Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs. Sixteen soybean genotypes were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using six YOLO (You Only Look Once) object-detection architectures (YOLOv7–YOLOv12) to identify the best-performing model for automated analysis. Among the tested object-detection architectures, YOLOv9 achieved the best overall performance for detecting germinated and non-germinated pollen grains in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P < 0.05), with a significant genotype × growth temperature interaction. Invitro incubation temperatures ranging from 10 °C to 45 °C produced a clear thermal response; however, no significant genotype × incubation temperature interaction was detected within either growth temperature regime. Although photosynthetic and physiological traits were measured exploring their relationship with pollen germination, their transient and complex response limited their reliability for predicting reproductive performance. The automated pipeline substantially reduced the time required to evaluate pollen germination. The pipeline processed nearly 5,000 images in approximately one hour, substantially increasing throughput and reducing reliance on manual counting. The findings demonstrate that pollen germination is a promising proxy trait for screening reproductive heat tolerance in soybean. Combining controlled environment phenotyping with YOLO-based object detection enabled efficient, accurate, and scalable pollen analysis, and represents the central methodological advance of this study. YOLOv9 performed best among the tested architectures, although discrepancies from manual counts in some images indicate that additional validation is needed. The weak associations with vegetative physiological traits further support the value of direct pollen-based phenotyping.
Why it matches plant phenotyping methods深層学習による花粉画像解析を中心に、花粉発芽という生殖形質を高速・自動測定するハイスループット表現型解析フレームワークを開発・比較・検証している。
abstractThis study presents a high-throughput phenotyping framework that integrates controlled-environment pollen germination assays with deep learning–based object detection for rapid, accurate, and scalable evaluation of reproductive heat tolerance in soybean breeding programs.
Reproduction assets foundThe authors state that all data supporting the study, including annotated pollen germination images, computational and statistical codes, and analysis tools, were deposited in Zenodo with a public DOI. This is a paper-specific, publicly actionable asset. LabelMe and Ultralytics YOLO are generic third-party tools, not作者Dataset · publicCommission.
Data availability
All data supporting the findings of this study, including annotated images, computational and
statistical codes, and analysis tools, have been deposited in the Zenodo data repository. Additional
data will be made available upon reasonable request following acceptance of the manuscript.
Repository: https://doi.org/10.5281/zenodo.21685593
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing Interests
Authors declared no competing interests
References
1. FAOSTAT: Crops and livestock products: soybean production data. https://www.fao.org/faostat/
(2022). Accessed 15 Feb 2026.
2. Patel D, Franklin KA. TemperaturOpen asset ↗Zenodo · 10.5281/zenodo.21685593pdf-raw-page:28 lines:1-34Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Why it matches plant phenotyping methods綿繊維横断面の画像分割、形質抽出、検証を目的とした再現可能な深層学習ワークフローの開発であり、植物フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Background Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow. Results Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40–100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia’s user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F 1 -scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. The platform reliably quantified epidermal traits despite variations in leaf morphology and image quality, supporting the integrated interpretation of stomatal and pavement-cell responses under stress. Conclusions We developed Stomatalia as a robust, user-friendly deep learning platform for automated, high-throughput analysis of bright-field leaf epidermal images across varying magnifications and resolutions. Stomatalia facilitates rapid, reproducible, and coordinated phenotyping of stomatal and pavement cells by integrating methodological standardisation, computational automation, and multi-trait extraction in a single analytical workflow. Its strongest current application is the analysis of high-quality dicot leaf-print images, particularly in species and imaging conditions similar to those used for model training and validation. Cross-species and benchmark analyses further define its current scope: stomatal detection can be transferred to some additional epidermal architectures, whereas robust pavement-cell segmentation in monocots or highly divergent species will require further annotation and model retraining. Within these defined boundaries, Stomatalia provides a flexible and extensible framework for studying stomatal and pavement cell morphology and environmental plasticity, while also supporting broader efforts to dissect and optimise plant responses to abiotic stress.
Why it matches plant phenotyping methods気孔・舗装細胞の形態形質を画像から自動抽出する深層学習プラットフォームを開発し、独立データで性能評価・比較検証しているため、植物フェノタイピング手法が中心である。
abstractwe developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits.
Reproduction assets foundThe authors publicly deposited the paper's test image datasets (raw/input leaf-print images, detection outputs, manual ground-truth counts, exported datasets) and the model file on Figshare, and provide a public Google Colab demo for running the Stomatalia algorithm. Both are paper-specific, public, and actionable.Dataset · publicThe test datasets and model file used in this work are available through the following link:
https://doi.org/10.6084/m9.figshare.32532672. The test_sets.zip archive contains the test image datasets
(cross-species and benchmark analysis), including raw/input images, detection output images, manual
ground-truth counts and exported datasets.Open asset ↗Figshare · 10.6084/m9.figshare.32532672pdf-page:25 lines:1-75Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published1 Jul 2026The Plant journal : for cell and molecular biologyCited by 0 · OpenAlex ↗
Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor the presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified the channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo. Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap.
Why it matches plant phenotyping methods植物組織における蛍光シグナルの分離、画像セグメンテーション、定量化ワークフローを開発・比較・検証しており、植物の細胞・細胞小器官状態を測定する方法が中心である。
abstractTo separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe segmentation and histograms were acquired using MATLAB script ( https://github.com/NIB‐SI/Nuclei‐segmentation ). The parameters used in the script to achieve appropriate segmentation are listed on GitHub, Case 1 ( https://github.com/NIB‐SI/Nuclei‐segmentation ).Open asset ↗NIB‐SI/Nuclei‐segmentationlines:255-341Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · Crossref · checked 15 Sept 2026
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
R ! .
Because the fitted plane passes through the centroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Single-molecule fluorescence in situ hybridization (smFISH) has emerged as a powerful tool for studying gene expression dynamics with unparalleled precision and spatial resolution in a variety of biological systems. Recent advancements have expanded its application to encompass plant studies, yet there remains a need for a simple and robust smFISH method adapted to plant tissue sections. Here, we present an optimized smFISH protocol, termed cryo-smFISH, for visualizing and quantifying single mRNA molecules in plant tissue cryosections. This method exhibits remarkable sensitivity, enabling the detection of low-expression transcripts, including long non-coding RNAs. By integrating a deep learning-based algorithm into our image analysis pipeline, our method enables precise assignment of RNA abundance in nuclear and cytoplasmic compartments. The method also enables robust integration with immunofluorescence, as cryosectioning enhances antibody penetration. This allows for the sequential visualization and quantification of both RNAs and endogenous proteins within the same cells. Finally, this study demonstrates the use of smFISH to validate single-cell RNA sequencing (scRNA-seq) expression patterns in plant tissues. By extending smFISH to plant cryosections, plant scientists will be able to exploit the full potential of quantitative transcript analysis at cellular and subcellular resolution.
Why it matches plant phenotyping methods植物組織向けcryo-smFISHプロトコルと画像解析法を開発し、RNA量を細胞・細胞内区画で定量する手法が研究の中心である。分子測定ではあるが、植物組織の状態を定量する方法として技術的貢献が明確。
abstractHere, we present an optimized smFISH protocol, termed cryo-smFISH, for visualizing and quantifying single mRNA molecules in plant tissue cryosections.
Reproduction assets foundThe authors deposit all data underlying graphs/heatmaps plus custom R/Python scripts and Cellpose segmentation models in a public GitHub repository specific to this paper. Third-party tools (FISH-quant, DeconvolutionLab2, Stellaris Designer) are generic and excluded.Code · publicAll custom code, including R/Python scripts and Cellpose segmentation models, is available at https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections .
Funding
This work was supported by Vetenskapsrådet (2023-03895), the Novo Nordisk Foundation (NFF24OC0093553 and NNF25OC0100533), and the Carl Tryggers Stiftelse (CTS 18- 325).
Acknowledgments
We thank A. Menkis for initial technical support with cryostat operation and Alexandre Berr for scientific feedback. We also thank memOpen asset ↗zhang_et_al_smFISH_cyrosectionslines:122-152Dataset · publictic ( Bolger et al., 2014 ). The raw gene-count matrix was obtained using the pseudoalignment software Kallisto ( Bray et al., 2016 ). RNA-seq reads were normalized as transcripts per million (TPM).
Data and code availability
The supplemental information and all data underlying the graphs and heatmaps presented are available at https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections .Open asset ↗zhang_et_al_smFISH_cyrosectionslines:106-121Code · publicech.com/stellaris-designer . For mRNA detection, the coding sequence of the target gene was entered into the program, which automatically generated a set of probes complementary to the target mRNA.
The sequences of the probes were then subjected to quality control using an automated local blast R script, available on GitHub at: https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections/tree/main/smFISHprobes . The smFISH probes used in this study and their respective fluorophores are shown in Supplemental Table 3 . The probes were diluted in Tris-EDTA buffer to a final stock concentration of 25 μM.
Cryo-smFISH
Sample preparationOpen asset ↗zhang_et_al_smFISH_cyrosectionslines:75-85Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
PollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks, whose annotation was fully verified by an expert palynologist to guarantee the taxonomic reliability of every published instance. The dataset is designed to close a concrete gap in existing palynological datasets, which typically combine low taxonomic diversity, few samples per class, and low-resolution crops restricted to bounding boxes. PollenBB16 contains 16,198 brightfield optical microscopy images at the native resolution of 3088 × 2064 pixels and 36,383 pixel-accurate polygons across 16 species from the Biobío Region, spanning endemic, native and exotic species of high ecological and melliferous value such as Eucryphia glutinosa and Quillaja saponaria (endemic), Gevuina avellana and Aristotelia chilensis (native), and Medicago sativa and Brassica rapa (introduced). Each spatial position is recorded at three focal planes. The displacement along the z axis reveals features of the exine together with information on the internal structure of the grain that remain inaccessible on a single plane. From this multifocal information, more robust convolutional networks can be trained with more accurate classification. The operational quality of the dataset is backed by a leakage-safe partition that keeps the three focal planes of the same position in the same subset to avoid metric inflation, complemented by a YOLO11n-seg baseline trained for 50 epochs that reaches 0.985 mask mAP@50 on the validation set, establishing a reproducible reference point. Beyond deep learning, PollenBB16 enables interdisciplinary applications in aerobiology, biodiversity monitoring under climate change, ecological restoration of the South American temperate forest, and botanical-origin authentication of Chilean monofloral honeys.
Why it matches plant phenotyping methods植物由来の花粉粒を対象とした高解像度画像データセットとセグメンテーション基準を構築し、深層学習による画像解析を再現可能な形で検証しているため、植物フェノタイピング手法・データセットとして中心的です。
abstractPollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks
Reproduction assets foundThe paper's PollenBB16 pollen image dataset (16,198 multifocal RGB microscopy images with pixel-accurate instance segmentation masks) and the accompanying authors' script polygons_to_bboxes.py are publicly deposited on Zenodo (10.5281/zenodo.19830051), per the Data Availability Statement.Code · publicThe only custom code distributed with this Data Descriptor is the Python script polygons_to_bboxes.py, which regenerates the YOLO bounding-box labels in labels_bb/ from the polygon labels in labels/. The script is packaged inside the scripts/ folder of the same Zenodo repository that hosts the dataset (10.5281/zenodo.19830051) and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, with no restrictions on access.Open asset ↗Zenodo · 10.5281/zenodo.19830051html-lines:731-797Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Understanding cellular growth dynamics in plants requires precise, long-term imaging of developing tissues. Cauline leaves are produced during the transition from vegetative to reproductive development and provide a useful system for studying how laminar organs diversify in form and function. While other laminar organs, such as rosette leaves and sepals, have been extensively studied, early cauline leaf development remains technically challenging to capture due to their concealed position, curved morphology, and the presence of dense trichomes. Here, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana . This method enables reproducible, high-resolution imaging of cauline leaves, supporting robust quantitative analysis of growth across developmental stages at cellular scale resolution. Key features • Fine dissection method for exposing initiating cauline leaves in Arabidopsis thaliana . • Long-term confocal live imaging of cauline leaf development at cellular resolution. • Optimized imaging parameters for high-fidelity 2.5D segmentation and growth analysis in MorphoGraphX.
Why it matches plant phenotyping methodsカウリン葉の成長を細胞レベルで定量化するための解剖、共焦点イメージング、2.5Dセグメンテーション、画像解析パイプラインが中心的に開発・提示されている。
abstractHere, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana .
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public2. MorphoGraphX 2.0.1 ( https://morphographx.org/software/ ) (access date, 2026-02-26) [10–11]
3. All codes have been deposited to OSF: https://osf.io/uth78/ (access date, 2026-02-26)
Procedure
A. Plant growth
1. Sow the seeds in pots filled with moist, room-temperature soil. Add a layer of water to the bottom of the tray and cover with a lid to maintain high humidity.
Note: Space seeds sufficiently to avoid contact between the developing plants and to prevent leaf damage; typicallyOpen asset ↗OSFlines:109-143Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens, we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis.
Why it matches plant phenotyping methods植物細胞表面形態から細胞壁弾性分布を推定する新規測定法を開発・検証しており、植物形質の取得手法が研究の中心である。
abstractwe introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all data and code (including code demonstrations for the surface morphology-based elasticity inference method) in a public GitHub repository, which is listed in allowed_urls.Code · publicData Availability: All relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-model .Open asset ↗rholee-xu/surface-modellines:127-138Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The actin cytoskeleton forms a dynamic network composed of filaments that remain flexible when bundled up, leading to complex filamentous structures in plant cells. Understanding the properties of these filamentous structures under different conditions and in different cell types can provide insight into their function. Yet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties. To address this problem, we devised a network-based approach termed Graph of Filaments over Time (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from two-dimensional time series imaging data. Our comparative analyses using both synthetic test cases and real-world actin cytoskeleton networks of Arabidopsis thaliana hypocotyls exposed to different treatments demonstrated that GraFT accurately traces and tracks actin filamentous structures. Moreover, GraFT facilitates automated quantification of properties for filamentous structures, providing fine-grained insights of effects of different treatments on the level of individual structures. Therefore, GraFT offers a substantial step toward an automated framework facilitating robust spatiotemporal studies of the plant actin cytoskeleton.
Why it matches plant phenotyping methods植物細胞の画像時系列からアクチン繊維構造を追跡・セグメント化し、その時空間特性を自動定量する手法の開発と検証が中心であるため。
abstractwe devised a network-based approach termed Graph of Filaments over Time (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from two-dimensional time series imaging data.
Reproduction assets foundThe paper's authors publicly release the GraFT tool and data-processing code on GitHub (MIT licensed) with an archived Zenodo version. The paper-specific data files are stated to be on Zenodo (DOI 10.5281/zenodo.10476058), but that URL is not among the allowed URLs, so only the code assets are reported. The SciencePlotCode · publicThe tool GraFT and code created for data processing can be found on the GitHub repository https://github.com/Oesterlund/GraFT and is MIT licensed together with an archived version for reproducibilityOpen asset ↗https://github.com/Oesterlund/GraFTlines:159-261Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Summary Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo . Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap. Significance statement Multiplexing genetically encoded biosensors in plants has been limited by overlapping fluorescent signals and strong autofluorescence. This study presents an optimized framework for linear unmixing and provides a MATLAB-based organelle segmentation tool, allowing precise quantification of multiple fluorescent reporters in vivo and advancing real-time visualization of complex cellular processes in plants.
Why it matches plant phenotyping methods植物組織における蛍光シグナルの分離、検出、セグメンテーション、定量化手法を開発・比較・検証しており、植物の細胞・細胞小器官状態を取得する方法が中心である。
abstractTo separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing.
Reproduction assets foundThe paper deposits raw confocal image data on Zenodo (10.5281/zenodo.19691651) and a MATLAB nuclei segmentation/quantification script on GitHub. Only the GitHub repository URL appears in the allowed URL list, so the code asset is reported; the Zenodo image deposit is noted but cannot be listed without a matching URL.Code · publici (ORCID: 0000-0002-6235-2816)
14
15 DATA AVAILABILITY
16 Raw image data supported with metadata were deposited to Zenodo:
17 10.5281/zenodo.19691651and can be opened with LAS X available at https://www.leica-
18 microsystems.com/products/microscope-software/p/leica-las-x-ls/downloads/. MATLAB script
19 was deposited to GitHub: https://github.com/NIB-SI/Nuclei-segmentation.
20 FUNDING
21 This research was funded by the Slovenian Research and Innovation Agency (research core
22 funding No. P4-0165, P4-0463, projects J4-1777, J4-60073, J4-70169 and ARIS program for
23 young researchers).
24 CONFLICT OF INTEREST
25 The authors declare no conflicts of interest. This article does not contain any Open asset ↗NIB-SI/Nuclei-segmentationpdf-layout-page:1 lines:1-34Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants defend against pathogens such as fungi by initiating coordinated structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx and calcium oscillations that drive defense gene expression, yet the mechanisms by which these signals encode stressor intensity and propagate systematically remain unclear. Here, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens (Hedw.) upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent imaging of cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon stimulus removal. We implemented an unbiased image segmentation algorithm using pixel-based k -means clustering to automatically locate regions with specific oscillatory signatures. Calcium dynamics were distinct across adjacent cells, distinguishable by cell type, and significantly modulated by circadian rhythm, adaptation time within the device, and stimulus timing. Cytosolic calcium oscillations, which rose and fell symmetrically within about 60 s, occurred spontaneously during the subjective night and following short adaptation periods. Chitin elicited strong oscillations with increased frequency, amplitude, and duration, and repeated pulses entrained regular, colony-wide oscillations at the stimulation interval. This study complements prior investigations of whole plant and growth tip dynamics and provides a quantitative framework to study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methods植物細胞のカルシウム動態を定量するマイクロ流体・蛍光イメージング系と自動画像セグメンテーションを開発し、植物の生理状態を抽出する方法が研究の中心である。
abstractHere, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens
Reproduction assets foundThe paper's Data Availability Statement explicitly makes analysis scripts and sample data publicly available on the authors' GitHub repository (albrechtLab/moss_calcium), and the MDPI supplementary materials (plants-15-00582-s001.zip) contain the paper's timelapse calcium-imaging videos and supplementary figures. Raw/全Code · publicData are available upon request. Analysis scripts and sample data are publicly available at https://github.com/albrechtLab/moss_calcium (accessed on 1 January 2026).Open asset ↗albrechtLab/moss_calciumlines:188-251Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Specialized host-microbe interfaces are central to cellular interactions in plants. Intracellular structures such as haustoria formed by filamentous pathogens mediate nutrient exchange and effector delivery to host cells. Despite their biological importance, the lack of quantitative frameworks has largely confined the study of these interfaces to qualitative observations, limiting our ability to compare infection strategies, cellular responses, and spatial organization across cells and tissues. Here, we present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images. Using an object-centric deep learning approach, HFinder enables robust identification of haustoria, microbial hyphae, and host organelles across diverse imaging conditions and pathosystems. We demonstrate that this framework supports quantitative analyses of subcellular processes at host-microbe interfaces, including effector secretion, perturbation of host cellular processes, and immune receptor accumulation at haustoria. HFinder provides a practical and scalable solution for the systematic digitalization of plant infection imaging data and establishes a general framework for quantitative studies of cellular dynamics at host-microbe contact zones.
Why it matches plant phenotyping methods植物と微生物の界面を共焦点画像から自動検出・分割し、ハウストリア等を定量解析する深層学習手法が中心であり、植物感染状態の画像ベース表現型解析に該当する。
abstractwe present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images.
Reproduction assets foundThe paper's HFinder pre-trained models (trained phenotyping models/checkpoints) are explicitly deposited on Zenodo with a public DOI matching an allowed URL. The training image dataset is also stated to be publicly available on Zenodo, but no separate authors' URL for it is given in the supplied blocks, so only the preModel / weights · publicFor
convenience, HFinder is distributed with pre-trained models that can be applied directly to confocal image
analysis (available on Zenodo: https://doi.org/10.5281/zenodo.17091805)Open asset ↗Zenodo · 10.5281/zenodo.17091805pdf-page:5 lines:1-47Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signaling pathways. In the root, the characteristic large central vacuole develops by endomembrane reorganization occurring mainly in the transition zone. The vacuole's bounding membrane-the tonoplast-can be visualized in vivo using fluorescent protein markers, allowing for quantitative analysis of confocal microscopy images. Tonoplast organization can thus serve as a sensitive indicator of changes to any of the processes involved in vacuole biogenesis. The Vacuolar Morphology Index (VMI) is widely accepted as a quantitative measure of vacuole structure. However, this metric has two drawbacks-it only reflects the size of the largest vacuolar compartment (missing therefore possible differences in the organization of smaller compartments), and its determination is labor intensive, limiting its use on large datasets. Results We developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI. We compared the performance of our protocol with VMI on a simulated dataset and on real data. To validate the methods´ performance, we used it to confirm the previously reported differences in vacuole shape and size between Arabidopsis thaliana roots grown on the surface of an agar medium compared to those embedded inside the agar. Both VMI and TTI could efficiently detect the relatively subtle changes in vacuole organization depending on the position of the root in the agar, and provided correlated results. However, only TTI produced data with close to normal value distribution, simplifying subsequent statistical evaluation. Conclusions We present the protocol for TTI determination as a two-stage semi-automated procedure involving microscopic image analysis employing an ImageJ macro and subsequent processing of numeric data in the Jupyter Notebook environment, together with benchmarking image data. Since this implementation is freeware-based, platform-independent and (relatively) user-friendly, we hope it will find its use as a high throughput, added value alternative to the VMI metric.
Why it matches plant phenotyping methods植物液胞構造を定量化する新規指標と半自動画像解析プロトコルを開発し、シミュレーションおよび実画像で既存指標と比較・検証しているため、植物フェノタイピング手法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI)
Reproduction assets foundThe paper deposits its benchmark confocal image dataset in the EMBL-EBI BioImage Archive (S-BIAD2226) and its TTI analysis software (ImageJ macro and Jupyter/Python scripts) on GitHub, both with explicit public availability statements.Dataset · publicImage data generated and analyzed in the current study are available in the EMBL-EBI BioImage Archive repository, accession number S-BIAD2226Open asset ↗EMBL-EBI BioImage Archive · S-BIAD2226lines:141-163Code · publicArchive copy, additional sample data and possible future updates of the software tool generated here are also available at [ https://github.com/GeorgeCaldarescu/TTI-Tonoplast-Topology-Index ] .Open asset ↗GitHub · GeorgeCaldarescu/TTI-Tonoplast-Topology-Indexlines:141-163Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published1 Jan 2026The Plant journal : for cell and molecular biologyCited by 4 · OpenAlex ↗
Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hamper automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers-so long as the cell contours can be identified-are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialists, it is expected to promote understanding of what happens at the sub- and cellular level with high-throughput outcomes.
Why it matches plant phenotyping methods植物細胞のライブ画像から細胞形状・先端位置・成長方向・成長動態を定量化する解析ソフトウェアを開発しており、植物表現型取得・抽出が研究の中心である。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public authors' code repositories (KymoTip analysis tool and SAM2 segmentation code) and a figshare deposit of the raw imaging data used for the tip-growth phenotyping measurements.Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTipOpen asset ↗blues0910/KymoTiphtml-lines:159-244Code · publicthe code for SAM2 segmentation is available at https://github.com/YusukeKimata‐Moo/SAM2‐segmentation/Open asset ↗YusukeKimata‐Moo/SAM2‐segmentationhtml-lines:159-244Dataset · publicThe raw data used in this paper are available on figshare: https://doi.org/10.6084/m9.figshare.30847580Open asset ↗figshare · 10.6084/m9.figshare.30847580html-lines:159-244Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Polyploidy (whole-genome duplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.
Why it matches plant phenotyping methodsiSPyは画像から組織内の核倍数性を空間的・高スループットに推定する教師なし学習パイプラインであり、Arabidopsisで実証されている。植物の状態を抽出する計算フェノタイピング手法が中心である。
abstractHere, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest.
Reproduction assets foundThe paper deposits its paper-specific phenotyping assets publicly: confocal images of A. thaliana, D. melanogaster, and human cardiomyocytes, ilastik segmentation files, and A. thaliana cotyledon flow cytometry data are all in an OSF repository, and the iSPy analysis code is available both on OSF and in a public GitLabDataset · publicAll data presented in the study are publicly available in the OSF data repository (https://osf.io/um7r3/; https://doi.org/10.17605/osf.io/um7r3).Open asset ↗10.17605/osf.io/um7r3html-lines:253-271Code · publicThe code for iSPy can also be found in the OSF data repository (https://osf.io/um7r3/; https://doi.org/10.17605/osf.io/um7r3), as well as in a GitLab repository, https://gitlab.gwdg.de/devplantpatterning/Publications/ispy-inferring-spatial-ploidy.Open asset ↗gitlab.gwdg.de · devplantpatterning/Publications/ispy-inferring-spatial-ploidyhtml-lines:253-271Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Plant cell walls are dynamic composites whose architecture determines growth, mechanics, and environmental resilience. Efforts to link pectin structure to function have been limited by the lack of molecular probes with sufficient specificity, a gap that becomes even more pronounced for the intricately branched rhamnogalacturonon-II (RG-II) subclass. Here we report the first fluorescent probes with defined specificity to RG-II, engineered from catalytic site mutants of Bacteroides thetaiotaomicron glycoside hydrolases BT1010 and BT0996. These enzyme-derived probes bind RG-II monomer with high affinity, discriminate against dimeric forms, and localize to cell corners and junctions in Arabidopsis thaliana stems, consistent with RG-II’s unique ability among wall polysaccharides to form borate-mediated, covalent crosslinkages between molecules. Application of these probes revealed spatial partitioning distinct from the homogalacturonan (HG)- and rhamnogalacturonan I (RG-I)-enriched middle lamella, highlighting functional specialization among pectic domains, with RG-II reinforcing cell junctions while HG and RG-I mediate wall flexibility. Our work establishes a generalizable framework for transforming CAZymes into high-precision imaging reagents, enabling molecular-level visualization of structurally complex polysaccharides in the cell wall.
Why it matches plant phenotyping methodsRG-IIを特異的に可視化する蛍光プローブを開発し、植物細胞壁内の空間分布という植物状態を画像で測定する手法を示しているため、フェノタイピング手法が中心的です。
abstractHere we report the first fluorescent probes with defined specificity to RG-II
Reproduction assets foundThe paper deposits its raw microscopy z-stacks and maximum projections on OSF and its custom MATLAB image-analysis code on GitHub, both with explicit availability statements and public URLs.Dataset · publicMicroscopy data that support the findings of this study have been deposited in Open Science Framework. Raw z-stacks, output maximum intensity projections, and annotated figure images in greyscale are available at (https://osf.io/8utvs/overview).Open asset ↗Open Science Frameworklines:319-349Code · publicMATLAB code used for image analysis is available at https://github.com/kristenthorne/GHprobes.git, with usage instructions and example input and output files provided.Open asset ↗GitHub · kristenthorne/GHprobeslines:319-349Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Mycorrhizal associations between fungi and plants are a fundamental aspect of terrestrial ecosystems. Mycorrhizas occur in c. 85% of extant plants, yet their geological record remains sparse. Rare fossil evidence from early terrestrial environments offers crucial insights into these ancient symbioses, but visualizing fossil fungi at the microscale within plant tissues is challenging. Here, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant from the Windyfield Chert, a stratigraphically distinct fossiliferous unit from Rhynie (Scotland). We also applied Raman spectroscopy to investigate the carbon framework of both fungal and plant tissues. This integrative approach revealed fungal structures in unprecedented detail. The fungus, Rugososporomyces lavoisierae gen. nov., sp. nov., exhibits features resembling extant Glomeromycotina arbuscular mycorrhizal fungi. This is the first record of mycorrhizas from the Windyfield Chert. FLIM further distinguished features at the subcellular level, while Raman spectroscopy showed that fungal arbuscules and vesicles of the plant water-conducting cells underwent geological alterations, resulting in a similar chemical composition. These findings expand our understanding of ancient and extremely rare plant-fungal symbioses and highlight the potential of confocal-FLIM for advancing palaeobotanical research.
Why it matches plant phenotyping methods植物組織内の微細構造を対象に、共焦点レーザー顕微鏡・FLIM・ラマン分光を組み合わせた観察法を中核としており、化石植物の細胞・菌根構造の状態を抽出している。
abstractHere, we combine confocal laser scanning microscopy and fluorescence lifetime imaging microscopy (FLIM) to investigate a newly identified fungus and cellular structures of a 407-Myr-old plant
Reproduction assets foundThe authors deposited all confocal imaging data used in this fossil mycorrhiza study (CLSM/FLIM datasets of Rugososporomyces lavoisierae in Aglaophyton majus) in a public Zenodo repository under a CC BY 4.0 license. This is a paper-specific, publicly accessible dataset of the phenotyping/imaging measurements.Dataset · publicAll confocal data collected and used in this study are deposited in the Zenodo repository under a Creative Commons Attribution 4.0 international license https://doi.org/10.5281/zenodo.15194427 (Strullu‐Derrien et al ., 2025 ).Open asset ↗Zenodo · 10.5281/zenodo.15194427lines:252-551Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Plant development and adaptation are highly dependent on cell morphology and growth. High turgor pressure in plants causes stress on the cell wall, followed by cell extension. In tip-growing cells, the localization of vesicles and cytoskeleton components has been well studied. However, there has been a lack of attention to the spatial profile of mechanical properties, specifically the cell wall elasticity. In this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells. Previous work is based on measurements from the wall meridional outline, a technique that cannot track the elastic deformation of the cell wall experimentally. Instead, we developed a way to infer the bulk modulus distribution from the cell surface by triangulating experimental marker points coming from fluorescent labeling. To justify the use of our protocol in tip-growing cells from the moss Physcomitrium patens , we replicated the experimental noise and moss morphology in simulated cells. In practice, we found that a larger triangulation improved robustness against noise, which agreed with our theoretical study. With multiple cell sampling, we determined that 10 cells were sufficient to recover the elasticity distribution with noise, but only when the elastic stretches were high enough. We then created a dimensionless map of inference error to verify a spatial change of P. patens bulk modulus within two folds. This technique will open the field to more comprehensive measurements of cell wall elasticity, providing a key step in understanding tip cell growth and morphogenesis. Author summary Tip-growing cells can be characterized by their fast growth concentrated at the cell’s apex. Their growth and morphogenesis are tightly regulated processes involving cell wall addition and rearrangement while the cell wall is under stress originating from the cell’s internal turgor pressure. We start by studying the cell wall’s elastic properties, one aspect of the cell growth process. We use a method of marker point tracking across the surface of the tip-growing cell to measure the wall’s elasticity profile. In this work, we present a parameter sensitivity study of this method on synthetic cells and report our results on experimental moss tip-growing cells. Our results suggest that this inference method can reliably measure a cell wall elasticity gradient under combined geometric and mechanical conditions that create elastic strains within 5% at the tip.
Why it matches plant phenotyping methodsコケの先端成長細胞における細胞壁弾性分布を、蛍光マーカーと表面形態から推定する新規測定法を開発し、シミュレーションおよび実細胞で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we introduce a new surface morphology-based method to measure the elasticity of the cell wall in tip-growing cells.
Reproduction assets foundThe authors' Data Availability statement explicitly deposits all relevant data and code, including code demonstrations, in a public GitHub repository (rholee-xu/surface-model), which contains the analysis code for the cell wall elasticity inference method.Code · publicAll relevant data and code, including code demonstrations, are available on the GitHub repository found here: https://github.com/rholee-xu/surface-modelOpen asset ↗rholee-xu/surface-modellines:45-63Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Summary Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hampers automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers —so long as the cell contours can be identified— are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialist, it is expected to promote understanding of what happens at the sub- and cellular level with high throughput outcomes. Significance statement Faced with fluctuations in cell coordinates and cell tip positions, position correction of live imaging data and accurate detection of tip position are key challenges in plant developmental biology. We solved them with a powerful and user-friendly tool, KymoTip, that can realize cell position correction, cell tip detection with cell centerline, and quantification of intracellular events.
Why it matches plant phenotyping methods植物細胞のライブイメージから細胞形状・先端位置・成長動態を定量化する解析ソフトウェアを開発しており、植物フェノタイピング手法が中心です。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's authors explicitly state that the KymoTip analysis code is publicly available on GitHub at https://github.com/blues0910/KymoTip, which is an allowed URL. This is the authors' own computational tool implementing the paper's tip-growth phenotyping analysis (segmentation, coordinate normalization, tip-bottom, Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTip.Open asset ↗blues0910/KymoTippdf-page:8 lines:1-44Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisSoybeanCell / cellular structureRootMorphology / geometry measurementRoot system architecture
Abstract Root system architecture (RSA), the three-dimensional arrangement of roots in soil, is a critical determinant of plant productivity, resource use efficiency, and resilience to environmental stress. Despite its agronomic importance, RSA remains a largely untapped breeding target due to historical technical barriers in root phenotyping. We present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems. Applying RADICYL to a genetically diverse panel of 371 soybean accessions, we combined 3D phenotyping with genome-wide association studies (GWAS), single-nucleus RNA sequencing (snRNA-seq), and gene co-expression network (GCN) analysis to identify RCE1 and NPR3 as central regulators of RSA, suggesting auxin and salicylic acid-mediated signaling impacts RSA in specific root tissues. Functional validation in Arabidopsis mutants revealed conserved effects on root width and lateral root development. Our findings position the endodermis and metaphloem as key regulatory cell types and demonstrate how multi-omic frameworks can accelerate the discovery of functional genes underlying complex traits. This study establishes a foundation for cell-type-targeted genome editing and climate-smart crop engineering, offering actionable genetic targets to optimize root systems for improved nutrient acquisition, drought resilience, and deep carbon sequestration. By bridging genotype, cellular context, and phenotype, this work redefines RSA as a tractable and transformative trait for the future of crop improvement.
Why it matches plant phenotyping methodsRADICYLという根系構造を定量化する高スループット3Dフェノタイピング基盤の開発・適用が研究の中心であり、15形質を測定している。
abstractWe present RADICYL (Root Architecture 3D Cylinder), a scalable, non-invasive, gel-based platform enabling high-throughput, high-resolution quantification of 15 RSA traits in intact root systems.
Reproduction assets foundThe paper's Data and code availability section names public repositories containing the authors' analysis code: a GitLab repo for WGCNA/single-cell network analysis, a GitHub repo for the RADICYL root image segmentation/phenotyping pipeline, and PyGNA2 on PyPI/GitLab. These are paper-specific, publicly actionable code/Code · publicn every 5°, resulting in 72 images per plant per timepoint for subsequent 3D root
1103 reconstruction. Phenotypic traits were quantified using the same automated pipeline described
1104 above for soybean.
1105
1106 Data and code availability
1107 The code to analyze the WGCNA network and single-cell data can be found here:
1108 https://gitlab.com/salk-tm/soybean-root-gwas/. RADYCL Segmentation pipeline for image
1109 analysis can be found here: https://github.com/Salk-Harnessing-Plants-Initiative/SSRAPC-Soy-
1110 Segmentation-Root-Architecture-Phenotyping-for-Cylinder.git. PyGNA2 is available on PyPI
1111 (https://pypi.org/project/pygna2/) and GitLab (https://gitlab.com/salk-tm/pygna2).
1112Open asset ↗salk-tm/soybean-root-gwaspdf-layout-page:30 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.
Why it matches plant phenotyping methods植物の蛍光イメージングからカルシウム動態という生理状態を抽出・定量するオープンソース解析ツールが中心であり、植物を含む複数生物種のデータで評価されている。
abstractHere, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem.
Reproduction assets foundThe paper describes CalciumInsights, an open-source R/Shiny tool for calcium transient analysis. The authors explicitly state their code is publicly available on GitHub, which constitutes the paper's computational analysis asset. No plant-phenotyping datasets, images, or trained models are described; the tool is tissueCode · publicnt for publication
All authors have reviewed the manuscript and approved the final draft for publication.
Resource availability
Lead contact: Further information and requests for data may be directed to and will be
fulfilled by Mauricio Cabrera (mauricio.cabrera1@upr.edu)
Code: All codes used are publicly available in GitHub at https://github.com/AOG-Lab/CalciumInsights
References
1. Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat
Rev Mol Cell Biol [Internet]. 2000 Oct [cited 2024 Oct 21];1(1):11–21. Available from:
https://www.nature.com/articles/35036035
2. Sanderson MJ, Charles AC, Boitano S, Dirksen ER. Mechanisms and function of intercellularOpen asset ↗AOG-Lab/CalciumInsightspdf-raw-page:20 lines:1-37Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Flow cytometry is widely applied to infer the ploidy and genome size (GS) of plant nuclei. The conventional approach of sample preparation, reliant on fresh plant material to release intact nuclei, often results in poor yields of nuclei in conditions when a plant material cannot be kept fresh due to logistical constraints. Previous attempts to use frozen plant material were mainly limited to ploidy analysis and relied on chopping methods, which restrict the material input and often result in poor nuclei yield, especially in frozen samples, due to incomplete disruption. Here, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal. Nuclei isolated from this protocol can also be used for DNA or RNA extraction. Genome size estimates from frozen material are similar to those from fresh material, with a reduction in error range, although not always significant (p > 0.05). In certain species, frozen samples can yield substantially more nuclei than fresh material. With the addition of specific debris compensation algorithms, coefficient of variation (CV%) can be maintained below 5%. This method has special value in estimating the GS of samples collected from remote locations and frozen for use in plant genome sequencing. Freezing preserves high-quality DNA and RNA, enabling the same sample to be used for both flow cytometry and genome sequencing.
Why it matches plant phenotyping methods凍結植物材料からフローサイトメトリーで植物ゲノムサイズを推定する改良プロトコルを開発・検証しており、植物形質取得法が研究の中心である。
abstractHere, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal.
Reproduction assets foundThe paper deposits its raw flow cytometry fluorescence dataset (genome size estimation of fresh vs frozen plant material) in FlowRepository and its supplementary materials (ANOVA table, histogram/peak-modeling figures, microscopy images, protocol) in a Zenodo database. Both are paper-specific, publicly accessible, andSupplement · publicg across diverse taxa and
storage durations, this method could significantly enhance field-based and conservation
genomics efforts.
Supplementary Materials: The following supplementary data can be accessed online from the
database titled “A flow cytometry protocol for measurement of plant genome size using frozen mate-
rial” at https://doi.org/10.5281/zenodo.14873353 (Accessed on 1 April 2025). Table S1: Results of
one-way ANOVA for all combinations of species, nuclei extraction method, and debris compensation
on genome size estimation. Figure S1. The process of nuclei isolation from frozen leaf material.
Figure S2. Conventional histogram analysis for the fluorescence data of fresh preparatOpen asset ↗zenodo · 10.5281/zenodo.14873353pdf-raw-page:12 lines:1-50Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Various high-throughput screening methods have been developed to explore plant phenotypes, primarily at the organ and whole plant levels. There is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap. This study used double-resonator piezoelectric cytometry biosensors to capture the dynamic changes in mechanical phenotypes of living cells of two rice species, drought-resistant Lvhan No. 1 and drought-sensitive 6527, under PEG6000 drought stress. In rice cells of Lvhan No. 1 and 6527, mechanomics parameters, including cell-generated surface stress (ΔS) and viscoelastic parameters (G', G″, G″/G'), were measured and compared under 5-25% PEG6000. Lvhan No. 1 showed larger viscoelastic but smaller surface stress changes with the same concentration of PEG6000. Moreover, Lvhan No. 1 cells showed better wall-plasma membrane-cytoskeleton continuum structure maintaining ability under drought stress, as proven by transient tension stress (ΔS > 0) and linear G'~ΔS, G″~ΔS relations at higher 15-25% PEG6000, but not for 6527 cells. Additionally, two distinct defense and drought resistance mechanisms were identified through dynamic G″/G' responses: (i) transient hardening followed by softening recovery under weak drought, and (ii) transient softening followed by hardening recovery under strong drought. The abilities of Lvhan No. 1 cells to both recover from transient hardening to softening and to recover from transient softening to hardening are better than those of 6527 cells. Overall, the dynamic mechanomics phenotypic patterns (ΔS, G', G″, G″/G', G'~ΔS, G″~ΔS) verified that Lvhan No. 1 has better drought resistance than that of 6527, which is consistent with the field data.
Why it matches plant phenotyping methods植物細胞の機械的表現型を取得する高スループットなバイオセンサー手法を用い、乾燥ストレス応答を定量化しており、表現型取得法が研究の中心である。
abstractThere is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap.
Reproduction assets foundThe paper's DRPC phenotyping measurements (frequency and motional resistance traces underlying the ΔS, G′, G″ analyses) are provided as downloadable supplementary figures at the MDPI supplementary URL. No standalone public dataset or author analysis code repository is stated; the Data Availability Statement only offersSupplement · publicansient softening under strong drought. The results presented in this work demonstrated the potential to develop a new cellular mechanical phenotype platform to screen for biotic and abiotic stress-resistant crop varieties, as shown in Figure 11 .
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bios15060334/s1 , Figure S1: Changes in frequency and motional resistance of 9 MHz AT and BT cut chips during the adhesions of Lvhan No.1 rice cells followed by the treatments of different concentrations of PEG6000 stresses. (A, B, C, D, E): AT cut, (A1, B1, C1, D1, E1): BT cut, (A, A1): 5% PEG6000, (B, B1): 10%PEG6000, (C, C1) 15%Open asset ↗lines:136-159Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Chloroplast density in cells varies among different types of cells and plants. In current single-cell spatiotemporal analysis, the automatic detection and quantification of chloroplasts at the single-cell level is crucial. We developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-chloroplasts), an AI tool for single-cell chloroplast detection and cell-type clustering. It utilizes You-Only-Look-Once (YOLO), a real-time detection algorithm, for accurate and efficient performance. DeepD&Cchl has been proved to identify chloroplasts in plant cells across various imaging types, including light microscopy, electron microscopy, and fluorescence microscopy. Integrated with an Intersection Over Union (IOU) module, DeepD&Cchl precisely counts chloroplasts in single- or multi-layered images, while eliminating double-counting errors. Furthermore, when combined with Cellpose, a single-cell segmentation tool, DeepD&Cchl enhances its effectiveness at the single-cell level. By counting chloroplasts within individual cells, it supports cell-type-specific clustering based on chloroplast number versus cell size, offering valuable morphological insights for single-cell studies. In summary, DeepD&Cchl is a significant advancement in plant cell analysis. It offers accuracy and efficiency in chloroplast identification, counting and cell-type classification, providing a useful tool for plant research.
Why it matches plant phenotyping methods植物細胞画像から葉緑体を検出・計数し、細胞型をクラスタリングするAIツールの開発が中心であり、植物の形態的状態を定量化するフェノタイピング手法に該当する。
abstractWe developed DeepD&Cchl (Deep-learning-based Detecting-and-Counting-chloroplasts), an AI tool for single-cell chloroplast detection and cell-type clustering.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe raw dataset, as well as the scripts for the DeepD&Cchl model training and 17application macro, were shared on GitHub https://github.com/Shaokai9/AI4LifeScience_ECNU/tree/main/Deep%20subcellular%20detection .Open asset ↗Shaokai9/AI4LifeScience_ECNUlines:400-410Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Grana are fundamental structural units of the intricate chloroplast membrane network. Investigating their nanomorphology is essential for understanding photosynthetic efficiency regulation. Here, we present GRANA (Graphical Recognition and Analysis of Nanostructural Assemblies), an artificial intelligence-enhanced, user-friendly software tool that recognizes grana on thylakoid network electron micrographs and generates a complex set of their structural parameters. GRANA employs 3 artificial neural networks of different architectures and binds them in a 1-click workflow. Its output is designed to facilitate hybrid intelligence analysis, securing fast and reliable results from large datasets. The GRANA tool is over 100 times faster compared with currently used manual approaches. As a proof of concept, we have successfully applied GRANA software to diverse grana structures across different land plant species grown under various conditions, demonstrating the wide range of potential applications for our software. GRANA tool supports large-scale analysis of grana nanomorphological features, facilitating advancements in photosynthesis-oriented studies.
Why it matches plant phenotyping methods葉緑体グラナの電子顕微鏡画像から構造パラメータを自動抽出するソフトウェアの開発であり、植物形態形質の取得・解析法が中心。
abstractan artificial intelligence-enhanced, user-friendly software tool that recognizes grana on thylakoid network electron micrographs and generates a complex set of their structural parameters.
Reproduction assets foundThe paper's raw TEM images used for grana nanomorphology analysis are publicly deposited under DOI 10.58132/HTWCC1. The authors' analysis code (github.com/center4ml/GRANA) is mentioned but that URL is not among the allowed URLs, so it cannot be listed.Dataset · publicRaw TEM data used for results in the manuscript are available at https://doi.org/10.58132/HTWCC1 .Open asset ↗10.58132/HTWCC1lines:184-235Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Plant cells are contained within a rigid network of cell walls. Cell walls serve as a structural material and a crucial signaling hub vital to all aspects of the plant life cycle. However, many features of the cell wall remain enigmatic, as it has been challenging to map its functional properties in live plants at subcellular resolution. Here, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls. CarboTag uses a small molecular motif, a pyridine boronic acid, that directs its cargo to the cell wall. We designed a suite of cell wall imaging probes based on CarboTag in various colors for multiplexing. Additionally, we developed new functional reporters for live quantitative imaging of key cell wall characteristics: network porosity, cell wall pH and the presence of reactive oxygen species. CarboTag paves the way for dynamic and quantitative mapping of cell wall responses at subcellular resolution. Subject terms: Plant cell biology, Fluorescence imaging
Why it matches plant phenotyping methods植物細胞壁のライブ機能イメージング用ツールボックスを開発し、孔隙率、pH、活性酸素などの細胞壁特性を定量化する手法が中心である。
abstractHere, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls.
Reproduction assets foundThe paper's Data availability and Code availability statements both point to a public 4TU repository DOI containing the raw imaging/phenotyping data and the analysis code for this paper's CarboTag cell wall imaging measurements.Dataset · publicThe raw data associated with the figures in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 . Source data are provided with this paper.Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code · publicCode developed to process and analyze data in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 .Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Ploidy, representing the number of homologous chromosome sets, can be estimated from flow cytometry data acquired on cells stained with a fluorescent DNA dye. This estimation relies on a combination of tools that often require scripting, individual sample curation, and additional analyses. Results To automate the ploidy estimation for multiple flow cytometry files, we developed MuPETFlow-a Shiny graphical user interface tool. MuPETFlow allows users to visualize cell fluorescence histograms, detect the peaks corresponding to the different cell cycle phases, perform a linear regression using standards, make ploidy or genome size predictions, and export results as figures and table files. The tool was benchmarked with known ploidy datasets from yeast and plant species, yielding consistent ploidy results. MuPETFlow's peaks detection and performance were also compared to those of other tools. Conclusions MuPETFlow stands out as the only tool offering in-app ploidy detection, multiple peak detection, multi-sample visualization, and automation capabilities. These features significantly accelerate the analysis, making it especially valuable for projects involving large datasets.
Why it matches plant phenotyping methods植物のフローサイトメトリーデータから倍数性・ゲノムサイズを推定する解析ツールを開発し、植物データセットでベンチマークしているため、植物表現型取得・解析手法が中心である。
abstractTo automate the ploidy estimation for multiple flow cytometry files, we developed MuPETFlow-a Shiny graphical user interface tool.
Reproduction assets foundThe paper's flow cytometry analysis assets are publicly available: the authors' MuPETFlow GitHub repository hosts the tool code and the newly generated S. cerevisiae FCS datasets, and the plant (Solanum pseudocapsicum) flow cytometry data used for ploidy estimation is deposited in FlowRepository under FR-FCM-Z45W. The Dataset · publicThe S. pseudocapsicum dataset is available
http://flowrepository.org/id/FR-FCM-Z45W.Open asset ↗pdf-page:5 lines:1-74Dataset · publicThe S. cerevisiae datasets are available at https://github.com/CintiaG/MuPETOpen asset ↗GitHubpdf-page:5 lines:1-74Code / dataset availability confirmedbioRxiv · Europe PMC · checked 13 Sept 2026
Polyploidy (whole-genome multiplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or by tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.
Why it matches plant phenotyping methodsArabidopsisを含む組織の核倍数性を画像から空間的に推定する新規計算パイプラインを開発しており、植物の状態計測手法が研究の中心である。
abstractwe present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public OSF data repository (containing the paper's imaging/phenotyping data) and a public GitLab repository for the iSPy analysis code, both with authors' URLs.Dataset · publicAll data are available in the main text, in the supplementary materials , and are publicly available in our OSF data repository https://osf.io/um7r3/ .Open asset ↗OSF · um7r3lines:234-294Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The thylakoid membrane is the site of the light-dependent reactions of photosynthesis. It is a continuous membrane, folded into grana stacks and the interconnecting stroma lamellae. The CURVATURE THYLAKOID1 (CURT1) protein family is involved in the folding of the membrane into the grana stacks. The thylakoid membrane remodels its architecture in response to light conditions, but its 3D organisation and dynamics remain incompletely understood. To resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner. Recently, we have used expansion microscopy, a technique that meets these criteria, to visualise the thylakoid membrane isolated from spinach. Here, we show that this protocol can also be used to visualise enveloped spinach chloroplasts. Additionally, we present an improved protocol for resolving the thylakoid structure of Arabidopsis thaliana. Using this protocol, we show the changes in thylakoid architecture in response to long-term far-red light acclimation and due to knocking out CURT1A. We show that far-red light acclimation results in higher grana stacks that are packed closer together. In addition, the distance between stroma lamellae, which are wrapped around the grana, decreases. In the curt1a mutant, grana have an increased diameter and height, and the distance between grana is increased. Interestingly, in this mutant, the stroma lamellae occasionally approach the grana stacks from the top. These observations show the potential of expansion microscopy to study the thylakoid membrane architecture.
Why it matches plant phenotyping methods植物のチラコイド膜構造を高解像度3D画像で取得する拡大顕微鏡法の改良・適用が中心であり、膜構造という植物形態形質を測定しているため。
abstractTo resolve these details, an imaging technique is needed that provides high-resolution 3D images in a high-throughput manner.
Reproduction assets foundThe article states that the data underlying the publication (expansion microscopy imaging/measurements of thylakoid architecture) are publicly available in the 4TU Research Data repository via the DOI 10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885. This is a paper-specific, publicly accessible data deposit with an authorDataset · publicUte Armbruster for providing the
seeds of the Ler0 curt1a-1 mutant. This work was supported by the Dutch
Organisation for Scientific Research (NWO) via a Vidi grant no. VI.Vidi
192.042 (E.W.) and by Wageningen Graduates Schools through a PhD
grant (J.B.).
Data availability
The data underlying this publication can be accessed at https://doi.org/10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885.References
[1] R.E. Blankenship, Molecular Mechanisms of Photosynthesis, John Wiley & Sons,
2021, https://doi.org/10.1002/9780470758472.
[2] H. Kirchhoff, Chloroplast ultrastructure in plants, New Phytol. 223 (2) (2019)
565–574, https://doi.org/10.1111/nph.15730.
[3] H. Kirchhoff, C. Hall, M. Wood, M. HerbstOpen asset ↗10.4121/75fa3c66-8505-4d6a-9bd9-16973e5ca885pdf-raw-page:9 lines:1-68Code / dataset availability confirmedCrossref · checked 6 Sept 2026
ArabidopsisMicroscopyCell / cellular structureRootTissueVisualization / data management
Abstract Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200–300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants' challenging optical properties. Expansion microscopy (ExM) improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that ExM can be applied to increase effective imaging resolution in Arabidopsis root specimens.
Why it matches plant phenotyping methods植物組織に適用可能な超解像イメージング手法を開発し、Arabidopsis根で解像度向上を実証しており、画像取得法が研究の中心である。
abstractHere, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy.
Reproduction assets foundThe paper's PlantEx expansion microscopy imaging data are deposited in ISTA's public repository, and the authors' custom analysis code (including the BigWarp-based expansion-factor script) is publicly available on GitHub. The Click-ExM repository is cited prior work whose method was adapted, not a paper-specific asset.Dataset · publicThe data that support the findings of this study are available via ISTA's data repository at https://doi.org/10.15479/AT:ISTA:18837 .Open asset ↗ISTA's data repository · 10.15479/AT:ISTA:18837lines:219-252Code · publicThe custom-written code used and described in this manuscript is available via Github ( https://github.com/danzllab/PlantEx ).Open asset ↗github.com/danzllab/PlantExlines:219-252Code · publicThe expansion factor was extracted as the linear scaling factor of the similarity transformation minimizing squared landmark residuals using the script https://github.com/danzllab/CATS/tree/master/rcats_image-analysis/bigwarp .Open asset ↗github.com/danzllab/CATSlines:154-159Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.
Why it matches plant phenotyping methodsシアノバクテリア細胞の画像セグメンテーションと細胞表現型分類を行うソフトウェアおよび機械学習手法の開発が中心であり、植物細胞の形態・生存状態を抽出するフェノタイピング手法に該当する。
abstractHere, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes.
Reproduction assets foundThe paper's segmentation/classification models and analysis code are publicly available in the authors' GitHub repository (cameronlab/cypose). The microscopy training datasets are not public and are available only upon request.Code · publicAll code and trained models can be downloaded from https://github.com/cameronlab/cypose .Open asset ↗cameronlab/cyposelines:298-383Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The present dataset combines transcriptomic and microscopic analyses to investigate the responses of winter oilseed rape (WOSR, Brassica napus L., cultivar Aviso) to soil drought, with a focus on differences between young and early-senescent old leaves. For microscopy, 36 scans of 1 to 5 leaf cross-sections were acquired from paraffin-embedded leaf disc samples using a scanner with a 40x lens (Pannoramic Confocal, 3DHistech), capturing a large field of view (8-mm-long observed leaf tissue). The raw scanned cross-sections and analyzed images are available under doi.org/10.57745/RK5PM3 in the Recherche Data Gouvrepository. These high-quality scans enable the differentiation of mesophyll cells and tissues. Software analysis yielded a dataset with 54 selected cross-sectional areas, 291 delimited surfaces of palisade, spongy, and vessel tissues, and 11,136 individually delimited cells from the palisade and spongy layers. For transcriptomics, an Illumina Novaseq sequencer was used to generate 390 Gb of mRNA paired-end reads. The raw reads were filtered, mapped, and assigned to genes from the Brassica napus reference genome Darmor-bzh v10, which were subsequently used to identify differentially expressed genes (DEGs) and to perform gene ontology enrichment analysis. The raw reads are accessible under accession PRJNA939927 at the NCBI Sequence Read Archive (SRA). This high-quality dataset provides insights into the molecular mechanisms underlying oilseed rape's response to soil drought and may aid in the development of drought-tolerant cultivars. A total of 17,975 DEGs were identified between well-watered and severe drought conditions across the contrasted leaf developmental stages.
Why it matches plant phenotyping methods葉の断面画像を取得・解析し、組織面積や個別細胞などの植物形態形質を構造化した再利用可能なデータセットを提供しており、画像ベースの表現型取得が実質的な構成要素である。
abstractFor microscopy, 36 scans of 1 to 5 leaf cross-sections were acquired from paraffin-embedded leaf disc samples using a scanner with a 40x lens
Reproduction assets foundThe article deposits its own plant-phenotyping assets publicly: raw and analyzed leaf cross-section microscopy scans (Recherche Data Gouv, doi:10.57745/RK5PM3) and the transcriptomic dataset (Recherche Data Gouv doi:10.57745/7HQSM3, mirrored at NCBI SRA under PRJNA939927). The analysis pipelines cited (nf-core/rnaseq, Dataset · publicThe raw scanned cross-sections and analyzed images are available under doi.org/10.57745/RK5PM3 in the Recherche Data Gouvrepository.Open asset ↗Recherche Data Gouv · 10.57745/RK5PM3lines:1-41Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.
Why it matches plant phenotyping methods葉表皮細胞の形態形質を画像から抽出・定量するコンピュータビジョン／画像処理ワークフローの開発と適用が研究の中心であるため、植物フェノタイピング手法として収載する。
abstractwe developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology.
Reproduction assets foundThe paper's quantitative phenotyping measurements (trichome types and morphometric parameters of leaf epidermal cells for nine wild potato species) are publicly available as Supplementary Tables S1 and S2 at the MDPI supplementary URL. Microscopy images are not publicly deposited and are available only upon request; noSupplement · publicObjects of the Institute of Cytology and Genetics SB RAS.
Abbreviations
The following abbreviations are used in this manuscript:
LSM
Laser scanning microscopy
PI
Propidium iodide
DAPI
4′,6-diamidino-2-phenylindole
PCA
Principal component analysis
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants13213084/s1 , Table S1: Trichome types for the studied wild potato species; Table S2: Morphometric parameters for leaf epidermal cells of the studied wild potato species, including Area, Length, Width, Elongation, Circularity, Rectangularity, Perimeter, Convex Hull Area, Convex Hull Perimeter, and Convex Hull Coverage.
Author Open asset ↗lines:139-176Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.
Why it matches plant phenotyping methods植物細胞構造の仮想染色と画像解析モデルを開発・評価し、細胞面積・形状・核拡大・葉緑体運動・生死などの表現型を定量化しているため、フェノタイピング手法が中心である。
abstractThe applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated.
Reproduction assets foundThe paper publicly releases its virtual-staining training/test image sets (bright-field inputs with paired confocal reference images) for BY-2 vacuole, BY-2 nucleus/plasma membrane, and E. densa chloroplast models on figshare under CC BY 4.0, via three DOIs listed in the Data Availability section. No author analysis orDataset · publicata pertaining to this article will be shared on reasonable request to the corresponding author. The
training and test image sets for BY-2 cells and E. densa, which are publicly accessible on figshare under the
CC BY 4.0 license, include images of wild-type tobacco BY-2 cells stained with BCECF for vacuolar lumen
visualization (https://doi.org/10.6084/m9.figshare.27247629.v1), transgenic tobacco BY-2 cells with
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted October 25, 2024.
;
hOpen asset ↗figshare · 10.6084/m9.figshare.27247629.v1pdf-raw-page:21 lines:1-32Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.
Why it matches plant phenotyping methods植物細胞・組織の分解状態を定量する4次元蛍光共焦点イメージングと計算ツールが研究の中心であり、植物状態の形態的変化を抽出する方法を開発している。
abstractIn this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales.
Reproduction assets foundThe paper's WallTrack computational pipeline (used to track and quantify 4D confocal imaging of poplar cell wall deconstruction) is publicly available on the authors' FARE laboratory GitLab repository. The underlying imaging/phenotype data are not publicly deposited; the authors state data will be made available on.Code · publicnano and micro
scales.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The WallTrack code is accessible through the FARE laboratory
GitLab repository at: https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d. Data will be made available on request.
Acknowledgments
The authors thank Anouck Habrant for her help in confocal imaging
and Grégoire Malandain, Solmaz Hossein Khani, Khadidja Ould Amer,
and Ali Faraj for their comments on the manuscript. This work was
supported by Agence Nationale de la Recherche (ANR) Open asset ↗https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d · refahi_et_al_4dpdf-raw-page:11 lines:1-66Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope. To investigate how differential growth of connected cell layers generate unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal (or plant tissues in general) is practically challenging. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals, and subsequent image processing. For live imaging early-stage sepals, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z- resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a 'voxel removal' technique to visualize the inner epidermal layer in MorphoGraphX image processing software. We also describe the MorphoGraphX parameters for creating a 2.5D mesh surface for the inner epidermis. Our parameters allow for the segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. While we have used sepals to illustrate our approach, the methodology will be useful for researchers intending to live-image and track growth of deeper cell layers in 2.5D for any plant tissue.
Why it matches plant phenotyping methods植物組織の深部をライブイメージングし、画像処理・細胞セグメンテーション・追跡によって成長を解析する方法自体が中心的に開発・最適化されているため。
abstractwe provide an optimized methodology for live imaging sepals, and subsequent image processing.
Reproduction assets foundThe paper's Data availability statement deposits the study's datasets (live-imaging/phenotyping data underlying the sepal growth analysis) in two public OSF repositories with explicit DOIs, making them paper-specific, public, and actionable.Dataset · publicg and Michelle Heeney for their comments on the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, WritinOpen asset ↗OSF · 10.17605/OSF.IO/UMW9Blines:234-260Dataset · publicon the manuscript. We thank Richard Smith (John Innes Centre, UK) for his advice on image processing.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.17605/OSF.IO/UMW9B , https://doi.org/10.17605/OSF.IO/P5Q39 .
Author contributions
AS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AR: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Writing – review & editing.
Conflict of intereOpen asset ↗OSF · 10.17605/OSF.IO/P5Q39lines:234-260Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
SUMMARY There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing the number and size of these organelles in the in leaf is a potential route to elevate leaf-level photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice and thus various attempts are being made to increase chloroplast content in this cell type. In this study we developed and applied a deep learning tool named Chloro-Count to demonstrate that loss of OsHAP3H function in rice increases chloroplast occupancy in bundle sheath cells by 50%. Although limited to a single season, when grown in the field Oshap3H mutants exhibited increased numbers of tillers and panicles as compared to controls or gain of function mutants. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function OsHAP3H mutants and facilitated a comparison between 2D and 3D quantification methods. In wild-type rice, as the dimensions of bundle sheath cells increase, the volume of individual chloroplasts also increases. However, the larger the chloroplasts the fewer there are per bundle sheath cell. This observation revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in Oshap3H mutants. The use of Chloro-Count also revealed that 2D quantification, upon which most previous studies have relied, is compromised by the positioning of chloroplasts within the cell. Chloro-Count is therefore a valuable tool for accurate and high-throughput quantification of chloroplasts that has enabled the robust characterization of OsHAP3H effects on chloroplast biogenesis in rice. Whereas previous studies have increased chloroplast occupancy in bundle sheath cells by increasing the size of individual chloroplasts, loss of OsHAP3H function leads to an increase in chloroplast numbers.
Why it matches plant phenotyping methodsChloro-Countという深層学習ツールを開発し、葉肉細胞内の葉緑体数・占有率を高精度かつハイスループットに定量する手法が研究の中心であるため。
abstractwe developed and applied a deep learning tool named Chloro-Count
Reproduction assets foundThe paper's Chloro-Count deep learning tool (Mask R-CNN segmentation of chloroplasts and bundle sheath cells) is the authors' own analysis code, explicitly stated to be publicly available on GitHub. No public image/phenotype dataset deposit is stated; training images and Table S1 raw data are not linked to a public URLCode · publicn validated, they are mapped to
566 individual organelles/cells for volumetric analysis. An overview of the system for detecting and
567 measuring volumes of chloroplasts is presented in Figure 3A. The process for detecting and
568 measuring bundle sheaths follows an analogous workflow. The Chloro-Count code is available on
569 https://github.com/pedropgusmao/chloro-count.
570
571 Data collection and pre-processing
572 A total of 327 slices from 39 different cells were used during the training of both image segmentation
573 networks. Images from 29 cells were used for training, five for validation and five for testing. A total of
574 3,790 segments of chloroplasts were used for training, 287Open asset ↗pedropgusmao/chloro-countpdf-layout-page:16 lines:1-47Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.
Why it matches plant phenotyping methods植物プロトプラストのクロマチン状態を高スループット画像とカスタム画像解析で定量し、100以上の特徴を抽出・ベンチマークしており、表現型取得法が研究の中心である。
abstractwe used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts.
Reproduction assets foundThe paper's chromatin feature datasets (Dryad doi:10.5061/dryad.pnvx0k6wp) and images (BioStudies S-BIAD1157) are paper-specific public assets, but their repository URLs are not among the allowed URLs, so they cannot be listed. The authors' adapted entropy analysis script is publicly available on the authors' GitHub (aCode · publicinterval (a new observation from the same group will fall inside the ellipse with probability P= 0.95).
Entropy analysis
The initial script for computing Shannon Entropy is described in Dussiau et al. (2022) and is available at https://osf.io/9mcwg/ . The adapted script for computing entropy of chromatin features is provided at https://github.com/barouxlab/ChromatinEntropy . When all cells (segmented nuclei) express the same value for a given feature, this entropy of the feature will be null. The more cell-to-cell variability for a given chromatin feature, the higher value of entropy.
Plots and statistical tests
Box plots, violin plots, scatter plots, 2D contours and histograms were created Open asset ↗barouxlab/ChromatinEntropylines:121-147Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Tissue morphogenesis remains poorly understood. In plants, a central problem is how the 3D cellular architecture of a developing organ contributes to its final shape. We address this question through a comparative analysis of ovule morphogenesis, taking advantage of the diversity in ovule shape across angiosperms. Here, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana. We introduce nerve-based topological analysis as a tool for unbiased detection of differences in cellular architectures and corroborate identified topological differences between two homologous tissues by comparative morphometrics and visual inspection. We find that differences in topology, cell volume variation and tissue growth patterns in the sheet-like integuments and the bulbous chalaza are associated with differences in ovule curvature. In contrast, the radialized conical ovule primordia and nucelli exhibit similar shapes, despite differences in internal cellular topology and tissue growth patterns. Our results support the notion that the structural organization of a tissue is associated with its susceptibility to shape changes during evolutionary shifts in 3D cellular architecture.
Why it matches plant phenotyping methods3Dデジタルアトラスと神経ベースのトポロジー解析、形態計測を用いて植物器官の細胞構造と形状を定量化しており、表現型取得・解析手法が研究の中心である。
abstractHere, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana.
Reproduction assets foundThe paper's topological analysis and statistical evaluation code is publicly available on GitHub (NADO repository), with explicit availability language. The paper-specific 3D digital ovule dataset (S-BIAD957) is deposited in BioStudies, but no matching allowed URL exists for it, so it cannot be listed as an actionable,Code · publicThe source code and the Dockerfiles can be obtained from the Github repository at https://github.com/fabian-roll/NADO .Open asset ↗https://github.com/fabian-roll/NADO · NADOlines:109-124Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.
Why it matches plant phenotyping methodsTradescantiaの雄しべ毛細胞の色を画像から自動分類・計数するコンピュータビジョン手法とデータセットを開発・評価しており、植物ストレス応答という細胞状態の取得が中心である。
abstractWe have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells
Reproduction assets foundThe paper's Trad-204 dataset of Tradescantia clone 4430 stamen hair cell images and the associated analysis are explicitly stated to be publicly available in the authors' GitHub repository.Dataset · publicThe datasets generated and analyzed for this study can be found in the GitHub repository: https://github.com/emiliomercuri/Trad-204 .Open asset ↗emiliomercuri/Trad-204 · Trad-204lines:382-419Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodelling, and timely deposition of polysaccharides, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions, through the behavior of yellow fluorescent protein (YFP)-tagged cytokinesis-specific GTPase RABA2a vesicles. We monitored the entire duration of cell plate development, from its first emergence, with the aid of YFP-RABA2a, in both the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.
Why it matches plant phenotyping methodsLLSMによる4次元画像取得と、細胞板の小胞体積を定量化する解析パイプラインの開発が研究の中心であり、植物細胞の形態・発達状態を抽出する方法として substantive です。
abstractUsing lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions
Reproduction assets foundThe paper deposits representative 4D lattice light sheet microscopy datasets (YFP–RABA2a cell plate imaging) used for its quantitative analysis on Zenodo, a paper-specific public asset. No author analysis code repository with explicit availability language is stated; the other URLs are method guidelines, not paper dataDataset · publicRepresentative datasets used in the study are available on Zenodo at https://doi.org/10.5281/zenodo.10515765 .Open asset ↗Zenodo · 10.5281/zenodo.10515765lines:85-93Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO 2 and O 2 , and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO 2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.
Why it matches plant phenotyping methodsRBAモデルを用いて植物細胞の生長率やC:N比などの表現型を定量予測する計算手法の開発が中心であり、単なる生物学的実験ではない。
abstractHere we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana
Reproduction assets foundThe authors publicly release the paper-specific RBA leaf model (XML) and the PlantCellRBA simulation/analysis software on Forgemia, with explicit availability statements in the Data availability and Supplementary material sections. No plant image/sensor/phenotype measurement datasets from this paper are deposited; the Code · publicinterest, such as the seed, in order to define and forecast quality
determinants under diverse environmental conditions. These insights will also be valuable in
fine-tuning plant breeding programs.
Data availability
The RBA leaf model (encoded in XML files) and the PlantCellRBA software for running simulations
are available at https://forgemia.inra.fr/anne.goelzer/rba-plant-cell-model.
Acknowledgements
We thank Wolfram Liebermeister, Ana Bulovic, Sophie Colombié and Jean-Denis Faure for critical
comments on the manuscript and the Métaprogramme Digitbio of INRAE for funding.
Author Contributions
AG and VF conceived the study. AG developed, implemented and simulated the different models
(RBA, Open asset ↗forgemia.inra.fr/anne.goelzer/rba-plant-cell-modelpdf-layout-page:28 lines:1-50Supplement · publicSupplementary Table 1) led to changes in growth rate
greater than 1% (Fig.Open asset ↗pdf-raw-page:20 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Correlative light and electron microscopy (CLEM) is an important tool for the localisation of target molecule(s) and their spatial correlation with the ultrastructural map of subcellular features at the nanometre scale. Adoption of these advanced imaging methods has been limited in plant biology, due to challenges with plant tissue permeability, fluorescence labelling efficiency, indexing of features of interest throughout the complex 3D volume and their re-localization on micrographs of ultrathin cross-sections. Here, we demonstrate an imaging approach based on tissue processing and embedding into methacrylate resin followed by imaging of sections by both, single-molecule localization microscopy and transmission electron microscopy using consecutive CLEM and same-section CLEM correlative workflow. Importantly, we demonstrate that the use of a particular type of embedding resin is not only compatible with single-molecule localization microscopy but shows improvements in the fluorophore blinking behavior relative to the whole-mount approaches. Here, we use a commercially available Click-iT ethynyl-deoxyuridine cell proliferation kit to visualize the DNA replication sites of wild-type Arabidopsis thaliana seedlings, as well as fasciata1 and nucleolin1 plants and apply our in-section CLEM imaging workflow for the analysis of S-phase progression and nucleolar organization in mutant plants with aberrant nucleolar phenotypes.
Why it matches plant phenotyping methods植物組織に適用するin-section CLEMおよび超解像イメージングのワークフローを開発・実証しており、植物細胞の構造・複製状態を取得する方法が研究の中心である。
abstractHere, we demonstrate an imaging approach based on tissue processing and embedding into methacrylate resin followed by imaging of sections by both, single-molecule localization microscopy and transmission electron microscopy using consecutive CLEM and same-section CLEM correlative workflow.
Reproduction assets foundThe paper's raw TEM and SMLM phenotyping image datasets (Arabidopsis nucleolar ultrastructure and DNA replication CLEM analysis) are publicly deposited in the BioImage Archive under accession S-BIAD700, with an explicit authors' URL. Source data quantification sheets are only provided with the paper, not as a separate址Dataset · publicature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.
Data availability
The raw datasets of TEM imaging (Spurr and Lowicryl) and SMLM data for quantitative analysis have been deposited to the BioImage Archive, under accession code S-BIAD700 ( https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD700 ). Source data are provided with this paper (sheet 1—IRF quantification, sheet 2—FC quantification, sheet 3—DBSCAN analysis). Source data are provided with this paper.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurOpen asset ↗BioImage Archive · S-BIAD700lines:116-150Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Plants undergo various natural changes that dramatically modify their genomes. One is polyploidization and the second is hybridization. Both are regarded as key factors in plant evolution and result in phenotypic differences in different plant organs. In Silene , we can find both examples in nature, and this genus has a seed shape diversity that has long been recognized as a valuable source of information for infrageneric classification. Methods Morphometric analysis is a statistical study of shape and size and their covariations with other variables. Traditionally, seed shape description was limited to an approximate comparison with geometric figures (rounded, globular, reniform, or heart-shaped). Seed shape quantification has been based on direct measurements, such as area, perimeter, length, and width, narrowing statistical analysis. We used seed images and processed them to obtain silhouettes. We performed geometric morphometric analyses, such as similarity to geometric models and elliptic Fourier analysis, to study the hybrid offspring of S. latifolia and S. dioica . Results We generated synthetic tetraploids of Silene latifolia and performed controlled crosses between diploid S. latifolia and Silene dioica to analyze seed morphology. After imaging capture and post-processing, statistical analysis revealed differences in seed size, but not in shape, between S. latifolia diploids and tetraploids, as well as some differences in shape among the parentals and hybrids. A detailed inspection using fluorescence microscopy allowed for the identification of shape differences in the cells of the seed coat. In the case of hybrids, differences were found in circularity and solidity. Overal seed shape is maternally regulated for both species, whereas cell shape cannot be associated with any of the sexes. Discussion Our results provide additional tools useful for the combination of morphology with genetics, ecology or taxonomy. Seed shape is a robust indicator that can be used as a complementary tool for the genetic and phylogenetic analyses of Silene hybrid populations.
Why it matches plant phenotyping methods種子画像を処理し、幾何学的形態計測と楕円フーリエ解析で種子形状・サイズを定量化する手法が研究の中心であり、植物器官の形態表現型を抽出している。
abstractWe used seed images and processed them to obtain silhouettes. We performed geometric morphometric analyses, such as similarity to geometric models and elliptic Fourier analysis, to study the hybrid offspring of S. latifolia and S. dioica .
Reproduction assets foundThe authors state that the raw seed images used for the morphometric phenotyping analyses are publicly deposited in Zenodo (DOI 10.5281/zenodo.8366177). This is a paper-specific, publicly accessible dataset of the seed/cell images underlying this study's measurements. No author analysis code with an explicit public URLDataset · publicRaw images used in this work are available in Zenodo DOI 10.5281/zenodo.8366177 .Open asset ↗Zenodo · 10.5281/zenodo.8366177lines:436-491Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published4 Feb 2024The Plant journal : for cell and molecular biologyCited by 0 · OpenAlex ↗
Confocal microscopy has greatly aided our understanding of the major cellular processes and trafficking pathways responsible for plant growth and development. However, a drawback of these studies is that they often rely on the manual analysis of a vast number of images, which is time-consuming, error-prone, and subject to bias. To overcome these limitations, we developed Dot Scanner, a Python program for analyzing the densities, lifetimes, and displacements of fluorescently tagged particles in an unbiased, automated, and efficient manner. Dot Scanner was validated by performing side-by-side analysis in Fiji-ImageJ of particles involved in cellulose biosynthesis. We found that the particle densities and lifetimes were comparable in both Dot Scanner and Fiji-ImageJ, verifying the accuracy of Dot Scanner. Dot Scanner largely outperforms Fiji-ImageJ, since it suffers far less selection bias when calculating particle lifetimes and is much more efficient at distinguishing between weak signals and background signal caused by bleaching. Not only does Dot Scanner obtain much more robust results, but it is a highly efficient program, since it automates much of the analyses, shortening workflow durations from weeks to minutes. This free and accessible program will be a highly advantageous tool for analyzing live-cell imaging in plants.
Why it matches plant phenotyping methods植物のライブセル画像から粒子密度・寿命・変位を自動抽出するソフトウェアを開発し、Fiji-ImageJと比較検証しており、表現型取得・解析手法が中心である。
abstractwe developed Dot Scanner, a Python program for analyzing the densities, lifetimes, and displacements of fluorescently tagged particles
Reproduction assets foundThe paper's own computational analysis tool, Dot Scanner (Python software for quantifying densities, lifetimes, and displacements of fluorescently labeled particles in plant tissues), is explicitly stated to be publicly available on GitHub with a full URL. No public phenotype/trait datasets or raw imaging data depositsCode · public= 2, blob size = 5, dot lower = 0.9, dot
upper = 4.5, and blob lower = 2, skips = 1, and remove edge
frames = false. All lifetimes that were 60 sec long were removed
from the analysis.
Dot scanner
Dot Scanner was developed using the Python programming lan-
guage. The software is available on GitHub, and the project home-
page (https://github.com/bdavis222/dotscanner) contains all the
documentation needed for its installation and use, including
the README file (https://github.com/bdavis222/dot-scanner/blob/main/README.md). As mentioned in the README, Python 3 must
be installed prior to Dot Scanner installation (https://www.python.org/downloads/).ACKNOWLEDGMENTS
We thank S. Bednarek for provOpen asset ↗bdavis222/dotscannerpdf-raw-page:9 lines:1-87Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract The actin cytoskeleton forms a dynamic network composed of filaments that remain flexible when bundled up, leading to complex filamentous structures in plant cells. Understanding the properties of these filamentous structures under different conditions and in different cell types can provide insight into their function. Yet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties. To address this problem, we devised a network-based approach termed Gra ph of F ilaments over T ime (GraFT) to trace and track filamentous structures in cytoskeleton networks extracted from imaging data. Our comparative analyses using both synthetic and real-world actin cytoskeleton networks of Arabidopsis thaliana hypocotyls exposed to different treatments demonstrated that GraFT accurately traces and tracks actin filamentous structures. Moreover, GraFT facilitates automated quantification of properties for filamentous structures, providing fine-grained insights of effects of different treatments on the level of individual structures. Therefore, GraFT offers a substantial step towards an automated framework facilitating robust spatiotemporal studies of the plant actin cytoskeleton.
Why it matches plant phenotyping methods植物細胞の画像からアクチン繊維構造を追跡・定量するGraFT手法を開発し、合成データと実画像で精度検証しており、表現型取得・抽出が研究の中心です。
abstractYet, despite developments in the study of the plant actin cytoskeleton, it remains challenging to segment and identify actin filamentous structures, preventing quantification of their spatiotemporal properties.
Reproduction assets foundThe preprint provides a public GitHub repository with the GraFT tool and data-processing code (MIT licensed), and states that all data files (the Arabidopsis actin cytoskeleton imaging datasets used for the phenotyping analyses) are deposited on Zenodo. The GitHub URL is an allowed URL; the Zenodo DOI is not among the,Code · publicuthors contributed to the discussion and manuscript preparation.
Competing interests
The authors declare no competing interests.
Availability of data
All data files can be found on Zenodo with DOI: 10.5281/zenodo.10476058
Code Availability
The tool GraFT and code created for data processing can be found on the GitHub repository https://github.com/Oesterlund/GraFT and is MIT licensed.
References
Østerlund, I., Persson, S. & Nikoloski, Z. Tracing and tracking filamentous structures across scales: A systematic review. Comput Struct Biotechnol J 21 , 452–462 (2023).
Takatani, S. et al. Microtubule Response to Tensile Stress Is Curbed by NEK6 to Buffer Growth Variation in the ArOpen asset ↗Oesterlund/GraFTlines:123-155Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique that has been playing an indispensable role in the fields of science, technology, and biomedical imaging. The technique is competent in imaging and analyzing label-free living cells and investigating reflective surfaces. Herein, we introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells that leverages several advantages, including being less-vibration sensitive to external perturbations due to its common-path configuration, also being compact in size, simple in optical design, highly stable, and cost-effective. A detailed description of the proposed DHM system, including its optical design, working principle, and capability for phase imaging, is presented. The applications of the proposed system are demonstrated through quantitative phase imaging results obtained from the reflective surface (USAF resolution test target) as well as transparent samples (living plant cells). The proposed system could find its applications in the investigation of several biological specimens and the optical metrology of micro-surfaces.
Why it matches plant phenotyping methods植物細胞を対象に定量位相画像を取得できる新規デジタルホログラフィック顕微鏡を開発しており、植物試料での性能実証も含むため、植物フェノタイピングに利用可能な画像計測法が中心です。
abstractwe introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells
Reproduction assets foundThe paper's only paper-specific public asset is the MDPI supplementary material containing the time-lapse retrieved wrapped phase imaging video of tobacco plant cells (the paper's plant-cell phenotyping measurements). No analysis code, datasets, or trained models are publicly deposited; the Data Availability Statement仅Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24030720/s1 , See the supplementary material for visualization of the time-lapse retrieved wrapped phase imaging video of tobacco plant cells captured five-minute intervals.
Click here for additional data file.
Author Contributions
Conceptualization: M.K.; methodology, M.K.; software, M.K.; validation, M.K., O.M. and T.M.; formal Open asset ↗lines:51-66Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and results We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. Conclusions CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.
Why it matches plant phenotyping methods葉表皮細胞の画像セグメンテーションと形態計測を自動化するFiji/Rツールの開発・比較検証であり、植物形質取得法が研究の中心です。
abstractCuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Reproduction assets foundThe paper's authors publicly release their Fiji macros and R filtering notebook (CuticleTrace toolkit) on GitHub, and the 50-image test set derives from the public Cuticle Database. Both are paper-specific, public, and actionable.Code · publicAll generated and analyzed data from this study are included in the published article and its Supporting Information (Appendix S1 ). The CuticleTrace User Manual, code for the Fiji macros, and the R Notebook for filtering cells are available in the GitHub repository ( https://github.com/benjlloyd/CuticleTrace ). A video tutorial is available at https://youtu.be/XLhWd-tpU70 .Open asset ↗benjlloyd/CuticleTracelines:354-354Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Chloroplasts are essential organelles in plants that are involved in plant development and photosynthesis. Accurate quantification of chloroplast numbers is important for understanding the status and type of plant cells, as well as assessing photosynthetic potential and efficiency. Traditional methods of counting chloroplasts using microscopy are time-consuming and face challenges such as the possibility of missing out-of-focus samples or double counting when adjusting the focal position. Here, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts. This approach utilizes a deep-learning-based object detection algorithm called You-Only-Look-Once (YOLO), along with the Intersection Over Union (IOU) strategy. The application of D&Cchl has shown excellent performance in accurately identifying and quantifying chloroplasts. This holds true when applied to both a single image and a three-dimensional (3D) structure composed of a series of images. Furthermore, by integrating Cellpose, a cell-segmentation tool, we were able to successfully perform single-cell 3D chloroplast counting. Compared to manual counting methods, this approach improved the accuracy of detection and counting to over 95%. Together, our work not only provides an efficient and reliable tool for accurately analyzing the status of chloroplasts, enhancing our understanding of plant photosynthetic cells and growth characteristics, but also makes a significant contribution to the convergence of botany and deep learning. One-sentence summary This deep learning-based approach enables the accurate complete detection and counting of chloroplasts in 3D single cells using microscopic image stacks, and showcases a successful example of utilizing deep learning methods to analyze subcellular spatial information in plant cells. The authors responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors ( https://academic.oup.com/plcell/ ) is: Zhao Dong ( dongzhao@hebeu.edu.cn ), Shaokai Yang, ( shaokai1@ualberta.ca ), Ningjing Liu ( liuningjing1@yeah.net ), and Qiong Zhao ( qzhao@bio.ecnu.edu.cn ).
Why it matches plant phenotyping methods植物細胞の顕微鏡画像から葉緑体数を自動検出・定量する深層学習手法を開発し、手動計数と比較して精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts.
Reproduction assets foundThe authors explicitly state that all code and the training dataset (annotated chloroplast microscopy images) are shared on their public GitHub repository, which is a paper-specific asset for this chloroplast counting study. Other URLs (labelImg, yolov7, ImageJ Falk plugins) are generic third-party tools, not paper-ownCode · publicltiple times during the stacking process. Through this approach, we
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successfully constructed a comprehensive 3D cell model from the series of 2D
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images, enabling more accurate chloroplast detection and counting in a 3D space.
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Code and software
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All the code and training dataset have been shared on GitHub
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(https://github.com/xiaoli111111111/-AI4CELLBIO-ECNU), with detailed
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explanations in the supplementary manual.
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References
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preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted December 18, 2023.
;
https://doi.org/10.1101/2023.12.Open asset ↗xiaoli111111111/-AI4CELLBIO-ECNUpdf-raw-page:16 lines:1-65Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice. Methods To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. Results We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. Conclusions The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
Why it matches plant phenotyping methods植物の3D顕微鏡画像から細胞形態を抽出するセグメンテーション手法を開発し、植物データセットで性能評価・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractWe propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data.
Reproduction assets foundThe paper provides public author code (CellStitch implementation and experiment-reproducing notebooks on GitHub) and the plant image datasets analyzed (Ovules, ATAS, Arabidopsis 3D Digital Tissue Atlas), all with explicit availability statements and URLs.Code · publicopen source code implementing the stitching algorithm from the top to the bottom layer is available at https://github.com/imyiningliu/cellstitchOpen asset ↗imyiningliu/cellstitchlines:116-127Dataset · publicAll the datasets analyzed in this paper are publicly available online. Ovules: https://osf.io/uzq3w/ ; ATAS: https://www.repository.cam.ac.uk/handle/1810/262530 ; Arabidopsis 3D Digital Tissue Atlas: https://osf.io/fzr56Open asset ↗lines:175-223Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
Abstract Background Plant defense activators offer advantages over pesticides by avoiding the emergence of drug-resistant pathogens. However, only a limited number of compounds have been reported. Reactive oxygen species (ROS) act as not only antimicrobial agents but also signaling molecules that trigger immune responses. They also affect various cellular processes, highlighting the potential ROS modulators as plant defense activators. Establishing a high-throughput screening system for ROS modulators holds great promise for identifying lead chemical compounds with novel modes of action (MoAs). Results We established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables. Our screening strategy comprised four phases: (1) development of a ROS inference system based on a deep neural network that combines ROS production data in plant cells and multidimensional chemical features of chemical compounds; (2) in silico extensive-scale screening of seven million commercially available compounds using the ROS inference model; (3) secondary screening by visualization of the chemical space of compounds using the generative topographic mapping; and (4) confirmation and validation of the identified compounds as potential ROS modulators within plant cells. We further characterized the effects of selected chemical compounds on plant cells using molecular biology methods, including pathogenic signal-triggered enzymatic ROS induction and programmed cell death as immune responses. Our results indicate that deep learning-based screening systems can rapidly and effectively identify potential immune signal-inducible ROS modulators with distinct chemical characteristics compared with the actual ROS measurement system in plant cells. Conclusions We developed a model system capable of inferring a diverse range of ROS activity control agents that activate immune responses through the assimilation of chemical features of candidate pesticide compounds. By employing this system in the prescreening phase of actual ROS measurement in plant cells, we anticipate enhanced efficiency and reduced pesticide discovery costs. The in-silico screening methods for identifying plant ROS modulators hold the potential to facilitate the development of diverse plant defense activators with novel MoAs.
Why it matches plant phenotyping methods植物細胞のROS蓄積という生理状態を推定する深層学習モデルと、実測による検証を組み合わせたスクリーニング手法の開発が中心である。
abstractWe established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe learning algorithm codes used during the current study are available on the GitHub address ( https://github.com/ma1206ko/in_silico_screening ).Open asset ↗ma1206ko/in_silico_screeninglines:168-249Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
One of the fundamental questions in plant developmental biology is how cell proliferation and cell expansion coordinately determine organ growth and morphology. An amenable system to address this question is the Arabidopsis root tip, where cell proliferation and elongation occur in spatially separated domains, and cell morphologies can easily be observed using a confocal microscope. While past studies revealed numerous elements of root growth regulation including gene regulatory networks, hormone transport and signaling, cell mechanics and environmental perception, how cells divide and elongate under possible constraints from cell lineages and neighboring cell files has not been analyzed quantitatively. This is mainly due to the technical difficulties in capturing cell division and elongation dynamics at the tip of growing roots, as well as an extremely labor-intensive task of tracing the lineages of frequently dividing cells. Here, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots. We also implemented a data sonification tool that facilitates human recognition of cell division synchrony. Using these tools, we revealed previously unnoted lineage-constrained dynamics of cell division and elongation, and their contribution to the root zonation boundaries.
Why it matches plant phenotyping methods生長中のシロイヌナズナ根端における細胞分裂・伸長という植物形態動態を、4D顕微鏡、AI画像処理、追跡、データソニフィケーションで半自動定量する手法を開発しており、フェノタイピング手法が中心である。
abstractHere, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes for the nuclei detection and cell tracking are available on the GitHub ( https://github.com/JerrySongCST/Arabidopsis_root_cortex_cell_tracking ).Open asset ↗JerrySongCST/Arabidopsis_root_cortex_cell_trackinglines:152-227Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana, including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.
Why it matches plant phenotyping methods植物画像からアクチンネットワークの定量的形質を自動抽出する画像解析アルゴリズムの開発が中心であり、植物組織・変異体・病害応答への適用も行っている。
abstractwe have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete DRAGoN analysis code (the computational tool that performs the paper's actin network extraction and phenotyping measurements) on a public GitHub repository, and the repository URL is also cited in the Results section.Code · publicData Availability: The authors confirm that all data underlying the findings are fully available without restriction. The complete code for this paper is available on a GitHub repository at https://github.com/JordanHembrow5/DRAGoN .Open asset ↗JordanHembrow5/DRAGoNlines:118-135Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
PremiseLeaf epidermal cell morphology is closely tied to plants evolutionary histories and growth environments, and is therefore of interest to many plant biologists. However, cell measurement can be time-consuming and restrictive with current methods. CuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and ResultsWe evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand-tracings across a taxonomically diverse 50-image dataset of variable image qualities. We observed [~]93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. ConclusionsCuticleTrace is broadly applicable, modular, and customizable, and integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape datasets.
Why it matches plant phenotyping methods葉の表皮細胞形態を画像からセグメンテーション・測定するソフトウェアを開発し、代替手法および専門家の手トレースと比較検証しており、植物表現型取得法が研究の中心です。
abstractCuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Reproduction assets foundThe authors publicly release the CuticleTrace FIJI macros and R filtering notebook used for the paper's epidermal cell phenotyping analysis on GitHub, with explicit availability language and URL.Code · publicSB and SWP supervised and
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directed the research.
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DATA AVAILABILITY
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All generated and analyzed data from this study are included in the published article and its
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Supporting Information (Fig. S2). The code for the FIJI macros as well as the R notebook for
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filtering cells is available in the GitHub repository: (https://github.com/benjlloyd/CuticleTrace).296
REFERENCES
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Aono, A. H., J. S. Nagai, G. da S. M. Dickel, R. C. Marinho, P. E. A. M. de Oliveira, J. P. Papa,
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and F. A. Faria. 2021. A stomata classification and detection system in microscope
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images of maize cultivars. PLOS ONE 16: e0258679.
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Barclay, R., J. Mcelwain, D. Dilcher, and B. Sageman. 2007. The COpen asset ↗benjlloyd/CuticleTracepdf-raw-page:13 lines:1-61Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MicroscopyCell / cellular structureFlowerTissue2D/3D reconstructionGrowth / development / phenology
Floral spurs are invaginations borne by perianth organs (petals and/or sepals) that have evolved repeatedly in various angiosperm clades. They typically store nectar and can limit the access of pollinators to this reward, resulting in pollination specialization that can lead to speciation in both pollinator and plant lineages. Despite the ecological and evolutionary importance of nectar spurs, the cellular mechanisms involved during spur development have only been described in detail in a handful of species, primarily with respect to epidermal cells. These studies show that the mechanisms involved are taxon-specific. Using confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae) and showed that the process is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic (directional) cell expansion. The comparison with Aquilegia , another taxon of Ranunculaceae with spurred petals, revealed that the convergence in form between the spurs of both taxa is obtained by partially similar developmental processes. The analytical pipeline designed here is an efficient method to visualize in 3D each cell of a developing organ, paving the way for future comparative studies of organ morphogenesis in multicellular eukaryotes. Highlight A new method of 3D analysis of plant tissues at the cellular level revealed that spur morphogenesis in Staphisagria picta is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic cell expansion. Floral spur development is analysed for the first time quantitatively, taking into account all tissues composing the organ, namely epidermis and parenchyma.
Why it matches plant phenotyping methods共焦点顕微鏡と自動3D画像解析による発生器官の細胞形態・増殖・異方的伸長の定量化手法が研究の中心であり、植物器官の表現型取得・解析に該当する。
abstractUsing confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae)
Reproduction assets foundThe paper's 3D segmentation/visualization pipeline (PlantSeg + MorphoLibJ + homemade Python scripts) is the paper-specific computational analysis, and the authors explicitly state the automation and visualization code is publicly available on GitHub. No separate public phenotype dataset or image deposit is stated; dataCode · public”.
235 Cell outliers, i.e. the 5% largest and smallest cells in terms of volume, were filtered out. To
236 visualize the interior of the petals, we relied on the opacity of the dots or on virtual sections.
237 The code that allowed the automation of the segmentations and the visualization of the data is
238 available on github [https://github.com/paulinedlpch/morphogenesis].Open asset ↗paulinedlpch/morphogenesispdf-layout-page:6 lines:1-57Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vivo microscopy of plants with high-frequency imaging allows observation and characterization of the dynamic responses of plants to stimuli. It provides access to responses that could not be observed by imaging at a given time point. Such methods are particularly suitable for the observation of fast cellular events such as membrane potential changes. Classical measurement of membrane potential by probe impaling gives quantitative and precise measurements. However, it is invasive, requires specialized equipment, and only allows measurement of one cell at a time. To circumvent some of these limitations, we developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3). In this protocol, we describe how to prepare experiments for agar media and microfluidics, and we detail the image analysis. We take an example of the rapid plasma membrane depolarization induced by the phytohormone auxin to illustrate the method. Relative membrane potential measurements using DISBAC 2 (3) fluorescence increase the spatio-temporal resolution of the measurements and are non-invasive and suitable for live imaging of growing roots. Studying membrane potential with a more flexible method allows to efficiently combine mature electrophysiology literature and new molecular knowledge to achieve a better understanding of plant behaviors. Key features Non-invasive method to relatively quantify membrane potential in plant roots. Method suitable for imaging seedlings root in agar or liquid medium. Straightforward quantification.
Why it matches plant phenotyping methods植物根の膜電位を蛍光画像から定量する非侵襲的手法の開発と画像解析プロトコルが中心であり、植物表現型計測法に該当する。
abstractwe developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3).
Reproduction assets foundThe protocol explicitly states that the raw imaging data re-analyzed in the paper are deposited on Zenodo and that all analysis scripts (R and Python) are available in a public SourceForge repository. Both are paper-specific, public, and actionable.Dataset · publicluorescence only in the root transition zone. Moreover, we focus on the interface between the cortex and the epidermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
QuantOpen asset ↗Zenodo · 4922659lines:168-214Code · publicermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
Quantify root elongation either as an average growth (agar experiment) or over time (microfluidics).
Normalize the microfluidOpen asset ↗SourceForge · disbac2-3-data-analysislines:168-214Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Plants can perceive and respond to mechanical signals. For instance, cortical microtubule (CMT) arrays usually reorganize following the predicted maximal tensile stress orientation at the cell and tissue level. While research in the last few years has started to uncover some of the mechanisms mediating these responses, much remains to be discovered, including in most cases the actual nature of the mechanosensors. Such discovery is hampered by the absence of adequate quantification tools that allow the accurate and sensitive detection of phenotypes, along with high throughput and automated handling of large datasets that can be generated with recent imaging devices. Results Here we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets following an ablation in the epidermis - a simple and robust method to change mechanical stress pattern. Our Fiji-based workflow puts together several plugins and algorithms under the form of user-friendly macros that automate the analysis process and remove user bias in the quantification. One of the key aspects is also the implementation of a simple geometry-based proxy to estimate stress patterns around the ablation site and compare it with the actual CMT arrays orientation. Testing our workflow on well-established reporter lines and mutants revealed subtle differences in the response over time, as well as the possibility to uncouple the anisotropic and orientational response. Conclusion This new workflow opens the way to dissect with unprecedented detail the mechanisms controlling microtubule arrays re-organization, and potentially uncover the still largely elusive plant mechanosensors.
Why it matches plant phenotyping methods植物細胞の微小管配向応答を定量化する画像解析ワークフローを開発し、自動化・バイアス低減・応力パターン推定まで扱うため、植物フェノタイピング手法が研究の中心である。
abstractHere we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets
Reproduction assets foundThe paper deposits its authors' Fiji/ImageJ analysis workflow code on GitHub (with a Zenodo code archive), the raw confocal microscopy time-lapse data at the Swedish National Data Service, and all intermediate processed data (projections, ROIs, quantifications) on Zenodo. All are paper-specific, public, and directly reCode · publicHere, we have put together a largely automated high-throughput image processing workflow ( https://github.com/VergerLab/MT_Angle2Ablation_Workflow ) [ 17 ] specifically designed to quantify CMT arrays response to tensile stress in 3D time-lapse datasets following an ablation in the epidermisOpen asset ↗VergerLab/MT_Angle2Ablation_Workflowlines:67-70Dataset · publicAll the microscopy data generated and analyzed for this study has been deposited at the Swedish National Data service ( https://doi.org/10.5878/17te-jg54 ).Open asset ↗10.5878/17te-jg54lines:72-78Dataset · publicAll intermediate processing data generated by the workflow for the analysis reported in this paper (SurfCut projections, cell contour preprocessing, ROIs, geometry-based proxy, FibrilTool output, and angle to ablation quantification) have also been deposited at https://zenodo.org/record/7436075#.Y5rmd-zMJF8 [ 32 ].Open asset ↗lines:107-115Dataset · publicDemes E, Verger S. Dataset of confocal microscopy from plant samples - high-throughput characterization of cortical microtubule arrays response to anisotropic tensile stressDataset of confocal microscopy from plant samples - high-throughput characterization of cortical microtubule arrays response to anisotropic tensile stress. Swedish University of Agricultural Sciences; 2023 [cited 2023 May 13]. Available from: https://snd.gu.se/catalogue/study/2022-252/1/2 .Open asset ↗lines:192-253Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMicroscopyCell / cellular structureMorphology / geometry measurement2D/3D reconstruction
Abstract The streptophyte green algal class Zygnematophyceae is the immediate sister lineage to land plants. Their special form of sexual reproduction via conjugation might have played a key role during terrestrialization. Thus, studying Zygnematophyceae and conjugation is crucial for understanding the conquest of land. Moreover, sexual reproduction features are important for species determination. We present a phylogenetic analysis of a field‐sampled Zygnema strain and analyze its conjugation process and zygospore morphology, both at the micro‐ and nanoscale, including 3D‐reconstructions of the zygospore architecture. Vegetative filament size (26.18 ± 1.07 μm) and reproductive features allowed morphological determination of Zygnema vaginatum, which was combined with molecular analyses based on rbcL sequencing. Transmission electron microscopy (TEM) depicted a thin cell wall in young zygospores, while mature cells exhibited a tripartite wall, including a massive and sculptured mesospore. During development, cytological reorganizations were visualized by focused ion beam scanning electron microscopy (FIB‐SEM). Pyrenoids were reorganized, and the gyroid cubic central thylakoid membranes, as well as the surrounding starch granules, degraded (starch granule volume: 3.58 ± 2.35 μm3 in young cells; 0.68 ± 0.74 μm3 at an intermediate stage of zygospore maturation). Additionally, lipid droplets (LDs) changed drastically in shape and abundance during zygospore maturation (LD/cell volume: 11.77% in young cells; 8.79% in intermediate cells, 19.45% in old cells). In summary, we provide the first TEM images and 3D‐reconstructions of Zygnema zygospores, giving insights into the physiological processes involved in their maturation. These observations help to understand mechanisms that facilitated the transition from water to land in Zygnematophyceae.
Why it matches plant phenotyping methodsFIB-SEM/TEMによる3D画像取得と再構成が研究の中心で、接合胞子の形態・細胞内構造・体積変化を定量的に評価しているため、画像ベースの植物表現型計測として含める。
abstractincluding 3D‐reconstructions of the zygospore architecture
Reproduction assets foundThe paper's FIB-SEM image stacks of Zygnema vaginatum zygospores (the raw imaging data underlying the 3D reconstructions) are publicly deposited as supplementary Movies S2–S4 on figshare. No author analysis code or trained models are explicitly deposited; other supporting data are available only upon request.Dataset · publicof a young Zygnema vaginatum zygospore. https://figshare.com/s/637190524e99459e5b4e .
MOVIE S3. FIB SEM generated stack of frames of an intermediate stage of Zygnema vaginatum zygospore maturation. https://figshare.com/s/30519eddaac8274263aa .
MOVIE S4. FIB SEM generated stack of frames of a mature Zygnema vaginatum zygospore. https://figshare.com/s/11bdf9f4e7ee785e7f9f .
ACKNOWLEDGMENTS
We thank Sabrina Obwegeser, MSc (University of Innsbruck, Austria) for expert technical help in TEM sectioning and image generation. We also thank Jiří Neustupa for his company and assistance during the fieldwork.
DATA AVAILABILITY STATEMENT
The supporting data of the present study are available upon requestOpen asset ↗figsharelines:180-264Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Background Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ . Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity among different dimensions; as a result, they have not been widely used in practice. Methods To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. Results We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. Conclusion The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
Why it matches plant phenotyping methods植物の3D細胞画像から形態を抽出するセグメンテーション手法を開発し、複数の植物データセットで性能をベンチマークしているため、植物フェノタイピング手法が中心である。
abstractWe propose CellStitch, a flexible pipeline that segments cells from 3D images
Reproduction assets foundThe paper provides explicit public availability for its CellStitch implementation and experiment-reproduction notebooks on GitHub, and evaluates on publicly available Arabidopsis thaliana image datasets, including the Arabidopsis 3D Digital Tissue Atlas hosted on OSF. These are paper-specific, public, actionable assetsCode · publicA python implementation of this method, CellStitch, is available at https://github.com/imyiningliu/300
cellstitch. The reproduction of all experiments presented herein can be accessed via https:
301
//github.com/imyiningliu/cellstitch/tree/main/notebooks.Open asset ↗imyiningliu/cellstitchpdf-raw-page:14 lines:1-70Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multicellular organisms result from complex developmental processes largely orchestrated through the quantitative spatiotemporal regulation of gene expression. Yet, obtaining absolute counts of messenger RNAs at a three-dimensional resolution remains challenging, especially in plants, owing to high levels of tissue autofluorescence that prevent the detection of diffraction-limited fluorescent spots. In situ hybridization methods based on amplification cycles have recently emerged, but they are laborious and often lead to quantification biases. In this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues. In addition, with the use of fluorescent protein reporters, our method also enables simultaneous detection of mRNA and protein quantity, as well as subcellular distribution, in single cells. With this method, research in plants can now fully explore the benefits of the quantitative analysis of transcription and protein levels at cellular and subcellular resolution in plant tissues.
Why it matches plant phenotyping methods植物組織内のmRNA・タンパク質量を細胞および細胞内解像度で可視化・定量するsmFISH法の開発であり、植物の状態を測定する方法が中心的です。
abstractIn this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues.
Reproduction assets foundThe authors openly deposited all raw microscopy images (WM-smFISH mRNA/protein imaging of Arabidopsis and barley tissues) used for their quantification pipeline on Figshare. No separate author analysis code repository with explicit availability language is stated in the supplied text.Dataset · publicAll the raw microscopy images used in this manuscript are openly available in Figshare at https://doi.org/10.6084/m9.figshare.22699132 .Open asset ↗Figshare · 10.6084/m9.figshare.22699132lines:110-216Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
NADP(H) is a central metabolic hub providing reducing equivalents to multiple biosynthetic, regulatory and antioxidative pathways in all living organisms. While biosensors are available to determine NADP + or NADPH levels in vivo, no probe exists to estimate the NADP(H) redox status, a determinant of the cell energy availability. We describe herein the design and characterization of a genetically-encoded ratiometric biosensor, termed NERNST, able to interact with NADP(H) and estimate E NADP(H) . NERNST consists of a redox-sensitive green fluorescent protein (roGFP2) fused to an NADPH-thioredoxin reductase C module which selectively monitors NADP(H) redox states via oxido-reduction of the roGFP2 moiety. NERNST is functional in bacterial, plant and animal cells, and organelles such as chloroplasts and mitochondria. Using NERNST, we monitor NADP(H) dynamics during bacterial growth, environmental stresses in plants, metabolic challenges to mammalian cells, and wounding in zebrafish. NERNST estimates the NADP(H) redox poise in living organisms, with various potential applications in biochemical, biotechnological and biomedical research.
Why it matches plant phenotyping methods植物を含む生体内でNADP(H)酸化還元状態を推定する遺伝子 encoded センサーを設計・特性評価しており、植物の生理状態を測定する方法開発が中心である。
abstractWe describe herein the design and characterization of a genetically-encoded ratiometric biosensor, termed NERNST, able to interact with NADP(H) and estimate E NADP(H) .
Reproduction assets foundThe paper's authors publicly deposited their image-analysis pipelines (ImageJ macros, CellProfiler/KNIME workflows, and R scripts) used to quantify NERNST biosensor fluorescence measurements, in a GitHub repository, and also cite a Zenodo archive of the same materials.Code · publicAnalysis pipelines for both software packages and R scripts are available in the GitHub repository found at https://github.com/PameeMolinari/NCOMMS-22-05375-T 94 .Open asset ↗PameeMolinari/NCOMMS-22-05375-Tlines:206-213Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background In tissues and organisms, the coordination of neighboring cells is essential to maintain their properties and functions. Therefore, knowing which cells are adjacent is crucial to understand biological processes that involve physical interactions among them, e.g. cell migration and proliferation. In addition, some signaling pathways, such as Notch or extrinsic apoptosis, are highly dependent on cell-cell communication. While this is straightforward to obtain from membrane images, nuclei labelling is much more ubiquitous for technical reasons. However, there are no automatic and robust methods to find neighboring cells based only on nuclear markers. Results In this work, we describe Nfinder, a method to assess the cell's local neighborhood from images with nuclei labeling. To achieve this goal, we approximate the cell-cell interaction graph by the Delaunay triangulation of nuclei centroids. Then, links are filtered by automatic thresholding in cell-cell distance (pairwise interaction) and the maximum angle that a pair of cells subtends with shared neighbors (non-pairwise interaction). We systematically characterized the detection performance by applying Nfinder to publicly available datasets from Drosophila melanogaster, Tribolium castaneum, Arabidopsis thaliana and C. elegans. In each case, the result of the algorithm was compared to a cell neighbor graph generated by manually annotating the original dataset. On average, our method detected 95% of true neighbors, with only 6% of false discoveries. Remarkably, our findings indicate that taking into account non-pairwise interactions might increase the Positive Predictive Value up to + 11.5%. Conclusion Nfinder is the first robust and automatic method for estimating neighboring cells in 2D and 3D based only on nuclear markers and without any free parameters. Using this tool, we found that taking non-pairwise interactions into account improves the detection performance significantly. We believe that using our method might improve the effectiveness of other workflows to study cell-cell interactions from microscopy images. Finally, we also provide a reference implementation in Python and an easy-to-use napari plugin.
Why it matches plant phenotyping methods植物を含む組織画像から細胞近傍という形態・構造状態を自動推定する画像解析法を開発し、手動アノテーションとの比較で性能検証しているため、植物フェノタイピング手法が中心です。
abstractwe describe Nfinder, a method to assess the cell's local neighborhood from images with nuclei labeling.
Reproduction assets foundThe paper's authors provide a public reference implementation (Python code and napari plugin) of the Nfinder cell-neighborhood analysis on GitHub, and the microscopy image datasets used for validation are publicly available (CIL datasets with DOIs).Code · publicSource code of a reference implementation in Python for 2D and 3D datasets as well as a user-friendly napari plugin can be found at https://github.com/santi-rodriguez/nfinder .Open asset ↗santi-rodriguez/nfinderlines:86-111Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Viruses are key players in marine ecosystems where they infect abundant marine microbes. RNA viruses are emerging as key members of the marine virosphere. They have recently been identified as a potential source of mortality in diatoms, a group of microalgae that accounts for roughly 40% of the primary production in the ocean. Despite their likely importance, their impacts on host populations and ecosystems remain difficult to assess. In this study, we introduce an innovative approach that combines automated 3D confocal microscopy with quantitative image analysis and physiological measurements to expand our understanding of viral infection. We followed different stages of infection of the bloom-forming diatom Guinardia delicatula by the RNA virus GdelRNAV-04 until the complete lysis of the host. From 20h after infection, we observed quantifiable changes in subcellular host morphology and biomass. Our microscopy monitoring also showed that viral infection of G. delicatula induced the formation of auxospores as a probable defense strategy against viruses. Our method enables the detection of discriminative morphological features on the subcellular scale and at high throughput for comparing populations, making it a promising approach for the quantification of viral infections in the field in the future.
Why it matches plant phenotyping methods自動3D共焦点顕微鏡と定量画像解析を組み合わせ、感染に伴う珪藻の細胞形態・バイオマスを高スループットに定量する手法が研究の中心である。
abstractwe introduce an innovative approach that combines automated 3D confocal microscopy with quantitative image analysis and physiological measurements
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe full annotated script can be found on https://github.com/mariescopy/Guinardia_ViralInfectionOpen asset ↗mariescopy/Guinardia_ViralInfectionlines:286-318Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Live imaging is the gold standard for determining how cells give rise to organs. However, tracking many cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. Results We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphoGraphX software for segmenting, tracking lineages, and measuring a suite of cellular properties. We also provide MorphoGraphX image processing scripts we developed to automate analysis of segmented images and data presentation. Conclusions Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is approachable and easy to use for leaf development live imaging.
Why it matches plant phenotyping methodsArabidopsis葉の生細胞イメージング、画像処理、細胞追跡・形質測定を統合した再利用可能な表現型解析パイプラインの開発が中心である。
abstractIn this work, we provide a comparably simple method for confocal live imaging entire Arabidopsis thaliana first leaves across early development.
Reproduction assets foundThe paper publicly deposits its live-imaging datasets (confocal imaging data for the figures) on OSF under CC-BY 4.0, and its MorphoGraphX/R analysis scripts on the authors' GitHub repositories, all explicitly linked in the Availability of data and materials section.Dataset · publicData for Figs. 1 , 2 , 3 , 4 , 5 A, B is available at https://doi.org/10.17605/OSF.IO/V2TKWOpen asset ↗OSF · 10.17605/OSF.IO/V2TKWlines:139-172Dataset · publicData for Figs. 5 C, 6 and 7 is available at https://doi.org/10.17605/OSF.IO/D7X3YOpen asset ↗OSF · 10.17605/OSF.IO/D7X3Ylines:139-172Code · publicavailable at https://github.com/kateharline/live_img_paper , https://github.com/kateharline/roeder_lab_projects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paperOpen asset ↗github.com/kateharline/roeder_lab_projectslines:139-172Code · publicavailable at https://github.com/kateharline/live_img_paper , https://github.com/kateharline/roeder_lab_projects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paperOpen asset ↗github.com/kateharline/jawd-paperlines:139-172Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
A bstract The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, three-dimensional, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana , including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.
Why it matches plant phenotyping methods植物の顕微鏡画像からアクチンネットワークの構造特性を自動抽出する画像解析手法を開発しており、植物状態の定量的表現型取得が中心である。
abstractwe have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level.
Reproduction assets foundThe paper's DRAGoN actin-network extraction algorithm (authors' analysis code) is explicitly stated to be freely available and open source on GitHub. No public phenotype dataset or image deposit is described in the supplied blocks.Code · publicery small amount. A much larger data set or perhaps an
artificial stimulation of the immune response (e.g. a microneedle assay[80]) may help in discerning these changes in
more detail.
To facilitate further development or optimisation for particular data sets, we have made the DRAGoN software freely
available and open source at https://github.com/JordanHembrow5/DRAGoN. The flexibility and non-specificity of this
tool is one of its main advantages and should enable it to be useful in a range of organisms, mutants, tissues, cell types
and environments. A number of key parameters (particularly those for the filtering and skeletonisation steps) can be
adjusted to best fit a given image modalityOpen asset ↗JordanHembrow5/DRAGoNpdf-layout-page:16 lines:1-48Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
Background and aims The surface area of mesophyll cells (Smes) and chloroplasts (Sc) facing the intercellular airspace (IAS) are important parameters for estimating photosynthetic activity from leaf anatomy. Although Smes and Sc are estimated based on the shape assumption of mesophyll cells (MCs), it is questionable if the assumption is correct for rice MCs with concave-convex surfaces. Therefore, in this study, we establish a reconstruction method for the 3-D representation of the IAS in rice leaf tissue to calculate the actual Smes and Sc with 3-D images and to determine the correct shape assumption for the estimation of Smes and Sc based on 2-D section images. Methods We used serial section light microscopy to reconstruct 3-D representations of the IAS, MCs and chloroplasts in rice leaf tissue. Actual Smes and Sc values obtained from the 3-D representation were compared with those estimated from the 2-D images to find the correct shape-specific assumption (oblate or prolate spheroid) in different orientations (longitudinal and transverse sections) using the same leaf sample. Key results The 3-D representation method revealed that volumes of the IAS and MCs accounted for 30 and 70 % of rice leaf tissue excluding epidermis, respectively, and the volume of chloroplasts accounted for 44 % of MCs. The shape-specific assumption on the sectioning orientation affected the estimation of Smes and Sc using 2-D section images with discrepancies of 10-38 %. Conclusions The 3-D representation of rice leaf tissue was successfully reconstructed using serial section light microscopy and suggested that estimation of Smes and Sc of the rice leaf is more accurate using longitudinal sections with MCs assumed as oblate spheroids than using transverse sections with MCs as prolate spheroids.
Why it matches plant phenotyping methodsイネ葉組織の3D再構成法を開発し、2D画像による葉肉細胞・葉緑体の表面積推定と比較検証しており、植物形質の取得・推定法が中心である。
abstractwe establish a reconstruction method for the 3-D representation of the IAS in rice leaf tissue to calculate the actual Smes and Sc with 3-D images
Reproduction assets foundThe paper's supplementary materials include 3-D reconstruction videos of the IAS and chloroplast regions facing the IAS (Videos S1 and S2) and a table of shape assumptions used for curvature correction factors (Table S1), all directly reproducing this paper's phenotyping measurements. These are hosted online via the OyDataset · publicSupplementary data are available online at https://academic.oup.com/aob and consist of the following:Open asset ↗lines:139-146Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Premise The shape of young cotton ( Gossypium ) fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of G. hirsutum and one of G. barbadense . Both narrow and wide fibers exist in G. hirsutum cv. Deltapine 90, which may impact the quality of our most abundant renewable textile material. More efficient cellular phenotyping methods are needed to empower future research efforts. Methods We developed semi-automated imaging methods for young cotton fibers and a novel machine learning algorithm for the rapid detection of tapered (narrow) or hemisphere (wide) fibers in homogeneous or mixed populations. Results The new methods were accurate for diverse accessions of G. hirsutum and G. barbadense and at least eight times more efficient than manual methods. Narrow fibers dominated in the three G. barbadense accessions analyzed, whereas the three G. hirsutum accessions showed a mixture of tapered and hemisphere fibers in varying proportions. Discussion The use or adaptation of these improved methods will facilitate experiments with higher throughput to understand the biological factors controlling the variable shapes of young cotton fibers or other elongating single cells. This research also enables the exploration of links between early cell shape and mature cotton fiber quality in diverse field-grown cotton accessions.
Why it matches plant phenotyping methods若い綿繊維の形状を対象に、半自動イメージングと機械学習による細胞形状検出法を開発し、精度と効率を検証しているため、植物表現型取得法が中心である。
abstractMore efficient cellular phenotyping methods are needed to empower future research efforts.
Reproduction assets foundThe paper publicly releases its authors' analysis code/workflow on GitHub and the cotton fiber images of six accessions used for phenotyping on USDA Ag Data Commons, both explicitly stated in the Data Availability statement and Open Data badge sections.Code · publicComputational tools and code supporting the project analysis are available through GitHub ( https://github.com/USDA-ARS-GBRU/Cotton_Fiber_Computer_Vision/ )Open asset ↗USDA-ARS-GBRU/Cotton_Fiber_Computer_Visionlines:217-287Dataset · publicimages of the six cotton accessions used are available through USDA Ag Data Commons ( https://data.nal.usda.gov/dataset/data-efficient-imaging-and-computer-vision-detection-two-cell-shapes-young-cotton-fibers )Open asset ↗lines:217-287Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The leaf epidermis is the interface between a plant and its environment. The epidermis is highly variable in morphology, with links to both phylogeny and environment, and this diversity is relevant to several fields, including physiology, functional traits, palaeobotany, taxonomy and developmental biology. Describing and measuring leaf epidermal traits remains challenging. Current approaches are either extremely labour-intensive and not feasible for large studies or limited to measurements of individual cells. Here, we present a method to characterise individual cell size, shape (including the effect of neighbouring cells) and arrangement from light microscope images. We provide the first automated characterisation of cell arrangement (from traced images) as well as multiple new shape characteristics. We have implemented this method in an R package, epidermalmorph, and provide an example workflow using this package, which includes functions to evaluate trait reliability and optimal sampling effort for any given group of plants. We demonstrate that our new metrics of cell shape are independent of gross cell shape, unlike existing metrics. epidermalmorph provides a broadly applicable method for quantifying epidermal traits that we hope can be used to disentangle the fundamental relationships between form and function in the leaf epidermis.
Why it matches plant phenotyping methods葉の表皮細胞の形状・配置などの植物形質を顕微鏡画像から自動定量する手法とRパッケージを開発しており、方法が研究の中心である。
abstractHere, we present a method to characterise individual cell size, shape (including the effect of neighbouring cells) and arrangement from light microscope images.
Reproduction assets foundThe paper's authors publicly released their analysis code, the epidermalmorph R package, on GitHub with documentation and tutorials, including example data ('podocarps') in the package. The expanded phenotype dataset is not yet available (request_only), but the code asset qualifies as public and paper-specific.Code · publicThe R package (including installation instructions) is on GitHub ( https://github.com/matildabrown/epidermalmorph ) with accompanying documentation and tutorials ( https://matildabrown.github.io/epidermalmorph/ ). Example data are available in the R package as the dataset ‘ podocarps ’. Figs 7 and 8 are examples of the output produced by this package; the expanded dataset used to generate these figures forms part of a forthcoming study (exOpen asset ↗matildabrown/epidermalmorphlines:188-571Code · publicThe R package (including installation instructions) is on GitHub ( https://github.com/matildabrown/epidermalmorph ) with accompanying documentation and tutorials ( https://matildabrown.github.io/epidermalmorph/ ). Example data are available in the R package as the dataset ‘ podocarps ’. Figs 7 and 8 are examples of the output produced by this package; the expanded dataset used to generate these figures forms part of a forthcoming study (expected publication in early 2023) and will be made available with this future paper. Please contaOpen asset ↗lines:188-571Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventually crop yield. A conventional method for assessing pollen viability is manual counting after staining, which is laborious and hinders high-throughput screening. We developed an automatic detection tool (PollenDetect) to distinguish viable and nonviable pollen based on the YOLOv5 neural network, which is adjusted to adapt to the small target detection task. Compared with manual work, PollenDetect significantly reduced detection time (from approximately 3 min to 1 s for each image). Meanwhile, PollenDetect can maintain high detection accuracy. When PollenDetect was tested on cotton pollen viability, 99% accuracy was achieved. Furthermore, the results obtained using PollenDetect show that high temperature weakened cotton pollen viability, which is highly similar to the pollen viability results obtained using 2,3,5-triphenyltetrazolium formazan quantification. PollenDetect is an open-source software that can be further trained to count different types of pollen for research purposes. Thus, PollenDetect is a rapid and accurate system for recognizing pollen viability status, and is important for screening stress-resistant crop varieties for the identification of pollen viability and stress resistance genes during genetic breeding research.
Why it matches plant phenotyping methods植物花粉の生存性という状態を画像から自動推定する深層学習ツールを開発し、手動計数および染色法と精度・速度を比較検証しているため、方法が研究の中心である。
abstractWe developed an automatic detection tool (PollenDetect) to distinguish viable and nonviable pollen based on the YOLOv5 neural network, which is adjusted to adapt to the small target detection task.
Reproduction assets foundThe paper's PollenDetect source code and model details are openly available on the authors' GitHub repository, as stated in the Data Availability Statement. The supplement describes dataset composition and annotation but does not explicitly state it contains the pollen images/annotations themselves, so only the code/reCode · publicDetails and source code of the PollenDetect model used in this study are openly available at https://github.com/Tanzhihao1998/Identification-of-pollen-activity.git/ (accessed on 1 April 2022).Open asset ↗https://github.com/Tanzhihao1998/Identification-of-pollen-activity.git/lines:99-142Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed. Results We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS-Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose-Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose-MorphoLibJ-LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios. Conclusions The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops.
Why it matches plant phenotyping methodsアブラナの種皮・胚・長角果壁の細胞形質を画像から自動定量する手法を開発・検証しており、フェノタイピング手法が研究の中心です。
abstractWe developed three methods to automatically process multicellular images of various tissues in rapeseed.
Live imaging is the gold standard for determining how cellular development gives rise to organs. However, tracking all individual cells across whole organs over large developmental time windows is extremely challenging. In this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development. Our imaging method works for both wild-type leaves and the complex curved leaves of the jaw-1D mutant. We find that dissecting the cotyledons, affixing a coverslip above the samples and mounting samples with perfluorodecalin yields optimal imaging series for robust cellular and organ level analysis. We provide details of our complementary image processing steps in MorphGraphX software for segmenting cells, tracking the cell lineages, and measuring a suite of cellular growth properties. We also provide MorphoGraphX image processing scripts that we developed to automate analysis of segmented images and data presentation. Our imaging techniques and processing steps combine into a robust imaging pipeline. With this pipeline we are able to examine important nuances in the cellular growth and differentiation of jaw-D versus WT leaves that have not been demonstrated before. Our pipeline is a practical starting place for researchers new to live imaging plant leaves, but also to anyone interested in improving the throughput and reliability of their live imaging process.
Why it matches plant phenotyping methods葉全体の共焦点ライブイメージング、細胞セグメンテーション・系譜追跡・成長特性測定を統合した実用的な表現型解析パイプラインの開発であり、方法が研究の中心です。
abstractIn this work, we provide a comparably simple method for confocal live imaging of Arabidopsis thaliana first leaves across early development.
Reproduction assets foundThe paper's phenotyping analysis code is publicly available in authors' GitHub repositories: MorphoGraphX processing/quantification scripts (iterative_growth_and_measures.py, multi_resize.py, batch_tiff.py) in roeder_lab_projects/mgx_scripts, ImageJ scripts, and R analysis/figure scripts in live_img_paper and jawdPaperCode · publice heat map representations of the
data with standardized parameters across time point comparisons and replicates (Video 4).
Data analysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The SchwaOpen asset ↗kateharline/live_img_paperpdf-raw-page:20 lines:1-63Code · publicndardized parameters across time point comparisons and replicates (Video 4).
Data analysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The Schwartz Research Fund Award (AHKR), and
the NationaOpen asset ↗kateharline/roeder_lab_proj-ectspdf-raw-page:20 lines:1-63Code · publicnalysis
All data processing, analysis and plotting was performed in RStudio (2020; 2021). Scripts used
to process the data and create figures are enclosed as Supplemental Information and available at
https://github.com/kateharline/live_img_paper, https://github.com/kateharline/roeder_lab_proj-ects/tree/master/imagej_scripts and https://github.com/kateharline/jawd-paper.Data availability
Imaging data will be deposited XXXX.
Funding
Kate Harline was supported by NSF Graduate Research Fellowship (DGE-1650441). This work
was funded by NSF MCB-2203275 (AHKR), The Schwartz Research Fund Award (AHKR), and
the National Institute Of General Medical Sciences of the National Institutes of Health under
Open asset ↗kateharline/jawd-paperpdf-raw-page:20 lines:1-63Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング枠組みと、大規模画像から生存性・細胞分裂・細胞同一性を解析する高スループット画像解析パイプラインを開発しており、表現型取得・解析が研究の中心的な技術的貢献である。
abstractIn this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their high-throughput confocal z-stack cell quantification pipeline (Python scripts wrapped in an R Shiny GUI), used to analyze the paper's bioprinted plant cell imaging datasets, on GitHub with a Zenodo deposit (10.5281/zenodo.7012765). The Zenodo record 5537065 in allowed_urls isCode · publicScripts for our high-throughput and automatic image analysis are available at https://github.com/LisaVdB/Confocal-z-stack-cell-detection and 10.5281/zenodo.7012765 .Open asset ↗LisaVdB/Confocal-z-stack-cell-detection · 10.5281/zenodo.7012765lines:247-261Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Mitochondria form highly dynamic populations in the cells of plants (and almost all eukaryotes). The characteristics and benefits of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently developed quantitative approach to reveal and analyse the physical and collective 'social' dynamics of mitochondria in an Arabidopsis msh1 mutant where the organelle DNA maintenance machinery is compromised. We use a newly created line combining the msh1 mutant with mitochondrially targeted green fluorescent protein (GFP), and characterize mitochondrial dynamics with a combination of single-cell time-lapse microscopy, computational tracking, and network analysis. The collective physical behaviour of msh1 mitochondria is altered from that of the wild type in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected, with greater potential efficiency for inter-organelle exchange-reflecting a potential compensatory mechanism for the genetic challenge to the mitochondrial DNA population, supporting more inter-organelle exchange. We find that these changes are similar to those observed in friendly, where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges, where physical dynamics of mitochondria may be altered to control the genetic structure of the mtDNA population.
Why it matches plant phenotyping methods植物ミトコンドリアの動態を、タイムラプス顕微鏡・計算追跡・ネットワーク解析で定量化する手法の実質的な適用であり、単なる生物学的ルーチン測定ではない。
abstractcharacterize mitochondrial dynamics with a combination of single-cell time-lapse microscopy, computational tracking, and network analysis.
Reproduction assets foundThe paper states that all data and analysis code for the mitochondrial dynamics phenotyping are publicly available on the authors' GitHub repository, which matches an allowed URL.Code · publicAll data and analysis codes are available from Github at https://github.com/StochasticBiology/plant-mito-dynamicsOpen asset ↗StochasticBiology/plant-mito-dynamicslines:109-163Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues. Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology. Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at the tissue level. We show that a support vector machine classifier based on the SAM network properties is predictive of cell division events, with test accuracy of 76%, which matches that based on cell size alone. Furthermore, we demonstrate that the combination of topological and biological properties, including cell size, perimeter, distance and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division. Using our classifiers, we demonstrate the importance of microtubule-mediated cell-to-cell growth coordination in influencing tissue-level topology. Together, the results from our network-based analysis demonstrate a feedback mechanism between tissue topology and cell division in A. thaliana SAMs.
Why it matches plant phenotyping methodsライブ細胞画像からSAMの細胞分裂イベントと組織トポロジー変化を推定するネットワーク表現・SVM分類法が研究の中心であり、植物の形態・発達状態を定量化している。
abstractwe use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at the tissue level.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the entire code and data to reproduce the SAM cell division prediction analysis (phenotyping measurements, features, and classifiers) in a public GitHub repository.Code · publicData availability
The entire code and data to reproduce the findings are available at https://github.com/matz2532/SAM_division_predictionOpen asset ↗matz2532/SAM_division_predictionlines:102-128Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Abstract Accurate delineation of plant cell organelles from electron microscope images is essential to understand subcellular behaviors and functions. Here, we develop a deep learning pipeline, organelle segmentation network (OrgSegNet) for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei, and vacuoles. OrgSegNet was evaluated on a large manually-annotated dataset of 6371 organelles collected from 13 plant species, and achieved a state-of-the-art segmentation performance of these organelles. To generalize the morphological characteristics of plant organelles, we defined three morphological metrics (shape-complexity, electron-density, and area), and released an open-source web tool “Plantorgan Hunter” allowing quantitative profiling of subcellular morphology. The functionalities of Plantorgan Hunter can be easily operated, and we believe that it will increase the efficiency and productivity of plant subcellular morphological characteristics for the plant science community.
Why it matches plant phenotyping methods植物細胞小器官の画像セグメンテーションと形態指標抽出を開発・評価し、データセットと公開ツールまで提供する、明確な植物フェノタイピング手法研究。
abstractHere, we develop a deep learning pipeline, organelle segmentation network (OrgSegNet) for pixel-wise segmentation to identify chloroplasts, mitochondria, nuclei, and vacuoles.
Reproduction assets foundThe paper publicly releases its manually-annotated TEM organelle dataset (Science Data Bank), the OrgSegNet code and trained models (GitHub), and a web tool (cropopen.com) for quantitative subcellular morphology profiling.Dataset · publicThe plant organelle dataset for the current study is available in the Sicence Data
Bank repository, https://www.scidb.cn/s/EBvqei.Open asset ↗pdf-page:27 lines:1-31Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Background The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. Results We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. Conclusion and availability NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis . The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE .
Why it matches plant phenotyping methods植物核内構造を3D画像から自動抽出・定量するImageJプラグインの開発と検証が中心であり、植物細胞状態の画像ベース表現型解析に該当する。
abstractWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin.
Reproduction assets foundThe paper provides its own analysis code (NODeJ, an ImageJ plugin) on a public GitLab repository with releases, and the 3D nuclear images used in the study are publicly available at an INDEPTH image site and a Recherche Data Gouv DOI. All are paper-specific, public, and actionable.Code · publicNODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysisOpen asset ↗https://gitlab.com/axpoulet/image2danalysislines:1-76Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. 3DCoordX enables rapid spatial annotation of cells even in highly curved biological shapes. Using 3DCoordX, we analyzed cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis (Arabidopsis thaliana). Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia (Marchantia polymorpha) and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia (Utricularia gibba). Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.
Why it matches plant phenotyping methods植物器官の3Dデジタル表現から細胞位置を注釈し、細胞成長や形態特徴を解析する専用ツールを開発しており、表現型取得・抽出手法が研究の中心です。
abstractWe developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX.
Reproduction assets foundThe paper deposits its phenotyping datasets (raw cell boundaries, PlantSeg predictions, segmented cells, annotated 3D cell meshes, and csv attribute files) in the BioStudies repository under accession S-BSST734, making the paper-specific 3D plant organ data publicly available.Dataset · publicThe datasets of this study have been deposited with the BioStudies data repository ( https://www.ebi.ac.uk/biostudies ) under the accession S-BSST734. Example dataset contains raw cell boundaries, cell boundaries, predictions from PlantSeg, nuclei images, segmented cells as well as the annotated 3D cell meshes, and the associated attribute files in csv format. The 3D meshes used in different manuscript figures are also available for download from the repository.Open asset ↗BioStudies · S-BSST734lines:161-178Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Biological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the ‘pseudo 3D’ morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.
Why it matches plant phenotyping methods植物細胞内オルガネラの3D形態を取得・解析する電子顕微鏡プロトコルと深層学習ワークフローが研究の中心であり、植物CCV形態の技術的スクリーニング手法を提供している。
abstractHere we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs)
Reproduction assets foundThe paper's Data Availability statement explicitly deposits example data (SEM replica images, training image pairs) and the analysis code (Cellpose-based CCV segmentation workflow) generated in this study at a public Zenodo DOI, making it a paper-specific, publicly actionable asset. The temography.com URLs are vendor/mCode · publicand round; LF, large and
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flat) using an area threshold of 8500 nm2
(a CCV diameter of 105 nm) and a 3D value of 1.52 (the
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average of the 3 smallest CCVs in control conditions determined to be spherical by the experimenter).
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Data Availability
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Example data and the code generated in this study is available at:
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https://doi.org/10.5281/zenodo.6563819
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Acknowledgements
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This research was supported by the Scientific Service Units of Institute of Science and Technology
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Austria (ISTA) through resources provided by the Electron Microscopy Facility, Lab Support Facility and
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the Imaging and Optics Facility. A.J. is supported by funding from the Austrian Science FundOpen asset ↗zenodo · 10.5281/zenodo.6563819pdf-raw-page:12 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions. RESULTS: High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis (AtBAG4). CONCLUSIONS: AtBAG4-expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.
Why it matches plant phenotyping methods植物プロトプラストの成長・増殖特性を大量画像から抽出する自動顕微鏡解析と画像処理パイプラインを開発・適用しており、表現型取得手法が研究の中心である。
abstractHigh-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the scripts and codes used in the analysis pipeline, additional downloaded plugins used in processing the images as well as sample data can be found in our Github page https://github.com/jodawson/cell_seg_tracking_analysis .Open asset ↗jodawson/cell_seg_tracking_analysislines:143-159Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Capturing cell-to-cell and cell-to-environment signals in a defined 3 dimensional (3D) microenvironment is key to study cellular functions, including cellular reprogramming towards tissue regeneration. A major challenge in current culturing methods is that these methods cannot accurately capture this multicellular 3D microenvironment. In this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis root cells and soybean meristematic cells. To analyze the large image datasets generated during these long-term viability studies, we developed an open source high-throughput image analysis pipeline. Furthermore, we showed the cell cycle re-entry of the isolated Arabidopsis and soybean cells leading to the formation of microcalli. Finally, we showed that the identity of isolated cells of Arabidopsis roots expressing endodermal markers maintained longer periods of time. The framework established in this study paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle re-entry towards tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング系と、長期画像データから細胞生存性・分裂・同一性を抽出するオープンソース解析パイプラインを開発しており、植物状態の取得・解析手法が中心である。
abstractIn this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their in-house confocal z-stack cell-counting image analysis pipeline (Python scripts wrapped in an R Shiny GUI) on GitHub, which directly reproduces the paper's computational analysis of bioprinted plant cell images. Generic dependencies (pyimageJ, OpenCV, ComDet, R shiny) and theCode · publicpipeline
contained in Python and further developed into an R Shiny application (44) can be easily accessed, along
with the usage instructions, from the Github repository at https://github.com/LisaVdB/Confocal-z-stack-
cell-detection.
Data availability
Scripts for our high-throughput and automatic image analysis are available at
https://github.com/LisaVdB/Confocal-z-stack-cell-detection.Open asset ↗LisaVdB/Confocal-z-stack-cell-detectionpdf-layout-page:10 lines:1-49Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
Arabidopsis (Arabidopsis thaliana) primary and lateral roots (LRs) are well suited for 3D and 4D microscopy, and their development provides an ideal system for studying morphogenesis and cell proliferation dynamics. With fast-advancing microscopy techniques used for live-imaging, whole tissue data are increasingly available, yet present the great challenge of analyzing complex interactions within cell populations. We developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells. The LiPlaCeT plugin contains ad hoc ergonomic curating tools, making it very simple to use for manual cell tracking, especially when the signal-to-noise ratio of images is low or variable in time or 3D space and when automated methods may fail. Performing time-lapse experiments and using cell-tracking data extracted with the assistance of LiPlaCeT, we accomplished deep analyses of cell proliferation and clonal relations in the whole developing LR primordia and constructed genealogical trees. We also used cell-tracking data for endodermis cells of the root apical meristem (RAM) and performed automated analyses of cell population dynamics using ParaView software (also publicly available). Using the RAM as an example, we also showed how LiPlaCeT can be used to generate information at the whole-tissue level regarding cell length, cell position, cell growth rate, cell displacement rate, and proliferation activity. The pipeline will be useful in live-imaging studies of roots and other plant organs to understand complex interactions within proliferating and growing cell populations. The plugin includes a step-by-step user manual and a dataset example that are available at https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip.
Why it matches plant phenotyping methods植物の4Dライブイメージングから細胞系譜・位置・長さ・成長率などの形態・成長表現型を抽出する解析プラグインとパイプラインの開発が中心である。
abstractWe developed a plugin "Live Plant Cell Tracking" (LiPlaCeT) coupled to the publicly available ImageJ image analysis program and generated a pipeline that allows, with the aid of LiPlaCeT, 4D cell tracking and lineage analysis of populations of dividing and growing cells.
Reproduction assets foundThe paper's LiPlaCeT Fiji plugin for 4D plant cell tracking is publicly available: source code on GitHub and an ImageJ plugin package including a dataset example and user manual on the authors' IBT-UNAM site.Code · publicThe source code is freely available at https://github.com/paul-hernandez-herrera/LiPlaCeT and the ImageJ plugin including a dataset example and the User Manual can be downloaded from https://www.ibt.unam.mx/documentos/diversos/LiPlaCeT.zip .Open asset ↗paul-hernandez-herrera/LiPlaCeTlines:203-225Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物の細胞から個体レベルの3D形態を取得するX線顕微鏡法と試料調製・計算セグメンテーションを中心に開発・提示しており、植物表現型取得手法が明確に主題である。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level.
Reproduction assets foundThe authors deposited fly-through animations of 2D image stacks and 3D volume rendering animations of the XRM scans shown in the paper's figures on figshare, directly reproducing this paper's plant phenotyping imaging data. No author analysis code or trained model checkpoints were explicitly deposited.Dataset · publicCanada) was used for data integration, visualization, and animation of the scan data, and to export image data as 2D 16-bit Tag Image File Format (TIFF) stacks. Fly-through animations of 2D image stacks for scans shown in all Figures, as well as 3D volume rendering animations of selected scans, are available for download from ( https://figshare.com/s/944efc8832e47fd4f203 ).
Image analysis and segmentation
Data from XRM scans were segmented using Amira software and with the assistance of a Wacom tablet for manual segmentation, in addition to ORS Dragonfly Deep Learning Module 2021.1.0.977 which is free for noncommercial use. Segmentation for Figure 1D combined automated and manual methods in Open asset ↗figsharelines:87-114Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Bicontinuous membranes in cell organelles epitomize nature's ability to create complex functional nanostructures. Like their synthetic counterparts, these membranes are characterized by continuous membrane sheets draped onto topologically complex saddle-shaped surfaces with a periodic network-like structure. Their structure sizes, (around 50-500 nm), and fluid nature make transmission electron microscopy (TEM) the analysis method of choice to decipher their nanostructural features. Here we present a tool, Surface Projection Image Recognition Environment (SPIRE), to identify bicontinuous structures from TEM sections through interactive identification by comparison to mathematical "nodal surface" models. The prolamellar body (PLB) of plant etioplasts is a bicontinuous membrane structure with a key physiological role in chloroplast biogenesis. However, the determination of its spatial structural features has been held back by the lack of tools enabling the identification and quantitative analysis of symmetric membrane conformations. Using our SPIRE tool, we achieved a robust identification of the bicontinuous diamond surface as the dominant PLB geometry in angiosperm etioplasts in contrast to earlier long-standing assertions in the literature. Our data also provide insights into membrane storage capacities of PLBs with different volume proportions and hint at the limited role of a plastid ribosome localization directly inside the PLB grid for its proper functioning. This represents an important step in understanding their as yet elusive structure-function relationship.
Why it matches plant phenotyping methods植物エチオプラストの膜構造をTEM画像から同定・定量解析するソフトウェアを開発し、植物器官の構造形質を抽出しているため、フェノタイピング手法が中心である。
abstractHere we present a tool, Surface Projection Image Recognition Environment (SPIRE), to identify bicontinuous structures from TEM sections through interactive identification by comparison to mathematical "nodal surface" models.
Reproduction assets foundThe paper's computational analysis tool SPIRE (used to identify and quantify prolamellar body cubic membrane structures from TEM micrographs) is explicitly released as open-source code with public URLs (SourceForge project and GitHub source repository), plus a video tutorial hosted at chloroplast.pl. No public deposit,Code · publicThe tool ( https://sourceforge.net/projects/spire-tool/ ) as well as the source code ( https://github.com/tohain/SPIRE ) and all dependencies are open source and thus freely and openly available.Open asset ↗spire-toollines:134-144Code · publicThe tool ( https://sourceforge.net/projects/spire-tool/ ) as well as the source code ( https://github.com/tohain/SPIRE ) and all dependencies are open source and thus freely and openly available.Open asset ↗SPIRElines:134-144Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In plants, secretion of cell wall components and membrane proteins plays a fundamental role in growth and development as well as survival in diverse environments. Exocytosis, as the last step of the secretory trafficking pathway, is a highly ordered and precisely controlled process involving tethering, docking, and fusion of vesicles at the plasma membrane (PM) for cargo delivery. Although the exocytic process and machinery are well characterized in yeast and animal models, the molecular players and specific molecular events that underpin late stages of exocytosis in plant cells remain largely unknown. Here, by using the delivery of functional, fluorescent-tagged cellulose synthase (CESA) complexes (CSCs) to the PM as a model system for secretion, as well as single-particle tracking in living cells, we describe a quantitative approach for measuring the frequency of vesicle tethering events. Genetic and pharmacological inhibition of cytoskeletal function, reveal that the initial vesicle tethering step of exocytosis is dependent on actin and myosin XI. In contrast, treatments with the microtubule inhibitor, oryzalin, did not significantly affect vesicle tethering or fusion during CSC exocytosis but caused a minor increase in transient or aborted tethering events. With data from this new quantitative approach and improved spatiotemporal resolution of single particle events during secretion, we generate a revised model for the role of the cortical cytoskeleton in CSC trafficking.
Why it matches plant phenotyping methods植物細胞内の小胞テザリング頻度を単一粒子追跡で定量する新しい測定手法を開発・適用しており、植物状態の取得・定量が研究の中心である。
abstractsingle-particle tracking in living cells, we describe a quantitative approach for measuring the frequency of vesicle tethering events.
Reproduction assets foundThe paper's supplementary materials include Video S1, a live-cell imaging movie of a CSC particle insertion event next to a cortical microtubule, which directly reproduces the paper's plant phenotyping (single-particle CSC trafficking) measurements. No author analysis code or datasets with explicit deposit language areSupplement · publicnt care and maintenance of plant materials and to all members of the Staiger laboratory for helpful discussions and input.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23010317/s1 , Video S1: A CSC particle is inserted next to a cortical microtubule and translocates on the microtubule during the steady movement phase.
Click here for additional data file.
Author Contributions
W.Z. and C.J.S. designed the research. W.Z. performed the experiments and analyzed the data. W.Z. and C.J.S. wrote the Open asset ↗lines:64-115Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
The three-dimensional (3D) arrangement of cells in tissues provides an anatomical basis for analyzing physiological and biochemical aspects of plant and animal cellular development and function. In this study, we established a protocol for tissue clearing and 3D imaging in rice. Our protocol is based on three improvements: clearing with iTOMEI (clearing solution suitable for plants), developing microscopic conditions in which the Z step is optimized for 3D reconstruction, and optimizing cell-wall staining. Our protocol successfully 3D imaged rice shoot apical meristems, florets, and root apical meristems at cellular resolution throughout whole tissues. Using fluorescent reporters of auxin signaling in rice root tips, we also revealed the 3D distribution of auxin signaling events that are activated in the columella, quiescent center, and multiple rows of cells in the stele of the root apical meristem. Examination of cells with higher levels of auxin signaling revealed that only the central row of cells was connected to the quiescent center. Our method provides opportunities to observe the 3D arrangement of cells in rice tissues.
Why it matches plant phenotyping methodsイネ組織を対象に、組織透明化・最適化した3D顕微鏡撮像・細胞壁染色による細胞配置の取得法を開発しており、植物表現型の画像取得が中心的な技術貢献である。
abstractIn this study, we established a protocol for tissue clearing and 3D imaging in rice.
Reproduction assets foundThe paper's 3D imaging datasets (supplementary videos S1–S6 of rice SAMs, florets, anthers, and root tips, plus figure data) are publicly available via the MDPI supplementary materials link. No separate analysis code repository is mentioned.Dataset · publiccquisition, which took approximately 2 h for 150 μm in depth, the images were processed using LASX software (Leica Microsystems, Tokyo, Japan).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/ijms23010040/s1 .
Click here for additional data file.
Author Contributions
M.S. and H.T. designed the research; M.S., H.A., Y.S. and S.M. performed the research; M.S. and H.T. analyzed the data; M.S. and H.T. wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supporOpen asset ↗lines:70-202Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
BackgroundThe three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. ResultsWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allows their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. Conclusion and availabilityNODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis. The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE.
Why it matches plant phenotyping methods植物核内構造を3D画像から自動抽出・解析するImageJプラグインを開発し、Arabidopsisデータセットで検証しているため、植物フェノタイピング手法が中心である。
abstractWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin.
Reproduction assets foundThe paper's authors publicly released NODeJ (source code and releases on GitLab) and the 3D nuclear images used for validation are publicly available via the INDEPTH image site and a data repository DOI.Code · publicNODeJ can be downloaded from
https://gitlab.com/axpoulet/image2danalysis/-/releases with source
code, documentation and further information avaliable at
https://gitlab.com/axpoulet/image2danalysis.Open asset ↗axpoulet/image2danalysispdf-page:1 lines:1-65Dataset · publicThe
images used in this report are available in these links: https://www.brookes.ac.uk/indepth/images/ and
https://doi.org/10.15454/1HSOIE.Open asset ↗10.15454/1HSOIEpdf-page:6 lines:1-75Dataset · publicThe images used in this
study are publicly available at
https://www.brookes.ac.uk/indepth/images/ and
https://doi.org/10.15454/1HSOIE.Open asset ↗pdf-page:1 lines:1-65Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportions of stomatal complexes (rg = 0.39-0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 = 0.42-0.82) across two field seasons. Thirty-six QTL were consistently identified for a given trait in both years. Twenty-four clusters of overlapping QTL for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.
Why it matches plant phenotyping methods光学トポメータ画像と機械学習による葉表皮形質の自動・高速取得パイプラインが研究の中心であり、測定精度も人手測定と比較検証されているため。
abstractA high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays).
Reproduction assets foundThe article explicitly deposits its optical topometry epidermal images (the paper's phenotyping input data) in the Illinois Data Bank with a public DOI. No author analysis code or trained model checkpoint is stated as publicly available; Mask R-CNN reference is a third-party library, and R packages are generic tools.Dataset · publicun with all SNPs included in the model. All the SNPs with a P -value smaller than 0.05 after the inclusion of all other retained SNPs were reported as putatively pleiotropic QTNs ( Supplemental Table S4 ).
Data availability
Data availabilityOptical tomography images from this article can be found in the Illinois Data Bank under https://doi.org/10.13012/B2IDB-8275554_V1 .
Supplemental data
The following materials are available in the online version of this article.
Supplemental Figure S1.
Examples of input images and the predictions of cell instances made for them across a range of epidermis morphology and image qualities.
Supplemental Figure S2.Open asset ↗Illinois Data Bank · 10.13012/B2IDB-8275554_V1lines:194-204Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs. RESULTS: We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the fluorescent levels in the cell walls. Micrographs are composed of pixels, which have information on position and intensity. From the pixel information we calculated three types of features (spatial, intensity and Haralick) in ER bodies corresponding to segmented cells. The spatial features include basic information on shape, e.g., surface area and perimeter. The intensity features include information on mean, standard deviation and quantile of fluorescence intensities within an ER body. Haralick features describe the texture features, which can be calculated mathematically from the interrelationship between the pixel information. Together these parameters were subjected to multivariate analysis to estimate the morphological diversity. Additionally, we calculated the displacement of the ER bodies using the positional information in time-lapse images. We captured similar morphological diversity and movement within ER body phenotypes in several microscopy experiments performed in different settings and scanned under different objectives. We then described differences in morphology and movement of ER bodies between A. thaliana wild type and mutants deficient in ER body-related genes. CONCLUSIONS: The findings unexpectedly revealed multiple genetic factors that are involved in the shape and size of ER bodies in A. thaliana. This is the first report showing morphological characteristics in addition to the movement of cellular components and it quantitatively summarises plant phenotypic differences even in plants that show similar cellular components. The estimation of morphological diversity was independent of the cell staining method and the objective lens used in the microscopy. Hence, our study enables a robust estimation of plant phenotypes by recognizing small differences in complex cell organelle shapes and their movement, which is beneficial in a comprehensive analysis of the molecular mechanism for cell organelle formation that is independent of technical variations.
Why it matches plant phenotyping methods植物細胞小器官の形態・移動を画像から抽出する特徴量計算法を開発し、異なる撮像条件で頑健性を検証しているため、表現型取得法が研究の中心です。
abstractHere we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe z-stack images were merged using specific criteria for the MaxContrastProjection package ( https://github.com/arpankbasak/ERB_DynaMo ).Open asset ↗arpankbasak/ERB_DynaMolines:96-99Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Mitochondria form highly dynamic populations in the cells of plants (and all eukaryotes). The characteristics of this collective behaviour, and how it is influenced by nuclear features, remain to be fully elucidated. Here, we use a recently-developed quantitative approach to reveal and analyse the physical and collective “social” dynamics of mitochondria in an Arabidopsis msh1 mutant where organelle DNA maintenance machinery is compromised. We use a newly-created line combining the msh1 mutant with mitochondrially-targeted GFP, and characterise mitochondrial dynamics with a combination of single-cell timelapse microscopy, computational tracking and network analysis. The collective physical behaviour of msh1 mitochondria is altered from wildtype in several ways: mitochondria become less evenly spread, and networks of inter-mitochondrial encounters become more connected with greater potential efficiency for inter-organelle exchange. We find that these changes are similar to those observed in friendly , where mitochondrial dynamics are altered by a physical perturbation, suggesting that this shift to higher connectivity may reflect a general response to mitochondrial challenges.
Why it matches plant phenotyping methods単なる生物学的測定ではなく、タイムラプス顕微鏡、計算追跡、ネットワーク解析を組み合わせて植物細胞内ミトコンドリアの動態状態を定量化する手法の実質的な適用である。
abstractwe use a recently-developed quantitative approach to reveal and analyse the physical and collective “social” dynamics of mitochondria
Reproduction assets foundThe paper explicitly states that all analysis code and data are available on the authors' GitHub repository, and a supplementary time-lapse microscopy video (phenotyping input) is hosted publicly. The Arabidopsis msh1 seed stock (N3372) used for the phenotyping is also publicly available from the NASC stock centre.Code · public14
average number of shortest paths crossing each node in the network. The mean connected
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component number is the average number of disconnected subgraphs within the network.
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Accession numbers
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All analysis code and data is available from Github at
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https://github.com/StochasticBiology/plant-mito-dynamics
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Acknowledgments
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J.M.C. is supported by the BBSRC and University of Birmingham via the MIBTP doctoral
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training scheme (grant number BB/M01116X/1). This project has received funding from the
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European Research Council (ERC) under the European Union’s Horizon 2020 research and
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innovation programme (grantOpen asset ↗StochasticBiology/plant-mito-dynamicspdf-raw-page:14 lines:1-67Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔と表皮細胞を検出・計数し、気孔指数を自動推定する画像解析パイプラインの開発とベンチマーク検証が研究の中心である。
abstractThe main aim of this research is to develop an automated stomatal index measurement pipeline.
Reproduction assets foundThe authors explicitly state the stomatal index pipeline code is fully open-source on GitHub and the wheat microscopic image dataset is downloadable as a zip release from the same repository. Both are paper-specific, public, and directly actionable.Code · publicThe code is fully open-source for academic usage and can be downloaded at https://github.com/WeizhenLiuBioinform/stomatal_indexOpen asset ↗WeizhenLiuBioinform/stomatal_indexlines:361-376Dataset · publicThe wheat dataset is available for downloading at https://github.com/WeizhenLiuBioinform/stomatal_index/releases/download/wheat1.0/wheat_dataset.zipOpen asset ↗WeizhenLiuBioinform/stomatal_index · wheat1.0lines:361-376Supplement · publicSupplementary Table 2
Description of the cuticle dataset used for training and testing the stomatal index measurement model.Open asset ↗lines:651-703Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract A key impediment to studying water-related mechanisms in plants is the inability to non-invasively image water fluxes in cells at high temporal and spatial resolution. Here, we report that Raman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions. Raman imaging of water-transporting xylem vessels in Arabidopsis thaliana mutant roots reveals faster xylem water transport in endodermal diffusion barrier mutants. Furthermore, transverse line scans across the root suggest water transported via the root xylem does not re-enter outer root tissues nor the surrounding soil when en-route to shoot tissues if endodermal diffusion barriers are intact, thereby separating ‘two water worlds’.
Why it matches plant phenotyping methodsRaman顕微分光と流体力学モデリングを組み合わせ、根組織内の水輸送を非侵襲・細胞解像度で測定する手法が研究の中心であり、植物の生理状態を定量化している。
abstractRaman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions.
Reproduction assets foundThe paper's MECHA 3D solute advection-diffusion model and MATLAB inverse modeling code are openly available on GitHub under a GPL.2 license. Other deposits (Raman data, hydraulic conductivity data, custom RMS analysis code on FigShare) exist but their URLs are not in the allowed list, so only the GitHub code asset is aCode · publicThe latest code of MECHA working in 3D with solute advection-diffusion and associated Matlab codes for inverse modeling schemes are openly available online under GPL.2 open-source licence at FigShare [10.6084/m9.figshare.14892408.v2] or GitHub [ https://github.com/MECHARoot/MECHA/blob/master/MECHA_4Dsolute.zip ].Open asset ↗MECHARoot/MECHA · MECHA_4Dsolute.ziplines:111-143Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
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fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
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montage image was then created to quickly estimate segmentation quality. The imageJ macro
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and sample images are available in the Zenodo repository,
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http://doi.org/10.5281/zenodo.4809980.573
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AI Segmentation
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The Machine learning approach is based on the “Waikato Environment for Knowledge
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Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
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classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
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middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background: Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs. Results: We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the fluorescent levels in the cell walls. Micrographs are composed of pixels, which have information on position and intensity. From the pixel information we calculated three types of features (spatial, intensity and Haralick) in ER bodies corresponding to segmented cells. The spatial features include basic information on shape, e.g., surface area and perimeter. The intensity features include information on mean, standard deviation and quantile of fluorescence intensities within an ER body. Haralick features describe the texture features, which can be calculated mathematically from the interrelationship between the pixel information. Together these parameters were subjected to multivariate analysis to estimate the morphological diversity. Additionally, we calculated the displacement of the ER bodies using the positional information in a time-lapse image. We captured similar morphological diversity and movement within ER body phenotypes on several microscopy experiments performed in different settings and scanned under different objectives. We then described differences in morphology and movement of ER bodies between A. thaliana wild type and mutants deficient in ER body-related genes. Conclusions: The findings unexpectedly revealed multiple genetic factors that are involved in the shape and size of ER bodies in A. thaliana . This is the first report showing morphological characteristics in addition to the movement of cellular components and quantitatively summarises plant phenotypic differences even in plants that show similar cellular components. The estimation of morphological diversity was independent of the cell staining method and the objective lens used in the microscopy. Hence, our study enables a robust estimation of plant phenotypes by recognizing small differences of complex cell organelle shapes and their movement, which is beneficial in a comprehensive analysis of the molecular mechanism for cell organelle formation that is independent of technical variations.
Why it matches plant phenotyping methods顕微鏡画像からERボディの形態・テクスチャ・移動を抽出し、異なる実験条件で頑健性を検証する植物表現型解析手法が中心である。
abstractHere we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.
Reproduction assets foundThe paper's authors publicly release their phenotyping analysis scripts (segmentation, feature extraction, dynamics, clustering) on GitHub and the conda analysis environment on Anaconda Cloud, both with explicit availability statements. Microscope images are said to be in a 'Bioimage database' but no URL is given, so aCode · publicell as the institutional core support by Małopolska Centre of Biotechnology, Jagiellonian University.
Availability of data and materials
The scripts used can be found in the GitHub repository, images can be found in the Bioimage database. Scripts used for pre-processing and analysis along with supplementary data can be found in http://www.github.com/arpankbasak/ERB_DynaMo The analysis environment erb_dynamo with all necessary dependencies have been uploaded to https://anaconda.org/arpankbasak/erb_dynamo/files . The plant materials are available from the Arabidopsis Biological Resource Center (ABRC) and Nottingham Arabidopsis Stock Center (NASC).
Ethics approval and consent to participate
NotOpen asset ↗arpankbasak/ERB_DynaMolines:342-369Code · publics
The scripts used can be found in the GitHub repository, images can be found in the Bioimage database. Scripts used for pre-processing and analysis along with supplementary data can be found in http://www.github.com/arpankbasak/ERB_DynaMo The analysis environment erb_dynamo with all necessary dependencies have been uploaded to https://anaconda.org/arpankbasak/erb_dynamo/files . The plant materials are available from the Arabidopsis Biological Resource Center (ABRC) and Nottingham Arabidopsis Stock Center (NASC).
Ethics approval and consent to participate
Not applicable.
Consent for publication
Consent and approval for publication from all the authors was obtained.
Competing Interests
The auOpen asset ↗arpankbasak/erb_dynamolines:342-369Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types, each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice (Oryza sativa) and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis (Arabidopsis thaliana). In addition, field-emission scanning electron microscopy observation revealed the presence of various cell types in haustoria. Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides cellular and structural insights into haustoria of S. hermonthica and P. japonicum and lays the foundation for understanding haustorium function.
Why it matches plant phenotyping methods植物器官の3次元画像再構成を中心に、ハウストリアの内部構造と細胞配置を可視化しており、形態状態の取得・抽出が研究の主要部分である。
titleThree-dimensional reconstructions of haustoria in two parasitic plant species in the Orobanchaceae
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAfter automated alignment adjustment, section alignment was manually checked and misaligned sections were re-registered by changing the registration parameters. The tools are available at https://github.com/yk-szk/ssrvtools .Open asset ↗yk-szk/ssrvtoolslines:86-98Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
MicroscopyCell / cellular structureTissueSegmentationGrowth / development / phenology
Background and Objective A variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. Methods We developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. Results The automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. Conclusions The proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.
Why it matches plant phenotyping methods発生中の胚の組織・細胞を対象に、ライトシート画像から形態関連の構造を自動抽出するCNN分割法を開発・評価しており、植物ではないため対象範囲外です。
abstractThus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way.
Reproduction assets foundThe paper's authors explicitly state that their source code, software, and annotated LSM image datasets (DAPI-Tissue, DAPI-Cells, PHH3-Cells) are publicly available in their GitHub repositories, which directly reproduce this paper's segmentation models and analysis.Code · publicion. For segmentation of
proliferating cells, the U-net was trained using PHH3-stained images with
corresponding manual segmentations. Finally, the three segmentations are
combined to create maps of relative proliferation in the mesenchyme. The
source code, software, and annotated datasets have been made publicly avail-
able at https://github.com/lucaslovercio/LSMprocessing.2. Materials and Methods
2.1. Image acquisition
Five E9.5 and five E10.5 mice embryos were harvested and fixed overnight
in 4% paraformaldehyde. After fixation, they were processed for clearing
and staining. The clearing step followed the CUBIC protocol [23]. Briefly
described, embryos were incubated overnight in Cubic1/HOpen asset ↗lucaslovercio/LSMprocessing.2pdf-raw-page:5 lines:1-47Code · publicof proliferating cells, tissues, and total cells. One
CNN model was trained for each segmentation problem, and the quantita-
tive evaluation suggests that all three models lead to segmentation results
within the range of the inter-observer agreement. The source code, soft-
ware, and annotated datasets are publicly available at https://github.com/lucaslovercio/LSMprocessing. The methods developed in this work
are integral to the larger goal of improving the understanding of development
and morphogenesis and how perturbations to development result in diseases.
22
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has graOpen asset ↗lucaslovercio/LSMprocessingpdf-raw-page:22 lines:1-45Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The importance of fluorescence light microscopy for understanding cellular and sub-cellular structures and functions is undeniable. However, the resolution is limited by light diffraction (~200-250 nm laterally, ~500-700 nm axially). Meanwhile, super-resolution microscopy, such as structured illumination microscopy (SIM), is being applied more and more to overcome this restriction. Instead, super-resolution by stimulated emission depletion (STED) microscopy achieving a resolution of ~50 nm laterally and ~130 nm axially has not yet frequently been applied in plant cell research due to the required specific sample preparation and stable dye staining. Single-molecule localization microscopy (SMLM) including photoactivated localization microscopy (PALM) has not yet been widely used, although this nanoscopic technique allows even the detection of single molecules. In this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM. The chromosomes were labeled by DAPI (4',6-diamidino-2-phenylindol), a DNA-specific dye, and with antibodies against topoisomerase IIα (Topo II), a protein important for correct chromatin condensation. Compared to the diffraction-limited methods, the combination of the three different super-resolution imaging techniques delivered tremendous additional insights into the plant chromosome architecture through the achieved increased resolution.
Why it matches plant phenotyping methods植物染色体の構造を対象に、複数の顕微鏡法を比較評価し、解像度向上による表現型（染色体アーキテクチャ）取得を中心課題としている。
abstractIn this study, we compared protein imaging within metaphase chromosomes of barley via conventional wide-field and confocal microscopy, and the sub-diffraction methods SIM, STED, and SMLM.
Reproduction assets foundThe paper's super-resolution microscopy measurements (SIM/STED/PALM imaging of barley metaphase chromosomes) are supported by supplementary materials, including PALM movies (Movies S1–S4) and supplementary figures, publicly available at the MDPI supplement URL. The main datasets themselves are only 'available from the Supplement · publicphotoactivated localization microscopy
rb
rabbit
RT
room temperature
SIM
structured illumination microscopy
SMLM
single molecule localization microscopy
STED
stimulated emission depletion
STORM
stochastic optical reconstruction microscopy
Topo II
topoisomerase IIα
Supplementary Materials
Supplementary Materials can be found at https://www.mdpi.com/1422-0067/22/4/1903/s1 .
Click here for additional data file.
Author Contributions
V.S. conceived the project. I.K., A.N., V.S. and K.W. conducted the study and processed the data. I.K., A.N. and V.S. wrote the manuscript. I.K., A.N., K.W., E.H. and V.S. discussed the results and contributed to manuscript writing. All authors have read and agreed tOpen asset ↗lines:237-298Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Published1 Feb 2021Proceedings of the National Academy of Sciences of the United States of AmericaCited by 35 · OpenAlex ↗
Artificial mechanical perturbations affect chromatin in animal cells in culture. Whether this is also relevant to growing tissues in living organisms remains debated. In plants, aerial organ emergence occurs through localized outgrowth at the periphery of the shoot apical meristem, which also contains a stem cell niche. Interestingly, organ outgrowth has been proposed to generate compression in the saddle-shaped organ-meristem boundary domain. Yet whether such growth-induced mechanical stress affects chromatin in plant tissues is unknown. Here, by imaging the nuclear envelope in vivo over time and quantifying nucleus deformation, we demonstrate the presence of active nuclear compression in that domain. We developed a quantitative pipeline amenable to identifying a subset of very deformed nuclei deep in the boundary and in which nuclei become gradually narrower and more elongated as the cell contracts transversely. In this domain, we find that the number of chromocenters is reduced, as shown by chromatin staining and labeling, and that the expression of linker histone H1.3 is induced. As further evidence of the role of forces on chromatin changes, artificial compression with a MicroVice could induce the ectopic expression of H1.3 in the rest of the meristem. Furthermore, while the methylation status of chromatin was correlated with nucleus deformation at the meristem boundary, such correlation was lost in the h1.3 mutant. Altogether, we reveal that organogenesis in plants generates compression that is able to have global effects on chromatin in individual cells.
Why it matches plant phenotyping methods植物組織内の核変形を経時イメージングで定量化する解析パイプラインを開発し、核の形態状態を抽出しているため、表現型取得法が研究上実質的に中心である。
abstractHere, by imaging the nuclear envelope in vivo over time and quantifying nucleus deformation, we demonstrate the presence of active nuclear compression in that domain.
Reproduction assets foundThe paper's Data Availability statement deposits original confocal phenotyping data (meristem/nucleus imaging) in the Cambridge repository and provides the authors' segmentation/quantification analysis pipeline scripts on the Sainsbury Laboratory GitLab. Both are paper-specific, public, and actionable.Dataset · publicOriginal confocal data are available via the University of Cambridge Data Repository ( https://doi.org/10.17863/CAM.64310 ).Open asset ↗University of Cambridge Data Repository · 10.17863/CAM.64310lines:76-106Code · publicScripts for the analysis pipeline are available via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamHJ/publications/fal_etal_2020 ).Open asset ↗Sainsbury Laboratory GitLab · slcu/teamHJ/publications/fal_etal_2020lines:76-106Code · publicScripts required to do the segmentation and quantitative analysis are provided via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamhj/publications/fal_et_al_2021 ), where also a more detailed protocol for executing the steps of the pipeline is provided.Open asset ↗Sainsbury Laboratory GitLab · slcu/teamhj/publications/fal_et_al_2021lines:76-106Code / dataset availability confirmedCrossref · checked 13 Sept 2026
A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana , enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.
Why it matches plant phenotyping methods機械学習による3Dデジタル画像解析と細胞・組織レベルの定量的アトラス構築が研究の中心であり、胚珠の形態・成長パターンを抽出する植物フェノタイピング手法に該当する。
abstractUsing machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana
Reproduction assets foundThe paper's 3D digital ovule datasets (raw images, PlantSeg predictions, segmented cells, annotated 3D cell meshes, and csv attribute files) are publicly deposited in EMBL-EBI BioStudies under accessions S-BSST475, S-BSST498, S-BSST497, and S-BSST513. Additionally, the PlantSeg 'generic_confocal_3D_unet' model was re/Dataset · publicAccession S-BSST475: the wild-type high-quality dataset and the additional dataset with more segmentation errors.Open asset ↗S-BSST475lines:655-758Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution 3D volumes of intact plant samples from the cell to whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High quality 3D volume data from our enhanced methods facilitate more sophisticated and effective computational segmentation and analyses than have previously been employed for X-ray based imaging. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物試料の細胞から個体までを対象に、X線顕微鏡によるマルチスケール3D画像取得、試料調製、計算セグメンテーション、相関イメージングの方法論を中心に提示しており、植物形態の取得・解析法が明確に中心です。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution 3D volumes of intact plant samples from the cell to whole plant level.
Reproduction assets foundThe preprint points to a public figshare collection containing the paper's high-resolution XRM image stacks ('flythroughs') and videos of the 3D plant datasets, which directly reproduce the paper's phenotyping imaging measurements. No author analysis code or trained model checkpoint is explicitly deposited; the deep-seDataset · publicof these improved techniques will
112
make a significant contribution to plant biology, expanding the reach of XRM as a
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routine tool for 3D imaging for plant scientists.
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RESULTS1
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Meristem Biology
119
1
high-resolution image stacks (“flythroughs”) and videos portraying the 3D data sets can be found here:
https://figshare.com/s/944efc8832e47fd4f203
.
CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted December 22, 2020.
;
https://doi.org/10.1101/2020.12.18.423480
doiOpen asset ↗figsharepdf-raw-page:4 lines:1-64Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
NucleusJ 1.0, an ImageJ plugin, is a useful tool to analyze nuclear morphology and chromatin organization in plant and animal cells. NucleusJ 2.0 is a new release of NucleusJ, in which image processing is achieved more quickly using a command-lineuser interface. Starting with large collection of 3D nuclei, segmentation can be performed by the previously developed Otsu-modified method or by a new 3D gift-wrapping method, taking better account of nuclear indentations and unstained nucleoli. These two complementary methods are compared for their accuracy by using three types of datasets available to the community at https://www.brookes.ac.uk/indepth/images/ . Finally, NucleusJ 2.0 was evaluated using original plant genetic material by assessing its efficiency on nuclei stained with DNA dyes or after 3D-DNA Fluorescence in situ hybridization. With these improvements, NucleusJ 2.0 permits the generation of large user-curated datasets that will be useful for software benchmarking or to train convolution neural networks.
Why it matches plant phenotyping methods植物細胞核の3D形態・クロマチン構造を画像から抽出するソフトウェアの開発、セグメンテーション手法の比較検証、植物試料での評価が中心であり、植物フェノタイピング手法に該当する。
abstractNucleusJ 2.0 is a new release of NucleusJ, in which image processing is achieved more quickly using a command-lineuser interface.
Reproduction assets foundThe paper's 3D nuclear bio-imaging datasets (7,313 images across six dataset types: digitized spheres, fluorescent microspheres, nuclear morphology, chromatin organization, and DNA FISH) are stored in the public OMERO-FSU repository under accession IDP3006_Dubos-Desset_2020, accessible via the INDEPTH website. The NuceDataset · publicDatasets were stored at OMERO-Florida State University (OMERO-FSU), a public repository under the accession number IDP3006 Dubos–Desset Nucleus 2020 that can be accessed through the INDEPTH COST-Action (CA16212) website at https://www.brookes.ac.uk/indepth/images/ . The INDEPTH image webpage provides a guideline to access and download the datasets that are freely available for research purposes.Open asset ↗OMERO-FSUlines:180-188Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Growth is one of the most studied plant responses. At the cellular level, plant growth is driven by cell division and cell expansion. A means to quantify these two cellular processes is through kinematic analysis, a methodology that has been developed and perfected over the past decades, with in-depth descriptions of the methodology available. Unfortunately, after performing the lab work, researchers are required to perform time-consuming, repetitive and error-prone calculations. To lower the barrier towards this final step in the analysis and to aid researchers currently applying this technique, we have created leafkin , an R-package to perform all the calculations involved in the kinematic analysis of monocot leaves using only four functions. These functions support leaf elongation rate calculations, fitting of cell length profiles, extraction of fitted cell lengths and execution of kinematic equations. With the leafkin package, kinematic analysis of monocot leaves becomes more accessible than before.
Why it matches plant phenotyping methods単子葉植物葉の成長速度・細胞長プロファイルなどのキネマティック形質を抽出・解析するRパッケージが中心であり、植物フェノタイピング解析ツールに該当する。
abstractwe have created leafkin , an R-package to perform all the calculations involved in the kinematic analysis of monocot leaves
Reproduction assets foundThe authors publicly release the leafkin R package, a sample kinematic phenotype dataset (maize leaf lengths, cell lengths, meristem sizes from cadmium treatments), and a tutorial analysis script on the IMPRES-lab GitHub page, with explicit availability statements.Code · publicThe leafkin package, sample data and tutorial script are available on the IMPRES lab GitHub page, https://github.com/impres-lab .Open asset ↗leafkinlines:119-213Dataset · publicThe data are provided as a set of tab-delimited text files on the IMPRES-lab GitHub page (https://github.com/impres-lab).Open asset ↗impres-lablines:88-102Code · publica full example dataset and tutorial script (available on https://github.com/impres-lab)Open asset ↗impres-lablines:28-38Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework. This dataset includes 9,811 unstained and 6,127 stained (safranin-o, toluidine blue-o, and lugol’s-iodine) images with three-fold annotation including physical, morphological, and tissue grading based on weight, different section area, and tissue zone respectively. In addition, we prepared ground truth segmentation labels for three different tuber weights. We have validated the pertinence of annotations by performing multi-label cell classification, employing convolutional neural network (CNN), VGG16, for unstained and stained images. The accuracy has been achieved up to 0.94, while, F2-score reaches to 0.92. Furthermore, the ground truth labels have been verified by semantic segmentation algorithm using UNet architecture which presents the mean intersection of union up to 0.70. Hence, the overall results show that the data are very much efficient and could enrich the domain of microscopy plant cell analysis for DL-framework.
Why it matches plant phenotyping methodsジャガイモ塊茎の細胞形態・組織特性を対象とする大規模画像データセットを構築し、分類・セグメンテーションで検証しており、植物フェノタイピング用データ資源が中心である。
titleA large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment
Reproduction assets foundThe paper's potato tuber microscopy image dataset (raw stained/unstained images plus ground truth segmentation labels) is publicly deposited on figshare by the authors.Dataset · publicThis dataset is publicly available on figshare47 (https://doi.org/10.6084/m9.figshare.c.4955669) which can be
downloaded as a zip file.Open asset ↗figshare · 10.6084/m9.figshare.c.4955669pdf-page:5 lines:1-35Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The plant cell wall (PCW) is a pecto-cellulosic extracellular matrix that envelopes the plant cell. By integrating extra-and intra-cellular cues, PCW mediates a plethora of essential physiological functions. Notably, it permits controlled and oriented tissue growth by tuning its local mechano-chemical properties. To refine our knowledge of these essential properties of PCW, we need an appropriate tool for the accurate observation of the native ( in muro ) structure of the cell wall components. The label-free techniques, such as AFM, EM, FTIR, and Raman microscopy, are used; however, they either do not have the chemical or spatial resolution. Immunolabeling with electron microscopy allows observation of the cell wall nanostructure, however, it is mostly limited to single and, less frequently, multiple labeling. Immunohistochemistry (IHC) is a versatile tool to analyze the distribution and localization of multiple biomolecules in the tissue. The subcellular resolution of chemical changes in the cell wall component can be observed with standard diffraction-limited optical microscopy. Furthermore, novel chemical imaging tools such as multicolor 3D dSTORM (Three-dimensional, direct Stochastic Optical Reconstruction Microscopy) nanoscopy makes it possible to resolve the native structure of the cell wall polymers with nanometer precision and in three dimensions. Here we present a protocol for preparing multi-target immunostaining of the PCW components taking as example Arabidopsis thaliana , Star fruit ( Averrhoa carambola) , and Maize thin tissue sections. This protocol is compatible with the standard confocal microscope, dSTORM nanoscope, and can also be implemented for other optical nanoscopy such as STED (Stimulated Emission Depletion Microscopy). The protocol can be adapted for any other subcellular compartments, plasma membrane, cytoplasmic, and intracellular organelles.
Why it matches plant phenotyping methods植物細胞壁成分の多重免疫染色と共焦点・超解像イメージングのプロトコル開発が中心であり、細胞壁構造・局在という植物状態の取得法を扱う。
abstractHere we present a protocol for preparing multi-target immunostaining of the PCW components
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicGrafeo (Custom made software for dSTORM data analysis and visualization, https://github.com/inatamara/Grafeo-dSTORM-analysis- (Open asset ↗inatamara/Grafeo-dSTORM-analysis-lines:208-269Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images. In this paper, we present to the plant community an introduction to and exploration of two machine learning approaches to address the problem of marker localization in confocal microscopy. First, a comparative study is conducted on the classification accuracy of common conventional machine learning algorithms, as a means to highlight challenges with these methods. Second, a 3D (volumetric) deep learning approach is developed and presented, including consideration of appropriate loss functions and training data. A qualitative and quantitative analysis of all the results produced is performed. Evaluation of all approaches is performed on an unseen time-series sequence comprising several individual 3D volumes, capturing plant growth. The comparative analysis shows that the deep learning approach produces more accurate and robust results than traditional machine learning. To accompany the paper, we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (FIJI) software. Network models and example datasets will also be available online.
Why it matches plant phenotyping methods植物の共焦点画像から細胞周期マーカーを自動検出・分割する機械学習手法を開発し、定量評価・比較検証しているため、植物表現型取得が中心である。
abstractAutomating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the confocal image datasets, annotations, and the annotation plugin in a public GitLab repository, matching the allowed URL.Code · publicample. As a pixel-wise segmentation is already produced by the network, but refined in post-processing to a single location in space, the network is already partially capable of generating meaningful 3D shape labels.
Data Availability Statement
The datasets and plugin used for this study can be found in the GitLab repository at https://gitlab.com/faraz.khan1/volumetric-segmentation-of-confocal-images .
Author Contributions
FK designed and implemented the computational algorithms, models and experiments. MP wrote the annotation tool and provided guidance. UV performed biological experiments and annotation. AF managed the project and helped design the approaches, with MP and FK. All authors coOpen asset ↗gitlab.com/faraz.khan1/volumetric-segmentation-of-confocal-imageslines:320-348Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Lignocellulosic biomass (LB) is recalcitrant to enzymatic hydrolysis due to its compact and complex cell wall structure. To identify the parameters behind LB recalcitrance, experimental data over hydrolysis time must be collected. Here, we describe a novel method to collect time-lapse images during cell wall deconstruction by enzymatic hydrolysis. The protocol includes instructions for sample preparation, layout of a custom designed incubation chamber and instructions for confocal time lapse acquisition. The protocol sets out a detailed plan where cross-sections of untreated and pretreated poplar samples are mounted in a sealed frame containing a buffer and an enzymatic cocktail. The sealed frame is then placed into an incubator to maintain the sample at a constant temperature of 50 °C, which is optimal for enzymatic reaction while avoiding enzymatic cocktail evaporation. Using lignin natural autofluorescence, confocal z-stacks of untreated and pretreated samples were acquired at regular time intervals during enzymatic hydrolysis for 24 h. Acquisition parameters were optimized to compromise between image resolution and reduced photo-bleaching. The acquired image might then be processed by further development of algorithms to extract precise quantitative information on cell wall deconstruction. This protocol is an important first step towards elucidating the underlying parameters of LB recalcitrance by allowing the acquisition of high-quality images of LB hydrolysis for extracting quantitative data on LB deconstruction.
Why it matches plant phenotyping methodsポプラ細胞壁の分解状態を時系列の共焦点3D画像で取得するプロトコル自体が中心であり、植物組織状態の定量的表現型抽出を可能にするため。
abstractHere, we describe a novel method to collect time-lapse images during cell wall deconstruction by enzymatic hydrolysis.
Reproduction assets foundThe paper's authors state that the scripts for computing photobleaching signal loss and image registration/analysis are publicly available in the FARE Laboratory GitLab repository, with an explicit URL matching an allowed URL.Code · publicels’ intensity
reduction in confocal image) between successive z-stacks. The signal loss was computed by subtracting
the voxels’ intensities between the registered floating image, It ◦ T It ←It+∆t , and the reference image It+∆t
and summing up the subtracted values (Scripts are available at the FARE Laboratory Gitlab Repository
https://gitlab.com/farelab/teamyr/publications/zoghlami_et_al_sus_chem_2020).
4. Results
Using the protocol, we acquired confocal images of pretreated poplar samples during hydrolysis
(Figure 7). We could visually observe that the cell walls gradually degraded over time. To illustrate
the advantages offered by using this protocol to achieve a quantitative characterizaOpen asset ↗gitlab.com/farelab/teamyr/publications/zoghlami_et_al_sus_chem_2020pdf-layout-page:8 lines:1-42Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Why it matches plant phenotyping methods植物組織を細胞単位で抽出する画像解析パイプラインを開発し、異なる組織・スケール・撮像条件で性能を示しているため、植物フェノタイピング手法が中心である。
abstractHere, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells.
Reproduction assets foundThe paper publicly deposits all plant phenotyping image/ground-truth datasets on OSF (https://osf.io/uzq3w), including ovule, lateral root, meristem, and leaf confocal/light-sheet volumes with hand-curated segmentations, and releases the PlantSeg analysis code and pre-trained 3D U-Net models on GitHub.Dataset · publicAll datasets used to support the findings of this study have been deposited in https://osf.io/uzq3w .Open asset ↗osf.io/uzq3wlines:38-47Code · publicThe code used for training and inference can be found at Wolny, 2020b
https://github.com/wolny/pytorch-3dunet copy archived at https://github.com/elifesciences-publications/pytorch-3dunet .Open asset ↗GitHub · wolny/pytorch-3dunetlines:212-223Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plasmodesmata are small channels that connect plant cells. While recent technological advances have facilitated analysis of the ultrastructure of these channels, there are limitations to efficiently addressing their presence over an entire cellular interface. Here, we highlight the value of serial block electron microscopy for this purpose. We developed a computational pipeline to study plasmodesmata distributions and detect the presence/absence of plasmodesmata clusters, or pit fields, at the phloem unloading interfaces of Arabidopsis ( Arabidopsis thaliana ) roots. Pit fields were visualized and quantified. As the wall environment of plasmodesmata is highly specialized, we also designed a tool to extract the thickness of the extracellular matrix at and outside of plasmodesmata positions. We detected and quantified clear wall thinning around plasmodesmata with differences between genotypes, including the recently published plm-2 sphingolipid mutant. Our tools open avenues for quantitative approaches in the analysis of symplastic trafficking.
Why it matches plant phenotyping methods植物組織の電子顕微鏡画像から原形質連絡の分布、クラスター、細胞壁厚を定量化する計算パイプラインとツールを開発しており、植物形態・構造形質の取得が中心です。
abstractWe developed a computational pipeline to study plasmodesmata distributions and detect the presence/absence of plasmodesmata clusters, or pit fields
Reproduction assets foundThe paper's authors publicly released their Matlab plugins for plasmodesmata distribution and cell-wall thickness analysis on GitHub, a guided R analysis pipeline tutorial, and the Col-0 SB-EM data sets with segmented wall models and PD annotations on Figshare. Generic tools (MIB, matGeom, CRAN packages) and the EMPIARCode · publicA guided tutorial with all the necessary code for this analysis is available at https://andreapaterlini.github.io/Plasmodesmata_dist_wall/ (last accessed March 2020).Open asset ↗lines:148-159Dataset · publicThe Col-0 data sets used in this article, with corresponding models and annotations, are available on Figshare ( https://doi.org/10.6084/m9.figshare.12488702.v1 ). They can be used as example data sets to test our pipeline.Open asset ↗figshare · 10.6084/m9.figshare.12488702.v1lines:148-159Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.
Why it matches plant phenotyping methods珪藻の顕微鏡動画から形態・細胞内特徴・群体運動を抽出する画像処理および深層学習手法が研究の中心であり、植物表現型解析手法の開発に該当する。
abstractTo acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources.
Reproduction assets foundThe paper's Bacillaria phenotyping data and analysis assets are publicly available: raw data, processed numeric/image data, and code in the authors' Digital-Bacillaria GitHub repository; skeleton-creation scripts in the Image-Skeletons subrepository; the OpenDevoCell segmentation platform (GitHub and web app); and a GfDataset · publicstrains contained in our primary data (videos) have been harvested from the Neckar
river in Germany (49°04'41.8"N 9°09'17.9"E). Samples were collected on September 14, 2019.
The average size of each cell (filament) is approximately 81µm.
Data Availability
Select unprocessed (raw) data are available at our Github repository
(https://github.com/devoworm/Digital-Bacillaria), processed numeric and image data (numeric
tables and skeletonized images), and select video files are available on the Open Science
Framework (DOI 10.17605/OSF.IO/AR8C3).
17Open asset ↗devoworm/Digital-Bacillariapdf-layout-page:17 lines:1-49Code · publicckground
color (select the background by color) to RGB value 0,0,0. To create a thick skeleton from a thin
skeleton, select the thin skeleton by color and then select the border function. The border width
should be set to 4, hard border, and filled with RGB value 0,217,0. The pseudo-code for GIMP
script-fu is located on Github (https://github.com/devoworm/Digital-Bacillaria/tree/master/Image-Skeletons).Image Tracking for Movement. We also employ image tracking for the primary microscopy
data. The tracking of a partial image (template) of a diatom can be used under certain conditions
to obtain its trajectory. In particular, a movement of the diatoms in a plane perpendicular to the
optical axiOpen asset ↗devoworm/Digital-Bacillariapdf-raw-page:10 lines:1-44Code · publicn-source software with a web interface called OpenDevoCell (based on DeepLearning 4J).
DeepLabv3 (Google, MountainView, California, USA) is a package for TensorFlow, and Deep
Learning 4J (Eclipse Foundation, Ottawa, Canada), a Java-based library that works with
TensorFlow. OpenDevoCell is open-source software located on Github
(https://github.com/devoworm/GSOC-2019/tree/master/OpenDevoCell) and as a web-based
application (https://open-devo-cell.herokuapp.com).11
.
CC-BY 4.0 International license
available under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (wOpen asset ↗devoworm/GSOC-2019pdf-raw-page:11 lines:1-35Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis . Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides structural insights into how organs interact between hosts and parasitic plants. One-sentence summary Three-dimensional image reconstruction was used to visualize the spatial organization of cell types in the haustoria of parasitic plants with special reference to their interaction with host roots.
Why it matches plant phenotyping methods寄生植物のハウストリア内部構造を3次元画像再構成で可視化することが研究の中心であり、植物器官の空間形態を抽出・比較している。
abstractHere, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe tools are available at https://github.com/yk-szk/ssrvtools.Open asset ↗yk-szk/ssrvtoolspdf-page:12 lines:1-46Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.
Why it matches plant phenotyping methods葉の病斑・損傷面積を染色と自動画像解析で定量する手法自体が中心的に開発・実証されており、植物病害・損傷状態の表現型計測に該当する。
abstractWe stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale.
Reproduction assets foundThe paper's LiMu image analysis pipeline (used for lesion quantification) is publicly available on PyPI. The leaf image datasets are only available from the corresponding author on request, and no public URL for the image data or supplements is present in the allowed list.Code · publicThe original LiMu code is made freely available in the Python Package Index (PyPI), and can be downloaded from https://pypi.org/project/limu/ .Open asset ↗limulines:184-227Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plasmodesmata are small channels that connect plant cells. While recent technological advances have facilitated the analysis of the ultrastructure of these channels, there are limitations to efficiently addressing their presence over an entire cellular interface. Here, we highlight the value of serial block electron microscopy for this purpose. We developed a computational pipeline to study plasmodesmata distributions and we detect presence/absence of plasmodesmata clusters, pit fields, at the phloem unloading interfaces of Arabidopsis thaliana roots. Pit fields can be visualised and quantified. As the wall environment of plasmodesmata is highly specialised we also designed a tool to extract the thickness of the extracellular matrix at and outside plasmodesmata positions. We show and quantify clear wall thinning around plasmodesmata with differences between genotypes, namely in the recently published plm-2 sphingolipid mutant. Our tools open new avenues for quantitative approaches in the analysis of symplastic trafficking. Sentence summary We developed computational tools for serial block electron microscopy datasets to extract information on the spatial distribution of plasmodesmata over an entire cellular interface and on the wall environment the plasmodesmata are in.
Why it matches plant phenotyping methods植物組織の電子顕微鏡画像から原形質連絡の分布や細胞壁厚を定量抽出する計算ツールとパイプラインが研究の中心であり、植物形態状態の測定法に該当する。
abstractWe developed a computational pipeline to study plasmodesmata distributions
Reproduction assets foundThe paper publicly releases its authors' MIB plugins for plasmodesmata distribution and wall-thickness analysis (GitHub), a guided R analysis tutorial/pipeline (GitHub Pages), and the Col-0 SB-EM datasets with segmented wall models and PD annotations (Google Drive), all with explicit availability statements and URLs.Code · publicA guided tutorial with all the necessary
code for this analysis is available at https://andreapaterlini.github.io/Plasmodesmata_dist_wall/Open asset ↗pdf-page:6 lines:1-49Dataset · publicThe Col-0 datasets used in this paper, with corresponding models and
annotation are available from https://drive.google.com/file/d/1g-Open asset ↗pdf-page:6 lines:1-49Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 9 Sept 2026
ABSTRACT Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, and acquisition settings. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Why it matches plant phenotyping methods植物組織を細胞単位で3Dセグメンテーションする画像解析パイプラインを開発し、異なる組織・スケール・撮像条件で検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells.
Reproduction assets foundThe paper (PlantSeg) publicly releases its plant phenotyping inputs and analysis: raw confocal/light-sheet images with hand-curated groundtruth segmentations on OSF, and the open-source PlantSeg pipeline including pre-trained networks and evaluation scripts on GitHub.Dataset · publicPlantSeg is open-source and publicly available https://github.com/hci-unihd/plant-seg. The repository
includes a complete user guide, the evaluation scripts used for quantitative analysis, and the employed datasets.Open asset ↗github · hci-unihd/plant-segpdf-page:7 lines:1-45Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 15 Sept 2026
Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and is presumed to function during leaf rolling, thereby reducing water loss during temperature extremes and drought. In this study, epidermal leaf impressions were collected from a genetically and anatomically diverse population of maize inbred lines. Subsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput. A genome-wide association study, combined with RNAseq analyses of the bulliform cell ontogenic zone, identified candidate regulatory genes affecting bulliform cell column number and cell width. This study is the first to combine machine learning approaches, transcriptomics, and genomics to study bulliform cell patterning, and the first to utilize natural variation to investigate the genetic architecture of this microscopic trait. In addition, this study provides insight toward the improvement of macroscopic traits such as drought resistance and plant architecture in an agronomically important crop plant.
Why it matches plant phenotyping methodsCNNを用いてトウモロコシ葉の微細なブルフォーム細胞形態を高スループット測定しており、表現型取得・抽出法が研究の中心です。
abstractSubsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the leaf epidermal glue-impression images (Cyverse zip), the authors' analysis scripts (LD calculation, image processing, U-net architecture, GWAS) on GitHub, trained U-net models (Cyverse File S1 zip), and supplemental material on figshare. All are paper-phenCode · publicwere deposited at NCBI SRT with SRA accession numbers PRJNA545465 and PRJNA400334. Leaf epidermal glue-impression images can be found at https://de.cyverse.org/dl/d/8CA8D72B-24AF-4887-8899-14460021887A/resized.zip. The scripts including LD calculation, image processing, U-net architecture, and running the GWAS are deposited in https://github.com/pengfei-qiao/Bulliform-cell-deep-learning.git. Trained U-net models are deposited as File S1 under https://de.cyverse.org/dl/d/B352A862-5B08-4373-87EB-9B48356028C6/FlieS1.zip. We request that this manuscript be cited when using these data. Supplemental material available at figshare: https://doi.org/10.25387/g3.9939623.Open asset ↗GitHub · pengfei-qiao/Bulliform-cell-deep-learninghtml-lines:236-236Code / dataset availability confirmedbioRxiv · checked 9 Sept 2026
The aerial epidermis of plants plays a major role in their environment interactions, and the development of its cellular components -trichomes, stomata and pavement cells- is still not fully understood. We have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques. Quantification of the trichome and stomatal densities in the ILs revealed 18 genomic regions with a low trichome density and 4 ILs with a high stomatal density. We also found ILs with abnormal proportions of different trichome types and aberrant trichome morphologies. This work has led to the identification of new, unexplored genomic regions with roles in trichome and stomatal formation and provides an important dataset for further studies on tomato epidermal development that is publically available to the research community.
Why it matches plant phenotyping methods走査電子顕微鏡を用いた葉表皮の画像取得と、毛状突起・気孔密度および形態の定量が研究の中心であり、再利用可能な表現型データセットも提供しているため。
abstractWe have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques.
Reproduction assets foundThe paper states that all SEM micrographs used for the trichome/stomatal phenotyping screen are publicly available in the BioStudies database under accession S-BSST262. This is a paper-specific public asset (the SEM images underlying the phenotyping measurements). No author analysis code was deposited.Dataset · public321 study are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) (McEntyre et al.,Open asset ↗BioStudiespdf-page:10 lines:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping to date typically comprises morphological and physiological profiling in a high-throughput manner. A powerful method that allows for subcellular characterization of organelle stoichiometric/functional characteristics is still missing. Organelle abundance and crosstalk in cell dynamics and signaling plays an important role for understanding crop growth and stress adaptations. However, microscopy can not be considered a high-throughput technology. The aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner. A combination of subcellular non-aqueous fractionation and liquid chromatography mass spectrometry was applied to assign membrane-marker proteins to cell compartmental abundances and functions of Pisum sativum leaves. Based on specific subcellular affiliation, proteotypic marker peptides of the chloroplast, mitochondria and vacuole membranes were selected and synthesized as heavy isotope labelled standards. The rapid and unbiased Mass Western approach for accurate stoichiometry and targeted absolute protein quantification allowed for a proportional organelle abundances measure linked to their functional properties. A 3D Confocal Laser Scanning Microscopy approach was developed to evaluate the Mass Western. Two P. sativum cultivars of varying morphology and physiology were compared. The Mass Western assay enabled a cultivar specific discrimination of the chloroplast to mitochondria to vacuole relations.
Why it matches plant phenotyping methods植物の細胞内オルガネラ量と機能的特性を高スループットに推定するフェノタイピング手法を開発し、3D共焦点顕微鏡で評価・検証しているため、方法が中心的である。
abstractThe aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner.
Reproduction assets foundThe paper's plant-phenotyping measurements and analysis outputs are available as public supplementary material hosted on the Frontiers article page: Tables S1–S4 (Mass Western target peptide lists, confocal organelle volume/area abundance values, NAF LFQ peak intensities, and proteotypic peptide subcellular localizatonSupplement · publicand Thomas Joch for plant cultivation at the department-associated greenhouse facility.
Footnotes
Funding. This study was funded by the Austrian Science Fund (FWF) [ P24870 -B22] and [W 1257-820], and supported by the COST action FA1306.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.00638/full#supplementary-material
Figure S1
Morphological phenotypes of the Pisum sativum cultivars Protecta (left) and Messire (right). Length of internodes and leaf weight n = 5 biol. replicates, error bars = standard error, p < 0.05 (Kruskal Wallis). ** p < 0.01, *** p < 0.005.
Click here for additional data filOpen asset ↗lines:102-128Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Many methods have been developed to quantify cell shape in 2D in tissues. For instance, the analysis of epithelial cells in Drosophila embryogenesis or jigsaw puzzle-shaped pavement cells in plant epidermis has led to the development of numerous quantification methods that are applied to 2D images. However, proper extraction of 2D cell contours from 3D confocal stacks for such analysis can be problematic. Results We developed a macro in ImageJ, SurfCut, with the goal to provide a user-friendly pipeline specifically designed to extract epidermal cell contour signals, segment cells in 2D and analyze cell shape. As a reference point, we compared our output to that obtained with MorphoGraphX (MGX). While both methods differ in the approach used to extract the layer of signal, they output comparable results for tissues with shallow curvature, such as pavement cell shape in cotyledon epidermis (as quantified with PaCeQuant). SurfCut was however not appropriate for cell or tissue samples with high curvature, as evidenced by a significant bias in shape and area quantification. Conclusion We provide a new ImageJ pipeline, SurfCut, that allows the extraction of cell contours from 3D confocal stacks. SurfCut and MGX have complementary advantages: MGX is well suited for curvy samples and more complex analyses, up to computational cell-based modeling on real templates; SurfCut is well suited for rather flat samples, is simple to use, and has the advantage to be easily automated for batch analysis of images in ImageJ. The combination of these two methods thus provides an ideal suite of tools for cell contour extraction in most biological samples, whether 3D precision or high-throughput analysis is the main priority.
Why it matches plant phenotyping methods植物表皮細胞の輪郭・形状を3D画像から抽出・定量するImageJパイプラインの開発と比較検証が中心であり、植物形態フェノタイピング手法に該当する。
abstractWe developed a macro in ImageJ, SurfCut, with the goal to provide a user-friendly pipeline specifically designed to extract epidermal cell contour signals, segment cells in 2D and analyze cell shape.
Reproduction assets foundThe paper's authors publicly released both the SurfCut analysis macro (GitHub and Zenodo DOI 10.5281/zenodo.2635737) and the confocal microscopy dataset of plant samples used for the phenotyping measurements (Zenodo DOI 10.5281/zenodo.2577053).Code · publicDevo” and ERASMUS grant (20016-1-TR01-KA103-026029).
Availability of data and materials
The datasets generated and analyzed in this study are available in the Zenodo repository ( https://zenodo.org /), DOI:10.5281/zenodo.2577053 [ 34 ].
The script of the SurfCut macro and a more detailed step-by-step user guide are available at https://github.com/sverger/SurfCut [ 35 ], Zenodo DOI:10.5281/zenodo.2635737 [ 28 ].
Authors’ contributions
OE, ML, and SV performed the experiments. SV wrote the ImageJ script “SurfCut.” OE analyzed the results. OE, ML, OH, and SV wrote the article. OH secured funding for this project. All authors read and approved the final manuscript.
Ethics approval and consOpen asset ↗sverger/SurfCutlines:84-107Dataset · publicThe datasets generated and analyzed in this study are available in the Zenodo repository ( https://zenodo.org /), DOI:10.5281/zenodo.2577053 [ 34 ].Open asset ↗Zenodo · 10.5281/zenodo.2577053lines:84-107Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
The endoplasmic reticulum (ER) is a highly dynamic polygonal membrane network composed of interconnected tubules and sheets (cisternae) that forms the first compartment in the secretory pathway involved in protein translocation, folding, glycosylation, quality control, lipid synthesis, calcium signalling, and metabolon formation. Despite its central role in this plethora of biosynthetic, metabolic and physiological processes, there is little quantitative information on ER structure, morphology or dynamics. Here we describe a software package (AnalyzER) to automatically extract ER tubules and cisternae from multi-dimensional fluorescence images of plant ER. The structure, topology, protein-localisation patterns, and dynamics are automatically quantified using spatial, intensity and graph-theoretic metrics. We validate the method against manually-traced ground-truth networks, and calibrate the sub-resolution width estimates against ER profiles identified in serial block-face SEM images. We apply the approach to quantify the effects on ER morphology of drug treatments, abiotic stress and over-expression of ER tubule-shaping and cisternal-modifying proteins.
Why it matches plant phenotyping methods植物ERの蛍光画像から構造・形態・動態を自動抽出・定量するソフトウェアを開発し、手動トレースおよびSEM画像で検証・較正しているため、植物表現型計測手法が中心である。
abstractHere we describe a software package (AnalyzER) to automatically extract ER tubules and cisternae from multi-dimensional fluorescence images of plant ER.
Reproduction assets foundThe authors deposited the AnalyzER MATLAB software (with manual, tutorial, and test data) plus all images, parameter files, and analysis results in the Oxford Research Archive at a public URL, directly reproducing this paper's plant ER phenotyping analysis.Dataset · publicAll images, parameter files used in the software to analyse them, and the results are available from the Oxford Research Archive (ORA) ( https://ora.ox.ac.uk/objects/uuid:cb0e2845-2a9c-495a-84f0-4dd2c5164463 ).Open asset ↗Oxford Research Archive (ORA) · uuid:cb0e2845-2a9c-495a-84f0-4dd2c5164463lines:161-264Code / dataset availability confirmedbioRxiv · Crossref · checked 15 Sept 2026
BackgroundOne of the main features of plant cells is their strong plasticity, and their propensity to regenerate an organism from a single cell. Plant protoplasts are basic plant cells units in which the pecto-cellulosic cell wall has been removed, but the plasma membrane is intact. One of the main features of plant cells is their strong plasticity, which in some species, can be very close from what is defined as cell totipotency. Methods and differentiation protocols used in plant physiology and plant biology usually involve macroscopic vessels and containers that make difficult, for example, to follow the fate of the same protoplast all along its full development cycle, but also to perform continuous studies of the influence of various gradients in this context. These limits have hampered the precise study of regeneration processes. ResultsHerein, we present the design of a comprehensive, physiologically relevant, easy-to-use and low-cost microfluidic and microscopic setup for the monitoring of Physcomitrella patens (P. patens) growth and development on a long-term basis. The experimental solution we developed is made of two parts (i) a microfluidic chip composed of a single layer of about a hundred flow-through microfluidic traps for the immobilization of protoplasts, and (ii) a low-cost, light-controlled, custom-made microscope allowing the continuous recording of the moss development in physiological conditions. We validated the experimental setup with three proofs of concepts: (i) the kinetic monitoring of first division steps and cell wall regeneration, (ii) the influence of the photoperiod on growth of the protonemata, and (iii) finally the induction of leafy buds using a phytohormone, cytokinin. ConclusionsWe developed the design of a comprehensive, physiologically relevant, easy-to-use and low-cost experimental setup for the study of P. patens development in a microfluidic environment. This setup allows imaging of P. patens development at high resolution and over long time periods.
Why it matches plant phenotyping methods植物の発生・成長を長期間画像モニタリングするマイクロ流体チップとカスタム顕微鏡を開発しており、表現型取得系が研究の中心である。
abstractwe present the design of a comprehensive, physiologically relevant, easy-to-use and low-cost microfluidic and microscopic setup for the monitoring of Physcomitrella patens (P. patens) growth and development on a long-term basis.
Reproduction assets foundThe paper's availability statement points to a public GitHub repository (FattaccioliLab/PlantsOnChip) containing the authors' microfluidic chip design files, microscope control Matlab scripts, Micromanager configuration, Arduino connection map, and bill of materials used for the plant protoplast phenotyping/imaging. NoCode · publicfile of the 35 mm Petri dish adapter to the SM1 threading of the xy manual stage
• Matlab programming script of the microscope and Micromanager configuration file
• Connection map of the Arduino Due board
• Bill of materials of the custom-made microscope (references, manufacturers, suppliers, prices)
Documents are available on https://github.com/FattaccioliLab/PlantsOnChip
Supplementary movies.
• Division of a protoplast and cell wall regeneration kinetics
• Chloronemata growth under continuous illumination
Competing interests. No financial competing interests are to be declared.
Funding. This work has received support of “Institut Pierre-Gilles de Gennes” (Laboratoire
d’excellence : ANR-10-Open asset ↗FattaccioliLab/PlantsOnChippdf-raw-page:11 lines:1-29Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Cell / cellular structureRootTissueMorphology / geometry measurementObject detectionRoot system architecture
The aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has the potential to further increase food production. However, the belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. To address this limitation, we propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images. Using the Faster Region-based Convolutional Neural Network (Faster R-CNN), the DL-RootAnatomy models detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes are used to estimate the root diameter, stele diameter, and late metaxylem number and average diameter. Experimental evaluation using standard object detection metrics, such as intersection-over-union and mean average precision, has shown that our models can accurately detect the root, stele and late metaxylem objects. Furthermore, the results have shown that the measurements estimated based on predicted bounding boxes have very small root mean square error when compared with the corresponding ground truth values, suggesting that DL-RootAnatomy can be used to accurately detect anatomical features. Finally, a comparison with existing approaches, which involve some degree of human interaction, has shown that the proposed approach is more accurate than existing approaches on a subset of our data. A webserver for performing root anatomy using our deep learning pre-trained models is available at https://rootanatomy.org, together with a link to a GitHub repository that contains code that can be used to re-train or fine-tune our network with other types of root-cross section images. The labeled images used for training and evaluating our models are also available from the GitHub repository.
Why it matches plant phenotyping methods根横断面画像から根径・中心柱径・後期後生木部の数と平均径を自動推定する深層学習手法を開発し、既存手法との比較および精度検証を行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose a fully automated approach, called Deep Learning for Root Anatomy (DL-RootAnatomy), for identifying anatomical traits in root cross-section images.
Reproduction assets foundThe authors publicly release the labeled rice root cross-section image dataset (with ground truth measurements), the source code, and the pre-trained Faster R-CNN models via a GitHub repository linked from the paper's Data Availability Statement and webserver description.Dataset · publicthe preliminary version. CW
815 designed and developed the webserver. All authors read and approved the
816 final manuscript.
817 Funding
818 Contribution No. 19-072-J from Kansas Agriculture Experiment Station.
819 Data Availability Statement
820 The image datasets used in this study can be found in a GitHub repository
821 at https://github.com/cwang16/Root-Anatomy-Using-Faster-RCNN.
822 Acknowledgments
823 An earlier version of this manuscript has been released as a Pre-Print at
824 https://www.biorxiv.org/content/10.1101/442244v2.article-info [65].
825 References
826 [1] J. L. Araus, G. A. Slafer, C. Royo, M. D. Serret, Breeding for yield
827 potential and stress adaptation in cereals, CrOpen asset ↗https://github.com/cwang16/Root-Anatomy-Using-Faster-RCNNpdf-layout-page:50 lines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).
Why it matches plant phenotyping methodsArabidopsisの細胞内オルガネラ量という植物状態を、標的プロテオミクスと計算推定で定量する手法を開発・検証し、オンラインツールとして提供しているため、測定法が中心である。
abstractA new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis.
Reproduction assets foundThe paper deposits its own shotgun proteomics raw data (whole plant and CSC samples) in PRIDE (PXD005408), its SRM organelle-marker transitions in PeptideAtlas (PASS00906), and integrates its MMAP analysis tool (NPAS + HC-marker based organelle abundance estimation) into the public SUBA4 web interface at suba.live. AllDataset · publicd ProtScore (Conf) > 2.0] and a Thorough ID was applied for the Search Effort. The data processing and matching by ProteinPilot results in recalibration of data, which were subsequently exported as MGF Peaklist(s) for HC‐data matching. These raw data for the whole plant ( n = 3) and CSCs ( n = 3) are available at PRIDE (Project https://doi.org/10.6019/pxd005408 ).
For Arabidopsis low/high‐light samples, analysis was undertaken with about 1 μg protein and performed with a Q‐Exactive+ (Thermo Fisher Scientific) with a nanoACQUITY UltraPerformance LC system (Waters), incorporating a C 18 reverse phase column (Waters; 100 μm × 100 mm, 1.7 μm particle, BEH130C18, column temperature 40°C).Open asset ↗PRIDElines:305-307Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Vibrational spectroscopy provides non-destructively the molecular fingerprint of plant cells in the native state. In combination with microscopy, the chemical composition can be followed in context with the microstructure, and due to the non-destructive application, in-situ studies of changes during, e.g., degradation or mechanical load are possible. The two complementary vibrational microspectroscopic approaches, Fourier-Transform Infrared (FT-IR) Microspectroscopy and Confocal Raman spectroscopy, are based on different physical principles and the resulting different drawbacks and advantages in plant applications are reviewed. Examples for FT-IR and Raman microscopy applications on plant cell walls, including imaging as well as in-situ studies, are shown to have high potential to get a deeper understanding of structure-function relationships as well as biological processes and technical treatments. Both probe numerous different molecular vibrations of all components at once and thus result in spectra with many overlapping bands, a challenge for assignment and interpretation. With the help of multivariate unmixing methods (e.g., vertex components analysis), the most pure components can be revealed and their distribution mapped, even tiny layers and structures (250 nm). Instrumental as well as data analysis progresses make both microspectroscopic methods more and more promising tools in plant cell wall research.
Why it matches plant phenotyping methods植物細胞壁の構造・化学組成を対象とする振動顕微分光法（FT-IRおよび共焦点ラマン）の植物への応用、画像化、データ解析を方法論としてレビューしており、植物状態の取得・抽出法が中心である。
abstractThe two complementary vibrational microspectroscopic approaches, Fourier-Transform Infrared (FT-IR) Microspectroscopy and Confocal Raman spectroscopy, are based on different physical principles and the resulting different drawbacks and advantages in plant applications are reviewed.
Reproduction assets foundThe review mentions an author-established public spectral database of plant cell wall reference components and spectra, hosted at bionami.at/spectra.html, which directly supports the paper's vibrational microspectroscopy measurements and band-assignment analysis. No code, models, or image datasets with explicit public-Dataset · publica spectral database, including reference components as well as different plant cell walls is currently established and made available to the scientific community ( http://bionami.at/spectra.html ).Open asset ↗bionami.atlines:98-107Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Pavement cells (PCs) are the most frequently occurring cell type in the leaf epidermis and play important roles in leaf growth and function. In many plant species, PCs form highly complex jigsaw-puzzle-shaped cells with interlocking lobes. Understanding of their development is of high interest for plant science research because of their importance for leaf growth and hence for plant fitness and crop yield. Studies of PC development, however, are limited, because robust methods are lacking that enable automatic segmentation and quantification of PC shape parameters suitable to reflect their cellular complexity. Here, we present our new ImageJ-based tool, PaCeQuant, which provides a fully automatic image analysis workflow for PC shape quantification. PaCeQuant automatically detects cell boundaries of PCs from confocal input images and enables manual correction of automatic segmentation results or direct import of manually segmented cells. PaCeQuant simultaneously extracts 27 shape features that include global, contour-based, skeleton-based, and PC-specific object descriptors. In addition, we included a method for classification and analysis of lobes at two-cell junctions and three-cell junctions, respectively. We provide an R script for graphical visualization and statistical analysis. We validated PaCeQuant by extensive comparative analysis to manual segmentation and existing quantification tools and demonstrated its usability to analyze PC shape characteristics during development and between different genotypes. PaCeQuant thus provides a platform for robust, efficient, and reproducible quantitative analysis of PC shape characteristics that can easily be applied to study PC development in large data sets.
Why it matches plant phenotyping methods葉表皮の舗装細胞形状を画像から自動抽出・定量するImageJツールを開発し、手動セグメンテーション等との比較検証も行っており、植物フェノタイピング手法が中心である。
abstractHere, we present our new ImageJ-based tool, PaCeQuant, which provides a fully automatic image analysis workflow for PC shape quantification.
Reproduction assets foundThe paper's PaCeQuant image analysis tool (ImageJ/MiToBo plugin) and its source code are publicly available under GPL v3.0 via the MiToBo website and GitHub, directly implementing the paper's pavement cell segmentation and 27-feature quantification workflow. The R analysis script is only described as supplemental, and Code · publicThe source code of MiToBo and the PaCeQuant plugin is available from the MiToBo website or on Github ( https://github.com/mitobo-hub/mitobo )Open asset ↗mitobo-hub/mitobolines:95-99Code / dataset availability confirmedbioRxiv · Europe PMC · checked 14 Sept 2026
How complex developmental-genetic networks are translated into organs with specific 3D shapes remains an open question. This question is particularly challenging because the elaboration of specific shapes is in essence a question of mechanics. In plants, this means how the genetic circuitry affects the cell wall. The mechanical properties of the wall and their spatial variation are the key factors controlling morphogenesis in plants. However, these properties are difficult to measure and investigating their relation to genetic regulation is particularly challenging. To measure spatial variation of mechanical properties, one must determine the deformation of a tissue in response to a known force with cellular resolution. Here we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties. Unlike classical extensometers, ACME is mounted on a confocal microscope and utilizes confocal images to compute the deformation of the tissue directly from biological markers, thus providing cellular scale information and improved accuracy. ACME is suitable for measuring the mechanical responses in live tissue. As a proof of concept we demonstrate that the plant hormone gibberellic acid induces a spatial gradient in mechanical properties along the length of the Arabidopsis hypocotyl.\n\nTerms
Why it matches plant phenotyping methods植物組織の力学的性質を細胞解像度で定量する自動共焦点マイクロ伸展計を開発し、画像から変形を抽出する方法を中心に実証しているため。
abstractHere we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe positioners are controlled by a SmarAct MCS3D (SmarAct GmbH) controller (Figure 1C,
label 15) accompanied by its software library, which in turn is controlled by custom-made software
(available here: https://github.com/ACME-Robinson/InstallPackage)Open asset ↗ACME-Robinson/InstallPackagepdf-page:14 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Root-knot nematodes induce galls that contain giant-feeding cells harboring multiple enlarged nuclei within the roots of host plants. It is recognized that the cell cycle plays an essential role in the set-up of a peculiar nuclear organization that seemingly steers nematode feeding site induction and development. Functional studies of a large set of cell cycle genes in transgenic lines of the model host Arabidopsis thaliana have contributed to better understand the role of the cell cycle components and their implication in the establishment of functional galls. Mitotic activity mainly occurs during the initial stages of gall development and is followed by an intense endoreduplication phase imperative to produce giant-feeding cells, essential to form vigorous galls. Transgenic lines overexpressing particular cell cycle genes can provoke severe nuclei phenotype changes mainly at later stages of feeding site development. This can result in chaotic nuclear phenotypes affecting their volume. These aberrant nuclear organizations are hampering gall development and nematode maturation. Herein we report on two nuclear volume assessment methods which provide information on the complex changes occurring in nuclei during giant cell development. Although we observed that the data obtained with AMIRA tend to be more detailed than Volumest (Image J), both approaches proved to be highly versatile, allowing to access 3D morphological changes in nuclei of complex tissues and organs. The protocol presented here is based on standard confocal optical sectioning and 3-D image analysis and can be applied to study any volume and shape of cellular organelles in various complex biological specimens. Our results suggest that an increase in giant cell nuclear volume is not solely linked to increasing ploidy levels, but might result from the accumulation of mitotic defects.
Why it matches plant phenotyping methods根こぶ線虫誘導巨大細胞の核体積・3D形態を取得する画像解析手法を提示し、AMIRAとVolumestを比較検証しているため、植物表現型計測法が中心です。
abstractHerein we report on two nuclear volume assessment methods which provide information on the complex changes occurring in nuclei during giant cell development.
Reproduction assets foundThe paper's nuclear volume measurements (individual GC and NGC nuclear volumes for Col-0, KRP3 OE, and KRP5 OE lines) are deposited in the article's public Supplementary File 1, available via the Frontiers supplementary-material URL. The Volumest plugin URL is a generic third-party tool, not a paper-specific asset, andSupplement · publicS-COFECUB (n°. sv 683/10 2011) project. RC has been supported by a doctoral scholarship in Brazil from CNPq (process number: 143030/2009-4) and in France from CAPES (process number: 6585-11-6).
1
http://lepo.it.da.ut.ee/~markkom/volumest/
Supplementary Material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2017.00961/full#supplementary-material
Click here for additional data file.
References
Banora M. Y. Rodiuc N. Baldacci-Cresp F. Smertenko A. Bleve-Zacheo T. Mellilo M. T.
( 2011 ).
Feeding cells induced by phytoparasitic nematodes require gamma-tubulin ring complex for microtubule reorganization.
PLoS Pathog.
7 : e10Open asset ↗lines:88-182Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Oscillations in pollen tubes have been reported for many cellular processes, including growth, extracellular ion fluxes, and cytosolic ion concentrations. However, there is a shortage of quantitative methods to measure and characterize the different dynamic regimes observed. Herein, a suite of open-source computational methods and original algorithms were integrated into an automated analysis pipeline that we employed to characterize specific oscillatory signatures in pollen tubes of Arabidopsis thaliana (Col-0). Importantly, it enabled us to detect and quantify a Ca2+ spiking behaviour upon growth arrest and synchronized oscillations involving growth, extracellular H+ fluxes, and cytosolic Ca2+, providing the basis for novel hypotheses. Our computational approach includes a new tip detection method with subpixel resolution using linear regression, showing improved ability to detect oscillations when compared to currently available methods. We named this data analysis pipeline 'Computational Heuristics for Understanding Kymographs and aNalysis of Oscillations Relying on Regression and Improved Statistics', or CHUKNORRIS. It can integrate diverse data types (imaging, electrophysiology), extract quantitative and time-explicit estimates of oscillatory characteristics from isolated time series (period and amplitude) or pairs (phase relationships and delays), and evaluate their synchronization state. Here, its performance is tested with ratiometric and single channel kymographs, ion flux data, and growth rate analysis.
Why it matches plant phenotyping methods花粉管の先端位置・成長・振動特性を定量化する新規チップ検出法とオープンソース解析パイプラインを開発しており、植物表現型取得・抽出が中心である。
abstracta suite of open-source computational methods and original algorithms were integrated into an automated analysis pipeline
Reproduction assets foundThe paper's raw phenotyping data (ratiometric kymographs, ion flux, tracking data) are deposited in Dryad, and the authors' CHUKNORRIS analysis pipeline code is on GitHub; both are paper-specific, public, and actionable.Dataset · publicData from all six ratiometric kymographs with corresponding MetaMorph tracking, as well as ion flux, tracking, and kymograph data of the highly oscillatory growth series are available at Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.6806cOpen asset ↗Dryad Digital Repository · 10.5061/dryad.6806clines:110-189Code · publicCode in the statistical programming language R, together with examples of its usage are available in the online repository GitHub: https://github.com/damineli/CHUKNORRIS , last accessed 15 February 2017.Open asset ↗GitHub · damineli/CHUKNORRISlines:110-189Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published28 Feb 2017Cytometry. Part A : the journal of the International Society for Analytical CytologyCited by 5 · OpenAlex ↗
Why it matches plant phenotyping methods植物プロトプラスト画像から薬剤による表現型変化を自動検出する画像解析・統計パイプラインを開発し、ハイコンテントスクリーニングに適用しており、表現型取得・抽出法が中心です。
abstractQuantitative image analysis procedures are necessary for the automated discovery of effects of drug treatment in large collections of fluorescent micrographs.
Reproduction assets foundThe paper's AVAILABILITY section states that a Reproducible Research Archive containing all raw data (the Arabidopsis protoplast fluorescence microscopy images), software, and processed results is publicly available from the authors' mur-phylab URL, which appears in the allowed URL list.Code · publicsoftware, and processed results is available from http://mur-phylab.cbd.cmu.edu/software.ACKNOWLEDGMENTS
We thank Dr. Armaghan Naik for helpful discussions, Dr.
Roland Nitschke for advice on microscopy, and Katja Rapp
for technical support.
LITERATURE CITED
1. Giuliano KA, De Biasio RL, Dunlay RT, Gough A, Volosky JM, Zock J, Pavlakis GN,
Taylor DL. High-content screening: A new approach to easing key bottlenecks in the
drug discoOpen asset ↗http://mur-phylab.cbd.cmu.edu/software.ACKNOWLEDGMENTSpdf-raw-page:9 lines:94-155