Plant phenotyping methods.

Subcellular Localization of Iron in Rhizophora mangle Leaves Revealed by Integrated Perls Reaction, TEM, STEM-HAADF, and EDS Analyses. ↗

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.

MC-SlotNet: Multiplicity-Consistent Slot-Based Full-Cell Instance Segmentation for Overlapping Plant Suspension-Culture Microscopy ↗

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.

Using CarboTrace 480 to detect protoplastation in pigment deficient mutant of Chlorella sorokiniana ↗

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.

Nuclear diversity in Charophyceae revealed by fluorescence staining and its implications for genome size estimation. ↗

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

A triple fluorescent marker for live imaging of plant cell morphogenesis ↗

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.

An optimised FISH-based approach for tissue and subcellular localisation of apple scar skin viroid in cucumber. ↗

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.

TipQuant: a robust algorithm for quantitative analysis of spatiotemporally dynamic activities in tip-growing cells. ↗

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

Ultrastructural characterization of secretory canals in Peucedanum praeruptorum roots using microscopic sectioning, transmission electron microscopy, and computed tomography. ↗

Plant secondary metabolites are mainly synthesized and stored in secretory tissues. Secretory canal development has been mainly characterized in Apiaceae. The secretory canals of Peucedanum praeruptorum contain pharmacologically active coumarins, but their organ-specific distribution and developmental dynamics remain poorly understood. This study integrated light microscopy (LM), transmission electron microscopy (TEM), X-ray microcomputed tomography (µ-CT), and high-performance liquid chromatography (HPLC) to investigate canal development, distribution, ultrastructure, 3D architecture, and coumarin accumulation in P. praeruptorum roots. Histological analysis showed that canals adjacent to the periderm originate from pericycle cells, whereas those in secondary phloem arise from parenchyma differentiation; both develop schizogenously. Canal quantity and dimensions varied temporally. Canals located in phloem showed density increasing toward the cambial zone, where cross-sectional areas were smaller. The canal density index increased from September to November, peaking on November 15, then declined. HPLC revealed dynamic accumulation of five major coumarins: content increased from September, peaked on November 15, then gradually decreased. TEM showed that epithelial cells surrounding the canal lumen were rich in Golgi, ER, mitochondria, plastids, starch grains, and osmiophilic droplets. µ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm). These dimensional characteristics aligned with developmental progression. This study characterizes the ontogeny, distribution, ultrastructure, and 3D architecture of secretory canals, providing a structural foundation for investigating correlations between secretory tissues and compound synthesis.

Why it matches plant phenotyping methods根の分泌道の密度・寸法・3D構造という植物器官形質を、µ-CTの体積解析・セグメンテーションと顕微鏡法で取得・抽出しており、画像計測が研究の中心的要素である。

abstractµ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm).

Advances in Imaging of Plant Ca2+ Signaling ↗

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.

Integrated in situ structural dynamics and transcriptomic analysis reveal starch remodeling mechanisms in fresh-consumption sweetpotato cultivars. ↗

Starch, a key biological macromolecule accounting for 50-80% of dry weight in sweetpotato (Ipomoea batatas [L.] Lam.) storage roots, underpins food and industrial applications. However, sweetpotato starch characterization is limited by local-sectioning approaches that fail to capture the whole-root granule dynamics. Here, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25). It was effective and convenient for in situ starch observation and analysis in sweetpotato roots. The whole-root in situ microscopy, starch physicochemical profiling, and transcriptomic correlation were integrated to resolve starch dynamics in Y25 and V100. We identified widespread simple starch granules (SSGs)-compound starch granule (CSG) coexistence across the whole root tissues, with Y25 exhibiting programmed CSG fragmentation driven by ARCs/FtsZ-mediated amyloplast envelope destabilization and concomitant AMY/BMY upregulation. Y25 had a higher amylose content and a higher proportion of medium/long chains, but the average degree of polymerization was slightly lower. Transcriptomic analyses revealed that the differentially expressed genes were annotated in pathways of carbohydrate metabolism, and the differentially expressed genes in the starch metabolism pathway were analyzed. Weighted gene co-expression network analysis further identified the hub genes from different modules and analyzed the co-expression networks. This work will not only advance the understanding of starch granule assembly and remodeling in sweetpotato, but also provide a robust methodological and transcriptome-guided framework for starch-focused germplasm screening and quality improvement.

Why it matches plant phenotyping methodsサツマイモ根全体のデンプン粒形態・動態を観察する新しい形態観察システムを構築し、その有効性を示しており、表現型取得法が研究の中心である。

abstractHere, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25).

MIRA: an open source and user-friendly software to automate counting and sizing of fungal spores ↗

Background The quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. Results We demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10⁶ spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense , a model for Pseudocercospora fijiensis , and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. Conclusions MIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

Why it matches plant phenotyping methods植物病害に関わる胞子の画像検出・計数・サイズ測定ソフトウェアを開発し、手動計数法とのベンチマーク検証も行っている。病害フェノタイピングのための画像解析手法が中心である。

abstractwe introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms.

Multivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones. ↗

Tolerance against winter freeze is the main focus of variety development in Louisiana, which represents the northernmost sugarcane-growing region worldwide. Antifreeze metabolites, xylem structure, and fiber content represent interrelated physicochemical properties contributing to freeze tolerance. This study first classified the cold tolerance of sugarcane cultivars using metabolites in juice as predictor variables. The best-fit model (XGBoost discriminant analysis) estimated the higher cold tolerance of the final on-station clone progeny to the stress tolerance-inducing wild germplasm line. Stalks of the tolerant sugarcane genotype contained higher fiber for mechanical support against cellular injury, compared to susceptible varieties. Fluorescence microscopy visualized phospholipids responsible for maintaining membrane fluidity during frost in lignin surrounding the vascular bundle. Thermal imaging is proposed for real-time monitoring of spatiotemporal temperature changes, as stalk injury is initiated by ice formation at sub-freeze temperatures during winter freeze. As additional datasets for independent prediction become available, developed methods could be used to explore the biomarkers for stress resistance in simpler multivariate discriminant analysis and the distribution of specific biomarkers in cellular components by microscopic imaging, and to trace stalk injury hot spots as a function of time and relationships with resistance markers.

Why it matches plant phenotyping methodsサトウキビの耐寒性という植物状態を、XGBoost判別モデルと画像・熱画像によって分類・評価する方法が研究の中心であり、単なる生物学的測定ではない。

titleMultivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones.

Dual Color tau-STED Super Resolution Microscopy in Arabidopsis Root Tip. ↗

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.

Tracking Nano- and Microplastics in Plants: Uptake Pathways, Tissue Distribution, and Analytical Strategies from Microscopy to Spectroscopy. ↗

Nano- and microplastics (NMPs) are now widely detected across agroecosystems and can act as physiological stressors in plants. Exposure occurs through contaminated soil, irrigation water, or airborne deposition, bringing particles into direct contact with roots and above-ground tissues. Reported entry routes include apoplastic transport, cracks formed at lateral root emergence, leaf stomata, and endocytosis once particles have crossed the cell wall. Once internalized, particles may translocate through the xylem and, in some cases, the phloem, accumulating in roots, stems, and leaves depending on particle size, surface charge, and plant structural characteristics. NMPs have been associated with oxidative stress, disrupted photosynthesis, and altered metabolic pathways. Detecting NMPs within heterogeneous, hydrated plant tissues remains challenging, as particles often show low contrast against biological structures and can be mistaken for cellular components. This review examines how microscopy techniques reveal NMPs size, surface attachment, tissue distribution, and cellular-level interactions, while noting that these approaches primarily provide morphological or localization information rather than confirming polymer identity. Complementary spectroscopic and mass-based analytical methods are discussed for their role in chemical confirmation and quantification. This review supports informed selection among imaging, spectroscopic, and quantitative techniques for studying plant-plastic interactions, while highlighting current analytical challenges facing the field.

Why it matches plant phenotyping methods植物組織内の粒子サイズ・付着・分布・細胞相互作用を測定する顕微鏡、分光、質量分析手法を中心にレビューしており、植物状態の観測手法が主題である。

abstractThis review examines how microscopy techniques reveal NMPs size, surface attachment, tissue distribution, and cellular-level interactions

Association of morphological markers of flower buds and anthers with the pollen developmental stage in Vicia faba L. ( Fabaceae ). ↗

The precise identification of microspore and pollen at the optimal developmental stages to be induced towards embryogenesis (vacuolated microspores and young pollen) is essential for induction of in vitro androgenesis in plants. Such identification is not always easy, and it is especially difficult in recalcitrant species such as Vicia faba . The present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry. We measured flower bud and anther length and width, grouping them at different intervals, and performed a detailed microscopical and anatomical analysis of buds, anthers and microspores/pollen at different stages. Our results demonstrated that flower buds in V. faba exhibit complex and irregular morphologies, with considerable variation in both sepal length and shape. Furthermore, the determination of microspore and pollen developmental stages in this species is constrained by pronounced developmental asynchrony and strong genotype dependence. Although anther length measurements correlate closely with microspore and pollen developmental stages, their practical use can be challenging. Therefore, measuring flower bud length, while excluding sepals, remains the most practical criterion for routine applications. Combining this refined morphometric approach with microscopic validation appears to be the most effective strategy for improving the identification of flower buds containing microspores or pollen at developmental stages suitable for androgenesis induction in this recalcitrant legume species.

Why it matches plant phenotyping methods花蕾・葯の形態計測と顕微鏡検証を用いて、微小胞子・花粉の発達段階を推定する実用的な植物フェノタイピング手法を評価しており、方法開発・検証が中心である。

abstractThe present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry.

AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize ↗

Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.

Why it matches plant phenotyping methods葉の気孔・葉脈形質を自動画像解析で同時定量する高スループット表現型解析プラットフォームが研究の中心であり、形質抽出手法も具体的に記述されている。

abstractusing a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis

Protocol for cell-specific ratiometric quantification of apoplastic pH in Arabidopsis seedlings. ↗

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.

Canonical Ru(ii) tris-polypyridyl complexes as confocal-compatible stains for lignified secondary-wall domains in Arabidopsis thaliana stems. ↗

Fluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing. Here, we evaluated two canonical Ru(ii) tris-polypyridyl luminophores, Ru1 ([Ru(deeb) 3 ] 2+ ) and Ru2 ([Ru(phen) 3 ] 2+ ), as non-derivatizing stains for fixed Arabidopsis thaliana stem sections. In situ spectral profiling defined practical 405-nm confocal detection windows, and both probes produced reproducible wall-associated photoluminescence enriched in secondary-wall-rich vascular domains, especially xylem vessels and interfascicular fibers. Their anatomical distribution showed qualitative concordance with Wiesner/Mäule lignin histochemistry and condition-validated Safranin O maps, supporting their use as spatial reporters of matrix-associated enrichment within anatomically defined lignified secondary-wall territories. The molecular determinants of this enrichment, including the relative contribution of lignin and other wall polymers, remain to be resolved. Sequential co-staining with Calcofluor White separated broad β-glucan-rich wall architecture from Ru-enriched secondary-wall domains, while spectral-overlap analysis identified far-red Alexa Fluor 647 excitation at 638 nm as the most orthogonal tested third-label configuration. Ligand-comparative DFT descriptors provided structure-property fingerprints summarizing differences in π-surface continuity and electrostatic anisotropy. Overall, these results position canonical Ru(ii) polypyridyl luminophores as confocal-compatible, chemically tractable scaffolds for anatomical imaging of lignified plant-wall territories.

Why it matches plant phenotyping methods植物の木化二次細胞壁を可視化・空間評価する共焦点蛍光染色法の開発と検証が中心であり、単なる生物学的測定ではない。

abstractFluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing.

Probing the living Plant Cell: AFM as a tool for Biomechanical research and development ↗

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.

Fpga-accelerated IoT Deployment of a Causal- Attention Multi-modal Deep Learning Network for Precision Crop Disease Monitoring ↗

Abstract Outbreaks of plant diseases are major threats to world food security particularly in areas where real-time monitoring and quick decision support are constrained by low-power edge gadgets and untrustworthy connectivity. In order to overcome these issues, this paper presents a FPGA-Accelerated IoT implementation of a Causal-Attention Multi-Modal Deep Learning Network, named EpiFusionNet-Edge, that can be applied to monitor crop diseases with real-world farming scenarios with high precision and scalability. The framework incorporates five data modalities that are complementary in nature and they include RGB leaf pictures, microscopic foldscope images, UAV hyperspectral signatures, microclimate IoT sensor measurements and region-specific pathogen/pest pressure indexes giving a complete picture of the health of the plant. Dual causal-attention mechanism is proposed to simulate both spatial and temporal environmental factor activation, which helps to detect and make predictions at the early stage and provides an explanatory logic behind the decisions. Multi-task learning enables classification of diseases, quantification of their intensity at the level of a micro-prediction and prediction of outbreaks in the short term (1–30 days). In order to achieve deployability in resource-constrained settings, the proposed deep learning architecture is ensemble-distilled, structurally pruned, and INT8-quantized, and hardened on a Xilinx Zynq-7000 FPGA platform. The FPGA accelerator is 43.2x faster inference, 88 percent less power usage, and less than 10 ms latency, which allows real-time execution of continuous field monitoring with IoT sensors. Cross-condition assessment on multi-domain datasets shows that there are great improvements on cross-environment generalization rates with 98.6% classification accuracy, 92.7% severity estimation accuracy and less than 3.5% degradation with domain shift. Grad-CAM + + and causal feature traceability further add interpretability with the focus of the model and the pathological indicators proven by experts. The findings show the promise of using a combination of IoT sensing, multi-modal AI fusion, and FPGA hardware acceleration to develop a deployable and scalable and transparent system with regard to precision agriculture. This paper creates a roadmap to a new generation of smart farming systems that are able to conduct disease surveillance and actively protect crops at the periphery in an autonomous manner.

Why it matches plant phenotyping methods植物病害の画像・センサー観測から病害強度を定量化するマルチモーダル・エッジ推論基盤を開発し、精度・速度・消費電力・ドメインシフト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。

abstractthis paper presents a FPGA-Accelerated IoT implementation of a Causal-Attention Multi-Modal Deep Learning Network, named EpiFusionNet-Edge

Pixel-registered multimodal synchrotron XRF and FTIR microscopies reveal salinity stress response mechanisms in pistachio. ↗

Background Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. Results We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. Conclusions We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity-treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.

Why it matches plant phenotyping methods植物組織の元素・生化学状態を空間的に取得するピクセル登録型マルチモーダル画像パイプラインを開発し、植物ストレス表現型解析への汎用的プラットフォームとして提示しているため。

abstractwe developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1

Multispectral and anatomical assessment of chromium and nickel accumulation in urban weeds. ↗

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.

A simple hand-sectioning approach for cellular-resolution imaging of anatomy and gene expression in small developing organs and boundary regions in Arabidopsis. ↗

Background Precise characterization of gene expression patterns across temporal, cellular, and tissue-specific contexts is fundamental to understanding plant development and function. Recent advances in ClearSee-based tissue clearing have enabled high-resolution visualization of internal structures and fluorescent reporter signals in plant tissues. Although hand sectioning can provide optical access to tissues that are not amenable to whole-mount clearing, its application to submillimeter-scale and fragile Arabidopsis organs and tissues, including developing inflorescence apices, flowers, fruits, and organ boundaries, has remained limited. Consequently, analysis of these tissues has largely depended on specialized microdissection techniques and labor-intensive histological workflows, such as wax- or resin-embedded microtomy, which restrict throughput, accessibility, and routine use. Results We developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues. This method relies only on gentle manual tissue processing under a stereomicroscope and readily available reagents, allowing reproducible preparation of delicate tissues without the need for embedding or specialized equipment. Combined with ClearSee-based clearing and fluorescent reporters, the approach enables high-resolution imaging of internal tissue architecture and gene expression, while preserving tissue integrity and fluorescence signals that are often compromised during conventional embedding and microtomy procedures. Conclusions Our method substantially reduces technical complexity, costs, preparation time, and labor associated with cellular-resolution imaging of small, fragile plant tissues. By providing a simple, scalable, and accessible alternative to conventional histological workflows, this approach facilitates routine analysis of internal developmental processes across diverse plant species.

Why it matches plant phenotyping methods小型・脆弱な植物組織の解剖学的構造と遺伝子発現を細胞解像度で取得する手法を開発・最適化しており、表現型取得法が研究の中心である。

abstractWe developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues.

Quantitative morphological phenotyping of infection structures in cucumber downy mildew and powdery mildew. ↗

Introduction: Cucumber diseases severely affect yield and quality. Deep learning-based analysis of microscopic pathogen images enables high-throughput identification and counting of pathogens, thereby facilitating early disease detection. However, most existing pathogen-recognition methods focus mainly on qualitative identification and cannot quantitatively characterize pathogen morphology, which limits their ability to reveal the developmental characteristics and functional differentiation of different infection structures from the perspective of pathogen morphology-function adaptability. Methods: To address this issue, this study focused on cucumber powdery mildew and downy mildew and achieved precise extraction and characterization of pathogen morphological features based on microscopic image instance segmentation. First, an in situ stained microscopic image dataset of cucumber pathogens was constructed. Second, an instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images. Finally, morphological analysis methods were used to quantitatively extract and characterize pathogen infection-structure features. Results: values greater than 0.90. In addition, category-wise morphological distribution analysis showed that different infection-structure types exhibited clear differentiation in size, contour complexity, and elongation. Discussion: This study provides an effective tool for high-throughput phenotyping of cucumber pathogen infection structures. The proposed method offers methodological support for disease diagnosis, pathogen morphological phenotyping, and precision disease management in horticultural production.

Why it matches plant phenotyping methods顕微鏡画像のインスタンスセグメンテーションを開発し、キュウリ病原体の感染構造の形態形質を定量抽出する手法が中心である。

abstractan instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images.

Three-dimensional reconstruction reveals distinct endodermal network topology associated with root ion transport characteristics in balsa ↗

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.

Plant Acoustics for Evaluating Vase Life in Chrysanthemums: A Novel and Noninvasive Method ↗

ABSTRACT Vase life is a key determinant of cut flower quality and market value. Conventional vase life assessment relies on visual inspection and physiological monitoring over several days to weeks, making it labor‐ and time‐intensive. This study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers. Six chrysanthemum cultivars were grown under two supplemental lighting treatments (Hybrid and LED) and two planting densities (54 and 74 plants m −2 ). Acoustic monitoring was compared with optical microscopy for the assessment of xylem vessel diameter, while conventional vase‐life testing was performed in parallel. Optical microscopy validated the acoustic measurements, with both methods consistently identifying vessel radii around 10 μm. The acoustic radius (), derived from pulse settling time measurements, showed cultivar‐ and planting‐density‐specific variation. Linear mixed‐effects modelling demonstrated that the relationship between acoustic radius and vase life differed significantly among cultivars, indicating that a universal relationship across cultivars is not supported. These findings show that acoustic monitoring provides a meaningful noninvasive proxy for vase‐life‐associated stem traits and may serve as a useful cultivar‐calibrated tool for evaluating postharvest longevity in cut chrysanthemums.

Why it matches plant phenotyping methods植物の茎の道管径に基づく音響的な非破壊測定法を開発し、光学顕微鏡および花持ち試験で検証しているため、表現型取得法が研究の中心です。

abstractThis study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers.

Physics-Informed Auto-Differentiation for Limited-Angle Tomography of Thick Amorphous Specimens Using BF-STEM ↗

Electron Tomography is a widely used 3D imaging tool for biological specimens because it offers higher resolution than optical imaging and greater accessibility than X-ray sources. While transmission electron microscopy (TEM) tomography can offer advantages for thin specimens under well-controlled imaging conditions, imaging thick, amorphous specimens becomes increasingly challenging due to reduced transmission and loss of usable contrast at large thicknesses. Alternatively, bright-field scanning transmission electron microscopy (BF-STEM) tomography, due to improved dose control and tolerance to multiple scattering, is preferred for thick samples, as it can image samples thicker than 400 nm while maintaining sufficient resolution and signal [1][2]. However, BF-STEM tomography of sheet-like laminar specimens remains strongly limited by incomplete tilt ranges, and the mismatch between conventional linear reconstruction algorithms and the underlying nonlinear image-formation physics limits reconstruction quality. In this work, we employ a physics-informed automatic differentiation (PIAD) based limited-angle tomography framework that uses a multislice TEM forward model to approximate the BF-STEM bright-field contrast, motivated by the reciprocity between TEM and STEM image formation in thick, amorphous specimens. By formulating reconstruction as a physics-informed inverse problem, the proposed approach enables stable three-dimensional recovery from severely limited angular data [3]. Comparisons with weighted back-projection (WBP) reconstruction demonstrate a substantial reduction of missing-wedge artifacts and improved morphological consistency across slices, highlighting the potential of PIAD-based multislice modeling for interpretable 3D reconstruction from BF-STEM data in regimes where conventional tomography fails. The physical basis for this improvement arises from the multislice model, which explicitly accounts for nonlinear multiple scattering in thick specimens. We chose multislice TEM, a simple plane-wave propagation, as our forward model for BF-STEM image formation, because the BF-STEM contrast can be approximated as the incoherent angular average of TEM multislice intensities over the probe convergence aperture [4]. We further simplify this weighted incoherent sum by noting that, for thick amorphous specimens, the dominant contributions arise from low-angle (near-zero-angle) components, as higher-angle components are preferentially scattered outside the bright-field acceptance. Under this assumption, BF-STEM image formation is well approximated by a blurred BF-TEM plane-wave multislice output, which we adopt as an effective forward model for thickness-dominated bright-field contrast in limited-angle tomography. To test this framework experimentally, we image a butterfly wing scale (Bicyclus anynana) with nanoscale features on a laminar sheet with lateral dimensions of a few hundred microns [5]. We acquired a tilt series of 35 BF-STEM images between -51° and 51°. In limited-angle reconstructions, conventional WBP reconstructions fail to resolve cross-rib structures and are affected by anisotropic smearing and missing-wedge artifacts (Fig. 1a & 1d). In contrast, the proposed PIAD reconstruction yields improved crossrib continuity and junction definition in both volume rendering (Fig. 1b) and orthogonal slices (Fig. 1c’ &1d’), enabling more interpretable 3D morphology of the crossrib architecture. To assess the predictive capability of the forward model, we perform a leave-one-out validation in which a single BF-STEM projection (Fig. 2a) is excluded from the reconstruction. The resulting volume is then forward-projected at the held-out angle using the TEM multislice model and compared with the unseen experimental projection. Despite this effective approximation for BF-STEM, the predicted projections capture the dominant contrast trends associated with the crossrib network (Fig. 2a-2b). The residual map shows differences in the cross-rib edges, as expected from blur, yet has an RMSE of 0.08 and a Pearson correlation coefficient of 0.86. The back-propagated loss gradients highlight spatial regions that are well constrained by the data. Although the cross-ribs are more clearly resolved in PIAD reconstructions, the lower lamina remains unresolved in both WBP and PIAD reconstructions due to the absence of projections near 90°; future PIAD reconstructions using a laminography geometry may address this limitation. Importantly, the present results demonstrate that PIAD reconstructions using an approximate forward model that accounts for multiple scattering outperform conventional linear projection methods for BF-STEM tomography under limited-angle acquisition. Physics-informed auto-differentiation improves recovery of crossrib morphology under limited-angle acquisition in BF-STEM tomography. a) Conventional weighted back-projection (WBP) reconstruction from a single-axis BF-STEM tilt series, showing preservation of coarse rib geometry but loss and anisotropic smearing of thin crossrib features (red dashed region). b) PIAD reconstruction using a TEM multislice forward model improves cross-rib continuity and reduces missing-wedge artifacts. The inset shows a schematic of the actual scale structure. (c,d) Representative xy- and xz-slices from the WBP reconstruction. (c′,d′) Corresponding slices from the PIAD reconstruction, demonstrating enhanced cross-rib definition and thickness consistency. Scale bar = 300 nm. Leave-one-out projection validation of a TEM multislice forward model for BF-STEM tomography. One BF-STEM projection (a) is excluded from reconstruction, and the resulting volume is forward-projected at the held-out angle using the TEM multislice model. The simulated projection (b) is compared with the unseen experimental projection after fitting a per-projection gain and offset (c), (RMSE 0.08; Pearson correlation 0.86). (d) Back-propagated loss gradients (projection alone z-axis) from the leave-one-out error localize data-supported regions of the volume, including crossrib features, distinguishing them from underconstrained regions dominated by missing-wedge artifacts. Scale bar = 500 nm.

Why it matches plant phenotyping methodsBF-STEMトモグラフィー画像から蝶翅の三次元形態を復元する物理情報型手法を開発し、従来法との比較およびleave-one-out検証を行っており、植物ではないが動物試料のため本インデックスの対象外です。

abstractthe proposed PIAD reconstruction yields improved crossrib continuity and junction definition

Validation of Machine Learning-Based Segmentation for Automated 3D Reconstruction in Electron Microscopy: Application in Life and Materials Science ↗

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.

Foliar Micromorphological Variability in Tea Landraces from Fuzhou: Insights from Scanning Electron Microscopy. ↗

Fuzhou represents a critical center for tea genetic diversity, yet the micromorphological basis for differentiating its local landraces remains poorly understood. Scanning electron microscopy (SEM) was employed to investigate the foliar micromorphology of 28 tea landraces from Fuzhou and to characterize structural differences among them. Adaxial epidermal wax ornamentation, stomatal architecture, and nonglandular trichome patterns provided important taxonomic characters for germplasm classification. Our analysis reveals that stomata are consistently paracytic and randomly oriented on the abaxial surface. However, their dimensions exhibit high phenotypic plasticity, with mean areas ranging from 421.92 to 822.26 µm2. Leaf surface ornamentation showed high phenotypic variability, with three identifiable types: straight, wrinkled, and undulated. The length, width, and type of nonglandular trichomes varied among the landraces, with values of nonglandular trichome length ranging from 269.99 to 632.31 µm and diameter from 9.72 to 14.62 μm. The nonglandular trichome ornamentation was categorized as smooth, long-stripe, and short-stick. The study demonstrated that SEM-based analysis of foliar micromorphological traits provides a valuable tool for tea germplasm identification and cultivar improvement. Specifically, the combination of adaxial epidermal wax ornamentation and nonglandular trichome surface ornamentation provides stable and reliable diagnostic micromorphological markers for accurate differentiation and identification of Fuzhou tea landraces, filling a critical micromorphological gap in the systematic study of local tea germplasm.

Why it matches plant phenotyping methodsSEM画像に基づく葉の微細形態形質の取得・分類を中心に、茶遺伝資源の識別へ応用しており、単なる生物学的測定ではなく植物フェノタイピング手法として中心的です。

abstractScanning electron microscopy (SEM) was employed to investigate the foliar micromorphology of 28 tea landraces from Fuzhou and to characterize structural differences among them.

Hijacked hydraulics: Verticillium dahliae-induced xylem dysfunction in pepper stems revealed by integrated hydraulic, imaging, and molecular analyses. ↗

Xylem tissue enables efficient long-distance water transport but is a primary target for vascular pathogens. This study investigates how systemic invasion by Verticillium dahliae impairs the hydraulic function of pepper (Capsicum annuum) plants, focussing on xylem colonisation and its anatomical and physiological effects. Real-time sap flow was continuously monitored with custom-built ExoBeat sensors, while periodic stem water potential measurements allowed calculation of changes in stem hydraulic conductance as an additional indicator of xylem performance. Fungal colonisation was assessed by quantitative polymerase chain reaction, and vessel occlusions and embolised conduits were visualised using scanning electron microscopy and micro-computed tomography, complemented by direct hydraulic conductivity measurements. By 14 d post inoculation, V. dahliae had progressed from roots to aboveground tissues, coinciding with a marked decrease in sap flow, water potential, and soil-to-stem hydraulic conductance, alongside the onset of dwarfing. Direct fungal blockage and anatomical changes were the primary contributors to hydraulic dysfunction. Vessel occlusion by tyloses, gels, and air embolisms played a negligible role. This study reveals how V. dahliae progressively impairs pepper hydraulics through systemic xylem colonisation, highlighting the value of real-time sap flow monitoring. Our integrative, multidisciplinary approach offers a powerful framework to unravel the complexity of dynamic plant-fungal vascular interactions.

Why it matches plant phenotyping methodsカスタムセンサーによるリアルタイム・サップフロー測定を中心に、植物の水理機能・病原体による機能低下を定量化しており、単なる生物学的測定にとどまらない実質的なフェノタイピング手法の適用である。

abstractReal-time sap flow was continuously monitored with custom-built ExoBeat sensors

DFA-YOLO: an enhanced YOLOv11-OBB and knowledge distillation-based maize stomata detection system. ↗

Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.

Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの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。

abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.

Imaging Double Fertilization in Maize ↗

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.

3D measurement of cell thickness and its dynamics in plants. ↗

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.

Fluorescence Lifetime Imaging in Plants: Practical guidelines for multiplexing, label-free imaging and data analysis ↗

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.

Subcellular metallomic networks orchestrate physiological outcomes: Single-cell mapping via an integrated SEM-FIB-TOF-SIMS platform. ↗

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.

Automated phenotyping of soybean stomatal responses to water deficit using YOLOv8 ↗

Artificial intelligence applied to plant phenotyping is crucial for consistent results, as stomata classification under stress impacts physiology, water use efficiency, and productivity. Manual analysis is laborious and error-prone, limiting the efficiency and accuracy of evaluations. In this context, this study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata. Soybean plants were grown under water deficit and well-watered conditions, generating significant variations in stomatal structural opening and associated gas exchange traits. To capture stomata variations, epidermal printing techniques were employed, with images obtained by microscopy. The dataset was labeled using the intelligent polygon tool of the Roboflow application, with 269 images of the adaxial and abaxial surfaces of leaves annotated in two categories: open and closed stomata. The images underwent geometric transformations to facilitate model training. The results demonstrated that the YOLOV8 neural network achieved precision recall and mAP greater than 90%, highlighting its effectiveness in detecting and classifying stomata. By integrating automated classification of aperture states (open and closed) with a defined physiological stress context in soybean, this work establishes a dataset specifically designed for functional analysis. This approach extends the applicability of deep learning toward stress-oriented plant physiology studies, offering a robust tool for evaluating crop adaptation under climate change scenarios.

Why it matches plant phenotyping methodsヨロウ豆の気孔開閉状態を画像から自動検出・分類するYOLOv8手法とデータセットを開発・評価しており、植物表現型取得が研究の中心である。

abstractthis study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata.

Quantitative Live Cell Imaging of Nuclear Shape and Chromatin Dynamics During Development and Environmental Stress in Arabidopsis thaliana Root. ↗

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.

Mamba-YOLO: A Hybrid Architecture with Linear-Complexity Selective Scan Mechanisms for Enhanced Microscopic Rice Disease Detection ↗

Abstract While automated visual inspection facilitates large-scale crop disease management, its deployment in field environments remains challenging. The morphological similarity of early-stage symptoms, combined with severe canopy occlusion, frequently degrades model accuracy. When applied to these unconstrained datasets, standard lightweight Convolutional Neural Networks (e.g., the YOLOv5-v11 series) tend to overfit, yielding an accuracy of only around 46% mAP@0.5 on microscopic targets. Even advanced NMS-free architectures like YOLO26 struggle to capture the long-range spatial dependencies required to decouple highly ambiguous, discrete lesions like early-stage Rice Blast.We address this fundamental structural limitation by introducing Mamba-YOLO. This hybrid architecture integrates the Visual State Space Model (VMamba) directly into a lightweight YOLO26-Nano baseline. We replaced standard deep bottleneck layers with Visual State Space (VSS) modules, leveraging a Selective Scan Mechanism (SSM) to model global context with linear computational complexity (𝑂(𝑁)). Our network perceives fragmented pathological features across the entire image without the massive quadratic computational burden typical of Vision Transformers.Our empirical evaluations on a multi-class rice disease dataset yield compelling insights. Mamba-YOLO achieves a highly competitive overall mAP@0.5 of 92.36%, performing on par with the heavily optimized pure-CNN baseline (92.41%). More importantly, under the strictly penalized mAP@0.5:0.95 metric, our architecture establishes a new peak of 55.6%. We recorded a critical +0.9% accuracy breakthrough for Rice Blast, the most challenging microscopic category. Beyond static accuracy, analysis of the training dynamics proves that the selective scan mechanism acts as a robust global regularizer, effectively collapsing the massive generalization gap that plagues traditional lightweight detectors.We achieve these structural breakthroughs with near-zero overhead. Mamba-YOLO maintains an ultra-low computational footprint of 5.9 GFLOPs and requires only 2.69 million parameters. This Pareto-optimal balance positions our architecture as a highly robust, field-ready solution for deploying high-precision diagnostics on resource-constrained agricultural edge devices.

Why it matches plant phenotyping methodsイネ病害の症状・病斑を画像から検出する新規深層学習アーキテクチャを開発し、複数の評価指標で性能検証しているため、植物表現型取得手法が中心である。

titleMamba-YOLO: A Hybrid Architecture with Linear-Complexity Selective Scan Mechanisms for Enhanced Microscopic Rice Disease Detection

ROOT-ExM: Super-Resolution Imaging of Proteins in Arabidopsis Roots by Expansion Microscopy. ↗

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.

Pathogen Inoculation and Rating Strategies for Studying Maize Diseases. ↗

Maize is a globally important staple that is used as food for human and animal consumption, fuel, and other industrial applications. Pathogens affect all stages of the plant life cycle and every plant organ, and lead to significant yield losses. An integrated strategy incorporating cultural and chemical management practices, as well as development of resistant plant varieties, is needed to prevent yield losses due to plant diseases. Large numbers of breeding material must be screened to develop pathogen-resistant maize varieties. Inoculation methods must be high-throughput to accommodate the large screening experiments. Additionally, there needs to be an extensive understanding of the plant-pathogen interaction to use a targeted biotechnology-based approach, which takes advantage of knowledge of the system to engineer resistance. To evaluate germplasm for breeding and biotechnology approaches, inoculation methods must replicate natural infection, and disease severity must be rated consistently to accurately screen germplasm or gather data on pathogens of interest. Here, we review inoculation and rating methods for Gibberella ear rot, seedling blight caused by Globisporangium ultimum var. ultimum , and Goss's wilt that are efficient and high-throughput. We also introduce fluorescence microscopy techniques for leaf samples infected with Exserohilum turcicum , the causal agent of northern corn leaf blight. These pathogens all cause significant yield losses, and in particular, Gibberella ear rot is associated with the accumulation of harmful mycotoxins. Understanding how pathogens cause disease and how plants defend against attack is a major goal of maize pathology studies and critical for developing integrated management strategies.

Why it matches plant phenotyping methodsトウモロコシ病害の接種および病害重症度評価法をレビューし、高スループットで一貫した植物病害表現型の取得を扱うため、方法論が中心です。感染葉の蛍光顕微鏡法も紹介されています。

abstractInoculation methods must be high-throughput to accommodate the large screening experiments.

High-Throughput Fluorescence Microscopy Using Aniline Blue Staining to Study the Maize -Exserohilum turcicum Pathosystem. ↗

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.

3D Reconstruction of Mature Arabidopsis Ovules Using FIB-SEM to Study Filiform Apparatus Morphology ↗

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.

EpiReasoner: An Integrated Artificial Intelligence Framework for Phenotype-to-Genotype Reasoning in Plant Epidermal Development ↗

Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L ( SKIP19L ), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.

Why it matches plant phenotyping methods植物の気孔・表皮細胞を対象に、画像解析と知識推論を統合したフェノタイピング手法を開発しており、形態・空間分布の定量化が中心的な貢献である。

abstractwe developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants.

A Streamlined Protocol for Single-Molecule Localization Microscopy in Arabidopsis Nuclei. ↗

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.

Phase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen ↗

Soil-borne vascular pathogens pose serious threats to agriculture with complex invasion strategies that remain poorly characterized compared to foliar pathogens. While foliar pathogens like Magnaporthe oryzae employ specialized appressoria to penetrate plant surfaces through a combination of mechanical force and enzymatic degradation, the invasion mechanisms of vascular pathogens that lack classical appressoria have remained largely theoretical. The nanoscale processes governing root penetration and colonization by these pathogens are particularly challenging to visualize due to technical limitations of conventional microscopy. Here we show, using phase-contrast X-ray computed microtomography and advanced microscopy, that Fusarium oxysporum (Fo) employs distinct mitogen-activated protein kinase (MAPK) cascades to orchestrate root invasion through unprecedented morphological plasticity. We identify previously undocumented appressoria-like structures that facilitate physical penetration, while demonstrating that Fo exhibits remarkable cellular adaptability, reducing hyphal diameter by more than 20-fold (from 5 μm to 220 nm) to navigate confined plant spaces, a dramatic morphological transition previously thought impossible. By using cellulase-deficient mutants, we demonstrate that cellulolytic activity is dispensable for surface breach and submicrometric hyphal colonization, establishing that mechanical force generation rather than enzymatic degradation is the primary determinant of successful host penetration. Three-dimensional reconstruction reveals a quantitative correlation between fungal proliferation and progressive embolism formation, with distinct MAPK pathways differentially regulating penetration force generation (Fmk1), osmotic adaptation during apoplastic colonization (Hog1), and directional growth toward vascular tissues (Mpk1). These findings provide a mechanistic framework for vascular wilt pathogenesis and reveal potential targets for controlling these economically devastating plant diseases.

Why it matches plant phenotyping methods位相コントラストX線マイクロトモグラフィーと3次元再構成を中核に、根の侵入・菌糸形態・塞栓形成という植物の病態と形態を定量化しており、病理学的機構研究であるものの表現型取得法の適用が実質的です。

titlePhase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen

Stomatal setpoints and environmental responsiveness are sculpted by developmental trajectories ↗

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.

The 2D and 3D ultrastructure of symbiosomes and associated vesicular structures in Lotus japonicus root nodule symbiosis ↗

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.

Technological advances in imaging and modelling of leaf structural traits: a review of heat stress in wheat ↗

Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic carbon assimilation and crop yield. Despite the advancement in various imaging technologies at the field, canopy, plant, tissue, cellular, and subcellular levels, phenotyping of imaging-based leaf structural traits (e.g. vein density, stomatal density, and stomatal aperture) for abiotic stresses is still time-consuming and expensive without the aid of artificial intelligence (AI) and machine learning (ML). This review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits. Recent high-resolution, non-destructive imaging technologies, including confocal laser scanning microscopy, X-ray computed tomography, and optical coherence tomography, have enabled in vivo visualization of plants. Integrating these imaging techniques with AI/ML facilitates high-throughput phenotyping and the modelling of stress responses. We emphasize the potential for future research to leverage these technological advancements in imaging and AI, combining imaging data with physiological and multi-omics studies to deepen the understanding of plant heat tolerance mechanisms. Such multidisciplinary integration in leaf structure phenotyping will accelerate the development of resilient wheat varieties, offering critical insights for crop improvement in the face of climate change.

Why it matches plant phenotyping methods植物の葉構造・機能形質を対象とする画像計測技術とAI/MLによる表現型解析を中心に整理したレビューであり、植物フェノタイピング手法レビューに該当する。

abstractThis review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits.

A cytological framework of female meiosis in Arabidopsis. ↗

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.

Identification of self-incompatibility in macadamia (Macadamia SPP.) using field-bagging and fluorescence microscopy ↗

Self-incompatibility (SI) significantly reduces crop yield, often far below its genetic potential. Developing self-compatible varieties is the most effective strategy for overcoming SI in crops. Most macadamia ( Macadamia SPP.) species exhibit SI or partial self-incompatibility (PSI), so the efficient identification of self-compatible germplasms has emerged as a crucial topic. To characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility. That is, the degree of SI was based on the final self-incompatibility index (F_SI), which was calculated based on the open-pollination final nut set per raceme (OP_FNS) and self-pollination final nut set per raceme (SP_FNS) values (strong SI: F_SI ≥ 0.7, medium SI: 0.4 ≤ F_SI 35%). Through comprehensive analysis of the field-bagging and fluorescence-microscopy observations, thirteen varieties with strong SI (816, 778, 842, Special, 812, D, 820, 246, 772, A16, A4, 951, and 695), six varieties with moderate SI (851, 828, 508, 936, O.C, and D4), and four varieties with weak SI (915, HY, 836, and 814) were identified. Our study provides a theoretical foundation and technical support for advancing germplasm resource innovation, and the genetic improvement and breeding of self-compatible macadamia varieties.

Why it matches plant phenotyping methods自家不和合性という植物状態を対象に、圃場袋掛けと蛍光顕微鏡観察を用いた標準化分類体系を確立しており、表現型の取得・評価法が研究の中心である。

abstractTo characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility.

Systematic Investigation of Microstructural and Spectral Characteristics in Citrus Midrib for Huanglongbing Detection. ↗

Citrus Huanglongbing (HLB) disease is a devastating disease faced by the global citrus industry, for which there is currently no effective cure. This study systematically investigated the anatomical characteristics and infrared spectral properties of different microstructures (phloem, xylem, pith, and cortical tissues) in the midribs of healthy and HLB-infected citrus leaves. Scanning electron microscopy observations revealed obvious phloem breakage and massive starch granule accumulation in various tissues of HLB-infected samples. Using micro-Fourier transform infrared spectroscopy, the spectral acquisition parameters were optimized (slice thickness: 10 μm, spectral resolution: 8 cm -1 , spatial resolution: 10 μm × 10 μm, number of scans: 256), and in-situ spectral information from different tissues were obtained. The results showed significant changes in the intensity and position of absorption peaks in the fingerprint region (1,800 to 675 cm -1 ) of all tissues after HLB infection, particularly enhanced carbohydrate absorption at bands such as 1,099, 1,060, and 1,033 cm -1 , indicating that abnormal carbohydrate accumulation is a typical symptom of HLB. A principal component analysis score plot based on spectral data from the phloem demonstrates a clear spatial separation trend between healthy and HLB-infected samples, providing a theoretical basis and methodological support for the fast, early, nondestructive detection of citrus HLB disease.

Why it matches plant phenotyping methodsミクロFTIRの取得条件を最適化し、健全・HLB感染葉の組織スペクトルから病害状態を識別する手法を検討しており、植物病徴の非破壊フェノタイピングが中心である。

abstractUsing micro-Fourier transform infrared spectroscopy, the spectral acquisition parameters were optimized (slice thickness: 10 μm, spectral resolution: 8 cm -1 , spatial resolution: 10 μm × 10 μm, number of scans: 256), and in-situ spectral information from different tissues were obtained.

A Precise Reproductive Calendar of Sexual and Apomictic Genotypes of Eragrostis curvula . ↗

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.

Optimization of LF-NMR-based methods for analysis of oil content and distribution in germinating oilseeds. ↗

Background Lipid metabolism is critical for seed germination, directly impacting their nutritional value as a food raw material. Conventional methods for oil analysis are destructive and fail to determine oil distribution. This study evaluated the feasibility of using low-field nuclear magnetic resonance (LF-NMR) coupled with magnetic resonance imaging (MRI) as a non-destructive approach for monitoring oil changes in germinating oilseeds. Results Four representative oilseed varieties - herbaceous (peanut, soybean) and woody (camellia, almond) - were investigated to analyze oil changes during germination. The accuracy of LF-NMR was validated against Soxhlet extraction and confocal laser scanning microscopy (CLSM). The results revealed that herbaceous seeds exhibited rapid oil mobilization germination, whereas woody seeds showed slower oil consumption. High correlations were observed between LF-NMR method and conventional method/CLSM imaging method (R 2 > 0.9). Notably, MRI-imaging oil ratio demonstrated the highest accuracy in quantifying both oil content and distribution. Greenness evaluation results show that LF-NMR is the greenest method for sample preparation and measurement process. Conclusion These findings confirm LF-NMR as an effective method for non-destructive monitoring of oil content and distribution during seed germination, which holds significant application potential in areas such as food raw material quality assessment and the optimization of oilseed processing pretreatment. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methods発芽油種子の油含量・分布という植物器官の状態を、LF-NMR/MRIで非破壊測定する手法を開発・検証しており、表現型取得法が研究の中心である。

abstractThis study evaluated the feasibility of using low-field nuclear magnetic resonance (LF-NMR) coupled with magnetic resonance imaging (MRI) as a non-destructive approach for monitoring oil changes in germinating oilseeds.

MBMSA-UNet: A Multi-Scale Attention-Based Instance Segmentation Model for Moso Bamboo Cells. ↗

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.

A Guide to Reproducible Cellulose Synthase Density and Speed Measurements in Arabidopsis thaliana . ↗

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 .

Deep learning-based seed germination prediction using morphological traits and RGB images. ↗

Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable expertise. Within the scope of smart farming applications, this study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images. The proposed model determines whether a seed will germinate using RGB images and morphological traits automatically extracted from these images. The dataset used in this study comprises a total of 3,645 images belonging to three different seed types. For each seed type (okra, eggplant, and tomato), 405 seed images were acquired from three distinct imaging sources (digital microscope, camera, and scanner), labeled, and subsequently sown in seed trays. The germination status of each sown seed was systematically monitored and matched with its corresponding image data. The dataset was partitioned into 80% training and 20% testing subsets. Following 5-fold stratified cross-validation, the proposed model achieved an average weighted F1-score of 0.95 on the training set and 0.93 on the testing set for germination capacity prediction. The performance of the proposed model was further compared with widely used deep convolutional neural network architectures, including VGG19, ResNet50, and EfficientNetB5. Comparative results demonstrate that the proposed model provides competitive and robust performance for seed germination prediction. Overall, the findings indicate that the proposed approach can effectively be utilized for automated seed germination prediction. Future research should evaluate the generalizability of the model by conducting performance assessments on additional seed types.

Why it matches plant phenotyping methodsRGB画像から種子の形態形質を自動抽出し、発芽状態を予測する深層学習手法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含める。

abstractthis study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images.

Ultrastructural and Energy-Dispersive Spectroscopy (EDS) study of Araucaria grandifolia leaf cuticles (Aptian, Patagonia): Implications for taxonomy and paleoecology ↗

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).

Quantitative imaging of apoplastic pH in plant roots via confocal microscopy. ↗

The regulation of apoplastic pH is critical for plant growth and development, affecting processes such as nutrient uptake, cell wall expansion and intercellular signaling. Conventional methods for measuring apoplastic pH, including pH indicators in growth media and ion-selective electrodes, often fall short of providing the spatial resolution and accuracy needed for detailed studies. Here we present a protocol for the quantitative imaging of apoplastic pH in Arabidopsis thaliana roots using confocal microscopy combined with the fluorescent pH probe 8-hydroxy-pyrene-1,3,6-trisulfonic acid trisodium salt, also called pyranine. This approach addresses the limitations of genetic sensors and traditional pH measurement techniques by offering a nontoxic, cost-effective and precise method for pH assessment at cellular resolution via ratiometric confocal imaging. In addition, we introduce an updated Fiji plugin for ratiometric image conversion. The new plugin enhances workflow efficiency by automating image processing while offering several options for customization, thereby ensuring reliable and reproducible results. The full procedure, from staining to image analysis, can be completed within ~2-4 h, depending on the number of samples and imaging depth. This protocol provides a robust tool for plant physiologists to investigate apoplastic pH dynamics with high spatial resolution and accuracy in plant tissues.

Why it matches plant phenotyping methods植物根のアポプラストpHを細胞解像度で定量画像化する手法と、画像解析プラグインを開発・提示しており、植物状態の取得法が中心である。

abstractHere we present a protocol for the quantitative imaging of apoplastic pH in Arabidopsis thaliana roots using confocal microscopy combined with the fluorescent pH probe 8-hydroxy-pyrene-1,3,6-trisulfonic acid trisodium salt, also called pyranine.

Imaging and genetic toolbox to study Arabidopsis embryogenesis ↗

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.

CryoFluorSEM – A new approach for fluorescence and EM imaging of cryofractured plant samples ↗

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.

Quantitative Approach for Simultaneous In Situ Profiling of Lignin, Cellulose, and Hemicellulose Using Confocal Raman Microscopy. ↗

Label-free confocal Raman microscopy (CRM) is characterized by its high chemical specificity, making it a promising tool for the in situ quantitative analysis of plant cell walls. However, the simultaneous quantification of components in Gramineous species remains challenging. This is due to the complex "lignin-ferulate-carbohydrate" cross-linked network, as well as the amorphous property of hemicellulose, specifically its weak Raman signal and severe spectral overlap with cellulose. To address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS). We acquired CRM mapping images of rice stems pretreated with acidified sodium chlorite (ASC) for varying durations. The CS values between the preprocessed cell wall spectra and reference spectra (milled wood lignin, microcrystalline cellulose, and xylan) were then calculated and used as quantitative indicators. The results showed that CS values allow for accurate profiling, exhibiting significant positive correlations with the contents of lignin, cellulose, and hemicellulose. These correlations follow piecewise linear relationships with high determination coefficients ( R 2 ) of 0.9728 and 0.9809 for lignin, 0.9592 and 0.9810 for cellulose, and 0.9004 and 0.9901 for hemicellulose. The CS-based method consistently outperforms the conventional characteristic peak intensity approach. In particular, it resolves the difficulty of accurately quantifying hemicellulose, a task where single-band methods typically underperform ( R 2 in situ simultaneous quantification of lignin, cellulose, and hemicellulose contents in rice stem cell walls during ASC pretreatment. Thus, the CRM-CS algorithm enables simultaneous in situ quantification in Gramineous cell walls, offering a valuable approach for crop breeding and the high-value utilization of lignocellulosic biomass.

Why it matches plant phenotyping methods植物細胞壁中のリグニン、セルロース、ヘミセルロース含量を定量するCRM-CS手法を開発・検証しており、植物形質の取得方法が研究の中心である。

abstractTo address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS).

Quantitative live cell imaging of nuclear shape and chromatin dynamics during development and environmental stress in Arabidopsis thaliana ↗

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.

Quantitative assessment and predictive modelling of stem damage during seedling separation in mechanical rice transplanting ↗

Rice seedling stems are particularly vulnerable to structural damage during the seedling separation phase of mechanical transplanting, especially under non-ideal plant-machine interactions. Owing to its internal and transient nature, such damage is inherently difficult to quantify or predict. This study presents a novel modelling framework for stem damage assessment, which establishes a quantitative relationship between the maximum impact load (Fₘₐₓ) during seedling separation and internal damage severity, quantified by the damaged area ratio (Dₐᵣ). High-speed imaging and triaxial force sensors were employed to measure Fₘₐₓ across seedlings aged 20, 30 and 40 d under varying transplanting speeds. Microscopic cross-sections of stems were analysed to calculate Dₐᵣ. A composite impact force model, incorporating stem bending rigidity, lateral needle–stem offset and contact duration, was developed to support experimental design. A strong positive correlation was observed between Fₘₐₓ and Dₐᵣ across all seedling age groups (ρ > 0.93, p 8 %. Age-specific linear regression models achieved high predictive accuracy and good calibration (cross-validated R² of 0.86–0.91; RMSE of 0.33–0.73 percentage points in Dₐᵣ), while extending these models with a restricted cubic spline further reduced errors in the upper damage tail. This framework offers theoretical insights into age- and speed-dependent stem damage and practical tools for optimising transplanting parameters and supporting real-time, damage-aware control strategies to mitigate mechanical damage risk and improve seedling survival and post-transplant performance.

Why it matches plant phenotyping methods稲苗の茎損傷を画像・力センサー・断面解析で定量化し、予測モデルを開発・検証しており、植物状態の取得・推定方法が研究の中心である。

abstractThis study presents a novel modelling framework for stem damage assessment

The ultrastructure of the mature wheat grain after resin embedding, as observed using atomic force microscopy ↗

This study examines the ultrastructure of the outer layers of hexaploid wheat (Triticum aestivum L.) seeds using atomic force microscopy (AFM) in air tapping mode. The specimens were resin-embedded after hydration. A standardised protocol for preparing specimens specifically for AFM investigations is presented, focusing on revealing the ultrastructure while minimising artefacts and optimising the resolution of the structural morphology. AFM provides a comprehensive histological description of the hydrated mature wheat seed, encompassing each layer from the outer pericarp to the starchy endosperm. This study highlights the ultrastructural details of the tissues in their hydrated state, particularly with regard to morphology and size. Thus, AFM shows great potential for revealing intricate details of plant tissues structure and ultrastructure.

Why it matches plant phenotyping methods成熟コムギ種子の組織形態・超微細構造をAFMで取得するための標準化試料調製プロトコルを提示しており、植物形態の観察手法が中心である。

abstractA standardised protocol for preparing specimens specifically for AFM investigations is presented, focusing on revealing the ultrastructure while minimising artefacts and optimising the resolution of the structural morphology.

Dynamic analysis of the infection process of cucumber powdery mildew based on instance segmentation ↗

Powdery mildew represents a significant threat to cucumber yield, with its infection process encompassing stages such as “attachment, colonisation, and dispersal.” With the advancement of deep learning, computer vision techniques are increasingly applied to study powdery mildew infection patterns. However, existing biological methods are low-throughput, subjective, and struggle to capture the dynamic characteristics of pathogen infection throughout the entire process. Current microscopic image analysis methods also fail to simultaneously recognise and segment various infection structures across different stages of infection, making it difficult to reveal the evolving infection patterns over time. To overcome these limitations, this paper proposes an integrated SR-QC-TA framework for modelling the infection behaviour of cucumber powdery mildew. First, a time-series dataset of microscopic images covering all stages of infection was constructed, systematically documenting the evolution of key infection structures from attachment to dispersal. Second, an instance segmentation algorithm, FS-YOLOv8s, was developed to achieve high-precision, multi-class recognition of pathogen structures in complex backgrounds. Additionally, a multi-dimensional quantitative characterisation method for pathogen infection features was designed, describing infection characteristics in terms of quantity, morphology, and location. Finally, based on these recognition and characterisation results, a temporal analysis framework was established to quantify dynamic changes in infection and reveal the stages of infection behaviour. Experimental results demonstrate that FS-YOLOv8s achieved mAPᵇᵒˣ@0.5 and mAPᵐᵃˢᵏ@0.5 scores of 91.8 % and 92.3 %, respectively, enabling high-precision segmentation across all infection stages. This research advances intelligent monitoring and control of cucumber powdery mildew and drives disease monitoring in horticultural crops toward bioengineering systems.

Why it matches plant phenotyping methodsキュウリうどんこ病の感染状態を顕微鏡画像からインスタンスセグメンテーションで抽出し、感染構造の数量・形態・位置と時間変化を定量化する手法が研究の中心であるため。

abstractan instance segmentation algorithm, FS-YOLOv8s, was developed to achieve high-precision, multi-class recognition of pathogen structures in complex backgrounds.

Integrating SEM-based phenotyping with GWAS reveals the genetic architecture of rice straw secondary cell wall and internode cell features ↗

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

Quantitative Ratiometric Analysis of FRET-Based Biosensors in Arabidopsis thaliana Enables Live Measurement of Analytes in Subcellular Compartments. ↗

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.

Plant Microtechnique with Resin: Towards Plant Histolomics ↗

Plant microtechnique involves a sequence of skill-intensive histological procedures that often yield poorly reproducible images and limited quantitative information. Nevertheless, it provides an essential cellular and tissue context needed to understand biological functions. In this work, we present an optimized resin-based microtechnique that replaces paraffin embedding, incorporates a chemically activated adhesive treatment for glass slides, and develop a trichrome stain for resin sections. All these improvements enhanced section stability and image reproducibility, enabled a broader color palette with sharp contrast of tissues, cells and organelles, and selected ultrastructural features using light microscopy. Based on these preparations, a quantitative micrograph analysis workflow was developed based on image segmentation and feature extraction using MATLAB (R2024a) and Adobe Photoshop (CS6). This approach enables the measurement of a wide range of morphometric and compositional features, generating structured histological datasets that we refer to as plant histolomes. As an illustrative application, this workflow was applied to leaves from several model plants species and integrated multiple anatomical traits into a composite feature, the “C4 Kranz-anatomy level”, enabling quantitative comparison along the C3-C4 anatomical transition. The resin-based microtechnique and the histolomics framework developed in this work provide a robust and reproducible basis for the quantitative plant histology, bridging classical microscopy with a data-driven tissue analysis.

Why it matches plant phenotyping methods樹脂包埋・染色による植物組織画像の再現性向上と、画像セグメンテーション／特徴抽出による形態・組成形質の定量化が研究の中心であり、植物フェノタイピング手法の開発・検証・適用に該当する。

abstractIn this work, we present an optimized resin-based microtechnique

A visualization framework for cell division activity and orientation in pre-anthesis ovaries of Prunus species ↗

Fruit size and shape, which influence horticultural quality, are determined by the number and the size of the cells in the local region. In fruit trees, however, the difficulty of applying molecular genetic approaches has hindered a detailed understanding of the localization and orientation of cell division in developing fruit tissues. In this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops, peach ( Prunus persica ), Japanese apricot ( P. mume ) and the interspecific hybrid Japanese apricot ( P. salicina x P. mume ), providing clear insight into the spatial distribution and orientation of dividing cells. We systematically optimized a 5-ethynyl-2′-deoxyuridine (EdU) labeling protocol for thick ovary tissues by adjusting infiltration conditions and fixation methods. In addition, electron microscopy combined with wide-view tiling visualization was applied to directly identify dividing cells, including those undergoing chromosome segregation and cell plate formation. By combining with machine learning-based detection, we efficiently and objectively identified dividing cells. Using these complementary approaches, we found that cell division activity was broadly distributed throughout pre-anthesis ovaries in all three crops, without pronounced spatial restriction. In contrast, analysis of division orientation revealed region-specific patterns: cells in the outermost exocarp divided predominantly anticlinally, whereas cells in the mesocarp divided largely periclinally, consistent with subsequent ovary (fruit) enlargement. The integrated framework presented here provides a foundation for understanding the spatial and three-dimensional regulation of fruit development and for future studies in fruit morphogenesis and horticulture.

Why it matches plant phenotyping methods植物組織内の細胞分裂という発生状態を可視化・定量する統合フレームワークを開発し、EdU標識、電子顕微鏡、広視野タイリング、機械学習検出を組み合わせて検証・適用しているため、植物フェノタイピング手法が中心である。

abstractIn this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops

Correlations between surface area and volume in cell size and growth in Arabidopsis thaliana. ↗

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.

Simultaneous triple staining for detecting cell-type specific spatio-temporal distribution of cell wall materials in monocot roots ↗

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.

Multimodal Dissection of UV‐B–Induced Plant Defense Against Insect in Tea Plants ↗

ABSTRACT Sustainable agriculture urgently requires innovative, pesticide‐free strategies to mitigate herbivory and safeguard food security. Ultraviolet‐B (UV‐B) irradiation, with tunable intensity and cost‐effectiveness, has emerged as a promising non‐chemical method to enhance plant resistance, yet its underlying mechanisms remain elusive. Here, using tea plant ( Camellia sinensis ) and its major pest Ectropis obliqua as a model, we developed a multimodal framework that integrates AI‐enhanced electronic nose technology for real‐time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses under precisely controlled UV‐B treatments. This approach identified herbivore‐induced volatiles—hexanal, (Z)‐3‐hexenol, octanal, and (Z)‐3‐hexenyl acetate—optimally induced at 1.2 kJ·m −2 UV‐B and linked to insect deterrence. SRS imaging further revealed elevated jasmonic acid derivatives and L‐phenylalanine, coupled with reduced protein levels and altered stomatal dynamics, all correlating with enhanced resistance. Transcriptomic and molecular analyses confirmed transcriptional regulation of these pathways. By bridging volatile detection, metabolic imaging, and molecular validation, this study pioneers a multimodal strategy that provides mechanistic insights into UV‐B–mediated plant defense and highlights the potential of multimodal methodologies as powerful tools for developing sustainable, pesticide‐free pest management solutions in precision agriculture.

Why it matches plant phenotyping methodsAI強化電子鼻とハイパースペクトルSRS顕微鏡を統合した植物防御応答のリアルタイム・多モーダル計測フレームワークが研究の中心であり、揮発性物質、代謝物、気孔動態などの植物状態を抽出している。

abstractwe developed a multimodal framework that integrates AI‐enhanced electronic nose technology for real‐time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses

Mechanical and Growth Anisotropy in Chara corallina: Challenging Green's Hypothesis ↗

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.

A live-imaging system for Arabidopsis leaf primordia at early stages ↗

Live imaging is one of the most powerful methods to reveal the morphogenesis of plant organs. However, the highly three-dimensional structure of plant organs always poses technical challenges. For example, the basal region of leaf primordia is rarely observed because of the shape of leaf primordia and the sudden shift in geometry at the point where the leaf primordium connects to the hypocotyl. In this work, we developed a new live-imaging system that is suitable for observing the developmental process of the basal region of Arabidopsis leaf primordia at early stages. Using this system, we achieved continuous observation of the basal region of early Arabidopsis leaf primordia for more than 50 hours.

Why it matches plant phenotyping methodsシロイヌナズナ葉原基の発生過程を継続観察する新規ライブイメージングシステムの開発が主題であり、植物器官の形態・発生状態の取得方法が中心的に扱われている。

abstractIn this work, we developed a new live-imaging system that is suitable for observing the developmental process of the basal region of Arabidopsis leaf primordia at early stages.

NucVerse3D: Generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities ↗

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

Complex networks for modeling texture and spectral features of hyperspectral images for environmental analysis. ↗

A complex network method is introduced for high-resolution hyperspectral image analysis and classification. The method is applied to detecting environmental pollution with the Jacaranda caroba plant species. Using confocal laser scanning microscopy (CLSM), detailed spectral data were captured from leaves exposed to different levels of potassium fluoride. Unlike most studies that focus on pixel- or patch-level classification, this work targets the classification of entire high-resolution hyperspectral images, requiring a method capable of capturing global spatial-spectral and texture relationships. Therefore, the limited number of samples and the high dimensionality of the hyperspectral data make conventional deep learning methods unsuitable, motivating the need for a robust and efficient alternative. To address this, we developed the hand-engineered technique named Directed Network of Angular Similarity (DNAS) which models the hyperspectral pixels as complex network vertices connected based on the angular similarity of their spectral bands. This technique allows for effective and efficient feature extraction, computing a compact image representation with only 36 descriptors. Coupled with a supervised classifier, our method achieves a classification accuracy of 92.6% when distinguishing Jacaranda caroba pollutant levels, surpassing both traditional and deep learning approaches. By leveraging the structural, spectral, and texture properties of hyperspectral data, the DNAS method provides a novel framework for detecting pollutant-induced changes in leaf structure, offering significant advantages in resource-limited scenarios. The results demonstrate the potential of Jacaranda caroba leaves, analyzed with this innovative technique, to serve as indicators of air quality.

Why it matches plant phenotyping methods葉のハイパースペクトル画像から汚染誘発変化を抽出・分類するDNAS手法の開発が中心であり、植物状態の画像ベース表現型評価に該当する。

abstractTherefore, the limited number of samples and the high dimensionality of the hyperspectral data make conventional deep learning methods unsuitable, motivating the need for a robust and efficient alternative.

Recommendations for assessing xylogenesis in angiosperm trees. ↗

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

Graphene/PEDOT:PSS Hybrid Ink Based Flexible and Eco-friendly Humidity Sensor for Early Plant Leaf Stress Monitoring ↗

In this work, we present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring. The humidity sensor was fabricated using graphene/PEDOT:PSS hybrid ink deposited via drop-casting method on interdigitated electrodes (IDEs) screen printed on a eco-friendly paper substrate. Contact angle measurement, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) studies were performed to demonstrate hydrophilic nature, surface morphology and elemental analysis, respectively, of the sensing layer. The sensor exhibited excellent sensing performance in the measured relative humidity (%RH) range from 25% RH to 94% RH having a maximum % response of 226.5%. The sensor demonstrated a nearly linear response (adj. R² = 0.99) in the considered range with a slope observed as 3.21%/%RH. Multi-cyclic repeatability and reproducibility analysis further confirmed high reliability and consistent performance of the developed sensor. Furthermore, the capability of the sensor was successfully evaluated through capturing variations in plant physiological health status (under different environmental conditions, such as un-watered, water availability and solar irradiation) via monitoring microclimatic relative humidity (%RH) variations on plant (Epiremnun aureum) leaves. Through establishing the %RH values for healthy crops or plants under normal (well-watered) and stress conditions (un-watered or excessive solar irradiations), sensor seems to demonstrate strong potential for smart agriculture i.e., detecting early plant leaf stress.

Why it matches plant phenotyping methods植物葉のストレス状態を相対湿度センサーで取得するセンサー開発と性能評価が中心であり、植物生理状態の早期モニタリングへ実証適用している。

abstractwe present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring

The ultrastructure of the mature wheat grain tissue in its native state, as observed using atomic force microscopy ↗

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.).

Integrating spectroscopy with machine learning and deep learning for monitoring mung plant responses to silicon dioxide nanoparticles ↗

This study investigates the potential of integration of confocal micro-Raman and UV-Vis spectroscopy with machine learning and deep learning algorithms to assess biochemical responses of mung bean plants exposed to silicon dioxide nanoparticles (SiO 2 NPs) at varying concentrations. The analysis of acquired Raman spectral data reveals a concentration dependent pattern where low concentrations (0.2-0.6 mM) reduce the intensities of key biomolecules such as carotenoids, lignin, pectin, protein, carbohydrate, and cellulose, while higher concentrations (1.2-1.4 mM) trigger enhancement in intensities. The estimation of photosynthetic pigments using UV-Vis spectroscopy complements the Raman spectroscopy results, with chlorophyll and carotenoid levels decreasing at lower concentrations before significantly increasing. Among computational approaches, the application of dimensionality reduction techniques such as LDA- significantly improve the performance of clustering algorithms learnings like AGNES (RI = 1.00), DBSCAN (RI = 0.99), and k-means (RI = 1.00) and deep learning models, achieving high classification accuracy. Supervised algorithms like random forest and support vector machine perform optimally without dimensionality reduction, showing accuracies of 78 % and 79 % respectively. This integrated spectroscopy-computational approach offers a non-invasive, label-free, and robust framework for monitoring plant-nanomaterial interactions.

Why it matches plant phenotyping methods植物の生化学的応答を分光計測と機械学習で推定する統合的な表現型取得・解析手法が研究の中心であり、単なる処理実験のルーチン測定ではない。

abstractThis integrated spectroscopy-computational approach offers a non-invasive, label-free, and robust framework for monitoring plant-nanomaterial interactions.

Morphological diversity of pollen and spores in a human-impacted highland forest-agriculture mosaic in northern Thailand. ↗

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.

A Comparative Study on the Identification of Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti Based on Three Microscopy Technology. ↗

Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti are morphologically similar fructus that are frequently misidentified. Xanthium italicum Moretti may possess inherent toxicity, and its adulteration of genuine medicinal materials poses a threat to clinical drug safety. Macroscopic observation and three microscopic techniques including stereo microscope, optical microscope, and 3D X-ray microscope were used for morphological identification of Xanthium sibiricum Patr ex Widder and Xanthium italicum Moretti in this study. 3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa. Intact fructus (n = 30 per species) were first screened macroscopically, then examined by stereo microscopy, optical microscopy, and 3D X-ray microscopy (0.3, 0.7, 1.5, 3.5, 18.06, 20.01 μm voxel size, Zeiss Xradia 520 Versa). The results showed that stereo microscopy, optical microscopy, and 3D X-ray microscopy collectively confirm the same conclusion from three distinct physical perspectives: surface topography, internal two-dimensional structure, and internal three-dimensional density distribution. The two Xanthium species differ significantly in burr spine morphology, fructus size and shape, the architecture and distribution of non-glandular and glandular trichomes, cotyledon conformation, and seed-coat cell patterning. In particular, 3D X-ray microscopy clearly resolves internal cotyledon spatial configurations and involucral cavity architectures, which furnishes critical endomorphic characters for taxonomic diagnosis. 3D X-ray microscopy provides unprecedented volumetric contrast of surface spines and internal seed architecture, permitting confident, non-destructive species identification. This study provides a basis for the safe clinical use of Xanthium sibiricum Patrin ex Widder. The frontier of 3D X-ray microscopy in plant systematics offers a novel, rapid, accurate and non-destructive protocol for the discrimination of morphologically elusive species.

Why it matches plant phenotyping methods3D X線顕微鏡を含む複数の画像計測法を用いて果実・種子の形態形質を抽出し、近縁2分類群の非破壊識別プロトコルとして比較・検証しており、植物フェノタイピング手法が中心である。

abstract3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa.

MorphoLearn: A morphology-driven workflow to decipher 3D electron microscopy segmentation in diatoms ↗

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.

Microfluidic Interrogation of Chitin-Induced Calcium Oscillations in the Moss Physcomitrium patens ↗

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.

Introduction of the Ribo-BiFC method to plants using a split mVenus approach. ↗

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.

WheatAI v1.0: An AI-Powered High Throughput Wheat Phenotyping Platform ↗

High-throughput, low-cost phenotyping remains a critical bottleneck in wheat breeding, genetics, and crop management. This is particularly evident in the measurement of complex yield components (i.e., spike and spikelet counts), disease and grain-quality traits related to Fusarium Head Blight (FHB) and Fusarium-Damaged Kernels (FDK), and microscale physiological traits such as density and size of stomata and aperture. We introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications. WheatAI v1.0 provides an accessible, browser-based interface that supports multiscale data ingestion from smartphones, Unmanned Aerial Vehicles (UAVs), and portable microscopes. The core functionalities of the platform include plot- and field-scale assessment via UAV- and smartphone-based wheat spike detection and counting, as well as smartphone-based spikelet counting. Additionally, it offers grain quality assessment through FDK ratio estimation and kernel morphometric measurements, such as length, width, and area, derived from smartphone images of kernel samples. For leaf-level analysis, WheatAI provides microscale phenotyping through automated stomatal counting, size, and aperture measurement from digital microscopy images. The system supports both single-image and bulk processing via a guided upload-and-run workflow. This platform is designed to reduce labor costs and rater subjectivity while accelerating field-to-lab decision cycles. By providing standardized, image-based outputs, WheatAI enables breeders, agronomists, and producers to implement high-throughput selection and precision scouting at scale.

Why it matches plant phenotyping methodsWheatAIは、画像から収量構成要素、病害関連形質、穀粒形態、気孔形質を抽出する高スループット植物フェノタイピング基盤そのものであり、方法・ソフトウェアの開発が中心です。

abstractWe introduce WheatAI (wheatai.net), an AI-powered web application designed to bridge the gap between advanced computer vision, AI and deep learning models, and high-throughput phenotyping (HTP) and practical agricultural applications.

FE-SEM visualization of cortical microtubules in plant cells using freeze-fracture techniques ↗

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).

3D Imaging, Segmentation, and Cell Annotation of the Ovule During Megaspore Mother Cell Differentiation in Paspalum spp. ↗

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

Quantification of Callose Deposition in the Phloem of Woody Stems Using Supervised Machine Learning-Driven Automated Image Analysis with the IlastiKlean R Package. ↗

Callose deposition in the phloem is an innate part of plant development and a response to biotic and abiotic stress, aiding in stress mitigation but potentially also compromising phloem functionality. Measuring callose using aniline blue staining is widely employed, but accurate quantification is hindered by image qualities such as texture and fluorescent artifacts. Here, we describe a method to quantify callose levels in the phloem of woody plants using aniline blue staining, confocal microscopy, and automated supervised machine learning-driven image analysis supported by the IlastiKlean R package. Bark peel samples from woody plants are collected from shoots, stained, and imaged to assess callose deposition. The microscopy images are preprocessed and analyzed using Fiji, Ilastik, and the IlastiKlean R package, which allows accurate quantification of the number, size, and distribution of callose deposits. This quantitative measure can be used to study, screen, and engineer plants that are better adapted to biotic or abiotic stresses, and it serves as an important tool for basic and foundational studies of callose deposition in the phloem.

Why it matches plant phenotyping methods木本植物の師部におけるカロース沈着を、画像解析とRパッケージで定量する手法の開発・記述が中心であり、植物の形態・生理状態を表す形質を抽出する。

abstractHere, we describe a method to quantify callose levels in the phloem of woody plants using aniline blue staining, confocal microscopy, and automated supervised machine learning-driven image analysis supported by the IlastiKlean R package.

A Dual-Reporter System for the Analysis of Phloem Structural and Signaling Responses In Vivo. ↗

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.

Characterization of Viruses in Phloem by Correlative X-Ray Microtomography (μCT)-Volume Electron Microscopy (vEM) Imaging. ↗

Studying virus-infected phloem is of significant importance, as it not only enhances our understanding of viral pathogenesis but also leverages viruses as tools to expand knowledge about plant phloem physiology. The uneven distribution pattern of phloem-infecting viruses poses methodological challenges for such studies-requiring both large field of view (FOV) and high-resolution imaging. A comprehensive anatomical analysis of the phloem necessitates global visualization, while resolving viral structures demands local high-resolution observation. This chapter describes a method, the X-ray microtomography (μCT)-volume electron microscopy (vEM) correlative imaging technique, which effectively addresses these methodological requirements, where μCT provides the large FOV for identification of regions of interest, followed by vEM acquisition of high-resolution images. It is a six-step protocol, including: (1) sample preparation, (2) flaw detection, (3) overview imaging by μCT, (4) identifying viral infection regions, (5) high-resolution imaging by vEM, and (6) image processing and analysis. In this workflow, the steps of sample preparation and identification of viral infection regions are critical. This protocol was originally established for investigating Southern rice black-streaked dwarf virus (SRBSDV) infection in rice phloem, with parameters optimized for plant reoviruses. We provide advice on how to adapt the approach for studying other viral infections.

Why it matches plant phenotyping methods植物のウイルス感染部位と師部構造をμCT・vEM相関イメージングで取得・解析する6段階プロトコルが中心であり、植物状態の画像ベース計測法に該当する。

abstractThis chapter describes a method, the X-ray microtomography (μCT)-volume electron microscopy (vEM) correlative imaging technique

Histochemical Staining of Arbuscular Mycorrhizal Roots for Quantification of Fungal Colonization, High-Resolution Imaging, and Localization of Symbiotic Gene Expression. ↗

Histochemical staining and microscopy-based techniques have been widely used to detect, quantify, and analyze the morphology of arbuscular mycorrhizal fungi (AMF) in roots. Here, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents. We also summarize common approaches for quantifying arbuscular mycorrhizal colonization. In addition, we present a simple fluorescent staining protocol, using wheat germ agglutinin-Alexa Fluor conjugates, for high-resolution imaging of fungal colonization patterns and arbuscule morphology in roots. Finally, we describe a GUS staining method for localizing the promoter activity of plant genes potentially involved in mycorrhization, using transformed mycorrhizal hairy roots carrying promoter-GUS fusions.

Why it matches plant phenotyping methodsAMF感染根の菌根菌コロニー形成量・形態を染色と顕微鏡で取得・定量する標準化プロトコルおよび高解像度画像法を中心に扱っており、植物状態の表現型計測法として中心的です。

abstractHere, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents.

Analysis of Gametophytic Apomixis Using Confocal Microscopy. ↗

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.

Methods for Autophagy Detection by Fluorescence Microscopy. ↗

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.

Preparation of Tissue Sections for Light-Microscopic Analysis of Phloem Anatomy. ↗

Accurate analysis and description of plant tissues often rely on the preparation of high-quality anatomical slides, a task that becomes particularly challenging when dealing with heterogeneous tissues such as phloem, which contains both soft and rigid components. This chapter provides a comprehensive protocol outlining key techniques for the optimal preparation of phloem tissue samples for light microscopy. The protocol encompasses essential steps such as fixation, softening, embedding, sectioning, staining, and mounting, and is adaptable for examining phloem and adjacent tissues in both woody and herbaceous stems and roots. Studying phloem anatomy is crucial for understanding nutrient transport, plant development, and responses to environmental stress, offering insights into both fundamental plant biology and practical applications in agriculture and forestry.

Why it matches plant phenotyping methods植物の師部解剖形態を観察・解析するための組織切片作製プロトコルが主題であり、植物形質取得の技術的方法が中心である。

abstractThis chapter provides a comprehensive protocol outlining key techniques for the optimal preparation of phloem tissue samples for light microscopy.

Evaluation of Pectin and Arabinogalactan Protein Distribution in Olive Pollen Tube Cell Walls Using Immunofluorescent Labeling. ↗

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

Multimodal Analysis of Phytoalexin Synthesis in Arabidopsis by Mass Spectrometry Imaging and Fluorescent Microscopy. ↗

Phytoalexins are plant secondary antimicrobial compounds that are rapidly and locally accumulated de novo upon pathogen attacks. They are strongly correlated with disease resistance; therefore, the timing and the location of their synthesis and accumulation have been explored transcriptionally and metabolically using various means separately. In this chapter, by focusing on the Arabidopsis camalexin (CA), we describe protocols for multimodal in situ detection of CA and elemental distribution, as well as the transcriptionally active region of its synthesis gene PHYTOALEXIN DEFICIENT 3 (PAD3) within the same leaf sample challenged with a pathogen.

Why it matches plant phenotyping methods病原体応答に関わる植物の防御状態を、同一葉で多モーダルに可視化・測定するプロトコルが研究の中心であり、単なる生物学実験の routine 測定ではない。

abstractwe describe protocols for multimodal in situ detection of CA and elemental distribution, as well as the transcriptionally active region of its synthesis gene PHYTOALEXIN DEFICIENT 3 (PAD3) within the same leaf sample challenged with a pathogen.

Recent advances in plant imaging technology: a concise review ↗

Imaging technologies have become indispensable tools in modern plant phenotyping, transforming visual information into measurable traits essential for analyzing morphology, physiology, biochemistry, and micro- to nanoscale structures. This concise review summarizes recent advances by dividing plant imaging into two major categories: (1) physiological and biochemical, which includes hyperspectral, multispectral, and fluorescence hyperspectral imaging, as well as terahertz imaging, surface-enhanced Raman scattering, and carbon dot-based techniques; and (2) structural and morphological, encompassing RGB, thermal, light detection and ranging (LiDAR), confocal microscopy, and optical coherence tomography. Together, these modalities deliver insights from the canopy to the molecular level, enabling precise monitoring of plant stress, disease, and developmental traits. By integrating these multimodal imaging techniques with artificial intelligence, the review highlights key developments, current challenges, and future perspectives in plant measurement and analysis.

Why it matches plant phenotyping methods植物フェノタイピングに用いる画像技術を体系的にレビューし、植物形質の測定・解析手法と課題を扱うことが中心である。

abstractThis concise review summarizes recent advances by dividing plant imaging into two major categories

3D imaging reveals robustness and plasticity of cell division in rice early embryogenesis ↗

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

Rice Straw Tissue Preparation for Reproducible Electron Microscopy Imaging and Analysis. ↗

Common problems in biological sample processing for scanning electron microscopy (SEM) include cell collapse and destruction. To overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples. Dried stems are rehydrated with a glycerol solution and fixed in formalin-acetic-alcohol to avoid cell wall collapse or organ distortion. The protocols detailed here comprise the first published method for preparing SEM images of dried plant tissue. The protocols offer a cost-effective approach to obtaining high-quality micrographs, facilitating the reconstruction of growth processes and the study of plant cell wall features. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Pretreatment and preparation of rice straw samples at the heading stage Basic Protocol 2: Paraffin infiltration and embedding Basic Protocol 3: Preparation of microscopic sections Basic Protocol 4: Transferring, adhering, and expanding sections on slides Support Protocol: Preparation of gelatin slides before sectioning to affix samples Basic Protocol 5: Preparation of samples for SEM imaging Basic Protocol 6: SEM analysis Basic Protocol 7: Processing and analysis of SEM images using ImageJ software.

Why it matches plant phenotyping methods乾燥イネ組織のSEM画像取得・処理・解析プロトコル自体が中心で、植物細胞壁形態などの表現型観察を可能にする方法開発である。

abstractTo overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples.

Role of stele and cortex in understanding and predicting root tensile properties. ↗

Background and aims The mechanical properties of plant roots are crucial for soil stabilization and vegetation restoration. To effectively employ bioengineering methods, understanding the tensile properties of plant roots is essential. In most studies, root diameter is used as a predictor of tensile strength but this fails to accurately describe root mechanical behaviour. The stele and cortex are two anatomical parts of the root whose actual mechanical behaviour and specific contributions to root biomechanisms remain unclear. Methods Tensile tests and scanning electron micrography were performed on roots of four typical species (Robinia pseudoacacia, Pinus tabuliformis, Vitex negundo and Syzygium aromaticum) in the Loess Plateau of China to investigate the roles of the stele and cortex in explaining the root's tensile strength. Then, based on the 'same strain' principle, a tensile strength prediction model was developed and validated using experimental data from plant root. Key results The stele and cortex of roots exhibited distinct mechanical behaviours: elastic plasticity and linear elasticity, respectively. Tensile strength was negatively correlated with diameter and stelar diameter and cortical thickness were positively correlated with diameter. The cortex had lower tensile strength, strain at maximum stress and thickness compared with the stele. The observed increase in scatter of tensile strength with decreasing root diameter was attributed to the higher coefficient of variation in cortical tensile strength compared with the stele. Notably, predicted results of intact root tensile strength fell within the 95 % prediction interval of the measured intact root tensile strength and could be enhanced 30-80 % by strengthening dataset quality. Conclusions Our results demonstrated the actual mechanical behaviour characteristics of cortex and stele, and provide a new perspective for addressing the mechanical properties of roots using composite materials mechanics. The findings of this study will provide a theoretical foundation for implementing plant-based ecological restoration and disaster prevention measures.

Why it matches plant phenotyping methods根の引張強度という植物形質を対象に、予測モデルを開発し実験データで検証しており、形質推定法が研究の中心である。

abstractbased on the 'same strain' principle, a tensile strength prediction model was developed and validated using experimental data from plant root.

Lighting up the underground: enhancing growth-ring detection in grassland subshrubs using autofluorescence and histochemistry ↗

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.

PLANT MICROTECHNIQUE WITH RESIN - TOWARDS PLANT HISTOLOMICS ↗

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.

Behavior Study of Plant Roots under Physical Obstacles Based on a Root Obstacle Microfluidic Chip. ↗

The behavior study of plant roots under physical obstacles is of significant importance for comprehending how plants adapt to the changes in the soil environment. Currently, there is no satisfactory method to simulate the soil obstacle environment and track the dynamic change of the root system under physical obstacles. In this work, based on the soil compaction and mechanical obstacles encountered by the root system in the soil, an obstacle microfluidic chip with seven different channels was designed. The obstacle microfluidic chip was fully utilized to take advantage of the variable structure of microfluidic chips to design chip architectures, making it convenient to study the plasticity of root systems under various barriers. The results demonstrated that the microfluidic system's high-resolution imaging capabilities enabled the visualization and quantification of the plant root system's growth behavior in the presence of mechanical obstacles. In addition, to account for the growth resistance or pressure experienced by the roots in the soil, the models were simulated by the fluid flow within the chip. Overall, the obstacle microfluidic chips designed in this study can be used for imaging and quantifying the plasticity of plant roots, which can be an effective tool for tracking the root system's response to mechanical stress.

Why it matches plant phenotyping methods根系の障害物応答を高解像度画像で可視化・定量化するマイクロ流体チップを設計した研究であり、根系形態・成長の取得手法が中心的です。

abstractthere is no satisfactory method to simulate the soil obstacle environment and track the dynamic change of the root system under physical obstacles.

Construction of a biocompatible supramolecular sensor for fluorescence imaging of Nitric oxide in plant tissues. ↗

Nitric oxide (NO) serves as a crucial signaling molecule regulating plant growth and stress responses, and its dynamic monitoring is crucial. This work presents a red-emitting aggregation-induced emission (AIE) supramolecular fluorescent sensor (β-CD/AIENAP) constructed through β-cyclodextrin encapsulation of organic AIE-active molecules (AIENAP). The extension of the material's conjugated structure red shifts the emission wavelength and improves the tissue penetration ability. Meanwhile, β-CD encapsulation restricts intramolecular motion, thereby enhancing fluorescence while improving biocompatibility and cell permeability. Density functional theory calculations verify both the luminescence mechanism and NO-responsive characteristics. The developed probe demonstrates rapid NO response (2 min) via specific triazole formation, exhibiting a large Stokes shift (180 nm), exceptional selectivity, and ultrahigh sensitivity (LOD = 77 nM). Through confocal imaging technology, the sensing system successfully realized the dynamic tracking of the dynamic spatial and temporal distribution of endogenous NO in plants, and systematically studied the NO response characteristics under different abiotic stresses in plants. Exogenous NO application experiments further validate stress resistance regulation. This study provides not only a novel nanosensor for plant NO detection but also an essential tool for analyzing NO signaling transduction and crop stress resistance mechanisms.

Why it matches plant phenotyping methods植物組織内の内生NOという生理状態を可視化・動態追跡する蛍光センサーを開発し、植物ストレス下で実証しており、表現型取得法が中心である。

abstractThis work presents a red-emitting aggregation-induced emission (AIE) supramolecular fluorescent sensor (β-CD/AIENAP) constructed through β-cyclodextrin encapsulation of organic AIE-active molecules (AIENAP).

A stomata imaging and segmentation pipeline incorporating generative AI to reduce dependency on manual groundtruthing. ↗

Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.

Why it matches plant phenotyping methods気孔形質を画像取得・検出・セグメンテーションで抽出する手法と、生成AIによる合成データを用いた種間適応を中心に開発しているため、植物フェノタイピング手法に該当する。

abstractThis study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence.

Accelerated Haustoria Segmentation Enables Rapid Gene Function Analysis in Cereal-Powdery Mildew Pathosystems. ↗

Reliable, high-throughput quantification of early fungal infection events is crucial for gene function studies, but it remains labor-intensive. We 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. Whole-slide images are captured with a commercial scanner, focus-projected, tiled, and analyzed by deep-learning models trained on expertly annotated datasets. A You Only Look Once ( YOLO ) network identifies GUS-positive cells, and a companion segmentation model pinpoints haustoria within each cell; automatic focus-layer selection preserves fine structural detail. The workflow runs in minutes per slide on a single workstation and maintains near-perfect agreement with manual counts in both barley and wheat, demonstrating robust cross-species transferability. By delivering single-cell readouts with minimal user input, the pipeline enables rapid functional validation screens and supports large-scale phenotyping of cereal-powdery mildew interactions. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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.

Live-Cell Monitoring of Piecemeal Chloroplast Autophagy. ↗

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.

Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation. ↗

Stomata are vital for controlling gas exchange and water vapor release, which significantly affect photosynthesis and transpiration. Characterizing stomatal traits such as size, density, and distribution is essential for adaptation to the environment. While microscopy is widely used for this purpose, manual analysis is labor-intensive and time-consuming that limit large scale studies. To overcome this, we introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement. Our approach provides a comprehensive analysis of stomatal morphology by examining both stomatal pores and guard cells. A key finding is the introduction of stomatal angles as a novel phenotyping trait, which can offer deeper insights into stomatal function. We developed a model using a carefully annotated dataset that accurately segments and analyzes stomatal guard cells from high-resolution images. Additionally, our study introduces a new opening ratio metric, calculated from the areas of the guard cells and the stomatal pore, providing a valuable morphological descriptor for future physiological research. This scalable system significantly enhances the precision and efficiency of large-scale plant phenotyping, offering a new tool to advance research in plant physiology.

Why it matches plant phenotyping methods深層学習による気孔画像解析と、気孔形態・角度・開口率の自動推定手法を開発しており、植物フェノタイピング手法が研究の中心である。

abstractwe introduce an automated, high-throughput method that leverages YOLOv8, an advanced deep learning model, for more accurate and efficient stomatal trait measurement.

Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm. ↗

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)

A machine learning-enabled approach to assess trade-offs between growth and stress tolerance in Pooideae grasses following domestication. ↗

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.

IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases. ↗

In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops. A custom-curated dataset was developed, comprising over 1,800 images acquired through smartphone cameras and foldscope devices under natural lighting conditions. The proposed CNN model achieved a classification accuracy of over 95%, with a disease quantification accuracy of 90.5%, calculated using pixel-level segmentation of infected regions. Experimental results revealed infection percentages ranging from 0.68% in early-stage cases to 13.98% in severely affected samples, enabling precise disease severity analysis. The framework includes a MATLAB-based graphical user interface (GUI) for real-time visualization of classification results and severity scores. Training convergence was demonstrated with a mini-batch loss reduction from 1.0879 to 0.0094 over 200 iterations, and classification confidence scores exceeding 90% for most disease categories. In addition to software implementation, the model was synthesized for hardware deployment using FPGA, demonstrating less than 5% LUT and 1% register usage for 512 × 512 images, ensuring resource-efficient performance in IoT environments. This work introduces a scalable, field-deployable tool for crop health monitoring, with potential to enhance sustainable farming practices through timely disease management.

Why it matches plant phenotyping methodsイネ・ジャガイモの病害画像を用いて感染領域と病害重症度を定量化するCNN・画像処理・GUI・FPGA実装を開発しており、植物表現型（病害状態）の取得・抽出が研究の中心です。

abstractThis study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops.

Label-free structural imaging of plant roots and microbes using third-harmonic generation microscopy ↗

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

Single Root hair growth under constant force: insights into wall mechanics ↗

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.

Plant Disease Detection using a Deep Learning approach: a Custom CNN ↗

With the global population expected to increase substantially, it raises a concern about feeding these populations, and it becomes essential to protect crops from diseases for food security. According to several studies, plant diseases and pests cause about 20–40% of the world's crop yield to be lost each year. Current plant disease detection methods include visual inspections, microscopy, culture-based procedures, molecular techniques, etc. These techniques are time-consuming, require specialized equipment and expertise, and are prone to human error. To address this problem, this study employs a customized Convolution Neural Network (CNN), which provides a more effective and scalable substitute for manual inspection and lab-based diagnostic techniques. The model uses CNN's sequential architecture along with softmax and ReLU activation functions. While ReLU introduces non-linearity in the model, which is essential for complex feature extraction, softmax helps in the normalization of vectors and multiclass classification. It has 3 blocks, each consisting of a convolution layer, a pooling layer, and a dropout layer. The model operates on a publicly available hybrid dataset taken from PlantVillage and DoctorP datasets, with a combined total of 5,721 images organized into sub-directories representing different diseases belonging to major groups like fungi, bacteria, virus, non-infectious conditions, nematodes and pests/insects. Images of each category were fed to the model, to identify diseases which are complex to be detected through images. Our model achieved an overall accuracy of 96.54%, illustrating the potential of CNN-based approaches for automated plant disease detection.

Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNを開発し、公開画像データセットで性能を評価しており、植物フェノタイピング手法が研究の中心です。

abstractthis study employs a customized Convolution Neural Network (CNN), which provides a more effective and scalable substitute for manual inspection and lab-based diagnostic techniques.

Novel staining-microscopy workflow visualizes microfibers in soil-plant systems: Implications for sustainable agriculture and food safety. ↗

Microfibers (MFs), primarily originating from sewage sludge and laundry effluents, are the most prevalent form of microplastics (MPs) in agricultural soils. While their ecological effects have been explored, the visualization, crop-level accumulation, and potential transport mechanisms of MFs within soil-plant systems remain poorly understood. This study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions. Three edible vegetables-lettuce, Chinese cabbage, and cherry radish-were used to evaluate species-specific response patterns. The results revealed clear differences in MF interactions across species: lettuce exhibited strong MF adsorption on root surfaces and subsequent penetration via crack-entry and apoplastic pathways without entering cells. In contrast, Chinese cabbage and cherry radish showed limited MF adsorption and no uptake. These patterns were associated with root permeability and antioxidative capacities, indicating that plant functional traits play a critical role in determining the transport capacity of MPs. Beyond introducing a novel method for MF visualization in complex terrestrial matrices, this study provides new insights into the risks posed by MFs to soil-plant systems. The findings also highlight potential threats to food safety and underscore the need to establish plant-specific thresholds and pollution mitigation strategies to support sustainable agriculture and protect public health.

Why it matches plant phenotyping methods植物体内のマイクロファイバー分布・吸着・蓄積・取り込みを可視化する新規蛍光染色・マルチモーダル顕微鏡ワークフローが研究の中心であり、植物状態の測定法として該当する。

abstractThis study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions.

E-NOSE Technology: An Eminent Tool for Early Detection of Plant Diseases ↗

Plant pathogens and pests pose a critical threat to global food security, necessitating early, accurate and cost-effective diagnostic tools. Conventional methods such as visual inspection, microscopy and molecular assays, while accurate, are often labor-intensive, expensive and unsuitable for large-scale field deployment. Recent advances in volatile organic compound (VOC) profiling have enabled the development of electronic nose (E-Nose) technology, which replicates the mammalian olfactory system through arrays of chemical sensors and pattern recognition algorithms. E-Noses offer rapid, non-invasive and non-destructive detection of plant stress and disease at pre-symptomatic stages, providing an essential advantage for timely intervention. Beyond disease diagnostics, E-Noses are increasingly applied in pest detection, crop quality monitoring, fertilizer and pesticide management and environmental surveillance, aligning closely with the objectives of precision agriculture. The integration of artificial intelligence, nanotechnology and machine learning has enhanced system accuracy and robustness, while challenges such as sensor drift, standardization and field-level validation remain. Overall, E-Nose technology holds significant promise as a practical and sustainable tool for advancing plant health management, promoting environmental sustainability and strengthening the resilience of modern agricultural systems.

Why it matches plant phenotyping methods植物の揮発性有機化合物を電子鼻で測定し、病害・ストレス状態を非破壊的に推定する手法を扱うレビューであり、植物フェノタイピング手法が中心です。

abstractRecent advances in volatile organic compound (VOC) profiling have enabled the development of electronic nose (E-Nose) technology

Discovery of new Australasian Rare Earth Element hyperaccumulator ferns from screening herbarium specimens ↗

Abstract Background and Aims Rare Earth Elements (REE) are essential for the development of clean technologies. Hyperaccumulator plants are metal-loving organisms that can be used to remove metals from contaminated soils. This study aimed to discover new REE hyperaccumulators in the Australasian region among the Blechnaceae and Gleicheniaceae families using specimens stored at the Queensland Herbarium. Methods A handheld X-ray fluorescence (XRF) instrument was harnessed to scan herbarium specimens, and this data was analysed with Dynamic Analysis in GeoPIXE. Selected specimens were further analysed to validate the XRF results: elemental analysis was conducted with inductively coupled plasma optical emission spectroscopy (ICP-OES), an elemental distribution map through micro-X-ray fluorescence (µXRF) and scanning electron microscopy (SEM) to rule out airborne contamination of plant samples. Results From the 3256 specimens analysed with the portable XRF, 73 specimens met the criteria to be considered REE hyperaccumulators (yttrium >50 µg g-1 on XRF analysis). Among this group, 11 new hyperaccumulator taxa were discovered, and the elemental analysis reported a total REE concentration around 1000 µg g-1, i.e. Diploblechnum neglectum (978 µg g-1), Sticherus flabellatus (1130 µg g-1), Sticheropsis milnei (1290 µg g-1). We validated the strong REE hyperaccumulating capacity of the previously reported ferns Blechnopsis orientalis (3850 µg g-1 total REEs) and Dicranopteris linearis (1280 µg g-1 total REEs). Conclusions The use of non-destructive portable XRF to scan herbaria collections is a tool to discover hyperaccumulator plants and this information could also be used as a bioprospecting tool to find REE deposits for potential REE phytomining.

Why it matches plant phenotyping methods携帯型XRFによる植物標本の非破壊スキャンを用いてREE蓄積形質を抽出し、ICP-OES等で検証しており、植物形質の取得・検証法が研究の中心である。

abstractA handheld X-ray fluorescence (XRF) instrument was harnessed to scan herbarium specimens, and this data was analysed with Dynamic Analysis in GeoPIXE.

AnatomyArray: A high-throughput platform for anatomical phenotyping in plants ↗

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.

Realistic Scenarios of Phenotypic Variation and Errors in High-Throughput Phenotyping Experiments Minimally Impact the Results of Quantitative Trait Locus Mapping Analysis ↗

High-throughput phenotyping technologies increase the efficiency of breeding programs, but with larger datasets, errors can accumulate. Plant breeders often conduct quantitative trait locus (QTL) mapping, where large sample size and accurate quantitative response estimates are important for detecting small-effect QTLs. This study examined how phenotype error, inconsistency, and replication changed QTL magnitude and location. Three real sets of phenotype data were used from microscopy robot analysis of grapevine powdery mildew ( Erysiphe necator) severity, which previously resulted in discovery of large ( R 2 = 85%), intermediate ( R 2 = 45%), and small ( R 2 = 9%) effect QTLs. Custom R scripts were written to induce several realistic sources of error, inconsistency, and varied replication. The results were remarkably robust to these changes. Swapping or shifting 2% of samples or changing disease severity by 50% on one replicate had negligible impact on QTLs. Unreplicated simulations produced the largest logarithm of the odds score range (5.55 to 8.27) and mean logarithm of the odds score deviation (−1.72 to −3.22; Cohen's D = 1.48 to 2.12). The large-effect-size QTL ( REN12) was always detected. The intermediate-effect-size QTL ( REN13) was detected except when three of the eight replicates were analyzed individually. Even for the small-effect-size locus ( NYVPLG9), error scenarios rarely (2 of 9,000 cases) eliminated significant QTL detection, versus no replication (9 of 10). Thus, the benefits of data volume associated with high-throughput phenotyping technologies outweigh the cost of the increased errors tested here. Instead, the focus should be on examining how each experimental replicate contributes to the results of the QTL mapping analysis.

Why it matches plant phenotyping methods高スループット画像表現型測定で生じる誤差・反復数・不整合がQTL解析結果に与える影響を系統的に評価しており、表現型取得データの技術的妥当性検証が中心である。

abstractThis study examined how phenotype error, inconsistency, and replication changed QTL magnitude and location.

Detection of roll rotation of naturally occurring crystals inside plant leaves and detection of adhesivity of the interior surface of leaf cell ↗

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

Multimodal Dissection of UV-B--Induced Plant Defense ↗

Sustainable agriculture urgently requires innovative, pesticide-free strategies to mitigate herbivory and safeguard food security. Ultraviolet-B (UV-B) irradiation, with tunable intensity and cost-effectiveness, has emerged as a promising non-chemical method to enhance plant resistance, yet its underlying mechanisms remain elusive. Here, using tea plant ( Camellia sinensis ) and its major pest Ectropis obliqua as a model, we developed a multimodal framework that integrates AI-enhanced electronic nose technology for real-time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses under precisely controlled UV-B treatments. This approach identified herbivore-induced volatiles—hexanal, (Z)-3-hexenol, octanal, and (Z)-3-hexenyl acetate—optimally induced at 1.2 kJ·m -2 UV-B and linked to insect deterrence. SRS imaging further revealed elevated jasmonic acid derivatives and L-phenylalanine, coupled with reduced protein levels and altered stomatal dynamics, all correlating with enhanced resistance. Transcriptomic and molecular analyses confirmed transcriptional regulation of these pathways. By bridging volatile detection, metabolic imaging, and molecular validation, this study pioneers a multimodal strategy that provides mechanistic insights into UV-B–mediated plant defense and highlights the potential of multimodal methodologies as powerful tools for developing sustainable, pesticide-free pest management solutions in precision agriculture.

Why it matches plant phenotyping methodsAI強化電子鼻とSRS顕微鏡を統合した植物防御応答の取得・解析フレームワークが研究の中心であり、揮発性物質、代謝、気孔動態などの植物状態を測定しているため。

abstractwe developed a multimodal framework that integrates AI-enhanced electronic nose technology for real-time volatile profiling with in situ hyperspectral stimulated Raman scattering (SRS) microscopy to characterize defense responses

SAMMBA is a high-throughput pipeline for isolating and phenotyping macroalgal strains ↗

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).

Root Anatomy: Preparing, Imaging, and Analyzing Maize Root Cross - Sections. ↗

Root anatomy plays a critical structural and functional role in the maize root system, and regulates edaphic stress tolerance. The function and genetic basis of several maize root anatomical traits for stress tolerance have been demonstrated. Leveraging root anatomical traits in maize thus holds great potential for developing cultivars with greater nutrient and water efficiency. Key for such approaches is the ability to characterize the root anatomy of plants of interest. Here, we outline a systematic method for preparing, imaging, and analyzing maize root cross-sections. The protocol describes root sectioning (by hand or using a vibratome), preparation of microscope slides and toluidine blue staining, imaging under a light microscope, and both manual and semiautomated methods for anatomical feature extraction from images. The protocol enables the visualization and quantification of various anatomical tissues and traits, and its simplicity, adaptability, and accessibility make it an ideal choice for both small- and large-scale phenotyping studies in maize and other plant species. This standardized protocol provides researchers with a comprehensive methodology to accurately dissect root structures, enabling in-depth analyses that are essential for understanding plant growth, development, and adaptive value for stress tolerance.

Why it matches plant phenotyping methodsトウモロコシ根の切片作製、顕微鏡画像化、画像からの解剖学的形質抽出を体系化したプロトコルであり、植物フェノタイピング手法が中心である。

abstractHere, we outline a systematic method for preparing, imaging, and analyzing maize root cross-sections.

End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality ↗

Seeds are essential to the agri-food industry. However, their quality is vulnerable to biotic and abiotic stresses during production and storage, leading to various types of deterioration. Real-time monitoring and pre-sowing screening offer substantial potential for improved storage management, field performance, and flour quality. This study investigated diverse deterioration patterns in wheat seeds by analyzing 1000 high-quality and 1098 deteriorated seeds encompassing mold, aging, mechanical damage, insect damage, and internal insect infestation. Hyperspectral imaging (HSI) and computer vision (CV) were employed to capture surface data from both the embryo (EM) and endosperm (EN). Internal seed quality was further assessed using scanning electron microscopy, dissection, and standard germination tests. Both conventional machine learning algorithms and deep convolutional neural networks (DCNN) were employed to develop discriminative models using independent datasets. Results revealed that each data source contributed valuable information for seed quality assessment (validation set accuracy: 65.1–89.2 %), with the integration of HSI and CV showing considerable promise. A comparison of early and late fusion strategies led to the development of an end-to-end deep fusion model. The decision fusion-based DCNN model, integrating HSI-EM, HSI-EN, CV-EM, and CV-EN data, achieved the highest accuracy in both training (94.3 %) and validation (93.8 %) sets. Applying this model to seed lot screening increased the proportion of high-quality seeds from 47.7 % to 93.4 %. These findings were further supported by external samples and visualizations. The proposed end-to-end decision fusion DCNN model simplifies the training process compared to traditional two-stage fusion methods. This study presents a potentially efficient alternative for rapid, individual kernel quality detection and control during wheat production.

Why it matches plant phenotyping methods小麦種子の品質状態をHSIとコンピュータビジョンで取得・推定する融合モデルを開発し、独立データセットおよび外部試料で検証しており、表現型取得・判定法が研究の中心です。

abstractHyperspectral imaging (HSI) and computer vision (CV) were employed to capture surface data from both the embryo (EM) and endosperm (EN).

Precision profiling of seed coat phenotypes in maize: 3D surface morphology, color, texture traits for the construction of phenotyping interaction networks ↗

The seed coat serves as a protective barrier between seeds and their environment. This structure plays fundamental roles in protection, environmental sensing, and germination regulation. Current phenotypic characterization methods typically measure the seed coat together with adjacent structures, including the aleurone layer and endosperm. Such combined measurements hinder accurate assessment of seed coat-specific traits. This study presents an integrated analytical approach for phenotyping isolated maize seed coats. The method combines microscopic hyperspectral imaging with atomic force microscopy (AFM), enabling quantitative assessment of 24 phenotypic indicators spanning roughness, light transmittance, color, and texture parameters. The investigation of phenotypic diversity focused on inbred lines from natural association populations. The analytical workflow involved kernel contour extraction from RGB images followed by detailed phenotypic mapping. Population-wide analysis revealed substantial phenotypic variation. Coefficients of variation ranged from 30 % to 45 % for light transmittance and color texture phenotypes, while exceeding 60 % for roughness parameters. A phenotypic interaction network was constructed to elucidate trait relationships, identifying VLD as key characteristic phenotypes in seed coat morphology. Dimensional reduction analysis highlighted 12 critical indicators: Rp, Ra, Rv, Rz, LAQ, VLI, LAD, TRGSD, TSGSH, TRGSE, CBAve, and SCAve. Germination studies demonstrated significant correlations between seed emergence rate (SER) and multiple seed coat traits, including light transmittance, color, and texture characteristics (R: −0.204 to −0.194, P < 0.05). Notable inbred lines, including Ry737, Dong46, CML486, and CML426, exhibited superior germination rates characterized by low seed coat roughness, high light transmittance, enhanced texture roughness, and increased color saturation and brightness. The methodological advances presented here provide novel insights into maize seed coat characteristics. These findings have significant implications for precise germplasm identification and the development of high-quality, high-vigor maize varieties.

Why it matches plant phenotyping methods分離したトウモロコシ種皮を対象に、顕微鏡ハイパースペクトル画像とAFMを統合し、形態・色・透過性・テクスチャなど24指標を定量化するフェノタイピング手法が研究の中心である。

abstractThis study presents an integrated analytical approach for phenotyping isolated maize seed coats.

Constant distance between leaf initiation sites permits non-destructive analysis of apical meristem activity during cactus shoot growth. ↗

Background and scope Trunks of saguaro cacti (Carnegiea gigantea) grow for many years, and during this time the shoot apical meristem (SAM) of each trunk not only grows in diameter, it also initiates new orthostichies (ribs). Several questions were examined. Is a saguaro SAM's diameter correlated with the number of orthostichies/ribs it is producing? Is SAM diameter tightly controlled, or does it vary among individuals of the same age? When saguaro trunks are ~3 m tall, their SAMs stop adding new orthostichies/ribs: do SAMs stop growing only after reaching a critical diameter, or do the SAMs vary in diameter when each stops growing? Methods Ribs were counted at various heights (corresponding to various ages) on saguaro plants in habitat. Shoot apical meristem diameter was measured by light microscopy in sectioned material. Shoot apical meristems of Echinocactus grusonii were also studied. Key results Shoot apical meristem diameter is strongly correlated with the number of ribs being maintained: the circumferential distance between newly initiated leaf primordia remains constant (145 ± 10.6 µm in C. gigantea; 193 ± 10.7 µm in E. grusonii) even as an SAM grows in diameter. An SAM's diameter and circumference can be estimated by counting the number of ribs it is maintaining. The diameter of each SAM of C. gigantea increases for many years but it eventually stabilizes; the final, stable diameter of each C. gigantea SAM varies from shoot to shoot. Conclusions Shoot apical meristem diameter in both species can be estimated non-destructively by simply counting the number of orthostichies/ribs the SAM is producing (or produced in the past). The growth rate of C. gigantea SAMs varies from plant to plant and can change with age. All C. gigantea SAMs stop increasing in diameter at some point, but that diameter varies from plant to plant.

Why it matches plant phenotyping methodsサボテンのシュート頂端分裂組織径という植物形態形質を、肋数から非破壊推定する測定法が研究の中心であり、手法の成立性と適用結果を示している。

abstractAn SAM's diameter and circumference can be estimated by counting the number of ribs it is maintaining.

A cytological framework of female meiosis in Arabidopsis ↗

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.

A Suite of Stains: Characterization of four fluorophores as complementary tools for visualizing neutral lipids in an extremophilic green alga ↗

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.

Live Leaf-Section Imaging for Visualizing Intracellular Chloroplast Movement and Analyzing Cell-Cell Interactions. ↗

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 mechanical properties of Arabidopsis thaliana roots adapt dynamically during development and to stress ↗

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.

From microstructure to multivariate prediction models: Decoding the biomechanical properties of tea stems via PLSR-Ridge regression and multifactorial orthogonal design ↗

Efficient harvesting and field management of tea is closely related to the mechanical properties of tea stems; however, there have no research on variables and models that can be used to predict them. In this paper, the relationship between factors (number of segments, diameter, stem length, density, moisture content, moment of inertia, and fracture deflection) affecting the mechanical properties (tensile and bending strength) of tea stems was analysed using a combination of partial least squares regression and ridge regression analyses, using three tea varieties, namely, Jinxuan, Yinghong, and Liannan, as the research subjects. Ultra depth of field electron microscopy was used to aid in the interpretation of the mechanical properties. The regression fitting results showed that the R² values of the prediction models for the tensile and flexural strengths of Jinxuan tea stems were 0.9361 and 0.9054, respectively, and the root mean square errors of prediction (RMSEP) were 0.7709 MPa and 1.9083 MPa, respectively. The R² values of the tensile and bending strengths of Liannan tea stems were 0.9161 and 0.9240, respectively, and the RMSEP were 0.3948 MPa and 1.2973 MPa, respectively. The R² values of Yinghong tea stems were 0.9292 and 0.9196, and the RMSEP values were 1.3207 MPa and 1.7489 MPa, respectively. The RPD of all the prediction models was greater than 3, indicating high prediction accuracy. The results of the comprehensive orthogonal test using four factors and three levels showed that the factors affecting the shear strength of tea stems were in the following order: stem segment, variety, moisture content, and shear speed. The results of the study will help to understand the biomechanical properties of tea stems, improve their resource utilisation and provide a reference for the optimal design of industrial cutting devices.

Why it matches plant phenotyping methods茶茎の tensile／bending strength という植物器官特性を、形態・物性変数からPLSR-Ridge回帰で予測するモデルを開発・評価しており、特性推定法が研究の中心である。

abstractthere have no research on variables and models that can be used to predict them.

Plant-Compatible Xenium In Situ Sequencing: Optimised Protocol for Spatial Transcriptomics in Medicago truncatula Roots and Nodules ↗

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.

Black ink staining protocol: A cost-effective substitute in quantifying arbuscular mycorrhizal colonization in plant roots ↗

Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).

Why it matches plant phenotyping methods植物根のAM菌根菌感染状態を染色とコンピュータビジョンで定量する手法の開発・比較評価が中心であり、単なる生物学的測定ではない。

abstractSix reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one.

Analyzing different phenotypic methods of soybean leaves under the high temperature stress with near-infrared spectroscopy, microscopic Image, and multispectral image ↗

High temperature stress (HT) plays an important role in soybean selection and breeding, it can cause changes in soybean physiological, biochemical and morphological traits, and directly affect the growth and yield of soybean plants. Among these changes, soybean leaves are particularly sensitive to HT during growth and development. It is important to establish a non-destructive method to distinguish the phenotypic differences between soybean plants under HT and control (CK). In this study, data from two years of soybean field trials were used. In the first year, phenotypic information was collected by near-infrared spectroscopy (NIR), microscopic images, and further difference analysis and classification modelling experiments were conducted. In the second year, multispectral image data were collected and analyzed by Soybean high temperature mask autoencoder (SHT_MAE). The SHT_MAE model with a 75% masking ratio achieved an accuracy of 89.16% and an F1-score of 89.18%. Compared with one-dimensional near-infrared and two-dimensional microscopic image fusion models, the classification accuracy of HT and CK is improved by 2.68%. The accuracy of SHT_MAE multispectral model was improved by 16.84% and 6.88%, respectively, compared with models using only NIR or microscopic images. Both spectral and imaging methods effectively distinguish the phenotypic differences between HT and CK soybean leaves, with the multispectral approach based on the SHT_MAE model demonstrating a clear advantage. This study realized the effective distinction of soybean leaves under HT and CK. It provides theoretical support for HT intelligent breeding (using artificial intelligence and data analysis to optimize breeding decisions) and high temperature grade prediction.

Why it matches plant phenotyping methods高温ストレス下のダイズ葉の表現型差を、近赤外分光・顕微鏡画像・マルチスペクトル画像と分類モデルで非破壊的に抽出・比較する手法研究であり、表現型取得と解析が中心である。

abstractIt is important to establish a non-destructive method to distinguish the phenotypic differences between soybean plants under HT and control (CK).

Expanding plant cell microscopy through artificial intelligence focusing on segmentation and virtual staining ↗

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.

Advanced illumination-imaging reveals photosynthesis-triggered pH, ATP and NAD redox signatures across plant cell compartments ↗

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

A simple and versatile plasma membrane staining method for visualizing living cell morphology in reproductive tissues across diverse plant species ↗

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.

Reconstructing the diversity dynamics of paleo-grasslands using deep learning on superresolution images of fossil Poaceae pollen ↗

Grass pollen is largely overlooked in investigating grassland evolution because the pollen of most species cannot be differentiated using traditional optical microscopy. However, deep learning can quantify small variations in pollen morphology visible under superresolution microscopy. We use the abstract features output by deep learning to estimate the taxonomic diversity and physiology of fossil grass pollen assemblages. Using a semi-supervised learning strategy, we trained convolutional neural networks (CNNs) on superresolution pollen images of modern grasses and unlabeled fossil Poaceae. Our models captured features that reflected both the taxonomic diversity of grass communities along an elevational gradient and morphological differences between C3 and C4 species. We applied our trained models to fossil grass pollen assemblages from a 25,000-year lake-sediment record from eastern equatorial Africa (Mt. Kenya) and correlated past shifts in grass diversity with atmospheric CO2 concentration and proxy records of local temperature, precipitation, and fire occurrence. We quantified changes in grass diversity using morphological variability of fossil pollen assemblages, approximated by the Shannon entropy of CNN features. Our data show that grassland species diversity was strongly reduced between 21,500 and 16,000 years ago, coincident with most severe regional cooling during the last ice age. C3:C4 ratios reconstructed using a gradient-boosted decision tree classifier infer a gradual decrease in C4 grasses since the late-glacial to Holocene transition, associated with decreasing fire activity and elevated temperatures. Our results demonstrate that CNN features of pollen morphology can advance palynological analysis, enabling robust estimation of grass diversity and C3:C4 ratio in ancient grassland ecosystems. SignificanceAlthough the pollen of most grass species are morphologically indistinguishable using traditional optical microscopy, we show that they can be differentiated through deep learning analyses of superresolution images. Abstracted morphological features derived from convolutional neural networks can be used to quantify the biological and physiological diversity of grass pollen assemblages, without a priori knowledge of the species present, and used to reconstruct past changes in the taxonomic diversity and relative abundance of C4 grasses in ancient grasslands. This approach unlocks ecological information previously unattainable from the fossil pollen record and demonstrates that deep learning can solve some of the most intractable identification problems in the reconstruction of past vegetation dynamics.

Why it matches plant phenotyping methods超解像花粉画像とCNN特徴量を用いて、花粉形態からイネ科の多様性およびC3:C4比を推定する手法を開発・適用しており、植物形質抽出が研究の中心です。

abstractdeep learning can quantify small variations in pollen morphology visible under superresolution microscopy

A long term time lapse microscopy technique for Arabidopsis roots. ↗

Time lapse microscopy is a transformative technique for plant cell and developmental biology. Light sheet microscopy, which manipulates the amount of light a sample is exposed to in order to minimize phototoxicity and maximize signal intensity, is an increasingly popular tool for time lapse imaging. However, many light sheet imaging systems are not designed with the unique properties of plant samples in mind. Recent advances have decreased the cost and increased the technical accessibility of light sheet microscopy, but plant samples still require special preparation to be compatible with these new systems. Here, we apply a novel light sheet microscopy system to regenerating Arabidopsis roots damaged via laser ablation. To adapt this system for Arabidopsis roots we establish a new protocol for sample mounting, as well as an automated root tip tracking system that requires no additional proprietary software. The methods presented here can be used to increase researcher access to long-term time-lapse imaging in Arabidopsis biology.

Why it matches plant phenotyping methodsArabidopsis根の長期タイムラプス撮像系を植物試料向けに適応し、試料マウント法と自動根端追跡を開発しており、表現型取得・抽出法が中心である。

abstractTo adapt this system for Arabidopsis roots we establish a new protocol for sample mounting, as well as an automated root tip tracking system that requires no additional proprietary software.

Computational Approaches to Revisiting Plant Cytoskeleton Organization and Dynamics ↗

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.

Using a Live Analysis System to Study Amyloplast Replication in Arabidopsis Ovule Integuments. ↗

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 .

Imaging of specialized plant cell walls by improved cryo-CLEM and cryo-electron tomography ↗

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.

Methods to Observe Plant Tissue Colonization by Fusarium oxysporum. ↗

Fusarium oxysporum, an important soil-borne pathogen, causes vascular wilts in more than 100 plant species, leading to billions of dollars in annual yield losses. Controlling Fusarium wilt diseases is challenging due to the persistence of pathogen spores in infested fields and the growing resistance to available fungicides. Understanding the molecular interactions between F. oxysporum and its host plants is crucial for developing novel control strategies, but studying these interactions is difficult because F. oxysporum invades plant roots long before wilt symptoms can be detected in above-ground tissues. To illuminate the hidden interactions between F. oxysporum and its plant hosts, we present three confocal microscopy protocols for visualizing fungal colonization in plant tissues and the associated plant responses. The first protocol employs wheat germ agglutinin-Alexa Fluor 488 and propidium iodide to stain fungal cells and plant host tissues, respectively. The second uses sirofluor to detect deposition of callose, a (1,3)-β-glucan polymer found in plant cell walls that plays a significant role in plant defense. The third utilizes fluorescent protein-tagged fungal isolates and a stable transgenic Arabidopsis thaliana line, providing a clean and easily accessible system for visualizing early infection stages. The protocols described here will shed light on underground plant-pathogen interactions, aiding researchers in unraveling the complex dynamics between diverse F. oxysporum pathotypes and their plant hosts.© 2025 Wiley Periodicals LLC. Basic Protocol 1: Observation of F. oxysporum cells in the tomato stem vasculature Basic Protocol 2: Observation of callose deposition in F. oxysporum-colonized tomato plant roots Basic Protocol 3: Observation of fungal colonization in an F. oxysporum-A. thaliana model system.

Why it matches plant phenotyping methods植物組織内の病原菌定着、植物組織、カロース沈着および感染応答を可視化する3種の共焦点顕微鏡プロトコルが研究の中心であり、植物の病態・応答の画像取得法に該当する。

abstractwe present three confocal microscopy protocols for visualizing fungal colonization in plant tissues and the associated plant responses.

Quantitative Assessment of Mitochondrial Volumetric Transitions in Arabidopsis thaliana. ↗

Mitochondria in plants typically appear as discrete spherical or slightly tubular organelles, with their morphology and volume serving as indicators of metabolic state and dysfunction. Measuring changes in mitochondrial volume is relatively straightforward in organisms lacking plastids. However, in chlorophyll-rich tissues, such assessments often require purification protocols that may compromise accuracy. Here, we present protocols for the quantitative assessment of mitochondrial volume transitions in leaf mesophyll cells of Arabidopsis thaliana. The methods are simple and highly sensitive and offer a reliable approach for studying mitochondrial morphology transitions under both physiological and stress conditions. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Leaf mesophyll treatment and mitochondrial imaging Basic Protocol 2: Leaf mesophyll mitochondrial volume assessment Basic Protocol 3: Mitochondrial volume statistics.

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.

Analysis of Autophagy Under Abiotic Stress in Arabidopsis Seedlings Expressing the GFP-ATG8 Autophagosome Marker. ↗

Plant autophagy is a catabolic process where cellular components such as protein aggregates and dysfunctional organelles are degraded and recycled to maintain homeostasis and facilitate stress resilience. Autophagy relies on a double-membrane vesicle called the autophagosome, which delivers cellular cargo to the vacuole for degradation. The Arabidopsis GFP-ATG8 reporter line is a valuable tool widely used to visualize and quantify autophagosomes via microscopy and monitor autophagic degradation via immunoblotting. Consistent assessment of autophagic activity requires standardized protocols for sample preparation, imaging, and data analysis. Here, we present protocols for monitoring autophagy in Arabidopsis seedlings expressing GFP-ATG8, including treatments to induce or inhibit autophagic flux, as well as imaging and image analysis procedures. These methods enable reliable evaluation of autophagic activity and can be adapted for diverse experimental conditions and genotypes. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Growth of Arabidopsis seedlings Basic Protocol 2: Activation of autophagy in Arabidopsis seedlings by abiotic stresses Basic Protocol 3: Inhibition of vacuolar degradation by concanamycin A treatment Basic Protocol 4: Quantification of GFP-ATG8-labeled autophagosomes in Arabidopsis seedlings via microscopy Basic Protocol 5: Analysis of autophagic degradation of GFP-ATG8 via immunoblotting.

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、GFP-ATG8蛍光イメージングと画像解析・免疫ブロッティングで定量する標準化プロトコルが中心であり、表現型取得法として適格。

abstractConsistent assessment of autophagic activity requires standardized protocols for sample preparation, imaging, and data analysis.

On‐Chip Analytical Method for Investigating Protrusive Forces in Growing Plant Roots† ↗

In this study, we present an on‐chip analytical method using a microfluidic device to characterize the mechanical properties in growing roots. Roots are essential organs for plants and grow under heterogeneous conditions in soil. Especially, the mechanical impedance in soil significantly affects root growth. Understanding the mechanical properties of roots and the physical interactions between roots and soil is important in plant science and agriculture. However, an effective method for directly evaluating the mechanical properties of growing roots has not been established. To overcome this technical issue, we developed a polydimethylsiloxane (PDMS) microfluidic device integrated with a cantilevered sensing pillar for measuring the protrusive force generated by the growing roots. Using the developed device, we analyzed the mechanical properties of the roots in a model plant, Arabidopsis thaliana . The root growth behavior and the mechanical interaction with the sensing pillar were recorded using a time‐lapse microscopy system. We successfully quantified the mechanical properties of growing roots including the protrusive force and apparent Young's modulus based on a simple physical model considering the root morphology. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Why it matches plant phenotyping methods成長中の植物根の突出力や見かけのヤング率を定量するマイクロ流体・カンチレバー型計測法を開発しており、根の機械的表現型取得が研究の中心である。

abstractwe developed a polydimethylsiloxane (PDMS) microfluidic device integrated with a cantilevered sensing pillar for measuring the protrusive force generated by the growing roots.

Rice-SVBDete: a detection algorithm for small vascular bundles in rice stem's cross-sections. ↗

Introduction Vascular bundles play a vital role in the growth, development, and yield formation of rice. Accurate measurement of their structure and distribution is essential for improving rice breeding and cultivation strategies. However, the detection of small vascular bundles from cross-sectional images is challenging due to their tiny size and the noisy background typically present in microscopy images. Methods To address these challenges, we propose Rice-SVBDete, a specialized deep learning-based detection algorithm for small vascular bundles in rice stem cross-sections. Our approach enhances the YOLOv8 architecture by incorporating three key innovations: Dynamic Snake-shaped Convolution (DSConv) in the Backbone network to adaptively capture intricate structural details of small targets. A Multi-scale Feature Fusion (MFF) mechanism, combining features from the Backbone, Feature Pyramid Network (FPN), and Path Aggregation Network (PAN), to better handle objects at multiple scales. A new Powerful Intersection over Union (PIoU) loss function that emphasizes spatial consistency and positional accuracy, replacing the standard CIoU loss. Results Experimental evaluations show that Rice-SVBDete achieves a precision of 0.789, recall of 0.771, and mean Average Precision (mAP@.5) of 0.728 at an IoU threshold of 0.50. Compared to the baseline YOLOv8, Rice-SVBDete improves precision by 0.179, recall by 0.201, and mAP@.5 by 0.227, demonstrating its effectiveness in small object detection. Discussion These results highlight Rice-SVBDete's potential for accurately identifying small vascular bundles in complex backgrounds, providing a valuable tool for rice anatomical analysis and supporting advancements in precision agriculture and plant science research.

Why it matches plant phenotyping methodsイネ茎断面画像から小維管束を検出・測定する深層学習手法を開発し、ベースライン比較で性能検証しているため、植物表現型取得が研究の中心である。

abstractAccurate measurement of their structure and distribution is essential for improving rice breeding and cultivation strategies.

Phenotyping-based spectral signatures uncover barley cultivars’ sensitivity to combined mildew and drought treatment ↗

The plant’s phenotype changes under biotic and abiotic stress, reflecting its adaptations in gene expression and metabolism. For crop management, rapid detection of plant stress responses is crucial. To facilitate rapid detection of stress responses in crops, we explored the potential of UCPH’s PhenoLab for assessing barley disease resistance under both biotic and abiotic stress. We used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts. At specific wavelengths, PhenoLab spectral signatures clearly distinguished cultivars with different levels of susceptibility to the obligate biotroph pathogen Blumeria graminis (powdery mildew). Microscope phenotyping at similar reflectance and fluorescence settings parallelled the PhenoLab-derived spectral signatures. However, a specific systemic resistance response emerged three days after inoculation, detectable only by microscopy when targeting infected and non-infected leaf areas. We hypothesized that combined stresses would work additively and used phenotyping to study the response of the resistant and susceptible barley cultivar to a combination of drought with powdery mildew infection. Surprisingly, drought made the resistant cultivar less resistant and the susceptible one less susceptible according to changes in reflectance and fluorescence at defined wavelengths. The spectroscopic absorbance assay confirmed this result biochemically. This proof-of-concept study showcases the potential of holistic functional phenomics, using non-invasive imaging to identify predictive spectral signatures for barley pathogen resistance.

Why it matches plant phenotyping methodsPhenoLabの高スループット反射・蛍光イメージングを用いて、病害抵抗性と複合ストレス応答を非破壊的に評価し、スペクトルシグネチャを検証しており、表現型取得法が研究の中心です。

abstractWe used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts.

Monitoring CO as a plant signaling molecule under heavy metal stress using carbon nanodots. ↗

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).

Sewing the future of cotton: a multi-omics study combining nanomechanics, transcriptomics, and phenotypic traits ↗

Cellulose microfibrils that are essential for mechanical strength and overall quality of cotton fibers. This study quantifies and compares the nanoscale structural and mechanical properties of cellulose microfibrils such as microfibril dimensions, crossover count and angles, roughness, and Youngs modulus for two popular cotton species: Gossypium hirsutum (Gh) and Gossypium barbadense (Gb) fibers across four growth stages (8, 12, 18, and 22 days post-anthesis) using atomic force microscopy (AFM). Our results revealed for the first time that Gb fibers exhibit a better alignment, finer dimensions, and higher stiffness compared to Gh fibers at nanoscale, resulting in smoother fiber surfaces, and improved quality at macroscale. We are also the first to develop machine-learning models to predict macroscale phenotypic traits specifically boll length and cellulose content using nanoscale features alone and in combination with multi-omics modalities, substantially enhancing the predictive accuracy and highlighting opportunities for robust cross-species modeling of cotton fiber traits.

Why it matches plant phenotyping methodsAFMによる綿繊維のナノスケール形態・力学形質の定量と、ナノスケール特徴からボール長などの植物表現型を予測する機械学習モデルの開発が研究の中心である。

abstractusing atomic force microscopy (AFM)

Hypoxia‐activated fluorescent probes as markers of oxygen levels in plant cells and tissues ↗

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.

Simultaneous quantification of cell wall elasticity and turgor pressure in live plant cells by elastic shell theory analysis and AFM ↗

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.

Towards ROXAS AI: automatic multi-species ring boundaries segmentation as regression in anatomical images. ↗

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.

Black ink staining protocol: A cost-effective substitute in quantifying arbuscular mycorrhizal colonization in plant roots. ↗

Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).

Why it matches plant phenotyping methods植物根の菌根コロニー形成を定量する染色・画像解析法の開発と試薬間比較検証が研究の中心であり、単なる生物学的測定ではない。

abstractSix reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one.

Advancing cotton fiber research with variable-pressure scanning electron microscopy. ↗

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.

From Bracts to Buds: Developing Reliable Methods for Trichome Density Assessment in Cannabis sativa L. ↗

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.

Camelot: a computer-automated micro-extensometer with low-cost optical tracking. ↗

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.

A Method to Visualize Cell Proliferation of Arabidopsis thaliana : A Case Study of the Root Apical Meristem. ↗

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.

Detection of Heavy Metal Copper Stress in Apple Rootstocks Using Surface-Enhanced Raman Spectroscopy. ↗

Excessive use of copper (Cu) chemicals has led to soil contamination. This study utilized surface-enhanced Raman spectroscopy (SERS) to investigate the effects of 10 commonly encountered concentrations of Cu stress in orchards on apple rootstocks. Spectral preprocessing methods were employed to eliminate baseline drift and fluorescence background interference from the Raman spectra, while data augmentation techniques were incorporated to develop a one-dimensional stacked autoencoder convolutional neural network (1D-SAE-CNN) for classifying Cu stress levels, resulting in evaluation indices greater than 0.9. Scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) quantified Cu distribution in root, stem, and leaf tissues, while micro-Raman imaging visualized lignin, cellulose, and pigments under Cu stress. The results indicate that SERS combined with a deep learning model enables rapid and accurate differentiation of Cu stress levels in apple rootstocks in orchards, while SEM-EDS and micro-Raman imaging techniques reveal the migration effect of Cu 2+ within apple rootstock tissues and the ″low concentration promotion, high concentration inhibition″ effect of Cu on apple rootstock growth. Therefore, this approach showcases rapid and accurate detection of heavy metal Cu stress in apple rootstock tissues and has great potential for analyzing various types of heavy metal pollution in agricultural orchard ecosystems.

Why it matches plant phenotyping methodsSERSと深層学習を用いてリンゴ台木の銅ストレスレベルを分類する手法が研究の中心であり、植物のストレス状態を直接推定している。

abstractSERS combined with a deep learning model enables rapid and accurate differentiation of Cu stress levels in apple rootstocks

A new method to quantify the spatiotemporal localization of SnRK1.1 ↗

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.

Detection and quantification protocols for investigating the early stages of Botrytis cinerea interaction with strawberry reveal different infection strategies ↗

Abstract Botrytis cinerea is a filamentous fungus that infects over 200 species of crops causing grey mold disease with devastating losses to agriculture worldwide. The heavy reliance on synthetic fungicides in the strawberry industry has led to the emergence of fungicide resistance in B. cinerea . Therefore, understanding the fundamental biology of B. cinerea is the first step in the search for novel antifungals. Although B. cinerea is one of the most serious pathogens of strawberry ( Fragaria x ananassa ), few protocols have been specifically developed to study this pathosystem. Consequently, early development of pathogen penetration in strawberry is poorly understood. Here we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection. These assays allow comparison of treatment effect on the same fruit, and facilitate the screening of fungicides or biocontrol agents. Through real-time PCR, chlorophyll fluorescence analysis, scanning electron and confocal microscopy, we quantified the lesion and fungal biomass of B. cinerea in the early stages of infection in fruit and petals, and demonstrated that B. cinerea penetrates through stomata of strawberry achenes, revealing a previously unrecognized infection route in this host. These data provide a deeper understanding of the B. cinerea -strawberry interaction and will serve as a foundation for future studies seeking novel antifungal treatments against B. cinerea .

Why it matches plant phenotyping methodsイチゴの病徴（病斑）と感染状態を定量するアッセイを開発し、顕微鏡・蛍光解析などによる表現型取得が研究の中心である。

abstractHere we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection.

Temperature signals drive grass secondary cell wall thickening ↗

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.

Exploring Carbon Dot as a Fluorescent Nanoprobe for Imaging of Plant Cells under Salt/Heat-Induced Stress Conditions. ↗

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.

An innovative fusion method with micro-vision and spectrum of wheat for detecting asymptomatic Fusarium head blight ↗

Fusarium head blight (FHB) poses a significant threat to global wheat health and seriously affects the quality of the wheat and its products. Therefore, detection of early FHB infection in wheat is crucial for preventing its rapid spread and ensuring food safety. This study proposed an innovative fusion method for detecting the severity of FHB invasion in wheat based on near-infrared spectroscopy and microscopic visual images. This method concatenated 512 features from near-infrared spectra and microscopic visual images of wheat and used neural architecture search (NAS) to build a model for fused features to achieve accurate classification of the degree of infection caused by pathogens in wheat, with accuracy of 90.60 % and F1-score of 90.95 %. This represented significant improvements of 20.80 % and 21.79 % over single spectral data modelling and 11.41 % and 12.67 % over single image data modelling, respectively. The study results showed that this method enables more accurate and non-destructive detection of FHB in wheat, providing a solution for the early identification of potential fungal diseases, which is valuable for improving the quality and yield of wheat.

Why it matches plant phenotyping methods小麦のFHB感染重症度という植物病害状態を、近赤外スペクトルと顕微鏡画像の融合およびNASモデルで非破壊推定する手法を開発・評価しており、フェノタイピング手法が中心である。

abstractThis study proposed an innovative fusion method for detecting the severity of FHB invasion in wheat based on near-infrared spectroscopy and microscopic visual images.

Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras. ↗

Here, we developed a vase-life monitoring system (VMS) to automatically and accurately assess the post-harvest quality and vase life (VL) of cut roses. The VMS integrates camera imaging with the YOLOv8 (You Only Look Once version 8) deep learning algorithm to continuously monitor major physiological parameters including flower opening, fresh weight, water uptake, and gray mold disease incidence. Our results showed that the VMS can automatically measure the main physiological factors of cut roses by obtaining precise and consistent data. The values measured for physiology and disease by the VMS closely correlated with those measured by observation (OBS). Additionally, YOLOv8 achieved a high performance in the model by obtaining an object detection accuracy of 90%. Additionally, the mAP0.5 supported the high accuracy of the model in evaluating the VL of cut roses. Regression analysis revealed a strong correlation between the VL, VMS, and OBS. The VMS incorporating the microscope detected physiological and disease factors in the early stages of development. These results show that the plant monitoring system incorporating a microscope is highly effective for evaluating the post-harvest quality of cut roses. The early detection method using the VMS could also be applied to the flower breeding process, which requires rapid measurements of important characteristics of flower species, such as VL and disease resistance, to develop superior cultivars.

Why it matches plant phenotyping methodsカメラ画像とYOLOv8を統合したシステムを開発・検証し、切り花の開花、鮮重、吸水、灰色かび病、花瓶寿命を自動評価しているため、植物表現型の取得手法が中心です。

abstractwe developed a vase-life monitoring system (VMS) to automatically and accurately assess the post-harvest quality and vase life (VL) of cut roses.

In vitro live cell imaging reveals nuclear dynamics and role of the cytoskeleton during asymmetric division of pollen mitosis I in Nicotiana benthamiana ↗

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

Three-dimensional interaction between Cinnamomum camphora and a sap-sucking psyllid insect (Trioza camphorae) revealed by nano-resolution volume electron microscopy. ↗

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

Experimental Modelling of Hydrophobic Surfaces from Plant Leaves ↗

Natural surfaces offer valuable insights into the mechanisms of hydrophobicity. Characterizing these surfaces through the contact angle of droplets provides a direct quantification. The widely used but debated Cassie-Baxter model attempts to relate contact angle with surface topography and liquid wetting properties. Surface tension establishes an initial chemical affinity in wetting, and surface roughness is known to enhance hydrophobicity. However, research lacks standardized metrics to explain how topography influences this behavior. In this study, we introduce a new model for droplet balance that complements the Cassie-Baxter model by considering the latest research findings on the significant effect of the triple line on droplet contact angle. We characterized the surfaces of accessible leaves using ISO standard roughness parameters, contact angle measurements, and surface topography analysis through confocal microscopy. Statistical screening of roughness parameters identified those with high correlation to contact angle model parameters, enabling the quantification of the effect of standard metrics of surface topography on contact angle through the model. Our results provide an enriched contact angle model that incorporates parameters capable of linking contact angle with the assessment of surface topography measured through engineering metrics, paving the way to emulate natural hydrophobicity on engineered surfaces.

Why it matches plant phenotyping methods植物葉表面の接触角・粗さ・表面トポグラフィーから疎水性を定量化する新規モデルを開発しており、植物表現型の取得・評価手法が中心である。

abstractIn this study, we introduce a new model for droplet balance that complements the Cassie-Baxter model

Protocol for detecting intracellular aggregations in Arabidopsis thaliana cell wall mutants using FM4-64 staining. ↗

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

A protocol for high-quality sectioning for tree-ring anatomy. ↗

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

Hypoxia-activated fluorescent probes as markers of oxygen levels in plant cells and tissues ↗

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.

The rational design of a Rhodamine fluorescent probe enables the selective detection and bioimaging of salicylic acid in plants under abiotic stress. ↗

Abiotic stress severely hinders plant growth and development, resulting in a considerable reduction in crop yields. Salicylic acid (SA) serves as a central signal mediating abiotic stress responses in plants. Real-time fluorescence tracking using specific probes can enhance our understanding of the SA-triggered modulation underlying these events. However, in complicated living plant microenvironments, selective recognition and bioimaging of SA is a great challenge for scientists due to the severe background interference and SA analogues. Herein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings. Experimental findings reveal that phoxrodam demonstrates exceptional selectivity (fluorescence intensity: I Phoxrodam+SA /I Phoxrodam+SA analogues > 4.29-fold), high sensitivity (limit of detection = 6.42 nM, fluorescence quantum yield: Φ Phoxrodam+SA = 0.36) and good anti-interference properties. Furthermore, we confirmed that phoxrodam accurately detects SA in the roots of salt-stressed wheat seedlings, the low-temperature resistance of Nicotiana benthamiana and the heavy metal resistance of pea seeds, using in vivo confocal imaging. This study provides a feasible strategy for efficiently tracking plant signalling molecules and promotes the in-depth research of SA-mediated physiological mechanisms, laying a key foundation for the future development of new immune activation inducers.

Why it matches plant phenotyping methods植物体内のサリチル酸を選択的に可視化・定量する蛍光プローブ技術の開発が中心であり、植物の生理状態を取得する実質的なフェノタイピング手法に該当する。

abstractHerein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings.

Quantitative vessel mapping on increment cores: a critical comparison of image acquisition methods. ↗

Introduction Quantitative wood anatomy is critical for establishing climate reconstruction proxies, understanding tree hydraulics, and quantifying carbon allocation. Its accuracy depends upon the image acquisition methods, which allows for the identification of the number and dimensions of vessels, fibres, and tracheids within a tree ring. Angiosperm wood is analysed with a variety of different image acquisition methods, including surface pictures, wood anatomical micro-sections, or X-ray computed micro-tomography. Despite known advantages and disadvantages, the quantitative impact of method selection on wood anatomical parameters is not well understood. Methods In this study, we present a systematic uncertainty analysis of the impact of the image acquisition method on commonly used anatomical parameters. We analysed four wood samples, representing a range of wood porosity, using surface pictures, micro-CT scans, and wood anatomical micro-sections. Inter-annual patterns were analysed and compared between methods from the five most frequently used parameters, namely mean lumen area ( MLA ), vessel density ( VD ), number of vessels ( VN ), mean hydraulic diameter ( D h ), and relative conductive area ( RCA ). A novel sectorial approach was applied on the wood samples to obtain intra-annual profiles of the lumen area ( A l ), specific theoretical hydraulic conductivity ( K s ), and wood density ( ρ ). Results Our quantitative vessel mapping revealed that values obtained for hydraulic wood anatomical parameters are comparable across different methods, supporting the use of easily applicable surface picture methods for ring-porous and specific diffuse-porous tree species. While intra-annual variability is well captured by the different methods across species, wood density ( ρ ) is overestimated due to the lack of fibre lumen area detection. Discussion Our study highlights the potential and limitations of different image acquisition methods for extracting wood anatomical parameters. Moreover, we present a standardized workflow for assessing radial tree ring profiles. These findings encourage the compilation of all studies using wood anatomical parameters and further research to refine these methods, ultimately enhancing the accuracy, replication, and spatial representation of wood anatomical studies.

Why it matches plant phenotyping methods木材解剖学的形質を抽出する画像取得法を比較・不確実性分析し、標準化ワークフローも提示しており、植物フェノタイピング手法が中心です。

abstractwe present a systematic uncertainty analysis of the impact of the image acquisition method on commonly used anatomical parameters.

Confocal Live Imaging of Reproductive Organs Development in Arabidopsis . ↗

Understanding how multicellular organisms are shaped requires high-resolution, quantitative data to unravel how biological structures grow and develop over time. In recent years, confocal live imaging has become an essential tool providing insights into developmental dynamics at cellular resolution in plant organs such as leaves or meristems. In the context of flowers, growth tracking has primarily been limited to sepals, the outermost floral organs, or the post-fertilization gynoecium, which are easily accessible for microscopy. Here, we describe a detailed pipeline for the preparation, dissection, and confocal imaging of the development of internal reproductive floral organs of Arabidopsis thaliana including both the stamen and gynoecium. We also discuss how to acquire high-quality images suitable for efficient 2D and 3D segmentation that allow the quantification of cellular dynamics underlying their development. Key features • Fine dissection of tiny and tightly enclosed floral organs. • Confocal live imaging method allowing long-term observation of plant reproductive morphogenesis. • Assessing the quality of acquired images for efficient segmentation at cellular resolution in 2D and 3D.

Why it matches plant phenotyping methods植物の生殖器官を対象とした共焦点ライブイメージングと、2D/3Dセグメンテーションによる細胞動態定量化の実験・解析パイプラインが中心であり、植物表現型取得法の開発に該当する。

abstractHere, we describe a detailed pipeline for the preparation, dissection, and confocal imaging of the development of internal reproductive floral organs of Arabidopsis thaliana including both the stamen and gynoecium.

Deep phenotyping platform for microscopic plant-pathogen interactions. ↗

The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro , a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision ’s open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

Why it matches plant phenotyping methods自動顕微鏡画像とソフトウェアを基盤とする高スループット植物病害表現型解析プラットフォームを開発し、精度・感度・再現性を検証しているため、方法が研究の中心である。

abstractwe developed BluVision Micro , a modular, machine learning-aided system designed for high-throughput microscopic phenotyping.

Computational wide-field imaging of poplar embolism and wound-response with a deployable microscope. ↗

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.

Pollen morphology and variability among Indian cultivars of Chrysanthemum morifolium and comparative analysis with genera of the Asteraceae family ↗

Chrysanthemums are important worldwide for their beauty and medicinal uses. This research analyzes the pollen morphology of 134 Chrysanthemum morifolium cultivars using scanning electron microscopy (SEM). Key pollen characteristics such as shape, spine length, ornamentation, and aperture type were observed in detail, highlighting the effectiveness of SEM in species and cultivar identification based on pollen features. Multivariate analyses, including hierarchical clustering and principal component analysis, categorized the cultivars according to their pollen traits. Notable findings among Chrysanthemum morifolium cultivars included Punjab Gold with larger pollen size, IAH Red with an equatorial diameter of 25.14 μm, and Kundan distinguished by a spine length of 13.42 μm. These differences suggest environmental factors may influence the observed variation. Comparison with other Asteraceae members, using data from the PalDat database, underscored the distinctiveness of chrysanthemum pollen morphology, validating its use as a classification tool. Pollen morphology study is vital for understanding plant biology, including reproduction, biodiversity, ecological interactions, and environmental adaptation. The findings have practical applications in agriculture and horticulture, enhancing knowledge of plant taxonomy and classification.

Why it matches plant phenotyping methodsSEMによる花粉形態の取得と多変量解析を研究の中心に据え、品種識別・分類への有効性を評価しているため、植物器官形質のフェノタイピング手法の実質的応用に該当する。

abstractThis research analyzes the pollen morphology of 134 Chrysanthemum morifolium cultivars using scanning electron microscopy (SEM).

Virtual staining from bright-field microscopy for label-free quantitative analysis of plant cell structures. ↗

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.

Distinct localization patterns of actin microfilaments during early cell plate formation in plants through deep learning-based image restoration ↗

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

Application of cryo-FIB-SEM for investigating ultrastructure in guard cells of higher plants ↗

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.

Shedding new light on the hidden complexity of seeds: chemically selective imaging of seed coats with stimulated Raman scattering microscopy. ↗

The seed coat plays a pivotal role in seed development and germination, acting as a protective barrier and mediating interac-tions with the external environment. Traditional histochemical techniques and analytical methods have provided valuable insights into seed coat composition and function. However, these methods often suffer from limitations such as indirect chemical signatures and lack of spatial resolution. Here, we introduce stimulated Raman scattering (SRS) microscopy as a novel analytical tool for non-destructive, label-free, high-resolution mapping of biopolymers, water and applied active ingre-dients (AIs) in intact seed coats. We demonstrate the capability of SRS microscopy to perform depth-resolved, chemically selective imaging of major seed coat biopolymers (pectin, tannin, and suberin). By comparing wild type arabidopsis thali-ana seeds with genetically modified mutants deficient in suberin and tannin, we illustrate the potential for semi-quantitative analysis of biopolymer content. Furthermore, we show that SRS microscopy can track the permeability of seed coats to wa-ter using deuterated water (D 2 O) uptake studies. Real-time imaging reveals differences in water permeation between wild type and suberin deficient seeds, highlighting the importance of seed coat composition in regulating water uptake during germina-tion. Additionally, we extend the application of SRS microscopy to large seeds, such as brassica oleracea, utilizing epi -detected imaging for surface studies. Finally, using a deuterated insecticide (clothianidin-d3), we demonstrate the capability of SRS microscopy to visualize the incorporation of AIs into seed coats. Our study presents SRS microscopy as a powerful tool for characterizing seed coat composition and understanding the diffusion of low molecular weight compounds into seeds. This technique offers new opportunities for designing seeds with tailored properties for improved germination and resilience to environmental stressors.

Why it matches plant phenotyping methodsSRS顕微鏡を用いて種皮の生体高分子、水分透過、化合物取り込みを非破壊・高解像度で可視化する手法を開発・実証しており、植物状態の取得方法が研究の中心である。

abstractOur study presents SRS microscopy as a powerful tool for characterizing seed coat composition and understanding the diffusion of low molecular weight compounds into seeds.

Actomyosin and the Arp2/3 Complex Are Involved in the Internalization of Cellulose Synthase Complexes ↗

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.

Plant paleoecophysiology traits in deep time: hydraulic conductivity and drought resistance in late Carboniferous Period plants ↗

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.

Analysis of stomatal characteristics of maize hybrids and their parental inbred lines during critical reproductive periods. ↗

The stomatal phenotype is a crucial microscopic characteristic of the leaf surface, and modulating the stomata of maize leaves can enhance photosynthetic carbon assimilation and water use efficiency, thereby playing a vital role in maize yield formation. The evolving imaging and image processing technologies offer effective tools for precise analysis of stomatal phenotypes. This study employed Jingnongke 728 and its parental inbred to capture stomatal images from various leaf positions and abaxial surfaces during key reproductive stages using rapid scanning electron microscopy. We uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution. Manual validation revealed high detection accuracies for stomatal density, width, and length, with R2 values of 0.92, 0.97, and 0.95, respectively. Phenotypic analyses indicated a significant positive correlation between stomatal density and the percentage of guard cells and pore area (r=0.36), and a negative correlation with stomatal area and subsidiary cell area (r=-0.34 and -0.46). Additionally, stomatal traits exhibited notable variations with reproductive stages and leaf layers. Specifically, at the monocot scale, stomatal density increased from 74.35 to 87.19 Counts/mm2 from lower to upper leaf layers. Concurrently, the stomatal shape shifted from sub-circular (stomatal roundness = 0.64) to narrow and elongated (stomatal roundness = 0.63). Throughout the growth cycle, stomatal density remained stable during vegetative growth, decreased during reproductive growth with smaller size and narrower shape, and continued to decline while increasing in size and tending towards a rounded shape during senescence. Remarkably, hybrid 728 differed notably from its parents in stomatal phenotype, particularly during senescence. Moreover, the stomatal density of the hybrids showed negative super parental heterosis (heterosis rate = -0.09), whereas stomatal dimensions exhibited positive super parental heterosis, generally resembling the parent MC01. This investigation unveils the dynamic variations in maize stomatal phenotypes, bolstering genetic analyses and targeted improvements in maize, and presenting a novel technological instrument for plant phenotype studies.

Why it matches plant phenotyping methodsYOLOv5sとUnetによる画像取得・分割手法で、トウモロコシ気孔の11形質を抽出し、手動検証で精度を評価しており、植物表現型取得法が中心である。

abstractWe uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution.

Camelot: a Computer Automated Micro Extensometer with Low-cost Optical Tracking ↗

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.

Machine learning segmentation tool trained on synthetic data for tracking cytoskeleton polymerisation and depolymerisation ↗

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.

DEGAST3D: Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images. ↗

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

A Protocol for Live Imaging of Arabidopsis Gynoecium Development Using Confocal Microscopy. ↗

During the Arabidopsis reproductive process, the female whorl of the flower, known as the gynoecium, passes seven of the 20 floral stages during its development. In each of these seven stages, specific developmental events occur, ranging from gynoecium primordium establishment to complex tissue and organ differentiation. Studying gynoecium development is important for its role in fruit and seed formation. Currently, there are many Arabidopsis lines with fluorescent proteins that provide relevant information on gynoecium patterning. However, the fluorescence of some proteins is affected during the steps of histological techniques. Furthermore, the complexity of gynoecium development makes live imaging difficult in the early stages and medial tissues. To address these issues at hand, we describe a methodology that facilitates the analysis of the fluorescent signal during gynoecium development, using as an example the pMIR164c::VENUS line.

Why it matches plant phenotyping methodsシロイヌナズナ雌ずいの発生を対象に、蛍光シグナルを生体で取得・解析する共焦点ライブイメージング法そのものを開発・提示しており、植物表現型取得が中心である。

titleA Protocol for Live Imaging of Arabidopsis Gynoecium Development Using Confocal Microscopy.

Detection of Reactive Oxygen Species (ROS) Levels in the Fruitlet Abscission Zone of Litchi. ↗

Reactive oxygen species (ROS) are pivotal in regulating plant organ abscission. The buildup of ROS in the fruitlet abscission zone (FAZ) actively triggers the abscission of litchi fruitlets. In this chapter, we present a simple method to measure intracellular ROS levels in the FAZ of litchi using 2,7-dichlorodi-hydrofluorescein diacetate (DCFH-DA). Litchi FAZ samples are transverse sectioned and then incubated with a 50 μM DCFH-DA solution at room temperature for 1 h. DCF fluorescence can be visualized using a laser scanning confocal microscope, and the fluorescence intensity is then analyzed with ImageJ software.

Why it matches plant phenotyping methodsリュウガン果実の離層における植物のROS状態を、蛍光イメージングと画像解析で測定する実験プロトコルが中心であり、植物生理フェノタイピング手法に該当する。

abstractwe present a simple method to measure intracellular ROS levels in the FAZ of litchi using 2,7-dichlorodi-hydrofluorescein diacetate (DCFH-DA).

Immunofluorescence for Detection of TOR Kinase Activity In Situ in Photosynthetic Organisms. ↗

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.

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density. ↗

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

Assembly and Evaluation of a Confocal Microscopy Image Analysis Pipeline Useful in Revealing the Secrets of Plant-Fungal Interactions ↗

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

Persephone’s Flower: Ecology and Development of Lathraea squamaria (Orobanchaceae), an Unusual Root Holoparasitic Plant ↗

The hidden lifestyle of the holoparasitic perennial Lathraea squamaria L. (common toothwort), which parasitizes the roots of deciduous trees in forests and woodlands, has led to significant knowledge gaps about the mechanisms underlying this plant symbiotic interaction. Here we present the first detailed structural examination of the interface between L. squamaria and its host root belowground using non-destructive micro-computed tomography (micro-CT). To clarify the physiology of seed germination in this elusive species, we demonstrated efficient in vitro germination of L. squamaria seeds. The terminology for the gross morphology and micromorphology of L. squamaria has been revised, and clear microscopic evidence for several previously described structures (e.g., prehaustoria, haustoria, seeds, elaiosomes, nectaries, and pollen) has been provided. Generally, this research aims to deepen our understanding of parasitic interactions, phenology, as well as the ecological significance of L. squamaria and its biotic associates (e.g., feeders and pollinators) within forest ecosystems.

Why it matches plant phenotyping methods非破壊マイクロCTを用いて寄生植物と宿主根の界面構造を詳細に可視化しており、植物の形態・構造状態の取得が研究の中心的手法の一つである。

abstractHere we present the first detailed structural examination of the interface between L. squamaria and its host root belowground using non-destructive micro-computed tomography (micro-CT).

Innovative multi-scale approach to study the phenotypic variation of seedling leaves in four weedy Amaranthus species. ↗

Plant phenotyping on morpho-anatomical traits through image analysis, from microscope images to large-scale acquisitions through remote sensing, represents a low-invasive tool providing insight into physiological and structural trait variation, as well as plant-environment interactions. High phenotype diversity in the genus Amaranthus includes annual weed species with high invasiveness and impact on important summer crops, and nutritive grain or vegetable crops. Identification of morpho-anatomical leaf characters at very young stages across weedy amaranths could be useful for better understanding their performance in agroecosystems. We used an innovative multi-scale approach with phenotype analyses of about 20 single-leaf morphometric traits of four Amaranthus species through processing confocal microscopy and camera acquisitions. The results highlight that determination of leaf traits at different investigation levels highlight species-specific traits at a juvenile stage, which are crucial for plant development, competition and establishment. Specifically, leaf circularity and hairiness Aspect Ratio better discriminated A. tuberculatus from other species. Also, leaf DW, hairiness area and perimeter variables allowed identification of dioecious amaranth species as distinct from monoecious species. The methodology used here provides a promising, reliable and low-impact approach for the functional characterization of phylogenetically related species and for statistical quantification of traits involved in taxonomy and biodiversity studies.

Why it matches plant phenotyping methods共焦点顕微鏡およびカメラ画像を用いた多尺度の葉形態形質取得・解析が研究の中心であり、複数の形質を定量化する手法を適用している。

abstractWe used an innovative multi-scale approach with phenotype analyses of about 20 single-leaf morphometric traits of four Amaranthus species through processing confocal microscopy and camera acquisitions.

Does domestication trade-off stress tolerance for leaf growth? A search for evidence across eight Pooideae grass species ↗

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

A new method for reconstructing the 3D shape of single cells in fruit ↗

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

Review of embedding and non-embedding techniques for quantitative wood anatomy ↗

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.

Cellular mechanism of polarized auxin transport on fruit shape determination revealed by time-lapse live imaging. ↗

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.

Fast and High-Resolution Imaging of Pollinated Stigmatic Cells by Tabletop Scanning Electron Microscopy. ↗

In plants, the first interaction between the pollen grain and the epidermal cells of the stigma is crucial for successful reproduction. When the pollen is accepted, it germinates, producing a tube that transports the two sperm cells to the ovules for fertilization. Confocal microscopy has been used to characterize the behavior of stigmatic cells post-pollination [1], but it is time-consuming since it requires the development of a range of fluorescent marker lines. Here, we propose a quick, high-resolution imaging protocol using tabletop scanning electron microscopy. This technique does not require prior sample fixation or fluorescent marker lines. It effectively captures pollen grain behavior from early hydration (a few minutes after pollination) to pollen tube growth within the stigma (1 h after pollination) and is particularly efficient for tracking pollen tube paths. Key features • Analysis of the pollen behavior in stigmatic cells of Arabidopsis thaliana but can be broadly used for other species. • Rapid and high-resolution imaging method. • Allows testing pollen grain hydration states, pollen tube paths on stigmatic cells from various genetic backgrounds, and also pollen tube phenotypes.

Why it matches plant phenotyping methods卓上走査電子顕微鏡を用いて花粉挙動、花粉管経路、花粉管表現型を取得する高速・高解像度イメージングプロトコルが研究の中心であり、植物表現型計測法の開発に該当する。

abstractHere, we propose a quick, high-resolution imaging protocol using tabletop scanning electron microscopy.

DNA imaging cytometry in plant analysis: a review ↗

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.

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning ↗

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Why it matches plant phenotyping methodsAI/ML画像解析による気孔形質推定を扱うレビューであり、植物フェノタイピング手法の開発・応用、性能と限界の評価が中心です。

abstractHere we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs.

Application of deep learning for the analysis of stomata: a review of current methods and future directions ↗

Plant physiology and metabolism rely on the function of stomata, structures on the surface of above-ground organs that facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore that make up the stomata, as well as the density (number per unit area), are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopy, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used, from data acquisition, pre-processing, DL architecture, and output evaluation to post-processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimize uptake, predominantly focusing on the sharing of datasets and generalization of models as well as the caveats associated with utilizing image data to infer physiological function.

Why it matches plant phenotyping methods気孔画像から形態・密度などの植物形質を推定する深層学習手法を体系的にレビューしており、フェノタイピング手法が中心である。

abstractHere we review the published literature on the application of DL for stomatal analysis.

Real-time monitoring of stromal NADPH levels in Arabidopsis using a metagenome-derived NADPH-binding fluorescent protein. ↗

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.

Reducing fluorescence interference for improved Raman spectroscopic analysis of plant cell walls ↗

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.

TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues. ↗

The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the world. Many biotic and abiotic plant health issues impede the efficient production of this crop, and laboratory-based diagnostics are inaccessible in many remote regions. Early detection of these plant health issues is essential for efficient and accurate response, prompting exploration of alternatives for field detection. Considering the availability of low-cost smartphones, artificial intelligence-based classification facilitated by mobile phone imagery can be a practical option. This study introduces a smartphone-attachable 30× microscopic lens, used to produce the novel tomato microimaging data set of 8,500 images representing 34 tomato plant conditions on the upper and lower sides of leaves as well as on the surface of tomato fruits. We introduce TOMMicroNet, a 14-layer convolutional neural network (CNN) trained to classify biotic and abiotic plant health issues, and we compare it against six existing pretrained CNN models. We compared two separate pipelines of grouping data for training TOMMicroNet, either presenting all data at once or separating the data into subsets based on the three parts of the plant. Comparing configurations based on cross-validation and F1 scores, we determined that TOMMicroNet attained the highest performance when trained on the complete data set, with 95% classification accuracy on both training and external data sets. Given TOMMicroNet's capabilities when presented with unfamiliar data, this approach has potential for the identification of plant health issues.

Why it matches plant phenotyping methodsスマートフォン顕微鏡画像からトマト葉・果実の生物的／非生物的健康状態を分類する画像取得系、データセット、CNNを開発・比較し、外部データで性能検証しているため、植物表現型取得・解析が中心である。

abstractThis study introduces a smartphone-attachable 30× microscopic lens, used to produce the novel tomato microimaging data set of 8,500 images representing 34 tomato plant conditions

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops ↗

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Why it matches plant phenotyping methods植物寄生性線虫による植物ストレス・感染状態の推定に関する画像処理、CNN、リモートセンシング、ハイパースペクトル手法を体系的にレビューしており、植物フェノタイピング手法が中心です。

abstractThis study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing.

In Situ Diagnosis and Digital Cataloguing of Plant Pathogenic Fungi Through Mobile‐Based Foldscope Microscopy ↗

ABSTRACT Agriculture confronts multifaceted challenges across the spectrum of crop production, with pest and disease management being a prominent concern. Timely diagnosis of crop diseases is imperative for mitigating production costs and curbing the adverse environmental impacts of chemical pesticides. In the present investigation, mobile phone‐based foldscope microscopy (MBFM) was used to diagnose various field samples infected with fungal diseases of field and horticultural crops, and the same was validated with the normal microscope pictures and field symptoms. The MBFM was also used to diagnose seed‐borne microflora associated with wheat and spores of commercial formulation of bioagents and validated. The MBFM utilises both symptoms and morphological structures of pathogen for in situ field diagnosis and hence advantages over the symptom‐based mobile Apps. This study underscores the utility of foldscope microscope as a potent technique for plant pathologists and extension workers to enable real‐time and in situ identification of diseases caused by fungal pathogens in various agricultural crops.

Why it matches plant phenotyping methods植物病害の症状と病原菌形態をモバイルFoldscopeで現場取得し、通常顕微鏡画像および圃場症状と検証する手法が研究の中心であり、植物の病害状態を評価するため、植物フェノタイピング手法として採用する。

abstractmobile phone‐based foldscope microscopy (MBFM) was used to diagnose various field samples infected with fungal diseases of field and horticultural crops, and the same was validated with the normal microscope pictures and field symptoms.

Novel CRM cosine similarity mapping strategy for simultaneous in-situ visual profiling lignocellulose in plant cell walls. ↗

Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.

Why it matches plant phenotyping methods植物細胞壁のリグニン・セルロース・ヘミセルロースを可視化・抽出するCRM画像解析手法を開発し、変異イネで有効性を検証しており、表現型取得法が研究の中心です。

abstractwe propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls.

FieldDino: High-throughput physio-morphological phenotyping of stomatal characteristics for plant breeding research ↗

Abstract Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy. The method allows coupled measurements to be collected in <15 s and consists of: (1) stomatal conductance measurements using a handheld porometer; (2) in situ collection of epidermal images with a digital microscope, 3D-printed leaf clip and Python-based app; and (3) automated deep learning analysis of stomatal features. The YOLOv8-M model trained on images collected in the field achieved strong performance metrics with an mAP@0.5 of 97.1% for stomatal detection. Validation in large field trials of 200 wheat genotypes with two irrigation treatments captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress. Highlight Chaplin et al., have developed FieldDino which enables rapid, high-throughput phenotyping of stomatal traits, advancing plant breeding research by integrating streamlined in-field measurements with automated deep learning analysis.

Why it matches plant phenotyping methodsFieldDinoは、圃場での気孔生理・解剖形質の高速取得、画像収集、深層学習による自動抽出を統合した植物フェノタイピング手法であり、開発と大規模検証が研究の中心です。

abstractWe present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy.

A high-throughput protocol for testing heat-stress tolerance in pollen. ↗

Viable pollen is crucial for fertilization, but pollen is generally highly susceptible to heat stress. A quick, reliable method for testing the heat-stress tolerance of pollen is needed to improve the heat-stress tolerance in plants, but current methods require considerable space and labor. In addition, many such methods only test tolerance to a single constant temperature, making it time-consuming to screen heat tolerance over a wide temperature range and to examine the dynamics of pollen viability at different temperatures. To address this issue, we aimed to: (1) develop an easy, reliable method for measuring pollen viability at different temperatures; and (2) identify the best temperature range for screening pollen with high heat-stress tolerance. We harvested mature pollen from wheat ( Triticum aestivum ) plants and transferred it to a 96-well plate filled with liquid medium containing sucrose. We placed the plate in a PCR machine operating under a gradient PCR program to simultaneously test a range of temperatures. After incubating the pollen for 4 h, at temperatures ranging from 21.9 to 47 °C, we examined the pollen grains under a light microscope and employed a specific image analysis pipeline to assess the effects of temperature on pollen morphology, germination, and tube growth. This method facilitated the high-throughput screening of many pollen samples, enabling rapid, reliable, and precise analysis of pollen viability in response to temperature. Our approach should be applicable to other plant species and could be used to identify quantitative trait loci or genes influencing heat stress tolerance in pollen for breeding programs. Supplementary information The online version contains supplementary material available at 10.1007/s42994-024-00183-3.

Why it matches plant phenotyping methods花粉の生存性・形態・発芽・花粉管成長を温度勾配下で測定する高スループット手法と画像解析パイプラインを開発しており、表現型取得法が研究の中心です。

abstractdevelop an easy, reliable method for measuring pollen viability at different temperatures

Protocol to analyse the structural composition by fluorescence microscopy and different conventional and fluorescence staining methods. ↗

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.

Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics ↗

Monitoring in-vivo stomatal conductance (gₛ) dynamics is essential for predicting crop water usage and yield sensitivity in response to climate change. Leaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring; however, the underlying mechanisms connecting leaf spectra with stomatal anatomical and behavioral traits, and their subsequent impacts on gₛ, remain underexplored. In this study, we conducted a wheat field trial, collecting comprehensive measurements of stomatal anatomical (i.e., size, density) and behavioral (i.e., opening ratio, pore area) traits by a customized, high-resolution microscope, leaf spectra via a handheld spectroradiometer, and gₛvia a handheld AP4 Leaf Porometer across various genotypes, nitrogen treatments, growth stages, and diurnal environments. We observed substantial gₛ variability, with stomatal anatomical and behavioral traits jointly accounting for 79% of this variability. We further examined the relationship between leaf spectra and stomatal traits/conductance using a partial least square regression (PLSR) model and discovered that a single PLSR spectral model accurately predicted the variability of each of these traits and gₛ across our datasets. Furthermore, we demonstrated a strong correspondence between spectral variations resulting from gₛ and spectral alternation induced by stomatal anatomical and behavioral traits. By analyzing the diurnal association between spectral and gₛ variability, we revealed important biophysical mechanisms underlying relationships among spectra, stomatal anatomical and behavioral traits, and gₛ. Collectively, our findings highlight the potential of leaf spectroscopy in advancing crop physiology monitoring, contributing to enhanced food security and sustainability.

Why it matches plant phenotyping methods小麦の気孔形質と気孔コンダクタンスを分光計測・PLSRで非破壊推定する手法を中心に検証しており、植物フェノタイピング手法の開発・検証に該当する。

abstractLeaf and canopy spectroscopy offer a non-destructive method for gₛ monitoring

Smart: Stoma Measurement, Analysis, Report Tool for Microscope Image and Its Application in Plant Phenotyping ↗

This manuscript describes a deep learning-based algorithm for inferring stomatal phenotypes from microscope images. Botanists spray compounds on leaf surfaces and observe their state under a microscope to study the effects of different compounds on stomatal opening and closing. We propose a stomatal orientation-based method that uses stomatal orientation to guide stomatal measurements. This method has three modules: a stomata detection module that locates the stoma region and orientation using deep learning-based object detection. A stoma segmentation module that segments the aperture, guard cell, and thick inner wall from the stoma ROI image. And a phenotype quantification module that calculates the phenotype parameters by analyzing the mask image. The experimental results show that the proposed method can resolve stomata with high accuracy (the average$R^{2}$of the previous method is 0.66, and the proposed method is 0.96). For the development of the community, we will release the algorithm and tool involved in this article in GitHub.

Why it matches plant phenotyping methods顕微鏡画像から気孔の検出・セグメンテーション・表現型パラメータ定量を行う深層学習手法とツールの開発が中心であり、植物表現型測定法に該当する。

abstractThis manuscript describes a deep learning-based algorithm for inferring stomatal phenotypes from microscope images.

Imaging Metabolic Flow of Water in Plants with Isotope-Traced Stimulated Raman Scattering Microscopy. ↗

Water plays a vital role in the life cycle of plants, participating in various critical biochemical reactions during both non-photosynthetic and photosynthetic processes. Direct visualization of the metabolic activities of water in plants with high spatiotemporal resolution is essential to reveal the functional utilization of water. Here, stimulated Raman scattering (SRS) microscopy is applied to monitor the metabolic processes of deuterated water (D 2 O) in model plant Arabidopsis thaliana (A. thaliana). The work shows that in plants uptaking D 2 O/water solution, proton-transfer from water to organic metabolites results in the formation of C-D bonds in newly synthesized biomolecules (lipid, protein, and polysaccharides, etc.) that allow high-resolution detection with SRS. Reversible metabolic pathways of oil-starch conversion between seed germination and seed development processes are verified. Spatial heterogeneity of metabolic activities along the vertical axis of plants (root, stem, and tip meristem), as well as the radial distributions of secondary growth on the horizontal cross-sections are quantified. Furthermore, metabolic flow of protons from plants to animals is visualized in aphids feeding on A. thaliana. Collectively, SRS microscopy has potential to trace a broad range of matter flows in plants, such as carbon storage and nutrition metabolism.

Why it matches plant phenotyping methods植物内の水・代謝物の流れをSRS顕微鏡で可視化・定量する手法が研究の中心であり、植物の生理状態を画像から抽出しているため。

abstractstimulated Raman scattering (SRS) microscopy is applied to monitor the metabolic processes of deuterated water (D 2 O) in model plant Arabidopsis thaliana

Metabolic imaging in living plants: A promising field for chemical exchange saturation transfer (CEST) MRI ↗

Magnetic resonance imaging (MRI) is a versatile technique in the biomedical field, but its application to the study of plant metabolism in vivo remains challenging because of magnetic susceptibility problems. In this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI. This method enables noninvasive access to the metabolism of sugars and amino acids in complex sink organs (seeds, fruits, taproots, and tubers) of major crops (maize, barley, pea, potato, sugar beet, and sugarcane). Because of its high signal detection sensitivity and low susceptibility to magnetic field inhomogeneities, CEST analyzes heterogeneous botanical samples inaccessible to conventional magnetic resonance spectroscopy. The approach provides unprecedented insight into the dynamics and distribution of sugars and amino acids in intact, living plant tissue. The method is validated by chemical shift imaging, infrared microscopy, chromatography, and mass spectrometry. CEST is a versatile and promising tool for studying plant metabolism in vivo, with many applications in plant science and crop improvement.

Why it matches plant phenotyping methods植物の生体内代謝を非侵襲的に測定するCEST-MRI法を確立し、複数手法で検証しており、植物表現型取得法が研究の中心である。

abstractIn this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI.

Implementation of Ribo-BiFC method to plant systems using a split mVenus approach ↗

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.

Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues ↗

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.

Application of cryo-FIB-SEM for investigating organelle ultrastructure in guard cells of higher plants ↗

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.

Intracellular positioning of mesophyll chloroplasts following to aggregative movement in Setaria viridis analysed three-dimensionally with a confocal laser scanning microscope ↗

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.

A novel workflow for unbiased 3D quantification of autophagosomes in Arabidopsis thaliana roots. ↗

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.

Comparing Different Methodologies to Quantify Particulate Matter Accumulation on Plant Leaves ↗

Urban air pollution poses a significant threat to human health, with metropolitan areas particularly affected due to high emissions from human activities. Particulate matter (PMx) is among the most harmful pollutants to human health, being composed of a complex mixture of substances related to severe pulmonary conditions. Urban green spaces play a vital role in mitigating air pollution by capturing PMx, and it is essential to select plant species with a high capacity for PMx accumulation to effectively enhance air quality. This study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves. Light microscopy allowed for the visualization of PMx deposition, revealing the species warranting further analysis using the filtration method. Among the species analyzed, Euonymus japonicus, Ligustrum lucidum, Alnus glutinosa, Rubus ulmifolius, and Laurus nobilis demonstrated the highest total PMx accumulation, exceeding 50 µg cm−2, making them particularly valuable for air pollution mitigation. This study examined the correlation between leaf traits such as specific leaf area (SLA), leaf area (LA), leaf dissection index (LDI), and leaf roundness and PMx accumulation across the 30 different plant species. A multiple linear regression analysis indicated that these leaf traits significantly influenced PMx accumulation, with SLA and LA showing negative correlations and leaf roundness exhibiting a positive correlation with PMx deposition. In conclusion, this study highlights the importance of selecting plant species with specific leaf traits for effective air quality improvement in urban environments particularly in highly polluted areas, to enhance air quality and public health.

Why it matches plant phenotyping methods葉面PM蓄積という植物状態の定量法について、光学顕微鏡法とろ過法の精度・相関を比較評価しており、測定手法の検証が研究の中心である。

abstractThis study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves.

Expanding super-resolution imaging versatility in organisms with multi-confocal image scanning microscopy. ↗

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

Neural network auto-segmentation of serial-block-face scanning electron microscopy images exhibit collagen fibril structural differences with tendon type and health. ↗

A U-Net machine learning algorithm was adapted to automatically segment tendon collagen fibril cross-sections from serial block face scanning electron microscopy (SBF-SEM) and create three-dimensional (3D) renderings. We compared the performance of routine Otsu thresholding and U-Net for a positional tendon that has low fibril density (rat tail tendon), an energy-storing tendon that has high fibril density (rat plantaris tendon), and a high fibril density tendon hypothesized to have disorganized 3D ultrastructure (degenerated rat plantaris tendon). The area segmentation of the tail and healthy plantaris tendon had excellent accuracy for both the Otsu and U-Net, with an Intersection over Union (IoU) of 0.8. With degeneration, only the U-Net could accurately segment the area, whereas Otsu IoU was only 0.45. For boundary validation, the U-Net outperformed Otsu segmentation for all tendons. The fibril diameter from U-Net was within 10% of the manual segmentation, however, the Otsu underestimated the fibril diameter by 39% in healthy plantaris and by 84% in the degenerated plantaris. Fibril geometry was averaged across the entire image stack and compared across tendon types. The tail had a lower fibril area fraction (58%) and larger fibril diameter (0.31 µm) than the healthy plantaris (67% and 0.21 µm) and degenerated plantaris tendon (66% and 0.19 µm). This method can be applied to a large variety of tissues to quantify 3D collagen fibril structure.

Why it matches plant phenotyping methodsU-NetによるSBF-SEM画像の自動分割を開発・検証し、腱のコラーゲン線維径や形状を定量化する手法が中心である。ただし植物ではなくラット腱を対象としているため、植物フェノタイピング索引の対象外。

abstractA U-Net machine learning algorithm was adapted to automatically segment tendon collagen fibril cross-sections from serial block face scanning electron microscopy (SBF-SEM) and create three-dimensional (3D) renderings.

An intelligent deep augmented model for detection of banana leaves diseases. ↗

One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healthy, Cordana, Sigatoka, and Pestalotiopsis. These types can be analyzed using four types of vision: RGB, night vision, infrared vision, and thermal vision. This paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. Each vision consisted of 1600 images with a size of (224 × 224). The training-testing approach was used to evaluate the performance of the hybrid model on Kaggle dataset, which was justified by various methods and metrics. The proposed model achieved a remarkable mean accuracy rate of 99.16% for RGB vision, 98.02% for night vision, 96.05% for infrared vision, and 96.10% for thermal vision for training and testing data. Microscopy employed in this research as a validation tool. The microscopic examination of leaves confirmed the presence and extent of the disease, providing ground truth data to validate and refine the proposed model. RESEARCH HIGHLIGHTS: The model can be helpful for internet of things -based drones to identify the large scale of banana leaf-disease detection using drones for images acquisition. Proposed an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. The model detected banana leaf disease with a 99.16% accuracy rate for RGB vision, 98.02% accuracy rate for night vision, 96.05% accuracy rate for infrared vision, and 96.10% accuracy rate for thermal vision The model will provide a facility for early disease detection which minimizes crop loss, enhances crop quality, timely decision making, cost saving, risk mitigation, technology adoption, and helps in increasing the yield.

Why it matches plant phenotyping methodsバナナ葉の病徴・病害状態を画像から分類する深層学習モデルを開発し、複数の撮像方式と顕微鏡による検証を含むため、植物病害フェノタイピング手法が中心である。

abstractThis paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision.

Predicting plasmodesmata-mediated interface permeability and intercellular diffusion ↗

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.

Calibration and validation of the application of low-cost, lightweight, and portable paper microscopes for monitoring pollen traits as a screening tool for drought tolerance ↗

An efficient, low-cost, lightweight, and portable paper microscope can be used to monitor real-time in vivo pollination, which is not feasible with a conventional compound microscope. The advantages of paper microscopes (foldscopes) remain unexplored for in vivo field-oriented assessment of pollination. In this study, experiments were carried out to calibrate and validate the capability of foldscopes to investigate in vitro pollen traits and to understand the feasibility of using in real-time in vivo field-level investigations. Comparison of optical images of pollen morphological traits (color, size, and shape) of diverse plant species (including crops) captured using a foldscope and a compound microscope revealed little to no differences. In addition, foldscopes were calibrated to monitor and estimate in vitro pollen viability in wheat, sorghum, sunflower, chickpea, soybean, and periwinkle and to determine in vitro pollen germination in chickpea, maize, and periwinkle. Pollen tube growth was observed by time-lapse imaging of pollen from periwinkle. The foldscope efficiently captured genotypic variation in in vitro pollen germination of twelve chickpea genotypes under drought revealing the possibility of foldscopes as a tool for field level, real-time in vivo monitoring of pollination under drought.

Why it matches plant phenotyping methods折り紙顕微鏡を用いた花粉形態・生存性・発芽・花粉管成長の画像計測について、校正と従来顕微鏡との比較検証を行っており、植物形質取得法が研究の中心である。

abstractIn this study, experiments were carried out to calibrate and validate the capability of foldscopes to investigate in vitro pollen traits

Time-resolved tracking of cellulose biosynthesis and microfibril network assembly during cell wall regeneration in live Arabidopsis protoplasts ↗

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

Fluorescent carbon dot embedded polystyrene: an alternative for micro/nanoplastic translocation study in leguminous plants. ↗

Micro/nanoplastics are widespread in terrestrial ecosystem. Even though many studies have been reported on the effects of these in marine environment, studies concerning their accumulation and impact on terrestrial ecosystem have been scanty. The current study was designed to determine how terrestrial plants, especially legumes, interact with micro/nanoplastics to gain insights into their uptake and translocation. The paper describes the synthesis of fluorescent carbon dot embedded polystyrene (CDPS) followed by its characterization. Translocation studies at different concentrations from 2 to 100% (v/v) for tracking the movement and accumulation of microplastics in Vigna radiata and Vigna angularis were performed. The optical properties of the synthesized CDPS were investigated, and their translocation within the plants was visualized using fluorescence microscopy. These findings were further validated by scanning electron microscopy (SEM) imaging of the plant sections. The results showed that concentrations higher than 6% (v/v) displayed noticeable fluorescence in the vascular region and on the cell walls, while concentrations below this threshold did not. The study highlights the potential of utilizing fluorescent CDPS as markers for investigating the ecological consequences and biological absorption of microplastics in agricultural systems. This method offers a unique technique for monitoring and analyzing the routes of microplastic accumulation in edible plants, with significant implications for both food safety and environmental health.

Why it matches plant phenotyping methods蛍光標識粒子と蛍光顕微鏡・SEMを用いて植物体内のマイクロプラスチック蓄積・移行を可視化する手法が研究の中心であり、植物の生理状態（吸収・転流）を測定する方法として該当する。

abstractThe paper describes the synthesis of fluorescent carbon dot embedded polystyrene (CDPS) followed by its characterization.

Spectral algal fingerprinting and long sequencing in synthetic algal-microbial communities ↗