Mangrove ecosystems are frequently exposed to high concentrations of iron (Fe) in sediments, resulting in Fe accumulation in plant tissues. Although Fe is an essential micronutrient involved in several metabolic processes, its excess requires efficient mechanisms of compartmentalization and storage to maintain cellular homeostasis. Histochemical detection using the Perls reaction has usually been applied to identify ferric iron (Fe 3+ ) in biological tissues; however, the combination of this technique with ultrastructural and elemental analyses remains relatively unexplored in plant cells. In this study, we investigated Fe localization in leaf tissues of Rhizophora mangle L. (Rhizophoraceae), a dominant mangrove species, by combining complementary approaches, including Perls cytochemical reaction, transmission electron microscopy (TEM), scanning transmission electron microscopy coupled with high-angle annular dark-field imaging (STEM-HAADF), and energy-dispersive X-ray spectroscopy (EDS). Perls-positive electron-dense deposits were visualized at the ultrastructural level, and their elemental composition was further characterized by EDS analyses. Fe-containing deposits were detected in the epidermis, mesophyll parenchyma, mucilage cells, and vascular tissues, as well as in multiple cellular compartments, including plastids, mitochondria, vacuoles, cell walls, intercellular spaces, and plasmodesmata, whereas sclerenchyma cells showed no detectable Fe-containing deposits. The combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level. This integrated methodology may facilitate the investigation of Fe distribution and compartmentalization in plant tissues under contrasting conditions of Fe availability.
Why it matches plant phenotyping methods植物葉の鉄分布・細胞内区画化という生理状態を対象に、複数の顕微鏡・元素分析法を統合した可視化および特性評価手法が研究の中心であるため。
abstractThe combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level.
Overlapping cells in plant suspension-culture microscopy pose a particular challenge, for instance, segmentation because a single pixel may belong to more than one cell. Most standard instance-segmentation methods are not designed for this setting and tend to treat overlapping objects as mutually exclusive regions. We instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding. This allows multiple predicted masks to occupy the same image region. We further introduce a mask-level multiplicity-consistency loss that encourages the predicted number of masks covering a pixel to agree with the underlying cell occupancy. We evaluate MC-SlotNet on a newly annotated dataset of 53 Siraitia grosvenorii suspension-culture micrographs containing 4131 full-cell instances acquired at 4×–40× magnification. Using grouped five-fold cross-validation and an overlap-preserving evaluation protocol, we compare the method with Mask R-CNN, SOLOv2, and Mask2Former. MC-SlotNet achieves the best performance on AP50 (0.800), mAP50:95 (0.565), F150 (0.842), all-ground-truth Dice (0.761), AJI+ (0.754), overlap-region Dice (0.705), and overlap-instance recall (0.828). Its AP75 (0.649) is comparable to Mask2Former’s (0.651). MC-SlotNet also has the lowest inference time among the evaluated methods, at 1.113 s/image. These results indicate that decoding full-cell masks independently, rather than enforcing an exclusive partition of image pixels, is well-suited to instance segmentation in plant suspension-culture microscopy images with substantial cell overlap.
Why it matches plant phenotyping methods植物細胞の重複画像から個々の細胞を分割・抽出する新規モデルを開発し、データセット上で既存手法と比較評価しており、植物表現型取得法が研究の中心である。
abstractWe instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding.
Background Understanding the structure of plant seeds cultivated for human consumption and food manufacturing is vital to provide sustainable products as well as to investigate early growth stages. This includes structural variation between different plant species, varieties and cultivars depending on genetic setup, as well as structural modifications upon germination, aging and storing or seed treatment during processing. For plant seeds as multi-component biological materials, structural characterization must extend across multiple length scales, from molecular organization to cellular architecture. Results We apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale, enabling a comprehensive analysis of hierarchical organization. To identify and characterize heterogeneous regions within the pea seeds, we implement a fitting-free, data-driven segmentation and analysis workflow based on machine learning tools. This approach allows for classification of structurally distinct domains and enables quantitative comparison across samples without relying on predefined models. Furthermore, we incorporate multi-modal analysis by combining structural imaging with complementary elemental information obtained from XRF. The integration of compositional and structural data provides deeper insight into structure-composition relationships. Conclusions This multi-scale, multi-modal approach opens new possibilities for investigating hierarchical structures and their development under diverse conditions and enables systematic comparison between different species or seeds at different developmental stages or exposed to different processing steps. The approach is broadly applicable to various kinds of samples and other hierarchically organized biological materials, which makes it a valuable technique for plant science as well as plant-based food science.
Why it matches plant phenotyping methods種子の構造・細胞領域をX線散乱／蛍光イメージングと機械学習ベースのセグメンテーションで定量解析する手法が研究の中心であり、植物器官の構造形質を抽出するため採用。
abstractWe apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale
Plant stress biology has traditionally relied on the analysis of snapshot measurements—including hormone levels, reactive oxygen species, transcripts, and physiological traits—which primarily characterize the current state of the cell, whereas the physical consequences of previous stress exposure remain considerably less accessible to direct measurement. This distinction may be particularly important under natural conditions, where plants experience recurrent, sequential, and combined stresses. This raises a fundamental question: can a cell, after physiological recovery, retain a measurable residual physical state that reflects aspects of its previous stress history and influences its response to subsequent stress? Here, we propose Environmental Phase Imprinting (EPI) as a testable biophysical hypothesis according to which environmental stress may leave a measurable imprint on the physical state of biomolecular condensates that persists after cessation of the initial exposure. EPI is not proposed as a new form of biological information or as an established mechanism of stress memory, but rather as a potential physical substrate, correlate, or consequence of previously described forms of cellular stress memory. To operationalize this hypothesis, we introduce the Plant Condensate Code (PCC), a multidimensional conceptual framework designed to move from a static “snapshot” of cellular state toward a time-resolved physical trajectory. PCC integrates complementary characteristics of condensate populations, including morphology, dynamics, molecular mobility, material state, and molecular composition, across a sequence of states encompassing baseline, stress, adaptation, recovery, and the post-stress state.We propose that the trajectory of condensate states, rather than any single measurement, may contain information about cellular stress history and may help explain differences in responses to recurrent or combined stress. Multimodal approaches, including live-cell imaging, fluorescence recovery after photobleaching (FRAP), molecular mobility analysis, microrheology, Brillouin microscopy, quantitative phase imaging, and molecular profiling, could provide complementary measurements of this physical state. PCC is further positioned within our broader conceptual research program encompassing Cytoplasmic Phase Homeostasis, Cytoplasmic Phase Sensing, and the Plant Threat Matrix (PTM)—a proposed six-state framework for describing plant physiological states under stress. Within this framework, physical measurements of condensate and cytoplasmic states may represent one possible approach for defining and quantitatively characterizing cellular physiological states. Finally, we discuss the potential application of this conceptual framework to stress-resilience phenotyping, evaluation of biostimulants, and selection for stress tolerance, while clearly distinguishing experimentally established phenomena from hypotheses and conceptual proposals. The proposed framework may provide a foundation for the development of a new direction in biophysical phenotyping of plant stress resilience, complementing molecular and physiological approaches to the study of stress memory.
Why it matches plant phenotyping methods植物ストレス履歴を測定・定量化するための新しい生物物理的フェノタイピング枠組みを中心に提案しており、単なるストレス生物学実験ではない。ただし実証ではなく概念的な方法開発提案である。
abstractTo operationalize this hypothesis, we introduce the Plant Condensate Code (PCC), a multidimensional conceptual framework designed to move from a static “snapshot” of cellular state toward a time-resolved physical trajectory.
The increasing world population necessitates new sustainable nutrient sources, making microalgae like Chlorella sorokiniana interesting due to its rich nutrient profile and sustainable cultivation methods. With genetic optimization tools like CRISPR/Cas9, microalgae as a nutrient source can be improved even further. However, degradation of the rigid cell wall of microalgae, and thereby developing protoplasts, is often necessary prior to transformation, but monitoring protoplast development in spherical, single-celled organisms like C. sorokiniana is challenging using bright-field microscopy. Carbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension. The enzymes Driselase, Glucanex, Snailase, and Saczyme were tested in different combinations to degrade the cell wall of the mutant, with Snailase as the most effective yielding ~60 % protoplasts. This study provides a quick and easy tool for monitoring protoplast development in the microalgae C. sorokiniana, the first step to improve C. sorokiniana as a sustainable nutrient source using genetic optimization tools like CRISPR/Cas9.
Why it matches plant phenotyping methods微細藻類の細胞壁状態とプロトプラスト形成を蛍光マーカーで識別する方法の開発・検証が研究の中心であり、植物状態の取得手法に該当する。
abstractCarbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension.
Main conclusion We established a protocol for reliable nuclear visualization in Charophyceae, revealed diverse nuclear organization across cell types and species, and identified suitable cells for genome size estimation via flow cytometry. Charophyceae are multicellular green algae closely related to land plants and are established model systems for understanding plant evolution. Yet key cellular parameters like genome size remain poorly characterized. We combined fluorescence microscopy, transmission electron microscopy (TEM), and flow cytometry to characterize nuclear diversity across cell types and species of Characeae and to identify a cell type suitable for genome size estimation. Among three DNA-intercalating fluorochromes, propidium iodide labeled nuclei most reliably. Nuclear morphology varied widely across cell types: mononucleated cells were found in vegetative apical cells, the coronula of oogonia, and spermatogenous filaments of antheridia, whereas multinucleation predominated in other tissues, e.g., cortical cells, spine cells, stipulodes and rhizoids. Nuclei in Chara hispida showed a significant gradient in cross-sectional area along the thallus axis. In the apical internodes, nuclei were larger and more heterogeneous, whereas in the basal internodes they were smaller and more uniform, which is consistent with possible endopolyploidy. TEM confirmed the nuclear identity of crescent-shaped structures. Relatively large nuclei were found in rhizoids and spine cells. Only Sphaerochara canadensis showed an organized nuclei pattern. Whole-thallus preparations did not yield a defined nuclear peak by flow cytometry, but antheridia of Chara tomentosa produced a sharp peak, from which a genome size of 5.32 pg (1C) was estimated. The protocol established here provides a simple, reproducible framework for visualizing nuclei and estimating genome size in Charophyceae, and helps address longstanding questions in this group, such as the mechanisms and functions of multinucleation, the site of meiosis, and genome evolution.
Why it matches plant phenotyping methodsシャジクモ類の核可視化とゲノムサイズ推定のための再現可能なプロトコルを開発・検証しており、表現型取得法が研究の中心である。
abstractWe established a protocol for reliable nuclear visualization in Charophyceae
The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.
Why it matches plant phenotyping methods植物細胞の電気的生理状態を測定・解析するEISベースのオンチップ基盤を開発しており、植物状態の取得方法が研究の中心である。
abstractwe present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Live imaging of plant subcellular structures is key to deciphering the spatiotemporal bases of cellular processes, and their functional impact on growth and morphogenesis at various biological scales. Live imaging of plant cells essentially relies on expression of fluorescent markers labeling cells or subcellular structures of interest. Simultaneous multi-channel imaging of several markers is still not routine practice in plant cell biology, owing to issues linked to genetic or spectral compatibility of markers, differences in expression levels, silencing, toxicity, etc. Here we designed a three-color marker in Arabidopsis thaliana and Capsella rubella , enabling high-resolution live imaging of plant morphogenesis, including labeling of the cell membrane, the nucleus and the microtubule cytoskeleton. Detection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells. The three- color marker allows visualization of the three-dimensional organization and dynamics of plant microtubules within the intracellular space with unprecedented precision, in various organs including the root and shoot meristems, the leaf, anther, and gynoecium. Our results demonstrate the potential of such single-construct strategy for cell biology studies in plants.
Why it matches plant phenotyping methods植物細胞の形態形成を可視化する三色ライブイメージング法と、植物細胞用に最適化した微小管マーカーの開発が研究の中心である。
abstractDetection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells.
Fluorescence in situ hybridisation (FISH) is a valuable technique for visualising RNA molecules in their native cellular context. Still, its application in plant tissues is often limited by tissue autofluorescence and the lack of optimised protocols. Here, we developed and validated a simple, reproducible FISH workflow to detect Apple scar skin viroid (ASSVd) in cucumber. Systematic optimisation of probe chemistry, tissue selection, and sampling stage significantly improved assay sensitivity and reproducibility. The AZDye594-labelled antisense riboprobe produced higher signal-to-background ratios and lower background fluorescence than fluorescein-labelled probes, enabling reliable detection of ASSVd in vascular-associated tissues. The optimised workflow consistently detected ASSVd in both leaves and stems. High-resolution confocal imaging further revealed predominant nuclear accumulation of ASSVd RNA in infected cells. Together, this study establishes a sensitive and accessible FISH workflow for localisation of ASSVd in cucumber and provides a practical platform for investigating the spatial distribution of viroid and other plant RNA pathogens.
Why it matches plant phenotyping methods植物組織内の病原体RNAの空間局在を可視化・定量可能にするFISHワークフローを開発・検証しており、植物状態の画像取得法が研究の中心である。
abstractHere, we developed and validated a simple, reproducible FISH workflow to detect Apple scar skin viroid (ASSVd) in cucumber.
Cell polarity and tip growth rely on the dynamic spatial organization of signaling and structural components. Quantitative characterization of these spatiotemporal dynamics is critical for understanding polarized cell growth, yet manual quantification is labor-intensive and existing computational tools often lack the flexibility and robustness needed to analyze molecular and structural dynamics in tip-growing cells. Tip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data, enabling analysis of the spatiotemporal dynamics of molecular and structural components in tip-growing cells. TipQuant accurately identified cell apices and quantified the spatiotemporal behavior of fluorescently labeled proteins and cellular structures in Arabidopsis thaliana pollen tubes and Fusarium graminearum hyphae, reproducing manual measurements while reducing user bias and improving efficiency, consistency, and analytical flexibility. The tool also revealed a strong positive correlation between rho-like GTPase from plants activity and apical Ca 2+ influx in Arabidopsis pollen tubes, demonstrating its utility for analyzing dynamic cellular processes. TipQuant is a robust analytical tool for quantifying spatiotemporal dynamics in tip-growing cells, providing a flexible alternative to manual image analysis and enabling studies of the molecular mechanisms underlying polarized growth.
Why it matches plant phenotyping methodsTipQuantはライブセル画像から植物の細胞先端位置、膜上の蛍光分布、先端細胞質内の動態を自動定量する解析ツールであり、画像ベースの植物表現型・状態取得が研究の中心です。
abstractTip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data
Abstract Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs. Sixteen soybean genotypes were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using six YOLO (You Only Look Once) object-detection architectures (YOLOv7–YOLOv12) to identify the best-performing model for automated analysis. Among the tested object-detection architectures, YOLOv9 achieved the best overall performance for detecting germinated and non-germinated pollen grains in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P < 0.05), with a significant genotype × growth temperature interaction. Invitro incubation temperatures ranging from 10 °C to 45 °C produced a clear thermal response; however, no significant genotype × incubation temperature interaction was detected within either growth temperature regime. Although photosynthetic and physiological traits were measured exploring their relationship with pollen germination, their transient and complex response limited their reliability for predicting reproductive performance. The automated pipeline substantially reduced the time required to evaluate pollen germination. The pipeline processed nearly 5,000 images in approximately one hour, substantially increasing throughput and reducing reliance on manual counting. The findings demonstrate that pollen germination is a promising proxy trait for screening reproductive heat tolerance in soybean. Combining controlled environment phenotyping with YOLO-based object detection enabled efficient, accurate, and scalable pollen analysis, and represents the central methodological advance of this study. YOLOv9 performed best among the tested architectures, although discrepancies from manual counts in some images indicate that additional validation is needed. The weak associations with vegetative physiological traits further support the value of direct pollen-based phenotyping.
Why it matches plant phenotyping methods深層学習による花粉画像解析を中心に、花粉発芽という生殖形質を高速・自動測定するハイスループット表現型解析フレームワークを開発・比較・検証している。
abstractThis study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs.
Reproduction assets foundThe authors state that all data supporting the study, including annotated pollen germination images, computational and statistical codes, and analysis tools, were deposited in Zenodo with a public DOI. This is a paper-specific, publicly actionable asset. LabelMe and Ultralytics YOLO are generic third-party tools, not作者Dataset · publicCommission.
Data availability
All data supporting the findings of this study, including annotated images, computational and
statistical codes, and analysis tools, have been deposited in the Zenodo data repository. Additional
data will be made available upon reasonable request following acceptance of the manuscript.
Repository: https://doi.org/10.5281/zenodo.21685593
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing Interests
Authors declared no competing interests
References
1. FAOSTAT: Crops and livestock products: soybean production data. https://www.fao.org/faostat/
(2022). Accessed 15 Feb 2026.
2. Patel D, Franklin KA. TemperaturOpen asset ↗Zenodo · 10.5281/zenodo.21685593pdf-raw-page:28 lines:1-34Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
ArabidopsisCell / cellular structureRootPhysiological trait estimationGrowth / development / phenology
Decoding how plants integrate multiple hormone signals to coordinate growth requires tools capable of resolving pathway interactions at cellular resolution in living tissue. Here we present ACE (Auxin–Cytokinin–Ethylene) and ACE2 , proof-of-concept single-locus reporters to simultaneously capture activity of multiple hormones. Deploying ACE alongside well-established reporters, exogenous hormone treatments, and reverse-genetic perturbations of hormone biosynthesis, signaling, and transport in three-day-old etiolated Arabidopsis seedlings, we dissect the spatiotemporal hierarchy governing primary root elongation and root apical meristem (RAM) size. We demonstrate that both ethylene- and cytokinin-triggered root growth inhibition involve a boost of TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1 (TAA1)-mediated auxin biosynthesis and AUXIN RESISTANT1 (AUX1)-dependent auxin redistribution. Two spatially distinct auxin responses underlie the respective root growth effects: ethylene expands TAA1-dependent auxin biosynthesis from the root vasculature into the epidermis and promotes AUX1-mediated auxin import into the transition and elongation zones to inhibit cell elongation, while cytokinin confines ethylene-dependent TAA1-boosted activity to the vasculature and drives auxin accumulation in lateral root cap cells to reduce RAM size. Together, these data establish a reciprocal regulatory loop between these hormones, positioning ethylene as a convergence node in auxin–cytokinin crosstalk, and cytokinin as a modulator of the ethylene–auxin interaction. Critically, the changes in cross-activated reporter patterns described for different genetic backgrounds, alongside quantitative assessment of hormone-specific inhibition of the mutants’ growth, were consistent with the multi-hormone network established over two decades of research, and added cell-type-resolved spatial detail and a proposed hierarchy for the etiolated seedling root. Finally, a second-generation reporter, ACE2 , overcomes key technical limitations of ACE , expanding the platform’s capacity toward a higher-order multi-hormone monitoring system. These resources expand the Arabidopsis genetic toolkit and provide a generalizable framework instrumental for dissecting multi-hormone signaling hierarchies at the cellular level.
Why it matches plant phenotyping methods多ホルモン活性を生体組織で同時可視化するACE/ACE2レポーターを開発・改良し、遺伝背景や根成長阻害との整合性を検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present ACE (Auxin–Cytokinin–Ethylene) and ACE2 , proof-of-concept single-locus reporters to simultaneously capture activity of multiple hormones.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.
Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。
abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.
Continuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression, revealing how the cellular microstructure governs its mechanical response. We introduce a dimensionless number Mi that is a function of the average interfacial contact area of cells, cell wall thickness, the average cell volume and tissue porosity, to describe tissue microstructure over different development stages. Mechanical softening during maturation aligned strongly with decreasing Mi, linking microstructure to effective Young's modulus, peak stress, and toughness. DVC revealed distinctive strain-distribution signatures: in young, low-porosity tissue, strain was initially diffuse with early-onset localization indicating progressive failure, whereas mature, high-porosity tissue exhibited sharply peaked strain distributions and highly localized fracture planes indicative of brittle collapse. These findings demonstrate how pore evolution, anisotropy, and cell packing jointly determine macroscopic deformation, establishing XCT-DVC as a powerful framework for connecting plant tissue architecture to mechanical function.
Why it matches plant phenotyping methods4D XCTとDVCを用いてリンゴ組織の内部三次元ひずみ、微細構造、破壊状態を定量化する手法が研究の中心であり、植物組織の構造・力学的状態を抽出しているため。
abstractContinuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Why it matches plant phenotyping methods綿繊維横断面の画像分割、形質抽出、検証を目的とした再現可能な深層学習ワークフローの開発であり、植物フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Calcium ions (Ca2+) function as ubiquitous second messengers that translate environmental and developmental cues into spatially and temporally defined cellular responses in plants. This review summarizes the cellular architecture and molecular mechanisms that generate, shape, and terminate Ca2+ signals, with emphasis on plasma-membrane channels, intracellular stores, pumps, exchangers, and organelle-associated transport systems. We also examine the development of live Ca2+ indicators, from chemical dyes and aequorin to ratiometric and single-fluorophore genetically encoded calcium indicators, and discuss principles for selecting sensors for different tissues and subcellular compartments. Recent studies have applied these tools to abiotic stress, plant immunity, polar growth, development, symbiosis, and systemic signaling. Accurate quantitative imaging nevertheless requires careful matching of sensor properties to the target cellular environment and rigorous control of motion, spectral interference, and analytical procedures. Combining improved indicators with advanced microscopy, genetic validation, and standardized data analysis should help connect distinct Ca2+ signatures with their molecular origins and physiological roles.
Why it matches plant phenotyping methods植物のCa2+シグナルを定量するライブイメージング指標、顕微鏡、解析手順を中心にレビューしており、生理状態の取得方法が主題である。
abstractWe also examine the development of live Ca2+ indicators, from chemical dyes and aequorin to ratiometric and single-fluorophore genetically encoded calcium indicators, and discuss principles for selecting sensors for different tissues and subcellular compartments.
Herein, we rationally designed and synthesized two types of D-π-A red-emitting fluorescent probes with intrinsic intramolecular charge transfer (ICT) and twisted intramolecular charge transfer (TICT) characteristics, for traditional methods for lipid droplet detection in food crops suffer from reliance on bulky instruments, tedious sample pretreatment and the impossibility of on-site quantification. To modulate the molecular skeleton, methoxy groups were introduced, and highly specific recognition of lipid droplet microenvironments was realized through the probes' synergistic spectral response to polarity and viscosity. Typical turn-on fluorescence responses and favorable lipophilicity were displayed by both probes, thus enabling accurate localization of the hydrophobic domains of lipid droplets. Possessing ratiometric fluorescence performance, the modified probe OCH 3 -Phe-FCN efficiently alleviated signal interference from complex matrices, and toward oleic acid was a lower limit of detection presented. By combining smartphone-based RGB colorimetry and image grayscale analysis was a portable detection platform constructed, with fluorescence signals employed to accomplish visual analysis of lipid droplets in food crops. Satisfactory biocompatibility and targeting capability were validated for the as-prepared probes via cell imaging and cytotoxicity evaluations, and far superior imaging quality and signal-to-noise ratio were demonstrated by the modified probe. With the aid of density functional theory (DFT) calculations, systematically elucidated was the spectral mechanism of excited-state transitions synergistically modulated by the two microenvironmental factors. Not only does this work deepen the insight into the response mechanism of dual-responsive fluorescent probes, but also a promising technical strategy is offered for in situ spectral detection of lipid droplets in food crops.
Why it matches plant phenotyping methods食品作物の脂質滴を蛍光プローブとスマートフォン画像解析で可視化・定量する検出法および携帯型プラットフォームの開発が中心であり、植物の細胞状態を測定する技術的貢献が明確です。
abstractBy combining smartphone-based RGB colorimetry and image grayscale analysis was a portable detection platform constructed, with fluorescence signals employed to accomplish visual analysis of lipid droplets in food crops.
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data management
Super-resolution microscopy has transformed our ability to visualize subcellular structures, but its application in plant biology remains challenging due to the optical complexity of plant tissues. Here, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity. This method leverages time-correlated single-photon counting (TCSPC) to separate fluorescence signals based on their lifetimes, enhancing signal specificity and enabling the visualization of elusive subcellular compartments in Arabidopsis thaliana root tips. The protocol covers sample preparation, fluorophore selection, microscope configuration, image acquisition, and data analysis, providing a step-by-step guide to optimize tau-STED imaging for plant cell biology. By addressing the unique challenges of plant tissue imaging, such as autofluorescence, refractive index mismatches, and light scattering, this approach facilitates super-resolution imaging of intracellular structures, including the plant endoplasmic reticulum-Golgi intermediate compartment (ERGIC). This protocol is designed to be accessible to researchers with basic microscopy experience and offers a robust framework for exploring subcellular dynamics in plants with unprecedented detail. Key features • tau-STED integrates STED signals with fluorescence lifetime via phasor analysis at confocal speeds, enabling low-noise super-resolution imaging. • Morphometry analysis workflow at super resolution.
Why it matches plant phenotyping methods植物組織の細胞内構造を超解像で取得・解析する顕微鏡プロトコルであり、植物表現型の画像取得法が中心。超解像下の形態計測ワークフローも含む。
abstractHere, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity.
Ammonium (NH 4 + ) is the primary inorganic nitrogen source for rice ( Oryza sativa L.). Substantial progress has been made in characterizing the functions of ammonium transporters (AMTs) in roots; however, the regulatory dynamics governing subcellular ammonium compartmentation after its entry into cells, particularly its vacuolar sequestration and efflux back to the external environment, remain poorly understood. This knowledge gap stems mainly from two factors: the difficulty of applying conventional detection methods at the organellar scale and interference caused by nonspecific ion adsorption to the cell wall of intact roots. To address these challenges, we present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT). The protocol covers the preparation of protoplasts and vacuoles from rice roots, the configuration and calibration of the NMT system, and the step-by-step measurement of net NH 4 + fluxes at three distinct biological levels (intact roots, protoplasts, and vacuoles). By employing a unified sample preparation and measurement strategy, this protocol enables quantification of net uptake fluxes across the plasma membrane, characterization of net efflux dynamics under specific conditions, and indirect estimation of vacuolar sequestration capacity using the isolated vacuole system. Overall, this protocol provides a flexible and robust framework for studying NH 4 + homeostasis in plants and is readily adaptable to different crop species, treatment conditions, and experimental objectives. Owing to its modular design and compatibility with standard NMT equipment, it can be readily adopted by laboratories seeking to investigate nitrogen transport mechanisms in plants. Key features • Allows for testing of NH 4 + fluxes in roots, protoplasts, and vacuoles. • Applicable to plants grown under different culture systems, including Arabidopsis thaliana grown in dishes and rice grown in hydroponic systems. • Supports both long-term and transient stress treatments. • Real-time monitoring.
Why it matches plant phenotyping methods植物根・プロトプラスト・液胞のNH4+フラックスをリアルタイム定量するNMT測定プロトコルが研究の中心であり、植物の生理状態を取得する方法を詳細に開発・標準化している。
abstractwe present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT).
Apoplastic pH dynamically regulates plant intercellular communication, but its measurement in internal tissues, such as the vasculature, remains technically challenging. Here, we present a protocol for ratiometric quantification of apoplastic pH in Arabidopsis seedlings using genetically encoded sensors. We describe seedling preparation, confocal imaging, and ratiometric image processing. Companion cell-specific expression of the pH sensor enables apoplastic pH readouts in the vasculature and supports in vivo comparative analyses of apoplastic pH across genotypes, treatments, and growth conditions in young seedlings. For complete details on the use and execution of this protocol, please refer to Xiong et al. 1 .
Why it matches plant phenotyping methods植物のアポプラストpHという生理状態を、遺伝子コード型センサー、共焦点撮像、画像処理で定量するプロトコルであり、表現型取得法が中心です。
abstractHere, we present a protocol for ratiometric quantification of apoplastic pH in Arabidopsis seedlings using genetically encoded sensors.
Abstract Plants live in a physical world governed by a multitude of mechanical processes which vary over time. The unique features of plant cells, which are turgor-inflated objects surrounded by the cell wall, present an intricate perception and response system for mechanical forces. A powerful tool to investigate how plants adapt and react to these cues is Atomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties. In the context of cell wall biomechanics, there remains some controversy on appropriate AFM measurement practices and suitable use of common terminologies. Specifically, the interpretation of plant cell indentation curves and derivation of the wall elasticity modulus can be challenging and continues to spark debate. In this Expert View, we discuss recent advances of AFM in plant science as well as best practices for the use of AFM and considerations for data interpretation with a focus on mechanical probing by indentation.
Why it matches plant phenotyping methods植物細胞の表面形態と力学特性をAFMで測定・解釈する実践と標準化を扱うレビューであり、植物形質取得法が中心です。
abstractAtomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties.
Hydrogen peroxide (H 2 O 2 ) is an important signaling molecule in plants under stress, and its level can be stimulated by abiotic stress and oxidative stress, which will seriously affect plant growth and development. Additionally, the presence of excessive residual H 2 O 2 in food can pose significant health risks to humans, because intake of H 2 O 2 can lead to serious pathological conditions. Therefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food. In this paper, we designed a fluorescence probe NBP, which has the advantages of high selectivity, low detection limit (80 nM) and long emission wavelength (648 nm). The imaging effect of exogenous H 2 O 2 was realized in the roots of Platycodon grandiflorum . By exploring the interplay between H 2 O 2 , plant metals, and drought stress, we can observe the up-regulation of H 2 O 2 in the roots of Platycodon grandiflorum under adverse conditions, and the root 3D imaging study could be realized. Then we combined the fluorescence probe with a smartphone, which enables on-site detection of residual H 2 O 2 in various milk samples. In addition, we investigated the fluorescence imaging of endogenous and exogenous H 2 O 2 in living cells using NBP. Therefore, this study provides a new way to assess the oxidative stress risk of Platycodon grandiflorum roots under abiotic stress, which is expected to improve plant production and has broad application prospects in food sample detection.
Why it matches plant phenotyping methods植物根におけるH2O2の蛍光イメージング手法を開発し、乾燥ストレス下の酸化ストレス状態を評価しているため、植物フェノタイピング手法が中心です。
abstractTherefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food.
Early detection of heavy metal stress in plants is essential for effective environmental monitoring, particularly in contaminated urban areas. This study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species. Five species were selected: Trifolium pratense, Rumex acetosa, Alcea rosea, Amaranthus retroflexus, and Plantago lanceolata. Visible plant injuries were assessed using Evans Blue staining and image-based anatomical analysis, which enabled distinguishing between living, partially damaged, and dead cells. Multispectral observations using a MicaSense RedEdge-M camera allowed calculation of the Normalized Difference Vegetation Index (NDVI) to detect stress-related changes in photosynthetic apparatus. The studied species differed in their capacity to accumulate and translocate Cr and Ni. Metal bioaccumulation was low in all species (bioconcentration factor < 1), with the highest Ni accumulation observed in Plantago lanceolata. Translocation of both metals was the greatest in Trifolium pratense and Amaranthus retroflexus. Hydrogen peroxide levels increased in roots and leaves of all species, particularly in Alcea rosea. Despite the absence of visible injuries, microscopic anatomical changes were detected in T. pratense and R. acetosa, while NDVI values differed between sites. In summary, this study indicates that no simple relationship was found between physiological stress parameter values and NDVI. It is important to emphasize the need for continued research under controlled conditions with specific doses of PTEs salts. This should clearly demonstrate the relationship between plant physiological responses to stress and the results of multispectral observations.
Why it matches plant phenotyping methodsリモートセンシング、画像ベースの解剖診断、NDVIを用いた植物ストレス検出法の評価が研究目的として明示されており、植物状態の取得・推定が中心的です。
abstractThis study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Field / plotCell / cellular structureWhole plant / canopy / plot / fieldGrowth / development / phenologyStress response / tolerance
Abiotic stress tolerance has been significantly weakened in modern crops during the domestication process. Regaining tolerance has become a critical task in light of current climate trends and their impact on global food security. Abiotic stress tolerance is an extremely complex trait and is conferred at various levels of plant functional organization and developmental stages, with regulatory mechanisms operating across multiple scales, from individual cells to tissues and the entire plant. The emergence of advanced molecular tools such as single-cell RNA sequencing and spatial omics technologies has revolutionized the field, advancing our understanding of plant responses to hostile environments. However, the implementation of this knowledge in crop breeding programmes is handicapped by the lack of appropriate phenotyping platforms. Here, we argue that current phenotyping methods may be excellent tools for functional validation of previously discovered traits but have limited predictive value in stress biology. We also propose that bridging the mismatch between omics technologies and phenotyping is the only way to account for cell-specific operation of key genes conferring stress tolerance and implementing them in breeding programmes. Some practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Why it matches plant phenotyping methods細胞ベースの植物フェノタイピング手法を扱い、蛍光色素や電気生理学的方法の例、限界、展望を論じる方法論レビューである。
abstractSome practical examples using cell-based phenotyping tools such as fluorescence dyes or electrophysiological methods are given, and current limitations and prospects of cell-based phenotyping are discussed.
Abstract Background Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow. Results Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40–100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia’s user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F 1 -scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. The platform reliably quantified epidermal traits despite variations in leaf morphology and image quality, supporting the integrated interpretation of stomatal and pavement-cell responses under stress. Conclusions We developed Stomatalia as a robust, user-friendly deep learning platform for automated, high-throughput analysis of bright-field leaf epidermal images across varying magnifications and resolutions. Stomatalia facilitates rapid, reproducible, and coordinated phenotyping of stomatal and pavement cells by integrating methodological standardisation, computational automation, and multi-trait extraction in a single analytical workflow. Its strongest current application is the analysis of high-quality dicot leaf-print images, particularly in species and imaging conditions similar to those used for model training and validation. Cross-species and benchmark analyses further define its current scope: stomatal detection can be transferred to some additional epidermal architectures, whereas robust pavement-cell segmentation in monocots or highly divergent species will require further annotation and model retraining. Within these defined boundaries, Stomatalia provides a flexible and extensible framework for studying stomatal and pavement cell morphology and environmental plasticity, while also supporting broader efforts to dissect and optimise plant responses to abiotic stress.
Why it matches plant phenotyping methods気孔・舗装細胞の形態形質を画像から自動抽出する深層学習プラットフォームを開発し、独立データで性能評価・比較検証しているため、植物フェノタイピング手法が中心である。
abstractwe developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits.
Reproduction assets foundThe authors publicly deposited the paper's test image datasets (raw/input leaf-print images, detection outputs, manual ground-truth counts, exported datasets) and the model file on Figshare, and provide a public Google Colab demo for running the Stomatalia algorithm. Both are paper-specific, public, and actionable.Dataset · publicThe test datasets and model file used in this work are available through the following link:
https://doi.org/10.6084/m9.figshare.32532672. The test_sets.zip archive contains the test image datasets
(cross-species and benchmark analysis), including raw/input images, detection output images, manual
ground-truth counts and exported datasets.Open asset ↗Figshare · 10.6084/m9.figshare.32532672pdf-page:25 lines:1-75Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
The endodermis plays a critical role in root function by regulating the movement of water and nutrients. Because endodermal function emerges from coordinated interactions among neighboring cells, the three-dimensional (3D) organization of cellular networks may influence how transport pathways are spatially arranged within root tissues. However, the 3D cellular network topology of the endodermis and its potential functional significance in woody plants remain poorly understood. Here, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta. We found that the balsa endodermis exhibits a distinct network topology characterized by higher local connectivity, lower closeness centrality, and lower edge betweenness centrality than that of Eucalyptus. Confocal Raman spectroscopy revealed broadly similar lignin and suberin signatures in the Casparian strip of the two species. Physiological measurements further showed that balsa roots exhibited significantly higher K + influx than Eucalyptus roots. Together, these observations indicate an association between variation in endodermal network organization and differences in root ion transport characteristics. This study highlights the value of integrating three-dimensional cellular reconstruction with network analysis to investigate structure-function relationships in plant tissues.
Why it matches plant phenotyping methodsLSFMによる3D細胞再構築とネットワーク解析が、根内皮の形態・構造特性を定量化する中心的手法として用いられているため、植物フェノタイピング手法の実質的応用に該当する。
abstractHere, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta.
A new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight. Transport of water and saccharides from plant stem to fruit cells is computed following biophysical rules. Saccharide fruit sink is based on sugar metabolism, rates of cell division and expansion, and starch and cell wall dynamics. Osmotic and hydraulic potentials in cells and their vacuoles drive water import at given cell-wall extensibility. The interaction of demand and transport determines saccharide flow and biomass. We incorporated physiological responses to temperature, pruning, and plant shading. Existing and new parameters were calibrated with data from fruit heating and fruit pruning experiments of contrasting tomato cultivars. Model validation for different strategies of fruit heating and pruning, and plant shading was successful. Increased fruit temperature was shown to reduce fruit weight, as expected. Growth response to fruit pruning or shading were fully explained by changes in phloem sucrose concentration. Hydraulic conductivity of vascular tissue as well as sucrose and hexose carrier capacities were crucial fruit properties determining sugar flux. Model scenarios on knockdown of sucrose synthase and active hexose uptake affected sugar composition. The model creates an important step towards predicting fruit quality and taste under diverse growth conditions.
Why it matches plant phenotyping methodsトマト果実の細胞動態、糖含量、重量、品質を予測する新規モデルを開発し、複数条件・品種で較正および検証しているため、植物形質推定手法が中心です。
abstractA new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight.
Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor the presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified the channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo. Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap.
Why it matches plant phenotyping methods植物組織における蛍光シグナルの分離、画像セグメンテーション、定量化ワークフローを開発・比較・検証しており、植物の細胞・細胞小器官状態を測定する方法が中心である。
abstractTo separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe segmentation and histograms were acquired using MATLAB script ( https://github.com/NIB‐SI/Nuclei‐segmentation ). The parameters used in the script to achieve appropriate segmentation are listed on GitHub, Case 1 ( https://github.com/NIB‐SI/Nuclei‐segmentation ).Open asset ↗NIB‐SI/Nuclei‐segmentationlines:255-341Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Background High temperatures during the reproductive stage of soybean severely disrupt reproductive processes and reduce yield. Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean. Sixteen soybean breeding lines (genotypes) were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using a deep learning–based object detection tool to reduce the manual labor and improve accuracy. Several advanced object detection models belonging to the YOLO (You Only Look Once) family, specifically, YOLOv7–YOLOv12, were evaluated to identify the most reliable model. Results Comparative evaluations of different object detection models indicated that YOLOv9 model achieved superior performance in evaluating pollen germination relative to other YOLO models, especially for detecting germinated and non-germinated pollen in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P
Why it matches plant phenotyping methodsダイズの耐暑性評価のため、花粉発芽を対象とした画像ベースの深層学習測定法を開発・比較検証しており、フェノタイピング手法が研究の中心である。
abstractThis experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean.
In recent years, automation of electron microscopy has enabled high-throughput acquisition of large, high-resolution serial-section image volumes. Three-dimensional reconstruction of these data has become essential for visualizing fine structural details in biological and material samples [1]. However, despite rapid advances in automated data acquisition, image analysis remains a major bottleneck. Conventional image processing methods, such as gray-level thresholding and manual annotation, require extensive labor and suffer from reduced reproducibility due to operator subjectivity [2]. To establish a highly efficient three-dimensional measurement workflow from imaging to quantitative analysis, we applied machine-learning (AI) to the most challenging image-analysis step and benchmarked its effectiveness against conventional methods. We prepared serial sections of Chlamydomonas for this evaluation. Continuous serial-section SEM images were acquired using a Hitachi High-Tech scanning electron microscope equipped with Auto Capture for Array Tomography (ACAT) and a focused ion beam scanning electron microscope (FIB-SEM) [3,4]. We performed appropriate sample pretreatment and optimized imaging conditions to clearly visualize the target chloroplast structures, followed by the automatic acquisition of continuous serial-section SEM image stacks. The obtained images were processed by cropping regions of interest, aligning images, adjusting contrast, and applying filters to facilitate structural identification. For segmentation and three-dimensional reconstruction, both a conventional method combining thresholding and manual correction [2] and a machine-learning-based approach (AIVIA, Leica Microsystems) trained on annotated data were employed [5]. In Figure 1(b), the region selected by the conventional method is shown in blue. Regions with contrast resembling that of the U-shaped chloroplast in Chlamydomonas were also selected. In contrast, the deep-learning-based method automatically extracted multi-scale features, such as intensity (gray-level), edges, and curvature, from the annotated regions and classified pixels individually. This facilitated the extraction of chloroplast regions, even in images containing structures with similar contrast (Figure 1(c)). The three-dimensional images reconstructed from the automatically segmented regions (Figures 2(a) and 2(b)) confirmed the presence of large openings and multiple micropores in the chloroplasts. Only 10 out of 60 annotation slices were required, significantly reducing manual annotation time compared to the conventional method. High reproducibility was also achieved in three-dimensional measurements. Furthermore, we acquired continuous serial-section SEM images of HIPS resin and an aluminum alloy using FIB-SEM and performed three-dimensional reconstruction combined with machine-learning-based segmentation. This presentation shows that integrating automated image acquisition with deep-learning-based segmentation streamlines the workflow from acquisition through three-dimensional reconstruction. It presents quantitative evaluation results and demonstrates the method’s utility for high-throughput three-dimensional analysis [7]. Comparison of chloroplast segmentation in Chlamydomonas. (a)An SEM image acquired using an FE-SEM equipped with ACAT, (b) the regions segmented using a threshold-based method, (c) the regions segmented using AIVIA. Three-Dimensional reconstruction of a Chlamydomonas chloroplast. (a) Three-dimensional reconstruction of the chloroplast obtained through automatic segmentation, (b) The same chloroplast viewed from a different orientation. The large opening and multiple micropores are found at the positions indicated by the white arrows.
Why it matches plant phenotyping methodsChlamydomonasの chloroplast 構造を対象に、機械学習セグメンテーションと3D再構築を従来法と比較・評価しており、植物構造の定量的取得ワークフローが研究の中心である。
abstractTo establish a highly efficient three-dimensional measurement workflow from imaging to quantitative analysis, we applied machine-learning (AI) to the most challenging image-analysis step and benchmarked its effectiveness against conventional methods.
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
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Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Abstract Hydrogen peroxide (H2O2) is a potent reactive oxygen species that plays a crucial role as a versatile signaling molecule for cellular function and vitality. Recent experimental evidence indicates that H2O2 affects cell-to-cell communication through plasmodesmata, tiny cytoplasmic nanopores connecting adjacent plant cells. H2O2-dependent systemic signaling has also been reported to involve plasmodesmal function in some contexts, although the dominant routes and messengers underlying rapid long-distance signaling remain under active debate. Nevertheless, direct monitoring of redox dynamics at plasmodesmata in live tissues has remained challenging. In this study, we developed a plasmodesmata-localized HyPer7 (Pd-HyPer7) reporter to investigate H2O2 dynamics at plasmodesmata in response to exogenous redox stressors and plant stresses, including cold and mechanical wounding. Pd-HyPer7 showed response characteristics that differed from the HyPer7 reporters localized to the cytosol, plasma membrane, and chloroplasts under the conditions tested, indicating that redox responses at plasmodesmata are distinguishable from these compartments. Notably, during mechanical wounding, both the cytosol and plasmodesmata showed transient redox responses with broadly similar temporal profiles in local tissues. In systemic tissues, however, the responses were temporally separated, with plasmodesmal oxidation peaking well after the cytosolic response. This timing relationship is consistent with plasmodesmata acting downstream of early systemic wound signaling, rather than simply mirroring cytosolic redox dynamics. Together, our results establish Pd-HyPer7 as a tool for monitoring plasmodesmal redox dynamics and support a model in which plasmodesmata participate in spatially and temporally regulated redox responses during plant stress.
Why it matches plant phenotyping methods植物ストレス時の原形質連絡における酸化還元動態を可視化するPd-HyPer7レポーターを開発し、植物組織での測定に適用・評価しているため、植物生理状態のフェノタイピング手法が中心である。
abstractdirect monitoring of redox dynamics at plasmodesmata in live tissues has remained challenging
MaizeMicroscopyCell / cellular structureSeed / grainVisualization / data management
Sexual reproduction in flowering plants relies on double fertilization, a process marked by two fusion events between the male and female gametes that lead to seed formation. Because this process unfolds within the embryo sac embedded deep inside the ovule, direct observation remains technically demanding, especially in maize, where the large size of female reproductive organs presents additional obstacles. The described method enables high-resolution visualization of cellular events unfolding during maize double fertilization. The approach integrates optimized fixation, clearing and confocal imaging of embryo sacs from ears pollinated with fluorescent pollen marker lines. Precise timing of embryo sac fixation is critical, allowing capture of key events such as pollen peri-germ cell membrane break-down or gamete karyogamy. The protocol provides detailed guidance for ovule dissection, fixation, preparation and renewal of the clearing solution and confocal imaging of embryo sacs. This method offers unprecedented access to the cellular events of double fertilization in maize, establishing a robust framework for studying reproductive processes and supporting future discoveries in plant reproduction.
Why it matches plant phenotyping methodsトウモロコシの二重受精過程を高解像度で可視化する固定・透明化・共焦点 imaging プロトコルが研究の中心であり、植物の生殖状態を取得する方法として該当する。
abstractThe described method enables high-resolution visualization of cellular events unfolding during maize double fertilization.
Main conclusion A novel and efficient method was developed to accurately measure thickness in 3D of many cells from a confocal stack, as well as to track changes in cell thickness overtime. Plant cells and organs are three-dimensional objects with a certain thickness. Among basic geometric parameters (length, width, depth/thickness), cell thickness is less accurately and comprehensively measured, probably because it cannot be directly seen. The current methods of cell thickness quantification have some limitations, such as measuring only from a cross-section, not accounting for the directionality of biological thickness, or not offering a way to track changes in thickness of individual cells over time. This research is an attempt to bridge the gap, by making the quantification of thickness and tracking its changes in many cells easier and more accurate. We devised a novel method to efficiently measure average cell thickness in 3D from cells imaged with confocal microscopy, the most popular technique to image live samples over many days. The method, in combination with the popular software MorphoGraphX, also allows accurate and efficient tracking of changes in thickness between different time points. We tested the method on various organs of the model plant Arabidopsis thaliana such as the shoot apical meristem, the hypocotyl, the cotyledon, and the sepal. We demonstrated that this new method can reliably measure the thickness of hundreds of cells at once in a short amount of time to reveal new biological insights. We believe this would be a useful tool for plant researchers to accurately characterize this hidden morphological dimension.
Why it matches plant phenotyping methods植物細胞の3D厚みと経時変化を効率・高精度に測定する手法を開発し、複数器官で検証しているため、植物形態フェノタイピング手法が中心である。
abstractA novel and efficient method was developed to accurately measure thickness in 3D of many cells from a confocal stack, as well as to track changes in cell thickness overtime.
Functional genomics have been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated cell sorting (RACS). The CRISPR/Cas-generated library consisted of 3567 microalgal mutants derived from 2397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unraveled mutations in the violaxanthin de-epoxidase (noVDE) or in the proteasome assembly chaperone 4 (noPAC4) genes. Knocking out all five known noVDEs revealed that the high carotenoid content is due to violaxanthin increase, whilst noPAC4 knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidence suggested two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this forward-genetics approach can greatly accelerate the discovery of genes and pathways.
Why it matches plant phenotyping methods微細藻類の単細胞カロテノイド含量という生理形質を、Raman-activated cell sortingでラベルフリーかつハイスループットに取得・選別するプラットフォームが研究の中心であり、単なる生物学的測定ではない。
abstractwe introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated cell sorting (RACS).
ABSTRACT Fluorescence Lifetime Imaging Microscopy (FLIM) is becoming a key technique for live-cell multiplexing and label-free detection of endogenous fluorescence in animal systems. Its potential in plant biology, however remains largely unexploited, despite its integration into a number of commercial microscopy setups. Here, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores. Lifetime imaging of different fluorescent reporters targeted to distinct organelles (nucleus, plasma membrane, endoplasmic reticulum, etc.) and subsequent analysis of the decay curves using different modes allowed us to simultaneously discriminate up to four spectrally overlapping fluorophores solely by lifetime differences in specific subcellular compartments. Remarkably, fluorophores with lifetimes differing by as little as 0.1 ns can be reliably discriminated using one of these modes, namely Phasor-based analysis. Moreover, we show that the same fluorophores exhibit compartment-specific lifetime shifts, enabling Phasor separation of identical tags residing in different organelles. Finally, we extended the Phasor approach to label-free imaging of endogenous plant fluorescence. Together, these results establish FLIM-Phasor as a versatile, multiplex-capable tool for plant cell biology, opening new avenues for imaging strategies that yield higher content information at both cellular and tissue-level resolution.
Why it matches plant phenotyping methods植物細胞・組織の蛍光状態を取得・解析するFLIM-Phasor法を体系的に構築・検証し、マルチプレックスおよびラベルフリー植物蛍光イメージングへの応用を示した、方法中心の研究である。
abstractHere, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores.
The spatial organization of essential, nonessential, and toxic metal(loid) elements (MEs) within plant cells underpins physiological function. Yet, comprehensive subcellular imaging of the full ME spectrum remains challenging due to trade-offs among spatial resolution, elemental coverage, and structural correlation. Here, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution. Applying this high-fidelity workflow to Arabidopsis , soybean, and wheat, we constructed single-cell metallome maps revealing an evolutionarily conserved subcellular architecture: chloroplasts enrich essential MEs (e.g., magnesium, iron, copper), whereas vacuoles compartmentalize nonessential [e.g., lanthanum (La)] and toxic MEs [e.g., cadmium (Cd), lead, arsenic]. We demonstrate that while this architecture remains stable under homeostasis, it undergoes dynamic, stimulus-specific, and dose-dependent remodeling under stress. Low-dose La(III) enhances pairwise and higher-order colocalizations of essential MEs within chloroplasts, correlating with improved photosynthetic efficiency and growth. High-dose La(III) induces nonphysiological La-ME associations and, critically, drives aberrant Cd(II) accumulation in chloroplasts-revealing a cross-toxicity mechanism wherein La(III) disrupts native sequestration barriers. In contrast, although high-dose Cd(II) is largely excluded from chloroplasts, it triggers a widespread redistribution of essential MEs, progressively eroding spatial organization. Thus, while both ions inhibit growth, they perturb metallomic networks via distinct mechanisms: La(III)-mediated disruption of sequestration vs. Cd(II)-induced systemic compartmental collapse. Our findings establish that subcellular ME networks are dynamically regulated and orchestrate physiological outcomes.
Why it matches plant phenotyping methods植物細胞内の金属元素分布と超微細構造を取得する統合イメージング基盤とワークフローの開発が中心であり、植物の生理状態・ストレス応答に結び付けて実証している。
abstractHere, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution.
Single-molecule fluorescence in situ hybridization (smFISH) has emerged as a powerful tool for studying gene expression dynamics with unparalleled precision and spatial resolution in a variety of biological systems. Recent advancements have expanded its application to encompass plant studies, yet there remains a need for a simple and robust smFISH method adapted to plant tissue sections. Here, we present an optimized smFISH protocol, termed cryo-smFISH, for visualizing and quantifying single mRNA molecules in plant tissue cryosections. This method exhibits remarkable sensitivity, enabling the detection of low-expression transcripts, including long non-coding RNAs. By integrating a deep learning-based algorithm into our image analysis pipeline, our method enables precise assignment of RNA abundance in nuclear and cytoplasmic compartments. The method also enables robust integration with immunofluorescence, as cryosectioning enhances antibody penetration. This allows for the sequential visualization and quantification of both RNAs and endogenous proteins within the same cells. Finally, this study demonstrates the use of smFISH to validate single-cell RNA sequencing (scRNA-seq) expression patterns in plant tissues. By extending smFISH to plant cryosections, plant scientists will be able to exploit the full potential of quantitative transcript analysis at cellular and subcellular resolution.
Why it matches plant phenotyping methods植物組織向けcryo-smFISHプロトコルと画像解析法を開発し、RNA量を細胞・細胞内区画で定量する手法が研究の中心である。分子測定ではあるが、植物組織の状態を定量する方法として技術的貢献が明確。
abstractHere, we present an optimized smFISH protocol, termed cryo-smFISH, for visualizing and quantifying single mRNA molecules in plant tissue cryosections.
Reproduction assets foundThe authors deposit all data underlying graphs/heatmaps plus custom R/Python scripts and Cellpose segmentation models in a public GitHub repository specific to this paper. Third-party tools (FISH-quant, DeconvolutionLab2, Stellaris Designer) are generic and excluded.Code · publicAll custom code, including R/Python scripts and Cellpose segmentation models, is available at https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections .
Funding
This work was supported by Vetenskapsrådet (2023-03895), the Novo Nordisk Foundation (NFF24OC0093553 and NNF25OC0100533), and the Carl Tryggers Stiftelse (CTS 18- 325).
Acknowledgments
We thank A. Menkis for initial technical support with cryostat operation and Alexandre Berr for scientific feedback. We also thank memOpen asset ↗zhang_et_al_smFISH_cyrosectionslines:122-152Dataset · publictic ( Bolger et al., 2014 ). The raw gene-count matrix was obtained using the pseudoalignment software Kallisto ( Bray et al., 2016 ). RNA-seq reads were normalized as transcripts per million (TPM).
Data and code availability
The supplemental information and all data underlying the graphs and heatmaps presented are available at https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections .Open asset ↗zhang_et_al_smFISH_cyrosectionslines:106-121Code · publicech.com/stellaris-designer . For mRNA detection, the coding sequence of the target gene was entered into the program, which automatically generated a set of probes complementary to the target mRNA.
The sequences of the probes were then subjected to quality control using an automated local blast R script, available on GitHub at: https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections/tree/main/smFISHprobes . The smFISH probes used in this study and their respective fluorophores are shown in Supplemental Table 3 . The probes were diluted in Tris-EDTA buffer to a final stock concentration of 25 μM.
Cryo-smFISH
Sample preparationOpen asset ↗zhang_et_al_smFISH_cyrosectionslines:75-85Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Plant tissues consist of diverse cell populations that collectively contribute to development, metabolism, environmental responses, and phenotype formation. Although single-cell and single-nucleus RNA sequencing have greatly advanced the study of plant cellular heterogeneity, their application to large sample cohorts remains limited by cost, technical complexity, tissue dissociation constraints, and throughput. In contrast, bulk RNA-seq datasets have accumulated extensively across plant species, tissues, developmental stages, and environmental conditions, yet the celltype-level information embedded in these datasets remains difficult to resolve because plant-oriented deconvolution frameworks are still lacking. Existing deconvolution methods have largely been developed in mammalian systems and have not been systematically optimized for plant transcriptomic features, leaving their applicability under plant-specific constraints unclear. Here, we present SEED, an adaptive deconvolution framework optimized for plant transcriptomic data. SEED integrates candidate reference-template construction with seven deconvolution strategies and automatically identifies an optimal combination for a given dataset. In grapevine simulated benchmarking, SEED showed its clearest advantage under low-replication conditions and remained broadly competitive, rather than uniformly dominant, when larger pseudo-bulk sample sizes were evaluated. SEED further performed robustly in public Arabidopsis thaliana and Nicotiana tabacum datasets. Finally, we applied SEED to bulk RNA-seq data generated in this study from Vitis vinifera cv . Cabernet Sauvignon berries collected from Yinchuan and Yantai, identifying terroir-associated cell subtypes and coordinated celltype interaction patterns. Together, these results establish SEED as a practical framework for plant transcriptome deconvolution and provide a new tool for dissecting cellular heterogeneity associated with environmental adaptation and phenotype formation in plants.
Why it matches plant phenotyping methods植物トランスクリプトームから細胞サブタイプと相互作用パターンを推定するSEEDを開発し、複数データセットでベンチマーク・検証している。分子データ解析だが、植物の細胞状態・表現型形成に結び付く再利用可能な推定手法が研究の中心である。
abstractHere, we present SEED, an adaptive deconvolution framework optimized for plant transcriptomic data.
The nucleus is the characteristic organelle of eukaryotic organisms. Unlike the classic textbook view of static nuclei, nuclear shape is dynamic in live cells. Altered or deformed nuclear shape is a hallmark of cancer in animal cells and environmental stress in plants. Nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, little is known about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To confront this issue, we developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition. This confocal imaging method utilizes a dual fluorescently tagged marker line - nuclear envelope protein and chromatin - to perform live cell imaging of the root in model plant Arabidopsis thaliana under control and salt-stressed conditions. These captured movies are analyzed to quantify nuclear and chromatin dynamics using open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. To validate this method, we imaged and quantified chromatin movement in control and salt-stressed roots, revealing a decrease in chromatin speed under salt-stressed conditions. This method allows for quantitative live cell imaging of root nuclear shape and chromatin dynamics during plant development and environmental stress, thus enabling analysis of changes in nuclear and chromatin dynamics caused by abiotic stressors.
Why it matches plant phenotyping methodsシロイヌナズナ根の核形状・クロマチン動態を定量する共焦点ライブイメージングと画像解析パイプラインを開発し、塩ストレス条件で検証しており、植物表現型取得が中心です。
abstractwe developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition.
ArabidopsisMicroscopyCell / cellular structureRootVisualization / data management
Conventional light microscopy is limited in resolution by the diffraction limit of light, restricting the visualization of the nanoscale organization of biomolecules. Expansion microscopy (ExM) has emerged as a powerful technique to overcome this barrier by physically expanding the specimen embedded in a swellable hydrogel without requiring specialized or high-cost imaging hardware. ExM is widely used in animal models, whereas its application to plant tissues has been challenging due to their multicellularity, in which each cell is encompassed by the rigid cell wall, which resists the expansion forces and prevents isotropic swelling. Here, we describe a robust and optimized ExM protocol specifically designed for Arabidopsis thaliana root tissues. This protocol details critical steps, including immunostaining, anchoring, gelation, denaturation, cell wall digestion, and expansion. Our method achieves an expansion factor of approximately 4.3×, enabling an effective lateral resolution of ~60 nm using a standard confocal microscope. We demonstrate the visualization of microtubules with preserved ultrastructure. This accessible protocol allows plant researchers to perform super-resolution imaging without specialized optical equipment, facilitating detailed structural analysis of plant cells. Key features • Expansion microscopy to break the diffraction barrier by increasing the physical distances between proteins while preserving relative spatial relationships and fluorescence signals. • 4-fold expansion of Arabidopsis root tissues. • 3D super-resolution imaging. • Deep-tissue imaging thanks to optical clearing associated with expansion of hydrogel-embedded specimens.
Why it matches plant phenotyping methods植物組織向けに最適化した超解像イメージングプロトコルを開発し、根の微細構造を定量・可視化する方法が中心である。
abstractHere, we describe a robust and optimized ExM protocol specifically designed for Arabidopsis thaliana root tissues.
MaizeMicroscopyCell / cellular structureLeafVisualization / data management
Maize is a globally important grain crop that is important for food and fuel. Northern corn leaf blight, caused by Exserohilum turcicum , is an important fungal foliar disease of maize that is highly prevalent and causes yield losses globally. Microscopy can be used to visualize plant-fungal interactions on a cellular level, which enables pathology and genetics studies. Host resistance and isolate aggressiveness can be characterized at different stages of disease development, which enables a more detailed understanding of the pathogenesis process and host-pathogen interactions. Our protocol outlines an efficient, cost-effective method for staining E. turcicum tissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope. This protocol uses KOH treatment followed by aniline blue staining, which stains glucans present in plant and fungal cell walls, and samples are visualized using fluorescence microscopy. Quantitative data about fungal structures including the conidia, hyphal structures, and appressoria, the structures formed to push through the plant leaf surface after conidia have germinated, can be obtained from the images generated using this technique. Visualization of these structures can help pathologists understand plant-pathogen interactions for maize and E. turcicum This method has advantages over other methods because the stain is less toxic than other available stains, samples can be processed in a more high-throughput manner than other protocols, and the required supplies are relatively inexpensive.
Why it matches plant phenotyping methodsトウモロコシ葉上の病原体感染構造を蛍光顕微鏡画像から定量する高スループット染色・画像化プロトコルが研究の中心であり、植物病態の表現型取得法に該当する。
abstractOur protocol outlines an efficient, cost-effective method for staining E. turcicum tissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope.
PollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks, whose annotation was fully verified by an expert palynologist to guarantee the taxonomic reliability of every published instance. The dataset is designed to close a concrete gap in existing palynological datasets, which typically combine low taxonomic diversity, few samples per class, and low-resolution crops restricted to bounding boxes. PollenBB16 contains 16,198 brightfield optical microscopy images at the native resolution of 3088 × 2064 pixels and 36,383 pixel-accurate polygons across 16 species from the Biobío Region, spanning endemic, native and exotic species of high ecological and melliferous value such as Eucryphia glutinosa and Quillaja saponaria (endemic), Gevuina avellana and Aristotelia chilensis (native), and Medicago sativa and Brassica rapa (introduced). Each spatial position is recorded at three focal planes. The displacement along the z axis reveals features of the exine together with information on the internal structure of the grain that remain inaccessible on a single plane. From this multifocal information, more robust convolutional networks can be trained with more accurate classification. The operational quality of the dataset is backed by a leakage-safe partition that keeps the three focal planes of the same position in the same subset to avoid metric inflation, complemented by a YOLO11n-seg baseline trained for 50 epochs that reaches 0.985 mask mAP@50 on the validation set, establishing a reproducible reference point. Beyond deep learning, PollenBB16 enables interdisciplinary applications in aerobiology, biodiversity monitoring under climate change, ecological restoration of the South American temperate forest, and botanical-origin authentication of Chilean monofloral honeys.
Why it matches plant phenotyping methods植物由来の花粉粒を対象とした高解像度画像データセットとセグメンテーション基準を構築し、深層学習による画像解析を再現可能な形で検証しているため、植物フェノタイピング手法・データセットとして中心的です。
abstractPollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks
Reproduction assets foundThe paper's PollenBB16 pollen image dataset (16,198 multifocal RGB microscopy images with pixel-accurate instance segmentation masks) and the accompanying authors' script polygons_to_bboxes.py are publicly deposited on Zenodo (10.5281/zenodo.19830051), per the Data Availability Statement.Code · publicThe only custom code distributed with this Data Descriptor is the Python script polygons_to_bboxes.py, which regenerates the YOLO bounding-box labels in labels_bb/ from the polygon labels in labels/. The script is packaged inside the scripts/ folder of the same Zenodo repository that hosts the dataset (10.5281/zenodo.19830051) and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, with no restrictions on access.Open asset ↗Zenodo · 10.5281/zenodo.19830051html-lines:731-797Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Key message CRaman imaging combined with a multi-layer perceptron neural network enables non-destructive, label-freeclassifi cation of tobacco BY-2 cells based on carotenoid composition. Abstract Carotenoids are natural tetraterpenoid pigments with important nutritional properties and broad industrial applications. Enhancing their production in plant-based biofactories offers a sustainable alternative to current manufacturing processes. In this work, we developed a label-free, single-cell analytical platform combining Raman imaging with a multi-layer perceptron neural network to classify tobacco BY-2 cells based on their carotenoid content. Carotenoid standards analysis, including astaxanthin, canthaxanthin, and β-carotene, was performed by surface-enhanced Raman scattering using hydrophobic gold nanostars due to the low concentration available. This analysis allowed the assignment of characteristic Raman peaks, specifically at 1160 cm −1 and 1520 cm −1 , of key carotenoids and their identification inside of the cells by Raman imaging. The Raman fingerprints were correlated with carotenoid profiles obtained by HPLC, enabling accurate differentiation between wild-type and transgenic cell lines. In the analyzed transgenic lines, carotenoids accumulated in vesicle-like structures near the nucleus and along the cytoplasmic membrane. This method provides a non-destructive, label-free approach with high classification accuracy and sorting potential based on carotenoid composition, and may be a useful tool for plant synthetic biology and metabolic engineering.
Why it matches plant phenotyping methods植物細胞内のカロテノイド組成をラマンイメージングと機械学習で非破壊・単細胞レベルに推定する分析プラットフォームを開発しており、植物表現型取得法が中心である。
abstractwe developed a label-free, single-cell analytical platform combining Raman imaging with a multi-layer perceptron neural network to classify tobacco BY-2 cells based on their carotenoid content.
Volume electron microscopy based on serial sectioning allows for three-dimensional (3D) visualization and analysis of the internal structures of tissues, cells, and organelles. One such technique, focused ion beam (FIB) scanning electron microscopy (SEM), has the advantages of nanoscale sectioning and high z-resolution, but the disadvantage of limited volume processing. Because of this limitation, targeting localized objects by FIB-SEM is difficult. Here, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule. In this protocol, plant samples are stained, embedded, trimmed, and carbon-coated while maintaining their orientation within the tissue. Then, sequential observations are performed using Cut & See function of FIB-SEM, followed by image processing for 3D reconstruction. Utilization of multi-scanning and image cropping from high-resolution data helps to identify localized targets within plant tissue. The filiform apparatus, which is an invaginated cell wall structure of the synergid cells, shows distinct contrast in each image, allowing for segmentation using brightness-based binarization. Such segmentation avoids the need to manually trace complex structures and facilitates 3D reconstruction by volume electron microscopy. Key features • Sampling and trimming of the resin block enable directionally loading in FIB-SEM. • Multi-scanning by FIB-SEM and target extraction by image processing software enable 3D reconstruction of local areas within the sample block. • Binarization using distinctive brightness of cellular structures enables segmentation without manual tracing of complex structures such as the filiform apparatus cell wall.
Why it matches plant phenotyping methods植物組織内の構造をFIB-SEMと画像処理で3D再構成・セグメンテーションするワークフローを開発しており、フィリフォーム装置形態という植物器官形質の取得が中心である。
abstractHere, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule.
Field / plotChlorophyll fluorescenceCell / cellular structureWhole plant / canopy / plot / field
The advent of spatial and quantitative biology has led to immense advances in understanding the complex inner workings of plants, down to the molecular scale. Functional imaging of live plants, which enables the spatial and quantitative mapping of biochemical cues, physicochemical properties of cellular structures, and the dynamics of physical and chemical signals with unprecedented resolution, has become a key technology for advancing the mechanistic understanding of plant cell biology. In this review, we highlight progress in live functional imaging in plants through the use and development of chemical fluorescent probes, which enable plant functional imaging without requiring genetic manipulation of the study object. We explain how probes sense, target, and report on functional features within the plant cell; discuss their limitations, including toxicity; and provide case studies to exemplify how these tools can complement biological studies to unravel the complex machinery that makes plants work. We conclude by outlining the expected future development of this field and identifying key challenges that lie ahead.
Why it matches plant phenotyping methods植物の生体機能を空間的・定量的に可視化する化学蛍光プローブの開発と利用を扱うレビューであり、植物イメージング手法が中心です。
abstractIn this review, we highlight progress in live functional imaging in plants through the use and development of chemical fluorescent probes
Understanding cellular growth dynamics in plants requires precise, long-term imaging of developing tissues. Cauline leaves are produced during the transition from vegetative to reproductive development and provide a useful system for studying how laminar organs diversify in form and function. While other laminar organs, such as rosette leaves and sepals, have been extensively studied, early cauline leaf development remains technically challenging to capture due to their concealed position, curved morphology, and the presence of dense trichomes. Here, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana . This method enables reproducible, high-resolution imaging of cauline leaves, supporting robust quantitative analysis of growth across developmental stages at cellular scale resolution. Key features • Fine dissection method for exposing initiating cauline leaves in Arabidopsis thaliana . • Long-term confocal live imaging of cauline leaf development at cellular resolution. • Optimized imaging parameters for high-fidelity 2.5D segmentation and growth analysis in MorphoGraphX.
Why it matches plant phenotyping methodsカウリン葉の成長を細胞レベルで定量化するための解剖、共焦点イメージング、2.5Dセグメンテーション、画像解析パイプラインが中心的に開発・提示されている。
abstractHere, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana .
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public2. MorphoGraphX 2.0.1 ( https://morphographx.org/software/ ) (access date, 2026-02-26) [10–11]
3. All codes have been deposited to OSF: https://osf.io/uth78/ (access date, 2026-02-26)
Procedure
A. Plant growth
1. Sow the seeds in pots filled with moist, room-temperature soil. Add a layer of water to the bottom of the tray and cover with a lid to maintain high humidity.
Note: Space seeds sufficiently to avoid contact between the developing plants and to prevent leaf damage; typicallyOpen asset ↗OSFlines:109-143Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published15 May 2026Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 0 · OpenAlex ↗
Effectively imaging the variation of heavy metal induce stress (HMIS) in plant is significantly important for stress resistance research in the fields of environmental and plant biology. However, due to the absence of distinctive parameter to reveal the relationship between HMIS and plant homeostasis, the reported fluorescence sensors fail to assess HMIS. Herein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants. Spectral experiments indicate that QVP exhibited selectivity, sensitive, photochemical stability, and pH adaptability for viscosity detection. Motivated by the robust detection capacities, QVP was further applied for clear fluorescence imaging of viscosity changes of plant cell (onion epidermis and scallion bulb) induced by HMIS (Cu 2+ , Au 3+ and Ag + ). Notably, the cellular viscosity was positively correlated with Cu 2+ concentration. More importantly, the sensor QVP had good penetration within plant tissues and enabled viscosity imaging of root hairs, leaves and other tissues. This work not only provides a novel molecular tool for understanding HMIS resistance of the plant by investigating the dynamic change of intracellular viscosity, but also provides an additional dimension for evaluating crop stress resistance.
Why it matches plant phenotyping methods植物細胞・組織の細胞内粘度を蛍光イメージングで測定し、金属イオンストレスを評価する新規センサーを開発・適用しており、植物状態の取得方法が研究の中心である。
abstractHerein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants.
Abstract Temperature fundamentally impacts plants growth and physiology. However, the mechanisms by which plants sense and response to environmental changes remain unclear due to the lack of effective methods for measuring internal plant temperatures. Here, by combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature in response to environmental temperature variations. We discovered a multilevel temperature regulation mechanism during the process by which plants establish thermal homeostasis. In Nicotiana benthamiana leaves, when environment temperature changes from approximately 24°C to 45°C, the maximum of internal plant temperature change is only approximately 10°C near cell wall, and less than 7°C in cytoplasm, while remaining nearly constant in chloroplasts (ΔTchl ≈ 1°C). Similar compartment-specific thermal regulation was observed in Arabidopsis thaliana and tomato, indicating that hierarchical regulation represents a conserved strategy for maintaining internal temperature stability in plants. Together, these findings provide direct evidence for multiscale thermal homeostasis in plants and establish a framework for understanding how cellular and subcellular organization contributes to temperature regulation.
Why it matches plant phenotyping methodsナノ温度計プローブと時間ゲート imaging により植物内部温度を測定する手法が研究の中心であり、植物の生理状態を直接定量している。
abstractby combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureVisualization / data management
While confocal fluorescence microscopy has provided valuable insights into chromatin organization in plant nuclei, its diffraction-limited resolution constrains the investigation of chromatin architecture, motivating the use of super-resolution techniques such as Single-Molecule Localization Microscopy (SMLM). Among these approaches, direct stochastic optical reconstruction microscopy (dSTORM) provides nanoscale resolution in individual cells, enabling precise visualization of chromatin domains, histone modifications, and nuclear organization. While such methods are increasingly applied in mammalian systems, their use in plant biology remains limited, largely due to technical challenges in sample preparation. Here, we present a streamlined and reproducible workflow for SMLM imaging of nuclei isolated from Arabidopsis thaliana. This protocol starts with seedling fixation to preserve nuclear morphology, followed by gentle tissue chopping and centrifugation to enrich intact nuclei. Isolated nuclei are then fluorophore-labeled in liquid medium and immobilized on low-melting agarose pads, a strategy that enhances stability during prolonged single-molecule imaging sessions. These steps collectively minimize background fluorescence, improve labeling consistency, and increase reproducibility across biological replicates. The resulting preparations provide enhanced clarity for visualizing chromatin modifications and nuclear architecture in plants. By lowering the technical barriers to implement SMLM imaging in Arabidopsis, this protocol provides a versatile means to investigate epigenetic regulation, chromatin organization, and nuclear topological variations at the nanoscale. This work establishes a methodological foundation for applying SMLM to plants, bridging the gap with mammalian cell biology and opening new opportunities to study how nuclear architecture contributes to genome regulation in response to developmental and environmental cues in plant systems.
Why it matches plant phenotyping methods植物核の形態・クロマチン構造を取得するSMLM画像化プロトコルの開発と再現性向上が研究の中心であり、植物フェノタイピング手法として適格です。
abstractHere, we present a streamlined and reproducible workflow for SMLM imaging of nuclei isolated from Arabidopsis thaliana.
Efficient gas and water exchange between plants and their environment largely depends on the number and distribution of stomata, cellular valves in leaf epidermis. Core genetic regulators of stomatal cell identity and pattern along with asymmetric stem-cell like divisions in stomatal precursors are hypothesized to customize stomatal production for optimal leaf performance. How these regulators work in concert and how division dynamics are modified and adjusted in different environments, however, are poorly understood. Here, we leveraged the variation in stomatal patterning in Arabidopsis thaliana accessions from diverse environments to define developmental rules and constraints in the stomatal lineage. The accessions subtle and quantitative variation enables us to identify which cellular parameters are flexible, revealing how developmental plasticity generates phenotypic plasticity. By developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins. Variation in final stomatal numbers is driven by differences in the relative contributions of stomatal initiation, cell size-based fate thresholds, general proliferative capacity, and coordination between sister and neighbor cell behaviors. Overall, diverse accessions converge toward two lineage regimes: one dominated by autonomous decisions with loose cell-cell coordination, the other by extensive cell-cell coordination. Challenging accessions with environmental fluctuations revealed regime-specific flexibility, with plasticity primarily mediated by a single division-related parameter. Our results show how cellular parameters integrate into alternative developmental strategies that shape environmental responsiveness.
Why it matches plant phenotyping methods葉の成長中の細胞挙動を追跡するライブセルイメージングツールを開発し、気孔密度の発生的起源を定量化しており、植物フェノタイピング手法が研究の中心である。
abstractBy developing live-cell imaging tools to track cellular behaviors during leaf growth under varying environmental conditions in these accessions, we could decompose stomatal density variation into its developmental origins.
Elucidating gene function in highly redundant genetic programs such as signaling pathways is challenging in model and nonmodel plants with current whole-plant genetic screening tools. Many of these challenges could be overcome if screens were instead carried out using individual cells harboring genetic perturbations. Here we report a single-cell screening platform, PIVOT (protoplast isolation after virus overexpression in planta), to accelerate identification and functional characterization of plant genes. We use Nicotiana benthamiana as a heterologous host to test gene libraries arrayed in a single leaf. PIVOT harnesses viral superinfection exclusion to ensure single multiplicity of infection per cell during pooled library delivery. Additionally, we engineer a cell-surface protein as a phenotypic marker for isolating cells of interest from a heterogeneous population. Using this system, we recover regulators of cytokinin signaling from an Arabidopsis open reading frame library. We anticipate PIVOT will be broadly applicable for high-throughput, single-cell functional genetic screening across the plant kingdom.
Why it matches plant phenotyping methods植物の単一細胞スクリーニング基盤を開発し、細胞表面の表現型マーカーで関心細胞を分離する技術が研究の中心であるため、植物表現型取得・選別法として含める。
abstractHere we report a single-cell screening platform, PIVOT (protoplast isolation after virus overexpression in planta), to accelerate identification and functional characterization of plant genes.
In root nodule symbiosis, symbiosome compartments accommodate nitrogen-fixing rhizobia inside the plant cell. Differentiated into bacteroids, the rhizobia are surrounded by a peribacteroid space and a plant-derived peribacteroid membrane, which separates them from the plant cytoplasm but allows signal and nutrient exchange between host and microbe. The morphological features of symbiosomes are primarily determined by ultrastructural single focal plane imaging, with limited information about spatial details. This study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants. The 3D model of a mature colonised root nodule cell region demonstrates a dense, puzzle-like arrangement of symbiosomes relative to one another and adjacent plant organelles. The symbiosome shape and size depends on the orientation and number of bacteroids within the compartment and features connective tubular structures. Furthermore, vesicular structures, some likely of bacterial origin, were present at the interface. The study presents a multi-angled analysis of symbiosome-related structures, highlighting their volumes, spatial distribution, and pronounced compactness. Interface associated vesicles, protrusions and connective structures hint towards a dynamic and flexible system that contributes to the plant-microbe crosstalk.
Why it matches plant phenotyping methods植物根粒内の共生体の形態・体積・空間分布を、2D/3D電子顕微鏡で解析することが研究の中心であり、植物組織の構造的表現型を取得する方法論的応用に該当する。
abstractThis study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants.
Female and male meiosis often differ in many aspects, such as their duration and the frequency as well as the positioning of crossovers. However, studying female meiosis is often very challenging and thus, much less is known about female versus male meiosis in many species including plants, where meiosis occurs deep within the ovules. To approach this gap, we developed a live-cell imaging system for female meiocytes in Arabidopsis (Arabidopsis thaliana) in this study. This allowed us to obtain a temporally resolved cytological framework of female meiosis in the wild type that serves as a guiding system for future studies. Subsequently, we have applied this imaging system here to study mutants in cyclin-dependent kinase inhibitors, in which a designated female meiocyte undergoes several mitotic divisions before entering meiosis. This mutant context enabled us to address when a meiocyte is committed to meiosis, a key question during reproductive development and in particular for the analysis of apomictic species in which meiosis is skipped.
Why it matches plant phenotyping methodsシロイヌナズナの雌性減数分裂を対象とするライブセルイメージングシステムを開発し、時間分解された細胞学的表現型の取得に用いており、フェノタイピング手法が研究の中心である。
abstractwe developed a live-cell imaging system for female meiocytes in Arabidopsis (Arabidopsis thaliana) in this study.
Branched broomrape (Phelipanche ramosa) is an obligate root parasitic weed that threatens tomato production in many regions. Progress in understanding host resistance mechanisms has been hindered by the parasite's subterranean life cycle and the technical limitations of traditional soil-based assays. Here, we introduce an integrated experimental framework that enables molecular, genetic, and cellular analysis of broomrape parasitism in tomato under controlled conditions. We implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots, and a dual-compartment in vitro co-culture system supporting parasite infection of transgenic hairy roots. This methodology enabled rapid functional testing of candidate host resistance genes, exemplified by CRISPR-edited mutants of the tomato transcription factor SCHIZORIZA (SlSCZ), which displayed localized lignin accumulation at the parasite entry site in the root. The observed lignification suggests a role for this gene in regulating inducible cell wall lignification against broomrape. Together, these tomato-focused integrated methods enable reproducible imaging, genetic perturbation, and high-resolution analysis of host-parasite interfaces. These provide a scalable platform for dissecting broomrape resistance and accelerating resistance gene discovery in tomato and a critical tool for combating the devastating consequences of this parasite on agriculture.
Why it matches plant phenotyping methodsトマト根上の寄生進展を非破壊・リアルタイムに観察する共培養系と再現可能なイメージングを開発し、植物の感染状態を取得する基盤が研究の中心である。
abstractWe implemented a transparent, soil-less co-cultivation system for non-destructive, real-time monitoring of broomrape development on tomato roots
Abstract The plant plasma membrane is a highly dynamic structure that is crucial for cell compartmentalization, the maintenance of (bio)chemical gradients, signaling and cell growth and responses to stress. In plants, plasma membranes are tightly connected to the cell walls that encase them. These cell walls can act as diffusion barriers and prevent the use of a wide range of synthetic fluorescent probes that have been developed to study animal cell membranes, which lack a cell wall, with live functional imaging. Here, we introduce LipoTag, a minimal chemical motif that, upon chemical conjugation, transforms hydrophobic fluorophores into water-soluble, membrane-targeted probes that can permeate plant cell walls to reach their intended location. LipoTag uses a localized positive charge in combination with a short aliphatic spacer to direct cargo to the plasma membrane. We used LipoTag to develop a suite of membrane-specific fluorescent probes that work in walled organisms beyond the plant kingdom. In addition, we used LipoTag to develop functional reporters for the quantitative imaging of membrane density, lipid order and membrane oxidation in living plant tissues. LipoTag forms a modular platform for exploring the plant plasma membrane with a suite of contemporary imaging modalities.
Why it matches plant phenotyping methods植物組織で膜密度・脂質秩序・膜酸化を定量イメージングする蛍光プローブと基盤を開発しており、植物状態の取得法が中心的です。
abstractwe used LipoTag to develop functional reporters for the quantitative imaging of membrane density, lipid order and membrane oxidation in living plant tissues.
Monitoring mercury ion (Hg 2+ ) accumulation and its phytotoxicity in plants requires analytical methods that provide both spatial and functional information beyond simple destructive quantification. We report HBTD-Hg , a dual-target fluorescent probe engineered to anchor to the cell membrane and exhibit a selective fluorescence turn-on response to Hg 2+ . This design enables simultaneous in situ imaging of Hg 2+ distribution and real-time assessment of membrane integrity in live plant tissues. The probe quantifies Hg 2+ with a detection limit of 49.7 nM. Importantly, it allows for the nondestructive tracking of Hg 2+ uptake dynamics directly through leaf imaging. Furthermore, it directly visualizes the ensuing Hg 2+ induced loss of cellular membrane integrity, including distinct vesiculation. This work provides a versatile tool for the real-time assessment of heavy metal stress, effectively linking environmental ion detection to observable cytological damage in plants.
Why it matches plant phenotyping methods植物組織の膜完全性・細胞障害を生体蛍光イメージングで可視化・追跡するプローブを開発しており、植物ストレス状態の取得法が中心的である。
abstractWe report HBTD-Hg , a dual-target fluorescent probe engineered to anchor to the cell membrane and exhibit a selective fluorescence turn-on response to Hg 2+ .
Summary (1) Rationale Quantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) Methods We computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key results ECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusion The ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.
Why it matches plant phenotyping methods植物形状を定量化・分類し、葉形状や葉脈を推定するECTベースの計算手法を中心に開発・評価しているため、植物フェノタイピング手法研究に該当する。
abstractQuantifying and predicting plant morphology is central to understanding development and evolution
Pavement cells in the Arabidopsis thaliana epidermis span a wide range of sizes and ploidy levels, but rules that generate this heterogeneity across an organ remain unclear. Clark et al. identify a shared genetic pathway that promotes large, polyploid pavement cells in both sepals and leaves, then ask whether the familiar "scattered" distribution of giant cells is truly random. By combining whole-tissue imaging with two independent computational randomization approaches that regenerate tissues from segmented images while preserving cell size distributions and key boundary constraints, together with a stochastic cell-autonomous model, the authors show how an initially random pattern can later appear clustered relative to a changing random baseline as tissues grow and subdivide. The study provides a quantitative framework for testing spatial organization in cellular mosaics where point-based methods fail, and it shows how proliferation history can convert early stochastic fate decisions into a statistically non-random mature pattern.
Why it matches plant phenotyping methods全組織イメージングと、セグメンテーション画像を用いた独立な計算的ランダム化・確率モデルを組み合わせ、植物組織の細胞サイズ・倍数性・空間パターンを定量解析する枠組みが研究の中心である。
abstractBy combining whole-tissue imaging with two independent computational randomization approaches that regenerate tissues from segmented images while preserving cell size distributions and key boundary constraints, together with a stochastic cell-autonomous model
This study showcases and validates a fully-printed, low-cost microneedles (MNs) device integrated with environmental sensors and NFC wireless readout for real-time monitoring of changes in plant total ionic conductivity. The Aerosol-Jet printed MNs patch enabled minimally invasive impedance measurements for the monitoring of leaf hydration and ion uptake. Inkjet-printed temperature and humidity sensors provided complementary environmental and leaf's microclimate data. Both sensing platforms were integrated in a cost-effective, easy-to-use wooden clip assembly, granting adhesion and reproducible MNs insertion. Dehydration and ions uptake tests demonstrated that the devices can detect ionic variations in different cellular compartments of the leaves, with distinct responses across plant species reflecting their physiological and anatomical differences. The NFC system validation confirmed that wireless, battery-free readout can be used to observe similar impedance trends with respect to the ones observed with conventional potentiostat measurements. Overall, the presented platform establishes a scalable approach toward simple, field-deployable plant monitoring systems, supporting future development of species-tailored and functionally enhanced sensors for precision agriculture.
Why it matches plant phenotyping methods植物の葉の水分状態・イオン吸収を測定する低侵襲センサーとNFC読出しプラットフォームの開発・検証が研究の中心であり、植物状態の表現型を直接取得している。
abstractThis study showcases and validates a fully-printed, low-cost microneedles (MNs) device integrated with environmental sensors and NFC wireless readout for real-time monitoring of changes in plant total ionic conductivity.
The plant plasma membrane is a highly dynamic structure that is crucial for cell compartmentalization, the maintenance of (bio)chemical gradients, signaling and cell growth and responses to stress. In plants, plasma membranes are tightly connected to the cell walls that encase them. These cell walls can act as diffusion barriers and prevent the use of a wide range of synthetic fluorescent probes that have been developed to study animal cell membranes, which lack a cell wall, with live functional imaging. Here, we introduce LipoTag, a minimal chemical motif that, upon chemical conjugation, transforms hydrophobic fluorophores into water-soluble, membrane-targeted probes that can permeate plant cell walls to reach their intended location. LipoTag uses a localized positive charge in combination with a short aliphatic spacer to direct cargo to the plasma membrane. We used LipoTag to develop a suite of membrane-specific fluorescent probes that work in walled organisms beyond the plant kingdom. In addition, we used LipoTag to develop functional reporters for the quantitative imaging of membrane density, lipid order and membrane oxidation in living plant tissues. LipoTag forms a modular platform for exploring the plant plasma membrane with a suite of contemporary imaging modalities.
Why it matches plant phenotyping methods植物細胞膜を対象とした蛍光プローブと機能レポーターを開発し、生体植物組織で膜密度・脂質秩序・膜酸化を定量画像化する方法を提示しており、表現型取得法が中心である。
abstractHere, we introduce LipoTag, a minimal chemical motif that, upon chemical conjugation, transforms hydrophobic fluorophores into water-soluble, membrane-targeted probes that can permeate plant cell walls to reach their intended location.
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens, we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis.
Why it matches plant phenotyping methods植物細胞表面形態から細胞壁弾性分布を推定する新規測定法を開発・検証しており、植物形質の取得手法が研究の中心である。
abstractwe introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all data and code (including code demonstrations for the surface morphology-based elasticity inference method) in a public GitHub repository, which is listed in allowed_urls.Code · publicData Availability: All relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-model .Open asset ↗rholee-xu/surface-modellines:127-138Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Eragrostis curvula serves as a valuable model for studying diplosporous apomixis due to its unique reproductive mode, wide ploidy range, and extensive genomic resources. A major limitation for reproductive studies in this species is the difficulty of isolating female tissues at precise developmental stages, for example, for transcriptomics studies, since different floral tissues can introduce expression noise from non-target tissues. To overcome this, we performed a detailed cytoembryological and morphometric characterization of male and female development in seven E. curvula genotypes with different ploidy levels (2X-7X) and reproductive modes (sexual, facultative apomictic, and obligate apomictic). Using differential interference contrast microscopy and methyl salicylate clarification, we described key cytological stages of male and female development. These stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars. Pistil length showed the strongest association with female developmental stage, particularly during the early phases of ovule development, enabling more precise staging. Synchrony between male and female development was also evaluated, revealing no consistent differences among reproductive modes or ploidy levels. This genotype-informed framework provides a practical tool for stage prediction and tissue selection, supporting future reproductive, developmental, and comparative studies in E. curvula and related grasses.
Why it matches plant phenotyping methods花器官の形態計測を細胞発生段階の推定に体系的に対応付け、遺伝子型別の発達カレンダーと再利用可能なステージ予測手法を構築しており、表現型取得が中心である。
abstractThese stages were then correlated with external floral parameters, including pistil, ovary, style, and anther length, to generate genotype-specific developmental calendars.
The actin cytoskeleton forms a dynamic network composed of filaments that remain flexible when bundled up, leading to complex filamentous structures in plant cells. Understanding the properties of these filamentous structures under different conditions and in different cell types can provide insight into their function. Yet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties. To address this problem, we devised a network-based approach termed Graph of Filaments over Time (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from two-dimensional time series imaging data. Our comparative analyses using both synthetic test cases and real-world actin cytoskeleton networks of Arabidopsis thaliana hypocotyls exposed to different treatments demonstrated that GraFT accurately traces and tracks actin filamentous structures. Moreover, GraFT facilitates automated quantification of properties for filamentous structures, providing fine-grained insights of effects of different treatments on the level of individual structures. Therefore, GraFT offers a substantial step toward an automated framework facilitating robust spatiotemporal studies of the plant actin cytoskeleton.
Why it matches plant phenotyping methods植物細胞の画像時系列からアクチン繊維構造を追跡・セグメント化し、その時空間特性を自動定量する手法の開発と検証が中心であるため。
abstractwe devised a network-based approach termed Graph of Filaments over Time (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from two-dimensional time series imaging data.
Reproduction assets foundThe paper's authors publicly release the GraFT tool and data-processing code on GitHub (MIT licensed) with an archived Zenodo version. The paper-specific data files are stated to be on Zenodo (DOI 10.5281/zenodo.10476058), but that URL is not among the allowed URLs, so only the code assets are reported. The SciencePlotCode · publicThe tool GraFT and code created for data processing can be found on the GitHub repository https://github.com/Oesterlund/GraFT and is MIT licensed together with an archived version for reproducibilityOpen asset ↗https://github.com/Oesterlund/GraFTlines:159-261Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Instance segmentation of moso bamboo cells is a critical step in quantitative structural analysis of bamboo materials and plant phenomics research. Moso bamboo tissues are mainly composed of vascular bundles and parenchyma cells. Within vascular bundles, fiber cells exhibit thick cell walls and extremely dense arrangements, whereas vessel cells are characterized by large diameters and complex internal structures. These features frequently lead to blurred boundaries, structural complexity, and local overexposure in microscopic images, making it difficult for traditional segmentation algorithms to achieve stable and accurate results. Although the U-Net has demonstrated outstanding performance in biological microscopic image analysis, its feature extraction capability and boundary recognition stability remain insufficient when dealing with the composite structure of moso bamboo. To address these challenges, this study proposes an improved model based on a multi-scale attention mechanism, termed MBMSA-UNet (Moso Bamboo Multi-Scale Attention U-Net). Building upon the encoder-decoder architecture of U-Net, the proposed model introduces a multi-scale channel-spatial attention block, aiming to handle the pronounced morphological and scale differences among vessels, fibers, and parenchyma cells. By adaptively reweighting features at different scales, the model enhances cross-layer feature fusion and strengthens responses to key regions, thereby effectively suppressing local overexposure interference and emphasizing boundary features between different cell types. Experimental results demonstrate that, compared with the U-Net and several of its improved variants, MBMSA-UNet achieves higher segmentation accuracy and greater robustness on microscopic images of moso bamboo, providing a solid foundation for fine-grained quantitative analysis of complex bamboo tissues.
Why it matches plant phenotyping methods竹組織の顕微鏡画像から細胞をインスタンス分割する手法を開発・比較検証しており、植物形態の定量解析に直結する方法が中心である。
abstractInstance segmentation of moso bamboo cells is a critical step in quantitative structural analysis of bamboo materials and plant phenomics research.
Cellulose synthase complexes (CSCs) play a central role in plant cell wall formation. Their dynamic behavior at the plasma membrane leads to the deposition of cellulose microfibrils into the apoplastic space, thereby shaping the architecture and mechanical properties of the cell wall. Although previous imaging studies have provided important insights into CSC dynamics and localization, standardized and reproducible workflows for quantitative measurements of CSC speed and density remain limited. Here, we present a reproducible live-cell imaging and analysis workflow for quantifying the speed and density of fluorescently labeled CSCs at the plasma membrane in Arabidopsis thaliana . The protocol integrates optimized spinning-disk confocal imaging, surface-based projection of z-stack recordings, automated detection of diffraction-limited CSCs foci, and kymograph-based speed measurements using freely available tools in Fiji. While selected steps, such as region of interest definition and parameter selection for spot detection or trajectory analysis, remain user-guided, these decisions are constrained to well-defined stages within an otherwise standardized pipeline, thereby reducing variability and improving reproducibility across experiments. The workflow has been validated across multiple tissues, reporter lines, genetic backgrounds, and perturbation conditions in Arabidopsis and enables robust comparative analysis of CSC dynamics. Beyond CSCs, this workflow is expected to be adaptable to other fluorescently labeled proteins that appear as diffraction-limited foci at or near the plasma membrane. Key features • Enables accurate CSC speed and density measurements during both primary and secondary cell wall formation using spinning-disk confocal time-lapse imaging. • Combines surface-projection, kymograph analysis, and high-throughput particle detection to quantify CSC dynamics even in crowded or low-signal plasma membrane regions. • Provides a standardized analysis workflow validated across multiple Arabidopsis genotypes, including inducible systems and mutant backgrounds that possess altered cell wall biosynthesis. • Applicable to any fluorescently labeled diffraction-limited foci at or near the plasma membrane, extending the workflow beyond CSCs.
Why it matches plant phenotyping methods植物細胞内のセルロース合成酵素複合体の速度・密度という観測可能な状態を、ライブイメージングと自動解析で定量する再現可能な手法を開発・検証した研究であり、方法が中心です。
abstractHere, we present a reproducible live-cell imaging and analysis workflow for quantifying the speed and density of fluorescently labeled CSCs at the plasma membrane in Arabidopsis thaliana .
Transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae). Well-preserved foliar compressions of this taxon were collected from the Punta del Barco Formation (Baqueró Group, Aptian, Patagonia, Argentina). TEM sections revealed six types of cell cuticles: two representing the ordinary epidermal cells (OEC) of the upper and lower cuticle, and four related to the stomatal apparatus and associated cells: subsidiary and guard cell cuticles, and inner and outer associated OEC cuticles. Cuticles comprise either a granular A2 layer (cuticle proper) and a spongy-fibrilous B1 layer (cuticular layer), or solely a B1 spongy layer, which is similar to that of Nothopehuen brevis and Brachyphyllum garciarum , two Cretaceous Araucariaceae from Patagonia. The statistical evaluation of quantitative measurements revealed the relationships and hierarchies between cell cuticle types and ultrastructural layers, revealing for the first time the precise identity of Araucariaceae cuticles. TEM-EDS revealed a significant presence of phosphorus (P) and chlorine (Cl), highlighting the potential taxonomic and paleoenvironmental relevance of the P/Cl ratio. Additionally, the six cell cuticle types found in A. grandifolia are shown in a dichotomous key, and a cuticle three-dimensional reconstruction is provided. Finally, the paleoenvironment conditions under which the A. grandifolia plant lived during the Aptian in Patagonia are also inferred.
Why it matches plant phenotyping methods葉のクチクラ微細構造・元素組成という植物器官形質を、TEM、EDS、各種顕微鏡、定量解析、3D再構成で体系的に取得・解析しており、観察が分類・古生態の補助的な routine 測定に留まらず、方法に基づく形質記載の中心となっている。
abstractTransmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae).
Summary Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo . Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap. Significance statement Multiplexing genetically encoded biosensors in plants has been limited by overlapping fluorescent signals and strong autofluorescence. This study presents an optimized framework for linear unmixing and provides a MATLAB-based organelle segmentation tool, allowing precise quantification of multiple fluorescent reporters in vivo and advancing real-time visualization of complex cellular processes in plants.
Why it matches plant phenotyping methods植物組織における蛍光シグナルの分離、検出、セグメンテーション、定量化手法を開発・比較・検証しており、植物の細胞・細胞小器官状態を取得する方法が中心である。
abstractTo separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing.
Reproduction assets foundThe paper deposits raw confocal image data on Zenodo (10.5281/zenodo.19691651) and a MATLAB nuclei segmentation/quantification script on GitHub. Only the GitHub repository URL appears in the allowed URL list, so the code asset is reported; the Zenodo image deposit is noted but cannot be listed without a matching URL.Code · publici (ORCID: 0000-0002-6235-2816)
14
15 DATA AVAILABILITY
16 Raw image data supported with metadata were deposited to Zenodo:
17 10.5281/zenodo.19691651and can be opened with LAS X available at https://www.leica-
18 microsystems.com/products/microscope-software/p/leica-las-x-ls/downloads/. MATLAB script
19 was deposited to GitHub: https://github.com/NIB-SI/Nuclei-segmentation.
20 FUNDING
21 This research was funded by the Slovenian Research and Innovation Agency (research core
22 funding No. P4-0165, P4-0463, projects J4-1777, J4-60073, J4-70169 and ARIS program for
23 young researchers).
24 CONFLICT OF INTEREST
25 The authors declare no conflicts of interest. This article does not contain any Open asset ↗NIB-SI/Nuclei-segmentationpdf-layout-page:1 lines:1-34Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Branching forms are ubiquitous in nature and have evolved repeatedly across scales and species. An important goal of developmental biology remains to identify similarities and differences in the regulatory mechanisms underlying branching. Here, we investigate the branching filaments that form upon spore germination in mosses, using Physcomitrium patens as a model species. To identify the macroscopic rules governing filament patterning, we developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations. Our quantitative analysis reveals that branch patterning in moss filaments can be captured by a simple probabilistic model in which subapical cells have a near-equal probability of producing – or not producing – a side-branch between successive apical cell divisions. This framework provides a quantitative basis for comparing the developmental rules driving branching morphogenesis within and beyond the plant kingdom.
Why it matches plant phenotyping methodsコケ植物のフィラメント形態を対象に、高解像度3D画像取得、単一細胞レベルの構造再構築、数学的表現による定量化パイプラインを開発しており、植物表現型の取得・抽出手法が研究の中心です。
abstractwe developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations
ArabidopsisChlorophyll fluorescenceMicroscopyLiDAR / point cloudCell / cellular structureRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Embryogenesis in the model plant Arabidopsis thaliana provides a framework for understanding how cell polarity and patterning coordinate with hormonal signalling to establish the plant body plan. Following fertilisation, the zygote divides asymmetrically to generate apical and basal lineages, establishing the apical-basal axis that defines future shoot and root poles. Genetic and molecular analyses of classical mutants including gnom, monopteros (mp), bodenlos (bdl) and topless revealed that localised auxin biosynthesis, directional transport and downstream transcriptional responses are central to apical-basal axis establishment and organ initiation. The main components of this regulation are polarly localised PIN auxin transporters and downstream modules involving MONOPTEROS and WUSCHEL-RELATED HOMEOBOX transcription factors. Advances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics. Live ovule imaging setups with confocal laser scanning and multiphoton microscopes enable real-time observation of embryo development, while laser-assisted cell ablation can be used to probe cell-to-cell communication and fate plasticity. Together, these methodological breakthroughs position Arabidopsis embryos as a prime model for dissecting the chemical and biophysical cues that shape plant development.
Why it matches plant phenotyping methods胚発生を解析するための蛍光イメージング、三次元再構成、ライブ撮像などの方法論と定量的な細胞形態・分裂方向測定が中心であり、植物フェノタイピング手法に該当する。
abstractAdvances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics.
Laboratory / benchtopChlorophyll fluorescenceMicroscopyCell / cellular structureFlowerRootTissueVisualization / data management
Abstract Cryo‐scanning electron microscopy (CryoSEM) permits the preparation and detailed imaging of bulky samples while keeping them in a hydrated state. For plant biology, cryofractures give information on cell ultrastructure and tissue organisation within a much larger context that is the whole organ or organism. To date, a method to locate fluorescence reporters on the cryofracture has not been reported. Our approach uses a stereofluorescence microscope with an 80 mm working distance and a high‐zoom ratio to image the fracture through a viewing port of the cryopreparation chamber while the sample is still frozen and under vacuum. We have applied this method to look at fluorescent reporters of auxin transport and signalling in plant shoot apices and seedlings, the expression of a poorly characterised gene in the young floral pedicel and nitrogen‐fixing rhizobial bacteria, expressing GFP, in root nodules. This method is applicable to any cryopreserved bulky sample that has a fluorescent output and paves the way for correlative light‐electron microscopy for cryoSEM‐based imaging.
Why it matches plant phenotyping methods植物試料の蛍光レポーターを凍結破断面上で位置特定・画像化する新規CryoFluorSEM法の開発であり、植物の構造・組織状態を取得する方法が中心である。
abstractTo date, a method to locate fluorescence reporters on the cryofracture has not been reported.
Heterogeneity of cellular and subcellular elemental distribution is poorly captured by conventional techniques due to limited sensitivity, throughput, and resolution. Single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) enables quantitative single-cell element analysis but remains challenging in multicellular plants because of plant cell complexity and lack of protocols. Herein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts. An optimized fixation protocol (1% (v/v) glutaraldehyde for 15 min for protoplasts; 2.5% (v/v) for 30 min for chloroplasts) was applied to preserve structural integrity, with endogenous P and Mg identified as specific indicator elements for protoplasts and chloroplasts, respectively. To reduce interference from broken cells, a broken-signal correction method was employed during data processing. Following isolation, purification, and glutaraldehyde fixation, Pisum sativum leaf protoplasts and chloroplasts were subjected to SC-ICP-MS analysis. Quantitative analysis revealed significant elemental heterogeneity, with P as the most abundant in protoplasts (67.1-135 fg cell -1 ) and Mg as the most abundant in chloroplasts (33.6-41.5 fg cell -1 ). This technique advances single-cell element analysis in plants, enabling new insights into nutrient distribution, metal accumulation, and cellular responses to environmental stress beyond conventional techniques.
Why it matches plant phenotyping methods植物細胞・葉緑体の元素分布という生理状態を定量するSC-ICP-MS法の開発が研究の中心であり、固定条件や破損シグナル補正も技術的に検討している。
abstractHerein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts.
The nucleus is the characteristic organelle for eukaryotic organisms. Unlike the classic textbook view of static two-dimensional nuclei, nuclear shape is dynamic inside the live cell. The alteration or deformed nuclear shape is the hallmark of cancer in animal cells and environmental stress in plants. The nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, we have limited knowledge about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To circumvent this issue, we are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root. The live cell imaging was performed in control and salt-stressed conditions. We utilized these captured movies to analyze through open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. Using this method, we have demonstrated that chromatin velocity is decreased in salt-treated conditions. This method will be widely applied to quantitative live cell imaging of nuclear shape and chromatin dynamics during plant development and environmental stress. Summary This process aims to simultaneously record nucleus and chromatin dynamics in Arabidopsis thaliana roots and investigate changes in these dynamics in response to developmental and environmental cues.
Why it matches plant phenotyping methods植物の核形状・クロマチン動態をライブイメージングと画像解析で定量化する手法が中心であり、環境ストレス下の植物状態を測定する再利用可能なワークフローを提示している。
abstractwe are utilizing a dual fluorescently tagged marker lines – nuclear envelope protein and chromatin – to perform live cell imaging in the model plant Arabidopsis thaliana root.
The movement, distribution, and interactions of organelles are cell-type specific, responding to fluctuating metabolic and environmental cues and governing the efficiency of plant physiology and stress response. The directional motility of various plant organelles is predominantly driven by the actomyosin system, yet the distinct functionality of these organelles across plant tissues presupposes organelle-specific regulation of motility, which requires the detection of subtle shifts in dynamics. Meanwhile, studies that comprehensively characterize and directly compare the simultaneous movement of multiple types of organelles within the same cell are limited. Here, we visualized peroxisomes, mitochondria, chloroplasts, Golgi bodies, and actin filaments simultaneously in tobacco (Nicotiana tabacum) to evaluate organelle organization and motility within the context of one another. Quantitative analysis of multiple motility factors enabled us to identify peroxisome motility in tobacco mesophyll as distinct from other organelles. Further analysis in Arabidopsis (Arabidopsis thaliana) revealed that both mitochondria and peroxisomes are slower in mesophyll cells compared to epidermis in normal growth conditions, but their motility patterns are unique from one another across leaf tissue after plants experienced conditions that induce photorespiration, a metabolic pathway requiring the concerted action of chloroplasts, peroxisomes, and mitochondria. Our quantitative analysis of thousands of organelles across species, cell type, and physiological conditions unveils distinct modulation of motility according to organelle identity and function. The extensive combinatorial characterizations of plant organelle movement provide a fundamental resource for the future discovery of molecular mechanisms driving the movement and distribution of diverse organelles.
Why it matches plant phenotyping methods複数オルガネラを同時可視化し、運動性を定量抽出する画像解析ワークフローが研究の中心で、植物細胞の生理状態を表す測定法として実質的に適用されている。
abstractHere, we visualized peroxisomes, mitochondria, chloroplasts, Golgi bodies, and actin filaments simultaneously in tobacco (Nicotiana tabacum) to evaluate organelle organization and motility within the context of one another.
Rice stem performs assimilate transport and promises sturdiness due to cell wall structure and composition. However, less is known about the genetic basis of its structural characteristics. In this study, for the first time, the scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits collected from the cross-sections of 147 rice accessions. Genome-wide association studies (GWAS) identified 54 significant single-nucleotide polymorphisms (SNPs; integrated into 28 quantitative trait loci) residing in the genic sequences of rice (promoter and coding DNA sequence), and classified into three groups: 1) cell wall-defining genes, 2) cell size-defining genes, and 3) transcription factors. DUF246 and DUF1218 , galactose oxidase , mitochondrial Rho GTPase , WUSCHEL-related homeobox 5 and scarecrow-like 9 are the novel genes identified among the 21 candidate genes. These genes may play roles in stem development traits, specifically the distance from the vascular bundle to the end of the parenchymal cells (DVBEPC) and the thickness of the straw cell wall in the protruding part (TSCWP). Post-GWAS analyses showed one significant haplotype on chromosome 4 and 25 significant epistatic interactions. Most notably, nine TF families were repeatedly detected among the significant QTL. Os07g0644300 (XPA-binding protein 2), located in the q7-1 genomic segment and associated with DVBEPC, was found to have a missense mutation. Phenotyping via SEM imaging provides precise genome-phenome association in understanding rice stem cell size and cell wall architecture, which ultimately can define biomass and lodging resistance. systematic scheme of the current study • This study pioneers the use of SEM imaging to digitally phenotype rice stem traits, revealing the genetic basis of cell size and wall structure using GWAS. • The GWAS studies identified 28 QTLs and 21 candidate genes, including novel ones linked to stem strength and architecture. • The findings from the study enhance our understanding of rice stem development and provide a foundation for improving biomass and lodging resistance through precise genome-phenome associations.
Why it matches plant phenotyping methodsSEM画像を用いたイネ茎のデジタル表現型取得法の開発が研究の中心で、18形質を定量化しGWASに適用しているため。
abstractthe scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits
Fluorescent biosensors provide a non-invasive and versatile approach to monitor dynamic changes in metabolite or ion concentrations within live cells. Specifically, FRET-based biosensors enable ratiometric measurements that report subcellular analyte levels while being independent of biosensor expression levels. We describe a comprehensive and standardized protocol for the quantitative ratiometric analysis of FRET-based biosensors in plants. The protocol guides users through live-sample preparation, confocal image acquisition of donors, FRET, and acceptor channels, binary mask generation for subcellular regions of interest, followed by regression-based ratiometric data analysis. The primary output is regression-derived ratiometric readout that enables quantitative comparisons between genotypes, tissues, developmental stages, and treatment conditions. Using the cpFLIPPi-5.3m biosensor for inorganic phosphate as an example, we demonstrate measurement of inorganic phosphate levels in the chloroplast stroma of A. thaliana. This analytical framework is broadly applicable to other FRET-based biosensors and model systems, enabling precise spatiotemporal quantification of metabolites and ions in vivo. This strategy delivers measurable insights into the subcellular dynamics of metabolites and ions, supporting comparisons under varied experimental settings.
Why it matches plant phenotyping methods植物の細胞内代謝物・イオン濃度を定量するFRET画像解析プロトコルの開発・実証であり、表現型取得と解析手法が中心である。
abstractWe describe a comprehensive and standardized protocol for the quantitative ratiometric analysis of FRET-based biosensors in plants.
Lignin plays a central role in the formation and function of secondary cell walls in vascular plants. However, the structural consequences of lignin modification for cell wall properties and cellular function in grasses remain poorly understood. Here, we investigated how cinnamyl alcohol dehydrogenase (CAD) deficiency alters vascular cell architecture in Sorghum bicolor, using the brown midrib-6 (bmr6) mutant as a model system. Biochemical and histochemical analyses confirmed altered lignin chemistry in bmr6, including increased incorporation of hydroxycinnamaldehyde residues and reduced tricin levels. We applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution. PXCT enabled measurements of wall thickness distribution and lumen shape along tracheary elements. Analyses revealed no significant differences in wall thickness between wild-type and bmr6 plants. However, three-dimensional morphometric descriptors indicated reduced lumen convexity in bmr6, suggesting localized modifications not detectable by conventional two-dimensional imaging. Water flow numerical simulations through PXCT-derived images indicated reduced vessel permeability and simulated hydraulic conductivity in bmr6, suggesting that subtle geometric changes may influence performance. These findings highlight the value of three-dimensional imaging for resolving cell wall organization and provide new insight into the architectural resilience of grass xylem in response to targeted lignin modification. HighlightThree-dimensional X-ray nano-imaging reveals alterations in the cell wall architecture that affect simulated hydraulic performance under reduced CAD activity in sorghum.
Why it matches plant phenotyping methods植物の木部細胞壁形状をナノスケール3D画像から定量化するPXCT手法が研究の中心であり、壁厚・内腔形状・形態記述子を抽出しているため、植物表現型計測の実質的な適用に該当する。
abstractWe applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution.
Analyzing morphological parameters and growth at the cell level is crucial for a better understanding of organ development. The most popular approach for quantitative analyses of development relies on confocal imaging of an organ expressing a fluorescent membrane marker over several time points, and analyzing the confocal dataset to quantify changes in morphological parameters and growth rate. These analyses are commonly done on the surface, with the assumption that changes in the surface of a cell reflect faithfully changes of the whole, volumetric cell. However, this assumption has not yet been systematically and explicitly tested. It is also not clear how the correlation between areal and volumetric measurements would change over time. Here, we combined time-series live imaging and three-dimensional reconstruction to compare surface and volumetric size and growth of cotyledon and sepal epidermal cells in Arabidopsis thaliana. We found that on average, surface area is tightly correlated with volume in both cell size and growth, supporting the use of surface area as a good proxy for volume at the population level. However, cells with similar surface areas or surface growth can display substantial differences in volume and volumetric growth. This happens due to variation in cell thickness, which in turn is controlled by microtubules. These findings highlight limitations of surface-based metrics, and call for volumetric analyses if a more accurate assessment of cell parameters is needed.
Why it matches plant phenotyping methods植物細胞の表面積・体積・成長をライブイメージングと3次元再構成で比較し、表面積を体積の代理指標として検証することが中心である。
abstractHere, we combined time-series live imaging and three-dimensional reconstruction to compare surface and volumetric size and growth of cotyledon and sepal epidermal cells in Arabidopsis thaliana.
Summary Anatomical and histochemical imaging of grass root systems relies on tissue sectioning and cell wall staining dyes because molecular reporter lines are limited for most organisms. Distinct staining dyes require variable incubation time and concentration across different tissues and organisms. As a result, staining with multiple dyes becomes time consuming or challenging. Here, we report a rapid method to perform simultaneous triple staining on a glass slide. The entire protocol requires ∼4 hours and a smaller volume of stain than traditional methods. We tested this method using the roots of two economically important crops, Triticum aestivum (wheat) and Zea mays (maize), as proof of concept. We have also demonstrated the presence of exodermis in wheat roots. Additionally, we identified the formation of polar lignin caps in maize exodermis using our simultaneous triple staining method. This method empowers a quantitative approach to cell biology by elucidating cell-type specific spatio-temporal distribution of cell wall materials in monocot root systems.
Why it matches plant phenotyping methods単子葉植物の根における細胞壁物質の細胞型別・時空間分布を定量的に可視化する同時三重染色法の開発が中心であり、植物状態の取得・解析手法に該当する。
abstractHere, we report a rapid method to perform simultaneous triple staining on a glass slide.
Plants defend against pathogens such as fungi by initiating coordinated structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx and calcium oscillations that drive defense gene expression, yet the mechanisms by which these signals encode stressor intensity and propagate systematically remain unclear. Here, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens (Hedw.) upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent imaging of cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon stimulus removal. We implemented an unbiased image segmentation algorithm using pixel-based k -means clustering to automatically locate regions with specific oscillatory signatures. Calcium dynamics were distinct across adjacent cells, distinguishable by cell type, and significantly modulated by circadian rhythm, adaptation time within the device, and stimulus timing. Cytosolic calcium oscillations, which rose and fell symmetrically within about 60 s, occurred spontaneously during the subjective night and following short adaptation periods. Chitin elicited strong oscillations with increased frequency, amplitude, and duration, and repeated pulses entrained regular, colony-wide oscillations at the stimulation interval. This study complements prior investigations of whole plant and growth tip dynamics and provides a quantitative framework to study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methods植物細胞のカルシウム動態を定量するマイクロ流体・蛍光イメージング系と自動画像セグメンテーションを開発し、植物の生理状態を抽出する方法が研究の中心である。
abstractHere, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens
Reproduction assets foundThe paper's Data Availability Statement explicitly makes analysis scripts and sample data publicly available on the authors' GitHub repository (albrechtLab/moss_calcium), and the MDPI supplementary materials (plants-15-00582-s001.zip) contain the paper's timelapse calcium-imaging videos and supplementary figures. Raw/全Code · publicData are available upon request. Analysis scripts and sample data are publicly available at https://github.com/albrechtLab/moss_calcium (accessed on 1 January 2026).Open asset ↗albrechtLab/moss_calciumlines:188-251Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Lipid droplets (LDs) play pivotal roles in crop physiology, stress adaptation, and product quality by serving as dynamic reservoirs of energy and essential nutrients. However, the lack of rapid and accurate methods for on-site quantitative detection of LDs has hindered their comprehensive analysis in agricultural systems. Herein, we report the rational design and synthesis of three near-infrared (NIR) fluorescent probes, YD-1 , YD-2 , and YD-3 , for the sensitive and specific detection of LDs in crops. These probes feature a hydrophobic donor-π-acceptor (D-π-A) framework comprising a 7-diethylaminoquinoline electron donor and distinct electron-accepting groups, including (5,5-dimethylcyclohex-2-en-1-ylidene)malononitrile (DCM), 4-fluorophenyl, and phenyl moieties. Among them, YD-1 exhibited the most pronounced fluorescence response to LDs, displaying intense NIR emission within LDs and remarkable quenching in non-LD regions, enabling high-fidelity LD imaging. TD-DFT calculations revealed that the superior sensitivity of YD-1 originates from its efficient intramolecular charge transfer (ICT) process, which is highly responsive to the polarity differences between LDs and their surroundings. YD-1 was successfully applied to monitor LDs dynamics in living cells, zebrafish under a high-fat diet, and soybeans at different growth stages. Furthermore, a custom-built mobile fluorescence analysis device was developed and coupled to probe YD-1 to achieve rapid, on-site quantitative detection of LDs in crop samples. This work provides a powerful analytical platform for on-site monitoring of LDs dynamics, offering insights into crop lipid metabolism and quality control.
Why it matches plant phenotyping methods作物の脂質滴を対象に、蛍光プローブと携帯型蛍光解析装置を開発し、生体内画像化および現場での定量検出を実現しているため、植物表現型取得法が研究の中心である。
abstractwe report the rational design and synthesis of three near-infrared (NIR) fluorescent probes, YD-1 , YD-2 , and YD-3 , for the sensitive and specific detection of LDs in crops.
MicroscopyCell / cellular structurePhysiological trait estimationGrowth / development / phenology
Paul Green hypothesized that growth anisotropy of plant cylindrical organs could be controlled by cell-wall elastic strain. The present study aimed to challenge this hypothesis through a robust experimental and analytical framework. By combining live-cell imaging of C. corallina internodal cells with controlled turgor pressure manipulation, we simultaneously measured, for the first time, both the growth strain rate tensor and the elastic compliance tensor derived from multiaxial mechanical testing in the same cell. Under Green’s hypothesis, a significant correlation should be observed between the two tensors in all directions. Our results revealed a moderate yet significant correlation between multiaxial elastic compliances and growth strain rates most pronounced in the axial direction. The ratio of axial-to-radial elastic compliance was significantly correlated with the ratio of radial-to-axial growth strain rates. In contrast, other quantities, such as the radial compliance components or the orientations of the two tensors relative to the cell axis showed no significant correlation. Furthermore the growth strain rate tensor was strongly age-dependent in both magnitude and orientation, unlike the elastic compliance. Finally, analysis of intra-tensor variability revealed that axial and radial components were strongly correlated for both tensors, with a lowered correlation in the principal axis decomposition.
Why it matches plant phenotyping methodsライブセルイメージングと力学試験を統合し、植物細胞の成長ひずみ率テンソルと弾性コンプライアンスを定量化する実験・解析手法が研究の中心であるため。
abstractThe present study aimed to challenge this hypothesis through a robust experimental and analytical framework.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro–computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA–volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights - NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC
Why it matches plant phenotyping methods3D核インスタンスセグメンテーション手法を開発し、多数のデータセットでベンチマークするとともに、核形態状態の定量的フェノタイピングへ応用しているため。
abstractHere we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation
Specialized host-microbe interfaces are central to cellular interactions in plants. Intracellular structures such as haustoria formed by filamentous pathogens mediate nutrient exchange and effector delivery to host cells. Despite their biological importance, the lack of quantitative frameworks has largely confined the study of these interfaces to qualitative observations, limiting our ability to compare infection strategies, cellular responses, and spatial organization across cells and tissues. Here, we present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images. Using an object-centric deep learning approach, HFinder enables robust identification of haustoria, microbial hyphae, and host organelles across diverse imaging conditions and pathosystems. We demonstrate that this framework supports quantitative analyses of subcellular processes at host-microbe interfaces, including effector secretion, perturbation of host cellular processes, and immune receptor accumulation at haustoria. HFinder provides a practical and scalable solution for the systematic digitalization of plant infection imaging data and establishes a general framework for quantitative studies of cellular dynamics at host-microbe contact zones.
Why it matches plant phenotyping methods植物と微生物の界面を共焦点画像から自動検出・分割し、ハウストリア等を定量解析する深層学習手法が中心であり、植物感染状態の画像ベース表現型解析に該当する。
abstractwe present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images.
Reproduction assets foundThe paper's HFinder pre-trained models (trained phenotyping models/checkpoints) are explicitly deposited on Zenodo with a public DOI matching an allowed URL. The training image dataset is also stated to be publicly available on Zenodo, but no separate authors' URL for it is given in the supplied blocks, so only the preModel / weights · publicFor
convenience, HFinder is distributed with pre-trained models that can be applied directly to confocal image
analysis (available on Zenodo: https://doi.org/10.5281/zenodo.17091805)Open asset ↗Zenodo · 10.5281/zenodo.17091805pdf-page:5 lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 5 Sept 2026
High-precision in vivo monitoring of ion fluxes is essential yet challenging studying plant electrophysiology such as growth regulation, signal transduction and stress responses. Existing methods for probing ion dynamics are limited by low sensitivity, high invasiveness that interferes physiological processes, and the inability to accurately resolve ion homeostasis with required spatial and temporal resolution. Here, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays manufactured on 1.2-μm-thick polymer substrates, which enable ultrasensitive and selective measurement of ionic current for month-long via scalable nanofabrication techniques. The fabricated PINE arrays have a smaller dimension than typical plant cells as well as less stiffness, facilitating minimally invasive integration with living plant cells. This subcellular-scale plant-electronic interface allows for reliable, selective detection of K + flux with a detection limit of ∼10⁻⁸ M, and thus allows continuous, stable monitoring of tomato stem cells over six weeks, capturing dynamic potassium fluctuations during all key growth stages. More importantly, the method permits long-term, real-time tracking of ion-specific dynamics without disrupting plant cellular structure or altering endogenous ion concentrations. Therefore, PINE provides unprecedented access to ion homeostasis and signaling networks, making it an excellent platform for precision agriculture and a foundational tool for future digital plant engineering.
Why it matches plant phenotyping methods植物細胞内のK+フラックスを長期間・リアルタイムに測定する超柔軟ナノ電極アレイを開発し、感度・選択性・長期安定性を実証した研究であり、植物生理状態の取得手法が中心である。
abstractHere, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays
MicroscopyCell / cellular structureClassificationGrowth / development / phenology
Understanding wood formation is critical for interpreting tree growth and carbon allocation under changing environmental conditions. While major progress has been made for gymnosperms, harmonized approaches for studying xylogenesis in angiosperms remain limited. Here, we present practical recommendations for observing and analysing xylogenesis in angiosperm trees, illustrated from examples from temperate and sub-Mediterranean forests. The perspective includes guidance on identifying xylem cell types in histological sections, defining developmental phenophases and establishing a workflow for data collection (and analysis). Annotated images are provided to support reproducibility and inter-observer consistency. We also discuss key challenges unique to angiosperms, including cell-type-specificities and wood type differences. Future research priorities include conserving histological images, extending xylogenesis to branches and coarse roots, enabling cross-biome comparisons and advancing kinetic analysis. This framework supports the coordinated expansion of angiosperm xylogenesis studies, enabling deeper insights into tree functioning in a changing world.
Why it matches plant phenotyping methods被子植物の木部形成（xylogenesis）を観察・分析するための標準化手順、組織学的画像、データ収集・解析ワークフローを中心に提示しており、植物の発達状態を測定する方法論的研究である。
abstractHere, we present practical recommendations for observing and analysing xylogenesis in angiosperm trees
This study presents a novel application of atomic force microscopy (AFM) for characterising the ultrastructure of dry (15 % moisture) native and mature wheat grains (Triticum aestivum L.). A key contribution is the standardisation of a meticulous sample preparation protocol that minimises artefacts. This protocol involves dry-cutting the grains using a device that enables precise surface smoothing via ultramicrotomy, ensuring perfect alignment for AFM scanning without the need for resin-embedding. The research provides a comprehensive histological description ranging from the outer pericarp to the starchy endosperm. The outer layers (pericarp, seed coat, and nucellar epidermis) appear as compact, continuous structures in the dry state, with stronger inter-layer adhesion. The study also discovered a previously undescribed left-handed helical twist in the tube cells of the inner pericarp, a feature that is hypothesised to be lost in conventional resin-embedding techniques. AFM is demonstrated to be a powerful tool for revealing intricate, hydration-dependent ultrastructural adaptations in plant tissues.
Why it matches plant phenotyping methods成熟コムギ粒の組織微細構造という植物形質を対象に、AFM imaging とアーティファクトを低減する試料調製法を中心的に開発・実証しているため、植物フェノタイピング手法として採用する。
abstractThis study presents a novel application of atomic force microscopy (AFM) for characterising the ultrastructure of dry (15 % moisture) native and mature wheat grains (Triticum aestivum L.).
Field / plotMicroscopyCell / cellular structureMorphology / geometry measurement
Pollen and spore morphology provides essential taxonomic reference data for floristic and environmental studies in tropical regions, where modern comparative datasets remain limited. This study documents the morphological characteristics of pollen and spores recovered from a shallow soil profile in a degraded mixed deciduous forest within Sri Nan National Park, northern Thailand. Using a non-acetolysis extraction protocol and systematic sub-sampling of a 30-cm profile, pollen and spores representing 37 plant families were identified, including lycophytes, bryophytes, monilophytes, gymnosperms, and angiosperms. Spore-producing taxa, particularly monilophytes, dominate the assemblage, while angiosperm pollen includes both arboreal and non-arboreal elements. More than 100 morphotypes are described based on aperture type, exine ornamentation, size, and symmetry, supported by high-resolution photomicrographs and standardized morphotype descriptions. The resulting dataset expands the regional palynological reference framework for northern Thailand and tropical Southeast Asia and supports consistent taxonomic identification in palynological, floristic, and comparative paleoecological studies, particularly in human-impacted forest-agriculture mosaics.
Why it matches plant phenotyping methods植物の花粉・胞子形態を標準化して記載し、高解像度画像を含む再利用可能な地域参照データセットを構築しており、形態取得・記述が研究の中心です。
abstractMore than 100 morphotypes are described based on aperture type, exine ornamentation, size, and symmetry, supported by high-resolution photomicrographs and standardized morphotype descriptions.
Abstract Sucrose is the central unit of carbon and energy in plants. As the product of photosynthesis, it is transported from source–to–sink tissues across both short and long distances. Subcellular sucrose concentrations strongly influence rates of transport within cells, tissues, and organs. Moreover, as a central metabolite, its concentration influences the rates of many enzymatic reactions. Measuring sucrose concentration with subcellular resolution remains challenging, especially for the cytosol, which hosts many critical enzymatic reactions and, in many cells, occupies only a thin layer between the vacuole and the plasma membrane. Here, we review the methods that have been utilized to measure subcellular sucrose concentrations in plant cells. The approaches covered include microautoradiography, non-aqueous fractionation, Fourier transform infrared (FTIR) microspectroscopy, Raman microspectroscopy, mass spectrometry imaging, Förster resonance energy transfer (FRET) nanosensors, direct sampling, and theoretical modelling. We provide perspectives on the use cases for these methods and discuss developments towards resolving subcellular sugar concentrations in live tissues.
Why it matches plant phenotyping methods植物組織内の細胞内ショ糖濃度という生理形質を測定する手法群をレビューし、各手法の利用場面と発展を論じているため、植物フェノタイピング手法レビューに該当する。
abstractHere, we review the methods that have been utilized to measure subcellular sucrose concentrations in plant cells.
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、シミュレーションおよび実画像で既存指標と比較・検証しているため、植物フェノタイピング手法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI)
Reproduction assets foundThe paper deposits its benchmark confocal image dataset in the EMBL-EBI BioImage Archive (S-BIAD2226) and its TTI analysis software (ImageJ macro and Jupyter/Python scripts) on GitHub, both with explicit public availability statements.Dataset · publicImage data generated and analyzed in the current study are available in the EMBL-EBI BioImage Archive repository, accession number S-BIAD2226Open asset ↗EMBL-EBI BioImage Archive · S-BIAD2226lines:141-163Code · publicArchive copy, additional sample data and possible future updates of the software tool generated here are also available at [ https://github.com/GeorgeCaldarescu/TTI-Tonoplast-Topology-Index ] .Open asset ↗GitHub · GeorgeCaldarescu/TTI-Tonoplast-Topology-Indexlines:141-163Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 15 Sept 2026
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole-cell volumes poses a significant challenge, especially in biologically diverse systems. Unlike medical and animal cell imaging, which often benefit from temporal redundancy and relatively uniform morphology, studies of microbial and microalgal biodiversity must rely on static snapshots. These snapshots exhibit high variability in cell shape, organelle organisation, and image contrast. Consequently, robust AI-assisted segmentation in this context requires models that learn directly from morphological features and can adapt to heterogeneous sample preparation. In this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets. This framework is specifically designed to address the challenges posed by morphological diversity and contrast variability while remaining within realistic computational constraints. We evaluate multiple lightweight 3D encoder-decoder architectures and identify VNet as the best option for balancing computational efficiency and volumetric accuracy in whole-cell segmentation. Using datasets from two strains of Phaeodactylum tricornutum and extending our analysis to cross-species comparisons, we demonstrate that training on specific regions of interest can lead to an overestimation of model performance. In contrast, performing whole-cell segmentation uncovers significant differences in architectural robustness. Moreover, we show that transfer learning and contrast-aware hybrid strategies allow for efficient adaptation to previously unseen datasets with minimal annotation. The incorporation of boundary-aware loss functions significantly enhances the delineation of closely associated organelles, such as chloroplasts and mitochondria, in multi-class segmentation tasks. Together, these findings establish a scalable, reproducible, and biologically informed AI framework for 3D FIB-SEM segmentation. This framework enables high-throughput analysis of cellular ultrastructure across diverse species and imaging conditions. Author SummaryCells exhibit a wide range of shapes, sizes, and internal structures, particularly among various microbial species. These morphological differences are not arbitrary; they indicate how cells adapt to their environments and manage essential biological functions. Modern three-dimensional electron microscopy can capture this structural diversity at the nanometre scale, but analysing the resulting data is often slow. This is due to the time-consuming process of manually outlining cellular structures, which also requires expert knowledge. Artificial intelligence (AI) has made significant advances in accelerating image analysis in medical and animal cell studies, typically by learning from repeated observations over time. However, studies focusing on microbial and microalgal biodiversity often rely on single snapshots of diverse cells prepared under varying imaging conditions. This complicates automated analysis since AI systems must learn from morphology directly rather than from temporal repetition. In this study, we developed and evaluated an AI-assisted segmentation framework specifically for whole-cell 3D electron microscopy data. By systematically comparing efficient neural network architectures and incorporating transfer learning and contrast-aware strategies, we demonstrate that accurate segmentation can be achieved even with limited training data and standard computing resources. Our approach facilitates faster, scalable, and reproducible analysis of cellular ultrastructure, paving the way for large-scale investigations into cell morphology, adaptation, and diversity across species. Significance statementQuantitative analysis of cellular ultrastructure across species is currently limited by challenges in segmenting large three-dimensional electron microscopy datasets. Unlike medical imaging, which often benefits from artificial intelligence due to its use of temporal repetition and consistent morphology, studies of microbial biodiversity depend on single snapshots that display extreme variations in cell shape and image contrast. This work presents a scalable, morphology-driven AI framework for whole-cell 3D segmentation that is resilient to biological diversity and variations in sample preparation. By enabling accurate analysis with minimal annotations and standard computational resources, this approach enhances access to high-throughput ultrastructural studies and facilitates comparative investigations of cellular adaptation across different species.
Why it matches plant phenotyping methods珪藻の細胞形態・細胞内構造を定量化する3D電子顕微鏡画像のAIセグメンテーション手法を開発・比較検証しており、表現型取得・抽出が研究の中心である。
abstractIn this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Low-temperature stress compromises the integrity of plant cell membranes, leading to lipid phase transitions and increased membrane permeability, which subsequently induce physiological damage. However, conventional methods for assessing cold resistance, such as relative electrolyte leakage measurement, growth recovery tests, and LT50 determination, are limited by their highly destructive nature, time-consuming procedures, or insufficient sensitivity. Electrical impedance spectroscopy (EIS), a non-destructive and efficient electrophysiological technique, has emerged as a valuable tool for evaluating cold resistance and screening cold-tolerant plant varieties. By applying multi-frequency alternating current to plant tissues and measuring the resulting impedance responses, EIS enables the extraction of key parameters such as extracellular resistance, intracellular resistance, and cell membrane capacitance. These parameters collectively reflect the structural integrity and physiological condition of cells from multiple perspectives. Notably, under low-temperature stress, plant genotypes with varying degrees of cold resistance exhibit distinct impedance spectral characteristics, allowing EIS to efficiently discriminate cold tolerance among different varieties or treatments. This review summarizes recent advances in EIS-based research on plant cold resistance, covering its underlying electrical principles, equivalent circuit models, and biophysical mechanisms. It also outlines practical applications, including the screening of cold-tolerant woody and herbaceous plants, as well as integration with traditional assessment methods, while highlighting the advantages of EIS in terms of accuracy, universality, and real-time monitoring. Furthermore, the review addresses key challenges such as species specificity, model standardization, and data analysis, and proposes future research directions, including integration with artificial intelligence, development of portable devices, and establishment of standardized stress resistance databases.
Why it matches plant phenotyping methods植物の低温耐性をEISで非破壊評価・識別する方法を中心に、原理、モデル、検証課題、実用化をレビューしており、植物フェノタイピング手法のレビューに該当する。
abstractElectrical impedance spectroscopy (EIS), a non-destructive and efficient electrophysiological technique, has emerged as a valuable tool for evaluating cold resistance and screening cold-tolerant plant varieties.
Plants defend against pathogens such as fungi by detecting an attack and initiating both structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx, calcium oscillations, and induces defense gene expression, yet the mechanisms by which these or other signals encode the external stressors or propagate signals plant-wide remain unclear. Here, we present a microfluidic system to examine intracellular calcium signals of the moss Physcomitrium patens upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent microscopy of juvenile moss cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon chitin removal. We developed an unbiased image segmentation algorithm to automatically locate regions with cell-specific oscillatory responses, using pixel-based k-means clustering, treating each time point as a separate dimension. Calcium dynamics were distinct across adjacent cells and distinguishable by cell type. Waves were dependent on time of day, adaptation time within the device, and stimulus timing. Cytosolic calcium waves, which rose and fell symmetrically within about 60 s, appeared spontaneously at night and with short adaptation time. Chitin increased wave frequency, amplitude, and duration, and repeated chitin pulses drove regular, plant-wide oscillations at a controlled frequency. This study complements prior investigations of whole plant and growth tip dynamics and provides new methods to comprehensively study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methodsマイクロ流体システム、蛍光イメージング、細胞特異的画像セグメンテーションを開発し、植物の細胞内カルシウム動態という生理状態を定量化することが研究の中心である。
abstractHere, we present a microfluidic system to examine intracellular calcium signals of the moss Physcomitrium patens upon precise and reversible exposure to fungal chitin oligosaccharides.
ArabidopsisMicroscopyCell / cellular structureVisualization / data management
Background Translation is a fundamental process for every living organism. In plants, the rate of translation is tightly modulated during development and in responses to environmental cues. However, it is challenging to measure the actual translation state of the tissues in vivo. Results Here, we report the introduction of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC. We combined a method originally developed for the fruitflies with an improved low background split-mVenus BiFC system previously described in plants. We labelled small subunit ribosomal proteins (RPS) and large subunit ribosomal proteins (RPL) of Arabidopsis thaliana with fragments of the mVenus fluorescent protein (FP). We tested the Ribo-BiFC method using transiently expressed recombinant ribosomal proteins in epidermal cells of Nicotiana benthamiana. The BiFC-tagged ribosomal proteins complemented the mVenus molecule and were detected by fluorescence microscopy, potentially visualizing the close proximity of translating assembled 80S ribosomal subunits. Although the resulting signal is less intense than that of known interactors, its detection points to the functionality of the system. Conclusions This Ribo-BiFC approach has further potential for use in stable transgenic lines in enabling the visualisation of translational rate in plant tissues and changing translation dynamics during plant development, under abiotic stress or in different genetic backgrounds.
Why it matches plant phenotyping methods植物組織内の翻訳状態・翻訳速度という生理状態を可視化する新規蛍光イメージング法を導入・検証しており、表現型取得法が研究の中心である。
abstractwe report the introduction of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC.
Pine wilt disease (PWD), caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus , is a globally destructive threat to coniferous forests, causing severe ecological and economic losses. Conventional resistance breeding is critically hampered by long life cycles of trees and field evaluation challenges. To address these limitations, we developed a three-tier biotechnology pipeline with a dual-output goal (generating both resistant germplasm and mechanistic insights) designed to bridge the in vitro-field gap. This strategy is founded upon the resolution of a longstanding pathogenesis debate, which established aseptic PWNs as a standardized research tool. The pipeline integrates high-throughput in vitro cellular screening (Tier 1), whole-plant validation via organogenesis (Tier 2), and scaled production coupled with mechanistic investigation through somatic embryogenesis (Tier 3). Tier 1 enables rapid phenotypic screening, Tier 2 validates resistance in whole plants, and Tier 3 facilitates mass production and in-depth study. It operates as a closed-loop, knowledge-driven system, simultaneously accelerating PWN-resistant germplasm development and empowering molecular mechanism discovery. Validated across Pinus massoniana and P. densiflora , this work provides a concrete, community-usable model system that directly addresses a core methodological bottleneck in forest pathology. This strategy effectively bridges the in vitro-field gap, offering a replicable model for perennial crop breeding and contributing to resilient forest management.
Why it matches plant phenotyping methodsマツ萎凋病抵抗性を評価する三層型の高速スクリーニング・全植物検証パイプライン自体が中心であり、植物の抵抗性表現型の取得と検証を技術的に扱っている。
abstractwe developed a three-tier biotechnology pipeline with a dual-output goal
Chemical imaging at high spatiotemporal resolution is crucial for advancing plant sciences and biotechnology. We demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues at high spatial ( 2 O 2 waves (100 μM) from plant mesophyll to stomata and pavement cells. Ca 2+ induced higher endogenous H 2 O 2 in mesophyll cells, whereas organelle electron transport chain disruptors and salt stress generated similar H 2 O 2 across all leaf cell types. Furthermore, the nanosensor quenching kinetics in photosynthetic mesophyll (0.018 s -1 ) and epidermal (0.004 s -1 ) cells enabled the detection of plant lipid corona formation. Optical nanosensors elucidate spatiotemporal dynamics of plant signaling molecules and advance our understanding of biocorona formation.
Why it matches plant phenotyping methods植物組織内のシグナル分子を高時空間分解能で可視化する光学ナノセンサーの開発・実証が研究の中心であり、植物の生理状態を測定する方法論に該当する。
abstractWe demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues
PoplarMicroscopyCell / cellular structureRootVisualization / data management
Abstract Background Cortical microtubules (CMTs), one of the components of cytoskeleton, control the orientation and localization of newly deposited cellulose microfibrils in cell walls, and thereby determine the shape, size, and structure of plant cells. Imaging of CMTs in plant tissues is generally performed using fluorescently labeled specimens under an optical fluorescence or confocal laser scanning microscope. However, optical microscopy has insufficient resolution to visualize individual CMTs, and its observation range is limited to superficial tissue layers that light can penetrate. In contrast, transmission electron microscopy offers high-resolution visualization of CMTs in plant cells but is restricted to slightly oblique ultrathin sections with an approximate thickness of 70–100 nm. Results Herein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM). We successfully observed the arrangement of CMTs in several plant specimens, including young branches of ginkgo ( Ginkgo biloba ), calli from the leaves of hybrid poplar ( Populus sieboldii × P. grandidentata ), and root tips of the adzuki bean ( Vigna angularis ). CMTs were visualized on the protoplasmic fracture face using both cryo-FE-SEM and conventional room-temperature FE-SEM. Conclusions The combination of freeze-fracture techniques with FE-SEM enables the visualization of CMT arrangement in plant tissues at a high resolution and across a broad area without the need for staining or extraction of cellular components. This technique is applicable to various plant tissues and allows for detailed observation of CMTs within these tissues, providing valuable insights into the role of microtubules in the division and differentiation of plant cells.
Why it matches plant phenotyping methods植物組織内の微小管配列を高解像度で可視化するFE-SEMと凍結割断の新規画像取得法を開発しており、植物細胞状態の観察手法が研究の中心です。
abstractHerein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM).
Tip-growing cells exhibit complex growth regimes in vitro, alternating between growing and non-growing intervals, oscillatory or more steady behavior. In Arabidopsis thaliana, pollen tube growth arrest is often accompanied by spiking behavior in intracellular ion concentrations and extracellular ion fluxes. Thus, selecting comparable growing regimes is critical for quantifying ion dynamics across cells and genotypes. Defining non-growing regimes is a fundamental step to filter out their associated data points. Here, we provide computational and statistical procedures for the quantitative phenotyping of ion fluxes associated with growth dynamics in tip-growing cells. The goal is to provide reliable estimates of ion fluxes given pairwise time-series comparisons (ion fluxes vs. growth rate), focused on growth-associated intervals and avoiding data stemming from regimes associated with growth arrest. We consider extracellular ion fluxes in growing tubes, but the analysis is applicable to other quantitative variables and tip-growing cells. After visualizing both series in a common timeframe, we extract the growth rate baseline and determine the non-growing regime threshold with a Gaussian Mixture Model, then predict the growth state at sampled flux times, filtering, and finally quantification. The protocol is presented in R but is of general use, since multiple software routines can yield similar results.
Why it matches plant phenotyping methods植物のイオンフラックスと成長状態を定量化する計算・統計プロトコルが研究の中心であり、表現型取得・抽出手法を具体的に開発している。
abstractHere, we provide computational and statistical procedures for the quantitative phenotyping of ion fluxes associated with growth dynamics in tip-growing cells.
Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hamper automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers-so long as the cell contours can be identified-are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialists, it is expected to promote understanding of what happens at the sub- and cellular level with high-throughput outcomes.
Why it matches plant phenotyping methods植物細胞のライブ画像から細胞形状・先端位置・成長方向・成長動態を定量化する解析ソフトウェアを開発しており、植物表現型取得・抽出が研究の中心である。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public authors' code repositories (KymoTip analysis tool and SAM2 segmentation code) and a figshare deposit of the raw imaging data used for the tip-growth phenotyping measurements.Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTipOpen asset ↗blues0910/KymoTiphtml-lines:159-244Code · publicthe code for SAM2 segmentation is available at https://github.com/YusukeKimata‐Moo/SAM2‐segmentation/Open asset ↗YusukeKimata‐Moo/SAM2‐segmentationhtml-lines:159-244Dataset · publicThe raw data used in this paper are available on figshare: https://doi.org/10.6084/m9.figshare.30847580Open asset ↗figshare · 10.6084/m9.figshare.30847580html-lines:159-244Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Reproductive development in apomictic plants diverges from the sexual pathway at different key steps. The early steps take place in the ovule, the female organ hosting female sporogenesis and gametogenesis. Cell identities are notably more plastic in the ovule of facultative aposporous plants, where somatic cells can shift to germinal fate. This plasticity likely starts during the early morphogenesis of the ovule, concomitant with gradual differentiation of the sexual megaspore mother cell (MMC). In sexual species, 3D morphogenetic analyses have shown that ovule shape conditions MMC plasticity. However, in aposporous grasses, the morphogenetic events shaping ovule primordia are currently undescribed in 3D and at the cellular level, largely due to the inaccessibility of this organ. To fill this gap, we propose here a comprehensive workflow from ovule sampling to the extraction of 3D cellular quantitative parameters, established for the tropical apomictic grass Paspalum rufum. First, this protocol describes 3D imaging of whole-mount ovules at successive developmental stages, covering MMC differentiation, using ClearSee clearing procedure and double cell walls/nuclei staining. Second, it provides a detailed image analysis workflow in the open-source platform, MorphoGraphX. The workflow enables semiautomatic 3D cell segmentation, cell location, and annotation according to tissue layers or adjacency networks, leading to the final extraction of cellular parameters that describe geometry and topology dynamics along with ovule primordia development. This protocol applies to various species of the Paspalum genus and is potentially useful for 3D studies of large, curved, and hidden organs in multiple plant species.
Why it matches plant phenotyping methods植物の胚珠を対象に、3D画像化・細胞セグメンテーション・注釈付けから細胞形態およびトポロジー形質を抽出する再利用可能なワークフローを開発した方法論中心の研究です。
abstractwe propose here a comprehensive workflow from ovule sampling to the extraction of 3D cellular quantitative parameters
Plants have evolved an effective defense mechanism in the phloem to prevent the spread of pathogens and minimize the loss of phloem sap following injury. Specific structural phloem proteins known as P-proteins rapidly seal affected sieve elements by plugging the sieve plates, a phenomenon defined as sieve element occlusion. This chapter describes a live cell imaging method for the analysis of P-protein responses and signal propagation in vivo without tissue sectioning or mechanical manipulation. It is based on an Arabidopsis thaliana dual-reporter line where P-proteins are labeled with fluorescent tags in a complementation background, allowing real-time visualization of the parietal protein network during sieve element occlusion. The calcium sensor Yellow Cameleon 3.6 is specifically expressed and anchored in the sieve elements, enabling the detection of calcium waves and their effects on P-protein structure over longer distances in vivo. Using this protocol, a wide range of external triggers-including chemical treatments, buffers, and wounding-can be applied with precision, allowing the analysis of P-protein functions and long-distance signaling in the phloem. The method is readily adaptable to other genetically encoded sensors and can be used to investigate P-protein-independent processes as well as the diverse signaling and structural responses of additional sieve element components.
Why it matches plant phenotyping methods植物の師部におけるタンパク質構造とカルシウムシグナルを生体内で可視化・解析するライブセルイメージング法が中心であり、植物状態の取得手法を具体的に提示している。
abstractThis chapter describes a live cell imaging method for the analysis of P-protein responses and signal propagation in vivo without tissue sectioning or mechanical manipulation.
Sieve elements in the phloem transport carbon and small molecules, such as RNA and phytohormones, throughout the plant body. Understanding the physical dimensions of sieve elements and phloem tissue is thus crucial for predicting how much carbon can be moved at any given time. Quantification of sieve element diameters and areas has previously been performed using transmission electron microscopy, scanning electron microscopy, and light microscopy, but sieve element identification is difficult because the phloem is a heterogeneous tissue. The recently identified LM26 antibody labels a pectin in the sieve element cell wall, allowing the identification of sieve elements and the measurement of their properties, such as diameter, relatively quickly and the quantification of their number in cross sections using image analysis software. Here, we describe methods for immunolabelling sieve elements in fresh or fixed tissue embedded in polyethylene glycol or methacrylate. The protocol is broadly adaptable to various fixation and sectioning methods, provided they do not alter the structure of pectins in the cell wall.
Why it matches plant phenotyping methods師部篩要素の同定、画像解析による直径・面積・数の定量を可能にする免疫標識プロトコルが中心で、植物形態形質の取得手法を提供している。
abstractallowing the identification of sieve elements and the measurement of their properties, such as diameter, relatively quickly and the quantification of their number in cross sections using image analysis software.
Apomixis is an asexual reproductive mechanism that takes place deeply inside the female reproductive organs of the plant, in ovules and seeds. In gametophytic apomixis, an unreduced female gametophyte is produced by a modified meiosis of the megaspore mother cell (dipolspory) or from a somatic initial cell (apospory). The unreduced, nonrecombined egg cell develops subsequently into an embryo by parthenogenesis. The cyto-embryological study of apomixis is challenging because of the inaccessibility of these structures. Consequently, images of apomeiosis and parthenogenesis with high definition are limited to a few species. In this chapter, we show the application of a Feulgen staining protocol combined with confocal microscopy for the study of nonreductional megasporogenesis and autonomous embryo formation in diplosporous apomictic Taraxacum officinale and aposporous apomictic Pilosella piloselloides var. praealta. Using a rapid and technically simple method, performed on whole-mount ovaries, we have obtained high-resolution images of the female reproductive cells. Furthermore, we highlight the application of this protocol for the study of loss-of-diplospory and loss-of-parthenogenesis mutants in the same species.
Why it matches plant phenotyping methods全載卵巣にFeulgen染色と共焦点顕微鏡を組み合わせ、雌性生殖細胞・胚形成を高解像度で可視化する技術を提示・適用しており、植物の生殖状態を取得する方法が中心である。
abstractUsing a rapid and technically simple method, performed on whole-mount ovaries, we have obtained high-resolution images of the female reproductive cells.
Fluorescence microscopy is pivotal for investigating autophagy's role in plant antiviral immunity. Here, we present a standardized procedure using complementary probes, CFP-ATG8f for autophagosomal structures and monodansylcadaverine (MDC) for autophagic vacuoles, to assess autophagy during viral infection. This combined CFP-ATG8f and MDC staining system provides a powerful, reproducible method for evaluating autophagic activity in plant-virus interactions.
Why it matches plant phenotyping methods植物のオートファジー活性という生理状態を蛍光顕微鏡と相補的プローブで測定する標準化・再現可能な手法が研究の中心であり、植物表現型測定法に該当する。
abstractHere, we present a standardized procedure using complementary probes, CFP-ATG8f for autophagosomal structures and monodansylcadaverine (MDC) for autophagic vacuoles, to assess autophagy during viral infection.
OliveMicroscopyCell / cellular structureVisualization / data management
The pollen tube is widely recognized as a suitable model for investigating the structure and spatial organization of cell wall components during polarized growth. This chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins, using specific monoclonal antibodies from the JIM series. JIM5 and JIM7 were employed to detect de-esterified and esterified homogalacturonan regions of pectin, respectively, while JIM8 and JIM13 were used to label distinct epitopes of arabinogalactan proteins. The protocol includes pollen germination, paraformaldehyde fixation, enzymatic digestion with cellulysin (for arabinogalactan protein detection only), and sequential antibody incubation, followed by confocal microscopy imaging using FITC filter settings. This approach enables precise visualization of the distribution patterns of pectins and arabinogalactan proteins in the pollen tube wall and provides a reliable framework for further studies on cell wall architecture in plant reproductive tissues.
Why it matches plant phenotyping methods植物花粉管細胞壁の成分分布を共焦点免疫蛍光で可視化するプロトコルが研究の中心であり、植物組織の空間的状態を測定する方法として扱える。
abstractThis chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins
Abstract Embryogenesis is an essential process involving a series of formative cell divisions that contribute to establishing the plant’s body axis. In many dicotyledons, the asymmetric cell division of the zygote gives rise to two daughter cells, which develop into two distinct cell lineages. In contrast, the fate of the two daughter cells and their contribution to the body axis formation remain poorly understood in the monocots. To address this question, we developed a method for three-dimensional imaging of early rice embryos. Our observations demonstrated that both an egg cell and two synergids are polarized prior to fertilization and are anchored to the micropylar end of the ovule via a cell wall-like structure stained with SR2200. Upon fertilization, the zygote undergoes an asymmetric cell division with a ventrally tilted division plane. The following cell divisions are not strictly synchronized between the apical and basal lineages, exhibiting non-stereotypic patterns up to the globular stage of embryogenesis. Furthermore, we examined the role of auxin signaling in rice embryogenesis using the auxin response sensor DR5rev::NLS-3xVENUS. The reporter activity was first detected at the center of the globular embryos and subsequently extended along the apical–basal axis as embryogenesis progressed. Our results highlight the importance of the progressive establishment of the body axes within cell populations during early embryogenesis.
Why it matches plant phenotyping methodsイネ胚の三次元画像化法を開発し、細胞配置・分裂パターンという植物形態状態を取得しているため、表現型取得法が研究の中心である。
abstractwe developed a method for three-dimensional imaging of early rice embryos
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Background Protoplasts, which are plant cells devoid of cell walls, are valuable tools in plant biotechnology. However, they are highly sensitive to mechanical and osmotic stress during isolation and early culture, often leading to significant loss of viability. Reliable and efficient methods for monitoring protoplast quality are essential for downstream applications. Results We applied impedance flow cytometry to assess the viability, cell size, and early division of freshly isolated protoplasts from Arabidopsis thaliana, Brassica napus, and Beta vulgaris. This label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations. Importantly, IFC-derived viability metrics strongly correlated with microcallus formation, demonstrating their predictive value for culture competence. Conclusions Impedance flow cytometry provides a robust, efficient and reproducible method for characterizing protoplast cultures. It enables rapid assessment of viability and growth potential, supporting quality control and optimization in plant cell culture workflows.
Why it matches plant phenotyping methodsインピーダンスフローサイトメトリーを用いて、植物プロトプラストの生存性・細胞サイズ・初期分裂を高速かつ高スループットに測定し、培養能力との相関で妥当性を検証しているため、植物表現型取得法が中心です。
abstractThis label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations.
Polyploidy (whole-genome duplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.
Why it matches plant phenotyping methodsiSPyは画像から組織内の核倍数性を空間的・高スループットに推定する教師なし学習パイプラインであり、Arabidopsisで実証されている。植物の状態を抽出する計算フェノタイピング手法が中心である。
abstractHere, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest.
Reproduction assets foundThe paper deposits its paper-specific phenotyping assets publicly: confocal images of A. thaliana, D. melanogaster, and human cardiomyocytes, ilastik segmentation files, and A. thaliana cotyledon flow cytometry data are all in an OSF repository, and the iSPy analysis code is available both on OSF and in a public GitLabDataset · publicAll data presented in the study are publicly available in the OSF data repository (https://osf.io/um7r3/; https://doi.org/10.17605/osf.io/um7r3).Open asset ↗10.17605/osf.io/um7r3html-lines:253-271Code · publicThe code for iSPy can also be found in the OSF data repository (https://osf.io/um7r3/; https://doi.org/10.17605/osf.io/um7r3), as well as in a GitLab repository, https://gitlab.gwdg.de/devplantpatterning/Publications/ispy-inferring-spatial-ploidy.Open asset ↗gitlab.gwdg.de · devplantpatterning/Publications/ispy-inferring-spatial-ploidyhtml-lines:253-271Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published4 Dec 2025Zenodo (CERN European Organization for Nuclear Research)Cited by 0 · OpenAlex ↗
Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.
Why it matches plant phenotyping methodsChlamydomonasの運動性・代謝・デンプン蓄積を対象に、ハイスループット追跡、ラマン分光、染色による定量的フェノタイピング手法を確立し、治療タンパク質評価のプラットフォームとして検証しているため。
abstractWe established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function.
Poster presented at CellBio 2025 in Philadelphia, PA. December 2025Abstract:Adenosine deaminase (ADA) deficiency causes severe combined immunodeficiency, and current treatments include enzyme replacement therapy with immunogenic bovine proteins. To develop improved therapeutic variants, robust model systems are needed for testing rationally designed enzymes in vivo. We used Zoogle (zoogle.arcadiascience.com), a computational dataset that selects model organisms based on conserved protein characteristics rather than sequence similarity, to identify Chlamydomonas reinhardtii as an optimal system for studying human ADA1 function. This approach can identify effective models that traditional phylogenetic methods might overlook. We characterized Chlamydomonas ADA1 mutants and found clear phenotypic defects in motility and cellular metabolism, particularly altered starch accumulation under nutrient stress. We established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function. These multi-modal readouts provided robust, reproducible measures of ADA1 activity in living cells. We're validating this system using wild-type human ADA1 and candidate variants designed through machine learning approaches to enhance stability and improve therapeutic properties. Initial results demonstrate that the algal system can detect functional differences in ADA1 variants, establishing a platform for screening computationally designed proteins. This approach enables systematic evaluation of engineered enzymes in a physiologically relevant cellular context. Our work establishes Chlamydomonas as an effective model for human metabolic enzymes and demonstrates the power of protein characteristic-based organism selection over traditional phylogenetic approaches. This validation platform enables rapid, cost-effective screening of designed therapeutic proteins before advancing to mammalian studies, potentially accelerating the development of next-generation enzyme replacement therapies for genetic diseases.
Why it matches plant phenotyping methodsChlamydomonasの細胞表現型を対象に、ハイスループット運動追跡、Raman分光、染色による定量的・再現可能な表現型評価系を構築し、ADA1変異体スクリーニングに適用しているため、フェノタイピング手法が中心である。
abstractWe established quantitative phenotyping approaches, including high-throughput motility tracking, metabolic profiling via Raman spectroscopy, and biochemical staining to assess cellular function.
The lamina joint is a critical determinant of leaf angle and crop architecture. While epidermal cells play a fundamental role in organ morphogenesis, influencing the overall shape and function of plants, their impact on lamina joint morphology has been largely overlooked. A live-imaging system for the rice lamina joint epidermis is established in this study, enabling precise tracking of cellular dynamics during leaf angle formation. It is found that asymmetric elongation between the lateral and medial edges, determined by spatial differences in the longitudinal elongation and number of epidermal cells, is a key factor in leaf angle formation. Mutations in the homeobox genes OsZHD1 and OsZHD2 disrupt the growth patterns of lamina joint epidermal cells, resulting in a decreased leaf angle. Epidermis-specific restoration of OsZHD1 expression rescues the reduced leaf angle phenotype of oszhd1 oszhd2, confirming the pivotal role of epidermal development in lamina joint morphogenesis. Transcriptomic analysis indicates that OsZHD1 and OsZHD2 regulate auxin activity, which modulates leaf angle by restricting lamina joint epidermal growth. This study underscores the significance of epidermal cells in shaping the lamina joint and elucidates the critical role of OsZHD1 and OsZHD2 in regulating epidermal cell behavior and leaf angle formation.
Why it matches plant phenotyping methodsイネ葉舌関節表皮の細胞動態を追跡するライブイメージング系を確立し、葉角形成に関わる形態・成長を定量的に解析しており、表現型取得法が研究の中核に含まれる。
abstractA live-imaging system for the rice lamina joint epidermis is established in this study, enabling precise tracking of cellular dynamics during leaf angle formation.
Under the intensifying impact of global climate change, the frequency of plant disease outbreaks is steadily increasing, posing significant challenges to healthy cultivation and precision management. To enable early diagnosis of plant disease stress, this study used two rose (Rosa chinensis) varieties, ’Red Cap’ and ’Carefree Wonder’ were used to conduct powdery mildew stress experiment. Throughout the stress period, leaf electrical impedance spectroscopy (EIS), physiological parameters, and ultrastructural observations, were collected. A novel lumped equivalent circuit model was proposed, incorporating plant cell electrophysiological properties. The model developed for both rose varieties-featuring Constant Phase Elements (CPE) and Warburg elements (W), successfully characterized the resistive properties of the leaf tissue, including the extracellular resistance (R₁), cell membrane resistance (R₂), intracellular resistance (R₃), and vacuole interior resistance (R₄). Model parameters R₁ and R₃ were significantly correlated with the above physiological indicators, and showed significant differences 3 to 11 days earlier than traditional physiological parameters, demonstrating strong potential for early detection of cellular damage. Overall, this study demonstrates that EIS technology can dynamically reflect electrical property changes in plant tissues under biotic stress, effectively overcoming the lag limitations of conventional physiological measurements, providing a promising tool for early disease diagnosis and resistance screening.
Why it matches plant phenotyping methodsバラ葉の病害ストレス状態を電気インピーダンス分光法で測定し、等価回路モデルを開発・検証して早期診断性能を評価しているため、植物フェノタイピング手法が中心である。
abstractA novel lumped equivalent circuit model was proposed, incorporating plant cell electrophysiological properties.
Field / plotMicroscopyCell / cellular structureRootTissueClassificationMorphology / geometry measurementGrowth / development / phenology
Growth rings in woody plants form in response to seasonal variation in the environment and are fundamental to dendrochronological studies, but estimating plant ages—especially in underexplored growth forms such as forbs, shrubs and subshrubs from grasslands—remains challenging. Here, we address a knowledge gap in the anatomy and histochemistry of subshrubs from natural Cerrado grasslands and evaluate their potential for dendrochronological applications. We studied underground woody organs of Jacaranda decurrens, Lippia lupulina, and Mandevilla longiflora, collected at the Santa Bárbara Ecological Station (Brazil). We used autofluorescence microscopy and a suite of histochemical tests targeting structural and non-structural compounds. Autofluorescence allowed spatial assessment of wood tissues without staining, and improved growth-ring visualization. FASGA staining increased contrast between fibers and parenchyma, facilitating tissue discrimination and growth-ring delimitation, while Mäule staining highlighted differences in cell-wall composition and guaiacyl/syringyl (G/S) ratios throughout growth-ring formation. Starch was consistently detected in parenchymatic cells of all species (lowest in J. decurrens, intermediate in L. lupulina, highest in M. longiflora), and its spatial association with parenchyma aided growth-ring identification. Combining fluorescence and histochemical approaches provides complementary insights into the anatomy and chemistry of underground organs and advances dendrochronological studies in grassland ecosystems.
Why it matches plant phenotyping methods自家蛍光顕微鏡と組織化学染色を用いて地下木質器官の成長輪を可視化・判別する方法が研究の中心であり、植物の年齢・成長状態という形質の取得に直接関与する。
abstractAutofluorescence allowed spatial assessment of wood tissues without staining, and improved growth-ring visualization.
Porosity and permeability are critical physical parameters for accurately modelling macroscale heat and mass transfer processes during the cooling, thermal processing, and storage of leafy vegetables. However, existing estimation methods primarily rely on lumped semi-empirical approaches, which overlook the realistic 3D structural information, limiting insights into microscale water transport behaviour. This study utilised low- and high-resolution X-ray computed tomography (CT) combined with advanced cell segmentation techniques to determine the porosity-permeability correlation of spinach, a representative easily dehydrated leafy vegetable. Experiments demonstrated that the Cellpose, dilation, and erosion algorithms effectively segmented adhering cells and generated lamina and petiole slices with varying porosity gradients. Using 3D reconstruction and seepage simulation, the porosity and permeability of representative elementary volumes (REVs) in the lamina and petiole tissues were calculated, and the pressure and flow rate distributions within the intercellular spaces were visualised. The porosity-permeability relationship was fitted using the Kozeny-Carman (KC) formula as κₗ = 4.839 × 10⁻¹¹φ¹.⁷⁰/(1 - φ)⁰.⁷⁰ for lamina REVs and κₚ = 1.128 × 10⁻¹⁰φ².¹⁶/(1 - φ)¹.¹⁶ for petiole REVs. Grayscale-porosity and porosity-permeability correlations were further applied to characterise the heterogeneity of porosity (2.742%–53.30%) and permeability (4.925 × 10⁻¹⁴ - 2.829 × 10⁻¹¹ m²) of intact spinach. The study aims to provide technical and theoretical support for multiscale modelling in the quality control of leafy vegetables.
Why it matches plant phenotyping methodsX線CT、細胞セグメンテーション、3D再構成を中核として、ホウレンソウ組織の空隙率・透過率という構造的な植物形質を定量化する手法を開発・適用しているため。
abstractThis study utilised low- and high-resolution X-ray computed tomography (CT) combined with advanced cell segmentation techniques to determine the porosity-permeability correlation of spinach
Abstract Plant cell walls are dynamic composites whose architecture determines growth, mechanics, and environmental resilience. Efforts to link pectin structure to function have been limited by the lack of molecular probes with sufficient specificity, a gap that becomes even more pronounced for the intricately branched rhamnogalacturonon-II (RG-II) subclass. Here we report the first fluorescent probes with defined specificity to RG-II, engineered from catalytic site mutants of Bacteroides thetaiotaomicron glycoside hydrolases BT1010 and BT0996. These enzyme-derived probes bind RG-II monomer with high affinity, discriminate against dimeric forms, and localize to cell corners and junctions in Arabidopsis thaliana stems, consistent with RG-II’s unique ability among wall polysaccharides to form borate-mediated, covalent crosslinkages between molecules. Application of these probes revealed spatial partitioning distinct from the homogalacturonan (HG)- and rhamnogalacturonan I (RG-I)-enriched middle lamella, highlighting functional specialization among pectic domains, with RG-II reinforcing cell junctions while HG and RG-I mediate wall flexibility. Our work establishes a generalizable framework for transforming CAZymes into high-precision imaging reagents, enabling molecular-level visualization of structurally complex polysaccharides in the cell wall.
Why it matches plant phenotyping methodsRG-IIを特異的に可視化する蛍光プローブを開発し、植物細胞壁内の空間分布という植物状態を画像で測定する手法を示しているため、フェノタイピング手法が中心的です。
abstractHere we report the first fluorescent probes with defined specificity to RG-II
Reproduction assets foundThe paper deposits its raw microscopy z-stacks and maximum projections on OSF and its custom MATLAB image-analysis code on GitHub, both with explicit availability statements and public URLs.Dataset · publicMicroscopy data that support the findings of this study have been deposited in Open Science Framework. Raw z-stacks, output maximum intensity projections, and annotated figure images in greyscale are available at (https://osf.io/8utvs/overview).Open asset ↗Open Science Frameworklines:319-349Code · publicMATLAB code used for image analysis is available at https://github.com/kristenthorne/GHprobes.git, with usage instructions and example input and output files provided.Open asset ↗GitHub · kristenthorne/GHprobeslines:319-349Plant phenotyping relevance match · UnverifiedbioRxiv · checked 13 Sept 2026
A methodology for achieving micrometer-scale 4D X-ray lab microscopy of living leaf tissue was developed to overcome challenges associated with delicate tissues, radiation damage, and motion artifacts during in vivo imaging. The study focused on optimizing laboratory based X-ray micro-computed tomography (microCT) parameters to balance high-resolution imaging with minimized physiological stress and radiation dose quantification. Assessing the dose-safe imaging window required comparing vertical and horizontal leaf mounting setups. Results demonstrated that the horizontal setup provided greater stability, preventing tissue degradation and maintaining sample viability during continuous acquisitions lasting up to 22 hours ([~]15600 Gy). MicroCT capacities were clearly able to resolve microstructures at the cellular level, achieving a pixel size down to 1 {micro}m. Furthermore, this optimized methodology confirmed the ability to track the spatiotemporal dynamics of applied compounds such as iohexol and aggregated nanoparticles within the leaf tissue. This work establishes that accessible laboratory based microCT enables the in vivo 4D monitoring of anatomical and physiological changes in living plants.
Why it matches plant phenotyping methods生葉を対象とした高解像度4D X線マイクロCTの撮像条件・線量・試料配置を開発し、生体内の解剖学的・生理学的変化を追跡する方法が研究の中心である。
abstractA methodology for achieving micrometer-scale 4D X-ray lab microscopy of living leaf tissue was developed to overcome challenges associated with delicate tissues, radiation damage, and motion artifacts during in vivo imaging.
ABSTRACT Plant microtechnique is a sequence of skill-intensive histological and microscopy procedures that often yield limited quantitative information. However, it provides the cellular context needed to uncover biomolecular functions. In this work, we developed an easier microtechnique and a novel histolomic approach for the quantitative analysis of histological features. We replaced paraffin with resin as the embedding medium, developed an adhesive treatment for glass slides, and developed a trichrome staining. These improvements provided superior tissue stability and greatly facilitated the skill-dependent steps. Unlike current stainings, our trichrome staining produced a broader color palette and sharply contrasted numerous organelles and ultrastructures in light microscopy. We leveraged these microtechnique advances through image segmentation and quantitative analysis in MATLAB and Adobe Photoshop to measure a wide range of morphometric and compositional features, thereby generating the histolome. To validate this workflow, we applied it comprehensively and systematically to several model plants and calculated their C 4 Kranz-anatomy level using a combination of characteristic histological features. The histolomes provided new insights into cellular functions and quantitative anatomical differentiation among species. The resin-based microtechnique and histolomic approach will help facilitate, standardize, and make plant histology research quantitative. GRAPHICAL ABSTRACT
Why it matches plant phenotyping methods樹脂包埋・染色・画像セグメンテーション・定量解析を統合し、植物組織の形態・構成特徴を抽出する新規ヒストロミクス手法を開発・検証しており、植物表現型取得が中心です。
abstractIn this work, we developed an easier microtechnique and a novel histolomic approach for the quantitative analysis of histological features.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum , Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L⁻¹) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.
Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を高解像度で追跡・定量するドロplet型マイクロ流体プラットフォームが研究の中心であり、植物状態の取得・解析手法を開発・評価している。
abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.
Why it matches plant phenotyping methods深層学習と画像処理により、感染植物細胞内のハウストリアを自動検出・セグメンテーションする公開パイプラインを開発し、手動計数および種間移植性を検証しているため、植物病害表現型の取得手法が中心である。
abstractWe report an openly available pipeline that automates the detection of β-glucuronidase (GUS)-stained epidermal cells and the intracellular haustoria formed by powdery mildew on barley and wheat leaves.
Mycorrhizal associations between fungi and plants are a fundamental aspect of terrestrial ecosystems. Mycorrhizas occur in c. 85% of extant plants, yet their geological record remains sparse. Rare fossil evidence from early terrestrial environments offers crucial insights into these ancient symbioses, but visualizing fossil fungi at the microscale within plant tissues is challenging. Here, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant from the Windyfield Chert, a stratigraphically distinct fossiliferous unit from Rhynie (Scotland). We also applied Raman spectroscopy to investigate the carbon framework of both fungal and plant tissues. This integrative approach revealed fungal structures in unprecedented detail. The fungus, Rugososporomyces lavoisierae gen. nov., sp. nov., exhibits features resembling extant Glomeromycotina arbuscular mycorrhizal fungi. This is the first record of mycorrhizas from the Windyfield Chert. FLIM further distinguished features at the subcellular level, while Raman spectroscopy showed that fungal arbuscules and vesicles of the plant water-conducting cells underwent geological alterations, resulting in a similar chemical composition. These findings expand our understanding of ancient and extremely rare plant-fungal symbioses and highlight the potential of confocal-FLIM for advancing palaeobotanical research.
Why it matches plant phenotyping methods植物組織内の微細構造を対象に、共焦点レーザー顕微鏡・FLIM・ラマン分光を組み合わせた観察法を中核としており、化石植物の細胞・菌根構造の状態を抽出している。
abstractHere, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant
Reproduction assets foundThe authors deposited all confocal imaging data used in this fossil mycorrhiza study (CLSM/FLIM datasets of Rugososporomyces lavoisierae in Aglaophyton majus) in a public Zenodo repository under a CC BY 4.0 license. This is a paper-specific, publicly accessible dataset of the phenotyping/imaging measurements.Dataset · publicAll confocal data collected and used in this study are deposited in the Zenodo repository under a Creative Commons Attribution 4.0 international license https://doi.org/10.5281/zenodo.15194427 (Strullu‐Derrien et al ., 2025 ).Open asset ↗Zenodo · 10.5281/zenodo.15194427lines:252-551Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Plant development depends on coordinated growth at cellular and organ scales, yet comparative analyses are hindered by inconsistent reporting of growth across studies. We conducted a meta-analysis of Arabidopsis thaliana growth dynamics, integrating data from 176 studies to create the first multiscale atlas of plant growth. We developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels. Analyses revealed both organ-specific and general growth strategies linked to size control. In the meristem, a conserved offset in cell expansion between central and peripheral zones was observed. Root elongation was driven mainly by cell expansion and differentiation in the elongation zone, rather than meristem activity. Hypocotyl and leaf growth showed unexpected parallels: early exponential elongation resembled primary morphogenesis, while later linear growth matched secondary morphogenesis. Comparing dark- vs light-grown hypocotyls and juvenile vs transition leaves showed that organ size was modulated by a trade-off between growth rate and duration of the scaling phase. Cellular-scale growth during early development was shown to influence final organ size, underscoring the need for early-stage measurements. This growth atlas provides benchmark values and a reference framework for interpreting mutant phenotypes, guiding experimental design, and advancing our understanding of growth regulation across plant organs.
Why it matches plant phenotyping methods植物の成長形質を統合・比較する数学的フレームワークと成長アトラスを構築しており、再利用可能な形質標準化・ベンチマークが研究の中心である。
abstractWe developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels.
When plants undergo senescence or experience carbon starvation, leaf cells degrade proteins in the chloroplasts on a massive scale via autophagy, an evolutionarily conserved process in which intracellular components are transported to the vacuole for degradation to facilitate nutrient recycling. Nonetheless, how portions of chloroplasts are released from the main chloroplast body and mobilized to the vacuole remains unclear. Here, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes. This protocol enabled us to track changes in chloroplast morphology during chloroplast-targeted autophagy on a timescale of seconds, and it could be adapted to monitor the dynamics of other intracellular processes in plant leaves. Key features • This protocol enables real-time monitoring of chloroplast morphology in living Arabidopsis leaves. • The method is based on confocal microscopy of transgenic plants that express fluorescent protein markers for specific organelles or suborganellar compartments. • We used this protocol to monitor the piecemeal autophagic degradation of chloroplasts, but it could also be extended to other intracellular phenomena.
Why it matches plant phenotyping methods生きた植物葉の葉緑体形態を共焦点画像でリアルタイム取得・追跡するプロトコルが中心であり、植物の形態状態を測定するフェノタイピング手法に該当する。
abstractHere, we developed a method to observe the autophagic transport of chloroplast proteins in real time using confocal laser-scanning microscopy on transgenic plants expressing fluorescently labeled chloroplast components and autophagy-associated membranes.
ArabidopsisMicroscopyCell / cellular structureRootTrackingGrowth / development / phenology
Arabidopsis is a widely used model plant to study physiology and development. Live imaging is an important technique to visualize and quantify processes in plant growth and cell division, where accurate cell tracking is essential. The commonly used software TrackMate adopts a tracking-by-detection approach, applying Laplacian of Gaussian (LoG) for blob detection and a Linear Assignment Problem (LAP) tracker for tracking. However, its performance declines when cells are densely arranged. To overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA) that incorporates knowledge of Arabidopsis root cellular patterns and spatial relationships among volumes. Our method follows a coarse-to-fine strategy: first performing relatively simple line-level tracking of nuclei, then refining associations based on the linear arrangement of cell files and their spatial relationships. We evaluated the method on long-term live imaging datasets of Arabidopsis root tips, and with minor manual correction, it achieved accurate nuclear tracking. To the best of our knowledge, this represents the first successful attempt to address a long-standing problem in time-lapse microscopy of the root meristem by providing an accurate tracking method for Arabidopsis root nuclei.
Why it matches plant phenotyping methods植物の3Dタイムラプス画像から根の細胞核を追跡する計算手法を開発・評価しており、植物の成長・細胞分裂の定量化を支える画像ベースの表現型取得法が中心です。
abstractTo overcome this limitation, we propose an accurate tracking method based on a Genetic Algorithm (GA)
Plant domestication may create trade-offs between growth and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. Trade-offs can also occur when traits optimized for favorable conditions perform less efficiently under stress. Deciphering these mechanisms is crucial for maintaining growth in changing environments. We examine one key aspect of vegetative growth, leaf elongation, in six species of grasses. We use a machine learning-enabled pipeline to extract cell dimensions and positions from leaf microscope images to study cell kinematics. We find that domesticated plants generally have longer leaves, larger division zones, and higher cell production rates. While no clear trade-off is observed between domestication and drought response in final leaf length, a trade-off occurs in development; wild species exhibit a smaller decrease in the elongation zone size under drought compared with domesticated species. This pattern points to compensatory mechanisms, such as extended elongation duration or increased cell production, mitigating drought effects in domesticated plants. These nuanced trade-offs associated with domestication highlight the importance of robustly phenotyping developmental and physiological traits, possibly informing breeding strategies to enhance crop resilience in future climates.
Why it matches plant phenotyping methods機械学習パイプラインによる葉の顕微鏡画像からの細胞寸法・位置抽出が、葉伸長と細胞動態の表現型評価の中心であるため。
abstractWe use a machine learning-enabled pipeline to extract cell dimensions and positions from leaf microscope images to study cell kinematics.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Laboratory / benchtopMicroscopyMultimodalCell / cellular structureRootTissueTrackingVisualization / data management
Abstract Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.
Why it matches plant phenotyping methodsTHG/3PFによる根の構造を高時空間分解能でラベルフリー取得するイメージング手法を開発・実証しており、植物表現型取得が中心である。
abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureRootPhysiological trait estimationGrowth / development / phenologyWater status / transpirationYield / yield components
Tip growth is a tightly regulated process that enables root hairs to explore their surroundings, enhancing plant development, particularly by improving nutrient uptake. While Lockhart's viscoplastic framework is widely used to describe this process, it has received limited experimental validation. By integrating optical microscopy with a custom microplate-based rheometer, we created a novel protocol to simultaneously measure, for individual growing root hairs, both the reduction in growth rate and the instantaneous compression in response to a step in applied axial force. The observed growth rate reduction aligns remarkably with a 1D Lockhart viscoplastic model, experimentally validating this framework in tip-growing cells. Additionally, the instantaneous compression upon force application provided an in situ estimate of turgor pressure. Together, these measurements allowed us to determine, for the first time in Arabidopsis root hairs, two critical parameters: the yield turgor pressure and cell wall viscosity. Our approach, including the technique, protocol, and analytical framework, can be readily adapted to other tip-growing species and diverse experimental conditions (e.g., varying nutrient availability or osmotic stress). This opens new opportunities to explore cell wall mechanosensitivity and its role in adapting tip growth to environmental signals.
Why it matches plant phenotyping methods個々の根毛の成長速度・圧縮・膨圧を測定する新規手法と解析枠組みが研究の中心であり、植物形質の取得とモデル検証を実施している。
abstractBy integrating optical microscopy with a custom microplate-based rheometer, we created a novel protocol to simultaneously measure, for individual growing root hairs, both the reduction in growth rate and the instantaneous compression in response to a step in applied axial force.
WheatMicroscopyCell / cellular structureRootTissueMorphology / geometry measurementRoot system architecture
The anatomy or the arrangement of cells often determines the organization and function of plant tissues. However, current methods in large-scale imaging and accurate quantification of anatomical traits face major limitations. To address these challenges, we introduce the AnatomyArray system, an integrated platform for multiplexed tissue sectioning and anatomical phenotyping in plants. This system includes a highly adaptable device for high-throughput paraffin sectioning and multichannel slide imaging of various plant tissues, along with AnatomyNet, a deep learning tool for analyzing tissue-scale patterns of cell arrangement and morphology. AnatomyNet delivers accurate, automated quantification of anatomical traits at both the tissue and cellular levels, outperforming existing tools in image analysis. Using the AnatomyArray system, we dissected the genetic basis of root anatomy in a diverse wheat (Triticum aestivum L.) population through anatomics-based genome-wide association studies. Among the candidate genes identified, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 14 (TaSPL14) was associated with stele and pericycle size in roots. Analysis of Taspl14 mutants confirmed that TaSPL14 plays a critical role in regulating root growth and tissue size by influencing phytohormone pathways. The AnatomyArray platform enables high-throughput characterization of cellular-level features and provides insights into the mechanisms shaping anatomical structure in plants.
Why it matches plant phenotyping methods植物組織の高スループット画像取得と、細胞・組織形態の自動定量を中核とするプラットフォームおよび解析ツールを開発しているため。
abstractwe introduce the AnatomyArray system, an integrated platform for multiplexed tissue sectioning and anatomical phenotyping in plants.
Cell / cellular structureTissuePhysiological trait estimationGrowth / development / phenology
The vascular cambium is a key lateral meristem responsible for secondary growth in woody plants, producing secondary xylem and phloem. Understanding its activity is crucial for studies on plant phenology, carbon sequestration, and environmental responses. However, defining the precise period of cambial activity remains challenging due to reliance on indirect indicators, such as cambial zone width or the presence of undifferentiated cells adjacent to the cambium. These parameters often misrepresent the true timing of cambial cell division, conflating it with subsequent differentiation processes. This study critically examines the limitations of indirect indicators and advocates for a more precise definition of cambial activity strictly as the process of cellular division. Direct cellular markers, such as mitotic figures, phragmoplasts, and newly formed tangential walls, provide more accurate assessment of cambial activity. By distinguishing cell division from differentiation, we can refine growth periodicity analyses and improve our understanding of environmental influences on cambial function. We review the structure and activity of the vascular cambium, demonstrating how indirect indicators can misrepresent cambial activity dynamics and lead to errors in determining its onset, duration, and cessation. By integrating direct cellular markers, we propose a more accurate methodology for assessing cambial activity, improving phenological studies and providing a clearer framework for evaluating plant responses to climatic variability.
Why it matches plant phenotyping methods形成層活動（細胞分裂）の評価方法を批判的に検討し、間接指標の限界と直接的な細胞マーカーを用いる改良方法を提案するレビューであり、植物状態の測定手法が中心です。
abstractDirect cellular markers, such as mitotic figures, phragmoplasts, and newly formed tangential walls, provide more accurate assessment of cambial activity.
Natural biocomposites such as wood and plant cell walls exhibit prominent mechanical properties largely attributed to the nanoscale organization of fibrous components, such as cellulose, which often adopt chiral arrangements. However, resolving the three-dimensional (3D) arrangement of these structures at the nanoscale remains a significant challenge, particularly in beam-sensitive materials. This study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution. By acquiring low-dose SED data at multiple tilt angles and applying a symmetry-based reconstruction algorithm, we resolved the 3D orientation of cellulose fibrils in native oat husk and birch wood. Our results reveal a multilayered cell wall architecture with alternating helical handedness, providing precise measurements of 3D fibril orientation. This method reveals complex hierarchical structures at the nanoscale, enabling rapid data acquisition and analysis using widely available instrumentation. The ability to resolve such chiral organization provides insights into material properties as well as opportunities for designing bioinspired materials with tunable mechanical and functional properties that extend far beyond natural biocomposite materials.
Why it matches plant phenotyping methods植物細胞壁のセルロース微繊維配向を定量的に再構成・測定する3D SED法が研究の中心であり、植物構造形質の取得手法に該当する。
abstractThis study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution.
Cell / cellular structureRootMorphology / geometry measurementPhysiological trait estimationGrowth / development / phenologyRoot system architecture
Sustainable phosphorus fertilization is a growing challenge in agriculture. Phosphorus is necessary for plant growth, but it is typically only bioavailable in its orthophosphate form. Phosphate fertilizers contribute to environmental damage as they leach into aquatic ecosystems. Therefore, it is imperative to develop new fertilization techniques such as controlled-release small-scale phosphate fertilizers. However, iteratively optimizing various new fertilizers using a comparable method is difficult. Here, we use three-dimensional bioprinting as a high-throughput screening platform to evaluate cellular phosphate uptake of various phosphate sources, including triple super phosphate, diammonium phosphate and struvite, which are composed of different chemistries and scales. As a result, we identified ideal phosphate fertilizer sources for the development of controlled-release phosphate fertilizers. Then, we evaluated whether plant growth and root architecture responded differently to the ideal controlled-release fertilizers. This study demonstrates the utility of this screening platform in developing a controlled-release phosphate fertilizer that effectively provides phosphate to plants at the microparticle scale.
Why it matches plant phenotyping methods3Dバイオプリンティングを用いた高スループットの植物リン吸収評価プラットフォームが研究の中心であり、植物のリン吸収および根系構造を測定しているため、単なる肥料試験を超える方法適用に該当する。
abstractHere, we use three-dimensional bioprinting as a high-throughput screening platform to evaluate cellular phosphate uptake of various phosphate sources
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens , we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis. Author summary Tip-growing cells can be characterized by their fast growth concentrated at the cell’s apex. Their growth and morphogenesis are tightly regulated processes involving cell wall addition and rearrangement while the cell wall is under stress originating from the cell’s internal turgor pressure. We start by studying the cell wall’s elastic properties, one aspect of the cell growth process. We use a method of marker point tracking across the surface of the tip-growing cell to measure the wall’s elasticity profile. In this work, we present a parameter sensitivity study of this method on synthetic cells and report our results on experimental moss tip-growing cells. Our results suggest that this inference method can reliably measure a cell wall elasticity gradient under combined geometric and mechanical conditions that create elastic strains within 5% at the tip.
Why it matches plant phenotyping methodsコケの先端成長細胞における細胞壁弾性分布を、蛍光マーカーと表面形態から推定する新規測定法を開発し、シミュレーションおよび実細胞で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe authors' Data Availability statement explicitly deposits all relevant data and code, including code demonstrations, in a public GitHub repository (rholee-xu/surface-model), which contains the analysis code for the cell wall elasticity inference method.Code · publicAll relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-modelOpen asset ↗rholee-xu/surface-modellines:45-63Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published16 Sept 2025Optical Trapping and Optical Micromanipulation XXIICited by 0 · OpenAlex ↗
A rigid body can have 3 degrees of rotational freedom. Of these, the yaw or in-plane rotation is well known and studied while the pitch or the first in-plane rotation is somewhat studied. However the roll or the second in-plane rotation has not been so well studied. It is here that we show how to detect roll rotation for a 4-fold symmetric Calcium Oxalate crystal inside a plant leaf cell by using the anisotropy of the scatter pattern while trapped in optical tweezers, behind a set of crossed polarizers. The difference in halves signal in the appropriate sense gives roll rotation while that in another sense gives pitch rotation of the crystal. We show that this can be used to perform nano-tribology of the interior surface of the leaf cell with a contact radius of about 500nm, without relying upon Atomic Force Microscopes, thus enabling soft probing.
Why it matches plant phenotyping methods植物葉細胞内の結晶回転を光トラップと散乱異方性で検出し、葉細胞内面の接着性を定量する新規測定法が中心であり、植物細胞の生理的状態を取得する方法開発に該当する。
abstractwe show how to detect roll rotation for a 4-fold symmetric Calcium Oxalate crystal inside a plant leaf cell by using the anisotropy of the scatter pattern while trapped in optical tweezers
Anthropogenic climate change is causing the decline of seaweed forests in many parts of the world. Despite successful preservation efforts, their immense biodiversity is still severely underrepresented in germplasm biobanks throughout the world. These culture libraries can preserve genetic diversity and provide inoculum for marine forest restoration and mariculture ventures, and potentially accelerate the selection and breeding of climate-resilient and high-yielding strains. However, the complex life cycles and body plans of seaweeds pose a huge challenge for the development of standardized phenotyping and isolating protocols for microscopic stages, especially with the efficiency necessary to deal with the current pace of global climatic changes. Here, we present SAMMBA (Seaweed Automatable Microplate Microscopy for Breeding Approaches), an end-to-end pipeline for the high-throughput isolation, phenotyping and storage of macroalgal cells in 384-well plates (384WP). By optimizing fluorescence microscopy imaging and analysis, along with a novel fragmentation method and dilution-to-extinction isolation, different unialgal seaweed tissues could be regrown after thousand-fold dilutions. In a single plate, we successfully isolated 68 singlet gametophyte fragments of Laminaria ochroleuca (39 males, 29 females; 17.7% efficiency) and 60 spores of Phyllariopsis purpurascens (31.25% efficiency). Furthermore, the taxonomic versatility of SAMMBA was demonstrated through the successful isolation of 60 unialgal cultures of red algae ( Halymenia sp., Hydrolithon sp., Erythrotrichia sp. ) and 10 strains of the green alga Ulva sp, without cross-contamination. The viability and unialgal nature of the isolated strains were verified by distributing a single L. ochroleuca strain across an entire 384-well plate and imaging each well over 30 days. We found that the average specific daily growth rates (daily SGR) per well were 0.130 ± 0.006 and 0.117 ± 0.01 day -1 for males and females, respectively, showing a significant difference between sexes (n = 768; p = 1.27e -53 ), while edge effects significantly reduced daily SGR in males but not in females. This approach dramatically increases experimental reproducibility and statistical power compared to conventional methods. Due to its modular design and cost-effectiveness, SAMMBA is readily adaptable to macroalgal repositories globally. It supports high-throughput, selective recovery of unialgal strains without reliance on robotic platforms, while remaining fully compatible with automation. This system significantly expands the experimental and operational capacity in macroalgal hatcheries, providing a scalable foundation for phenomics, domestication programs, and standardized, verifiable biobanking of unialgal strains. Ultimately, SAMMBA could provide critical support for breeding strategies required to ensure the resilience of marine forests and aquaculture in a rapidly changing ocean.
Why it matches plant phenotyping methodsマクロ藻類の高スループットな単離・表現型取得を目的としたSAMMBAパイプラインを開発し、蛍光顕微鏡画像解析と増殖測定を技術的に検証しているため、植物表現型手法が研究の中心である。
abstractHere, we present SAMMBA (Seaweed Automatable Microplate Microscopy for Breeding Approaches), an end-to-end pipeline for the high-throughput isolation, phenotyping and storage of macroalgal cells in 384-well plates (384WP).
The danger of heavy metal pollution has drawn the global concern of researchers in recent decades, especially multiple heavy metal pollution. Heavy metal pollution induced stress may cause oxidative stress and redox balance disruption in living organism, and further trigger other secondary stresses. Fluorescent imaging analysis is considered to be effective method for real-time visualization of complex bioactive molecules in situ due to their non-destruction, high sensitivity and specificity. It is still relatively rare that bifunctional fluorescent probe for imaging redox-correlated molecules under heavy metal stress. Herein, we designed and synthesized a smart dual responsive fluorescence probe (D-P) with dual detection sites for the individual detection of HClO and SO₂ derivatives with different channels. The probe D-P was applied to effectively dual monitor the dynamic change of HClO and SO₂ derivatives in plant, zebrafish and human cells. Importantly, probe D-P successfully observed dynamic changes of HClO and SO₂ derivatives mediated redox status in living organisms under single and combined exposure to heavy metal stress, and further during DTT-induced ER stress and hypoxia and ischemia stress, confirming that probe D-P is a powerful tool to real-time image of the dynamic balance of HClO and SO₂ derivatives for assessing the redox homeostasis under environmental stress, which can be monitored and adopted as the early warning in environmental stress conditions and disease.
Why it matches plant phenotyping methods植物内の酸化還元関連分子を可視化する二重応答蛍光プローブを開発し、重金属ストレス下の植物で動態を測定することが中心であるため、植物の生理状態を取得するフェノタイピング手法に該当する。
abstractHerein, we designed and synthesized a smart dual responsive fluorescence probe (D-P) with dual detection sites for the individual detection of HClO and SO₂ derivatives with different channels.
Microfluidic technologies offer powerful tools for miniaturized and highly controlled biological experiments, yet their application in plant research remains underexploited. In this study, we present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution. Protoplasts isolated from leaves of Nicotiana tabacum, Brassica juncea , and Kalanchoe daigremontiana were used to evaluate the platform’s suitability across diverse plant species. Our results demonstrate species-dependent responses to microfluidic cultivation, with tobacco protoplasts showing the highest viability. The system permits dynamic tracking of cell fate within individual droplets and supports the quantification of stochastic and concentration-dependent responses to chemical stimuli. Using tobacco protoplasts, we further investigated the effect of low concentrations of cytokinins (BAP) and auxins (NAA) for the early protoplast culture, up to the first division. Low concentrations (20–80 µg·L −1 ) significantly enhanced cell survival and cell growth, while higher doses did not yield additional benefits. This work underscores the potential of droplet-based microfluidics as a high-resolution, low-volume platform for protoplast-based assays and dose-response screening, with applications across diverse plant biotechnology studies.
Why it matches plant phenotyping methods植物プロトプラストの生存、成長、細胞運命を単一細胞レベルで追跡・定量するマイクロ流体プラットフォームが研究の中心であり、植物状態の取得手法として適格。
abstractwe present a droplet-based microfluidic platform tailored for the encapsulation and cultivation of plant protoplasts, enabling long-term observation of cell development at nearly single-cell resolution.
Summary Female and male meiosis often differ in many aspects, such as their duration and the frequency as well as the positioning of crossovers. However, studying female meiosis is often very challenging and thus, much less is known about female versus male meiosis in many species including plants. To approach this gap, we have developed a live-cell imaging system for female meiocytes in Arabidopsis. This allowed us to obtain a temporally resolved cytological framework of female meiosis in the wildtype that serves as a guiding system for future studies. Here, we have applied this imaging system to study mutants in cyclin- dependent kinase inhibitors, in which a designated female meiocyte undergoes several mitotic divisions before entering meiosis. This enabled us to address when a meiocyte is committed to meiosis, a key question during reproductive development and in particular for the analysis of apomictic species in which meiosis is skipped. Highlights Establishment of a live-cell imaging system captures dynamic features of female meiosis. Identification of cytological landmarks ensures robust assignment of meiotic stages. Time-lapse imaging enables quantitative dissection of meiotic phases. Application of the framework reveals great plasticity in the commitment to meiosis.
Why it matches plant phenotyping methodsアラビドプシス雌性減数分裂を対象とするライブセル画像化システムを開発し、時間分解・定量的な細胞状態の抽出と変異体への応用を行っており、画像ベースの植物表現型取得が中心です。
abstractwe have developed a live-cell imaging system for female meiocytes in Arabidopsis.
MicroscopyCell / cellular structureVisualization / data management
Understanding lipid metabolism in algae is critical to advancing our knowledge on fundamental algal physiology and for harnessing these organisms as platforms for the sustainable production of high-energy lipids. BODIPY is the most prevalently used fluorescent dye for the visualization of lipid droplets (LDs) in algae; however, its limitations warrant exploration of alternatives. Here we evaluate and compare four lipophilic fluorophores (BODIPY, DAF, Cou, DPAS) for their effectiveness in staining LDs in the extremophilic green alga Chlamydomonas priscui. We assess each dyes photophysical properties, synthetic accessibility, LD specificity, cellular toxicity, and suitability for microscopy and flow cytometry. All four dyes successfully stain LDs, but their performance diverges under different experimental conditions. BODIPY permits long-term incubation allowing quantification in time-course studies but exhibits poor LD specificity and high susceptibility to photobleaching. DAF enables polarity-sensitive staining but is highly toxic on prolonged exposure or during cellular stress. Cou and DPAS yield strong LD-specific signals with low cytotoxicity, making them ideal for studies involving environmental stress. However, DPAS requires room-temperature incubation, pointing toward greater potential utility for non-extremophilic algae. These results expand the toolbox for lipid biotechnology research in extremophiles and underscore the importance of tailoring dye selection and experimental conditions to algal physiology.
Why it matches plant phenotyping methods藻類細胞の脂質滴を可視化・定量する蛍光染色法を比較評価し、顕微鏡およびフローサイトメトリーへの適用性、特異性、毒性、光退色を検証しているため、表現型取得法が中心である。
abstractHere we evaluate and compare four lipophilic fluorophores (BODIPY, DAF, Cou, DPAS) for their effectiveness in staining LDs in the extremophilic green alga Chlamydomonas priscui.
Deep understanding of the structural composition and growth of biological specimens is becoming increasingly important for the development of bio-based and sustainable material systems. Full-field nano-computed tomography is particularly suitable for this purpose as it allows for non-destructive 3D imaging at high spatial resolution. However, most biological samples are functionalized by water and respond sensitively to any changes in climate conditions, specifically relative humidity, by adjusting their material moisture content. To date, only a limited number of tomography instruments offer an in situ climate control option to users. These, however, are limited either by the range of relative humidity states, the long times required to change the climate state, or obstruction or attenuation of the beam. Here, the first fully automatized climate cell for in situ full-field nanotomography is presented. It has been designed, built and integrated at the nanotomography station at the P05 imaging beamline, operated by Hereon at the DESY storage ring PETRA III, Germany. The highly flexible and windowless design allows the humidity dependent swelling and shrinking of lignified plant cell walls to be studied in situ, using phase contrast nanotomography. The concept of this climate chamber can easily be integrated into other setups. It operates in the relative humidity range of 0-90% and can be controlled in a temperature range of 10-50°C. Climate conditions can be adjusted at any time, remotely from the control hutch by using a humidity generator. Results show that the developed setup maintains a stable climate during the entire duration of a tomographic scan at different humidities and does not obstruct the sample or hinder the imaging conditions. During the tomographic investigation the sample remains stable in the flow of the air stream and shows typical cell wall swelling and shrinking behaviour depending on the equilibrium moisture content. This new climate cell is now available to all users of the P05 nanotomography instrument for conditioning samples, serving a wide range of scientific applications.
Why it matches plant phenotyping methods植物細胞壁の膨潤・収縮を非破壊に取得するナノトモグラフィー用の自動環境制御セルを開発・統合し、安定性と撮像性能を検証しているため、植物表現型取得法が中心である。
abstractHere, the first fully automatized climate cell for in situ full-field nanotomography is presented.
Genetic screens in whole plants are a powerful tool for functional genetics. However, elucidating gene function in highly redundant genetic programs such as signaling pathways remains challenging in both model and non-model plants. Here, we report a single-cell screening platform, PIVOT (Protoplast Isolation after Virus Overexpression in planTa ), to accelerate identification and functional characterization of plant genes. We used Nicotiana benthamiana as a heterologous host to test gene libraries arrayed in a single leaf. Two elements of our system made pooled screens possible in planta : (1) we harnessed viral superinfection exclusion to ensure single multiplicity of infection per cell during pooled library delivery, and (2) we engineered a cell surface protein as a phenotypic marker for isolating cells of interest from a heterogeneous population. Using this system, we recovered known and new regulators of cytokinin signaling from an Arabidopsis open reading frame library. We anticipate PIVOT will be broadly applicable for high-throughput, single-cell functional genetic screening across the plant kingdom.
Why it matches plant phenotyping methods植物細胞の表現型マーカーを利用して関心細胞を単離する単一細胞スクリーニング基盤そのものの開発であり、表現型取得・選別が研究の中心です。
abstractHere, we report a single-cell screening platform, PIVOT (Protoplast Isolation after Virus Overexpression in planTa ), to accelerate identification and functional characterization of plant genes.
In response to environmental changes, chloroplasts, the cellular organelles responsible for photosynthesis, undergo intracellular repositioning, a phenomenon known as chloroplast movement. Observing chloroplast movement within leaf tissues remains technically challenging in leaves consisting of multiple cell layers, where light scattering and absorption hinder deep tissue visualization. This limitation has been particularly problematic when analyzing chloroplast movement in the mesophyll cells of C 4 plants, which possess two distinct types of concentrically arranged photosynthetic cells. In response to stress stimuli, mesophyll chloroplasts aggregate toward the inner bundle sheath cells. However, conventional methods have not been able to observe these chloroplast dynamics over time in living cells, making it difficult to assess the influence of adjacent bundle sheath cells on this movement. Here, we present a protocol for live leaf section imaging that enables long-term and detailed observation of chloroplast movement in internal leaf tissues without chemical fixation. In this method, a leaf blade section prepared either using a vibratome or by hand was placed in a groove made of a silicone rubber sheet attached to a glass slide for microscopic observation. This technique allows for the quantitative tracking of chloroplast movement relative to the surrounding cells. In addition, by adjusting the sectioning angle and thickness of the unfixed leaf sections, it is possible to selectively inactivate specific cell types based on their size and shape differences. This protocol enables the investigation of the intercellular interactions involved in chloroplast dynamics in leaf tissues. Key features • Thin leaf sections prepared while still alive enable prolonged microscopic observation of chloroplast movement within the leaf tissue. • Selective cell inactivation can be achieved by adjusting the slice thickness and angle. • This method is applicable to a wide range of plant species.
Why it matches plant phenotyping methods生葉切片のライブイメージングにより、葉内部の葉緑体運動を長時間観察・定量追跡する手法を開発しており、植物表現型の取得が研究の中心である。
abstractHere, we present a protocol for live leaf section imaging that enables long-term and detailed observation of chloroplast movement in internal leaf tissues without chemical fixation.
The redox state of the plastoquinone pool (PQ-redox) acts as a central element in a variety of intracellular signal pathways. Several methods for determining PQ-redox have been established. Although some of these methods may be quantitative, such as those based on liquid chromatography, they are typically sensitive to sample preparation. Here, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803). The method, based on the evaluation of relative fluorescence yield at the J-step of the OJIP transient (VJ, VJ), has already been reported; however, thus far, it has been used mostly for studying dark-acclimated leaves, which limits its range of application. Here, we show that the OJIP transient can be used for semi-quantitative estimation of PQ-redox in algal and cyanobacterial cell cultures, in addition to plants. We further show that it can reflect PQ-redox in both dark-acclimated and light-acclimated samples. Our systematic comparison of Multi-Color PAM, AquaPen, and FL 6000 fluorometers demonstrates that accurate measurement of VJ and VJ parameters in suspension cultures requires low culture density and a high-intensity saturation pulse. We further show that with increasing light intensity to which the cells are exposed, the state of photosystem II (PSII) changes due to light-induced reduction of quinone A (QA-) and conformational changes, which in turn influence both the sensitivity and dynamic range of the VJ parameter towards PQ-redox estimation. A comparison of fluorescence transients in Chlorella and Synechocystis revealed high homeostatic control over PQ-redox in Synechocystis, maintained by terminal oxidases present at the thylakoid membrane. While we discuss certain limitations, our systematic assessment suggests that the OJIP method has great potential to become a routine tool for semi-quantitative PQ-redox estimation under a wide range of experimental conditions in green algae and cyanobacteria.
Why it matches plant phenotyping methodsOJIP蛍光法による植物・藻類・シアノバクテリアのPQ-redox推定を中心に、複数蛍光計の系統比較、測定条件、感度・適用範囲を評価しており、植物の生理状態を取得する方法の検証研究である。
abstractHere, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803).
Mechanical properties of plant cells and tissues change dynamically, influencing plant growth, development, and interactions with the environment. Despite their central roles in plant life, current knowledge of how these properties change in vivo is very limited. Here we have combined Brillouin microscopy and molecular rotors to investigate stiffness, viscosity and porosity in living Arabidopsis thaliana seedling roots during differentiation and in response to stress and genetic manipulation. We found that mechanical properties change in a cell- and tissue-specific manner. The properties change dynamically during differentiation to support directional cell expansion. Cell-type-specific adaptations are induced within hours in response to stress or changes in cell wall metabolism. The findings form the foundation for future studies to characterize regulatory mechanisms linking biochemical signaling and mechanical properties.
Why it matches plant phenotyping methods生きた植物体での硬さ・粘性・多孔性をBrillouin顕微鏡と分子ローターにより測定する手法適用が研究の中核であり、植物の生理状態・組織特性を定量化している。
abstractHere we have combined Brillouin microscopy and molecular rotors to investigate stiffness, viscosity and porosity in living Arabidopsis thaliana seedling roots during differentiation and in response to stress and genetic manipulation.
Abstract Functional genomics has been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS). The CRISPR/Cas-generated library consists of 3,567 microalgal mutants derived from 2,397 effective guide RNAs. Label-free sorting of the library for high carotenoid content by RACS unravels mutations in the violaxanthin de-epoxidase ( noVDE ) or in the proteasome assembly chaperone 4 ( noPAC4 ) genes. Knocking out all five known noVDE s reveal that the high carotenoid content is due to violaxanthin increase, whilst noPAC4 knockout boosted carotenoid content with elevations in violaxanthin, zeaxanthin, and β-carotene. Genetic and transcriptomic evidences suggest two previously unknown modes of carotenogenesis regulation mediated by noPAC4: epigenetic mechanisms via histone deacetylase (HDAC) and post-translational controls by the 26S proteasome. Therefore, by label-freely sorting single-cell metabolic phenotype and rapidly yet unambiguously tracing it to a genotype, this new forward-genetics approach can greatly accelerate the discovery of new genes and pathways.
Why it matches plant phenotyping methods単細胞のカロテノイド含量という植物状態をラベルフリーで取得・選別するRaman-activated Cell Sorting（RACS）プラットフォームが研究の中心であり、ゲノム編集ライブラリへの実質的な適用も行っている。
abstractwe introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated Cell Sorting (RACS).
Sequential drought and heat stress pose a growing threat to forest ecosystems in the context of climate change, yet systematic evaluation methods for woody plants remain limited. This study aimed to develop a comprehensive screening platform for identifying woody plant species tolerant to sequential drought and heat stress among 27 native species growing in Korea. A sequential stress protocol was applied: drought stress for 2 weeks, followed by high-temperature exposure at 45 °C. Physiological indicators, including relative water content (RWC) and electrolyte leakage index (ELI), were used for preliminary screening, supported by phenotypic observations, Evans blue staining for cell death, and DAB staining to assess oxidative stress and recovery ability. The results revealed clear differences among species. Chamaecyparis obtusa , Quercus glauca , and Q. myrsinaefolia exhibited strong tolerance, maintaining high RWC and low ELI values, while Albizia julibrissin was highly susceptible, showing severe membrane damage and low survival. DAB staining successfully distinguished tolerance levels based on oxidative recovery. Additional species such as Camellia sinensis , Q. acuta , Q. phillyraeoides , Q. salicina , and Ternstroemia japonica showed varied responses: Q. phillyraeoides demonstrated high tolerance, T. japonica showed moderate tolerance, and Q. salicina was relatively sensitive. The integrated screening system effectively differentiated tolerant species through multiscale analysis-physiological, cellular, and morphological-demonstrating its robustness and applicability. This study provides a practical and reproducible framework for evaluating sequential drought and heat stress in trees and offers valuable resources for urban forestry, reforestation, and climate-resilient species selection.
Why it matches plant phenotyping methods順次乾燥・高温ストレスに対する樹木の耐性を、生理・細胞・形態指標で評価する統合スクリーニング基盤そのものを開発・提示しており、表現型取得法が研究の中心である。
abstractThis study aimed to develop a comprehensive screening platform for identifying woody plant species tolerant to sequential drought and heat stress among 27 native species growing in Korea.
Elucidating the spatial and temporal regulation of gene expression during plant organogenesis is crucial for enabling precise crop improvement strategies that incorporate beneficial traits into crops while avoiding adverse effects. Root nodules, specialised organs formed in symbiosis with nitrogen-fixing bacteria, provide a valuable system to study cell-type-specific gene networks in a symbiosis-induced developmental context. However, capturing these dynamics at cellular resolution in intact plant tissues remains technically challenging. Spatial transcriptomics technologies developed for animal systems are often not directly transferable to plant tissues due to fundamental differences in tissue composition between plants and animals, including rigid and heterogeneous plant cell walls, high cell wall autofluorescence, and large vacuoles in plant cells that complicate probe access and signal detection. To address these challenges, we present an optimised protocol for applying the Xenium in situ sequencing platform to formalin-fixed paraffin-embedded (FFPE) sections of plant tissues, including Medicago truncatula roots and nodules. Key technical adaptations include customised tissue preparation, optimised section thickness, hybridisation conditions, post-Xenium staining, imaging, and downstream image analysis, all tailored specifically for plant samples. To mitigate autofluorescence and enhance detection sensitivity, we employed a strategic approach to codeword selection during probe design. Furthermore, we developed a modular probe design approach combining a custom 380-gene standalone panel with a 100-gene add-on panel. This design allows flexibility for addressing diverse research questions and includes orthologous gene sequences from two Medicago ecotypes, ensuring compatibility for downstream functional validation using mutant lines available in both genetic backgrounds. We validated the protocol across nodules at multiple developmental stages using both the 50-gene panel targeting mature nodule cell identity and the extended 480-gene panel, which includes markers across different cell types and developmental stages, as well as genes of interest identified from prior single-cell and bulk RNA-seq analyses. This optimised workflow provides a reproducible and scalable method for high-resolution spatial transcriptomics in plant tissues, establishing a robust foundation for adaptation to other plant species and developmental systems.
Why it matches plant phenotyping methods植物組織向け空間トランスクリプトミクスの技術適応・最適化と検証が研究の中心であり、植物器官の細胞状態を高解像度で取得する再現可能なワークフローを開発している。
abstractwe present an optimised protocol for applying the Xenium in situ sequencing platform to formalin-fixed paraffin-embedded (FFPE) sections of plant tissues, including Medicago truncatula roots and nodules.
Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue, respectively, outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark. The model successfully labeled vascular bundles with a Dice Similarity Coefficient (DSC) of 0.51 in apple tissue and 0.79 in pear tissue, although thin vasculature in apple remained more challenging to segment. The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue. Despite evaluating different methods to enhance segmentation quality, none improved test performance beyond that of the model trained on the standard dataset. The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images, without extensive sample preparation such as contrast labelling. The developed method, if not replaces, drastically accelerates conventional human-in-the-loop analysis of such images.
Why it matches plant phenotyping methods植物組織の3DマイクロCT画像から微細構造を自動セグメンテーションし、形態計測する手法の開発・比較検証が中心であるため。
abstracta 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images.
Summary Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hampers automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers —so long as the cell contours can be identified— are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialist, it is expected to promote understanding of what happens at the sub- and cellular level with high throughput outcomes. Significance statement Faced with fluctuations in cell coordinates and cell tip positions, position correction of live imaging data and accurate detection of tip position are key challenges in plant developmental biology. We solved them with a powerful and user-friendly tool, KymoTip, that can realize cell position correction, cell tip detection with cell centerline, and quantification of intracellular events.
Why it matches plant phenotyping methods植物細胞のライブイメージから細胞形状・先端位置・成長動態を定量化する解析ソフトウェアを開発しており、植物フェノタイピング手法が中心です。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's authors explicitly state that the KymoTip analysis code is publicly available on GitHub at https://github.com/blues0910/KymoTip, which is an allowed URL. This is the authors' own computational tool implementing the paper's tip-growth phenotyping analysis (segmentation, coordinate normalization, tip-bottom, Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTip.Open asset ↗blues0910/KymoTippdf-page:8 lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jul 2025Environmental and experimental botany
Wheat and barley are known as important staple food worldwide, but their growth and yield are severely affected by soil salinity, prompting a need for regaining their stress tolerance lost during domestication, to meet food security targets under current climate scenarios. The bottle neck in this process is plant phenotyping. In the past decades, approach to plant phenotyping for salinity stress tolerance was predominantly driven by the need for a high throughput screening and focused on the whole-plant level traits by advocating various non-destructive and/or analytical methods. This approach, though useful for assessing overall plant performance under salinity stress, fails to account for tissue- and cell-specific operation of contributing mechanisms and, as a result, lack the predictive power. In this work, we propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H₂O₂-induced K⁺ and Ca²⁺ flux responses from mature root epidermis. By screening 44 barley, 20 durum and 20 bread wheat accessions, we show that tolerant genotypes reduce sensitivity of cation (Na⁺, K⁺ and Ca²⁺) permeable ion channels to ROS and argue that such desensitization may allow plants to efficiently regulate its ionic homeostasis in a cell- and tissue-specific manner, without compromising stress-induced ROS signaling to downstream targets, for transcriptional regulation purposes. Being conducted on young (4-d old) seedlings, this cell-based phenotyping platform offer breeders a possibility to target new (previously unexplored) traits and may be instrumental for assisting breeders in engineering salinity stress tolerance in future breeding programs.
Why it matches plant phenotyping methods塩ストレス耐性を評価する新しい細胞ベースの生理フェノタイピング手法を提案・検証し、イオンフラックス応答を用いて遺伝子型をスクリーニングしているため、手法が研究の中心である。
abstractwe propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H₂O₂-induced K⁺ and Ca²⁺ flux responses from mature root epidermis
Plant mitochondria are in continuous motion. While providing ATP to other cellular processes, they also constantly consume ATP to move rapidly within the cell. This movement is in part related to taking up, converting and delivering metabolites and energy to and from different parts of the cell. Plant mitochondria have varying amounts of DNA, even within a single cell, from none to the full mitochondrial genome. Because mitochondrial dynamics are altered in an Arabidopsis mutant with disrupted DNA maintenance, we hypothesised that exchanging DNA templates for repair is one of the functions of their movement and interactions. Here, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana. In addition to staining mitochondrial DNA with SYBR Green, we have developed and implemented a fluorescent mitochondrial DNA binding protein that will enable future understanding of mitochondrial dynamics, genome maintenance and replication. We demonstrate that mitochondria without mtDNA have altered physical behaviour and lower immediate connectivity to the rest of the population, further supporting a link between the physical and genetic dynamics of these complex organelles.
Why it matches plant phenotyping methods植物ミトコンドリアのDNA可視化と位置追跡を行うイメージング手法を開発・実装し、運動性や接続性という細胞内状態を定量化しているため、方法開発が中心である。
abstractHere, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Lignin, a major component of plant cell walls, plays a critical role in structural support and stress resistance. Despite its importance, the transport and deposition dynamics of the glycosylated lignin monomer during lignification in living cells remain poorly understood, hindering advances in biomass utilization. To address this challenge, alkyne-labeled glycosylated lignin precursors (pGCA ALK and CF ALK ) were synthesized by introducing propargyl groups at the ortho position of aromatic rings. These precursors were successfully incorporated into lignin polymers in flax (a herbaceous plant) and ginkgo (a gymnosperm), enabling the real-time tracking of lignification via fluorescent click chemistry. Quantitative imaging revealed that lignification initiates at cell corners and the middle lamella and then progressively extends into secondary cell walls. Distinct deposition patterns were observed: parenchyma cells exhibited continuous lignin accumulation, whereas fiber tracheids underwent rapid lignification, followed by cell death. Specialized pit structures displayed "tunnel-like" lignin deposition in longitudinal pits and unilateral patterns in transverse pits. In vitro synthesis of dehydrogenation polymers (DHP) and extraction of the cellulolytic enzyme lignin (CEL) from ginkgo confirmed the biocompatibility of labeled monomers. LC-MS analysis further demonstrated that alkynyl groups formed oxygen-containing cyclic structures without disrupting natural β-O-4 and β-5 lignin linkages. Application of this labeling method in biomass utilization indicated that lower overall fluorescence intensity correlates with more efficient lignin removal during pretreatment. These results provide new insights into the spatiotemporal dynamics of lignification and establish a bioorthogonal platform for lignin research, offering promising strategies for optimizing plant biomass in industrial applications.
Why it matches plant phenotyping methods蛍光クリック化学による生細胞内リグニン形成の時空間追跡と定量イメージング手法を開発・適用しており、植物状態の取得方法が研究の中心である。
abstractenabling the real-time tracking of lignification via fluorescent click chemistry.
Fluorescence imaging has become a central tool in plant cell biology, enabling detailed analysis of cellular structures and dynamics. However, challenges such as phototoxicity, photobleaching, and the invasiveness of fluorescent labeling have driven the development of artificial intelligence (AI)-based alternatives. Among these, deep learning-based segmentation and virtual staining have shown significant promise for advancing plant cell microscopy. Compared with traditional methods reliant on manual operations or simple thresholding algorithms, segmentation powered by AI-based image transformation offers enhanced accuracy and reproducibility in quantifying cellular features. Moreover, virtual staining transforms bright-field images into synthetic fluorescence images, enabling non-invasive, high-resolution analyses while bypassing the need for physical labeling. Together, these techniques expand the analytical capabilities of plant cell microscopy, facilitating efficient and precise imaging workflows. Despite their potential, these approaches face technical challenges. Virtual staining relies heavily on high-quality bright-field images and is currently constrained when applied to three-dimensional analyses of complex plant tissues. Future efforts must focus on developing diverse training datasets and advancing AI technologies to overcome these limitations. By offering automated segmentation and virtual staining, AI is transforming plant cell microscopy into a more versatile and powerful tool, paving the way for groundbreaking discoveries and broader applications in plant cell biology.
Why it matches plant phenotyping methods植物細胞画像におけるAIセグメンテーションと仮想染色を扱うレビューで、細胞特徴の定量化と再現性向上を目的とした画像解析手法が中心である。
abstractdeep learning-based segmentation and virtual staining have shown significant promise for advancing plant cell microscopy.
Photosynthesis provides energy and organic substrates to most life. In plants, photosynthesis dominates chloroplast physiology but represents only a fraction of the tightly interconnected metabolic network that spans the entire cell. Here, we explore how photosynthetic activity affects energy physiology within and beyond the chloroplast. We developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology to investigate pH, MgATP2- and NADH/NAD+ dynamics at dark-light transitions in Arabidopsis mesophyll cells. Our findings reveal a stromal alkalinization signature induced by photosynthetic proton pumping, extending to the cytosol and mitochondria as an alkalinization wave. Photosynthesis leads to increased MgATP2- levels in both the stroma and cytosol. Additionally, we observed reduction of the NAD pool driven by photosynthesis-derived electron export. Arabidopsis lines defective in chloroplast NADP- and mitochondrial NAD-dependent malate dehydrogenases show more reduced cytosolic NAD redox status even in darkness, highlighting the involvement of chloroplasts and mitochondria in shaping cytosolic redox metabolism via malate metabolism. Our study sets a novel methodological standard for precision live-monitoring of photosynthetic cell physiology. Applying this technology reveals signatures of photosynthetic physiology within and beyond the chloroplast with unprecedented resolution. Those signatures link photosynthetic activity and the fundamental biochemical functions of phototrophic cells. Significance statementBy applying novel live microscopy monitoring using fluorescent protein biosensors in plant cells, we reveal that dark-light transitions trigger profound re-orchestration of subcellular pH, ATP and NAD redox physiology not limited to chloroplasts but extending into the cytosol and the mitochondria.
Why it matches plant phenotyping methods植物細胞内のpH、ATP、NAD酸化還元状態を測定するライブイメージング手法を開発し、技術標準として提示・適用しており、表現型取得法が研究の中心である。
abstractWe developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 15 Sept 2026
Plant reproduction involves dynamic spatiotemporal changes that occur deep within maternal tissues. In ovules of Arabidopsis thaliana (A. thaliana), one of the two synergid cells degenerates at fertilization, while the fertilized egg cell (zygote) undergoes directional elongation followed by asymmetric division to initiate embryonic patterning. However, morphological analysis of these events has been hampered by the limitations of conventional cell wall staining, which fails to label cells lacking complete walls, and by the requirement for transgenic fluorescent reporters to visualize cell outlines. Here, we report that the membrane-specific fluorescent dye FM4-64 readily permeates ovules, allowing clear visualization of reproductive cell morphology both before and after fertilization. This staining method supports high-resolution time-lapse imaging and quantitative analysis of early embryogenesis in living tissues. Importantly, it is applicable not only to the angiosperm A. thaliana but also to the liverwort Marchantia polymorpha (M. polymorpha) and the fern Ceratopteris richardii (C. richardii), enabling the visualization of live reproductive cell structures within maternal tissues and revealing fertilization-associated morphological changes. This simple and robust method thus provides a valuable tool for spatiotemporal and quantitative analyses of reproductive processes across a broad range of plant species, without the need to generate transgenic lines.
Why it matches plant phenotyping methods生きた植物生殖組織の細胞形態を可視化・定量化する蛍光染色法を開発し、複数種で適用・検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we report that the membrane-specific fluorescent dye FM4-64 readily permeates ovules, allowing clear visualization of reproductive cell morphology both before and after fertilization.
ArabidopsisSoybeanCell / cellular structureRootMorphology / geometry measurementRoot system architecture
Abstract Root system architecture (RSA), the three-dimensional arrangement of roots in soil, is a critical determinant of plant productivity, resource use efficiency, and resilience to environmental stress. Despite its agronomic importance, RSA remains a largely untapped breeding target due to historical technical barriers in root phenotyping. We present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems. Applying RADICYL to a genetically diverse panel of 371 soybean accessions, we combined 3D phenotyping with genome-wide association studies (GWAS), single-nucleus RNA sequencing (snRNA-seq), and gene co-expression network (GCN) analysis to identify RCE1 and NPR3 as central regulators of RSA, suggesting auxin and salicylic acid-mediated signaling impacts RSA in specific root tissues. Functional validation in Arabidopsis mutants revealed conserved effects on root width and lateral root development. Our findings position the endodermis and metaphloem as key regulatory cell types and demonstrate how multi-omic frameworks can accelerate the discovery of functional genes underlying complex traits. This study establishes a foundation for cell-type-targeted genome editing and climate-smart crop engineering, offering actionable genetic targets to optimize root systems for improved nutrient acquisition, drought resilience, and deep carbon sequestration. By bridging genotype, cellular context, and phenotype, this work redefines RSA as a tractable and transformative trait for the future of crop improvement.
Why it matches plant phenotyping methodsRADICYLという根系構造を定量化する高スループット3Dフェノタイピング基盤の開発・適用が研究の中心であり、15形質を測定している。
abstractWe present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems.
Reproduction assets foundThe paper's Data and code availability section names public repositories containing the authors' analysis code: a GitLab repo for WGCNA/single-cell network analysis, a GitHub repo for the RADICYL root image segmentation/phenotyping pipeline, and PyGNA2 on PyPI/GitLab. These are paper-specific, publicly actionable code/Code · publicn every 5°, resulting in 72 images per plant per timepoint for subsequent 3D root
1103 reconstruction. Phenotypic traits were quantified using the same automated pipeline described
1104 above for soybean.
1105
1106 Data and code availability
1107 The code to analyze the WGCNA network and single-cell data can be found here:
1108 https://gitlab.com/salk-tm/soybean-root-gwas/. RADYCL Segmentation pipeline for image
1109 analysis can be found here: https://github.com/Salk-Harnessing-Plants-Initiative/SSRAPC-Soy-
1110 Segmentation-Root-Architecture-Phenotyping-for-Cylinder.git. PyGNA2 is available on PyPI
1111 (https://pypi.org/project/pygna2/) and GitLab (https://gitlab.com/salk-tm/pygna2).
1112Open asset ↗salk-tm/soybean-root-gwaspdf-layout-page:30 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.
Why it matches plant phenotyping methods植物の蛍光イメージングからカルシウム動態という生理状態を抽出・定量するオープンソース解析ツールが中心であり、植物を含む複数生物種のデータで評価されている。
abstractHere, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem.
Reproduction assets foundThe paper describes CalciumInsights, an open-source R/Shiny tool for calcium transient analysis. The authors explicitly state their code is publicly available on GitHub, which constitutes the paper's computational analysis asset. No plant-phenotyping datasets, images, or trained models are described; the tool is tissueCode · publicnt for publication
All authors have reviewed the manuscript and approved the final draft for publication.
Resource availability
Lead contact: Further information and requests for data may be directed to and will be
fulfilled by Mauricio Cabrera (mauricio.cabrera1@upr.edu)
Code: All codes used are publicly available in GitHub at https://github.com/AOG-Lab/CalciumInsights
References
1. Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat
Rev Mol Cell Biol [Internet]. 2000 Oct [cited 2024 Oct 21];1(1):11–21. Available from:
https://www.nature.com/articles/35036035
2. Sanderson MJ, Charles AC, Boitano S, Dirksen ER. Mechanisms and function of intercellularOpen asset ↗AOG-Lab/CalciumInsightspdf-raw-page:20 lines:1-37Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
MicroscopyCell / cellular structureMorphology / geometry measurementSegmentationVisualization / data management
ABSTRACT Live‐cell imaging has enabled the visualization of cytoskeletal dynamics with high spatiotemporal resolution, producing vast, and complex datasets. Recent advancements in live‐cell imaging techniques have significantly increased data dimensionality and throughput, challenging conventional qualitative analysis methods. Computational approaches, including machine learning‐based image processing, have emerged as powerful tools for extracting quantitative features from these datasets, facilitating systematic analysis of cytoskeletal organization and dynamics. In this review, we outline image analysis techniques for quantification of cytoskeletal structures, focusing on microscopic image transformation and feature extraction. We discuss classical image‐processing methods, such as filtering and segmentation, as well as recent applications of deep learning in cytoskeletal analysis. Furthermore, we revisit classical studies on cortical microtubule reorganization after plant cytokinesis, and explore how modern computational techniques can provide new insights into traditional concepts.
Why it matches plant phenotyping methods植物細胞骨格の組織化・動態を顕微鏡画像から定量化する画像解析手法を中心に扱うレビューであり、植物の形態・細胞状態の表現型抽出に直接関連する。
abstractIn this review, we outline image analysis techniques for quantification of cytoskeletal structures, focusing on microscopic image transformation and feature extraction.
Amyloplasts, non-photosynthetic plastids specialized for starch synthesis and storage, proliferate in storage tissue cells of plants. To date, studies of amyloplast replication in roots and the ovule nucelli from various plant species have been performed using electron and fluorescence microscopy. However, a complete understanding of amyloplast replication remains unclear due to the absence of experimental systems capable of tracking their morphology and behavior in living cells. Recently, we demonstrated that Arabidopsis ovule integument could provide a platform for live-cell imaging of amyloplast replication. This system enables precise analysis of amyloplast number and shape, including the behavior of stroma-filled tubules (stromules), during proplastid-to-amyloplast development in post-mitotic cells. Here, we provide technical guidelines for observing and quantifying amyloplasts using conventional fluorescence microscopy in wild-type and several plastid-division mutants of Arabidopsis . Key features • Novel approach for investigating amyloplast differentiation and replication in plant cells. • Detection of stroma-labeled amyloplasts in whole-mount ovules using conventional fluorescence microscopy. • Facilitates quantitative and comparative analysis of amyloplast proliferation using various Arabidopsis resources. • Enables high-resolution analysis of changing amyloplast and stromule morphologies in living cells.
Why it matches plant phenotyping methods生細胞蛍光イメージングによりアミロプラストの数・形状・増殖を定量する技術の技術指針を提示しており、植物表現型の取得・解析方法が研究の中心である。
abstractHere, we provide technical guidelines for observing and quantifying amyloplasts using conventional fluorescence microscopy in wild-type and several plastid-division mutants of Arabidopsis .
Flow cytometry is widely applied to infer the ploidy and genome size (GS) of plant nuclei. The conventional approach of sample preparation, reliant on fresh plant material to release intact nuclei, often results in poor yields of nuclei in conditions when a plant material cannot be kept fresh due to logistical constraints. Previous attempts to use frozen plant material were mainly limited to ploidy analysis and relied on chopping methods, which restrict the material input and often result in poor nuclei yield, especially in frozen samples, due to incomplete disruption. Here, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal. Nuclei isolated from this protocol can also be used for DNA or RNA extraction. Genome size estimates from frozen material are similar to those from fresh material, with a reduction in error range, although not always significant (p > 0.05). In certain species, frozen samples can yield substantially more nuclei than fresh material. With the addition of specific debris compensation algorithms, coefficient of variation (CV%) can be maintained below 5%. This method has special value in estimating the GS of samples collected from remote locations and frozen for use in plant genome sequencing. Freezing preserves high-quality DNA and RNA, enabling the same sample to be used for both flow cytometry and genome sequencing.
Why it matches plant phenotyping methods凍結植物材料からフローサイトメトリーで植物ゲノムサイズを推定する改良プロトコルを開発・検証しており、植物形質取得法が研究の中心である。
abstractHere, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal.
Reproduction assets foundThe paper deposits its raw flow cytometry fluorescence dataset (genome size estimation of fresh vs frozen plant material) in FlowRepository and its supplementary materials (ANOVA table, histogram/peak-modeling figures, microscopy images, protocol) in a Zenodo database. Both are paper-specific, publicly accessible, andSupplement · publicg across diverse taxa and
storage durations, this method could significantly enhance field-based and conservation
genomics efforts.
Supplementary Materials: The following supplementary data can be accessed online from the
database titled “A flow cytometry protocol for measurement of plant genome size using frozen mate-
rial” at https://doi.org/10.5281/zenodo.14873353 (Accessed on 1 April 2025). Table S1: Results of
one-way ANOVA for all combinations of species, nuclei extraction method, and debris compensation
on genome size estimation. Figure S1. The process of nuclei isolation from frozen leaf material.
Figure S2. Conventional histogram analysis for the fluorescence data of fresh preparatOpen asset ↗zenodo · 10.5281/zenodo.14873353pdf-raw-page:12 lines:1-50Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Cryo-focused ion beam scanning electron microscopy (cryo-FIBSEM) has become essential for preparing electron-transparent lamellae from cryo-plunged and high-pressure frozen specimens. However, targeting specific cellular features within large, complex organs remains challenging. Here we present a series of technical improvements significantly enhancing the efficiency and accessibility of the Serial Lift-Out and SOLIST (Serialized On-grid Lift-In Sectioning for Tomography) procedures that are revolutionizing the field. We were able to extend the cryo-FIBSEM session from 24 hours to 5 days without interruptions. In addition, we describe a modified silver-plated EasyLift TM needle that eliminates the need of the copper or gold block between the original tungsten needle and the sample. Moreover, we describe a strategy that significantly reduces curtaining effects. Finally, we report a precise routine to target a lamella with a precision of approximately 1 micrometer in X,Y and Z. Together, these modifications considerably reduce contamination risk and preparation time, making cryo-lift-out techniques more accessible for routine structural biology applications on any type of tissue. Here, we demonstrate the power of our technique by targeting several specific wall structures that are of crucial importance for root function in plants and that were previously inaccessible to cryo-electron tomography (cryo-ET). High-pressure freezing (HPF) of plant tissues presents unique challenges for cryo-electron microscopy sample preparation due to the overall sample size, the individual cells size, their rigid cell wall and finally, their large vacuoles, which contain large amounts of rather diluted water solutions compared to cytosol. The internal root structures targeted are the Casparian strip (CS), suberin lamellae (SL), as well as secondary wall of xylem vessels, requiring reaching a targeting precision of 5 micrometers in a 3 millimeters long and 80-120 micrometers thick root tip. Our technological improvements for the cryo-correlative light and electron microscopy (cryo-CLEM) workflow enabled successful, targeted cryo-ET in plant roots. We noticed that, despite ice formation in vacuoles and to some degree in the cytosol, the plasma membranes and cell walls are remarkably well preserved, providing stunning insights into the native, hydrated nano-structure of plant cell walls, previously only observable with contrasting agents and in a dehydrated state.
Why it matches plant phenotyping methods植物根の細胞壁構造を対象とするcryo-CLEM/cryo-ETワークフローの技術改良と実証が中心であり、植物組織の構造的表現型を画像取得する方法論に該当する。
abstractOur technological improvements for the cryo-correlative light and electron microscopy (cryo-CLEM) workflow enabled successful, targeted cryo-ET in plant roots.
Why it matches plant phenotyping methods植物葉肉細胞のミトコンドリア体積・形態を画像化して定量するプロトコルが研究の中心であり、植物の生理状態・ストレス状態に関連する形態形質の取得法を提示している。
abstractHere, we present protocols for the quantitative assessment of mitochondrial volume transitions in leaf mesophyll cells of Arabidopsis thaliana.
Phenotype characterization with single-cell resolution can enable deep and nuanced insights into microbiological systems. Currently, Flow Cytometry and Imaging Flow Cytometry (IFC) offer numerous advantages, but are marred by barriers to accessibility: (1) high instrument costs; (2) labor-intensive, technically demanding sample preparation; and (3) reliance on consumable reagents (i.e., fluorescent labels). To achieve phenotype characterization without these constraints, we evaluated the low-cost, low-input ARTiMiS IFC as a potential alternative instrument technology. To demonstrate this approach, we used intracellular lipid content in microalgae, an important phenotype for production of biofuels and high-value bioproducts, as the phenotype of interest. Variational Auto-Encoder (VAE) unsupervised deep learning methodology was implemented to encode phenotype variation from un-annotated training data. The VAE embeddings were compared with other label-free predictor modalities to evaluate the stability of VAE data encoding across replicates and its predictive power to estimate the target phenotype. The VAE embeddings were robust and consistent between culture batches, and yielded accurate, consistent predictions of the demonstration phenotype in a high-throughput, non-destructive, dye-free methodology. In this proof-of-concept study, we demonstrate that VAE-enabled ARTiMiS IFC may serve as a viable alternative for cell phenotype characterization while overcoming several of the key drawbacks of traditional high-fidelity techniques. SynopsisLabel-free Imaging Flow Cytometry data was processed by a Variational Auto-Encoder to accurately predict lipid content in microalgal cells.
Why it matches plant phenotyping methods微細藻類細胞の脂質含量という表現型を、ラベルフリー画像フローサイトメトリーとVAEで推定する手法が研究の中心であり、性能と再現性も評価している。
abstractTo achieve phenotype characterization without these constraints, we evaluated the low-cost, low-input ARTiMiS IFC as a potential alternative instrument technology.
Various high-throughput screening methods have been developed to explore plant phenotypes, primarily at the organ and whole plant levels. There is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap. This study used double-resonator piezoelectric cytometry biosensors to capture the dynamic changes in mechanical phenotypes of living cells of two rice species, drought-resistant Lvhan No. 1 and drought-sensitive 6527, under PEG6000 drought stress. In rice cells of Lvhan No. 1 and 6527, mechanomics parameters, including cell-generated surface stress (ΔS) and viscoelastic parameters (G', G″, G″/G'), were measured and compared under 5-25% PEG6000. Lvhan No. 1 showed larger viscoelastic but smaller surface stress changes with the same concentration of PEG6000. Moreover, Lvhan No. 1 cells showed better wall-plasma membrane-cytoskeleton continuum structure maintaining ability under drought stress, as proven by transient tension stress (ΔS > 0) and linear G'~ΔS, G″~ΔS relations at higher 15-25% PEG6000, but not for 6527 cells. Additionally, two distinct defense and drought resistance mechanisms were identified through dynamic G″/G' responses: (i) transient hardening followed by softening recovery under weak drought, and (ii) transient softening followed by hardening recovery under strong drought. The abilities of Lvhan No. 1 cells to both recover from transient hardening to softening and to recover from transient softening to hardening are better than those of 6527 cells. Overall, the dynamic mechanomics phenotypic patterns (ΔS, G', G″, G″/G', G'~ΔS, G″~ΔS) verified that Lvhan No. 1 has better drought resistance than that of 6527, which is consistent with the field data.
Why it matches plant phenotyping methods植物細胞の機械的表現型を取得する高スループットなバイオセンサー手法を用い、乾燥ストレス応答を定量化しており、表現型取得法が研究の中心である。
abstractThere is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap.
Reproduction assets foundThe paper's DRPC phenotyping measurements (frequency and motional resistance traces underlying the ΔS, G′, G″ analyses) are provided as downloadable supplementary figures at the MDPI supplementary URL. No standalone public dataset or author analysis code repository is stated; the Data Availability Statement only offersSupplement · publicansient softening under strong drought. The results presented in this work demonstrated the potential to develop a new cellular mechanical phenotype platform to screen for biotic and abiotic stress-resistant crop varieties, as shown in Figure 11 .
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bios15060334/s1 , Figure S1: Changes in frequency and motional resistance of 9 MHz AT and BT cut chips during the adhesions of Lvhan No.1 rice cells followed by the treatments of different concentrations of PEG6000 stresses. (A, B, C, D, E): AT cut, (A1, B1, C1, D1, E1): BT cut, (A, A1): 5% PEG6000, (B, B1): 10%PEG6000, (C, C1) 15%Open asset ↗lines:136-159Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Chloroplast density in cells varies among different types of cells and plants. In current single-cell spatiotemporal analysis, the automatic detection and quantification of chloroplasts at the single-cell level is crucial. We developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-chloroplasts), an AI tool for single-cell chloroplast detection and cell-type clustering. It utilizes You-Only-Look-Once (YOLO), a real-time detection algorithm, for accurate and efficient performance. DeepD&Cchl has been proved to identify chloroplasts in plant cells across various imaging types, including light microscopy, electron microscopy, and fluorescence microscopy. Integrated with an Intersection Over Union (IOU) module, DeepD&Cchl precisely counts chloroplasts in single- or multi-layered images, while eliminating double-counting errors. Furthermore, when combined with Cellpose, a single-cell segmentation tool, DeepD&Cchl enhances its effectiveness at the single-cell level. By counting chloroplasts within individual cells, it supports cell-type-specific clustering based on chloroplast number versus cell size, offering valuable morphological insights for single-cell studies. In summary, DeepD&Cchl is a significant advancement in plant cell analysis. It offers accuracy and efficiency in chloroplast identification, counting and cell-type classification, providing a useful tool for plant research.
Why it matches plant phenotyping methods植物細胞画像から葉緑体を検出・計数し、細胞型をクラスタリングするAIツールの開発が中心であり、植物の形態的状態を定量化するフェノタイピング手法に該当する。
abstractWe developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-chloroplasts), an AI tool for single-cell chloroplast detection and cell-type clustering.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe raw dataset, as well as the scripts for the DeepD&Cchl model training and 17application macro, were shared on GitHub https://github.com/Shaokai9/AI4LifeScience_ECNU/tree/main/Deep%20subcellular%20detection .Open asset ↗Shaokai9/AI4LifeScience_ECNUlines:400-410Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 180 recombinant inbred lines of maize ( Zea mays (L.)) were analyzed by a new Stomatal Patterning Phenotype (SPP) to: (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g. SD) into a set of component traits that were present in HTP data but not previously exploited.
Why it matches plant phenotyping methods新しい空間解析手法（SPP）により、気孔密度を細胞サイズ・配置・位置確率などの表現型構成要素へ分解し、HTPデータから形質を抽出・解析しているため、植物フェノタイピング手法が研究の中心である。
abstracthigh-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach
Carbon monoxide (CO) is widely recognized as a significant environmental pollutant and is associated with numerous instances of accidental poisoning in humans. However, it also serves a pivotal role as a signaling molecule in plants, exhibiting functions analogous to those of other gaseous signaling molecules, including nitric oxide (NO) and hydrogen sulfide (H 2 S). In plant physiology, CO is synthesized as an integral component of the defense mechanism against oxidative damage, particularly under abiotic stress conditions such as drought, salinity, and exposure to heavy metals. Current research methodologies have demonstrated a lack of effective tools for monitoring CO dynamics in plants during stress conditions, particularly in relation to heavy metal accumulation across various developmental stages. Therefore, development of a sensor capable of detecting CO in living plant tissues is essential, as it would enable a deeper understanding of its biological functions, underlying mechanisms, and metabolic pathways. In response to this gap, the present study introduces a novel technique for monitoring CO production and activity in plants using nitrogen-doped carbon quantum dots (N-CQDs). These nanodots exhibited exceptional biocompatibility, low toxicity, and environmentally sustainable characteristics, rendering them an optimal tool for CO detection via fluorescence quenching mechanism, with a detection limit (LOD) of 0.102 μM. This innovative nanomarker facilitated the detection of trace quantities of CO within plant cells, providing new insights into plant stress responses to heavy metals such as Cu, Zn, Pb, Ru, Cr, Cd, and Hg, as well as the processes involved in seed germination. Additionally, confocal microscopy validated the interaction between CO and N-CQDs, yielding visual evidence of CO binding within plant cells, further enhancing the understanding of CO's role in plant biology.
Why it matches plant phenotyping methods植物組織内のCO動態を検出する蛍光ナノセンサーを開発し、検出限界と共焦点顕微鏡で検証しており、植物の生理状態取得法が研究の中心である。
abstractthe present study introduces a novel technique for monitoring CO production and activity in plants using nitrogen-doped carbon quantum dots (N-CQDs).
Summary Low oxygen signalling in plants is important in development and stress responses. Measurement of oxygen levels in plant cells and tissues is hampered by a lack of chemical tools with which to reliably detect and quantify endogenous oxygen availability. We have exploited hypoxia‐activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues. We applied 4‐nitrobenzyl (4NB‐) resorufin and methyl‐indolequinone (MeIQ‐) resorufin to Arabidopsis thaliana whole cells and seedlings exposed to hypoxia (1% O 2 ) and normoxia (21% O 2 ). Confocal microscopy and fluorescence intensity measurements were used to visualise regions of resorufin fluorescence. Both probes enter A. thaliana whole cells and are activated to fluoresce selectively in hypoxic conditions. Similarly, incubation with A. thaliana seedlings resulted in hypoxia‐dependent activation of both probes and observation of fluorescence in hypoxic roots and leaf tissue. MeIQ‐Resorufin was used to visualise endogenous hypoxia in lateral root primordia of normoxic A. thaliana seedlings. Oxygen measurement in plants until now has relied on invasive probes or genetic manipulation. The use of these chemical probes to detect and stain applied and endogenous hypoxia has the potential to facilitate a greater understanding of oxygen concentrations in plant cells and tissues, allowing the correlation of oxygen availability with acclimative and developmental responses to hypoxia.
Why it matches plant phenotyping methods植物細胞・組織の低酸素状態を可視化・定量する蛍光プローブを開発・検証しており、植物状態の取得方法が研究の中心である。
abstractWe have exploited hypoxia‐activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues.
Main conclusion Our method can simultaneously quantify cell wall elasticity and turgor pressure of live plant cells through AFM measurements and calculations based on elastic shell theory. Abstract The morphological behavior of plant cells depends on their mechanical properties. Cell wall elasticity ( E ) and turgor pressure ( P ) are main factors that dominate the behavior. A method to simultaneously quantify them in live cells has yet to be established, hindering progress in plant mechanobiology. Recently, atomic force microscopy (AFM) has been used to analyze single cells based on Hertz’s contact theory (HCT). However, HCT cannot evaluate P . Several groups have attempted to evaluate P by adapting elastic shell theory (EST), but it is still difficult to estimate both E and P from the indentation data and EST alone. Herein an analytical method is proposed based on EST using the cell indentation and surface geometry from the AFM measurements. We also demonstrate the reliability of our approach under various osmotic pressure conditions and simultaneously determine the values of P and E in epidermal monolayer cells of an Allium cepa L. Supplementary Information The online version contains supplementary material available at 10.1007/s00425-025-04683-4.
Why it matches plant phenotyping methodsAFM測定と弾性殻理論に基づき、生細胞の細胞壁弾性と膨圧を定量する解析手法を開発し、浸透圧条件下で信頼性を検証しており、植物表現型取得が研究の中心です。
abstractOur method can simultaneously quantify cell wall elasticity and turgor pressure of live plant cells through AFM measurements and calculations based on elastic shell theory.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Productivity and fitness of cultivated plants are influenced by genetic heritage and environmental interactions, shaping certain phenotypes. Phenomics is the -omics methodology providing applicative approaches for the analysis of multidimensional phenotypic information, essential to understand and foresee the genetic potential of organisms relevant to agriculture. While plant phenotyping provides information at the whole organism level, cellular level phenotyping is crucial for identifying and dissecting the metabolic basis of different phenotypes and the effect of metabolic-related genetic modifications. Phenotype Microarray (PM) is a high-throughput technology developed by Biolog ™ for metabolic characterization studies at cellular level, which is based on colorimetric reactions to monitor cellular respiration under different conditions. Nowadays, PM is widely used for bacteria, fungi, and mammalian cells, but a procedure for plant cells characterization has not yet been developed, due to difficulties linked in identifying a suitable reporter of cell activities. Here, we tested for the first time, PM technology on plant cells using protoplasts as a means of evaluating metabolic activity. Indeed, studying the metabolism of plant protoplasts can be a valuable method for predicting the inherent metabolic potential of an entire plant organism. Protoplasts are indeed valuable tools in plant research and biotechnology because they offer a simplified, isolated cellular system where researchers can focus on intracellular processes without interference from the cell wall. As a proof-of-principle, we used protoplasts of Solanum tuberosum L . as model system. Protoplasts were isolated from leaf tissue of in vitro-grown plants, purified and then diluted until desired concentration. Microplates were inoculated with protoplasts suspension and various markers of redox potential as indicators of cell activity were tested. After identifying the optimal conditions for PM testing, metabolic tests were extended to protoplasts from S. lycopersicum L., evaluating plant response to different NaCl concentrations and some of the toxic compounds present in pre-configured Biolog ™ microplates. The standardized high-throughput system developed was effective for the metabolic characterization of plant protoplasts. This method lays the foundation for plant cell metabolic phenotype studies enabling comparative studies at cellular level among cultivars, species, wild-type organisms, and genome-edited plants.
Why it matches plant phenotyping methods植物プロトプラストの代謝表現型を取得するためのフェノタイプマイクロアレイ手法を開発し、条件最適化と複数種・処理で実証した研究であり、表現型取得法が中心である。
abstracta procedure for plant cells characterization has not yet been developed
Introduction Quantitative wood anatomy (QWA) along a time series of tree rings (known as tree-ring anatomy or dendroanatomy) has proven to be very valuable for reconstructing climate and for investigating the responses of trees and shrubs to environmental influences. A major obstacle to a wider use of QWA is the time- consuming data production, which also requires specialized equipment and expertise. This is why the research community has been striving to reduce these limitations by defining and improving tools and protocols along the entire data production chain. One of the remaining bottlenecks is the analysis of anatomical images, which broadly consists of cell and ring segmentation, followed by manual editing, measurements, and output. While dedicated software such as ROXAS can perform these tasks, its accuracy and efficiency are limited by its reliance on classical image analysis techniques. However, the reliability and accuracy of automatic cell and ring detection are key to efficient QWA data production. Methods In this paper, we target automatic ring segmentation and deliberately focus on the most challenging case, circular ring structures in arctic angiosperm shrubs with partly very narrow and wedging rings. This shape requires high precision combined with a large global context, which is a challenging combination for instance segmentation approaches. We present a new iterative regression-based method for more precise and reliable segmentation of tree rings. Results and discussion We show a performance increase in mean average recall of up to 18.7 percentage points compared to previously published results on the publicly available MiSCS (Microscopic Shrub Cross Sections) dataset. The newly added uncertainty estimation of our method allows for faster and more targeted validation of our results, saving a large amount of human labor. Furthermore, we show that panoptic quality performance on unseen species is more than doubled using multi-species training compared to single-species training. This will be another key step toward an AI-based version of the currently available ROXAS implementation.
Why it matches plant phenotyping methods樹木年輪の解剖画像からリング境界を自動抽出する画像解析法を開発し、公開データセット上で既存法と性能比較・検証しているため、植物フェノタイピング手法が研究の中心です。
abstractWe present a new iterative regression-based method for more precise and reliable segmentation of tree rings.
Wheat and barley are known as important staple food worldwide, but their growth and yield are severely affected by soil salinity, prompting a need for regaining their stress tolerance lost during domestication, to meet food security targets under current climate scenarios. The bottle neck in this process is plant phenotyping. In the past decades, approach to plant phenotyping for salinity stress tolerance was predominantly driven by the need for a high throughput screening and focused on the whole-plant level traits by advocating various non-destructive and/or analytical methods. This approach, though useful for assessing overall plant performance under salinity stress, fails to account for tissue- and cell-specific operation of contributing mechanisms and, as a result, lack the predictive power. In this work, we propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H 2 O 2 -induced K + and Ca 2+ flux responses from mature root epidermis. By screening 44 barley, 20 durum and 20 bread wheat accessions, we show that tolerant genotypes reduce sensitivity of cation (Na + , K + and Ca 2+ ) permeable ion channels to ROS and argue that such desensitization may allow plants to efficiently regulate its ionic homeostasis in a cell- and tissue-specific manner, without compromising stress-induced ROS signaling to downstream targets, for transcriptional regulation purposes. Being conducted on young (4-d old) seedlings, this cell-based phenotyping platform offer breeders a possibility to target new (previously unexplored) traits and may be instrumental for assisting breeders in engineering salinity stress tolerance in future breeding programs.
Why it matches plant phenotyping methods塩ストレス耐性を評価する細胞ベースの表現型取得法を提案・検証し、イオンフラックスを用いた再利用可能なフェノタイピング基盤として中心的に扱っている。
abstractIn this work, we propose and validate a new approach for phenotyping cereal crops for salinity stress tolerance by measuring H 2 O 2 -induced K + and Ca 2+ flux responses from mature root epidermis.
Grana are fundamental structural units of the intricate chloroplast membrane network. Investigating their nanomorphology is essential for understanding photosynthetic efficiency regulation. Here, we present GRANA (Graphical Recognition and Analysis of Nanostructural Assemblies), an artificial intelligence-enhanced, user-friendly software tool that recognizes grana on thylakoid network electron micrographs and generates a complex set of their structural parameters. GRANA employs 3 artificial neural networks of different architectures and binds them in a 1-click workflow. Its output is designed to facilitate hybrid intelligence analysis, securing fast and reliable results from large datasets. The GRANA tool is over 100 times faster compared with currently used manual approaches. As a proof of concept, we have successfully applied GRANA software to diverse grana structures across different land plant species grown under various conditions, demonstrating the wide range of potential applications for our software. GRANA tool supports large-scale analysis of grana nanomorphological features, facilitating advancements in photosynthesis-oriented studies.
Why it matches plant phenotyping methods葉緑体グラナの電子顕微鏡画像から構造パラメータを自動抽出するソフトウェアの開発であり、植物形態形質の取得・解析法が中心。
abstractan artificial intelligence-enhanced, user-friendly software tool that recognizes grana on thylakoid network electron micrographs and generates a complex set of their structural parameters.
Reproduction assets foundThe paper's raw TEM images used for grana nanomorphology analysis are publicly deposited under DOI 10.58132/HTWCC1. The authors' analysis code (github.com/center4ml/GRANA) is mentioned but that URL is not among the allowed URLs, so it cannot be listed.Dataset · publicRaw TEM data used for results in the manuscript are available at https://doi.org/10.58132/HTWCC1 .Open asset ↗10.58132/HTWCC1lines:184-235Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Plant cells are contained within a rigid network of cell walls. Cell walls serve as a structural material and a crucial signaling hub vital to all aspects of the plant life cycle. However, many features of the cell wall remain enigmatic, as it has been challenging to map its functional properties in live plants at subcellular resolution. Here, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls. CarboTag uses a small molecular motif, a pyridine boronic acid, that directs its cargo to the cell wall. We designed a suite of cell wall imaging probes based on CarboTag in various colors for multiplexing. Additionally, we developed new functional reporters for live quantitative imaging of key cell wall characteristics: network porosity, cell wall pH and the presence of reactive oxygen species. CarboTag paves the way for dynamic and quantitative mapping of cell wall responses at subcellular resolution. Subject terms: Plant cell biology, Fluorescence imaging
Why it matches plant phenotyping methods植物細胞壁のライブ機能イメージング用ツールボックスを開発し、孔隙率、pH、活性酸素などの細胞壁特性を定量化する手法が中心である。
abstractHere, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls.
Reproduction assets foundThe paper's Data availability and Code availability statements both point to a public 4TU repository DOI containing the raw imaging/phenotyping data and the analysis code for this paper's CarboTag cell wall imaging measurements.Dataset · publicThe raw data associated with the figures in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 . Source data are provided with this paper.Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code · publicCode developed to process and analyze data in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 .Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
CottonMicroscopyCell / cellular structureSeed / grainVisualization / data managementGrowth / development / phenology
Cotton fibers, as highly extended, thickened epidermal seed structures, are a crucial renewable resource in textile production. Cotton plants produce two main types of fiber cells: wide, hemisphere-shaped fibers and narrow, tapered fibers. Both types stabilize through secondary cell wall development, with the mature narrow fibers being particularly valued for spinning into fine, strong yarns, suitable for premium cotton fabrics. Traditional methods for studying fiber development and cell types, such as scanning electron microscopy (SEM), are often time-intensive and costly. SEM preparation steps, including fixation, dehydration, and sputter coating, can cause shrinkage and other image distortions, limiting the accuracy of observations. Variable-pressure scanning electron microscopy (VP-SEM) offers an alternative approach, operating under low pressure rather than a high-vacuum environment, which can be advantageous for imaging live samples with minimal sample preparation. In this study, we applied VP-SEM to observe fiber cell initiation and early elongation in the conventional upland cotton cultivar UGA 230 at 0 and 1-day post-anthesis. Two SEM detectors, the ultra-variable-pressure detector and backscattered electrons, were used to capture detailed images. Optimal imaging conditions were identified with a 15 keV accelerating voltage and a 50 Pa pressure setting, enabling clear visualization of early fiber development without the need for extensive preparation. This VP-SEM protocol not only facilitates high-resolution imaging of cotton fibers at early developmental stages but also reduces time and expense, minimizing sample damage. Additionally, this optimized approach can be adapted for other fresh biological samples, making it a versatile tool for real-time imaging across various studies in plant biology and beyond.
Why it matches plant phenotyping methods綿花繊維の発生・伸長を高解像度で取得するVP-SEMプロトコルの条件最適化と技術的利点を中心に扱っており、植物形質取得法が主題である。
abstractOptimal imaging conditions were identified with a 15 keV accelerating voltage and a 50 Pa pressure setting, enabling clear visualization of early fiber development without the need for extensive preparation.
Trichomes in Cannabis sativa are specialized structures responsible for cannabinoid and terpene biosynthesis, making their density a critical factor for both research and industrial applications. Despite their importance, trichome density analysis is hindered by variability across plant structures and the lack of standardized protocols. This study evaluates different plant structures—bracts, sugar leaves, calyxes, and the main cola—to determine the most reliable site for trichome counting. Among these, bracts emerge as the most consistent due to their homogeneous trichome distribution and high cannabinoid concentration. While sugar leaves and calyxes also contribute to trichome yield assessments, their variability necessitates careful sampling. Moreover, trichome shape and size must be taken into consideration when correlating trichome density with secondary metabolite levels. The integration of microscopic imaging and software-assisted counting enhances accuracy and reproducibility in trichome density analysis. Establishing a standardized protocol for trichome assessment will improve cannabinoid yield optimization, quality control, and overall Cannabis research methodologies. Incorporating morphological data (trichome density, distribution, shape, and size) with chemical assays (cannabinoid and terpene identification and quantification) thus provides a more robust assessment of Cannabis potency and value. Future work should refine imaging techniques and sampling strategies to further enhance trichome analysis reliability.
Why it matches plant phenotyping methodsトリコーム密度・形態を顕微鏡画像とソフトウェアで測定する標準化・再現性向上が研究の中心であり、植物器官の形態形質を取得する方法開発・評価に該当する。
abstractThis study evaluates different plant structures—bracts, sugar leaves, calyxes, and the main cola—to determine the most reliable site for trichome counting.
Background Plant growth and morphogenesis is a mechanical process controlled by genetic and molecular networks. Measuring mechanical properties at various scales is necessary to understand how these processes interact. However, obtaining a device to perform the measurements on plant samples of choice poses technical challenges and is often limited by high cost and availability of specialized components, the adequacy of which needs to be verified. Developing software to control and integrate the different pieces of equipment can be a complex task. Results To overcome these challenges, we have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress. It consists of three primary components: a force sensor with a sample attachment point, an actuator with a second attachment point, and a camera. To monitor force, we use a parallel beam sensor, commonly used in digital weighing scales. To stretch the sample, we use a stepper motor with a screw mechanism moving a stage along linear rail. To monitor sample deformation, a compact digital microscope or a microscope camera is used. The system is controlled by MorphoRobotX, an integrated open-source software environment for mechanical experimentation. We first tested the basic Camelot setup, equipped with a digital microscope to track landmarks on the sample surface. We demonstrate that the system has sufficient accuracy to measure the stiffness in delicate plant samples, the etiolated hypocotyls of Arabidopsis, and were able to measure stiffness differences between wild type and a xyloglucan-deficient mutant. Next, we placed Camelot on an inverted microscope and used a C-mount microscope camera to track displacement of cell junctions. We stretched onion epidermal peels in longitudinal and transverse directions and obtained results similar to those previously published. Finally, we used the setup coupled with an upright confocal microscope and measured anisotropic deformation of individual epidermal cells during stretching of an Arabidopsis leaf. Conclusions The portability and suitability of Camelot for high-resolution optical tracking under a microscope make it an ideal tool for researchers in resource-limited settings or those pursuing exploratory biomechanics work.
Why it matches plant phenotyping methods植物試料の力学的形質（弾性、クリープ、降伏応力、剛性、変形）を光学追跡で測定する装置とソフトウェアを開発しており、植物表現型の取得手法が研究の中心である。
abstractwe have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress.
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data managementGrowth / development / phenology
Plant growth and development rely on a delicate balance between cell proliferation and cell differentiation. The root apical meristem (RAM) of Arabidopsis thaliana is an excellent model to study the cell cycle due to the coordinated relationship between nucleus shape and cell size at each stage, allowing for precise estimation of the cell cycle duration. In this study, we present a method for high-resolution visualization of RAM cells. This is the first protocol that allows for simultaneous high-resolution imaging of cellular and nuclear stains, being compatible with DNA replication markers such as EdU, including fluorescent proteins (H2B::YFP), SYTOX DNA stains, and the cell wall stain SR2200. This protocol includes a clarification procedure that enables the acquisition of high-resolution 3D images, suitable for detailed subsequent analysis.
Why it matches plant phenotyping methodsArabidopsis根端分裂組織の細胞・核形態と増殖状態を取得する高解像度3Dイメージング手法の開発が中心であり、植物状態の定量的解析に再利用可能な方法を提示している。
abstractIn this study, we present a method for high-resolution visualization of RAM cells.
Maintaining energy homeostasis is a major challenge for plants in the current context of climate change. The Sucrose-non fermenting 1 (SNF1)-related kinase 1 (SnRK1) complex, a member of the SNF1-AMP-activated protein kinase (AMPK)-SnRK1 family of kinase complexes, is a central player in the regulation of cell energy homeostasis. The α-subunit of the complex, which possesses kinase activity and is known as SnRK1.1 or KIN10, plays a role in sensing energy status and coordinating metabolic reprogramming to counter any energy imbalance. The discovery of a dual and dynamic intracellular distribution of SnRK1.1 suggests that the activity and function of SnRK1 might be regulated by spatiotemporal changes. To investigate the spatiotemporal distribution of SnRK1.1, we developed a protocol to quantify its intracellular distribution using fluorescence confocal images acquired along the z-axis in plants expressing SnRK1.1–eGFP. Using the open-source software Fiji/ImageJ, we calculated the ratio between nuclear and non-nuclear SnRK1.1 fractions and defined this as the N/ER index. We validated our method by analyzing the response of SnRK1.1 to photosynthesis inhibition by DCMU, including changes in protein levels and phosphorylation status. In addition, comparison with results obtained using a commercial software-based approach confirmed the compatibility of the N/ER index with different segmentation and quantification tools. Originally designed for leaf tissue images, this protocol can be broadly applied to assess the role of intracellular spatiotemporal changes in a wide range of kinases or fluorescently tagged recombinant proteins. Finally, SnRK1.1 intracellular distribution may also serve as a proxy to assess changes in cellular energy status. One sentence summary New method to track SnRK1.1 distribution and changes in plant cell energy status
Why it matches plant phenotyping methods植物細胞内の蛍光画像からSnRK1.1の核／非核分布を定量する画像解析プロトコルを開発し、検証・他ソフトウェアとの比較も行っており、植物状態の測定法が中心である。
abstractwe developed a protocol to quantify its intracellular distribution using fluorescence confocal images acquired along the z-axis in plants expressing SnRK1.1–eGFP.
Identifying mutant traits is essential for improving crop yield, quality, and stress resistance in plant breeding. Historically, the efficiency of breeding has been constrained by throughput and accuracy. Recent significant advancements have been made through the development of automated, high-accuracy, and high-throughput equipment. However, challenges remain in the post-processing of large-scale image data and its practical application and evaluation in breeding. This study presents a comparative analysis of human and machine recognition, with validation of a randomly selected mutant at the physiological level performed on wild-type Arabidopsis thaliana and a candidate mutant of the M 3 generation, which was generated through mutagenesis with heavy ion beams (HIBs) and 60 Co-γ radiation. The mutant populations were subjected to image acquisition and automated screening using the High-throughput Plant Imaging System (HTPIS), generating approximately 10 GB of data (4,635 image datasets). We performed Principal Components Analysis (PCA), scatter matrix clustering, and Logistic Growth Curve (LGC) analyses, and compared these results with those obtained from traditional manual screening based on human visual assessment, and randomly selected #197 candidate mutants for validation in terms of growth and development, chlorophyll fluorescence, and subcellular structure. Our findings demonstrate that as the confidence interval level increases from 75 to 99.9%, the accuracy of machine-based mutant identification decreases from 1 to 0.446, while the false positive rate decreases from 0.817 to 0.118, and the false negative rate increases from 0 to 0.554. Nevertheless, machine-based screening remains more accurate and efficient than human assessment. This study evaluated and validated the efficiency (greater than 80%) of high-throughput techniques for screening mutants in complex populations of radiation-induced progeny, and presented a graphical data processing procedure for high-throughput screening of mutants, providing a basis for breeding techniques utilizing HIBs and γ-ray radiation, and offering innovative approaches and methodologies for radiation-induced breeding in the context of high-throughput big data.
Why it matches plant phenotyping methods植物画像取得・自動スクリーニングと機械／人手認識の比較検証が研究の中心であり、表現型選抜ワークフローの技術評価に該当する。
abstractThis study presents a comparative analysis of human and machine recognition
In grasses, stem elongation is driven by intercalary meristems at node-internode junctions, where cells divide, elongate, and in some cell types secondary wall maturation. Cellulose is the predominant polymer in plant cells and the most abundant biopolymer on Earth. It is synthesized at the plasma membrane by multi-protein complexes that include CELLULOSE SYNTHASE A (CESA) proteins. To investigate the spatiotemporal regulation of cellulose deposition during development, we developed a CESA8 luciferase gene expression reporter system in Brachypodium distachyon. High bioluminescence was observed in stem nodes, a specific region of elongating internodes, and the inflorescence, indicating sites of active secondary wall deposition. Within internodes, luminescence followed a distinct pattern, with a "dark zone" directly above the node with minimal signal, followed by a "bright zone" approximately 5 mm above the node where bioluminescence peaked. Histological, biophysical, and transcript analysis confirmed that luminescence intensity correlates with thickened secondary cell walls, increased cellulose crystallinity, and elevated CESA8 transcript levels. Time-lapse imaging revealed that CESA8 expression follows a robust diurnal rhythm governed by thermocycles alone, with peak expression occurring in the early morning. Temperature pulse experiments revealed an immediate but transient response of CESA8 to temperature shifts, which we modeled as an incoherent feed-forward loop. Finally, we found a strong correlation between CESA8 expression and stem elongation, highlighting the role of secondary cell wall thickening in supporting upright growth. These findings provide new insights into the regulation of secondary wall formation and its integration with environmental cues, advancing our understanding of grass stem development. SIGNIFICANCEUnderstanding how grasses build strong stems is essential for improving biomass production and crop resilience. In grasses, stem elongation and secondary cell wall thickening occur in distinct zones, yet the precise timing and regulation of this process remain unclear. To investigate this phenomenon, we developed a real-time imaging system to track the expression of CESA8, a key gene involved in cellulose synthesis. Our findings reveal that secondary wall thickening follows a daily rhythm controlled by temperature rather than light. These insights provide a foundation for optimizing plant architecture in bioenergy crops, improving their efficiency and sustainability.
Why it matches plant phenotyping methodsCESA8ルシフェラーゼによるリアルタイム画像計測系を開発し、発光を二次細胞壁肥厚や茎伸長と検証・関連付けており、表現型取得法が研究の中心である。
abstractwe developed a CESA8 luciferase gene expression reporter system in Brachypodium distachyon.
The versatile fluorescent dyes are essential for specifically labeling plant cell walls in vivo, monitoring plasma membrane damage, and assessing cell viability. However, such dyes are rare and often discovered accidentally due to a lack of design principles. Propidium iodide, a well-known example, has limitations like low brightness, high toxicity, and poor bacterial differentiation. To address these challenges, we developed VersaDye, a modular probe designed for specific imaging of live plant cell walls and monitoring plasma membrane damage in plant cells, human cells, and certain bacteria. The design integrates impermeability principles and environment-dependent fluorophore scaffolds. VersaDye enables bright, wash-free labeling of plant cell walls and can stain various plant organs for constructing 3D tissue organization. Notably, it can selectively distinguish live Gram-positive from Gram-negative bacteria, a feature absent in other dyes. Its impermeability and targeting ability also allow it to probe membrane damage caused by physical, chemical, and biological stimuli. This study marks the first use of VersaDye in analyzing cell damage in live plants under salt stress. VersaDye offers a robust platform for wash-free, in vivo membrane damage monitoring and simultaneous cell wall labeling. Additionally, its design suggests adaptability for regulating permeability to meet specific diagnostic needs, such as identifying membrane-compromised cells in diseases or enabling high-throughput antibiotic screening targeting specific bacteria.
Why it matches plant phenotyping methods植物細胞壁の画像化と細胞膜損傷・生存状態の検出を目的とした蛍光プローブを開発しており、植物状態の取得法が研究の中心である。
abstractVersaDye enables bright, wash-free labeling of plant cell walls and can stain various plant organs for constructing 3D tissue organization.
Carbon dots (CDs) have emerged as promising nanomaterials for bioimaging and stress monitoring due to their unique optical and functional properties. CDs were synthesized using citric acid and o -phenylenediamine via microwave-assisted heating, named as CP-CDs. High-resolution transmission electron microscopy observed an average particle size of 3.65 ± 0.40 nm with graphitic cores. Raman spectroscopy and Fourier transform infrared spectroscopy confirmed diverse functional groups. The CDs exhibited excitation-dependent fluorescence with a peak emission at 432 nm, a high quantum yield of 54.91%, and a fluorescence lifetime of 9.50 ± 0.15 ns, making them highly suitable for bioimaging. Confocal microscopy demonstrated tissue-specific localization in lettuce plant cells. In stem cells, CP-CDs predominantly targeted mitochondria, confirmed by a colocalization with Mito-Tracker Red. In contrast, leaf cells showed selective accumulation at the stomatal openings. Under salt- and heat-induced stress, stem cells exhibited an increase in mitochondrial fluorescence, indicating stress-responsive interactions, whereas leaf cells maintained consistent stomatal localization. Further, enhanced fluorescence from chloroplasts under stress conditions suggested synergistic effects with chlorophyll. Also, stress conditions caused CP-CDs to accumulate at the cell boundaries in stem cells, highlighting their sensitivity to stress-induced changes. These findings demonstrate the optical properties, tissue-specific uptake, and organelle-level localization of CP-CDs, underlining their potential for bioimaging, stress detection, and targeted delivery systems in plants.
Why it matches plant phenotyping methods植物細胞のストレス応答を蛍光ナノプローブと共焦点イメージングで検出する手法の開発・実証が中心であり、単なる生物学的測定ではない。
titleExploring Carbon Dot as a Fluorescent Nanoprobe for Imaging of Plant Cells under Salt/Heat-Induced Stress Conditions.
Viscosity and hypochlorite (ClO - ) are two crucial microenvironmental species that play significant roles in biological activities. Their abnormal levels are closely associated with numerous common diseases. Therefore, accurate and real-time detection of hypochlorite and viscosity related to inflammatory microenvironment conduces to elucidate the pathogenesis and further diagnose the disease. In this work, based on the strategy of the phenothiazine (PTZ)-dicyanoisophorone (DCO) dyad system, a new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo. The free sensor emits weak fluorescence in aqueous solution thanks to twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET). However, in a high-viscosity system, the fluorescence emission of the sensor at 459 nm was significantly enhanced. Upon introduction of ClO - in aqueous buffer solution, the PBI exhibited apparent fluorescence enhancement at 577 nm, and showed large Stokes shift (177 nm). The fluorescence responsive mechanism was confirmed using HRMS, 1 H NMR and DFT calculation analysis. Onion and lotus root cells imaging of PBI towards ClO - was implemented. Furthermore, PBI has been successfully applied to the fluorescence imaging of viscosity and exogenous/endogenous hypochlorite in zebrafish.
Why it matches plant phenotyping methods植物細胞内の粘度と次亜塩素酸を可視化する蛍光センサーの開発が中心であり、植物の生理状態を画像計測する方法として植物細胞で実証されている。
abstracta new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo.
Pollen grain size relates to plant community structure via pollen dispersal, plant resource allocation into regenerative processes, plant phylogeny and plant genetics (ploidy), or it can be used as a decisive trait for pollen species distinction. However, the availability of pollen size data is limited because of labor- and time-consuming methodological constraints and is classically based on fewer than 50 measured pollen grains per species, thus restricting our knowledge of the temporal and spatial variability of pollen size in response to biotic and abiotic conditions. We addressed this data gap by using imaging flow cytometry (IFC), which allows for high-throughput assessment of pollen size and measured > 500 000 single pollen from 100 anemophilous species that were sampled between 2018 and 2022. We present a workflow for high-throughput data analysis, show the agreement of IFC estimates with literature size estimates and assess pollen size variability in the context of plant phylogeny. Our approach allows us to make statistically robust measurements of pollen size that are not limited by sampling effort and sample throughput to answer broad ecological questions at large temporal and spatial scales.
Why it matches plant phenotyping methodsイメージングフローサイトメトリーを用いた花粉サイズのハイスループット測定・解析ワークフローを開発し、文献値との一致を検証しているため、植物形質取得法が研究の中心である。
abstractWe addressed this data gap by using imaging flow cytometry (IFC), which allows for high-throughput assessment of pollen size
Pollen is a male gametophyte of angiosperms. Following meiosis, the microspore undergoes an asymmetric division called pollen mitosis I (PMI), which produces two cells of different sizes: a large vegetative cell and a small generative cell. Polarized nuclear migration and positioning during PMI are important for successful pollen development and cell differentiation. However, analyzing the pollen development process in real-time is challenging in many model plants with tricellular pollen, including Arabidopsis and rice. In this study, we established a method for live confocal imaging of microtubule and actin dynamics using suspension cultures with biolistic delivery of plasmid DNAs during PMI in Nicotiana benthamiana (Benthams tobacco), containing bicellular pollen. Pharmacological studies have indicated that actin filaments are crucial for microspore nuclear positioning before PMI, cell plate expansion during cytokinesis, and chromatin dispersion in vegetative cell nucleus after PMI. By contrast, inhibition of microtubule assembly resulted in abnormal chromosome segregation and nuclear behavior after PMI, although nuclear positioning and asymmetric division were observed. Our in vitro live cell imaging system for PMI provides insights into the importance of cytoskeletal regulation in asymmetric division and differentiation during pollen development.
Why it matches plant phenotyping methods花粉の細胞分裂・核動態をリアルタイム取得するライブ共焦点イメージング法を確立しており、画像取得系自体が研究の中心である。
abstractwe established a method for live confocal imaging of microtubule and actin dynamics using suspension cultures with biolistic delivery of plasmid DNAs during PMI
Background Ploidy, representing the number of homologous chromosome sets, can be estimated from flow cytometry data acquired on cells stained with a fluorescent DNA dye. This estimation relies on a combination of tools that often require scripting, individual sample curation, and additional analyses. Results To automate the ploidy estimation for multiple flow cytometry files, we developed MuPETFlow-a Shiny graphical user interface tool. MuPETFlow allows users to visualize cell fluorescence histograms, detect the peaks corresponding to the different cell cycle phases, perform a linear regression using standards, make ploidy or genome size predictions, and export results as figures and table files. The tool was benchmarked with known ploidy datasets from yeast and plant species, yielding consistent ploidy results. MuPETFlow's peaks detection and performance were also compared to those of other tools. Conclusions MuPETFlow stands out as the only tool offering in-app ploidy detection, multiple peak detection, multi-sample visualization, and automation capabilities. These features significantly accelerate the analysis, making it especially valuable for projects involving large datasets.
Why it matches plant phenotyping methods植物のフローサイトメトリーデータから倍数性・ゲノムサイズを推定する解析ツールを開発し、植物データセットでベンチマークしているため、植物表現型取得・解析手法が中心である。
abstractTo automate the ploidy estimation for multiple flow cytometry files, we developed MuPETFlow-a Shiny graphical user interface tool.
Reproduction assets foundThe paper's flow cytometry analysis assets are publicly available: the authors' MuPETFlow GitHub repository hosts the tool code and the newly generated S. cerevisiae FCS datasets, and the plant (Solanum pseudocapsicum) flow cytometry data used for ploidy estimation is deposited in FlowRepository under FR-FCM-Z45W. The Dataset · publicThe S. pseudocapsicum dataset is available
http://flowrepository.org/id/FR-FCM-Z45W.Open asset ↗pdf-page:5 lines:1-74Dataset · publicThe S. cerevisiae datasets are available at https://github.com/CintiaG/MuPETOpen asset ↗GitHubpdf-page:5 lines:1-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Cellulose, the most abundant polysaccharide on earth composing plant cell walls, is synthesized by coordinated action of multiple enzymes in cellulose synthase complexes embedded within the plasma membrane. Multiple chains of cellulose fibrils form intertwined extracellular matrix networks. It remains largely unknown how newly synthesized cellulose is assembled into an intricate fibril network on cell surfaces. Here, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly on Arabidopsis thaliana protoplast surfaces as the primary cell wall regenerates. Our observations provide the basis for a model of cellulose fibril network development in protoplasts driven by an interplay of multiscale dynamics that includes rapid diffusion and coalescence of nascent cellulose fibrils, processive elongation of single fibrils, and cellulose fibrillar network rearrangement during maturation. This study provides fresh insights into the dynamic and mechanistic aspects of cell wall synthesis at the single-cell level.
Why it matches plant phenotyping methods生きた植物細胞でセルロース合成と繊維ネットワークを連続可視化する画像プラットフォームの確立が研究の中心であり、植物の細胞壁状態を抽出する手法に該当する。
abstractHere, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly on Arabidopsis thaliana protoplast surfaces as the primary cell wall regenerates.
Polyploidy (whole-genome multiplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or by tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.
Why it matches plant phenotyping methodsArabidopsisを含む組織の核倍数性を画像から空間的に推定する新規計算パイプラインを開発しており、植物の状態計測手法が研究の中心である。
abstractwe present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public OSF data repository (containing the paper's imaging/phenotyping data) and a public GitLab repository for the iSPy analysis code, both with authors' URLs.Dataset · publicAll data are available in the main text, in the supplementary materials , and are publicly available in our OSF data repository https://osf.io/um7r3/ .Open asset ↗OSF · um7r3lines:234-294Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Phloem-feeding insects present significant economic threats worldwide and remain challenging to understand due to their specialized feeding strategies. Significant advances in genetics, genomics, and biochemistry have greatly enriched our comprehension of phloem-insect interactions. However, existing studies relying on two-dimensional discrete images have limited our understanding of visible morphological details. In this study, we leverage volume electron microscopy (vEM) technology to unveil a nanometer-resolution interaction mode between plant and the phloem-feeding insect, Camphor psyllid (Trioza camphorae, Hemiptera: Psyllidae). The stylets penetrate each cell on the way to the feeding site (sieve tube), and new cell walls will form around the salivary sheath, ultimately fusing with the original cell walls to form remarkably thickening cell walls. Our reconstruction findings on pit gall tissues suggest that a significant decrease in cell volume and a drastic increase in cell layers are the primary processes during pit gall formation. These unique findings will set the stage for a robust discussion on the plant cellular response induced by phloem-feeding insects.
Why it matches plant phenotyping methodsvEMによるナノメートル分解能の3次元画像取得と再構築が研究の中心で、植物組織の細胞体積や細胞層数などの形態状態を定量化しているため、画像ベースの植物フェノタイピング応用に該当する。
abstractwe leverage volume electron microscopy (vEM) technology to unveil a nanometer-resolution interaction mode between plant and the phloem-feeding insect
The thylakoid membrane is the site of the light-dependent reactions of photosynthesis. It is a continuous membrane, folded into grana stacks and the interconnecting stroma lamellae. The CURVATURE THYLAKOID1 (CURT1) protein family is involved in the folding of the membrane into the grana stacks. The thylakoid membrane remodels its architecture in response to light conditions, but its 3D organisation and dynamics remain incompletely understood. To resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner. Recently, we have used expansion microscopy, a technique that meets these criteria, to visualise the thylakoid membrane isolated from spinach. Here, we show that this protocol can also be used to visualise enveloped spinach chloroplasts. Additionally, we present an improved protocol for resolving the thylakoid structure of Arabidopsis thaliana. Using this protocol, we show the changes in thylakoid architecture in response to long-term far-red light acclimation and due to knocking out CURT1A. We show that far-red light acclimation results in higher grana stacks that are packed closer together. In addition, the distance between stroma lamellae, which are wrapped around the grana, decreases. In the curt1a mutant, grana have an increased diameter and height, and the distance between grana is increased. Interestingly, in this mutant, the stroma lamellae occasionally approach the grana stacks from the top. These observations show the potential of expansion microscopy to study the thylakoid membrane architecture.
Why it matches plant phenotyping methods植物のチラコイド膜構造を高解像度3D画像で取得する拡大顕微鏡法の改良・適用が中心であり、膜構造という植物形態形質を測定しているため。
abstractTo resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner.
Reproduction assets foundThe article states that the data underlying the publication (expansion microscopy imaging/measurements of thylakoid architecture) are publicly available in the 4TU Research Data repository via the DOI 10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885. This is a paper-specific, publicly accessible data deposit with an authorDataset · publicUte Armbruster for providing the
seeds of the Ler0 curt1a-1 mutant. This work was supported by the Dutch
Organisation for Scientific Research (NWO) via a Vidi grant no. VI.Vidi
192.042 (E.W.) and by Wageningen Graduates Schools through a PhD
grant (J.B.).
Data availability
The data underlying this publication can be accessed at https://doi.org/10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885.References
[1] R.E. Blankenship, Molecular Mechanisms of Photosynthesis, John Wiley & Sons,
2021, https://doi.org/10.1002/9780470758472.
[2] H. Kirchhoff, Chloroplast ultrastructure in plants, New Phytol. 223 (2) (2019)
565–574, https://doi.org/10.1111/nph.15730.
[3] H. Kirchhoff, C. Hall, M. Wood, M. HerbstOpen asset ↗10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885pdf-raw-page:9 lines:1-68Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Single cells offer a simplified model for investigating complex mechanisms such as cell-cell adhesion. Protoplasts, plant cells without cell walls (CWs), have been instrumental in plant research, industrial applications, and breeding. However, due to the absence of a CW, protoplasts are not considered “true” plant cells and making them less relevant for biophysical studies. Current protocols for CW recovery in protoplasts vary widely among laboratories and starting materials, requiring lab-specific optimizations that often depend on expert knowledge and qualitative assessments. To address this, we have developed a user-friendly streamlined workflow, the Q-Warg pipeline, which enables quantitative comparison of various conditions for CW recovery post-protoplasting. This pipeline employs fluorescence imaging and tailored processing to measure parameters such as morphometry, cell viability, and CW staining intensity. Using this approach, we optimized culture conditions to obtain single plant cells (SPCs) with recovered CWs. Additionally, we demonstrated the robustness and versatility of the workflow by quantifying different fluorescent signals in protoplast suspensions. Overall, the Q-Warg pipeline provides a widely available and user-friendly solution for robust and unbiased characterization of protoplasts culture. The quantitative data generated by the pipeline may be useful in the future to decipher the mechanisms regulating protoplast viability and regeneration. Significance statement Several fields of plant biology, ranging from biotechnology to biomechanics, have recently regained a strong interest in using and studying protoplasts and single cells. Here, we developed a widely accessible quantitative workflow to characterize cell culture recovery after protoplasting along with the demonstration of its usefulness and versatility in various cases. We hope this tool can help other research groups to streamline the procedure needed to establish single plant cell approaches in their lab.
Why it matches plant phenotyping methods植物プロトプラストの形態、 viability、細胞壁染色強度を蛍光画像と専用処理で定量するワークフローを開発し、培養条件の比較・最適化に用いた研究であり、植物状態の取得・抽出法が中心である。
abstractwe have developed a user-friendly streamlined workflow, the Q-Warg pipeline, which enables quantitative comparison of various conditions for CW recovery post-protoplasting.
Here, we present a step-by step protocol to visualize intracellular aggregations in Arabidopsis mutants with cell wall secretion defects using FM4-64, a lipophilic styryl dye. We describe steps for growing seedlings, staining them with FM4-64, and identifying intracellular aggregates in cell wall synthesis and/or secretion mutants in root and hypocotyl epidermal cells via confocal microscopy. Additionally, we provide troubleshooting suggestions for common pitfalls. For complete details on the use and execution of this protocol, please refer to Hoffmann and McFarlane. 1 .
Why it matches plant phenotyping methodsArabidopsis細胞内凝集体をFM4-64染色と共焦点顕微鏡で可視化・同定する実行手順とトラブルシューティングが中心であり、植物細胞状態の画像計測プロトコルに該当する。
abstractwe present a step-by step protocol to visualize intracellular aggregations in Arabidopsis mutants
Quantitative wood anatomy (QWA), which involves measuring wood cell anatomical characteristics commonly on dated tree rings, is becoming increasingly important within plant sciences and ecology. This approach is particularly valuable for studies that require processing a large number of samples, such as those aimed at millennial-long climatic reconstructions. However, the field faces significant challenges, including the absence of a publicly available comprehensive protocol for efficiently and uniformly producing high-quality wood thin sections for QWA along dated tree-ring series. This issue is especially critical for more brittle subfossil wood, in addition to fresh material from living trees. Our manuscript addresses these challenges by providing a detailed protocol for producing thin anatomical sections of wood and digital images, specifically tailored for long chronologies of tree-ring anatomy with an emphasis on conifer wood. The protocol includes step-by-step procedures for sample preparation, sectioning, and imaging, ensuring consistent and high-quality results. By offering this well-tried-and-tested protocol, we aim to facilitate reproducibility and accuracy in wood anatomical studies, ultimately advancing research in this field. It aims to serve as a reference for researchers and laboratories engaged in similar work, promoting standardized practices and enhancing the reliability of QWA data.
Why it matches plant phenotyping methods樹木年輪の木材解剖学的形質を定量化するための試料調製・薄切・デジタル画像化プロトコルが論文の中心であり、植物形質取得の再現性と標準化を目的としている。
abstractproviding a detailed protocol for producing thin anatomical sections of wood and digital images, specifically tailored for long chronologies of tree-ring anatomy
O_LILow oxygen signalling in plants is important in development and stress responses. Measurement of oxygen levels in plant cells and tissues is hampered by a lack of chemical tools with which to reliably detect and quantify endogenous oxygen availability. We have exploited hypoxia-activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues. C_LIO_LIWe applied 4-nitrobenzyl (4NB-) resorufin and methyl-indolequinone (MeIQ-) resorufin to Arabidopsis thaliana whole cells and seedlings exposed to hypoxia (1% O2) and normoxia (21% O2). Confocal microscopy and fluorescence intensity measurements were used to visualise regions of resorufin fluorescence. C_LIO_LIBoth probes enter A.thaliana whole cells and are activated to fluoresce selectively in hypoxic conditions. Similarly, incubation with A.thaliana seedlings resulted in hypoxia-dependent activation of both probes and observation of fluorescence in hypoxic roots and leaf tissue. MeIQ-Resorufin was used to visualise endogenous hypoxia in lateral root primordia of normoxic A.thaliana seedlings. C_LIO_LIOxygen measurement in plants until now has relied on invasive probes or genetic manipulation. Use of these chemical probes to detect applied and endogenous hypoxia has the potential to facilitate a greater understanding of oxygen dynamics in plant cells and tissues, allowing correlation of oxygen concentrations with adaptive and developmental responses to hypoxia. C_LI
Why it matches plant phenotyping methods植物細胞・組織の低酸素状態を蛍光プローブで可視化・測定する化学的フェノタイピング手法の開発と検証が中心であり、単なる生物学的応用ではない。
abstractWe have exploited hypoxia-activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues.
Mature leaf area (LA) is a showcase of diversity - varying enormously within and across species, and associated with the productivity and distribution of plants and ecosystems. Yet, it remains unclear how developmental processes determine variation in LA. We introduce a mathematical framework pinpointing the origin of variation in LA by quantifying six epidermal 'developmental traits': initial mean cell size and number (approximating values within the leaf primordium), and the maximum relative rates and durations of cell proliferation and expansion until leaf maturity. We analyzed a novel database of developmental trajectories of LA and epidermal anatomy, representing 12 eudicotyledonous species and 52 Arabidopsis experiments. Within and across species, mean primordium cell number and maximum relative cell proliferation rate were the strongest developmental determinants of LA. Trade-offs between developmental traits, consistent with evolutionary and metabolic scaling theory, strongly constrain LA variation. These include trade-offs between primordium cell number vs cell proliferation, primordium mean cell size vs cell expansion, and the durations vs maximum relative rates of cell proliferation and expansion. Mutant and wild-type comparisons showed these trade-offs have a genetic basis in Arabidopsis. Analyses of developmental traits underlying LA and its diversification highlight mechanisms for leaf evolution, and opportunities for breeding trait shifts.
Why it matches plant phenotyping methods葉の発達軌跡と表皮解剖から6つの発達形質を定量化する数学的枠組みとデータベースが研究の中心であり、葉面積の表現型抽出・解析手法として有用。
abstractWe introduce a mathematical framework pinpointing the origin of variation in LA by quantifying six epidermal 'developmental traits'
Low-cost, minimally invasive microscopy for tracking cellular dynamics in living plants within their natural ecosystems is crucial for addressing fundamental questions in plant ecology and biology. However, existing solutions are constrained by coarse resolution, limited field-of-view (FoV), and poor deployability in natural settings. Here, we utilize a compact, portable microscope ("miniscope") for label-free (autofluorescence) imaging in living poplar wood. We systematically implement and evaluate multiple computational methods to enhance resolution and FoV. Our optimal computational pipeline, comprising maximal intensity projection, deconvolution, and flat-field correction, increases resolution by up to 39% on-axis and up to 49% at the field edges, resolving features of 2.87 μm, averaged over a FoV of ∼1 mm (diameter), compared with a 4.34 μm baseline. We demonstrate microscopy within the tissue of a living poplar plant in our greenhouse, observing the embolism of vessel elements, wound response, and tissue deformation from moisture evaporation.
Why it matches plant phenotyping methods生体ポプラ組織の細胞動態・木部塞栓・創傷応答を観察する携帯型顕微鏡と画像処理パイプラインを開発・評価しており、植物状態の取得手法が中心である。
abstractWe systematically implement and evaluate multiple computational methods to enhance resolution and FoV.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Plant bioengineering is a time-consuming and labor-intensive process with no guarantee of achieving desired traits. Here, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB) in maize (Zea mays) and Nicotiana benthamiana. FAST-PB enables genome editing and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single-cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrated that FAST-PB could streamline Golden Gate cloning, with the capacity to construct 96 vectors in parallel. Using FAST-PB in protoplasts, we found that PEG2050 increased transfection efficiency by over 45%. For proof-of-concept, we established a reporter-gene-free method for CRISPR editing and phenotyping via mutation of high chlorophyll fluorescence 136. We show that diverse lipids were enhanced up to 6-fold using CRISPR activation of lipid controlling genes. In callus cells, an automated transformation platform was employed to regenerate plants with enhanced lipid traits through introducing multigene cassettes. Lastly, FAST-PB enabled high-throughput single-cell lipid profiling by integrating MALDI-MS with the biofoundry, protoplast, and callus cells, differentiating engineered and unengineered cells using single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering and change what is possible using single-cell metabolomics in plants.
Why it matches plant phenotyping methods植物の遺伝子改変と連動した自動フェノタイピング基盤を開発し、単一細胞MALDI-MSによる脂質プロファイリングと葉緑素蛍光を用いた表現型評価を中核的に扱っているため。
abstractHere, we present a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB)
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.
Why it matches plant phenotyping methods植物細胞構造の仮想染色を用いた画像ベースの形態計測法を開発・検証しており、細胞面積や形状などの表現型抽出が研究の中心である。
abstractThe applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated.
Phragmoplasts are plant-specific intracellular structures composed of microtubules, actin microfilaments (AFs), membranes, and associated proteins. Importantly, they are involved in the formation and expansion of cell plates that partition daughter cells during cell division. While previous studies have revealed the important role of cytoskeletal dynamics in the proper functioning of the phragmoplast, the localization and role of AFs in the initial phase of cell plate formation remain controversial. Here, we used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage, enabling us to investigate the dynamics of AFs during the initial phase of cell plate formation in transgenic tobacco BY-2 cells labeled with Lifeact-RFP or RFP-ABD2 (actin binding domain 2). This computational approach overcame the limitation of conventional imaging, namely laser-induced photobleaching and phototoxicity. The restored images indicated that RFP-ABD2 labeled AFs were predominantly localized near the daughter nucleus, whereas Lifeact-RFP labeled AFs were found not only near the daughter nucleus but also around the initial cell plate. These findings, validated by imaging with a long exposure time, highlight distinct localization patterns between the two AF probes and suggest that Lifeact-RFP labeled AFs play a role in initiating cell plate formation.
Why it matches plant phenotyping methods深層学習による画像復元を開発・検証し、植物細胞内のアクチン局在と動態を高解像度4D画像から取得しているため、植物表現型取得法が中心である。
abstractwe used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage
Stomata are vital for CO 2 and water vapor exchange, with guard cells' aperture and ultrastructure highly responsive to environmental cues. However, traditional methods for studying guard cell ultrastructure, which rely on chemical fixation and embedding, often distort cell morphology and compromise membrane integrity. In contrast, plunge-freezing in liquid ethane rapidly preserves cells in a near-native vitreous state for cryogenic electron microscopy. Using this approach, we applied Cryo-Focused Ion Beam-Scanning Electron Microscopy (cryo-FIB-SEM) to study the guard cell ultrastructure of Vicia faba, a higher plant model chosen for its sensitivity to external factors and ease of epidermis isolation, advancing beyond previous cryo-FIB-SEM applications in lower plant algae. The results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state. 3D models of organelles such as stromules, chloroplast protrusions, chloroplasts, starch granules, mitochondria, and vacuoles were reconstructed from cryo-FIB-SEM volumetric data, with their surface area and volume initially determined using manual segmentation. Future studies using this near-native volume imaging technique hold promise for investigating how environmental factors like drought or salinity influence stomatal behavior and the morphology of guard cells and their organelles.
Why it matches plant phenotyping methods高等植物のガードセルを対象に、cryo-FIB-SEMによる近天然状態の3D画像取得とオルガネラ形態の再構築・定量を主要な技術貢献として扱っているため、植物フェノタイピング手法研究に該当する。
abstractThe results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state.
Studying cell-to-cell heterogeneity is essential to understand how unicellular organisms respond to stresses. We introduce a single-cell analysis framework that enables the study of intercellular heterogeneity of photosynthetic traits, particularly their interactions within individual cells that have identical genotypes, cellular contexts and histories. Our approach combines single-cell imaging of chlorophyll a fluorescence with machine learning and we study light stress responses in Chlamydomonas reinhardtii as a proof-of- concept. This framework allows us to score the extent of high-light responses such as state transitions (qT) and high-energy quenching (qE), to reveal significant cell-to-cell heterogeneity and to reveal a strong correlation between qT and qE, undetectable in bulk measurements. This study highlights the value of single-cell phenotypic analysis for for investigating light stress responses in unicellular organisms. We detail the key aspects that come into play to generalize the method to other complex stress responses involving multiple traits.
Why it matches plant phenotyping methods単一細胞のクロロフィル蛍光イメージングと機械学習を組み合わせ、光ストレス応答などの植物生理形質を抽出・評価する分析フレームワークが研究の中心である。
abstractWe introduce a single-cell analysis framework that enables the study of intercellular heterogeneity of photosynthetic traits
The coupling of exo- and endocytic trafficking of Cellulose Synthase Complexes (CSCs) has been proposed to be important for maintaining the population of active CSCs at the plasma membrane (PM) and thus appropriate levels of cell wall assembly. Although actin and myosin are known to participate in the late stages of exocytosis of CSCs, their exact role during CSC internalization events remains controversial. We constructed a functional, photoconvertible fluorescent mEOS2-CESA6 reporter and developed single-particle live-cell imaging approaches to visualize and quantify the dynamic behavior of CSCs at the PM during internalization. Using the small molecule inhibitor of clathrin, Endosidin 9-17 or ES9-17, we confirmed that clathrin-mediated endocytosis is a major pathway for CSC internalization. We also found that the actin cytoskeleton is involved in CSC internalization. Genetic or chemical inhibition of actin, myosin, or the Arp2/3 complex significantly reduced the frequency of CSC internalization events and prolonged the CSC pause time prior to internalization. Additionally, we found that the Arp2/3 complex contributes to the late stage of exocytosis of CSCs into the PM. These results reveal a role for actomyosin and the Arp2/3 complex in both CSC secretion as well as internalization that was previously undescribed in plant cells. One sentence summaryDirect visualization of individual CSC internalization events reveals that actomyosin participates in CSC internalization and the Arp2/3 complex contributes to both exocytosis and internalization of CSCs through regulating the dynamic homeostasis of the cortical actin cytoskeleton.
Why it matches plant phenotyping methods植物細胞内のセルロース合成複合体を対象に、機能的蛍光レポーターと単一粒子ライブセル画像解析を開発し、内在化動態を定量化しているため、画像ベースの植物状態計測が中心である。
abstractWe constructed a functional, photoconvertible fluorescent mEOS2-CESA6 reporter and developed single-particle live-cell imaging approaches to visualize and quantify the dynamic behavior of CSCs at the PM during internalization.
Plants have been a key interface in the global carbon and water cycles for nearly 475 million years. The magnitude of vegetational effects has waxed and waned dynamically because plant abundance and community composition have changed over time. Unravelling how plant communities have shaped, and been shaped by, global biogeochemical cycles relies upon reconstructing the paleoecology and paleoecophysiology of plants, and this process can be challenging in deep time, when plant communities contained organisms with traits that are rare in—or absent from—present-day ecosystems. Fortunately, the archive of how plants have shaped and responded to environmental change is preserved in the fossil record, because the traits and properties of extinct plants can be interpreted from fossilized anatomy in a qualitative, semi-quantitative, and quantitative way. Traits related to water transport in plants. including drought resistance and hydraulic supply to leaves, are particularly useful and important because these traits link individual plant performance to the water and carbon cycles.The collapse of tropical everwet rainforests end of the Carboniferous Period (~300 Ma) provides an illustration of how plant water transport traits influenced, and were shaped by, the water and carbon cycles. These traits are quantified by combining mathematical models of stem hydraulic conductivity and drought resistance with anatomical measurements from scanning electron and light microscopy images of fossilized plant water transport cells, called xylem. Analysis of stem hydraulic traits in five lineages of extinct Carboniferous plants—arborescent lycophytes, stem group seed plants, stem group tree ferns, coniferophytes, and sphenophytes—reveals differential hydraulic capacity and drought resistance among these plants, despite their simultaneous presence in tropical everwet ecosystems. Significant differences in these two traits are not only present between these five lineages, but can also be observed within several of these plant groups: for example, key parameters may vary by more than an order of magnitude in related plants. High hydraulic capacity and low drought resistance traits were associated with a decline in relative abundance toward the close of the Carboniferous Period, whereas plants with lower hydraulic capacity and higher drought resistance traits increased in relative abundance and survived this floral transition. This change in relative abundance within these communities shaped the hydrologic and carbon cycles which, in turn, amplified environmental stress that, consequently, further altered plant community composition. Implementing this analysis in trait-aware paleoecosystem models illustrates the effect of plant traits on global environments, and vice versa, yielding insight into plant performance during extreme environmental change that is analogous to anthropogenic impacts predicted for the late 21st century and beyond.
Why it matches plant phenotyping methods化石植物の解剖学的画像測定と数学モデルを組み合わせ、木部の水理伝導度・乾燥抵抗性という植物生理形質を定量化しており、形質取得・推定手法が研究の中心的要素です。
abstractThese traits are quantified by combining mathematical models of stem hydraulic conductivity and drought resistance with anatomical measurements from scanning electron and light microscopy images of fossilized plant water transport cells, called xylem.
Abstract Background: Plant growth and morphogenesis is a mechanical process controlled by genetic and molecular networks. Measuring mechanical properties at various scales is necessary to understand how these processes interact. However, obtaining a device to perform the measurements on plant samples of choice poses technical challenges and is often limited by high cost and availability of specialized components, the adequacy of which needs to be verified. Developing software to control and integrate the different pieces of equipment can be a complex task. Results: To overcome these challenges, we have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress. It consists of three primary components: a force sensor with a sample attachment point, an actuator with a second attachment point, and a camera. To monitor force, we use a parallel beam sensor, commonly used in digital weighing scales. To stretch the sample, we use a stepper motor with a screw mechanism moving a stage along linear rail. To monitor sample deformation, a compact digital microscope or a microscope camera are used. The system is controlled by MorphoRobotX, an integrated open-source software environment for mechanical experimentation. We first tested the basic Camelot setup, equipped with a digital microscope to track landmarks on the sample surface. We demonstrate that the system has sufficient precision to measure the stiffness in delicate plant samples, the etiolated hypocotyls of Arabidopsis , and were able to measure stiffness differences between wild type and a xyloglucan-deficient mutant. Next, we placed Camelot on an inverted microscope and used C-mount microscope camera to track displacement of cell junctions. We stretched onion epidermal peels in longitudinal and transverse directions and obtained results similar to those previously published. Finally, we used the setup coupled with an upright confocal microscope and measured anisotropic deformation of individual epidermal cells during stretching of an Arabidopsis leaf. Conclusions: The portability and suitability of Camelot for high-resolution optical tracking under a microscope make it an ideal tool for researchers in resource-limited settings or those pursuing exploratory biomechanics work.
Why it matches plant phenotyping methods植物サンプルの力学的形質（弾性、クリープ、降伏応力、剛性、変形）を測定する低コスト装置とソフトウェアを開発しており、植物表現型の取得方法が研究の中心である。
abstractwe have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress.
ArabidopsisMicroscopyCell / cellular structureRootTissueVisualization / data management
Abstract Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200–300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants' challenging optical properties. Expansion microscopy (ExM) improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that ExM can be applied to increase effective imaging resolution in Arabidopsis root specimens.
Why it matches plant phenotyping methods植物組織に適用可能な超解像イメージング手法を開発し、Arabidopsis根で解像度向上を実証しており、画像取得法が研究の中心である。
abstractHere, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy.
Reproduction assets foundThe paper's PlantEx expansion microscopy imaging data are deposited in ISTA's public repository, and the authors' custom analysis code (including the BigWarp-based expansion-factor script) is publicly available on GitHub. The Click-ExM repository is cited prior work whose method was adapted, not a paper-specific asset.Dataset · publicThe data that support the findings of this study are available via ISTA's data repository at https://doi.org/10.15479/AT:ISTA:18837 .Open asset ↗ISTA's data repository · 10.15479/AT:ISTA:18837lines:219-252Code · publicThe custom-written code used and described in this manuscript is available via Github ( https://github.com/danzllab/PlantEx ).Open asset ↗github.com/danzllab/PlantExlines:219-252Code · publicThe expansion factor was extracted as the linear scaling factor of the similarity transformation minimizing squared landmark residuals using the script https://github.com/danzllab/CATS/tree/master/rcats_image-analysis/bigwarp .Open asset ↗github.com/danzllab/CATSlines:154-159Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Podocarpus pollen morphology is shaped by both phylogenetic history and the environment. We analyzed the relationship between pollen traits quantified using deep learning and environmental factors within a comparative phylogenetic framework. We investigated the influence of mean annual temperature, annual precipitation, altitude, and solar radiation in driving morphological change. We used trait-environment regression models to infer the temperature tolerances of 31 Neotropical Podocarpidites fossils. Ancestral state reconstructions were applied to the Podocarpus phylogeny with and without the inclusion of fossils. Our results show that temperature and solar radiation influence pollen morphology, with thermal stress driving an increase in pollen size and higher UV-B radiation selecting for thicker corpus walls. Fossil temperature tolerances inferred from trait-environment models aligned with paleotemperature estimates from global paleoclimate models. Incorporating fossils into ancestral state reconstructions revealed that early ancestral Podocarpus lineages were likely adapted to warm climates, with cool-temperature tolerance evolving independently in high-latitude and high-altitude species. Our results highlight the importance of deep learning-derived features in advancing our understanding of plant environmental adaptations over evolutionary timescales. Deep learning allows us to quantify subtle interspecific differences in pollen morphology and link these traits to environmental preferences through statistical and phylogenetic analyses.
Why it matches plant phenotyping methods深層学習による花粉形態形質の定量が研究の中心であり、植物器官の観測形質を抽出して環境・系統解析に利用しているため、植物フェノタイピング手法の応用に該当する。
abstractWe analyzed the relationship between pollen traits quantified using deep learning and environmental factors within a comparative phylogenetic framework.
The cytoskeleton is important in controlling the growth and morphology of plant cells, so tracking its morphological changes is essential. Here, we develop a new machine learning based segmentation tool for microtubules (MTs), which can distinguish between polymerised and depolymerised fibres. To circumvent the low abundance of data, we trained on synthetic images of microtubules from a computational micro-tubule model, pre-processed to reproduce microscope effects and partial depolymerisation. We used this tool to investigate how the MT network in an Arabidopsis thaliana root hair cell repolymerises after depolymerisation under Oryzalin (OZ) drug treatments. Specifically, we show the network initially repolymerises from the shank region. This work demonstrates the viability of using synthetic data to train machine learning systems handling cytoskeletal image data.
Why it matches plant phenotyping methods植物細胞の微小管画像から重合・脱重合状態を抽出する機械学習セグメンテーション手法の開発が中心であり、植物細胞の形態・状態の表現型計測に該当する。
abstractwe develop a new machine learning based segmentation tool for microtubules (MTs), which can distinguish between polymerised and depolymerised fibres.
Tracking plant cells in three-dimensional (3D) tissue captured through light microscopy presents significant challenges due to the large number of densely packed cells, non-uniform growth patterns, and variations in cell division planes across different cell layers. In addition, images of deeper tissue layers are often noisy, and systemic imaging errors further exacerbate the complexity of the task. In this paper, we propose a novel learning-based method DEGAST3D: Learning Deformable 3D GrAph Similarity to Track Plant Cells in Unregistered Time Lapse Images exploits the tightly packed 3D cell structure of plant cells to create a three-dimensional graph for accurate cell tracking. We also propose a novel algorithm for cell division detection and an effective three-dimensional registration, improving state-of-the-art algorithms. On a public dataset, our novel cell pair matching method outperforms the baseline by $6.83 \%$, $5.96 \%$, $6.40 \%$ in precision, recall, and F-1 score, respectively. On the same dataset, our proposed novel cell division technique improves the results of the baseline method by $15.38 \%$ and $14.78 \%$ in terms of recall and F1-score, respectively.
Why it matches plant phenotyping methods植物細胞の3D画像から細胞追跡・分裂検出・画像登録を行う手法を開発し、公開データセットで性能評価しているため、植物フェノタイピング手法が中心です。
abstractIn this paper, we propose a novel learning-based method DEGAST3D: Learning Deformable 3D GrAph Similarity to Track Plant Cells in Unregistered Time Lapse Images
Subcellular RNA localization is an underexplored regulatory layer crucial for properly adapting cells to cellular or environmental conditions. Most studies describing RNA localization have been performed by cell fractionation and subsequent RNA quantification from pools of cells, thereby missing information about cell-to-cell variability. RNA single-molecule fluorescent in situ hybridization (smFISH) is an effective technique for detecting single RNA molecules and identifying subcellular accumulation patterns. Nevertheless, obtaining quantitative results from smFISH can be challenging in tissues with high autofluorescence, like in plants. Here, we describe an automated pipeline to detect and quantify nucleocytoplasmic RNA levels from Arabidopsis root smFISH images. This pipeline utilizes free image preprocessing, segmentation, and RNA detection software. The method permits users with any programming skills to analyze batches of images. Suggestions and recommendations for image acquisition, processing, and data analysis are included. This pipeline allows quantitative differences in nucleocytoplasmic distribution at the single-cell level to be studied under different cellular, environmental, and genetic contexts.
Why it matches plant phenotyping methodsArabidopsis根のsmFISH画像から細胞内RNA分布を自動検出・定量する画像解析パイプラインを開発しており、植物の状態を測定する方法が中心である。
abstractHere, we describe an automated pipeline to detect and quantify nucleocytoplasmic RNA levels from Arabidopsis root smFISH images.
Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue, respectively, outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark. The model successfully labelled vascular bundles with a Dice Similarity Coefficient (DSC) of 0.51 in apple tissue and 0.79 in pear tissue, although thin vasculature in apple remained more challenging to segment. The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue. Despite evaluating different methods to enhance segmentation quality, none improved test performance beyond that of the model trained on the standard dataset. The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images, without extensive sample preparation such as contrast labelling. The developed method, if not replaces, drastically accelerates conventional human-in-the-loop analysis of such images.
Why it matches plant phenotyping methods植物果実組織の3D微細構造をマイクロCT画像から自動抽出・定量化する深層学習手法を開発し、既存手法およびベンチマークと性能比較しているため、植物フェノタイピング手法が研究の中心です。
abstracta 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images.
Fungal pathogens present a severe risk to food systems; however, complex crop-microbe interactions are challenging to study using tools developed for model species. In particular, efficient screening and rapid assessment of microbial effectors is hindered by a lack of cloned resistance (R) genes and difficulty in validating large numbers of predicted effector candidates. This chapter describes a protocol for preparing wheat protoplasts to enable positive identification of host defense induction without overexpression of a cloned R gene, increasing the available pool of host resistance genes for screening. The assay uses polyethylene glycol (PEG)-calcium-mediated transient transfection to introduce candidate effector gene constructs into wheat protoplasts, with a defense-activated reporter for inducing a positive readout with internal normalization, indicating host recognition. This protocol provides a valuable tool for the study of host-pathogen interactions in wheat, contributing to improved resources for the development of disease-resistant crops and genome-informed pathogen surveillance.
Why it matches plant phenotyping methodsコムギプロトプラストで宿主防御誘導を定量的に読み出すスクリーニング assay のプロトコル開発が中心であり、植物の防御状態を表現型として取得する方法に該当する。
abstractThis chapter describes a protocol for preparing wheat protoplasts to enable positive identification of host defense induction
ArabidopsisCherryCell / cellular structureFlowerVisualization / data management
Petal abscission involves cell death and reactive oxygen species (ROS) accumulation in the cells at the base of petals. Visualizing changes in the properties of these cells is crucial for analyzing and understanding petal abscission, a trait with important implications, especially for ornamental flower crops. This protocol describes the guidelines, experimental setups, and conditions for visualizing cell death by trypan blue staining and ROS accumulation by 3,3'-diaminobenzidine (DAB) staining in petals. Additionally, it provides instructions for staining and sectioning the entire Arabidopsis thaliana flower to give an improved view of the cells crucial for abscission. This protocol can be used to study the mechanism of petal abscission, including temporal changes at the base of petals during abscission and comparisons with mutants. Although Arabidopsis thaliana and cherry (Prunus sp.) blossoms are used as examples here, this protocol can easily be adapted for other plant species.
Why it matches plant phenotyping methods花弁離脱に関連する細胞死とROS蓄積を可視化する染色プロトコルが研究の中心であり、植物の状態を測定する方法として実質的に記述されている。
abstractThis protocol describes the guidelines, experimental setups, and conditions for visualizing cell death by trypan blue staining and ROS accumulation by 3,3'-diaminobenzidine (DAB) staining in petals.
The target of rapamycin (TOR) is a central hub kinase that promotes growth and development in all eukaryote cells. TOR induces protein synthesis through the phosphorylation of the S6 kinase (S6K), which, in turn, phosphorylates ribosomal S6 protein (RPS6) increasing this anabolic process. Therefore, S6K and RPS6 phosphorylation are generally used as readouts of TOR activity. Protein phosphorylation levels are measured by a western blot (WB) technique using an antibody against one specific phosphosite in cell extracts. However, at the tissue/cell-specific level, there is a huge gap in plants due to the lack of alternative techniques for the evaluation of TOR activity as there are for other organisms such as mammals. Here, we describe an in vivo protocol to detect S6K phosphorylation in tissues/cells of model photosynthetic organisms such as Arabidopsis thaliana and Chlamydomonas reinhardtii . Our proposed method consists of the immunolocalization of a phosphorylated target of TOR kinase using a fluorescent secondary antibody by confocal microscopy. The protocol involves four main steps: tissue/cell fixation, permeabilization, and incubation with primary and secondary antibodies. It is an easy technique that allows handling different samples at the same time. In addition, different ultrastructural cell markers can also be used, such as for nucleus and cell wall detection, allowing a detailed analysis of cell morphology. To our knowledge, this is the first protocol to detect TOR activity in situ in photosynthetic organisms; we consider that it will pave the research on the TOR kinase, opening new possibilities to better understand its complex signaling. Key features • The protocol is an easy and non-destructive method to detect S6K phosphorylation at the cellular level for plants and algae. • First method for in situ immunolocalization of target proteins of TOR kinase in photosynthetic organisms.
Why it matches plant phenotyping methods植物・藻類組織内のTOR活性をリン酸化S6Kの免疫蛍光と共焦点顕微鏡で細胞レベルに測定する新規プロトコルであり、植物の生理状態取得法が中心である。
abstractOur proposed method consists of the immunolocalization of a phosphorylated target of TOR kinase using a fluorescent secondary antibody by confocal microscopy.
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.
Why it matches plant phenotyping methods植物細胞の画像から細胞骨格密度を定量抽出する深層学習セグメンテーション法が研究の中心であり、精度評価と複数の植物細胞モデルへの適用も行っている。
abstractwe examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images
BarleySugar beetWheatLaboratory / benchtopMicroscopyLiDAR / point cloudCell / cellular structureLeafSegmentationVisualization / data management
The ability of laser scanning confocal microscopy to generate high-contrast 2D and 3D images has become essential in studying plant-fungal interactions. Techniques such as visualization of native fluorescence, fluorescent protein tagging of microbes, green fluorescent protein (GFP)/red fluorescent protein (RFP)-fusion proteins, and fluorescent labeling of plant and fungal proteins have been widely used to aid in these investigations. Use of fluorescent proteins has several pitfalls, including variability of expression in planta and the requirement of gene transformation. Here, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline that uses propidium iodide (PI), which stains RNA and DNA, and wheat germ agglutinin labeled with fluorescein isothiocyanate (WGA-FITC), which stains chitin, to visualize fungal colonization of plants. This pipeline relies on the use of KOH to remove the cutin layer of the leaf, increasing its permeability, allowing the different stains to penetrate and effectively bind to their targets, resulting in a consistent visualization of cellular structures. To expand the utility of this pipeline, we used the staining techniques in conjunction with machine learning to analyze fungal biomass through volume analysis, as well as quantifying nuclear breakdown, an early indicator of programmed cell death (PCD). This pipeline is simple to use, robust, consistent across host and fungal species, and can be applied to most plant-fungal interactions. Therefore, this pipeline can be used to characterize model systems as well as nonmodel interactions where transformation is not routine. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.
Why it matches plant phenotyping methods植物-真菌相互作用を可視化し、真菌バイオマスと核崩壊を画像から定量する染色・共焦点顕微鏡・機械学習パイプラインの開発と評価が中心である。
abstractHere, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline
Key message A novel fluorescent i-motif DNA silver nanoclusters system has been developed for visualization of reactive oxygen species in plants, enabling the detection of intracellular signaling in plant cells. Reactive oxygen species (ROS) are crucial in plant growth, defense, and stress responses, making them vital for improving crop resilience. Various ROS sensing methods for plants have been developed to detect ROS in vitro and in vivo. However, each method comes its own advantages and disadvantages, leading to an increasing demand for a simple and effective sensory system for ROS detection in plants. Here, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants. Two sensors, C 20 /AgNCs and FAM-C 20 /AgNCs-Cy5, detect intracellular ROS signaling in response to stimuli, such as abscisic acid, salicylic acid, ethylene, and bacterial peptide elicitor flg22. Notably, FAM-C 20 /AgNCs-Cy5 exceeds the sensing capabilities of HyPer7, a widely recognized ROS sensor. Taken together, we suggest that fluorescent i-motif DNA/AgNCs system is an effective tool for visualizing ROS signals in plant cells. This advancement is important to advancing our understanding of ROS-mediated processes in plant biology.
Why it matches plant phenotyping methods植物細胞内ROSという生理状態を可視化する新規蛍光センサーを開発し、既存センサーと比較して性能を示しているため、フェノタイピング手法が中心である。
abstractHere, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants.
Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.
Why it matches plant phenotyping methodsシアノバクテリア細胞の画像セグメンテーションと細胞表現型分類を行うソフトウェアおよび機械学習手法の開発が中心であり、植物細胞の形態・生存状態を抽出するフェノタイピング手法に該当する。
abstractHere, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes.
Reproduction assets foundThe paper's segmentation/classification models and analysis code are publicly available in the authors' GitHub repository (cameronlab/cypose). The microscopy training datasets are not public and are available only upon request.Code · publicAll code and trained models can be downloaded from https://github.com/cameronlab/cypose .Open asset ↗cameronlab/cyposelines:298-383Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Rapid cell expansion pushes the Arabidopsis hypocotyl (juvenile stem) through the soil until blue light, acting first through phototropin 1 (phot1) and then through cryptochrome 1 (cry1), suppresses elongation to produce a length characteristic of established, photosynthetically capable seedlings. To determine where these two different blue-light receptors act to suppress hypocotyl elongation, we measured relative elemental growth rate, specifically along the hypocotyl midline at 5-min intervals before and during blue light, using a machine-learning-based image analysis pipeline designed specifically for this kinematic analysis of growth. In darkness, hypocotyl material expanded most rapidly (approximately 4% h -1 ) in a broad zone approximately 1 mm below the apical terminus of the hypocotyl (cotyledonary node). Blue light, acting through phot1, rapidly inhibited expansion in this zone, while simultaneously stimulating unexpanded cells in a very narrow, more apical region. Nuclear cry1, and not its cytoplasmic pool, counteracted the phot1-initiated expansion of the small cells in this apical region, preventing them from entering the more basal elongation zone. In a cry1 mutant, expansion of these apical cells proceeded unchecked, reaching rates as high as 6% h -1 to produce the iconic cry1 long-hypocotyl phenotype. The new spatial information shows where to focus future cell and molecular studies of cry1 and phot1 signaling mechanisms and, ecologically, indicates that a seedling may use an apical reservoir of elongation potential to reenter a lit environment should a natural darkening event such as soil disturbance deactivate cry1.
Why it matches plant phenotyping methods機械学習画像解析パイプラインを用いて、低い時間間隔で胚軸の空間的な成長速度を抽出しており、植物形質の取得・解析が研究上の主要な技術要素である。
abstractwe measured relative elemental growth rate, specifically along the hypocotyl midline at 5-min intervals before and during blue light, using a machine-learning-based image analysis pipeline designed specifically for this kinematic analysis of growth.
Plant developmental biology necessitates precise three-dimensional (3D) tracking of dynamic processes in live plants, and the 3D imaging technique in developmental bioimaging requires suitable fluorophores to achieve single-cell resolution imaging. Herein, we have designed a series of plasma membrane fluorescent dyes with a number of excellent properties and established a single-cell resolution imaging tool based on these dyes for three-dimensional imaging of various tissues and organs in living plants. The designed plasma membrane fluorescent dyes not only have the advantages of rapid wash-free staining, highly specific targeting, high brightness and high contrast imaging, ultralong imaging time and low biotoxicity, but also effectively avoid the autofluorescence interference of chlorophyll in cells, allowing for the development of a three-dimensional imaging approach of living plant organs with single-cell resolution. The three-dimensional histological structures of various organs of adult Arabidopsis thaliana, including roots, leaves, flowers, and fruits, were successfully reconstructed with single-cell resolution using this model plant. Furthermore, the 3D imaging method was employed to track the dynamic changes in tissue and organ morphology at the single-cell level during key plant developmental processes, including seed germination, root development, leaf growth, and anther development.
Why it matches plant phenotyping methods植物器官の3D形態を単一細胞解像度で取得・追跡する蛍光イメージング手法と色素を開発し、複数の器官・発生過程で実証しており、表現型取得法が研究の中心です。
abstractestablished a single-cell resolution imaging tool based on these dyes for three-dimensional imaging of various tissues and organs in living plants
Macroscopic phenotypic changes in plants are frequently employed as a means of evaluating the biological response of plants to external environmental stresses. However, the lack of effective observational tools at the microscopic cellular level hinders the ability to fully comprehend the intricacies of this response. Herein, we developed a plasma membrane fluorescent dye with target-activated green emission complemented with conventional FM dyes, and established a four-dimensional (4D) imaging approach based on this dye for spatio-temporal monitoring of plasma membrane dynamics during cellular responses to external environmental stress. A green fluorescent dye, designated FMG-DBO, was constructed by modifying the bridged unit between the aniline donor and the pyridinium acceptor. Its green emission can be combined with that of conventional FM dyes, enabling high-resolution imaging of plant leaf cells containing chlorophyll. The anchoring ability of the dyes was enhanced by incorporating a rigid diaza[2.2.2]octane unit as an anti-permeability group. The long retention time of the FMG-DBO dye in the plasma membrane enables the tracking of three-dimensional dynamics of the plasma membrane of plant cells. Consequently, an FMG-DBO-based four-dimensional imaging approach was established to monitor dynamic changes of plant cells under external environmental stress at the cellular level. The biological responses of two different drought-tolerant rice root cells to drought stress were examined by this four-dimensional imaging approach. It was observed that the two types of rice root cells exhibited disparate responses to the drought environmen. This approach offers alternative cell-level visualization tools for evaluating the biological responses of plant cells under environmental stress.
Why it matches plant phenotyping methods植物細胞の膜動態を可視化・追跡する蛍光色素と4Dイメージング手法を開発し、環境ストレス応答の観測に適用しており、表現型取得法が中心的です。
abstractdeveloped a plasma membrane fluorescent dye
Plant domestication is thought to create trade-offs between high yield and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. However, trade-offs can also occur when traits (such as yield in agriculture) optimized for favorable conditions perform less efficiently in stressful conditions. Deciphering the mechanisms driving these trade-offs is crucial for maintaining yield in changing environments. We examine leaf growth, a key trait influencing carbon assimilation, in eight species of grasses. We use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics, finding that domesticated plants generally have longer leaves, larger division zones and higher cell production rates. We found no clear evidence of trade-off between domestication and drought response in final leaf length. However, a trade-off is observed in development as wild species exhibited a smaller decrease in elongation zone size under drought than their domesticated counterparts. These nuanced trade-offs associated with domestication highlight the importance of examining physiological traits and mechanisms in greater detail, possibly informing breeding strategies to enhance crop resilience in the face of climate change. Highlight This study uses a high throughput pipeline to characterize leaf elongation responding to drought stress across eight species including barley, wheat, oat and wild relatives.
Why it matches plant phenotyping methods葉の顕微鏡画像から細胞寸法・位置を自動抽出する機械学習パイプラインが、葉の成長特性評価の中心的手法として用いられているため。
abstractWe use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics
Fruit cells’ shape generally reflects the physiological state and quality of the fruit, and indirectly dictates its economics. In this study, a new bio-microscope including three independent and orthogonal channels of opto-electromechanical microscopic observation systems was developed to obtain the three views (e.g., front view, top view, side view) of a single fruit cell using tomato and strawberry at two ripening stages as fruit samples. The obtained three-view images were used to reconstruct the 3D real shape of a single cell based on the 3D geometrical modelling method using Solidworks CAD design software and then compared with the actual geometric size. The average relative errors for the major diameter, minor diameter 1, minor diameter 2, projection perimeter and projection area were 4.04 %, 6.25 %, 5.71 %, 1.69 % and 3.79 %, respectively. This good accuracy makes the newly developed bio-microscope together with the proposed 3D geometrical modelling method a promising 3D shape reconstruction technology for a single fruit cell to extract real and detailed cell morphology information. Furthermore, this method can find applications in other fields such as human and animal cells where soft particles’ 3D shape analysis is important.
Why it matches plant phenotyping methods果実細胞の3D形状・形態を取得する顕微鏡と再構成手法の開発および精度検証が研究の中心であり、植物の形態形質を直接推定している。
abstracta new bio-microscope including three independent and orthogonal channels of opto-electromechanical microscopic observation systems was developed to obtain the three views
The plant cell wall, composed mainly of polysaccharides, lignin, and structural proteins, supports the architecture, mechanics, and functions of plants. Developing appropriate chemical imaging methods to study spatiotemporal changes of cell wall structural components at the microscopic level is important for understanding plant growth and senescence. In this study, tobacco (Nicotiana tabacum L.), a widely cultivated economic crop and model plant, was selected as the research object. Based on Raman confocal imaging combined with a multivariate curve resolution model, a label-free, in situ, high-throughput and high specificity imaging method for cellulose, high methylated pectin, and low methylated pectin in tobacco leaf cell wall was established to study their microscopic spatiotemporal changes during leaf growth and senescence (flue-curing) processes. The results based on the proposed method revealed that cellulose and pectin levels in the midrib cell wall gradually increased as the leaves matured, from appeared mainly at the cell corners and middle lamella respectively, to appeared in the cell corners, middle lamella, and cell wall. The same trend was observed in the lateral vein cell walls, where cellulose and pectin levels gradually increased. During the flue-curing process, cellulose and highly methylated pectin degraded. The proposed chemical imaging method is expected to provide a label-free, in situ, and high-throughput cell imaging technique for investigating the microscopic spatiotemporal distribution of the main structural components of the leaf cell wall.
Why it matches plant phenotyping methods葉の細胞壁成分を対象に、ラマン顕微鏡画像と多変量曲線分解による化学イメージング手法を開発し、セルロース・ペクチンの空間および時系列分布を抽出しているため、植物表現型取得法が中心である。
abstractBased on Raman confocal imaging combined with a multivariate curve resolution model, a label-free, in situ, high-throughput and high specificity imaging method for cellulose, high methylated pectin, and low methylated pectin in tobacco leaf cell wall was established
In recent decades, xylem anatomical traits have become increasingly important in dendrochronological research, as they offer the unique opportunity to assess eco-physiological drivers of tree growth at intra-annual resolution. However, standard protocols for generating such data are still missing, leading to methodological uncertainty, and complicating data exchange among laboratories. Here, we compare protocols for high-quality permanent slide preparation in dendroanatomy and address the effects of paraffin embedding vs. non-embedding approaches. Tests are conducted on both gymnosperm and angiosperm wood types of widely distributed European tree species, considering cell wall thickness (CWT), mean lumen area (MLA), and hydraulic diameter (Dh). Results indicate that non-embedding does not significantly alter the qualitative and quantitative characteristics of permanent slides compared to embedded samples. Whereas the mean chronologies of MLA and Dh and their non-embedded counterparts share substantial high-frequency variance, the CWT chronologies reveal slightly larger discrepancies at inter-annual scale. However, methodological differences do not exceed 11.1 % for any parameter. While these results show high similarity between the two approaches, we recommend adopting the non-embedding procedure, since it saves resources and therefore allows to produce larger datasets. Regardless of the protocol used to build wood anatomical datasets, assembling large-scale networks of wood anatomical data could transform our understanding of forest responses to global changes.
Why it matches plant phenotyping methods木材解剖形質（細胞壁厚、管腔面積、水理直径）を取得する永久切片作製法について、埋包・非埋包プロトコルを比較検証しており、植物形質測定法が研究の中心である。
abstractHere, we compare protocols for high-quality permanent slide preparation in dendroanatomy and address the effects of paraffin embedding vs. non-embedding approaches.
Background Nitroxyl (HNO) is an emerging signaling molecule that plays a significant regulatory role in various aspects of plant biology, including stress responses and developmental processes. However, understanding the precise actions of HNO in plants has been challenging due to the absence of highly sensitive and real-time in situ monitoring tools. Consequently, it is crucial to develop effective and accurate detection methods for HNO. Establishing such methodologies will enable researchers to elucidate the functional roles of HNO in plant physiological processes, thereby advancing our knowledge of plant resilience and adaptation under environmental stressors. Result Herein, we successfully constructed a near-infrared fluorescent probe, DCIF-HNO, based on the dicyanoisophorone platform as fluorophore and 2-(diphenylphosphino)benzoate as HNO recognition site for identifying HNO in plants. Probe DCIF-HNO exhibited rapid response, excellent selectivity, and high sensitivity to HNO in vitro spectroscopic tests, while also demonstrating low toxicity and biocompatibility. A rapid and portable smartphone sensing platform for HNO in actual samples was successfully constructed based on probe DCIF-HNO and color recognition application. Moreover, probe DCIF-HNO was successfully applied to plant cells and tissues, enabling real-time visualization and detection of HNO and revealing the complex network of HNO interactions during H 2 S/NO crosstalk in plants. Furthermore, the increase in HNO levels in plants response to high salt and Cr stress was observed using probe DCIF-HNO. Transcriptome sequencing and differential metabolites analysis were employed to gain insight into the mechanism of HNO production under Cr stress. Significance Due to the optical properties and high-resolution imaging capabilities of DCIF-HNO, this study offers a novel framework for elucidating the signaling role of HNO in plant stress responses. The precise visualization of HNO dynamics enhances our understanding of the complex molecular pathways involved in plant adaptation to abiotic stressors. This research not only advances plant physiology but also has significant implications for developing strategies to enhance agricultural resilience in challenging environmental conditions.
Why it matches plant phenotyping methods植物内HNOのリアルタイム可視化・検出プローブとスマートフォン計測基盤を開発し、ストレス応答という植物の生理状態を測定しているため、方法が中心的である。
abstractwe successfully constructed a near-infrared fluorescent probe, DCIF-HNO
As a common pollutant, cadmium (Cd) poses a serious threat to the growth and development of plants. Currently, there is no effective method to elucidate the protective mechanism of Cd 2+ in plant cells. For the first time, we designed a Cd 2+ fluorescent probe to observe the adsorption and sequestration of Cd 2+ in rice cell walls and vacuoles. Specifically, Cd 2+ is blocked by the Casparian strip and electrostatically attracted to hemicellulose, which is abundantly adsorbed and fixed to the cell walls of the endodermis. For Cd 2+ that successfully entered the endodermis, one part entered the cells and was compartmentalised and fixed in the vacuoles, while the other part entered the vascular bundles and precipitated in the cell walls of the sclerenchyma through the ion exchange effect. Furthermore, with prolonged exposure to Cd 2+ , compartmentalised bodies that were strongly labelled by fluorescence gradually appeared in the vacuoles, which were assumed to be a new heavy metal protective mechanism activated by plants in response to continuous Cd 2+ exposure. In conclusion, this study provides an innovative and effective method for the detection of adsorption, transportation, and accumulation of Cd 2+ in plant tissues, which can be employed for the rapid identification of crops with low Cd accumulation.
Why it matches plant phenotyping methodsイネ組織内のCd2+の吸着・輸送・蓄積を可視化する蛍光プローブを開発し、植物組織の状態を測定する手法自体が中心であるため。
abstractFor the first time, we designed a Cd 2+ fluorescent probe to observe the adsorption and sequestration of Cd 2+ in rice cell walls and vacuoles.
Key message Polarized auxin transport regulates fruit shape determination by promoting anisotropic cell growth. Angiosperms produce organs with distinct shape resultant from adaptive evolution. Understanding the cellular basis underlying the development of plant organ has been a central topic in plant biology as it is key to unlock the mechanisms leading to the diversification of plants. Variations in the location of synthesis, polarized auxin transport (PAT) have been proposed to account for the development of diverse organ shapes, but the exact cellular mechanism has yet to be elucidated. The Capsella rubella develops a perfect heart-shaped fruit from an ovate shape gynoecium that is tightly linked to the localized auxin synthesis in the valve tips and provides a unique opportunity to address this question. In this study, we studied auxin movement in the fruits and the cellular effect of N-1-Naphthylphthalamic Acid (NPA) on the fruit shape determination by constructing the pCrPIN3:PIN3:GFP reporter and live-imaging. We found PAT in the valve epidermis is in congruent with fruit shape development and NPA treatment disrupts the heat-shaped fruit development mainly by repressing cell anisotropic growth with minor effect on division. As the Capsella fruit is unusually big in size, we also included a detailed step-by-step protocol on how to conduct live-imaging experiment. We further test the utility of this protocol by conducting a live-imaging analysis of the gynophore in Arachis hypogaea. Collectively, the results of this study elucidated the mechanism on how auxin signal was translated into instructions guiding cell growth during organ shape determination. In addition, the description of the detailed live-imaging protocol will encourage further studies of the cellular mechanisms underlying shape diversification in angiosperms.
Why it matches plant phenotyping methods果実および細胞成長を観察・抽出するライブイメージング手法の詳細プロトコルを提示し、別種でも有用性を検証しており、手法的貢献が明示されています。
abstractwe also included a detailed step-by-step protocol on how to conduct live-imaging experiment.
Abstract Reactive oxygen species (ROS) are crucial in plant growth, defense, and stress responses, making them vital for improving crop resilience. Various ROS sensing methods for plants have been developed to detect ROS in vitro and in vivo . However, each method comes its own advantages and disadvantages, leading to an increasing demand for a simple and effective sensory system for ROS detection in plants. Here, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants. Two sensors, C 20 /AgNCs and FAM-C 20 /AgNCs-Cy5, detect intracellular ROS signaling in response to stimuli such as abscisic acid, salicylic acid, ethylene, and bacterial peptide elicitor flg22. Notably, FAM-C 20 /AgNCs-Cy5 exceeds the sensing capabilities of HyPer7, a widely recognized ROS sensor. Taken together, we suggest that fluorescent i-motif DNA/AgNCs system is an effective tool for visualizing ROS signals in plant cells. This advancement is important to advancing our understanding of ROS-mediated processes in plant biology.
Why it matches plant phenotyping methods植物細胞内ROSという生理状態を可視化する新規DNA/AgNCsセンサーを開発・比較評価しており、植物表現型の取得法が研究の中心である。
abstractHere, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants.
The present dataset combines transcriptomic and microscopic analyses to investigate the responses of winter oilseed rape (WOSR, Brassica napus L., cultivar Aviso) to soil drought, with a focus on differences between young and early-senescent old leaves. For microscopy, 36 scans of 1 to 5 leaf cross-sections were acquired from paraffin-embedded leaf disc samples using a scanner with a 40x lens (Pannoramic Confocal, 3DHistech), capturing a large field of view (8-mm-long observed leaf tissue). The raw scanned cross-sections and analyzed images are available under doi.org/10.57745/RK5PM3 in the Recherche Data Gouvrepository. These high-quality scans enable the differentiation of mesophyll cells and tissues. Software analysis yielded a dataset with 54 selected cross-sectional areas, 291 delimited surfaces of palisade, spongy, and vessel tissues, and 11,136 individually delimited cells from the palisade and spongy layers. For transcriptomics, an Illumina Novaseq sequencer was used to generate 390 Gb of mRNA paired-end reads. The raw reads were filtered, mapped, and assigned to genes from the Brassica napus reference genome Darmor-bzh v10, which were subsequently used to identify differentially expressed genes (DEGs) and to perform gene ontology enrichment analysis. The raw reads are accessible under accession PRJNA939927 at the NCBI Sequence Read Archive (SRA). This high-quality dataset provides insights into the molecular mechanisms underlying oilseed rape's response to soil drought and may aid in the development of drought-tolerant cultivars. A total of 17,975 DEGs were identified between well-watered and severe drought conditions across the contrasted leaf developmental stages.
Why it matches plant phenotyping methods葉の断面画像を取得・解析し、組織面積や個別細胞などの植物形態形質を構造化した再利用可能なデータセットを提供しており、画像ベースの表現型取得が実質的な構成要素である。
abstractFor microscopy, 36 scans of 1 to 5 leaf cross-sections were acquired from paraffin-embedded leaf disc samples using a scanner with a 40x lens
Reproduction assets foundThe article deposits its own plant-phenotyping assets publicly: raw and analyzed leaf cross-section microscopy scans (Recherche Data Gouv, doi:10.57745/RK5PM3) and the transcriptomic dataset (Recherche Data Gouv doi:10.57745/7HQSM3, mirrored at NCBI SRA under PRJNA939927). The analysis pipelines cited (nf-core/rnaseq, Dataset · publicThe raw scanned cross-sections and analyzed images are available under doi.org/10.57745/RK5PM3 in the Recherche Data Gouvrepository.Open asset ↗Recherche Data Gouv · 10.57745/RK5PM3lines:1-41Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
The review focuses on image cytometry of plant cells, used to determine the ploidy level and genome size of plants. The review presents examples of basic plant studies using the method of analyzing static images of nuclei, the dyes used, sample preparation methods, data analysis, laboratory equipment and software. It also provides an application of the image cytometry in the study of vascular plants. An important parameter of any method is reproducibility and comparative characteristics relative to other methods. The article compares absolute values of plant genome size measurements made by image cytometry and flow cytometry, as well as the necessary minimum parameters to ensure measurement accuracy and limitations of the method. The review will be useful when planning an experiment on DNA content analysis without using expensive equipment – flow cytometers, but only on the basis of optical or fluorescence microscopy data.
Why it matches plant phenotyping methods植物細胞の画像サイトメトリーによる倍数性・ゲノムサイズ測定法を中心に、試料調製、画像解析、ソフトウェア、再現性、フローサイトメトリーとの比較検証を扱うレビューであり、植物表現型測定法が中核です。
abstractThe review focuses on image cytometry of plant cells, used to determine the ploidy level and genome size of plants.
The light irradiation to the plant chloroplasts drives NADPH and ATP synthesis in the stroma via the electron transport chains within the thylakoid membranes. Conventional methods for assessing photosynthetic light reactions are often invasive or require specific conditions. While detection markers do not significantly affect plant growth itself, developing a method for the real-time and non-invasive detection of NADPH is a highly impactful and important research area in plant physiology and biochemistry. This study introduces a genetically encoded NADPH-binding blue fluorescent protein (mBFP) targeted to the chloroplast stroma or thylakoid membrane in Arabidopsis thaliana and Nicotiana benthamiana. Using two-photon microscopy, we monitored real-time stromal NADPH levels in transgenic leaves of Arabidopsis in response to light exposure. A mutant mBFP construct targeted to the thylakoid membrane allowed us to detect the stromal NADPH levels in real time under different light conditions. This in planta biosensor provides a non-invasive tool for studying photosynthetic responses to light more quantitatively and holds potential for optimizing light conditions in controlled-environment agriculture, such as indoor vertical farms, to improve crop productivity.
Why it matches plant phenotyping methods植物体内のNADPH量をリアルタイム・非侵襲的に測定する蛍光バイオセンサーと二光子顕微鏡ワークフローを開発しており、植物生理状態の取得手法が研究の中心である。
abstractdeveloping a method for the real-time and non-invasive detection of NADPH is a highly impactful and important research area in plant physiology and biochemistry.
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.
Why it matches plant phenotyping methods葉表皮細胞の形態形質を画像から抽出・定量するコンピュータビジョン／画像処理ワークフローの開発と適用が研究の中心であるため、植物フェノタイピング手法として収載する。
abstractwe developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology.
Reproduction assets foundThe paper's quantitative phenotyping measurements (trichome types and morphometric parameters of leaf epidermal cells for nine wild potato species) are publicly available as Supplementary Tables S1 and S2 at the MDPI supplementary URL. Microscopy images are not publicly deposited and are available only upon request; noSupplement · publicObjects of the Institute of Cytology and Genetics SB RAS.
Abbreviations
The following abbreviations are used in this manuscript:
LSM
Laser scanning microscopy
PI
Propidium iodide
DAPI
4′,6-diamidino-2-phenylindole
PCA
Principal component analysis
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants13213084/s1 , Table S1: Trichome types for the studied wild potato species; Table S2: Morphometric parameters for leaf epidermal cells of the studied wild potato species, including Area, Length, Width, Elongation, Circularity, Rectangularity, Perimeter, Convex Hull Area, Convex Hull Perimeter, and Convex Hull Coverage.
Author Open asset ↗lines:139-176Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Lignin fluorescence in plant cell walls significantly interferes with Raman spectroscopic signals, resulting in compromised analytical accuracy and resolution. To address this issue, a strategy was implemented to both reduce the absolute lignin content in samples and prepare thinner plant tissue sections. This approach involved embedding plant samples in LR White resin, complemented by an ultrathin sectioning technique. Additionally, algorithms were developed to eliminate the impact of resin spectra on the imaging process. These advancements collectively enhanced the performance of Raman spectroscopy by effectively diminishing the disruptive effects of lignin fluorescence. Further analysis with confocal laser scanning microscopy (CLSM) elucidated the presence of aggregation-induced luminescence (AIE) in plant tissues, revealing a direct correlation with lignin concentration. These findings not only offer a new perspective for the application of Raman spectroscopy in plant science, but also pave the way for advancements in tip-enhanced Raman spectroscopy (TERS) detection.
Why it matches plant phenotyping methods植物細胞壁のリグニンを対象とするRaman分光イメージングの干渉低減、薄切片調製、樹脂スペクトル除去アルゴリズムを開発し、植物組織の化学的形質測定法の性能向上を中心に扱っているため。
abstractTo address this issue, a strategy was implemented to both reduce the absolute lignin content in samples and prepare thinner plant tissue sections.
Nanomaterial-mediated plant genetic engineering holds promise for developing new crop cultivars but can be hindered by nanomaterial toxicity to protoplasts. We present a fast, high-throughput method for assessing protoplast viability using resazurin, a non-toxic dye converted to highly fluorescent resorufin during respiration. Protoplasts isolated from hypocotyl canola (Brassica napus L.) were evaluated at varying temperatures (4, 10, 20, 30 ˚C) and time intervals (1-24 h). Optimal conditions for detecting protoplast viability were identified as 20,000 cells incubated with 40 µM resazurin at room temperature for 3 h. The assay was applied to evaluate the cytotoxicity of silver nanospheres, silica nanospheres, cholesteryl-butyrate nanoemulsion, and lipid nanoparticles. The cholesteryl-butyrate nanoemulsion and lipid nanoparticles exhibited toxicity across all tested concentrations (5-500 ng/ml), except at 5 ng/ml. Silver nanospheres were toxic across all tested concentrations (5-500 ng/ml) and sizes (20-100 nm), except for the larger size (100 nm) at 5 ng/ml. Silica nanospheres showed no toxicity at 5 ng/ml across all tested sizes (12-230 nm). Our results highlight that nanoparticle size and concentration significantly impact protoplast toxicity. Overall, the results showed that the resazurin assay is a precise, rapid, and scalable tool for screening nanomaterial cytotoxicity, enabling more accurate evaluations before using nanomaterials in genetic engineering.
Why it matches plant phenotyping methodsカノーラプロトプラストの生存性を測定する高速・高スループットな蛍光アッセイを最適化し、ナノ材料毒性評価に適用しており、植物状態の取得方法が中心的です。
abstractWe present a fast, high-throughput method for assessing protoplast viability using resazurin
The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.
Why it matches plant phenotyping methods植物細胞構造の仮想染色と画像解析モデルを開発・評価し、細胞面積・形状・核拡大・葉緑体運動・生死などの表現型を定量化しているため、フェノタイピング手法が中心である。
abstractThe applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated.
Reproduction assets foundThe paper publicly releases its virtual-staining training/test image sets (bright-field inputs with paired confocal reference images) for BY-2 vacuole, BY-2 nucleus/plasma membrane, and E. densa chloroplast models on figshare under CC BY 4.0, via three DOIs listed in the Data Availability section. No author analysis orDataset · publicata pertaining to this article will be shared on reasonable request to the corresponding author. The
training and test image sets for BY-2 cells and E. densa, which are publicly accessible on figshare under the
CC BY 4.0 license, include images of wild-type tobacco BY-2 cells stained with BCECF for vacuolar lumen
visualization (https://doi.org/10.6084/m9.figshare.27247629.v1), transgenic tobacco BY-2 cells with
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted October 25, 2024.
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hOpen asset ↗figshare · 10.6084/m9.figshare.27247629.v1pdf-raw-page:21 lines:1-32Plant phenotyping relevance match · UnverifiedbioRxiv · checked 13 Sept 2026
Maize (Zea mays L.) performs highly efficient C4 photosynthesis by dividing photosynthetic metabolism between mesophyll and bundle sheath cells. In vivo physiological measurements are indispensable for C4 photosynthesis research as any isolated cells or sectioned leaf often show interrupted and abnormal photosynthetic activities. Yet, direct in vivo observation regarding bundle sheath cells in the delicate anatomy of the C4 leaf is still challenging. In the current work, we used two-photon fluorescence-lifetime imaging microscopy (two-photon-FLIM) to access the photosynthetic properties of bundle sheath cells on intact maize leaves. The results provide spectroscopic evidence for the diminished total PSII activity in bundle sheath cells at its physiological level and show that the single PSIIs could undergo charge separation as causal. We also report an acetic acid-induced chlorophyll fluorescence quenching on intact maize leaves, which might be a physiological state related to the nonphotochemical quenching mechanism.
Why it matches plant phenotyping methods二光子FLIMを用いて無傷葉の葉鞘細胞の光合成特性・生理状態を直接画像計測しており、植物生理表現型の取得法が研究の中心です。
abstractwe used two-photon fluorescence-lifetime imaging microscopy (two-photon-FLIM) to access the photosynthetic properties of bundle sheath cells on intact maize leaves.
The protocol shows the effectiveness of using safranin-fast green stain for fluorescence microscopy. This staining technique has been used in conventional microscopy to perform anatomical characterizations of plants. However, this protocol describes the procedure for using samples stained with safranin-fast green in conjunction with fluorescence microscopy. The strength of the protocol lies in the fact that the samples are permanent and allows for effective differentiation of lignified and cellulosic walls unlike conventional fluorescence microscopy stains such as Congo red-acridine orange, calcofluor, and autofluorescence. The protocol for making fluorescence intensity measurements is also standardized, allowing the data to be used for statistical analysis and inference about the chemical composition of plant cell walls.
Why it matches plant phenotyping methods植物細胞壁のリグニン化・セルロース性を蛍光顕微鏡と標準化した蛍光強度測定で評価するプロトコルが研究の中心であり、植物組織の構造・化学的形質を取得する方法開発に該当する。
abstractthis protocol describes the procedure for using samples stained with safranin-fast green in conjunction with fluorescence microscopy.
Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.
Why it matches plant phenotyping methods植物細胞・組織の分解状態を定量する4次元蛍光共焦点イメージングと計算ツールが研究の中心であり、植物状態の形態的変化を抽出する方法を開発している。
abstractIn this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales.
Reproduction assets foundThe paper's WallTrack computational pipeline (used to track and quantify 4D confocal imaging of poplar cell wall deconstruction) is publicly available on the authors' FARE laboratory GitLab repository. The underlying imaging/phenotype data are not publicly deposited; the authors state data will be made available on.Code · publicnano and micro
scales.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The WallTrack code is accessible through the FARE laboratory
GitLab repository at: https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d. Data will be made available on request.
Acknowledgments
The authors thank Anouck Habrant for her help in confocal imaging
and Grégoire Malandain, Solmaz Hossein Khani, Khadidja Ould Amer,
and Ali Faraj for their comments on the manuscript. This work was
supported by Agence Nationale de la Recherche (ANR) Open asset ↗https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d · refahi_et_al_4dpdf-raw-page:11 lines:1-66Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Climate change-driven drought events are becoming unescapable in an increasing number of areas worldwide. Understanding how plants are able to adapt to these changing environmental conditions is a non-trivial challenge. Physiologically, improving a plant's intrinsic water use efficiency (WUEi ) will be essential for plant survival in dry conditions. Physically, plant adaptation and acclimatisation are constrained by a plant's anatomy. In other words, there is a strong link between anatomical structure and physiological function. Former research predominantly focused on using 2D anatomical measurements to approximate 3D structures based on the assumption of ideal shapes, such as spherical spongy mesophyll cells. As a result of increasing progress in 3D imaging technology, the validity of these assumptions is being assessed, and recent research has indicated that these approximations can contain significant errors. We suggest to invert the workflow and use the less common 3D assessments to provide corrections and functions for the more widely available 2D assessments. By combining these 3D and corrected 2D anatomical assessments with physiological measurements of WUEi , our understanding of how a plant's physical adaptation affects its function will increase and greatly improve our ability to assess plant survival.
Why it matches plant phenotyping methods3Dおよび補正2Dによる植物解剖形質の評価ワークフローを中心に論じ、植物構造の測定・推定法の改善を提案しているため、方法論的レビュー／開発に該当する。
abstractFormer research predominantly focused on using 2D anatomical measurements to approximate 3D structures
Hydrogen sulfide (H 2 S) has emerged as a crucial signaling molecule in plant stress responses, playing a significant role in regulating various physiological and biochemical processes. In this study, we report an aggregation-induced emission (AIE)-based ratiometric fluorescent probe TPN-H 2 S for the highly selective detection of H 2 S in plant tissues. The probe exhibited excellent sensitivity and selectivity towards H 2 S over other analytes, enabling real-time monitoring of H 2 S dynamics in living cell. Furthermore, the AIE-based ratiometric probe TPN-H 2 S allowed for accurate quantification of H 2 S levels, providing valuable insights into the spatiotemporal distribution of Cys metabolism produces H 2 S. Importantly, the physiological pathways and signaling mechanisms of H 2 S production of was investigated in plant tissues under Cr and nano-plastics stress. Utilizing a high-throughput screening approach, we identified exogenous substances such as calcium chloride (CaCl 2 ) and abscisic acid (ABA) that could induce higher level of H 2 S production during the stress response in plants. Overall, those findings demonstrate the potential of the AIE-based ratiometric fluorescent probe TPN-H 2 S as a powerful tool for unraveling the role of H 2 S in plant stress responses and pave the way for further exploration of H 2 S-mediated signaling pathways in plants.
Why it matches plant phenotyping methods植物組織内のH2Sをリアルタイム・定量検出する蛍光プローブを開発し、感度・選択性を評価している。植物の生理状態・ストレス応答の取得法が研究の中心である。
abstractwe report an aggregation-induced emission (AIE)-based ratiometric fluorescent probe TPN-H 2 S for the highly selective detection of H 2 S in plant tissues.
Abstract Translation is a fundamental process for every living organism. In plants, the rate of translation is tightly modulated during development and in response to environmental cues. However, it is difficult to measure the actual translation state of the tissues in vivo . Here, we report the implementation of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC. We combined method originally developed for fruit-fly with an improved low background split-mVenus BiFC system previously described in plants. We labelled Arabidopsis thaliana small subunit ribosomal protein (RPS) and large subunit ribosomal protein (RPL) with fragments of the mVenus fluorescent protein. Upon the assembly of the 80S ribosome, the mVenus fragments complemented and were detected by fluorescent microscopy. We show that these recombinant proteins are in close proximity in the tobacco epidermal cells, although the signal is reduced when compared to BiFC signal from known interactors. This Ribo-BiFC method system can be used in stable transgenic lines to enable visualisation of translational rate in plant tissues and could be used to study translation dynamics and its changes during plant development, under abiotic stress or in different genetic backgrounds.
Why it matches plant phenotyping methods植物組織内の翻訳速度という生理状態を可視化するRibo-BiFC法を実装・検証しており、表現型取得法が研究の中心である。
abstractHere, we report the implementation of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named large-volume fully automated cell reconstruction (LVACR), enabling the exploration of 3D cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (Populus simonii Carr. and Populus canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem of Pinus tabuliformis Carr. seedlings. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.
Why it matches plant phenotyping methods植物組織の3D細胞形態・接続性を画像から抽出するLVACRを開発し、ベンチマーク検証と実データ適用を行っており、植物フェノタイピング手法が中心である。
abstractwe established a set of frameworks named large-volume fully automated cell reconstruction (LVACR), enabling the exploration of 3D cytological features and cellular connectivity in plant tissues.
ArabidopsisChlorophyll fluorescenceCell / cellular structureRootVisualization / data management
Polar transport of the phytohormone auxin plays a crucial role in plant growth and response to environmental stimuli. Small-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth. In this study, we developed a new fluorescent auxin probe, BODIPY-IAA2, which effectively visualizes auxin distribution in various plant tissues. We designed this probe to be transported by auxin transporters while lacking the ability to elicit auxin signaling. Using BODIPY as the fluorophore provides bright and stable fluorescence signals, making it suitable for live-imaging under standard fluorescent microscopy. We tested the probe with auxin reporter lines in Arabidopsis and performed yeast two-hybrid assays. The results showed that BODIPY-IAA2 did not activate auxin signaling through the auxin receptor TIR1. However, BODIPY-IAA2 did mildly compete with both exogenous and endogenous auxins for transport, indicating that the probe is transported by auxin transporters in vivo. The probe not only enables visualization of its tissue distribution but also allows sub-cellular staining, including the endoplasmic reticulum and tip regions in elongating cells in moss. We also observed unusual staining patterns in the main root of non-model parasitic plants where genetic transformation is not feasible. Our new fluorescent auxin probe demonstrates significant potential for detailed studies on auxin transport and distribution across diverse plant species.
Why it matches plant phenotyping methods植物体内のオーキシン分布を可視化する蛍光プローブを開発し、植物組織・細胞でのライブイメージング性能を検証しており、表現型取得手法が研究の中心である。
abstractSmall-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth.
Chromosome number is the most fundamental trait of a karyotype. Accurate chromosome counting is essential for further analyses including cytogenomics, taxonomic, evolutionary, and genomic studies. Despite its importance, miscounting is common, especially in early publications on species with small and morphologically similar chromosomes. Vellozia Vand. is a genus mainly distributed throughout South America belonging to the angiosperm family Velloziaceae, a dominant taxon in the Brazilian “campos rupestres”. Cytogenetic studies within the group have been rare and have shown conflicting chromosome counts, even within the same species. These discrepancies are associated with the presence of a few small chromosome-like structures, which were previously classified as possible satellites. Here, to accurately determine the chromosome number of species belonging to the genus, we used different cytogenomics approaches, including the immunostaining of the KNL1 kinetochore protein combined with chromosome spread preparation using tissue culture-derived samples. Our results revealed 2 n = 18 chromosomes for all six species studied. This finding suggests that the basic chromosome number for Vellozia is x = 9 and not x = 8, as previously proposed. The immunolocalization of functional centromeres was fundamental for undoubtedly identifying the smaller chromosome pair as real chromosomes and accurately determining the correct chromosome number of these species. This will provide substantial support for further studies, including investigations into karyotype evolution and the generation of reference genomes for the species of the family.
Why it matches plant phenotyping methods機能的セントロメアの免疫局在を用いて、従来誤同定されていた染色体を検証し、染色体数という植物形質を正確に測定する手法が研究の中心である。
abstractHere, to accurately determine the chromosome number of species belonging to the genus, we used different cytogenomics approaches, including the immunostaining of the KNL1 kinetochore protein combined with chromosome spread preparation using tissue culture-derived samples.
Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope. To investigate how differential growth of connected cell layers generate unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal (or plant tissues in general) is practically challenging. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals, and subsequent image processing. For live imaging early-stage sepals, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z- resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a 'voxel removal' technique to visualize the inner epidermal layer in MorphoGraphX image processing software. We also describe the MorphoGraphX parameters for creating a 2.5D mesh surface for the inner epidermis. Our parameters allow for the segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. While we have used sepals to illustrate our approach, the methodology will be useful for researchers intending to live-image and track growth of deeper cell layers in 2.5D for any plant tissue.
Why it matches plant phenotyping methods植物組織の深部をライブイメージングし、画像処理・細胞セグメンテーション・追跡によって成長を解析する方法自体が中心的に開発・最適化されているため。
abstractwe provide an optimized methodology for live imaging sepals, and subsequent image processing.
Reproduction assets foundThe paper's Data availability statement deposits the study's datasets (live-imaging/phenotyping data underlying the sepal growth analysis) in two public OSF repositories with explicit DOIs, making them paper-specific, public, and actionable.Dataset · publicg and Michelle Heeney for their comments on the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, WritinOpen asset ↗OSF · 10.17605/OSF.IO/UMW9Blines:234-260Dataset · publicon the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Writing – review & editing.
Conflict of intereOpen asset ↗OSF · 10.17605/OSF.IO/P5Q39lines:234-260Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Stomata are vital for CO2 and water vapor exchange, with guard cells’ aperture and ultrastructure highly responsive to environmental cues. However, traditional methods for studying guard cell ultrastructure, which rely on chemical fixation and embedding, often distort cell morphology and compromise membrane integrity, leaving no suitable methodology until now. In contrast, plunge-freezing in liquid ethane rapidly preserves cells in a near-native vitreous state for cryogenic electron microscopy. Using this approach, we applied Cryo-Focused Ion Beam-Scanning Electron Microscopy (cryo- FIB-SEM) to study the guard cell ultrastructure of Vicia faba , a higher plant model chosen for its sensitivity to external factors and ease of epidermis isolation, advancing beyond previous cryo-FIB-SEM applications in lower plant algae. The results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state. 3D models of organelles such as stromules, chloroplast protrusions, chloroplasts, starch granules, mitochondria, and vacuoles were reconstructed from cryo-FIB-SEM volumetric data, with their surface area and volume initially determined using manual segmentation. Future studies using this near-native volume imaging technique hold promise for investigating how environmental factors like drought or salinity influence stomatal behavior and the morphology of guard cells and their organelles.
Why it matches plant phenotyping methods高等植物の細胞・オルガネラ形態を取得するcryo-FIB-SEM 3Dイメージング手法を導入し、体積データから表面積・体積を定量化しており、表現型取得法が研究の中心です。
abstractThe results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state.
Chloroplasts can change their intracellular position responding to environmental conditions. In addition to the well-known photorelocation movements, i.e., accumulation and avoidance movements in response to light, mesophyll chloroplasts in C₄ plants change their intracellular positioning from the cell periphery to the bundle sheath cell side (aggregative movement) in response to environmental stresses such as drought, salinity, and hyperosmosis, under light. Previous studies on the aggregative movement have been limited in two-dimensional (2D) information on the transverse sections of leaves, because aggregated mesophyll chloroplasts gather inside the leaf tissues, which need to be cut for observation. However, the 2D analysis on the cross sections is difficult to investigate accurately the aggregated chloroplasts overlapped each other in the depth direction. Therefore, there are few studies examining the anatomical features of the aggregated chloroplasts at the cellular level. Here in this study, we established the workflow for three-dimensional (3D) observation using a confocal laser scanning microscope (CLSM), which can investigate a thick section as a stack of optical sections, followed by 3D reconstruction of mesophyll cells and chloroplasts. Using this method, we visualized the 3D representations of mesophyll cells of green foxtail (Setaria viridis), which is a model of C₄ plant, and investigated the chloroplasts individually and quantified their structures or intracellular positions before and after the aggregative movement. The 3D data of individual chloroplasts in a whole cell revealed that the aggregated positioning is independent with chloroplast volume or surface area, and that chloroplasts did not change their shape before and after the movement.
Why it matches plant phenotyping methodsCLSMによる厚切片の3D観察、再構成、個々の葉緑体の位置・構造定量ワークフローを確立しており、植物表現型の取得法が研究の中心です。
abstractwe established the workflow for three-dimensional (3D) observation using a confocal laser scanning microscope (CLSM), which can investigate a thick section as a stack of optical sections, followed by 3D reconstruction of mesophyll cells and chloroplasts.
Macroautophagy is often quantified by live imaging of autophagosomes labeled with fluorescently tagged ATG8 protein (FP-ATG8) in Arabidopsis thaliana. The labeled particles are then counted in single focal planes. This approach may lead to inaccurate results as the actual 3D distribution of autophagosomes is not taken into account and appropriate sampling in the Z-direction is not performed. To overcome this issue, we developed a workflow consisting of immunolabeling of autophagosomes with an anti-ATG8 antibody followed by stereological image analysis using the optical disector and the Cavalieri principle. Our protocol specifically recognized autophagosomes in epidermal cells of Arabidopsis root. Since the anti-ATG8 antibody recognizes multiple AtATG8 isoforms, we were able to detect a higher number of immunolabeled autophagosomes than with the FP-AtATG8e marker, that most probably does not recognize all autophagosomes in a cell. The number of autophagosomes per tissue volume positively correlated with the intensity of autophagy induction. Compared with the quantification of autophagosomes in maximum intensity projections, stereological methods were able to detect the autophagosomes present in a given volume with higher accuracy. Our novel workflow provides a powerful toolkit for unbiased and reproducible quantification of autophagosomes and offers a convenient alternative to the standard of live imaging with FP-ATG8 markers.
Why it matches plant phenotyping methods植物根におけるオートファゴソーム数を3D画像解析・立体計測で定量する新規ワークフローを開発し、既存法と精度・再現性を比較しており、表現型取得法が研究の中心である。
abstractwe developed a workflow consisting of immunolabeling of autophagosomes with an anti-ATG8 antibody followed by stereological image analysis using the optical disector and the Cavalieri principle.
Root anatomy plays a crucial role in regulating essential processes such as the absorption and movement of water and nutrients in plants. Root anatomy also impacts the energy costs of building and sustaining root tissues, tissue mechanics, and interactions with other organisms. Although several studies in maize have confirmed the functional utility of numerous root anatomical traits, such as that of cortical cell size and number for stress adaptation, there have been significant obstacles in measuring and analyzing root anatomical characteristics. This has resulted in gaps in our understanding of the genetic control and range of phenotypic variations among different cultivars, and how this diversity relates to overall fitness. Here, we review root anatomical phenotypes in maize and their function in stress adaptation, and briefly discuss phenotyping methods available for root anatomy. We further introduce a simple and accessible phenotyping approach that enables a comprehensive investigation of maize root anatomy. Detailed characterization of root traits and the implementation of robust methods for root anatomical phenotyping could have wide-ranging benefits across various areas of plant science, from fundamental research to enhancing crop breeding efforts.
Why it matches plant phenotyping methodsトウモロコシ根の解剖学的形質を対象に、既存手法のレビューと新しい根解剖フェノタイピング手法の導入を行っており、方法が中心的です。
abstractHere, we review root anatomical phenotypes in maize and their function in stress adaptation, and briefly discuss phenotyping methods available for root anatomy.
Plant mitochondria are in continuous motion. While providing ATP to other cellular processes, they also constantly consume ATP to move rapidly within the cell. This movement is in part related to taking up, converting and delivering metabolites and energy to and from different parts of the cell. Plant mitochondria have varying amounts of DNA even within a single cell, from none to the full mitochondrial genome. Because mitochondrial dynamics are altered in an Arabidopsis mutant with disrupted DNA maintenance, we hypothesised that exchanging DNA templates for repair is one of the functions of their movement and interactions. Here, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana . In addition to staining mitochondrial DNA with SYBR Green, we have developed and implemented a fluorescent mitochondrial DNA binding protein that will also enable future understanding of mitochondrial dynamics, genome maintenance and replication. We demonstrate that mitochondria without mtDNA have altered physical behaviour and have a lower immediate connectivity to the rest of the population, further supporting a link between the physical and genetic dynamics of these complex organelles.
Why it matches plant phenotyping methods植物ミトコンドリアのDNA可視化と位置追跡を中心に、蛍光DNA結合タンパク質を開発・実装し、ミトコンドリアの挙動と接続性という細胞内植物状態を定量化しているため。
abstractHere, we image mitochondrial DNA by two distinct methods while tracking mitochondrial position to investigate differences in the behaviour of mitochondria with and without DNA in Arabidopsis thaliana .
Resolving complex three-dimensional (3D) subcellular dynamics noninvasively in live tissues demands imaging tools that balance spatiotemporal resolution, field-of-view and phototoxicity. Image scanning microscopy (ISM), as an advancement of confocal laser scanning microscopy, provides a 2-fold 3D resolution enhancement. Nevertheless, the relatively low imaging speed has been the major obstacle for ISM to be further employed in in vivo imaging of biological tissues. Our proposed solution, multi-confocal image scanning microscopy (MC-ISM), aims to overcome the limitations of existing techniques in terms of spatiotemporal resolution balancing by optimizing pinhole diameter and pitch, eliminating out-of-focus signals, and introducing a frame reduction reconstruction algorithm. The imaging speed is increased by 16 times compared with multifocal structured illumination microscopy. We further propose a single-galvo scan, akin to the Archimedes spiral in spinning disk confocal systems, to ensure a high-speed and high-accuracy scan without the galvanometer's inertial motion. Benefitting from its high photon efficiency, MC-ISM allows continuous imaging of mitochondria dynamics in live cells for 1000 frames without apparent phototoxicity, reaching an imaging depth of 175 μm. Noteworthy, MC-ISM enables the observation of the inner membrane structure of living mitochondria in Arabidopsis hypocotyl for the first time, demonstrating its outstanding performance.
Why it matches plant phenotyping methods生体組織の高速度・高分解能イメージング手法を開発し、Arabidopsisの生細胞ミトコンドリア動態・膜構造の観察に適用しており、植物状態の取得法が中心である。
abstractOur proposed solution, multi-confocal image scanning microscopy (MC-ISM), aims to overcome the limitations of existing techniques in terms of spatiotemporal resolution balancing
Compared to animal cells, phenotypic characterization of single plant cells on microfluidic platforms is still rare. In this work, we collated population statistics on the morphological, biochemical, physical and electrical properties of Arabidopsis protoplasts under different external and internal conditions, using progressively improved microfluidic platforms. First, we analyzed the different effects of three phytohormones (auxin, cytokinin and gibberellin) on the primary cell wall (PCW) regeneration process using a microfluidic flow cytometry platform equipped with a single-channel fluorescence sensor. Second, we correlated the intracellular reactive oxygen species (ROS) level induced by heavy metal stress with the concurrent PCW regeneration process by using a dual-channel fluorescence sensor. Third, by integrating contraction channels, we were able to effectively discriminate variations in cell size while monitoring the intensity of intracellular ROS signaling. Fourth, by combining an electrical impedance electrode with the contraction channel, we analyzed the differences in electrical and mechanical properties of wild-type and mutant plant cells before and after primary cell wall regeneration. Overall, our work demonstrates the feasibility and sensitivity of microfluidic flow cytometry in high-throughput phenotyping of plant cells and provides a reference for assessing metabolic and physiological indicators of individual plant cells in multiple dimensions.
Why it matches plant phenotyping methodsマイクロ流体フローサイトメトリーを用いて植物細胞の形態・生化学・物理・電気的形質を多次元かつ高スループットに取得するプラットフォーム研究であり、表現型取得法が中心です。
ABSTRACT The economic value of cotton is based on its long, thin, strong, and twisted trichoblasts that emerge from the ovule epidermis. The mature dried fiber cell reflects the outcome of a rapid tapering of the nascent trichoblast, weeks of polarized diffuse growth, followed by a transition to persistent secondary cell wall synthesis. Highly conserved and dynamic microtubule and cellulose microfibril-based anisotropic growth control modules are central to all of these phases. In this paper, we developed novel quantitative phenotyping and computational modeling pipelines to analyze fiber growth behaviors at a daily resolution. We uncovered unexpected variability in growth rate, cell wall properties, and cell geometry across a critical window of fiber development. Finite element computational modeling of fiber growth was used to analyze the instability of cell diameter control and predict how spatial gradients of fiber and matrix material properties can interact to dictate the patterns of shape change. As an initial step toward gaining insight into the molecular orchestration of cellulose biosynthesis, expression profiles of a broad set of relevant genes were quantified across the same developmental timeline and correlated with fiber phenotypes. This analysis identified specific candidate genes that may serve as targets for fiber quality improvement.
Why it matches plant phenotyping methods綿花繊維の成長挙動を日次で定量化するフェノタイピングおよび計算モデリングのパイプライン開発が中心である。
abstractwe developed novel quantitative phenotyping and computational modeling pipelines to analyze fiber growth behaviors at a daily resolution.
Gibberellins (GAs) are a class of phytohormones, important for plant growth, and very difficult to distinguish because of their similarity in chemical structures. Herein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA 3 and GA 4 , triggering near-infrared (NIR) fluorescence intensity changes. Using a new coupled Raman/NIR fluorimeter that enables self-referencing of nanosensor NIR fluorescence with its Raman G-band, we demonstrated detection of cellular GA in Arabidopsis, lettuce, and basil roots. The nanosensors reported increased endogenous GA levels in transgenic Arabidopsis mutants that overexpress GA and in emerging lateral roots. Our approach allows rapid spatiotemporal detection of GA across species. The reversible sensor captured the decreasing GA levels in salt-treated lettuce roots, which correlated remarkably with fresh weight changes. This work demonstrates the potential for nanosensors to solve longstanding problems in plant biotechnology.
Why it matches plant phenotyping methods植物ホルモン濃度という植物の生理状態を、開発したナノセンサーとNIR測定系で検出する手法が研究の中心であり、植物内での適用実証も行っている。
abstractHerein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA 3 and GA 4 , triggering near-infrared (NIR) fluorescence intensity changes.
Intercellular communication is essential for plant development and responses to biotic and abiotic stress. A key pathway is diffusive exchange of signal molecules and nutrients via plasmodesmata. These cell wall channels connect the cytoplasms of most cells in land plants. Their small size, with a typical diameter of about 50 nm, and complex structure have hindered the quantification plasmodesmata-mediated intercellular diffusion. This measure is essential for disentangling the contributions of diffusive and membrane transporter-mediated movement of molecules that, together, define cell interactions within and across tissues. We compared the two most promising methods to measure plasmodesmata-mediated interface permeability, live-cell microscopy with fluorescent tracer molecules and transmission electron microscopy-based mathematical modeling, to evaluate the potential for obtaining absolute quantitative values. We applied both methods to 29 cell-cell interfaces from nine angiosperm species and found a stronger association between the modelled and experimentally determined interface permeabilities than between the experimentally-determined permeability and any single structural parameter. By feeding the values into a simulation of an artificial Arabidopsis leaf, we illustrate how interface permeabilities can help to predict diffusion patterns of defense-related molecules, such as glucosinolates and transcription factors.
Why it matches plant phenotyping methods植物細胞間の原形質連絡を介した界面透過性を測定する2手法を比較・評価しており、植物の生理状態を定量する方法の技術的検証が中心である。
abstractWe compared the two most promising methods to measure plasmodesmata-mediated interface permeability, live-cell microscopy with fluorescent tracer molecules and transmission electron microscopy-based mathematical modeling, to evaluate the potential for obtaining absolute quantitative values.
Plant cell walls are composed of polysaccharides among which cellulose is the most abundant component. Cellulose is processively synthesized as bundles of linear β-1,4-glucan homopolymer chains via the coordinated action of multiple enzymes in cellulose synthase complexes (CSCs) embedded within the plasma cell membrane. Plant cell walls are composed of multiple layers of cellulose fibrils that form highly intertwined extracellular matrix networks. However, it is not yet clear as to how cellulose fibrils synthesized by multiple CSCs are assembled into the intricate cellulose network deposited on plant cell surfaces. Herein, we have established an in vivo time-resolved imaging platform for visualizing cellulose during its biosynthesis and assembly into a complex fibrillar network on the surface of Arabidopsis thaliana mesophyll protoplasts as the primary cell wall regenerates. We performed total internal reflection fluorescence microscopy (TIRFM) with fluorophore-conjugated tandem carbohydrate binding modules (tdCBMs) that were engineered to specifically bind to nascent cellulose fibrils. Together with a well-controlled environment, it was possible to monitor in vivo cellulose fibril synthesis dynamics in a time-resolved manner for nearly one day of continuous cell wall regeneration on protoplast cell surfaces. Our observations provide the basis for a novel model of cellulose fibril network development in protoplasts driven by complex interplay of multi-scale dynamics that include: rapid diffusion and coalescence of short nascently synthesized cellulose fibrils; processive elongation of single fibrils; and cellulose fibrillar network rearrangement during cell wall maturation. This platform is valuable for exploring mechanistic aspects of cell wall synthesis while visualizing cellulose microfibrils assembly.
Why it matches plant phenotyping methods生細胞上のセルロース微 fibril の形成・ネットワーク構築を時系列で可視化するイメージング基盤を確立しており、植物状態の取得手法が研究の中心である。
abstractwe have established an in vivo time-resolved imaging platform for visualizing cellulose during its biosynthesis and assembly into a complex fibrillar network
Redox regulations and antioxidant defence play a central role in the acclimation of plants to their environment. Glutathione represents an essential component of the cellular antioxidant defence system, which keeps levels of reactive oxygen species (ROS) under control. High-performance liquid chromatography (HPLC) separation with fluorescence detection is a sensitive method that enables analysis of reduced and oxidised glutathione levels in small samples of plant tissues or plant cell culture. We aimed to optimise the method to obtain more accurate information about the total level of glutathione and the proportion of the reduced form (GSH) by choosing the most suitable reduction reagent and the conditions under which the reduction occurs. The applicability of the developed method was verified by analysing tobacco cells treated with hydrogen peroxide, which caused a decrease in the GSH/total glutathione ratio. Significant changes in the level of glutathione as well as in the GSH/total glutathione ratio were also observed during tobacco cell culture development.
Why it matches plant phenotyping methods植物細胞の酸化ストレス状態を推定するグルタチオンHPLC測定法の条件最適化と適用検証が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
titleThe optimised method of HPLC analysis of glutathione allows to determine the degree of oxidative stress in plant cell culture.
O_LISynthetic biology has made progress in creating artificial microbial and algal communities, but technical and evolutionary complexities still pose significant challenges. C_LIO_LITraditional methods for studying microbial and algal communities, such as microscopy and pigment analysis, are limited in throughput and resolution. In contrast, advancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints, offering more precise and comprehensive analysis than conventional flow cytometry. C_LIO_LIThis study demonstrates the use of full-spectrum cytometry for analyzing synthetic algal-microbial communities, facilitating rapid species identification and enumeration. The workflow involves recording individual spectral signatures from monocultures, utilizing autofluorescence to distinguish them from noise, and subsequent creation of a spectral library for further analysis. The obtained library is used then to analyze mixtures of unicellular cyanobacteria and synthetic phytoplankton communities, revealing differences in spectral signatures. The synthetic consortium experiment monitored algal growth, comparing results from different instruments and highlighting the advantages of the spectral virtual filter system for precise population separation and abundance tracking. This approach demonstrated higher flexibility and accuracy in analyzing multi-component algal-microbial assemblages and tracking temporal changes in community composition. C_LIO_LIBy capturing the complete emission spectrum of each cell, this method enhances the understanding of algal-microbial community dynamics and responses to environmental stressors. With development of standardized spectral libraries, our work demonstrates an improved characterization of algal communities, advancing research in synthetic biology and phytoplankton ecology. C_LI
Why it matches plant phenotyping methods藻類の個体スペクトル計測とスペクトルライブラリを用いて、群集の構成・個体数・増殖を高スループットに測定する技術が研究の中心であり、植物状態の取得法として実質的です。
abstractadvancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints
Abstract Sandwich freezing is a method of rapid freezing by sandwiching specimens between two copper disks, and it has been used for observing exquisite close-to-native ultrastructure of living yeast and bacteria. Recently, this method has been found to be useful for preserving cell images of glutaraldehyde-fixed cultured cells, as well as animal and human tissues. In the present study, this method was applied to observe the fine structure of living Arabidopsis plant tissues and was found to achieve excellent ultrastructural preservation of cells and tissues. This is the first report of applying the sandwich freezing method to observe plant tissues.
Why it matches plant phenotyping methods植物組織の細胞・組織微細構造を保存・観察するためのサンドイッチ凍結法を植物へ適用した技術研究であり、表現型取得法が中心である。
abstractIn the present study, this method was applied to observe the fine structure of living Arabidopsis plant tissues and was found to achieve excellent ultrastructural preservation of cells and tissues.
In the field of photosynthesis, only a limited number of approaches of super-resolution fluorescence microscopy can be used, as the functional architecture of the thylakoid membrane in chloroplasts is probed through the natural fluorescence of chlorophyll molecules. In this work, we have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy, to obtain 2D images and 3D-reconstucted volumes of isolated chloroplasts, obtained from pea (Pisum sativum), spinach (Spinacia oleracea) and Arabidopsis thaliana. In agreement with previous studies, SPiRI images exhibit larger thylakoid grana diameters when extracted from plants under low-light regimes. The three-dimensional thylakoid architecture, revealing the complete network of the thylakoid membrane in intact, non-chemically-fixed chloroplasts can be visualized from the volume reconstructions obtained at high resolution. From such reconstructions, the stromal connections between each granum can be determined and the fluorescence intensity in the stromal lamellae compared to those of neighboring grana.
Why it matches plant phenotyping methods植物葉緑体のチラコイド膜構造を高解像度・3D画像から抽出するカスタム蛍光顕微鏡法を開発・適用しており、植物形態・細胞内構造の表現型取得が中心である。
abstractwe have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy
Plant cells are contained inside a rigid network of cell walls. Cell walls are highly dynamic structures that act both as a structural material and as a hub for a wide range of signaling processes. Despite its crucial role in all aspects of the plant life cycle, live dynamical imaging of the cell wall and its functional properties has remained challenging. Here, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls. CarboTag relies on a small molecular motif, a pyridine boronic acid, that targets its cargo to the cell wall, is non-toxic and ensures rapid tissue permeation. We designed a suite of cell wall imaging probes based on CarboTag in any desired color for multiplexing. Moreover, we created new functional reporters for live quantitative imaging of key cell wall features: network porosity, cell wall pH and the presence of reactive oxygen species. CarboTag opens the way to dynamical and quantitative mapping of cell wall responses at subcellular resolution.
Why it matches plant phenotyping methods植物細胞壁のライブ・定量イメージング用ツールを開発し、細胞壁の孔隙率、pH、活性酸素などの状態を取得する方法が研究の中心である。
abstractHere, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls.
MicroscopyCell / cellular structureMorphology / geometry measurementVisualization / data management
Ensuring global food security is pressing among challenges like population growth, climate change, soil degradation, and diminishing resources. Meeting the rising food demand while reducing agriculture's environmental impact requires innovative solutions. Nanotechnology, with its potential to revolutionize agriculture, offers novel approaches to these challenges. However, potential risks and regulatory aspects of nanoparticle (NP) utilization in agriculture must be considered to maximize their benefits for human health and the environment. Understanding NP-plant cell interactions is crucial for assessing risks of NP exposure and developing strategies to control NP uptake by treated plants. Insights into NP uptake mechanisms, distribution patterns, subcellular accumulation, and induced alterations in cellular architecture can be effectively drawn using transmission electron microscopy (TEM). TEM allows direct visualization of NPs within plant tissues/cells and their influence on organelles and subcellular structures at high resolution. Moreover, integrating TEM with stereological principles, which has not been previously utilized in NP-plant cell interaction assessments, provides a novel and quantitative framework to assess these interactions. Design-based stereology enhances TEM capability by enabling precise and unbiased quantification of three-dimensional structures from two-dimensional images. This combined approach offers comprehensive data on NP distribution, accumulation, and effects on cellular morphology, providing deeper insights into NP impact on plant physiology and health. This report highlights the efficient use of TEM, enhanced by stereology, in investigating diverse NP-plant tissue/cell interactions. This methodology facilitates detailed visualization of NPs and offers robust quantitative analysis, advancing our understanding of NP behavior in plant systems and their potential implications for agricultural sustainability.
Why it matches plant phenotyping methodsTEMと設計ベースステレオロジーを統合し、植物細胞内のナノ粒子分布・蓄積と細胞形態を定量評価する方法論が中心であるため、植物表現型計測手法として含める。
abstractintegrating TEM with stereological principles, which has not been previously utilized in NP-plant cell interaction assessments, provides a novel and quantitative framework to assess these interactions.
Dopamine, alongside norepinephrine and epinephrine, belongs to the catecholamine group, widely distributed across both plant and animal kingdoms. In mammals, these compounds serve as neurotransmitters with roles in glycogen mobilization. In plants, their synthesis is modulated in response to stress conditions aiding plant survival by emitting these chemicals, especially dopamine that relieves their resilience against stress caused by both abiotic and biotic factors. In present studies, there is a lack of robust methods to monitor the operations of dopamine under stress conditions or any adverse situations across the plant's developmental stages from cell to cell. In our study, we have introduced a groundbreaking approach to track dopamine generation and activity in various metabolic pathways by using the simple nitrogen and sulfur co-doped carbon quantum dots (N, S-CQDs). These CQDs exhibit dominant biocompatibility, negligible toxicity, and environmentally friendly characteristics using a quenching process for fluorometric dopamine detection. This innovative nanomarker can detect even small amounts of dopamine within plant cells, providing insights into plant responses to strain and anxiety. Confocal microscopy has been used to corroborate this occurrence and to provide visual proof of the process of binding dopamine with these N, S-CQDs inside the cells.
Why it matches plant phenotyping methods植物細胞内のドーパミンを検出・可視化するナノマーカーと蛍光測定法を開発しており、植物の生理状態を測定する方法が中心である。
abstractwe have introduced a groundbreaking approach to track dopamine generation and activity in various metabolic pathways by using the simple nitrogen and sulfur co-doped carbon quantum dots (N, S-CQDs).
Chlorophyll (Chl) plays a crucial role in photosynthesis, functioning as a photosensitizer. As an integral component of this process, energy absorbed by this pigment is partly emitted as red fluorescence. This signal can be readily imaged by fluorescence microscopy and provides a visualization of photosynthetic activity. However, due to limited resolution, signals cannot be assigned to specific subcellular/organellar membrane structures. By correlating fluorescence micrographs with transmission electron microscopy, researchers can identify sub-cellular compartments and membranes, enabling the monitoring of Chl distribution within thylakoid membrane substructures in cyanobacteria, algae, and higher plant single cells. Here, we describe a simple and effective protocol for correlative light-electron microscopy (CLEM) based on the autofluorescence of Chl and demonstrate its application to selected photosynthetic model organisms. Our findings illustrate the potential of this technique to identify areas of high Chl concentration and photochemical activity, such as grana regions in vascular plants, by mapping stacked thylakoids.
Why it matches plant phenotyping methods葉緑素自家蛍光と電子顕微鏡を相関させるCLEMプロトコルを開発・適用し、チラコイド構造内の葉緑素分布と光化学活性を可視化する手法が中心であるため。
abstractHere, we describe a simple and effective protocol for correlative light-electron microscopy (CLEM) based on the autofluorescence of Chl and demonstrate its application to selected photosynthetic model organisms.
SUMMARY There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing the number and size of these organelles in the in leaf is a potential route to elevate leaf-level photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice and thus various attempts are being made to increase chloroplast content in this cell type. In this study we developed and applied a deep learning tool named Chloro-Count to demonstrate that loss of OsHAP3H function in rice increases chloroplast occupancy in bundle sheath cells by 50%. Although limited to a single season, when grown in the field Oshap3H mutants exhibited increased numbers of tillers and panicles as compared to controls or gain of function mutants. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function OsHAP3H mutants and facilitated a comparison between 2D and 3D quantification methods. In wild-type rice, as the dimensions of bundle sheath cells increase, the volume of individual chloroplasts also increases. However, the larger the chloroplasts the fewer there are per bundle sheath cell. This observation revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in Oshap3H mutants. The use of Chloro-Count also revealed that 2D quantification, upon which most previous studies have relied, is compromised by the positioning of chloroplasts within the cell. Chloro-Count is therefore a valuable tool for accurate and high-throughput quantification of chloroplasts that has enabled the robust characterization of OsHAP3H effects on chloroplast biogenesis in rice. Whereas previous studies have increased chloroplast occupancy in bundle sheath cells by increasing the size of individual chloroplasts, loss of OsHAP3H function leads to an increase in chloroplast numbers.
Why it matches plant phenotyping methodsChloro-Countという深層学習ツールを開発し、葉肉細胞内の葉緑体数・占有率を高精度かつハイスループットに定量する手法が研究の中心であるため。
abstractwe developed and applied a deep learning tool named Chloro-Count
Reproduction assets foundThe paper's Chloro-Count deep learning tool (Mask R-CNN segmentation of chloroplasts and bundle sheath cells) is the authors' own analysis code, explicitly stated to be publicly available on GitHub. No public image/phenotype dataset deposit is stated; training images and Table S1 raw data are not linked to a public URLCode · publicn validated, they are mapped to
566 individual organelles/cells for volumetric analysis. An overview of the system for detecting and
567 measuring volumes of chloroplasts is presented in Figure 3A. The process for detecting and
568 measuring bundle sheaths follows an analogous workflow. The Chloro-Count code is available on
569 https://github.com/pedropgusmao/chloro-count.
570
571 Data collection and pre-processing
572 A total of 327 slices from 39 different cells were used during the training of both image segmentation
573 networks. Images from 29 cells were used for training, five for validation and five for testing. A total of
574 3,790 segments of chloroplasts were used for training, 287Open asset ↗pedropgusmao/chloro-countpdf-layout-page:16 lines:1-47Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.
Why it matches plant phenotyping methods植物プロトプラストのクロマチン状態を高スループット画像とカスタム画像解析で定量し、100以上の特徴を抽出・ベンチマークしており、表現型取得法が研究の中心である。
abstractwe used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts.
Reproduction assets foundThe paper's chromatin feature datasets (Dryad doi:10.5061/dryad.pnvx0k6wp) and images (BioStudies S-BIAD1157) are paper-specific public assets, but their repository URLs are not among the allowed URLs, so they cannot be listed. The authors' adapted entropy analysis script is publicly available on the authors' GitHub (aCode · publicinterval (a new observation from the same group will fall inside the ellipse with probability P= 0.95).
Entropy analysis
The initial script for computing Shannon Entropy is described in Dussiau et al. (2022) and is available at https://osf.io/9mcwg/ . The adapted script for computing entropy of chromatin features is provided at https://github.com/barouxlab/ChromatinEntropy . When all cells (segmented nuclei) express the same value for a given feature, this entropy of the feature will be null. The more cell-to-cell variability for a given chromatin feature, the higher value of entropy.
Plots and statistical tests
Box plots, violin plots, scatter plots, 2D contours and histograms were created Open asset ↗barouxlab/ChromatinEntropylines:121-147Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
The organization of cytoskeletal elements is pivotal for coordinating intracellular transport in eukaryotic cells. Several quantitative measures based on image analysis have been proposed to characterize morphometric features of fluorescently labeled actin networks. While helpful in detecting differences in actin organization between treatments or genotypes, the accuracy of these measures could not be rigorously assessed due to a lack of ground-truth data to which they could be compared. To overcome this limitation, we utilized coarse-grained computer simulations of actin filaments and crosslinkers to generate synthetic actin networks with varying levels of bundling. We converted the simulated networks into pseudo-fluorescence images similar to images obtained using confocal microscopy. Using both published and novel analysis procedures, we extracted a series of morphometric parameters and benchmarked them against analogous measures based on the ground-truth actin configurations. Our analysis revealed a set of parameters that reliably reports on actin network density, orientation, ordering, and bundling. Application of these morphometric parameters to root epidermal cells of Arabidopsis thaliana revealed subtle changes in network organization between wild-type and mutant cells. This work provides robust measures that can be used to quantify features of actin networks and characterize changes in actin organization for different experimental conditions.
Why it matches plant phenotyping methods植物細胞内アクチンネットワークの画像解析指標を開発・ベンチマークし、根表皮細胞への適用で構造形質を定量化しており、表現型取得・抽出手法が中心である。
abstractUsing both published and novel analysis procedures, we extracted a series of morphometric parameters and benchmarked them against analogous measures based on the ground-truth actin configurations.
Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cells' mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. In this study, we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the surface stress (ΔS) generated and viscoelasticity (storage modulus G' and loss modulus G″) of protoplasts and suspension cells of rice under a drought stress of 5-25% PEG6000. The findings demonstrate that rice suspension cells and protoplasts react mechanically differently to 5-15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress; they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.
Why it matches plant phenotyping methodsDRPCによる植物細胞の力学特性を動的・非侵襲的に測定する手法が研究の中心であり、ストレス応答という植物状態を定量化している。
abstractthe lack of a workable method for consistently tracking plant cells' mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress.
Heavy metals, including Hg 2+ , Cr 6+ and Cd 2+ , have always been a major issue in environmental pollution, leading to abnormal changes in the levels of biologically active molecules including Cys in plants, seriously affecting all aspects of the growth and development of plants. This makes it essential to develop a simple and practical method to study the potential impact of heavy metals on plants. In this paper, our research group has developed near-infrared fluorescent probe WRM-S, which has the advantages of fast response, sensitivity to Cys, and successfully applying it to cells and zebrafish. Moreover, it combined the close relationship between heavy metal stress on plants and Cys, using Cys as the detection target, monitoring the internal environment changes of two plants under Hg 2+ , Cr 6+ , and Cd 2+ stress in the environment, and then conducting 3D imaging. The results indicated that the probe has strong penetration ability in plant tissues, and revealed abnormal changes in plant Cys levels caused by heavy metal stress-induced cellular oxidative stress or cytotoxicity. Thus, the in-situ imaging detection of this probe provides a direction for the physiological dynamics research of plant environmental stress.
Why it matches plant phenotyping methods植物組織内のCys変動を可視化・定量する近赤外蛍光プローブを開発し、重金属ストレス下の植物生理状態を3Dイメージングする方法が研究の中心である。
abstractour research group has developed near-infrared fluorescent probe WRM-S
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Arabidopsis root is a classic model system in plant cell and molecular biology. The sensitivity of plant roots to local environmental perturbation challenges data reproducibility and incentivizes further optimization of imaging and phenotyping tools. Here we present RoPod, an easy-to-use toolkit for low-stress live time-lapse imaging of Arabidopsis roots. RoPod comprises a dedicated protocol for plant cultivation and a customizable 3D-printed vessel with integrated microscopy-grade glass that serves simultaneously as a growth and imaging chamber. RoPod reduces impact of sample handling, preserves live samples for prolonged imaging sessions, and facilitates application of treatments during image acquisition. We describe a protocol for RoPods fabrication and provide illustrative application pipelines for monitoring root hair growth and autophagic activity. Furthermore, we showcase how the use of RoPods advanced our understanding of plant autophagy, a major catabolic pathway and a key player in plant fitness. Specifically, we obtained fine time resolution for autophagy response to commonly used chemical modulators of the pathway and revealed previously overlooked cell type-specific changes in the autophagy response. These results will aid a deeper understanding of the physiological role of autophagy and provide valuable guidelines for choosing sampling time during end-point assays currently employed in plant autophagy research.
Why it matches plant phenotyping methodsRoPodは植物根の非侵襲的ライブタイムラプス撮像と表現型解析のための専用ツールキットであり、撮像容器・栽培プロトコル・応用ワークフローの開発が研究の中心です。
abstractHere we present RoPod, an easy-to-use toolkit for low-stress live time-lapse imaging of Arabidopsis roots.
Plant cells are uniquely characterized by exhibiting cell walls, pigments, and phenolic compounds, which can impede microscopic observations by absorbing and scattering light. The concept of clearing was first proposed in the late nineteenth century to address this issue, aiming to render plant specimens transparent using chloral hydrate. Clearing techniques involve chemical procedures that render biological specimens transparent, enabling deep imaging without physical sectioning. Drawing inspiration from clearing techniques for animal specimens, various protocols have been adapted for plant research. These procedures include (i) hydrophobic methods (e.g., Visikol™), (ii) hydrophilic methods (ScaleP and ClearSee), and (iii) hydrogel-based methods (PEA-CLARITY). Initially, clearing techniques for plants were mainly utilized for deep imaging of seeds and leaves of herbaceous plants such as Arabidopsis thaliana and rice. Utilizing cell wall-specific fluorescent dyes for plants and fungi, researchers have documented the post-penetration behavior of plant pathogenic fungi within hosts. State-of-the-art plant clearing techniques, coupled with microbe-specific labeling and high-throughput imaging methods, offer the potential to advance the in planta characterization of plant microbiomes.
Why it matches plant phenotyping methods植物試料を透明化して深部画像を取得する技術を体系的に扱うレビューであり、植物の形態や植物—微生物相互作用の観察に用いる画像取得法が中心です。
abstractClearing techniques involve chemical procedures that render biological specimens transparent, enabling deep imaging without physical sectioning.
Plant bioengineering is a time-consuming and labor-intensive process, with no guarantee of achieving the desired trait. Here we report a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB). FAST-PB achieves gene cloning, genome editing, and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrate that FAST-PB can streamline the Golden Gate cloning process, with the capacity to construct 96 vectors in parallel. To prove the concept, using FAST-PB, we first found that PEG2050 significantly increases transfection efficiency by over 45%. To validate the pipeline, we established a reporter-gene-free method for CRISPR editing via mutation of HCF136 , affecting cellular chlorophyll fluorescence. Next, we applied this pipeline for lipid production and found that diverse lipids were significantly enhanced up to sixfold through introducing multi-gene cassettes via CRISPR activation, and regenerated plant using this platform. Lastly, we harnessed FAST-PB to achieve high-throughput single-cell lipid profiling through the integration of MALDI-MS with the biofoundry, and differentiated engineered and unengineered cells using the single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering, and change what is possibly using single-cell metabolomics in plants.
Why it matches plant phenotyping methods植物細胞の高スループット表現型取得を組み込んだ自動化プラットフォームの開発であり、クロロフィル蛍光と単一細胞脂質プロファイリングによる評価が技術的中心の一部となっている。
titleEnhancing lipid production in plant cells through high-throughput genome editing and phenotyping via a scalable automated pipeline
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.
Why it matches plant phenotyping methods植物細胞画像から細胞骨格密度を定量化する深層学習セグメンテーション法を開発・評価しており、植物状態の表現型抽出が研究の中心である。
abstractwe examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images
Tissue morphogenesis remains poorly understood. In plants, a central problem is how the 3D cellular architecture of a developing organ contributes to its final shape. We address this question through a comparative analysis of ovule morphogenesis, taking advantage of the diversity in ovule shape across angiosperms. Here, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana. We introduce nerve-based topological analysis as a tool for unbiased detection of differences in cellular architectures and corroborate identified topological differences between two homologous tissues by comparative morphometrics and visual inspection. We find that differences in topology, cell volume variation and tissue growth patterns in the sheet-like integuments and the bulbous chalaza are associated with differences in ovule curvature. In contrast, the radialized conical ovule primordia and nucelli exhibit similar shapes, despite differences in internal cellular topology and tissue growth patterns. Our results support the notion that the structural organization of a tissue is associated with its susceptibility to shape changes during evolutionary shifts in 3D cellular architecture.
Why it matches plant phenotyping methods3Dデジタルアトラスと神経ベースのトポロジー解析、形態計測を用いて植物器官の細胞構造と形状を定量化しており、表現型取得・解析手法が研究の中心である。
abstractHere, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana.
Reproduction assets foundThe paper's topological analysis and statistical evaluation code is publicly available on GitHub (NADO repository), with explicit availability language. The paper-specific 3D digital ovule dataset (S-BIAD957) is deposited in BioStudies, but no matching allowed URL exists for it, so it cannot be listed as an actionable,Code · publicThe source code and the Dockerfiles can be obtained from the Github repository at https://github.com/fabian-roll/NADO .Open asset ↗https://github.com/fabian-roll/NADO · NADOlines:109-124Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 7 Sept 2026
Cell / cellular structureLeafObject detectionPhysiological trait estimationGrowth / time-series analysisVisualization / data managementGrowth / development / phenology
Auxins, particularly indole-3-acetic acid (IAA), is a phytohormone critical for plant growth, development, and response to environmental stimuli. Despite its importance, there is a lack of species-independent sensors that allow direct and reversible detection of IAA. Herein, we introduce a novel near infrared fluorescent nanosensor for spatial and temporal measurement of IAA in planta using Corona Phase Molecular Recognition. The IAA nanosensor shows high specificity to IAA in vitro and was validated to localize and function in plant cells. The sensor works across different plant species without optimization and allows visualization of dynamic changes to IAA distribution and movement in leaf tissues. The results highlighted the utility of IAA nanosensor for understanding IAA dynamics in planta .
Why it matches plant phenotyping methods植物体内のIAAを空間・時間的に可視化する蛍光ナノセンサーを開発し、植物細胞で検証しており、植物生理状態の取得法が研究の中心です。
abstractwe introduce a novel near infrared fluorescent nanosensor for spatial and temporal measurement of IAA in planta using Corona Phase Molecular Recognition.
Abstract The study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting. In this approach, sub-nanosecond laser pulses with a repetition rate of 10 MHz are applied to excite the sample, followed by the analysis of arrival times of the emitted fluorescence photons. Photon statistics are generated by iteratively fitting the sum of two exponential functions. The tool was tested on both plastid and in vivo leaf samples of Savoy cabbage ( Brassica oleracea var. sabauda) with 3–4 subsequent leaves giving a complete sample coverage starting from the outermost. The Chl-a fluorescence lifetime exhibited a gradual increase in both the isolated plastid suspensions and the in vivo leaf samples towards the innermost leaf layers explained by an increase of natural absence of light (etiolation syndrome). Furthermore, cadmium stress and iron deficiency were investigated on treated sugar beet ( Beta vulgaris ) samples in vivo using TCSPS measurements. The reduced fluorescence quenching resulted in an increased fluorescence lifetime. Finally, a long-term (10 week) testing of the setup was carried out on Chl-retaining resurrection Haberlea rhodopensis plants protecting themselves by an elevated non-photochemical quenching yielding a decrease of fluorescence lifetime during their desiccation.
Why it matches plant phenotyping methods植物の生体クロロフィル蛍光寿命を測定する装置を開発し、複数の植物試料・ストレス条件・長期試験で検証しており、植物生理状態の取得法が研究の中心である。
abstractThe study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting.
Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.
Why it matches plant phenotyping methodsTradescantiaの雄しべ毛細胞の色を画像から自動分類・計数するコンピュータビジョン手法とデータセットを開発・評価しており、植物ストレス応答という細胞状態の取得が中心である。
abstractWe have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells
Reproduction assets foundThe paper's Trad-204 dataset of Tradescantia clone 4430 stamen hair cell images and the associated analysis are explicitly stated to be publicly available in the authors' GitHub repository.Dataset · publicThe datasets generated and analyzed for this study can be found in the GitHub repository: https://github.com/emiliomercuri/Trad-204 .Open asset ↗emiliomercuri/Trad-204 · Trad-204lines:382-419Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
It was previously reported that stress induces a cellular production of abscisic acid in plants, but no direct method shows the evidence. Here, an electrochemical microsensor involving an abscisic acid receptor PYL2 modified carbon fiber microelectrode was fabricated by self-assembly method, where the Cu 2+ combined with the histidine tag of PYL2 on the surface of microelectrode was used as the detection probe, the mediated reaction between Cu + and ferricyanide realized the amplification responses and provided the microsensor with a high sensitivity for detection of abscisic acid with a detection limit of 0.8 nM. With use of this microsensor, an increase of extracellular abscisic acid from single rice protoplast induced by sulfate, osmotic and salinity stress was real-time monitored. Direct measurement of free extracellular abscisic acid in single plant cells might offer important new insights into its role in plants challenged by abiotic stresses.
Why it matches plant phenotyping methods単一植物細胞からのABA放出をリアルタイム測定する電気化学マイクロセンサーを開発し、ストレス応答という植物生理状態への適用も行っており、測定法が研究の中心である。
abstractHere, an electrochemical microsensor involving an abscisic acid receptor PYL2 modified carbon fiber microelectrode was fabricated by self-assembly method
Societal Impact Statement Parasitic plants that deprive crops of water and nutrients are an increasingly concerning food security issue, affecting the livelihood of millions of subsistence, small‐ and mid‐scale farmers. An in‐depth understanding of parasite–host interactions is required to develop species‐specific and ecologically sustainable parasite management methods. The non‐invasive visualization of herbaceous contact zones, applicable to diverse parasite–host pathosystems presented in this study, brings methodological advance to the research of biotic interactions between crops and plant parasites belonging to the most devastating parasitic plant family (Orobanchaceae). This work also provides first insights into how the parasites' feeding organ displaces host tissue beyond the direct parasite–host interface. Summary High‐resolution X‐ray computed tomography (HRXCT) enables sectioning‐free two‐dimensional imaging of biological structures and reconstruction of three‐dimensional objects. Although its application is common in many areas of biomedicine and despite its flexibility regarding resolution levels, the technology remains underutilized in the plant sciences. Here, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution. We tested various sample preparation methods and contrast stains for their efficiency to improve the imaging of haustorium samples. In doing so, we achieved cellular resolution with the visible cellular organization of haustorial structures, especially of the vascular system. Fresh stained and dehydrated sample preparation of soft haustoria enables the highest spatial resolution with fine‐cellular discrimination of haustorium versus host cells. Application of cell‐level resolved HRXCT to five pathosystems: Alectra ‐cowpea, Phelipanche ‐tomato, Phtheirospermum ‐tomato, Rhamphicarpa ‐tomato, and Striga ‐sorghum highlighted a life history‐specific organization and uncovered an as yet undescribed internal displacement of host tissue at parasite–host interfaces. Following image‐based training, our HRXCT approach could invoke AI‐based cell recognition for automated parasite cell–host cell differentiation. Superseding extensive microsectioning for 3D imaging, the newly established HRXCT protocol for 2D‐ and 3D‐visualization of herbaceous plant–plant contact zones and the first insights gained from it, is useful for mid‐throughput, comparative studies of parasitic plant–host interactions.
Why it matches plant phenotyping methodsHRXCTによる植物組織の2D・3D画像取得プロトコルを開発し、試料調製・染色を比較検証したうえで、寄生植物と宿主の接触領域を細胞レベルで可視化する方法が中心である。
abstractHere, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution.
Rapeseed / canolaMRI / PETCell / cellular structureLeafTissueClassificationPhysiological trait estimationGrowth / development / phenologyStress response / toleranceWater status / transpiration
Background Characterisation of the structure and water status of leaf tissues is essential to the understanding of leaf hydraulic functioning under optimal and stressed conditions. Magnetic Resonance Imaging is unique in its capacity to access this information in a spatially resolved, non-invasive and non-destructive way. The purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration. Results By combining transverse relaxation maps with a classification scheme, we were able to distinguish specific zones of areoles and veins. The tissue heterogeneity observed in young leaves still occurred in mature and senescent leaves, but with different distributions of T 2 values in accordance with the basipetal progression of leaf blade development, revealing changes in tissue structure. When subjected to severe water stress, all blade zones showed similar behaviours. Conclusion This study demonstrates the great potential of Magnetic Resonance Imaging in assessing information on the structure and water status of leaves. The feasibility of in planta leaf measurements was demonstrated, opening up many opportunities for the investigation of leaf structure and hydraulic functioning during development and/or in response to abiotic stresses.
Why it matches plant phenotyping methods葉の構造と水分状態を定量MRIで空間的に測定する新規手法を開発し、分類法と組み合わせて葉組織・葉脈を評価しており、植物フェノタイピング手法が中心である。
abstractThe purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration.
MicroscopyCell / cellular structureMorphology / geometry measurementVisualization / data management
The ever increasing breadth of biological knowledge has led to recent efforts to combine information from various fields into cell- or tissue atlases. Anatomical features are the structural basis for such efforts, but unfortunately large scale analysis of subcellular anatomical traits is currently a missing feature. Similarly, small phenotypic alterations of organelle- or cell-specific anatomical traits, such as an increase of the total volume or the number of mitochondria in response to certain stimuli, are currently hard to quantify. To provide tools to extract quantitative information from available 3D microscopic datasets generated with methods such as serial block face scanning electron microscopy we a) developed much improved fixation and embedding protocols for plants to drastically reduce processing artifacts and b) generated an easy-to-use AI tool for quantitative analysis and visualization of large-scale data sets. We make this tool available as open source.
Why it matches plant phenotyping methods植物の3D顕微鏡データから細胞・細胞小器官の構造形質を大規模定量するAIツールを開発しており、植物向け試料調製法も改良しているため、表現型取得・解析手法が研究の中心である。
titleAnatomics MLT, an AI tool for large scale quantification of ultrastructural traits
β-Galactosidase (β-gal), an enzyme related to cell wall degradation, plays an important role in regulating cell wall metabolism and reconstruction. However, activatable fluorescence probes for the detection and imaging of β-gal fluctuations in plants have been less exploited. Herein, we report an activatable fluorescent probe based on intramolecular charge transfer (ICT), benzothiazole coumarin-bearing β-galactoside (BC-βgal), to achieve distinct in situ imaging of β-gal in plant cells. It exhibits high sensitivity and selectivity to β-gal with a fast response (8 min). BC-βgal can be used to efficiently detect the alternations of intracellular β-gal levels in cabbage root cells with considerable imaging integrity and imaging contrast. Significantly, BC-βgal can assess β-gal activity in cabbage roots under heavy metal stress (Cd 2+ , Cu 2+ , and Pb 2+ ), revealing that β-gal activity is negatively correlated with the severity of heavy metal stress. Our work thus facilitates the study of β-gal biological mechanisms.
Why it matches plant phenotyping methods植物細胞内のβ-ガラクトシダーゼ活性を可視化・定量する蛍光プローブを開発し、重金属ストレス下の根で実証しており、植物状態の取得法が中心である。
abstractHerein, we report an activatable fluorescent probe based on intramolecular charge transfer (ICT), benzothiazole coumarin-bearing β-galactoside (BC-βgal), to achieve distinct in situ imaging of β-gal in plant cells.
Quantitative analysis of vessel characteristics at the cellular scale is of great significance for understan-ding plant adaptation strategies to environment. The direct grinding combined with stereo-microscope imaging is one of the main approaches to examine the anatomical structure of xylem (conifer tracheid and hardwood vessel) wood structure, which inevitably damages xylem cells, hindering the accurate understanding of anatomical structures. In this study, we applied X-ray micro-computed tomography (μCT) and stereo-microscope technology to quantitatively measure the diameter and area of vessels of seven Canadian broadleaved tree species ( Acer saccharum , Betula papyrifera , Fraxinus americana , Ostrya virginiana , Populus grandidentata , Quercus rubra , and Carya cordiformis ). We fitted the results by linear model and tested the feasibility of μCT technology in quantifying the vessel size of broadleaved species. We found that the results of the two methods for measuring vessel size were highly similar ( R 2 =0.98). The goodness of fit of the vessel diameter results measured by the two methods for the ring-porous wood species ( C. cordiformis , R 2 =0.98; F. americana , R 2 =0.96; Q. rubra , R 2 =0.99) was higher than that of the diffuse-porous wood species ( B. papyrifera , R 2 =0.88; O. virginiana , R 2 =0.73; A. saccharum , R 2 =0.68; P. grandiden-tata , R 2 =0.88). The goodness of fit of small vessels (diameter≤200 μm, R 2 =0.94) measured by the two methods was higher than that of large vessels (diameter>200 μm, R 2 =0.92). Thus, the μCT technique provided a new non-destructive detection method for quantifying xylem vessels of broadleaved tree species.
Why it matches plant phenotyping methodsμCTを用いた木部道管サイズ測定法をステレオ顕微鏡法と比較検証し、非破壊的な植物形質取得法として実証しているため。
abstractWe fitted the results by linear model and tested the feasibility of μCT technology in quantifying the vessel size of broadleaved species.
Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodelling, and timely deposition of polysaccharides, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions, through the behavior of yellow fluorescent protein (YFP)-tagged cytokinesis-specific GTPase RABA2a vesicles. We monitored the entire duration of cell plate development, from its first emergence, with the aid of YFP-RABA2a, in both the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.
Why it matches plant phenotyping methodsLLSMによる4次元画像取得と、細胞板の小胞体積を定量化する解析パイプラインの開発が研究の中心であり、植物細胞の形態・発達状態を抽出する方法として substantive です。
abstractUsing lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions
Reproduction assets foundThe paper deposits representative 4D lattice light sheet microscopy datasets (YFP–RABA2a cell plate imaging) used for its quantitative analysis on Zenodo, a paper-specific public asset. No author analysis code repository with explicit availability language is stated; the other URLs are method guidelines, not paper dataDataset · publicRepresentative datasets used in the study are available on Zenodo at https://doi.org/10.5281/zenodo.10515765 .Open asset ↗Zenodo · 10.5281/zenodo.10515765lines:85-93Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Cell / cellular structurePhysiological trait estimationTrackingVisualization / data management
The advent of fluorescent probes and the characterization of their photochemical properties in the past years allowed significant advances in the studies of spatiotemporal cellular processes within complex and crowded systems. Dyes are indeed extremely useful tools for the visualization of cellular and subcellular structures present in living cells, as well as to study their dynamic and molecular composition or physiological changes. There are some areas of plant cell biology that have been more challenging to explore due to the physiology and organization of certain endomembrane compartments. In this study we characterize the labeling properties of cresyl violet as quick and inexpensive imaging agent for tracking endosomes, vacuole compartments which are usually very differentiated and categorized as more acidic as well as acidic plant compartments. Its photobleaching, labelling and cytotoxic properties are compared with other well-known and currently most used synthetic and molecular probes.
Why it matches plant phenotyping methods植物細胞内区画の可視化・追跡用蛍光プローブを開発・特性評価し、既存プローブと比較しているため、植物状態の画像取得法が中心です。
abstractwe characterize the labeling properties of cresyl violet as quick and inexpensive imaging agent for tracking endosomes, vacuole compartments which are usually very differentiated and categorized as more acidic as well as acidic plant compartments.
To survive and grow, plant cells must regulate the properties of their cellular microenvironment in response to ever changing external factors. How the biomechanical balance across the cells internal structures is established and maintained during environmental variations remains a nurturing question. To provide insight into this issue we used two micro-mechanical imaging techniques, namely Brillouin light scattering and BODIPY-based molecular rotors Fluorescence Lifetime Imaging, to study Nicotiana tabacum suspension BY-2 cells long-term adapted to high concentrations of NaCl and mannitol. We discuss our results in terms of molecular crowding in cytoplasm and vacuoles, as well as tension in plasma membrane. The viscoelastic behavior was elucidated relative to cells external environments revealing the difference between the responses of cytoplasm and vacuole in the adapted cells. To understand how sudden changes in osmolarity affect cellular mechanics, the response of control and already adapted cells to further short-term osmotic stimulus was also examined. The applied correlative approach provides evidence that adaptation to hyperosmotic stress leads to different ratios of protoplast and environmental qualities that help to maintain cell integrity. Presented results demonstrate that the viscoelastic properties of protoplasts are an element of plant cells adaptation to high osmolarity.
Why it matches plant phenotyping methods植物細胞の粘弾性という生理・力学的形質を、Brillouin光散乱と蛍光寿命イメージングで測定する手法の実質的な適用が研究の中心であり、単なるルーチン測定ではない。
abstractwe used two micro-mechanical imaging techniques, namely Brillouin light scattering and BODIPY-based molecular rotors Fluorescence Lifetime Imaging, to study Nicotiana tabacum suspension BY-2 cells
Why it matches plant phenotyping methodsSBF-SEMによる植物細胞と感染構造の3D画像取得・再構築法を確立し、感染に伴う宿主細胞の形態変化を解析しており、画像ベースの植物表現型取得が中心です。
abstractOverall, we established a method for obtaining serial SBF-SEM images, each with a lateral ( x-y ) pixel resolution of 10 nm and an axial ( z ) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD.
Abstract Background Dissection of complex plant cell wall structures demands a sensitive and quantitative method. FTIR is used regularly as a screening method to identify specific linkages in cell walls. However, quantification and assigning spectral bands to particular cell wall components is still a major challenge, specifically in crop species. In this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand plant cell wall composition. This method was validated by analysing different varieties of mungbean which is one of the most important legume crop grown widely in Asia. Results Using standards and extraction of a specific component of cell wall components, we assigned 1050-1060 cm -1 and 1390-1420 cm -1 wavenumbers that can be widely used to quantify cellulose and lignin, respectively, in Arabidopsis, Populus , rice and mungbean. Also, using KBr as a diluent, we established a method which can relatively quantify the cellulose and lignin composition among different tissue types of the above species. We further used this method to quantify cellulose and lignin in field-grown mungbean genotypes. The ATR-FTIR-based study revealed the cellulose content variation ranges from 27.9% to 52.37%, and the lignin content variation ranges from 13.77% to 31.6% in mungbean genotypes. Conclusion Cell wall composition in different mungbean genotypes was determined by the developed FT-IR-based method, which was cross-validated using canonical wet-chemistry methods. Overall, our data suggested that ATR-FTIR can be used for the relative quantification of lignin and cellulose in different plant species. This method can be used for rapid screening of cell wall composition in large number of germplasms of different crops including mungbean.
Why it matches plant phenotyping methodsATR-FTIRによる植物細胞壁組成（セルロース・リグニン）の定量法を開発し、標準物質・湿式化学法で検証した研究であり、植物形質の取得手法が中心である。
abstractIn this study, we addressed these challenges using ATR-FTIR spectroscopy as it is a high throughput, cost-effective and non-destructive approach to understand plant cell wall composition.
ABSTRACT Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF fluorescence signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.
Why it matches plant phenotyping methodsTHG・3PFによる生根の構造と細胞内特徴を、ラベルフリーかつ生体内で可視化するイメージング手法が研究の中心であり、植物形態・状態の表現型取得に直接関与する。
abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution
Cell / cellular structureTrackingVisualization / data management
Long-term visualization of changes in plasma membrane dynamics during important physiological processes can provide intuitive and reliable information in a 4D mode. However, molecular tools that can visualize plasma membranes over extended periods are lacking due to the absence of effective design rules that can specifically track plasma membrane fluorescent dye molecules over time. Using plant plasma membranes as a model, we systematically investigated the effects of different alkyl chain lengths of FMR dye molecules on their performance in imaging plasma membranes. Our findings indicate that alkyl chain length can effectively regulate the permeability of dye molecules across plasma membranes. The study confirms that introducing medium-length alkyl chains improves the ability of dye molecules to target and anchor to plasma membranes, allowing for long-term imaging of plasma membranes. This provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes. Using the amphiphilic amino-styryl-pyridine fluorescent skeleton, we discovered that the inclusion of short alkyl chains facilitated rapid crossing of the plasma membrane by the dye molecules, resulting in staining of the cell nucleus and indicating improved cell permeability. Conversely, the inclusion of long alkyl chains hindered the crossing of the cell wall by the dye molecules, preventing staining of the cell membrane and demonstrating membrane impermeability to plant cells. The FMR dyes with medium-length alkyl chains rapidly crossed the cell wall, uniformly stained the cell membrane, and anchored to it for a long period without being transmembrane. This allowed for visualization and tracking of the morphological dynamics of the cell plasma membrane during water loss in a 4D mode. This suggests that the introduction of medium-length alkyl chains into amphiphilic fluorescent dyes can transform them from membrane-permeable fluorescent dyes to membrane-staining fluorescent dyes suitable for long-term imaging of the plasma membrane. In addition, we have successfully converted a membrane-impermeable fluorescent dye molecule into a membrane-staining fluorescent dye by introducing medium-length alkyl chains into the molecule. This molecular engineering of dye molecules with alkyl chains to regulate cell permeability provides a simple and effective design rule for long-term visualization of the plasma membrane, and a convenient and feasible means of chemical modification for efficient transmembrane transport of small molecule drugs.
Why it matches plant phenotyping methods植物細胞膜の長期蛍光イメージング用色素を分子設計・評価し、水分喪失時の膜形態動態を可視化する方法開発が中心である。
abstractThis provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes.
Single-molecule imaging enables the observation of individual molecules in living cells (DEste et al., 2024; Kusumi et al., 2014; Lelek et al., 2021; Nguyen et al., 2023). In plants, however, the tracking of single molecules is typically limited to a few hundred milliseconds (Bayle et al., 2021; Gronnier et al., 2017; Hosy et al., 2015), precluding the observation of dynamic cellular processes at molecular resolution. Here, we describe photochromic reversion, an imaging modality that enables long-term single-molecule tracking of genetically encoded translational fusions. Using this approach, we achieve minute-long tracking of individual cell-surface receptors and reveal previously inaccessible dynamic spatial arrest events of single plasma membrane proteins. We further developed and benchmarked computational analysis of spatial arrests (CASTA), a machine learning-based tool that automatically detects and analyses spatial, temporal, and diffusional properties of these events, thereby enabling precise nanoscale kinetic measurements. Together, these advances provide a powerful framework for deciphering the principles governing membrane dynamics and function.
Why it matches plant phenotyping methods植物細胞表面受容体の動態を長時間単一分子イメージングで取得し、空間停止イベントを解析するCASTAも開発・ベンチマークしており、植物状態の測定法が中心である。
abstractHere, we describe photochromic reversion, an imaging modality that enables long-term single-molecule tracking of genetically encoded translational fusions.
ABSTRACT Single-molecule fluorescence in situ hybridization (smFISH) has emerged as a powerful tool to study gene expression dynamics with unparalleled precision and spatial resolution in a variety of biological systems. Recent advancements have expanded its application to encompass plant studies, yet a demand persists for a simple and robust smFISH method adapted to plant tissue sections. Here, we present an optimized smFISH protocol (cryo-smFISH) for visualizing and quantifying single mRNA molecules in plant tissue cryosections. This method exhibits remarkable sensitivity, capable of detecting low-expression transcripts, including long non-coding RNAs. Integrating a deep learning-based algorithm in our image analysis pipeline, our method enables us to assign RNA abundance precisely in nuclear and cytoplasmic compartments. Compatibility with Immunofluorescence also allows RNA and endogenous proteins to be visualized and quantified simultaneously. Finally, this study presents for the first time the use of smFISH for single-cell RNA sequencing (scRNA-seq) validation in plants. By extending the smFISH method to plant cryosections, an even broader community of plant scientists will be able to exploit the multiple potentials of quantitative transcript analysis at cellular and subcellular resolutions.
Why it matches plant phenotyping methods植物組織切片向けにsmFISHプロトコルと画像解析を最適化し、細胞内RNA量を定量する方法を開発しており、植物状態の測定手法が中心である。
abstractHere, we present an optimized smFISH protocol (cryo-smFISH) for visualizing and quantifying single mRNA molecules in plant tissue cryosections.
Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.
Why it matches plant phenotyping methods植物組織の3D画像再構成・細胞インスタンス分割・形態解析を行う手法が研究の中心であり、植物組織データセットで検証されています。
abstractThis paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images.
Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.
Why it matches plant phenotyping methodsDRPCによる植物細胞の機械特性（応力・粘弾性）の動的かつ非侵襲的測定法を開発・評価しており、植物ストレス表現型の取得が中心である。
abstractwe used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress.
Development of a living organism is a highly regulated process during which biological materials undergo constant change. De novo material synthesis and changes in mechanical properties of materials are key for organ development; however, few studies have attempted to produce quantitative measurements of the mechanical properties of biological materials during growth. Such quantitative analysis is particularly challenging where the material is layered, as is the case for the plant cuticle on top of the plant epidermal cell wall. Here, we focus on Hibiscus trionum flower petals, where buckling of the cuticle forms ridges, producing an iridescent effect. This ridge formation is hypothesised to be due to mechanical instability, which directly depends upon the mechanical properties of the individual layers within the epidermal cells. We present measurements of the mechanical properties of the surface layers of petal epidermal cells through atomic force microscopy (AFM) and the uniaxial tensile tester for ultrathin films (TUTTUT), across growth stages. We found that the wavelength of the surface ridges was set at the ridge formation stage, and this wavelength was preserved during further petal development, most likely because of the plasticity of the material. Our findings suggest that temporal changes in biological material properties are key to understanding the development of biological surface patterns.
Why it matches plant phenotyping methods植物花弁表皮の機械特性をAFMとTUTTUTで定量測定する手法が研究の中心であり、成長段階に伴う植物表面特性を評価している。
abstractWe present measurements of the mechanical properties of the surface layers of petal epidermal cells through atomic force microscopy (AFM) and the uniaxial tensile tester for ultrathin films (TUTTUT), across growth stages.