Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole-genome sequencing and mapping of the candidate mutation. This screen led to the identification of a point mutation in CAP-D2 (capd2-2), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery.
Why it matches plant phenotyping methods花粉生存性を画像から自動推定するセグメンテーション手法とソフトウェアPATの開発が研究の中心であり、植物表現型計測ツールとして明確に該当する。
titlePAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PAT pollen-phenotyping tool (the software implementing the paper's computational analysis, including the fine-tuned CPSAM segmentation model support) as open source on GitHub. Note: the full repository URL in the text (https://github.com/Riha-429[Code · public17
Data availability
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Pollen Analysis tool (PAT) is available as open source tool at Github repository (https://github.com/Riha-429
Lab/Pollen-Analysis-Tool).
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Figure legends
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Fig. 1. Cellpose performance on Alexander-stained anther cross-sections across varying pollen
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densities.
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Representative cross-sections of Alexander-stained anthers showing a range of pollen densities, from
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low (top rows, light staining) to high (bottom rows, dense reOpen asset ↗Pollen-Analysis-Toolpdf-raw-page:17 lines:1-64Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementSegmentationRoot system architecture
Introduction Plant parasitism by sedentary plant-parasitic nematodes is a dynamic and continuously evolving process, accompanied by profound remodelling of host root system architecture across distinct infection stages. However, the physiology and anisotropic growth of Arabidopsis thaliana roots under Heterodera schachtii infection, together with complex lateral root proliferation and increasingly dense, overlapping morphology, pose substantial challenges for accurate image segmentation. Methods Here, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features. These adaptations address the unique morphological complexity of the infected root system. Additionally, we integrated Root-TransUNet into a high-throughput phenotyping pipeline and applied it to an existing dataset of ~120,000 images of 362 A. thaliana MAGIC recombinant inbred lines collected over several months of infection. By extracting root system architecture traits, including root surface area and estimated root volume across infection stages, we enabled stage-specific association analyses between host root growth and nematode performance across these genotypes. Results Root-TransUNet achieved strong segmentation performance, demonstrating improved structural continuity and boundary precision compared with widely used CNN- and Transformer-based baselines, including UNet++. Stage-specific analyses revealed that the relationship between host root traits and nematode performance changed as infection progressed. During establishment, nematode number was largely independent of initial root size and varied strongly among genotypes, whereas during the reproductive phase (10-30 dpi), greater root expansion coincided with reduced estimated nematode volume accumulation. Notably, nematode burden was largely independent of host root size before infection, indicating that root quantity was generally not a limiting factor for infection in this experiment. Discussion These results demonstrate that Root-TransUNet can robustly segment infected root systems across a wide range of nematode infection densities, providing a scalable image-analysis framework for studying plant-parasitic nematode parasitism in combination with host root phenotyping.
Why it matches plant phenotyping methods感染根系の画像セグメンテーション手法を開発し、高スループット表現型解析パイプラインに統合して根系形態形質を抽出しており、表現型取得・抽出法が中心的である。
abstractHere, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features.
Reproduction assets foundThe paper analyzes a public BioImages dataset (S-BIAD2402) of ~400,000 RGB root/nematode infection images and provides authors' analysis code on GitHub; both are paper-specific, public, and actionable.Code · publicng molecular signatures, deepening our understanding of host-parasite resource allocation strategies, and establishing a foundation for the discovery of novel resistance mechanisms.
Code and data availability
Python-based source code for automating root analysis using the datasets above is accessible via our GitHub repository ( https://github.com/JieZhou1025/Root-nematode-interaction ).
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2402 .
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
AuthorOpen asset ↗JieZhou1025/Root-nematode-interactionlines:412-424Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Plants employ non-photochemical quenching (NPQ) to protect their photosynthetic apparatus from photodamage. The response latency of NPQ following changes in light intensity is thought to significantly decrease photosynthetic efficiency. The amount of NPQ is commonly quantified from chlorophyll-fluorescence techniques using the Stern–Volmer equation, which requires fully closed reaction centres (RCs) of photosystem II, yielding NPQ in the absence of photochemical quenching ( $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed ). However, in nature, NPQ and photochemical quenching are normally present simultaneously. Therefore, to obtain a full understanding of this process, NPQ should also be explored when the RCs are open. Here we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements. A detailed comparison in Arabidopsis thaliana plants reveals that the value of $${\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Open is ~35% lower than that of $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed . This difference is consistently observed across all measurements and is seen both upon closing ( $${\rm{NPQ}}^{\rm{Open}}\to {\rm{NPQ}}^{\rm{Closed}}$$ NPQ Open → NPQ Closed ) and upon reopening ( $${\mathrm{NPQ}}^{\mathrm{Closed}}\to {\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Closed → NPQ Open ) of the RCs. We show that this difference can be explained by the presence of RC-induced ‘instantaneous’ switching of the NPQ quenching rate. This means that, in plants, NPQ is much more economical than is widely believed, it is large when its presence is needed, and it decreases instantaneously when the need disappears.
Why it matches plant phenotyping methods植物の光合成状態（NPQ）を測定するための蛍光寿命・蛍光収率に基づく2つの方法を開発し、比較検証しているため、方法開発が中心である。
abstractHere we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements.
Reproduction assets foundThe paper's custom ultrafast fluorescence analysis code (ICA-based PSII/PSI deconvolution and NPQ calculations) is explicitly deposited by the authors on GitHub, alongside the original data contributions.Code · publicr(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Funding
This work was supported by ‘Nanoscale regulators of photosynthesis’ NWO research project (project number: OCENW.GROOT.2019.86).
Data availability
The original contributions presented in the study are available via GitHub at https://github.com/L-Ramakers/Heimdall .
Code availability
The custom analysis code used in the study is available via GitHub at https://github.com/L-Ramakers/Heimdall .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afOpen asset ↗L-Ramakers/Heimdalllines:88-125Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
Why it matches plant phenotyping methods深層学習による葉の自動セグメンテーション・追跡を開発的に適用し、時系列画像から葉レベルおよび植物体レベルの発達形質を定量化しているため、表現型取得・抽出が研究の中心である。
abstractleaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants
Reproduction assets foundThe paper explicitly states that the authors' leaf segmentation software (AraLeaf_segmentation), the GreenLab Arabidopsis model (AraGreenlab), and the statistical analysis code (AraParameters_statistical_analyses) are open-source and publicly hosted on forge.inrae.fr, and that the data and results used in the paper areCode · publicThe leaf segmentation software (AraLeaf_segmentation) and the GreenLab model of Arabidopsis thaliana (AraGreenlab) are open-source software, and distributed under the GNU GPL v3 licence. The two software packages, and the code to run statistical analyses (AraParameters_statistical_analyses) are available at:https://forge.inrae.fr/phenoscope-public/plant_phenomics_suppmat.Open asset ↗phenoscope-public/plant_phenomics_suppmathtml-lines:419-444Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Code / 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 confirmedarXiv · OpenAlex · checked 11 Sept 2026
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
Modern plant phenotyping faces the challenge of interpreting complex, high-dimensional data. Traditional analytical tools often fail to capture the non-linear, hierarchical, and temporal relationships that define plant responses under multifactorial conditions. We present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space. Unlike conventional Euclidean approaches, HTDA-Mapper preserves the hierarchical structure of phenotypic traits, improves cluster resolution, and reveals hidden growth trajectories across treatments and time, offering a powerful means to explore latent phenoms. The pipeline supports both quantitative data and images. When integrated with unsupervised contrastive learning, HTDA-Mapper identifies similarities and differences in raw image data without requiring manual labelling or post hoc processing. We applied this framework to a high-throughput phenotyping (HTP) dataset of over 27,000 images of Arabidopsis thaliana seedlings exposed to varying nutrient levels and priming agents at different concentrations over seven days. Using cubical complexes, HTDA-Mapper mapped relationships between treatment variables, compound concentrations, and phenotypic outcomes. Furthermore, it reliably detected compound-specific effects, uncovered dynamic trait–environment interactions, revealed phenotypic trajectories not captured by conventional methods, and facilitated biologically meaningful interpretation of the complex dataset. By preserving the geometry and temporal evolution of plant development, HTDA-Mapper sets a new standard for HTP analysis. Beyond phenomics, it is a versatile tool for other omics, such as transcriptomics and metabolomics, where structured, high-dimensional data is prevalent. HTDA-Mapper can accelerate data-driven crop improvement by uncovering effective compounds, robust genotypes, and adaptive growth strategies that enhance plant resilience.
Why it matches plant phenotyping methods植物フェノミクスの高次元画像・形質データを解析するHTDA-Mapperアルゴリズムを開発し、27,000枚超の植物画像データで適用・評価しているため、解析手法が中心的である。
abstractWe present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space.
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 · publicUpon acceptance, the codes and all material used in this research will be freely available at HYPERLINK: https://github.com/JZdrazilX/MML and data at ZENODO: 10.5281/zenodo.17952279.Open asset ↗JZdrazilX/MMLhtml-lines:222-260Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Code / 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
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 5 Sept 2026
Doubled haploid (DH) technology can fast-track crop breeding. Haploid induction yields haploids with only one set of genomes, which are usually sterile. Haploid fertility (HF) is the ability of haploid plants to set seed, and it is a critical bottleneck in DH pipelines. Genetic mechanisms to restore HF hold immense potential in DH crop breeding, yet its phenotyping remains manual, destructive, and inconsistent. While recent advances in imaging and machine learning have improved throughput for general plant traits, no curated image dataset exists for Arabidopsis thaliana that explicitly represents HF. Here, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification. AutoSiQ includes high-resolution scanned inflorescences annotated with a seven-class ontology encompassing green siliques, green fertile siliques, mature siliques, fertile siliques, cracked fertile siliques, cracked siliques, and flowers. This multi-class annotation scheme preserves biologically meaningful information beyond binary fertile/non-fertile distinctions, enabling reliable fertility estimation and future phenotyping applications. We release baseline object detection models (YOLOv5), trained using the AutoSiQ dataset, and evaluate their performance across confidence thresholds. Model predictions strongly correlate with manual counts, achieving R² up to 0.94 for total silique number estimation. We further demonstrate AutoSiQ's utility for automated haploid fertility rate (HFR) estimation and genotype discrimination between two contrasting genotypes (WT and bmf2 mutant). A longitudinal analysis identifies ~60 days after sowing (DAS) as the optimal harvest time for maximizing mature silique counts by balancing between the number of immature buds and silique shattering. By releasing both the dataset and baseline code, AutoSiQ provides a reproducible and extensible foundation for high-throughput fertility phenotyping in haploid Arabidopsis .
Why it matches plant phenotyping methodsハプロイド稔性を画像から定量するデータセットと深層学習パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification.
Reproduction assets foundThe paper's AutoSiQ dataset (annotated scanned Arabidopsis inflorescence images with seven-class silique ontology and manual fertility counts) is publicly deposited on Zenodo per the data availability statement. The YOLOv5 GitHub repository is a generic third-party library, not an authors' code asset.Dataset · publicThe 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://zenodo.org/records/17905566 .Open asset ↗zenodo · 17905566lines:367-402Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で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 present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / 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
Introduction Understanding plant responses to abiotic stress requires an insight into plant redox activity. This study proposes a novel and cost-effective method for assessing the redox state of plants. Methods The method utilizes the electrochromic properties of polyoxometalate phosphomolybdic acid hydrate (PMA). PMA is reduced proportionally by glutathione (GSH) and ascorbic acid (AsA), which results in a measurable color change. The validity of this method was confirmed through empirical experimentation in Arabidopsis thaliana under conditions of salinity and UV radiation. Results Salinity treatments revealed a non-significant, two-phase trend in redox activity with an increase at moderate levels followed by a decrease. UVC radiation led to a substantial decrease in redox activity, indicating distress. In contrast, UVA promoted resilience, also known as eustress. Notably, UVB significantly increased redox activity, suggesting the activation of an emergency antioxidant response. Discussion A demonstrable correlation has been identified between the redox activity of plants and various stress types. This correlation facilitates the classification of responses into two distinct categories: adaptive eustress and detrimental distress. This advancement contributes to the enhancement of plant metabolic and stress tolerance evaluation.
Why it matches plant phenotyping methods植物のレドックス状態を測定する新規手法を開発し、シロイヌナズナでストレス条件下の妥当性を検証しており、表現型取得が研究の中心である。
abstractThis study proposes a novel and cost-effective method for assessing the redox state of plants.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.17795112) containing the study's datasets, which underpin the PMA-based redox activity measurements (absorbance at 852 nm) in Arabidopsis thaliana under salinity and UV stress. No author analysis code or trained models are attDataset · publicThe 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/10.5281/zenodo.17795112 .Open asset ↗zenodo · 10.5281/zenodo.17795112lines:416-483Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomatal pores, formed by guard cells, govern the critical trade-off between carbon assimilation and water loss in plants. Their dynamic responses to environmental stresses, such as stomatal oscillations and drought “stress memory” (hysteresis), have lacked a unified mechanistic explanation. While abscisic acid (ABA) is believed to play key roles in water stress responses, no model has linked its core regulatory kinetics to these complex stomatal behaviors. Here, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation. We demonstrate that this framework predicts accurate, genotype-specific stomatal regulation across wildtype, ABA-insensitive mutant ( ost1-3 ), and ABA-synthesis mutant ( aao3-2 ) in Arabidopsis thaliana ( At ) and that non-linear feedbacks in ABA autoregulation can drive both stomatal oscillations and hysteresis. This work unifies genetic, signaling, and membrane processes with leaf-scale physiological dynamics, providing a new predictive foundation for understanding and modulating plant management of water use and water stress.
Why it matches plant phenotyping methods葉の水理とABA制御を統合した予測モデルを開発し、遺伝子型別の気孔コンダクタンス制御を検証しており、植物生理表現型の取得・予測手法が中心である。
abstractHere, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation.
Reproduction assets foundThe paper's Code Availability section explicitly archives all MATLAB code used to generate the study's stomatal conductance modeling results in a Zenodo repository (DOI 10.5281/zenodo.17888362) and on GitHub (desai-sahil/sys-bio-gs), both listed as allowed URLs. This is author analysis code directly reproducing the hydCode · publicn analysis are provided in SI sections S5. Comprehensive tables listing all model parameters,
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their sources, and the methodology for parameter fitting are provided in SI section S7.
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All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
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on steps to run the code to reproduce the results in main text.
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. BeldiOpen asset ↗Zenodo · 10.5281/zenodo.17888362pdf-raw-page:9 lines:1-74Code · publicnd the methodology for parameter fitting are provided in SI section S7.
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All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
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on steps to run the code to reproduce the results in main text.
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. Belding, and P. Jain for insightful
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discussions. This work was supported by the Center for Research on Programmable Open asset ↗GitHub · desai-sahil/sys-bio-gspdf-raw-page:9 lines:1-74Code / dataset availability confirmedCrossref · checked 5 Sept 2026
ABSTRACT Sample preparation is an important first step to obtain high quality mass spectrometry imaging (MSI) data. Preparing plant tissues is especially challenging for MSI of thin tissues along the lateral dimensions. The unique challenges involved with plant tissues, such as fragile cell walls, hydrophobic barriers, and specific tissue structures, often lead to inefficiency and difficulties in sample preparation. Imprinting plant tissues onto porous polytetrafluoroethylene (pPTFE) sheet has been widely used to extract internal metabolites in leaves and petals while keeping spatial resolution for MSI. However, pressure applications were typically made manually using a vise or pliers leading to low reproducibility and resolution in MS images. In this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting. To evaluate the performance of the new device, Lemna minor fronds, Arabidopsis thaliana , and Bacopa monnieri leaves were imprinted onto the pPTFE with PNP, vise, or pliers, and matrix‐assisted laser desorption/ionization (MALDI) MSI was obtained on the imprints. The PNP showed dramatic improvements in reproducibility and image quality compared to manual pressure application tools.
Why it matches plant phenotyping methods植物組織の空間的な代謝物情報を再現性よく取得するための空気圧式インプリンティング装置を開発し、手動法と性能比較している。植物表現型取得に関わる試料調製・イメージング手法が中心である。
abstractIn this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting.
Reproduction assets foundThe paper's MALDI-MSI data (imzML files of imprinted Lemna minor, Arabidopsis, and Bacopa tissues) are openly deposited in a paper-specific METASPACE project, as stated in the Data Availability Statement. No author analysis code or trained models are disclosed.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in METASPACE (https://metaspace2020.eu/project/pnp_ptfe_imprinting_plant).Open asset ↗METASPACE · pnp_ptfe_imprinting_planthtml-lines:230-307Code / 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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Plant seeds are one of the most important food sources for humans. As a result, seed morphology and the concentrations of essential and toxic elements in seeds have important implications not only for seed yield and quality, but also for human health. To identify natural variation in the accumulation of various elements in seeds and in seed morphology, high-throughput phenotyping methods are needed. Here, we employed X-ray fluorescence microscopy (μ-XRF) as a method for rapid and high-throughput phenotyping of seed libraries and developed a computer vision-based algorithmic workflow to automatically the extraction of elemental and morphological data from single seeds. This workflow enables rapid segmentation of individual seeds from a genome-wide association study (GWAS) panel with 1163 A. thaliana accessions, and facilitates the extraction of elemental and morphological traits at the individual seed level from the μ-XRF image. A total of 7 and 10 loci, respectively associated with the morphology and elemental concentration of A. thaliana seeds, were identified. The high-throughput and nondestructive method for automated phenotyping of plant seed libraries developed in this study provides a tool for investigating natural genetic variation controlling the seed mineral accumulation and seed morphogenesis.
Why it matches plant phenotyping methods種子の元素濃度・形態をμ-XRFとコンピュータビジョンで高速・自動取得する手法を開発しており、植物表現型取得が研究の中心である。
abstracthigh-throughput phenotyping methods are needed
Reproduction assets foundThe authors explicitly state that the u-XRF source code and algorithm (the computer vision workflow used for seed segmentation and trait extraction from μ-XRF images) are distributed under the MIT License and publicly available at their GitHub repository, making it a paper-specific, public, actionable code asset.Code · publicThe source code and algorithm of u-XRF are distributed under the MIT License, which permits academic use, distribution, and reproduction subject to the terms of the license ( https://opensource.org/license/MIT/ ), unless otherwise specified. Supporting source code, Web of Science Global Science Publications data, and additional datasets can be accessed at https://github.com/The-Wang-Lab-NAU/u-XRF/ for download and upload.Open asset ↗The-Wang-Lab-NAU/u-XRFlines:291-309Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
High-throughput phenotyping of growth kinetics and organ size in the model plant Arabidopsis thaliana requires rapid and precise methods for trait estimation. To address this need, we developed the Arabidopsis Phenotypic Trait Estimation System, APTES, an open-access, high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs. The enhanced segmentation model Cascade Mask Region-based Convolutional Neural Network (Mask R-CNN) achieved precision (measure of positive prediction accuracy), recall (sensitivity in detection), and F1 score values (harmonic mean of precision and recall) of 0.965, 0.958, and 0.961, respectively, for individual leaf segmentation. These metrics demonstrated a consistent improvement of approximately 1 percentage point over the baseline model. For silique segmentation, our enhanced DetectoRS model for silique segmentation attained precision, recall, and F1 scores of 0.954, 0.930, and 0.942, respectively. Notably, precision increased by 1%, while the F1 score improved by 2 percentage points. Trait parameters were automatically calculated with coefficient of determination values for leaf and silique traits ranging from 0.776 to 0.976 and mean absolute percentage error values from 1.89% to 7.90%. We phenotyped 166 Arabidopsis accessions, using APTES, and subjected the resulting values to a genome-wide association study (GWAS), revealing 1,042 single-nucleotide polymorphisms (SNPs) as being significantly associated with 18 leaf and silique traits, and one significant SNP on chromosome 3 linked to silique number. Furthermore, we validated APTES across other public Arabidopsis databases and other plant species, with segmentation results demonstrating its applicability across diverse datasets. In conclusion, APTES is a valuable automated tool for leaf and silique segmentation and trait estimation, which should offer benefits to the broader plant science community. Supplementary information The online version contains supplementary material available at 10.1007/s42994-025-00239-y.
Why it matches plant phenotyping methods植物の葉・莢の形質を画像から抽出する深層学習システムを開発し、性能検証・他データセットでの妥当性確認まで行っており、フェノタイピング手法が研究の中心である。
abstractwe developed the Arabidopsis Phenotypic Trait Estimation System, APTES, an open-access, high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs.
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2件を確認しました。Code · publicThe executable tool and software packages are available at https://drive.google.com/drive/folders/1i9IariiIrxuFtVIaRiaIzqvb8Gfg3xTc or http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:292-292Code · publicThe executable tool and software packages are available at https://drive.google.com/drive/folders/1i9IariiIrxuFtVIaRiaIzqvb8Gfg3xTc or http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:292-292Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Summary Thermal imaging is a key plant phenotyping and monitoring technique but faces major bottlenecks in accurately and efficiently inferring stomatal conductance (gsw) from leaf temperature. The conductance index (Ig) was previously proposed to estimate gsw from thermography by linking temperature differences between real and artificial leaves (ALs) based on the leaf energy balance. However, Ig is highly sensitive to environmental fluctuations, hampering interpretation and reducing reproducibility. We developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw. This was achieved by capturing temperature variations in a set of ALs with a range of known constant pore conductances. This approach provided the Ig–conductance relationship, using ALs as a reference, to infer gsw of real leaves from their measured Ig. In fluctuating environments, gsw estimated using DynG showed greater accuracy and stability than gsw calculated from Ig alone, and was in good agreement with gsw determined using lysimetric and gas exchange methods. DynG's power was further showcased in distinguishing gsw of Arabidopsis genotypes differing in stomatal traits (Col‐0, epf1epf2, and EPF2OE). We conclude that Ig corrected with DynG can reliably estimate gsw in fluctuating environments without complex modeling, opening new avenues for gsw phenotyping and monitoring.
Why it matches plant phenotyping methods熱画像から気孔コンダクタンスを推定する補正係数を開発し、変動環境下で既存法と比較検証した、植物フェノタイピング手法の中心的研究である。
abstractWe developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw.
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 · publicRelated codes are available on GitHub ( https://github.com/jiayu0903/dynamic‐conductance‐index.git ).Open asset ↗https://github.com/jiayu0903/dynamic‐conductance‐index.gitlines:805-819Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Image‐based plant phenotyping has diverse applications, ranging from providing quantitative traits for genetic breeding to enhancing management practices for indoor and outdoor production systems. Misidentification of cell lines or ecotypes/varieties is a major problem across all biological research disciplines. With the 1000 Arabidopsis Genome Project facilitating the use of various ecotypes, it is crucial to verify the identity of ecotypes in discovery‐based genetic screens involving hundreds of ecotypes. To address this issue, an RGB image analysis pipeline was established for the accurate recognition of different Arabidopsis thaliana ecotypes. In the developed pipeline, the most crucial aspects for accurately capturing traits and training deep learning models were identified as follows: (i) assessment of data complexity using spatial‐temporal features of the RGB spectrum and data entropy, the latter defined as the variability within the dataset; (ii) data redefinition in instances of high data complexity; and (iii) data partitioning based on extracted morphological similarity among ecotype replicates. The pipeline includes several supervised deep learning models integrated into an auto‐optimization subsystem. Extensive hyperparameter tuning was performed to identify the best‐performing models for single‐image and image‐sequence ecotype classification. Two external datasets were evaluated to demonstrate the robustness of the pipeline, regardless of how they were collected. A graphical user interface is provided to prepare these images for input into the pipeline in cases of extreme variability. The pipeline can automatically verify ecotypes in large‐scale studies and extract traits for further analysis and correlation, as needed, using datasets from a variety of sources.
Why it matches plant phenotyping methodsRGB画像から植物形態情報を抽出し、深層学習による分類・検証を行うパイプライン自体が中心的な貢献であり、外部データセットで頑健性も評価しているため。
abstractan RGB image analysis pipeline was established for the accurate recognition of different Arabidopsis thaliana ecotypes.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the source code of the RGB image analysis pipeline used for Arabidopsis ecotype classification, directly reproducing this paper's computational analysis. Supporting Information also contains the extracted rosette area and Code · publican be found in Sup-
porting Information Data S1 and S2. Installation file along
with user manual for developed GUI for color enhancement
and background suppression can be found in GUI Package
in the Supporting Information. The source code of the RGB
image analysis pipeline components is available at the fol-
lowing GitHub link: https://github.com/pisyntor/Computer_
based_Recognition_of_Arabidopsis_thaliana_Ecotypes.
O RC I D
RijadSarić https://orcid.org/0000-0002-7554-2555
James Whelan https://orcid.org/0000-0001-5754-025X
R E F E R E N C E S
1001 Genomes Consortium. (2016). 1,135 Genomes reveal the global
pattern of polymorphism in Arabidopsis thaliana. Cell, 166(2), 481–
491. https://doOpen asset ↗pisyntor/Computer_pdf-raw-page:22 lines:1-89Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Background The plant vacuole arises by orchestrated interplay of membrane trafficking, cytoskeletal rearrangements and a variety of signalling 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植物の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、既存指標との比較・実データおよびシミュレーションによる検証、ベンチマークデータを提示しており、表現型取得・抽出法が中心である。
abstractWe developed an alternative metric for describing vacuole organization, named the Tonoplast Topology Index (TTI), which overcomes the above-mentioned shortcomings of the VMI.
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 software tool generated here are also available at https://github.com/GeorgeCaldarescu/TTI-Open asset ↗GeorgeCaldarescu/TTI-pdf-page:9 lines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Shade avoidance response in plants includes a higher leaf elevation angle. A cost-effective and noninvasive high throughput image analysis technique was used to measure the dynamics of leaf elevation angles during shade avoidance response in Arabidopsis . Time-lapse images were taken from the top and the side of a plant using Raspberry Pi computers. The leaf elevation index for each plant is determined from the plant dimensions measured by an image analysis software package PlantCV . This method was used to monitor the dynamics of changing leaf elevation angles in wild-type plants and in shade avoidance mutants pif4-2pif5-3 and pif7-2 plants.
Why it matches plant phenotyping methodsRaspberry Piと画像解析を用いて葉の仰角を定量化する高スループット手法が研究の中心であり、植物形質の時系列取得に実質的に適用されている。
abstractA cost-effective and noninvasive high throughput image analysis technique was used to measure the dynamics of leaf elevation angles during shade avoidance response in Arabidopsis .
Reproduction assets foundThe paper deposits its authors' PlantCV-based python analysis script (example_workflow.py) as an Extended Data software item with a public DOI (Caltech DATA). No phenotype dataset or image deposit is described.Code · publicts used in this study
Ecotype
Genotype
Available From
Columbia
Wild type
Columbia
pif4-2pif5-3
ABRC # CS68096
Columbia
pif7-2
ABRC # CS71656
I thank Dr. Noah Fahlgren at the Donald Danforth Plant Science Center for his help with
PlantCV
.
Extended Data
Description: python script used in this study. Resource Type: Software. DOI:
https://doi.org/10.22002/q71sw-5vz65
BerryJC
FahlgrenN
PokornyAA
BartRS
VeleyKM
2018104An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping.PeerJ62167-8359e5727e572710.7717/peerj.572730310752PMC6174877
DevlinPF
HallidayKJ
HarberdNP
WhitelamGC
1996121The rosette habit of Arabidopsis thaliana is dependeOpen asset ↗10.22002/q71sw-5vz65html-lines:128-241Code / dataset availability confirmedEurope PMC · bioRxiv · checked 13 Sept 2026
ArabidopsisRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana , we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.
Why it matches plant phenotyping methodsRSAのコスト効率トレードオフを定量化する半自動ソフトウェアを開発し、植物形態形質の解析に適用しており、表現型取得・抽出手法が研究の中心である。
abstractWe developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework
Reproduction assets foundThe paper's authors developed the Ariadne software used for all RSA phenotyping and Pareto analysis in this study, and explicitly state it is publicly available on PyPI and provide a GitHub code availability URL. Both are paper-specific, public, actionable code assets. No public phenotype dataset deposit is stated; theCode · publicCode availability : https://github.com/Salk-Harnessing-Plants-Initiative/AriadneOpen asset ↗Salk-Harnessing-Plants-Initiative/Ariadnelines:235-276Code / 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 · Europe PMC · checked 14 Sept 2026
Plants adapt seed traits in response to different environmental triggers, supporting the survival of the next generation. To elucidate the mechanistic understanding of such adaptations it is important to characterize the distributions of seed traits by phenotyping seeds on an individual scale and to correlate these traits with corresponding plant properties. Here we introduce a seed-to-plant-tracking pipeline which enables automated handling and high precision phenotyping of Arabidopsis seeds as well as germination detection and early growth quantification of emerging plants. It includes previously published measurement platforms ( pheno Seeder, Growscreen), which were improved for very small seeds. We demonstrate the performance of the pipeline by comparing seeds from two consecutive generations of elevated temperature during flowering with control seeds. Relative standard deviation of repeated seed mass measurements was reduced to 0.2%. We identified an increase in seed mass, volume, length, width, height, and germination time as well as a darkening of the seeds under the treatment. A correlation analysis revealed relationships between seed and plant traits, e.g., a highly significant negative correlation between seed brightness and germination time, and a positive correlation between seed mass and early growth rate, but no correlation between time of emergence and morphometric seed traits (e.g., mass, volume). Thus, the seed-to-plant tracking provides the basis for investigating the mechanism of seed and plant trait variation and transgenerational inheritance.
Why it matches plant phenotyping methods種子から植物までを追跡し、種子形質の高精度自動計測、発芽検出、初期成長定量を行うパイプラインを開発・改良しており、表現型取得法が研究の中心である。
abstractHere we introduce a seed-to-plant-tracking pipeline which enables automated handling and high precision phenotyping of Arabidopsis seeds as well as germination detection and early growth quantification of emerging plants.
Reproduction assets foundThe paper deposits its seed and plant phenotyping datasets in Jülich DATA (DOI 10.26165/JUELICH-DATA/KZDQYD), explicitly stated in the data availability statement. Supplementary tables also contain the paper's measurement data. No author analysis code repository is stated.Dataset · publicThe author(s) declare that no financial support was received for the research and/or publication of this article.
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/10.26165/JUELICH-DATA/KZDQYD .
Author contributions
DK: Formal Analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing. AF: Investigation, Methodology, Resources, Software, Writing – review & editing. VS: Formal Analysis, Investigation, Methodology, Resources, Software, Writing – review & editing. JK: MethodOpen asset ↗JUELICH-DATA · 10.26165/JUELICH-DATA/KZDQYDlines:333-387Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1539424/full#supplementary-material
Supplementary Table 1
Data of seed mass vs volume and projected seed area, respectively, shown in
Figure 6
.
Supplementary Table 2
Data of repeatability measurements analysed in
Table 1
and
2
.
Supplementary Table 3
Data of leaf area time series used for estimation of plant growOpen asset ↗lines:333-387Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Molecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping. Using genetic markers and data from high-throughput phenotyping of a maize multiparent advanced generation inter-cross population and an Arabidopsis thaliana diversity panel, we show that dynamicGP outperforms a baseline genomic prediction approach for the multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and environment over plant development to improve the prediction accuracy of agronomically relevant traits.
Why it matches plant phenotyping methods遺伝マーカーと高スループット表現型データを統合し、植物形態・幾何・色彩形質の時系列を予測する計算手法dynamicGPが研究の中心であるため。
abstractHere we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their dynamicGP R implementation on GitHub and mirrored code plus genotyping data on Zenodo, and the maize HTP phenotype dataset is deposited on e!DAL (IPK). All are paper-specific, public, and directly actionable.Code · publicAn R implementation of algorithms 1 and 2 is available at https://github.com/dobby978/dynamicGP .Open asset ↗dobby978/dynamicGPlines:134-161Code · publicAll code that was used to generate the results of this study is available via GitHub at https://github.com/dobby978/dynamicGP and via Zenodo at https://doi.org/10.5281/zenodo.14959484 (ref. 32 ).Open asset ↗Zenodo · 10.5281/zenodo.14959484lines:181-276Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recordings of electric potential changes on plant surfaces have been utilized to identify the components and mechanisms involved in the formation and transmission of systemic signals elicited by stimuli such as herbivory, wounding, or burning. The recorded responses, commonly referred to as slow wave or variation potentials, exhibit striking variability in their waveform. The extent to which this variability is due to differences in experimental procedures or plant biological variability remains unclear. Here, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding. This protocol serves as a comprehensive tutorial covering plant growth, procedures for reproducible mechanical wounding, critical aspects of electrophysiological recordings, and statistical analysis of surface potential recordings. It particularly emphasizes the construction and maintenance of electrodes, placement of the reference or ground electrode, mechanisms for wounding, and data analysis. This protocol aims to promote and facilitate the adoption, standardization, and interoperability of plant surface potential recordings among research groups, thereby increasing the reproducibility and comparability of data within the field. Key features • Recording electric potential changes on the petiole of 5-week-old Arabidopsis plants using noninvasive surface electrodes, improving the wounding procedure, reproducibility, and data processing from [1]. • Genotype-independent method for phenotyping, including parallel recordings from multiple plants. • Guidelines for plant growth conditions, unambiguous leaf assignment by order of emergence, and detailed instructions for electrode fabrication and maintenance. • Instructions for constructing devices for standardized, reproducible mechanical wounding along with a custom script for unbiased and semi-automated data analysis.
Why it matches plant phenotyping methods植物の表面電位を再現性高く記録・解析する電気生理学的フェノタイピング手法の詳細プロトコルであり、電極、標準化創傷、データ解析、再現性・相互運用性が中心的に扱われている。
abstractHere, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding.
Reproduction assets foundThe authors publicly deposit their paper-specific assets on GitHub: the SWPanalyzer.Rmd analysis script, raw surface potential recordings, and 3D-printed wounding grid designs, all directly used for this protocol's phenotyping measurements and analysis.Code · publicis thaliana Col-0 ecotype, other ecotypes may be used. However, differences in rosette morphology could affect petiole accessibility for electrode placement.
2. Prior to the measurement, plants should be acclimatized to the new conditions.
3. The raw data, analysis, R script, and 3D design can be found under the following link: https://github.com/jucbca/SWP-data_analysis
Troubleshooting
Recording:
Problem: You are unable to record any changes in electric potentials.
Solutions:
1. If you do not detect a signal in the wounded leaf:
a. Use a lighter to burn the leaf from the bottom. This method is the most reliable trigger of SWPs and serves as a positive control for your setup.
b. Check with aOpen asset ↗jucbca/SWP-data_analysislines:260-334Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
How robust three-dimension (3D) organ shape emerges during morphogenesis is a fundamental question in biology. Addressing this question requires a comprehensive quantification of organ geometry in 3D. To tackle these issues, we considered the sepal of Arabidopsis as a model. Using a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways. The results of a Principal Component Analysis reveal sepal curvature as an important parameter accounting for variations in sepal morphology within genotypes. Unexpectedly, despite genetic homogeneity of the wild-type plants and reproducible culture conditions, we found a significant level of variability in sepal morphology. Our data also show that sepal shape from wild-type plants is more robust (less variable) than sepal size, hinting to a possible selective pressure on shape parameters.
Why it matches plant phenotyping methods3D萼片形態を復元・定量する独自パイプラインが研究の中心であり、器官形状・曲率という植物表現型を解析している。
abstractUsing a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways.
Reproduction assets foundThe paper's data availability statement explicitly deposits the phenotyping assets (original sepal images, segmented images, and sepal measurements) on recherche.data.gouv.fr under DOI 10.57745/LTTTBK. No custom code was developed; analysis used standard Python libraries, so no code asset qualifies.Dataset · publicAll the data that were used for the statistical analysis in this study (original images, segmented images, as well as sepal measurements) are openly available as a published dataset from the French national data platform recherche.data.gouv.fr at https://doi.org/10.57745/LTTTBK . No custom code or scripts were developed for the analysisOpen asset ↗recherche.data.gouv.fr · 10.57745/LTTTBKlines:128-315Code / 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 confirmedEurope PMC · checked 6 Sept 2026
Ontologies are highly prevalent in biology and medicine and are always evolving. Annotating biological text, such as observed phenotype descriptions, with ontology terms is a challenging and tedious task. The process of annotation requires a contextual understanding of the input text and of the ontological terms available. While text-mining tools are available to assist, they are largely based on directly matching words and phrases and so lack understanding of the meaning of the query item and of the ontology term labels. Large Language Models (LLMs), however, excel at tasks that require semantic understanding of input text and therefore may provide an improvement for the auto-annotation of text with ontological terms. Here we describe a series of workflows incorporating OpenAI GPT's capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms, aiming for results that resemble manually curated annotations. These workflows make use of an LLM to intelligently parse phenotypes into short concepts, followed by finding appropriate ontology terms via embedding vector similarity or via Retrieval-Augmented Generation (RAG). The RAG model is a state-of-the-art approach that augments conversational prompts to the LLM with context-specific data to empower it beyond its pre-trained parameter space. We show that the RAG produces the most accurate automated annotations that are often highly similar or identical to expert-curated annotations.
Why it matches plant phenotyping methods植物の表現型観察記述をオントロジー語に自動アノテーションするLLM/RAGワークフローの開発・精度評価が中心であり、再利用可能な計算ツールとして植物表現型データを処理する。
abstractHere we describe a series of workflows incorporating OpenAI GPT's capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms
Reproduction assets foundThe paper's phenotype descriptors, gold-standard annotations, LLM-parsed concepts, auto-annotations, and evaluation scores are publicly available as supplementary files, and the authors' analysis code (DE, DCE, DCRAG workflows) is publicly deposited on GitHub. A specific AraPheno trait (#278) used as an input example/`Code · publicCode to execute the DE, DCE and DCRAG workflows is available at https://github.com/dkainer/LLMannotator .Open asset ↗github.com/dkainer/LLMannotatorlines:229-257Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The likelihood that a host will be susceptible to infection is influenced by the interaction of diverse biotic and abiotic factors. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between the host, the environment, and biotic factors such as the microbiome. For example, pathogen infection success is known to vary by host genotype, bacterial strain identity and dose, and pathogen dose. Elucidating the interactions between these factors in vivo has been challenging because testing combinations of these variables quickly becomes experimentally intractable. Here, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health. Using an Arabidopsis-Pseudomonas host-microbe model, we found that host genotype and bacterial strain order of arrival predict host susceptibility to infection, but pathogen and non-pathogenic bacterial dose can overwhelm these effects. Host susceptibility to infection is therefore driven by complex interactions between multiple factors that can both mask and compensate for each other. However, regardless of host or inoculation conditions, the ratio of pathogen to non-pathogen emerged as a consistent correlate of disease. Our results demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host susceptibility to disease. Increasing the scale at which we can screen drivers of disease, such as microbiome community structure, will facilitate both disease predictions and treatments for medicine and agricultural applications.
Why it matches plant phenotyping methodsMYCroplantersという植物の高スループット生育・感染評価システム自体を開発・記述し、宿主の感染感受性（病害状態）を測定する方法が研究の中心であるため。
abstractHere, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits paper-specific assets: raw plant scan images and 3D printing files in Dryad, and processed data plus analysis/figure-generation code on GitHub. Both are public, actionable, and directly reproduce this paper's plant-phenotyping measurements and analysis.Dataset · publicThe datasets (raw images and 3D printing files) supporting the conclusions of this article are available in the Dryad repository ( https://doi.org/10.5061/dryad.w9ghx3fxd ).Open asset ↗Dryad · 10.5061/dryad.w9ghx3fxdlines:199-208Code · publicProcessed data, code for analysis and figure generation, and a copy of 3D printing files can be found on our GitHub ( https://github.com/mech3132/mycroplanter ).Open asset ↗GitHub · mech3132/mycroplanterlines:199-208Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant-pathogenic bacteria colonise their hosts using various strategies, exploiting both natural openings and wounds in leaves and roots. The vascular pathogen Xanthomonas campestris pv. campestris (Xcc) enters its host through hydathodes, organs at the leaf margin involved in guttation. Subsequently, Xcc breaches the hydathode-xylem barrier and progresses into the xylem vessels causing systemic disease. To elucidate the mechanisms that underpin the different stages of an Xcc infection, a need exists to image bacterial progression in planta in a non-invasive manner. Here, we describe a phenotyping setup and Python image analysis pipeline for capturing 16 independent Xcc infections in Arabidopsis thaliana plants in parallel over time. The setup combines an RGB camera for imaging disease symptoms and an ultrasensitive CCD camera for monitoring bacterial progression inside leaves using bioluminescence. The method reliably quantified bacterial growth in planta for two bacterial species, that is, vascular Xcc and the mesophyll pathogen Pseudomonas syringae pv. tomato (Pst). The camera resolution allowed Xcc imaging already in the hydathodes, yielding reproducible data for the first stages prior to the systemic infection. Data obtained through the image analysis pipeline was robust and validated findings from other bioluminescence imaging methods, while requiring fewer samples. Moreover, bioluminescence was reliably detected within 5 min, offering a significant time advantage over our previously reported method with light-sensitive films. Thus, this method is suitable to quantify the resistance level of a large number of Arabidopsis thaliana accessions and mutant lines to different bacterial strains in a non-invasive manner for phenotypic screenings.
Why it matches plant phenotyping methods植物感染を非侵襲的に画像化・定量するフェノタイピング装置とPython解析パイプラインを開発し、複数の細菌感染で検証しているため、方法が中心的です。
abstractHere, we describe a phenotyping setup and Python image analysis pipeline for capturing 16 independent Xcc infections in Arabidopsis thaliana plants in parallel over time.
Reproduction assets foundThe paper's Python image analysis pipeline (Digital phenotyper) for quantifying bioluminescent bacterial infection in Arabidopsis is explicitly and publicly deposited by the authors on GitHub.Code · publiccsv file and an overlayed image (.png file) of the RGB and CCD image was created for visual inspection. The pipeline features an environment file in which the different parameters can be adjusted to optimise the pipeline for other setups. All available parameters, code and instructions for this pipeline are provided on GitHub ( https://github.com/MolPlantPathology/Digital_phenotyper ).
2.3
Digital Phenotyping Quantifies Disease Severity at Different Stages of Infection
To confirm the validity of our method, we benchmarked our digital phenotyping pipeline against other well‐established methods. To do so, we performed spray inoculations of Xcc8004 Δ xopAC Tn 7:lux on three Arabidopsis genotypeOpen asset ↗MolPlantPathology/Digital_phenotyperlines:101-107Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis. We improved the DeepLabv3+ model, naming it DFMA, and introduced a novel ASPP structure, PSPA-ASPP. On our self-constructed rice seedling dataset, the model achieved a mean Intersection over Union (mIoU) of 81.72%. On publicly available datasets, including Arabidopsis thaliana, Brachypodium distachyon, and Sinapis alba, detection scores reached 87.69%, 91.07%, and 66.44%, respectively, outperforming existing models. The model generates detailed segmentation masks, capturing structures such as the embryonic shoot, axis, and root, while a seedling length measurement algorithm provides precise parameters for component development. This approach offers a comprehensive, automated solution, improving phenotyping analysis efficiency and addressing the challenges of traditional methods.
Why it matches plant phenotyping methods幼苗画像から器官を分割し、幼苗長を自動測定する深層学習ベースのフェノタイピング手法を開発・検証しており、方法が中心的です。
abstractwe propose an efficient deep learning approach to enhance plant seedling phenotyping analysis.
Reproduction assets foundThe paper uses a public Kaggle plant segmentation dataset (Arabidopsis thaliana, Brachypodium distachyon, Sinapis alba) for model validation, which is a paper-specific, publicly actionable asset. The authors' homemade rice seedling dataset and any code/models are not publicly deposited; the data availability statement仅Dataset · publicthis way, a homemade labeled dataset with the file suffix “.json” was obtained. Processed by the program, 115 sets of images were finally obtained. The sample image is shown in
Figure 1B
.
The public dataset was created using the Plant Segmentation Dataset, which was made public on the Kaggle platform by Orsolya Dobos et al. ( https://www.kaggle.com/tivadardanka/plant-segmentation ) in 2019. This dataset contains images of three seedlings, including Arabidopsis thaliana , Brachypodium distachyon , and Sinapis alba . The authors manually placed seedlings of these three plants on the surface of 1% agar plates and collected images using an EPSON PERFECTION V30 scanner. Images were saved in “.tiOpen asset ↗Kaggle · tivadardanka/plant-segmentationlines:45-67Code / 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 confirmedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Abstract Deep learning can revolutionise high-throughput image-based phenotyping by automating the measurement of complex traits, a task that is often labour-intensive, time-consuming, and prone to human error. However, its precision and adaptability in accurately phenotyping organ-level traits, such as fruit morphology, remain to be fully evaluated. Establishing the links between phenotypic and genotypic variation is essential for uncovering the genetic basis of traits and can also provide an orthologous test of pipeline effectiveness. In this study, we assess the efficacy of deep learning for measuring variation in fruit morphology in Arabidopsis using images from a multiparent advanced generation intercross (MAGIC) mapping family. We trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs. Our model achieved strong performance with an average precision of 88.0% for detection and 55.9% for segmentation. Quantitative trait locus analysis of the derived phenotypic metrics of the MAGIC population identified significant loci associated with fruit morphology. This analysis, based on automated phenotyping of 332,194 individual fruits, underscores the capability of deep learning as a robust tool for phenotyping large populations. Our pipeline for quantifying pod morphological traits is scalable and provides high-quality phenotype data, facilitating genetic analysis and gene discovery, as well as advancing crop breeding research.
Why it matches plant phenotyping methods深層学習による果実形態の画像ベース表現型抽出パイプラインを開発し、検出・セグメンテーション性能を評価しており、植物フェノタイピング手法が研究の中心である。
abstractWe trained an instance segmentation model and developed a pipeline to phenotype Arabidopsis fruit morphology, based on the model outputs.
Reproduction assets foundThe paper's phenotyping pipeline (MorphPod/silique-detector), its annotation tool (GIMP Image Annotator), versioned releases, a Software Heritage archive, and DOME-ML registry annotations are publicly available with explicit availability statements. The Zenodo phenotype/genotype dataset is referenced but its URL is notCode · publich University, Aberystwyth SY23 3EE, UK.
John H Doonan,
National Plant Phenomics Centre, IBERS, Aberystwyth University, Aberystwyth SY23 3EE, UK.
Chuan Lu,
Computer Science Department, Aberystwyth University, Aberystwyth SY23 3DB, UK.
Availability of Source Code
MorphPod: Deep learning phenotyping of Arabidopsis fruit morphology
https://github.com/kieranatkins/silique-detector/ [ 65 ]
Operating system: Platform independent
Programming language: Python, R
Other requirements: see public environment file
released under GNU GPL v3
bio.tools: biotools:morphpod
RRID: MorphPod ( RRID:SCR_026174 )
This code has also been archived in Software Heritage [ 66 ].
GIMP Image Annotator (GIÀ): a lightweight Open asset ↗kieranatkins/silique-detectorlines:235-276Code · publicgramming language: Python, R
Other requirements: see public environment file
released under GNU GPL v3
bio.tools: biotools:morphpod
RRID: MorphPod ( RRID:SCR_026174 )
This code has also been archived in Software Heritage [ 66 ].
GIMP Image Annotator (GIÀ): a lightweight GIMP plug-in for computer vision-assisted image annotation
https://github.com/kieranatkins/gimp-image-annotator [ 67 ]
Operating system: Platform independent
Programming language: Python
Other requirements: GIMP
released under GNU GPL v3
bio.tools: biotools:gimp_image_annotator
RRID: gimp_image_annotator ( RRID:SCR_026175 )
Workflow hub: 10.48546/workflowhub.workflow.1229.1 [ 68 ]
Additional Files
Supplementary Fig. S1 . DataOpen asset ↗kieranatkins/gimp-image-annotatorlines:235-276Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Hormonal mechanisms associated with cell elongation play a vital role in the development and growth of plants. Here, we report Nextflow-root (nf-root), a novel best-practice pipeline for deep-learning-based analysis of fluorescence microscopy images of plant root tissue from A. thaliana. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images. This also includes apoplastic pH measurements, which is useful for modeling hormone signaling and cell physiological responses. We show that this nf-core standard-based pipeline successfully automates tissue zone segmentation and is both high-throughput and highly reproducible. In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. We observed a high statistical similarity between the manually generated results and the output of the nf-root.
Why it matches plant phenotyping methods植物根組織の発達ゾーンを画像から自動抽出し、アポプラストpHを測定する再現可能な深層学習パイプラインを開発・検証しており、植物表現型取得が中心である。
abstractThis bioinformatics pipeline performs automatic identification of developmental zones in root tissue images.
Reproduction assets foundThe paper publicly releases the PHDFM fluorescence microscopy image dataset, a test dataset, the trained U-Net^2 segmentation model, the nf-root Nextflow pipeline, the segmentation training module, and the prediction package implementing uncertainty/interpretability, all with explicit availability statements and ZenodoDataset · publicThe PHDFM dataset is available at https://zenodo.org/record/5841376/ .Open asset ↗zenodo · 5841376lines:127-159Dataset · publicthe test dataset for the pipeline ( https://zenodo.org/record/5949352/ ) are publicly available online.Open asset ↗zenodo · 5949352lines:127-159Code · publicsoftware and hardware information are also available in the module ( https://github.com/qbic-pipelines/root-tissue-segmentation-core ). We used version 1.0.1 of the segmentation training module.Open asset ↗github · qbic-pipelines/root-tissue-segmentation-corelines:106-126Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology.
Why it matches plant phenotyping methodsNIRスペクトルから植物形質を予測するソフトウェアおよびデータベースを開発しており、形質取得・推定手法が研究の中心である。
abstractWe developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits
Reproduction assets foundThe paper's NIRS spectra and 81 trait measurements for 5,325 Arabidopsis thaliana individuals are publicly hosted in the authors' NIRSpredict Shiny application, and the application's R code is deposited on the authors' GitHub repository (AxelVaillant/NirsPredict), as stated in the Data availability section. Both are直接,Dataset · publicTrait values are publicly available in the NIRSpredict database atOpen asset ↗pdf-page:10 lines:1-65Code · publicThe R code of the application is available on a GitHubOpen asset ↗pdf-page:10 lines:1-65Code / 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
Temperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed for in silico identification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.
Why it matches plant phenotyping methods温度依存性の酵素制約代謝モデルを開発し、成長関連表現型の予測と変異体による検証を行っており、植物表現型推定手法が研究の中心です。
abstractHere, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures.
Reproduction assets foundThe paper's computational analysis code (simulations and statistics), the machine-learning tool for protein thermostability optima, and the refined AraCore metabolic model are all explicitly deposited in public GitHub repositories by the authors. No standalone public phenotype dataset URL is given; compiled RGR and CO2Code · public30
Declaration of interests
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The authors declare no competing interests.
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Code availability
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Custom computer code that was developed for simulations and statistical analyses in this
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study are publicly available at https://github.com/pwendering/AraTModel. The code
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developed for machine learning of protein thermostability optima was deposited in a
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separate repository, which is publicly available at https://github.com/pwendering/topt-689
predict. The refined AraCore model can be retrieved from
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https://github.com/pwendering/ArabidopsisCoreModel. All remaining data areOpen asset ↗pwendering/AraTModelpdf-raw-page:30 lines:1-31Code · public686
study are publicly available at https://github.com/pwendering/AraTModel. The code
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developed for machine learning of protein thermostability optima was deposited in a
688
separate repository, which is publicly available at https://github.com/pwendering/topt-689
predict. The refined AraCore model can be retrieved from
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https://github.com/pwendering/ArabidopsisCoreModel. All remaining data are provided
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with this manuscript and supplementary material.
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693
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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 foOpen asset ↗pwendering/ArabidopsisCoreModelpdf-raw-page:30 lines:1-31Code / 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 · checked 14 Sept 2026
Bioassay with an insect herbivore is a common approach to studying plant defense. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analysing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatio-temporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record the time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analysing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of the feeding behavior of insects. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only a photo scanner and a laptop computer and does not require any specialized equipment. The analysis software is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of the feeding behavior of insects on leaves, facilitating understanding of plant defense mechanisms.
Why it matches plant phenotyping methods葉の摂食痕をスキャン画像と解析パイプラインで定量し、摂食イベントごとの消費葉面積や時空間的な摂食部位を抽出する方法が研究の中心であるため、植物の損傷状態を測定するフェノタイピング手法として採用する。
abstractHere, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves.
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 software and documentation for the analysis pipeline is available online ( https://github.com/nsotta/feeding-mark-analysis ).Open asset ↗nsotta/feeding-mark-analysis · nsotta/feeding-mark-analysislines:91-152Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.
Why it matches plant phenotyping methods植物画像を生成し、そこから植物個体別形質を推定する二段階の画像ベース表現型解析フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained.
Reproduction assets foundThe paper's authors explicitly state that source code and links to the phenotyping datasets (Arabidopsis, GrowliFlower, MixedCrop) are publicly available in their GitHub repository, which implements the multi-conditional CWGAN crop growth simulation and growth estimation framework.Code · publicSource code and links to the datasets are publicly available at https://github.com/luked12/crop-growth-cgan .Open asset ↗luked12/crop-growth-cganlines:216-253Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
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The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
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Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
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details of models’ weights, hyperparameters, training scripts and datasets can be found at
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https://github.com/William-Yao0993/FD_detection.128
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Model evaluation
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Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
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calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
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F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLeafTrackingArchitecture / morphology / geometryGrowth / development / phenology
Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semiautomated image analysis pipeline. Using this setup, we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.
Why it matches plant phenotyping methods低コスト画像計測プラットフォームとオープンソース解析パイプラインを開発し、葉の伸長・運動・角度を高時間分解能で抽出する方法が研究の中心である。
abstractHere, we present a modular and low-cost imaging setup, based on small Raspberry Pi computers that can track leaf movements and elongation growth with high temporal resolution.
Reproduction assets foundThe paper's authors publicly deposited their Python image-analysis scripts and R analysis/statistical scripts at the Pierik-Lab GitHub organization, with explicit open-source availability language. The underlying phenotype data, however, is only available on request.Code · publicThe full, open-source scripts with descriptions per step are accessible at https://github.com/Pierik-Lab .Open asset ↗Pierik-Lablines:90-103Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software ( https://github.com/MolPlantPathology/ScAnalyzer ) has the potential to standardize the analysis of disease assays between different groups.
Why it matches plant phenotyping methods植物葉の病徴と病原体拡散を画像処理で定量化するツールおよび解析パイプラインが研究の中心であり、植物の病害状態を直接推定するため。
abstractimage processing provides a more accurate and objective quantification of plant disease symptoms.
Reproduction assets foundThe paper's ScAnalyzer Python/R analysis pipeline and the raw leaf/luminescence images are publicly available on the authors' GitHub repository, along with the printable leaf-grid sheet used for phenotyping.Code · publicThe code is available on GitHub ( https://github.com/MolPlantPathology/ScAnalyzer ).Open asset ↗MolPlantPathology/ScAnalyzerlines:124-131Code / 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
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 confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis’s 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model’s classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model’s performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model’s generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.
Why it matches plant phenotyping methods深層学習による植物画像からの系統識別・生育状態推定を開発し、時間間隔データで検証しており、植物表現型取得・推定手法が研究の中心である。
abstractThis paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle).
Reproduction assets foundThe paper's plant-phenotyping analysis is built on the Arabidopsis thaliana time-series image dataset from Namin et al., which the authors explicitly state is publicly available for download. No author analysis code or trained model is shared.Dataset · publicData is publicly available at: http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zip (accessed on 16 April 2024).Open asset ↗phenocam.anu.edu.au · 2017-Namin-et-al-DeepPheno.ziplines:226-239Code / 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
The periderm is a vital protective tissue found in the roots, stems, and woody elements of diverse plant species. It plays an important function in these plants by assuming the role of the epidermis as the outermost layer. Despite its critical role for protecting plants from environmental stresses and pathogens, research on root periderm development has been limited due to its late formation during root development, its presence only in mature root regions, and its impermeability. One of the most straightforward measurements for comparing periderm formation between different genotypes and treatments is periderm (phellem) length. We have developed PAT (Periderm Assessment Toolkit), a high-throughput user-friendly pipeline that integrates an efficient staining protocol, automated imaging, and a deep-learning-based image analysis approach to accurately detect and measure periderm length in the roots of Arabidopsis thaliana . The reliability and reproducibility of our method was evaluated using a diverse set of 20 Arabidopsis natural accessions. Our automated measurements exhibited a strong correlation with human-expert-generated measurements, achieving a 94% efficiency in periderm length quantification. This robust PAT pipeline streamlines large-scale periderm measurements, thereby being able to facilitate comprehensive genetic studies and screens. Although PAT proves highly effective with automated digital microscopes in Arabidopsis roots, its application may pose challenges with nonautomated microscopy. Although the workflow and principles could be adapted for other plant species, additional optimization would be necessary. While we show that periderm length can be used to distinguish a mutant impaired in periderm development from wild type, we also find it is a plastic trait. Therefore, care must be taken to include sufficient repeats and controls, to minimize variation, and to ensure comparability of periderm length measurements between different genotypes and growth conditions.
Why it matches plant phenotyping methods植物根の表現型（周皮長）を自動画像取得・深層学習解析で定量するパイプラインを開発し、専門家測定との相関で信頼性と再現性を検証しており、方法が研究の中心である。
abstractWe have developed PAT (Periderm Assessment Toolkit), a high-throughput user-friendly pipeline that integrates an efficient staining protocol, automated imaging, and a deep-learning-based image analysis approach to accurately detect and measure periderm length in the roots of Arabidopsis thaliana .
Reproduction assets foundThe authors publicly release the PAT pipeline (analysis code/scripts) and a test dataset of Col-0 and wox4-1 TIFF microscopy images via their GitHub repository. Full-resolution TIFF images of the 20 natural accessions are only available upon request (request_only, not listed as an allowed URL).Dataset · publicroved the manuscript.
Competing interests: W.B. is a cofounder of Cquesta, a company that works on crop root growth and carbon sequestration.
Data Availability
All raw data and datasets have been included in the Supplementary Materials. The PAT pipeline and its associated code are accessible via the following GitHub repository: https://github.com/Salk-Harnessing-Plants-Initiative/PAT-Pipeline-for-Analysis-of-Periderm . Additionally, the test dataset comprising Col-0 and wox4-1 TIFF images is available on the same GitHub repository. Full-resolution TIFF images corresponding to the natural accessions (Table 1 ) can be obtained from the corresponding author upon request.
Supplementary MaterialsOpen asset ↗Salk-Harnessing-Plants-Initiative/PAT-Pipeline-for-Analysis-of-Peridermlines:391-421Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS: Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS: AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
Why it matches plant phenotyping methods植物病徴を画像から定量化するソフトウェアの開発が研究の中心であり、苗の色・形態パラメータを自動抽出して病害症状を評価する。
abstractIn this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.
Reproduction assets foundThe paper's authors publicly released the AraDQ software package (system code, pretrained deep learning models, installation manual) and the datasets generated and analyzed in the study, including the case-study image files, in their GitHub repository.Code · publicThe portable software, system code, and installation manual are available at https://github.com/kist-smartfarm/AraDQ .Open asset ↗kist-smartfarm/AraDQlines:94-101Dataset · publicThe image files used in this case study are provided in the released dataset on GitHub.Open asset ↗lines:137-212Dataset · publicThe AraDQ software package, including the installation manual, and the datasets generated and analyzed during the current study are available in the GitHub repository at https://github.com/kist-smartfarm/AraDQ .Open asset ↗kist-smartfarm/AraDQlines:137-212Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely used in plant biology to determine growth rates and other phenotypic characters, with segmentation into foreground and background being a primary challenge. Statistical clustering and learning approaches can reduce the need for user input into this process, though these are computationally demanding, can generalise poorly and are not intuitive to end users. As such, simple strategies that rely on the definition of a range of target colors are still frequently adopted. These are limited by the geometries in color space that are implicit to their definition; i.e. thresholds define cuboid volumes and selected colors with a radius define spheroid volumes. A more comprehensive specification of target color is a hull, in color space, enclosing the set of colors in the image foreground. We developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors. We implemented convex hulls and then alpha-hulls, i.e. a limit applied to hull edge length, to support concave surfaces and disjoint color volumes. AlGrow also provides automated annotation by detecting internal circular markers, such as pot margins, and applies relative indexes to support movement. Analysis of publicly available Arabidopsis image series and metadata demonstrated effective automated annotation and mean Dice coefficients of >0.95 following training on only the first and last images in each series. AlGrow provides both graphical and command line interfaces and is released free and open-source with compiled binaries for the major operating systems.
Why it matches plant phenotyping methods植物画像から面積・成長などの表現型を抽出する画像解析ソフトウェアを開発し、Arabidopsis画像系列で性能検証しているため、方法が中心的である。
abstractWe developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors.
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。Dataset · public163 obtained from https://www.plant-phenotyping.org/datasets-home, as these are also able to demonstrateOpen asset ↗pdf-page:4 lines:1-55Code / 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 confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ArabidopsisMesh / voxelMicroscopyFlowerSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
Background 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 [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. 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. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis. Results For live imaging sepals across all tissue layers at early stages of development, 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 [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. Conclusion We provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.
Why it matches plant phenotyping methodsライブイメージング、画像処理、細胞セグメンテーションと追跡を統合した、萼片の成長・形態解析パイプラインの最適化が中心であり、植物表現型取得法に該当する。
abstractwe provide an optimized methodology for live imaging sepals and subsequent image processing
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。Dataset · publicThe images of the WT flowers, as well as the final edited images can be accessed at https://doi.org/10.17605/OSF.IO/UMW9B . The images shown in this manuscript correspond to WT replicate 2.Open asset ↗OSF.IO/UMW9Blines:137-166Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background: Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in an quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, it provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results: Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from diseased (yellow) leaf area. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar way as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion: Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.
Why it matches plant phenotyping methods植物葉の病徴面積と病原体拡散を画像解析で自動定量するソフトウェアを開発・提示しており、植物表現型の取得・抽出が研究の中心である。
abstractimage processing provides a more accurate and objective quantification of plant disease symptoms
Reproduction assets foundThe preprint states that all code and raw images generated during the study are available at the authors' GitHub repository (https://github.com/MolPlantPathology/ScAnalyzer), which contains the ScAnalyzer Python/R analysis pipeline; the repository also hosts the printable leaf-sampling grid (grid.pdf) used as the phenpCode · publicThe code is available on GitHub ( https://github.com/MolPlantPathology/ScAnalyzer ).Open asset ↗MolPlantPathology/ScAnalyzerlines:85-109Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
BACKGROUND: The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic. RESULTS: We tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background. CONCLUSION: The method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.
Why it matches plant phenotyping methodsCNNによる葉のセグメンテーション・追跡手法を開発し、検出・追跡精度を検証した植物フェノタイピング研究である。
abstractHere, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants.
Reproduction assets foundThe paper's authors explicitly state that the full project library (leaf detection/tracking code and dataset) is available as a public GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicof the PyTorch-Vision GitHub repository was used for the model training, specifically the reference scripts found in the folder detection. These scripts are included in the project GitHub under the modelTraining folder without relevant modifications. The full library of this project is available as a public repository at GitHub https://github.com/Fedjurrui/Leaf-Tracking .
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
No competing interests declared.
References
1.Open asset ↗Fedjurrui/Leaf-Trackinglines:174-269Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisTissueClassificationCalibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
Transcriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly available Arabidopsis thaliana data with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing. Author Summary Large datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly available Arabidopsis thaliana RNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.
Why it matches plant phenotyping methodsRNA-seqデータから植物の年齢・組織型を予測する機械学習について、必要サンプル数と正規化法を大規模Arabidopsisデータで評価しており、表現型推定手法の検証が中心である。
abstractWe use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required.
Reproduction assets foundThe paper's normalized gene expression matrices, curated phenotype annotation datasets, and intermediary files are publicly deposited on Zenodo, and all analysis code is publicly available on GitLab. These directly reproduce the paper's plant-phenotyping measurements (Arabidopsis age/tissue annotations) and modeling/MLDataset · public(NoNo). TMM normalization [24,29] and MRN normalization [25] were performed using
the Python “conorm” package 1.2.0 [30]. TPM and NoNo normalization values were an
output of Kallisto [27]. How these normalizations impacted sample count is visualized as
S2 Figure. We have made these GEMs publicly available on Zenodo at the link
https://zenodo.org/records/10183151
Sample Phenotype Annotations Pre-Processing
Sample phenotype annotations were retrieved from the NCBI BioProject database
[16,17] using BioSampleParser which was slightly modified to check for successful data
retrieval [31]. Phenotype annotations were retrieved for 48696 NCBI BioSamples,
representing data from 2643 BioProjects.Open asset ↗Zenodopdf-raw-page:9 lines:1-55Dataset · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. BostanciOpen asset ↗Zenodo · 10.5281/zenodo.10183150pdf-raw-page:43 lines:1-51Code · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning
Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Open asset ↗GitLabpdf-raw-page:43 lines:1-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The analysis of telomere length is an important component of many studies aiming to characterize the role of telomere maintenance mechanisms in cellular lifespan, disease, or in general chromosome protection and DNA replication pathways. Several powerful methods to accurately measure the telomere length from Southern blots have been developed, but their utility for large-scale genomic studies has not been previously evaluated. Here, we performed a comparative analysis of two recently developed programs, TeloTool and WALTER, for the extraction of mean telomere length values from Southern blots. Using both software packages, we measured the telomere length in two extensive experimental datasets for the model plant Arabidopsis thaliana , consisting of 537 natural accessions and 65 T-DNA (transfer DNA for insertion mutagenesis) mutant lines in the reference Columbia (Col-0) genotype background. We report that TeloTool substantially overestimates the telomere length in comparison to WALTER, especially for values over 4500 bp. Importantly, the TeloTool- and WALTER-calculated telomere length values correlate the most in the 2100-3500 bp range, suggesting that telomeres in this size interval can be estimated by both programs equally well. We further show that genome-wide association studies using datasets from both telomere length analysis tools can detect the most significant SNP candidates equally well. However, GWAS analysis with the WALTER dataset consistently detects fewer significant SNPs than analysis with the TeloTool dataset, regardless of the GWAS method used. These results imply that the telomere length data generated by WALTER may represent a more stringent approach to GWAS and SNP selection for the downstream molecular screening of candidate genes. Overall, our work reveals the unanticipated impact of the telomere length analysis method on the outcomes of large-scale genomic screens.
Why it matches plant phenotyping methods植物のテロメア長という形質をSouthern blotから抽出する2つの解析ツールを大規模データで比較・評価しており、測定法の技術的妥当性が研究の中心である。
abstractHere, we performed a comparative analysis of two recently developed programs, TeloTool and WALTER, for the extraction of mean telomere length values from Southern blots.
Reproduction assets foundThe paper's plant-phenotyping measurements (TeloTool- and WALTER-derived telomere length datasets for 537 Arabidopsis accessions and 65 T-DNA mutant lines, used for GWAS) are stated to be contained in Supplemental Data S1 and S2, publicly downloadable from the MDPI supplement URL listed in the allowed URLs. No author-cSupplement · publicvant for telomere biology studies and the functional analysis of candidate genes in other systems, including large-scale genomic screens in other models and in humans.
Acknowledgments
We thank Jae Choi (University of Kansas) for the insightful discussions.
Supplementary Materials
The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms242417194/s1 .
Click here for additional data file.
Author ContributionsOpen asset ↗lines:273-287Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The quantification of stomatal pore size has long been a fundamental approach to understand the physiological response of plants in the context of environmental adaptation. Automation of such methodologies not only alleviates human labor and bias but also realizes new experimental research methods through massive analysis. Here, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from bright-field microscopy images containing mesophyll tissue as noisy backgrounds. By combining a You Only Look Once X-based stomatal detection submodule and a U-Net-based pore segmentation submodule, we achieved a mean average precision with an intersection of union (IoU) threshold of 50% value of 0.875 (stomata detection performance) and an IoU of 0.745 (pore segmentation performance) against images of leaf discs taken with a bright-field microscope. Moreover, we designed a portable imaging device that allows easy acquisition of stomatal images from detached/undetached intact leaves on-site. We demonstrated that this device in combination with fine-tuned models of the pipeline we generated here provides robust measurements that can substitute for manual measurement of stomatal responses against pathogen inoculation. Utilization of our hardware and pipeline for automated stomatal aperture measurements is expected to accelerate research on stomatal biology of model dicots.
Why it matches plant phenotyping methods葉画像から気孔開度を自動抽出する画像解析パイプラインと携帯型撮像装置を開発・性能評価しており、植物表現型取得が中心である。
abstractwe present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' ONNX model weights and pipeline code, plus masked/unmasked test images, on a public GitHub repository and Zenodo (DOI 10.5281/zenodo.7549843). These are paper-specific, publicly actionable assets for the stomatal aperture phenotyping pipeline. The YCode · publicPlant Cell Physiol. 00(00): 1–10 (2023) doi:https://doi.org/10.1093/pcp/pcad018
Supplementary Data
Supplementary data are available at PCP online.
Data Availability
The model weights and codes in ONNX format to execute
the Arabidopsis stomata quantification pipeline and mask
and unmasked test data are available at https://github.com/phytometrics/arabidopsis_leaf_stomata_quantification.Model weights and the test images are also available on Zen-
odo with the following DOI: https://doi.org/10.5281/zenodo.7549843. The software used to process image streams from the
portable device is under development at https://github.com/phytometrics/cvgui_linux.Funding
Grant-in-Aid for Transformative ResearcOpen asset ↗phytometrics/arabidopsis_leaf_stomata_quantificationpdf-raw-page:9 lines:1-84Dataset · publicData Availability
The model weights and codes in ONNX format to execute
the Arabidopsis stomata quantification pipeline and mask
and unmasked test data are available at https://github.com/phytometrics/arabidopsis_leaf_stomata_quantification.Model weights and the test images are also available on Zen-
odo with the following DOI: https://doi.org/10.5281/zenodo.7549843. The software used to process image streams from the
portable device is under development at https://github.com/phytometrics/cvgui_linux.Funding
Grant-in-Aid for Transformative Research Areas (21H05151 and
21H05149 to A.M. and 21H05152 to Y.T.), Grant-in-Aid for Sci-
entific Research (B) (19H02960 to A. M.), and Grant-in-Aid foOpen asset ↗10.5281/zenodo.7549843pdf-raw-page:9 lines:1-84Code / 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
ArabidopsisLaboratory / benchtopRGB / grayscaleRootTrackingRoot system architecture
Plant roots elongate when cells produced in the apical meristem enter a transient period of rapid expansion. To measure the dynamic process of root cell expansion in the elongation zone, we captured digital images of growing Arabidopsis roots with horizontal microscopes and analyzed them with a custom image analysis program (PatchTrack) designed to track the growth-driven displacement of many closely spaced image patches. Fitting a flexible logistics equation to patch velocities plotted versus position along the root axis produced the length of the elongation zone (mm), peak relative elemental growth rate (% h -1 ), the axial position of the peak (mm from the tip), and average root elongation rate (mm h -1 ). For a wild-type root, the average values of these kinematic traits were 0.52 mm, 23.7% h -1 , 0.35 mm, and 0.1 mm h -1 , respectively. We used the platform to determine the kinematic phenotypes of auxin transport mutants. The results support a model in which the PIN2 auxin transporter creates an area of expansion-suppressing, supraoptimal auxin concentration that ends 0.1 mm from the quiescent center (QC), and that ABCB4 and ABCB19 auxin transporters maintain expansion-limiting suboptimal auxin levels beginning approximately 0.5 mm from the QC. This study shows that PatchTrack can quantify dynamic root phenotypes in kinematic terms.
Why it matches plant phenotyping methodsPatchTrackによる画像解析で根の動的な伸長・細胞伸長形質を抽出する手法とプラットフォームを開発・実証しており、表現型取得が研究の中心です。
abstractanalyzed them with a custom image analysis program (PatchTrack) designed to track the growth-driven displacement of many closely spaced image patches.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' PatchTrack image-analysis code (used to produce the kinematic phenotyping measurements) on a public GitHub repository. No phenotype dataset or image deposit is stated.Code · publicThe computer code for PatchTrack is available at https://github.com/phytoMorph/phytoMorph_kinematics .Open asset ↗phytoMorph/phytoMorph_kinematicslines:96-111Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".
Why it matches plant phenotyping methods葉色画像から個体を自動検出し、深層学習で葉色およびアントシアニン含量を推定する撮像・解析手法を開発し、大規模フィールドデータで適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically.
Reproduction assets foundThe paper deposits its field time-series plant images and annotation/labeling data in two Dryad repositories, and the PlantServation demo set (scripts plus demo data) on Zenodo. All are paper-specific, public, and actionable.Dataset · publicThe time-series image data for the Swiss site generated in this study have been deposited in a Dryad repository [ https://doi.org/10.5061/dryad.1g1jwsv11 ] 93 .Open asset ↗Dryad · 10.5061/dryad.1g1jwsv11lines:200-258Dataset · publicThe time-series image data for Japanese site generated in this study as well as the labeling data for image analysis used in this study are available in a Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Dryad · 10.5061/dryad.h70rxwdnklines:200-258Code · publicThe PlantServation demo set (ca. 600 MB) including scripts and demo data for PlantServation software is available at Zenodo [ https://zenodo.org/record/7321725 ] 95 accessible via Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Zenodo · 7321725lines:200-258Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 10 Sept 2026
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
Why it matches plant phenotyping methodsOPEN leafは、葉単位の動的表現型を取得・定量するオープンソース撮像プラットフォームと画像処理パイプラインの設計・実装を主題としており、植物フェノタイピング手法が中心である。
abstractHere we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.
Reproduction assets foundThe paper's SMART image-analysis pipeline (used for all rosette and leaf-specific phenotyping measurements in this study) is explicitly stated to be publicly available as source code on GitHub and as a prepackaged Docker container, with authors' URLs given in the text.Code · public161 pipeline used are available as source code on GitHub (https://github.com/Computational-Plant-Open asset ↗Computational-Plant-pdf-page:6 lines:1-34Code / 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 confirmedEurope PMC · checked 7 Sept 2026
Abstract Stomata act as a pathway for air and water vapor during respiration, transpiration and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high throughput stoma is a key issue. However, current existing methods usually suffer from detection error or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired in a destructive way, and the maize stomatal data set acquired in a nondestructive way, enabling one-stop automatic collection of phenotypic such as the location, density, length and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. And the experimental results showed that the prediction results of the method are consistent with those of manual labeled. The test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).
Why it matches plant phenotyping methods気孔の検出と開度・密度・寸法などの表現型を自動抽出する画像解析手法を開発し、複数作物で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。
abstractThe proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time and intelligent phenotype analysis of stomata and apertures.
Reproduction assets foundThe paper explicitly states that the test sets, system code, and usage instructions are publicly available at the authors' GitHub repository (https://github.com/AITAhenu/RotatedStomataNet). This is a paper-specific asset: the RotatedStomataNet system code for rotated object detection of stomata and apertures, together,Code · publicThe test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).Open asset ↗AITAhenu/RotatedStomataNetpdf-page:3 lines:1-49Code / 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 confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 14 Sept 2026
Abstract Nondestructive plant phenotyping is fundamental for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-through phenotyping facilities can further our understanding of plant development and stress responses, their high costs significantly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration. We paired these devices with a suite of computational pipelines for integrated and straightforward data analysis. We validated the suitability of our system for large screens by evaluating a cowpea diversity panel for responses to drought stress. The observed natural variation was subsequently used for Genome-Wide Association Study, where we identified nine genetic loci that putatively contribute to cowpea drought resilience during early vegetative development. We validated the homologs of the identified candidate genes in Arabidopsis using available mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of species.
Why it matches plant phenotyping methods低コストの画像・重量ベース装置と計算パイプラインを開発し、植物成長・蒸発散を測定するフェノタイピングシステムとして検証しており、手法が研究の中心である。
abstractwe developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration.
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 · public142 how to build and program the device can be found at https://github.com/ok84-star/AAWSMO. DetailsOpen asset ↗ok84-star/AAWSMOpdf-page:6 lines:1-42Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognized as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst nematode and root-knot nematode tolerance limits in A. thaliana through classical modelling approaches. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable a new mechanistic understanding of tolerance to below-ground biotic stress.
Why it matches plant phenotyping methods根圏線虫感染による植物の耐性を定量化するため、画像による緑色キャノピー面積の測定と、960個体を同時測定する高スループット表現型解析プラットフォームを開発しており、表現型取得法が研究の中心である。
abstractThrough imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe paper's authors publicly deposited the full plant image dataset (green canopy phenotyping pictures) on figshare and the analysis code/model (SYLM and R growth analysis scripts) on a WUR GitLab repository, both explicitly linked in the Data availability statement.Dataset · publicAlso, the full picture dataset has been made available at doi: https://doi.org/10.6084/m9.figshare.23518923.v1 .Open asset ↗figshare · 10.6084/m9.figshare.23518923.v1lines:263-263Code · publicUsing these equations, the tolerance limit T SYLM and the minimum yield m were estimated (model and code available via gitlab: https://git.wur.nl/published_papers/willig_2023_camera-setup ).Open asset ↗git.wur.nl · published_papers/willig_2023_camera-setuplines:53-66Code / dataset availability confirmedCrossref · checked 8 Sept 2026
AppleArabidopsisLaboratory / benchtopMultispectral / hyperspectralLeafTissueClassificationObject detectionWater status / transpiration
Abstract Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana , and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Why it matches plant phenotyping methods植物組織培養におけるハイパーヒドリシティという植物状態を、分光計測と機械学習で自動検出・分類する手法を開発し、別のハイパースペクトル画像取得で妥当性検証しているため。
abstractThe objective of this study was to characterize optical features to allow an automated detection of HH.
Reproduction assets foundThe paper's RGB image dataset of hyperhydric in vitro explants (used for CNN-based HH detection) is publicly available on Roboflow, explicitly stated in the Data availability section and cited as Bethge (2023). Spectral datasets and trained spectral classifier are only available on request.Dataset · publicRGB image dataset analysed during the current study available in the Bethge ( 2023 ) repository, [ https://universe.roboflow.com/hains/hh-detection-in-vitro/dataset/8 ].Open asset ↗Roboflow · hh-detection-in-vitrolines:203-234Code / 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 confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognised as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana , since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst- and root-knot nematode tolerance limits in A. thaliana through classical modelling of tolerance limits. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable further studies into a mechanistic understanding of tolerance to below-ground biotic stress. Highlight The mechanisms of tolerance to root-parasitic nematodes remain unknown. We developed a high-throughput phenotyping system that enables unravelling the underlying mechanisms of tolerance to nematodes.
Why it matches plant phenotyping methods線虫耐性を評価するためのキャノピー画像計測と高スループット表現型解析プラットフォームの開発が研究の中心である。
abstractThrough imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe authors state that custom R scripts and functions used to analyse the high-throughput green canopy area growth data are publicly available via their GitLab repository at git.wur.nl/published_papers/willig_2023_camera-setup, and the data availability statement points to the same repository. The protocols.io link is Code · publiclimits (T) and relative minimum yield (m) were estimated for all
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measurements.
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Plant growth analysis using the high-throughput phenotyping platform
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To analyse the growth data of the plants obtained from the high-throughput platform, custom scripts and
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functions were written in “R” (available via gitlab:
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https://git.wur.nl/published_papers/willig_2023_camera-setup). For analysis we used the median daily
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leaf area (cm2), which was calculated by taking the median leaf area of the daily measurements (15 per
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day). The data was log2-transformed before analysis for normalization. The rate of growth was
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determined per day per plant by
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𝑅𝑥,𝑡 = log2(𝐴𝑥,𝑡−1 − 𝐴𝑥,𝑡Open asset ↗git.wur.nl/published_papers/willig_2023_camera-setuppdf-raw-page:11 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLeaf2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.
Why it matches plant phenotyping methods静的データから器官の発生時系列と成長軌跡を再構成するモデルベース手法が研究の中心であり、葉の形態形成・成長という植物表現型を推定している。
abstractWe introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data.
Reproduction assets foundThe paper's data availability statement explicitly provides three paper-specific public assets: a new version of the MorphoLeaf phenotyping application, COPASI and R scripts for temporal calibration parameter estimation, and the leaf datasets used in the analysis, each with a public URL.Dataset · publicLeaf datasets are available at https://doi.org/10.15454/BMELNY .Open asset ↗10.15454/BMELNYlines:210-440Code · publicCOPASI and R scripts used to estimate temporal calibration parameters are available at https://doi.org/10.15454/DPFU1T .Open asset ↗10.15454/DPFU1Tlines:210-440Code / 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 · 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 · checked 8 Sept 2026
Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
Why it matches plant phenotyping methods根系感染像の取得プラットフォームと画像解析ソフトウェアを開発し、線虫数・サイズや感染前根面積を測定する手法が研究の中心であるため。
abstractwe describe an open-source, easy to adopt, imaging hardware and trait analysis software method
Reproduction assets foundThe paper's authors publicly release the imaging tower hardware design (STL files) and all ImageJ/Python analysis scripts used for nematode counting, root area quantification, and colour normalization in a GitHub repository, explicitly stated in the Availability of data and materials section.Code · publicAll scripts used in this experiment are available under the following github repository: https://github.com/OlafKranse/A_low_cost_imaging_tower .Open asset ↗OlafKranse/A_low_cost_imaging_towerlines:141-197Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS: We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS: We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
Why it matches plant phenotyping methods根系の静的・動的形質を画像から自動抽出する高スループット手法と解析パイプラインを開発・検証しており、表現型取得法が研究の中心である。
abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking
Reproduction assets foundThe paper's root reconstruction/phenotyping pipeline (RootSystemTracker) is released as open-source code on GitHub with an ImageJ plugin documentation page; an example time-lapse movie of the reconstruction is also available on YouTube. No public dataset of the 1000 time-lapse images or RSML outputs is stated in thesupCode · publicThe architecture reconstruction pipeline is supplied as an ImageJ plugin with online documentation (Plugin page: https://imagej.net/plugins/rootsystemtracker [ 9 ]) and as open-source code on GitHub ( https://github.com/Rocsg/RootSystemTracker ).Open asset ↗Rocsg/RootSystemTrackerlines:208-277Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
Why it matches plant phenotyping methods複数の表現型予測モデルを植物種の実データとシミュレーションで系統比較・評価しており、計算による植物形質推定が研究の中心である。
abstractour aim is to systematically compare 12 different phenotype prediction models
Reproduction assets foundThe paper's authors publicly release their analysis code (easyPheno framework and the phenotype_prediction repository containing simulated phenotypes, hyperparameter optimization results, GWAS results, and figure-generation code), plus the Arabidopsis SNP matrix (figshare) and four AraPheno phenotype datasets used in aCode · publicAll simulated phenotypes, detailed results of the whole hyperparameter optimization, precomputed permutation-based GWAS results, and the code for conducting the simulations and generating all figures can be freely downloaded from our GitHub repository: https://github.com/grimmlab/phenotype_prediction .Open asset ↗grimmlab/phenotype_predictionlines:619-631Dataset · publicThe fully imputed SNP matrix data for Arabidopsis thaliana is publicly available and can be downloaded from https://doi.org/10.6084/m9.figshare.11346893.v1 .Open asset ↗10.6084/m9.figshare.11346893.v1lines:619-631Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
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 14 Sept 2026
Premise The agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping. Methods We developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images. Results The rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28. Discussion These results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.
Why it matches plant phenotyping methodsArabidopsis幼苗のロゼット面積という植物形質を画像から推定するPython画像処理ワークフローを開発・検証しており、表現型取得手法が研究の中心である。
abstractWe developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes.
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 workflows for each image set can be found at https://github.com/massivejords/Agar-plate-leaf-area as Jupyter Notebooks (Kluyver et al., 2016 ), along with the batch analysis Python script used to process the large image set.Open asset ↗massivejords/Agar-plate-leaf-arealines:85-97Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
High temperatures inhibit plant growth. A proposed strategy for improving plant productivity under elevated temperatures is the use of plant growth-promoting rhizobacteria (PGPR). While the effects of PGPR on plant shoots have been extensively explored, roots—particularly their spatial and temporal dynamics—have been hard to study, due to their below-ground nature. Here, we characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II. The platform uses custom-made agar plates, which allow air exchange to occur with the agar medium and enable the shoot to grow outside the compartment. The platform provides light protection to the roots, the exposure of it to the shoots, and the non-invasive phenotyping of both organs. Arabidopsis thaliana, co-cultivated with Paraburkholderia phytofirmans PsJN at elevated and ambient temperatures, showed increased lengths, growth rates, and numbers of roots. However, the magnitude and direction of the growth promotion varied depending on root type, timing, and temperature. The root length and distribution per depth and according to time was also influenced by bacterization and the temperature. The shoot biomass increased at the later stages under ambient temperature in the bacterized plants. The study offers insights into the timing of the tissue-specific, PsJN-induced morphological changes and should facilitate future molecular and biochemical studies on plant–microbe–environment interactions.
Why it matches plant phenotyping methodsGrowScreen-Agar IIという非侵襲的な高解像度フェノタイピング・イメージングプラットフォームを用い、根とシュートの形態を時空間的に測定することが研究の中心的手法です。
abstractwe characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II.
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。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212927/s1 , Figure S1: WinRhizo analyzed root lengths and root and shoot biomass; Figure S2: Root sampling and bacterial colonization confirmation; Figure S3: Sample root images generated by the GrowScreen-Agar II; Figure S4: Agar plates for GrowScreen-Agar II; Figure S5: Magazines for GrowScreen-Agar II; Figure S6: Imaging station of GrowScreen-Agar II; Table S1: Mean values and standard error of different root type morphological traits; Table S2: Mean values and standard error of different root system traits describing distribution and spread.Open asset ↗lines:106-120Code / 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
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis , over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.
Why it matches plant phenotyping methodsロボティクスによる根系画像取得の自動化と画像解析パイプライン開発が中心で、根系構造・成長動態という植物形質を抽出するフェノタイピング基盤を提示している。
abstractwe present the automation of GLO-Roots using robotics and the development of image analysis pipelines
Reproduction assets foundThe paper deposits its root phenotyping imaging data, image analysis pipelines/scripts, RShiny exploration apps, and rhizotron build files on Zenodo, plus robotics software on GitHub — all paper-specific, public, and actionable.Dataset · publicThe raw data is available through Zenodo at https://doi.org/10.5281/zenodo.5709009 .Open asset ↗Zenodo · 10.5281/zenodo.5709009lines:160-163Code · publicImage analysis pipelines and scripts are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708430 .Open asset ↗Zenodo · 10.5281/zenodo.5708430lines:224-389Code · publicGeneral code for software operating robotics available: GitHub: https://github.com/rhizolab/rhizo-server .Open asset ↗GitHub · rhizolab/rhizo-serverlines:224-389Code / 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 · bioRxiv · checked 15 Sept 2026
Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii , replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.
Why it matches plant phenotyping methods植物寄生性線虫感染の画像取得・解析を自動化する低コストの装置とソフトウェアを開発し、線虫数・サイズおよび根面積を測定する手法が中心であるため。
abstractHere, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method
Reproduction assets foundThe paper's ImageJ analysis scripts (root surface area, colored-agar variant, leaf surface count) and a custom Python color-normalization script are explicitly deposited in the authors' public GitHub repository (OlafKranse/A_low_cost_imaging_tower), directly reproducing this paper's phenotyping analysis. No phenotype/тCode · publici.org/10.1101/2022.07.14.500020; this version posted July 15, 2022. The copyright holder for this preprint
(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 available under a CC-BY 4.0 International license.
described in the script
(https://github.com/OlafKranse/A_low_cost_imaging_tower/blob/main/Imaging and
Analyses/automated_root_surface_area.ijm). A slightly adjusted script was used for plates
containing dye (https://github.com/OlafKranse/A_low_cost_imaging_tower/blob/main/Imaging and
Analyses/automated_root_surface_area_colored_agar.ijm). The root surface area for all the images
in the folderOpen asset ↗OlafKranse/A_low_cost_imaging_towerpdf-layout-page:6 lines:1-37Code · publicing and quantifiable traits
Automatic counting was performed on images taken as described above. Depending on the
treatment a different script was used to calculate the number and size of females. Before isolation,
the colour histogram for all images was normalised to the first image in the dataset using a custom
python script (https://github.com/OlafKranse/A_low_cost_imaging_tower/tree/main/Imaging and
Analyses/Normalise colour). The images were then processed in ImageJ for two different nematode
life stages: i) tanned cyst nematodes; ii) female nematodes.Open asset ↗OlafKranse/A_low_cost_imaging_towerpdf-layout-page:6 lines:1-37Code / 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 8 Sept 2026
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
Why it matches plant phenotyping methods植物画像からのセグメンテーションと葉数推定を中核とする深層学習フェノタイピング手法の開発であり、植物形質の自動抽出性能も評価しているため。
abstractwe focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework
Reproduction assets foundThe paper's experiments use the public CVPPP 2017 Leaf Counting Challenge dataset (Arabidopsis and tobacco plant images with segmentation masks and leaf counts), which the authors explicitly link in the data availability statement. No author code or trained models are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017 .Open asset ↗CVPPP2017 · CVPPP2017lines:527-601Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Confocal imaging is a well-established method for investigating plant phenotypes on the tissue and organ level. However, many differences are difficult to assess by visual inspection and researchers rely extensively on ad hoc manual quantification techniques and qualitative assessment. Here we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces. We successfully demonstrate the applicability of the approach using confocal imaging of aerial organs in Arabidopsis thaliana. Automatic identification of flower primordia using the surface curvature as an indication of outgrowth allows for high-throughput quantification of divergence angles and further analysis of individual flowers. We demonstrate the throughput of our method by quantifying geometric features of 1065 flower primordia from 172 plants, comparing auxin transport mutants to wild type. Additionally, we find that a paraboloid provides a simple geometric parameterisation of the shoot inflorescence domain with few parameters. We utilise parameterisation methods to provide a computational comparison of the shoot apex defined by a fluorescent reporter of the central zone marker gene CLAVATA3 with the apex defined by the paraboloid. Finally, we analyse the impact of mutations which alter mechanical properties on inflorescence dome curvature and compare the results with auxin transport mutants. Our results suggest that region-specific expression domains of genes regulating cell wall biosynthesis and local auxin transport can be important in maintaining the wildtype tissue shape. Altogether, our results indicate a general approach to parameterise and quantify plant development in 3D, which is applicable also in cases where data resolution is limited, and cell segmentation not possible. This enables researchers to address fundamental questions of plant development by quantitative phenotyping with high throughput, consistency and reproducibility.
Why it matches plant phenotyping methods植物組織の3D画像から形態形質を自動抽出・定量する手法を開発し、高スループット性と再現性を実証しているため、フェノタイピング手法が中心である。
abstractHere we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces.
Reproduction assets foundThe paper's data availability statement explicitly deposits all original source data (confocal phenotyping data of Arabidopsis shoot apical meristems) in the Cambridge Apollo repository and all analysis/segmentation/quantification scripts in a public Sainsbury Laboratory GitLab repository. Both are paper-specific,公开,直接Dataset · publicAll original source data files used in this study are available via the Cambridge University Apollo Repository ( https://doi.org/10.17863/CAM.82442 ).Open asset ↗Cambridge University Apollo Repository · 10.17863/CAM.82442lines:369-397Code · publicAll scripts and software for segmentation, quantification, analysis and visualisation are available via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamHJ/publications/aahl_etal_2022 ).Open asset ↗Sainsbury Laboratory GitLab repositorylines:369-397Code / 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 confirmedbioRxiv · OpenAlex · Europe PMC · checked 8 Sept 2026
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability. The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner. Significance StatementMany bottlenecks exist in high-throughput phenotyping involving computing power for processing and a lack of focus on abiotic stresses that has prevented an advancement in phenotyping on par with genotyping. Therefore, we create an automated HTP system that performs nutrient studies on Arabidopsis thaliana with cloud-based image processing that quantifies plant traits at a whole and leaf-level.
Why it matches plant phenotyping methodsOPEN leafは、葉単位の形態形質を画像から自動取得・定量するオープンソース高スループット表現型解析システムの設計・実装が中心であり、明確に収載対象です。
abstractHere we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.
Reproduction assets foundThe paper's image-analysis pipeline (SMART) used for rosette and leaf-specific phenotyping is explicitly released as public source code on GitHub and as a prepackaged Docker container. Phenotype data tables are only in supplementary material without a public URL, and other code repos (OPEN Controller, OPEN-leaf-cloud)'Code · public209 available as source code on GitHub (https://github.com/Computational-Open asset ↗pdf-page:8 lines:1-41Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 8 Sept 2026
Abstract Background Color distortion is an inherent problem in image-based phenotyping systems that are illuminated by artificial light. This distortion is problematic when examining plants because it can cause data to be incorrectly interpreted. One of the leading causes of color distortion is the non-uniform spectral and spatial distribution of artificial light. However, color correction algorithms currently used in plant phenotyping assume that a single and uniform illuminant causes color distortion. These algorithms are consequently inadequate to correct the local color distortion caused by multiple illuminants common in plant phenotyping systems, such as fluorescent tubes and LED light arrays. We describe here a color constancy algorithm, ColorBayes, based on Bayesian inference that corrects local color distortions. The algorithm estimates the local illuminants using the Bayes’ rule, the maximum a posteriori, the observed image data, and prior illuminant information. The prior is obtained from light measurements and Macbeth ColorChecker charts located on the scene. Results The ColorBayes algorithm improved the accuracy of plant color on images taken by an indoor plant phenotyping system. Compared with existing approaches, it gave the most accurate metric results when correcting images from a dataset of Arabidopsis thaliana images. The software is available at https://github.com/diloc/Color_correction.git .
Why it matches plant phenotyping methods植物フェノタイピング画像の局所的な色歪みを補正するアルゴリズムを開発し、既存手法およびArabidopsis画像データセットで精度を検証しているため、方法が中心的である。
abstractWe describe here a color constancy algorithm, ColorBayes, based on Bayesian inference that corrects local color distortions.
Reproduction assets foundThe paper's ColorBayes color-correction algorithm code is explicitly stated as publicly available on the authors' GitHub repository. The green fabric ground-truth image dataset and Arabidopsis plant image datasets are described but no public deposit is stated for them.Code · publicThe code of the color correction algorithm is available for reuse at https://github.com/diloc/Color_correction.git.Open asset ↗diloc/Color_correctionpdf-page:15 lines:1-82Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementTrackingGrowth / development / phenologyRoot system architecture
Directional root growth control is crucial for plant fitness. The degree of root growth deviation depends on several factors, whereby exogenous growth conditions have a profound impact. The perception of mechanical impedance by wild-type roots results in the modulation of root growth traits, and it is known that gravitropic stimulus influences distinct root movement patterns in concert with mechanoadaptation. Mutants with reduced shootward auxin transport are described as being numb towards mechanostimulus and gravistimulus, whereby different growth conditions on agar-supplemented medium have a profound effect on how much directional root growth and root movement patterns differ between wild types and mutants. To reduce the impact of unilateral mechanostimulus on roots grown along agar-supplemented medium, we compared the root movement of Col-0 and auxin resistant 1-7 in a root penetration assay to test how both lines adjust the growth patterns of evenly mechanostimulated roots. We combined the assay with the D-root system to reduce light-induced growth deviation. Moreover, the impact of sucrose supplementation in the growth medium was investigated because exogenous sugar enhances root growth deviation in the vertical direction. Overall, we observed a more regular growth pattern for Col-0 but evaluated a higher level of skewing of aux1-7 compared to the wild type than known from published data. Finally, the tracking of the growth rate of the gravistimulated roots revealed that Col-0 has a throttling elongation rate during the bending process, but aux1-7 does not.
Why it matches plant phenotyping methodsD-rootシステムと根貫通アッセイを組み合わせ、根の成長パターン・伸長速度を追跡して評価する測定ワークフローが研究の中心であるため。
abstractWe combined the assay with the D-root system to reduce light-induced growth deviation.
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。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11050650/s1 , Table S1: raw data.Open asset ↗lines:48-103Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary Revealing the contributions of genes to plant phenotype is frequently challenging because loss‐of‐function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana . An image segmentation‐based method using the software I mage J and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation‐based method was error‐prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection‐based algorithm yielded near perfect seed counts ( r 2 = 0.9996) and highly accurate fruit counts ( r 2 = 0.980). Comparing seed counts for wild‐type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.
Why it matches plant phenotyping methodsFaster R-CNNによる種子・果実数という植物形質の画像ベース測定法を開発・比較検証し、解析パイプラインとモデルを提示しているため、フェノタイピング手法が中心的です。
abstractAn image segmentation‐based method using the software I mage J and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all analysis scripts and the final seed and fruit counting models (trained Faster R-CNN phenotyping models) in a public GitHub repository under the authors' ShiuLab account, making it a paper-specific, publicly actionable asset.Code · publicAll the scripts used in this study and the final seed and fruit counting models are available on GitHub at: https://github.com/ShiuLab/Manuscript_Code/tree/master/2022_Arabidopsis_seed_and_fruit_count .Open asset ↗ShiuLab/Manuscript_Code · 2022_Arabidopsis_seed_and_fruit_countlines:244-645Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Code / 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 · checked 8 Sept 2026
In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.
Why it matches plant phenotyping methods植物の遺伝子型から288形質を予測するエンドツーエンドのニューラルネットワーク手法を開発・評価しており、表現型推定手法が研究の中心である。
abstractOur model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data.
Reproduction assets foundThe paper's authors explicitly state that the Galiana model code is freely available from their public Bitbucket repository, which is a paper-specific computational analysis asset. The phenotype data come from third-party databases (1001 Genomes, AraPheno) and are not paper-specific deposits; supplementary tables are aCode · publicWe implemented the model using pytorch ( 26 ). The code is freely available from our git repository https://bitbucket.org/eddiewrc/galiana/src/master/ .Open asset ↗bitbucket.org/eddiewrc/galiana · eddiewrc/galianalines:44-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The root system of a plant provides vital functions including resource uptake, storage, and anchorage in soil. The uptake of macro-nutrients like nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) from the soil is critical for plant growth and development. Small signaling peptide (SSP) hormones are best known as potent regulators of plant growth and development with a few also known to have specialized roles in macronutrient utilization. Here we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients. The SSP, CEP1 (C-TERMINALLY ENCODED PEPTIDE) enhanced nitrate uptake rate per unit root length in Medicago truncatula plants deprived of N in the high-affinity transport range. Single structural variants of M. truncatula and Arabidopsis thaliana specific CEP1 peptides, MtCEP1D1:hyp4,11 and AtCEP1:hyp4,11, enhanced uptake not only of nitrate, but also phosphate and sulfate in both model plant species. Transcriptome analysis of Medicago roots treated with different MtCEP1 encoded peptide domains revealed that hundreds of genes respond to these peptides, including several nitrate transporters and a sulfate transporter that may mediate the uptake of these macronutrients downstream of CEP1 signaling. Likewise, several putative signaling pathway genes including LEUCINE-RICH REPEAT RECPTOR-LIKE KINASES and Myb domain containing transcription factors, were induced in roots by CEP1 treatment. Thus, a scalable method has been developed for screening synthetic peptides of potential use in agriculture, with CEP1 shown to be one such peptide.
Why it matches plant phenotyping methods植物の栄養吸収率を測定する高スループット表現型解析プラットフォームを開発し、合成ペプチドのスクリーニングに適用しているため、測定手法が研究の中心である。
abstractHere we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients.
Reproduction assets foundThe paper's nutrient uptake rate calculations were performed with R code publicly available on Zenodo (Griffiths et al., 2021), which the authors state they used with minor modifications for this paper's phenotyping analysis. The NCBI BioProject (PRJNA764762) is an RNA-seq omics deposit and is excluded per criteria; TrCode · publicdata processing to determine specific nutrient uptake rates was conducted using R version 3.6.0 (Team, 2020)( R Core Team, 2020 ) with minor modification to the R code available at https://doi.org/10.5281/zenodo.3893945 ( Griffiths et al., 2021 )Open asset ↗zenodo · 10.5281/zenodo.3893945lines:318-326Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 9 Sept 2026
A plant's architecture contributes to its ability to acquire resources and reduce mechanical load. Arabidopsis thaliana is the most common model plant in molecular biology, and there are several mutants and transgenic lines with modified plant architecture regulation, such as lazy1 mutants, which have reversed angles of lateral branches. Although some phenotyping methods have been used in larger agricultural plants, limited suitable methods are available for three-dimensional reconstruction of Arabidopsis, which is smaller and has more uniform surface textures and structures. An inexpensive, easily adopted three-dimensional reconstruction system that can be used for Arabidopsis is needed so that researchers can view and quantify morphological changes over time. We developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method, which uses a fixed camera to capture plant images from multiple directions while the plant slowly rotates. We then developed a script to autogenerate stack images from the obtained input movie and visualized the plant architecture by rendering the output stack image using the general bioimage analysis software. We successfully three-dimensionally and time-sequentially scanned wild-type and lazy1 mutant A. thaliana plants and measured the angles of the lateral branches. This non-contact, non-destructive method requires no specialized equipment and is space efficient, inexpensive and easily adopted by Arabidopsis researchers. Consequently, this system will promote three- and four-dimensional phenotyping of this model plant, and it can be used in combination with molecular genetics to further elucidate the molecular mechanisms that regulate Arabidopsis architecture.
Why it matches plant phenotyping methodsシロイヌナズナの3次元画像再構成と枝角度測定を目的とする、植物表現型取得システムの開発が中心である。
abstractWe developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method
Reproduction assets foundThe authors publicly release their Python scripts for three-dimensional reconstruction, skeletonization, and curvature measurement (the paper's core phenotyping analysis) on their University of the Ryukyus website. No phenotype datasets or raw plant images are stated as deposited.Code · publicThis script was implemented in the Python language ( https://www.python.org/ ) and is executable in Windows and macOS. The script is only for academic purposes and freely available at our website ( https://ie.u-ryukyu.ac.jp/∼kunita/download_plant3d.html ).Open asset ↗lines:64-69Code / 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
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above ground, but largely unexplored below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time. This dataset describes the developmental dynamics of 93 accessions and reveals highly complex and polygenic RSA traits that show significant correlation with climate variables.
Why it matches plant phenotyping methodsロボティクスによる表現型取得の自動化と画像解析パイプライン開発が中心で、根系構造の時系列形質を抽出するフェノタイピング基盤を提示している。
abstractIn this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time.
Reproduction assets foundThe paper's data availability statement deposits the GLORIAv2 phenotyping robot hardware, the image analysis pipelines/scripts used to extract root traits, the RShiny RSA exploration app, and the raw imaging data/images on Zenodo, all directly reproducing this paper's root phenotyping measurements and analysis.Dataset · public10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of Biological and
Environmental Research (DE-SC0008769 and DE-SC0018277) and the Carnegie Institution for
SOpen asset ↗Zenodo · 10.5281/zenodo.5709009pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of BiologOpen asset ↗Zenodo · 10.5281/zenodo.5708422pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.528Open asset ↗Zenodo · 10.5281/zenodo.5574925pdf-raw-page:13 lines:1-35Code / 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 confirmedEurope PMC · Crossref · checked 14 Sept 2026
ArabidopsisMaizeLiDAR / point cloudRGB / grayscaleLeafWhole plant / canopy / plot / fieldClassificationMorphology / geometry measurementSegmentationGrowth / development / phenology
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.
Why it matches plant phenotyping methodsRoADはロボット、RGBカメラ、レーザープロフィロメータ、画像処理による植物形質抽出を中核とする自動フェノタイピングシステムであり、方法開発と実証が主目的です。
abstractwe developed the Robotic Assay for Drought (RoAD) system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' Arabidopsis image-processing source code (the pipeline that produced the paper's phenotypic trait measurements) on GitHub, making it a paper-specific, publicly actionable analysis code asset. No public phenotype dataset or image deposit is stated;Code · publicMN, ME, YY, YB, LT, SHH, and JWW. Funding
acquisition, YY, LT, JWW, and SHH.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
All relevant data can be found within the manuscript and
its supporting materials. The source code for Arabidopsis
image processing is available on GitHub at https://github.com/lr-xiang/RoAD-image-processing.SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. PCZ and BRZ responses of Arabidopsis accessions.
Figure S2. Drought responses in Arabidopsis using RoAD end-
point drought mode.
Figure S3. Validation results for maize plants.
Figure S4. ComparisonOpen asset ↗lr-xiang/RoAD-image-processingpdf-raw-page:15 lines:80-150Code / 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 confirmedCrossref · checked 15 Sept 2026
Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
Why it matches plant phenotyping methods光学トモグラフィーと機械学習ツールによる気孔密度測定を中心的な方法として開発・適用し、ガス交換等の表現型を大規模集団で評価しているため。
abstractThis study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD).
Reproduction assets foundThe paper's optical tomography leaf images (the sensor inputs used for machine-learning stomatal density phenotyping) are publicly deposited in the Illinois Data Bank. Phenotypic trait data (Supplemental Table S12) are public but only via the article's supplemental material without a listed URL; RNA-seq (PRJNA522466) GDataset · publichttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA522466/ .
Genotyping-by-sequencing data are available at: https://doi.org/10.5281/zenodo.5019227 . Phenotypic data are available as
part of the supplemental
material ( Supplemental Table
S12 ). Optical tomography images from this article can be found in the Illinois
Data Bank under: https://doi.org/10.13012/B2IDB-1411926_V1 .
Supplemental data
The following materials are available in the online version of this article.Open asset ↗10.13012/B2IDB-1411926_V1lines:985-1051Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.
Why it matches plant phenotyping methods根の屈曲角度を画像から自動抽出するソフトウェアとワークフローの開発が研究の中心であり、植物形態表現型の定量手法に該当する。
abstractwe developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images.
Reproduction assets foundThe paper explicitly releases the ACORBA software (source code, trained models, annotated training libraries, notebooks, user manual) on SourceForge and the raw microscopy/scanner image stacks used for the root-angle measurements on Zenodo (DOI 10.5281/zenodo.5105719). Both are paper-specific, public, and actionable.Code · publicand
online Python image analysis and machine learning tutorials.
Availability of data and materials
The latest versions of ACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (GOpen asset ↗sourceforge.net/projects/acorbapdf-raw-page:17 lines:1-45Dataset · publicACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (Grant No. 803048), Charles
University Primus (Grant No. PRIMUS/19/SCI/09).
Author contributions
NBCS and MF conceived the pOpen asset ↗ZENODO · 10.5281/zenodo.5105719pdf-raw-page:17 lines:1-45Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Summary Revealing the contributions of genes to plant phenotype is frequently challenging because the effects of loss of gene function may be subtle or be masked by genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana . An image segmentation-based (ImageJ) and a Faster Region Based Convolutional Neural Network (R-CNN) approach were used for measuring two Arabidopsis fitness traits: seed and fruit counts. Although straightforward to use, ImageJ was error-prone (correlation between true and predicted seed counts, r 2 =0.849) because seeds touching each other were undercounted. In contrast, Faster R-CNN yielded near perfect seed counts (r 2 =0.9996) and highly accurate fruit counts (r 2 =0.980). By examining seed counts, we were able to reveal fitness effects for genes that were previously reported to have no or condition-specific loss-of-function phenotypes. Our study provides models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits in the study of gene functions.
Why it matches plant phenotyping methods画像分割とFaster R-CNNを用いて種子数・果実数という植物形質を高スループット測定し、精度比較・検証を行うことが中心であるため。
titleHigh throughput measurement of Arabidopsis thaliana fitness traits using transfer learning
Reproduction assets foundThe paper's Data availability statement explicitly deposits all analysis scripts and the final seed and fruit counting models (trained Faster R-CNN phenotyping models) on the authors' public GitHub repository, which is listed in allowed_urls.Code · public, PD, SH,
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NLP, EV, EW, JKC, PJK, and MDL performed data collection and analysis. PW, FM,
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MDL, and SHS wrote the manuscript. All authors read and approved the final manuscript.
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Data availability
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All the scripts used in this study and the final seed and fruit counting models are available
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on Github at:
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https://github.com/ShiuLab/Manuscript_Code/tree/master/2021_Arabidopsis_seed_and_f
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ruit_count
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References
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Abadi M, Barham P, Chen JM, Chen ZF, Davis A, Dean J, Devin M, Ghemawat S,
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Irving G, Isard M et al. 2016. TensorFlow: A system for large-scale machine
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learning. 12th USENIX Symposium on Operating Systems Design and
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Implementation. USENIXOpen asset ↗ShiuLab/Manuscript_Code · 2021_Arabidopsis_seed_and_fpdf-raw-page:33 lines:1-52Code / 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area ( R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight – a yield component trait ( R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997 , a maize homolog of the Arabidopsis HCF244 ( high chlorophyll fluorescence 244 ), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.
Why it matches plant phenotyping methodsUAV画像から作物プロットの被覆率・緑色度を抽出するパイプラインを開発し、地上測定との相関で検証した研究であり、フェノタイピング手法が中心です。
abstractA pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed.
Reproduction assets foundThe paper's raw UAV RGB imagery used for the maize phenotyping pipeline is publicly deposited on CyVerse (DOI: 10.25739/4t1v-ab64), as stated in the supplied text. No author analysis code or trained models are described with public availability.Dataset · publicThe original UAV images
taken for this study are available at CyVerse (DOI: 10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-page:5 lines:1-38Code / 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 · checked 9 Sept 2026
The seeds of Arabidopsis thaliana become encapsulated by a layer of mucilage when imbibed. This polysaccharide-rich hydrogel is constituted of two layers, an outer layer that can be easily extracted with water and an inner layer that must be examined in situ in order to study its properties and structure in a non-destructive manner or disintegrated through hydrolysis or physical means in order to analyze its constituents. Mucilage production is an adaptive trait and we have exploited 19 natural accessions previously found to have atypical and varied outer mucilage characteristics. A detailed study using biochemical, histological and Time-Domain NMR analyses has been used to generate three related datasets covering 33 traits measured in four biological replicates. This data will be a rich resource for genetic, biochemical, structural and functional analyses investigating mucilage constituent polysaccharides or their role as adaptive traits.
Why it matches plant phenotyping methodsアラビドプシス種子の粘液形質を対象に、33形質・4反復の再利用可能なデータセットを生成した研究であり、植物形質データセットの構築が中心です。
abstractA detailed study using biochemical, histological and Time-Domain NMR analyses has been used to generate three related datasets covering 33 traits measured in four biological replicates.
Reproduction assets foundThe paper deposits its plant-phenotyping measurements in three Data INRAE datasets. Two of them (dataset 1: 33 mucilage/seed traits; dataset 3: individual microscopy measurements of mucilage and seed width) have DOIs matching allowed_urls entries and are directly citable public assets. Dataset 2's DOI (10.15454/EYABB2)Dataset · publicCambert, M. et al. Seed mucilage traits for Arabidopsis thaliana natural accessions with atypical outer mucilage - dataset 1. Portail
Data INRAE https://doi.org/10.15454/1MZ1ZC (2021).Open asset ↗10.15454/1MZ1ZCpdf-page:9 lines:1-70Dataset · publicBerger, A., Sallé, C. & North, H. M. Measurements of inner mucilage and seed width for Arabidopsis natural accessions - dataset 3.
Portail Data INRAE https://doi.org/10.15454/LBUN4X (2021).Open asset ↗Portail Data INRAE · 10.15454/LBUN4Xpdf-page:9 lines:1-70Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The physical presence of roots and the compounds they release affect the cohesion between roots and their environment. However, the plant traits that are important for these interactions are unknown and most methods that quantify the contributions of these traits are time-intensive and require specialist equipment and complex substrates. Our lab developed an inexpensive, high-throughput phenotyping assay that quantifies root-substrate adhesion in Arabidopsis thaliana. We now report that this method has high sensitivity and versatility for identifying different types of traits affecting root-substrate adhesion including root hair morphology, vesicle trafficking pathways, and root exudate composition. We describe a practical protocol for conducting this assay and introduce its use in a forward genetic screen to identify novel genes affecting root-substrate interactions. This assay is a powerful tool for identifying and quantifying genetic contributions to cohesion between roots and their environment.
Why it matches plant phenotyping methods根—基質接着を定量する高スループット表現型測定法を開発・検証し、遺伝子スクリーニングへの応用も示すため、手法が研究の中心である。
abstractOur lab developed an inexpensive, high-throughput phenotyping assay that quantifies root-substrate adhesion in Arabidopsis thaliana.
Reproduction assets foundThe paper's data availability statement points to a public University of Bristol (data.bris) repository deposit containing the study's centrifuge assay datasets (root-gel detachment measurements and associated analyses). No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://doi.org/10.5523/bris.21loiw3fpw372g99l93meaja1 .Open asset ↗10.5523/bris.21loiw3fpw372g99l93meaja1lines:526-555Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Tissue clearing methods combined with confocal microscopy have been widely used for studying developmental biology. In plants, ClearSee is a reliable clearing method that is applicable to a wide range of tissues and is suitable for gene expression analysis using fluorescent reporters, but its application to the Arabidopsis thaliana embryo, a model system to study morphogenesis and pattern formation, has not been described in the original literature. Here, we describe a ClearSee-based clearing protocol which is suitable for obtaining 3D images of Arabidopsis thaliana embryos. The method consists of embryo dissection, fixation, washing, clearing, and cell wall staining and enables high-quality 3D imaging of embryo morphology and expression of fluorescent reporters with the cellular resolution. Our protocol provides a reliable method that is applicable to the analysis of morphogenesis and gene expression patterns in Arabidopsis thaliana embryos.
Why it matches plant phenotyping methodsArabidopsis胚の形態を細胞解像度で3D取得するClearSeeベースのクリアリング・画像化プロトコルが研究の中心であり、植物表現型の取得方法を開発している。
abstractHere, we describe a ClearSee-based clearing protocol which is suitable for obtaining 3D images of Arabidopsis thaliana embryos.
Reproduction assets foundThe paper's supplementary materials include Movie S1, the Z-stack confocal image data (157 serial optical sections) used for the paper's 3D embryo visualization analysis, publicly available at the MDPI supplementary URL. No author analysis code or trained models are reported.Supplement · publicThe following are available online at https://www.mdpi.com/2223-7747/10/2/190/s1 , Movie S1: Z-stack images of 157 serial optical sections used for Figure 2 ; Table S1: Primers used in this study.
Click here for additional data file.
Author Contributions
Conceptualization, M.A.; methodology, M.A.; validation, A.I. and M.A.; formal analysis, A.I.; investigation, A.I., M.Y., T.S., A.O., and M.A.; resources, TOpen asset ↗lines:58-85Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (F v /F m ) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
Why it matches plant phenotyping methods植物病害の症状・重症度・進展を画像から定量化する方法の開発とワークフロー提示が中心であり、植物状態の表現型取得に該当する。
abstractHere, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves.
Reproduction assets foundThe authors explicitly state that all R and ImageJ scripts and the study data are openly available in their public GitHub repository, which directly reproduces this paper's Botrytis symptom phenotyping analysis.Code · publicAll R and ImageJ script generated to process are available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Dataset · publicThe data presented in this study are openly available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
ArabidopsisLaboratory / benchtopSeed / grainObject detectionGrowth / development / phenology
A high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.
Why it matches plant phenotyping methods種子発芽を自動画像解析で測定する高スループット表現型解析法を開発し、再現性・精度・既存法との一致を検証しており、測定手法自体が中心である。
abstractA high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed
Reproduction assets foundThe paper's seed-by-seed germination time measurements (the core phenotyping data) are published as online supplementary XLSX files (Supplementary Data S3, S4, S5), and a ZIP supplement (Supplementary Information 2) corresponds to the image time series (Supplementary Data S2). These are hosted with the open-access (CC Dataset · publicAll the germination time seed by seed in the analyses are provided in the Supplementary Data S3 online for Col-0 accession in water condition, in Supplementary Data S4 online for comparison with standard assays and in Supplementary Data S5 online for the analyse of Col-0 and L er ABA dose response.Open asset ↗lines:101-105Code / 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 · bioRxiv · checked 9 Sept 2026
Summary Soil is a heterogenous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically-based variation in the elemental composition ‘ionome’, of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool-IonExplorer to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory QTL, GWA studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.
Why it matches plant phenotyping methods大規模植物イオノーム取得のための高スループット実験・正規化パイプラインとデジタルツールを開発し、再利用可能なデータ資源として提示しているため、測定法が中心的です。
abstractWe present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection.
Reproduction assets foundThe paper's species-wide leaf and seed ionomic dataset for 1,135 A. thaliana accessions is made publicly accessible through the authors' interactive web tool Ion Explorer, which allows filtering, analysis, and download of the complete dataset as .csv files. This is a paper-specific, public, actionable asset. No author-Dataset · publicwe present an interactive web-based tool made available online: Ion Explorer
https://ffionexplorer.nottingham.ac.uk/ionmap/. Ion Explorer allows interactive visualisation,
analysis and comparison of the two large datasets.Open asset ↗Ion Explorerpdf-page:11 lines:1-44Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.
Why it matches plant phenotyping methods植物画像から葉の状態を分割・定量するオープンソースの表現型解析パイプライン開発が中心であり、植物フェノタイピング手法に該当する。
abstractWe have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner.
Reproduction assets foundThe paper deposits its ARADEEPOPSIS Nextflow pipeline on GitHub, its manually annotated ground-truth training images on Zenodo (3946393), its trained DeepLabV3+ model checkpoints on Zenodo (3946618), and its model training code on GitHub. All are paper-specific, public, and actionable.Code · publicencourage researchers to use and build upon the training code ( https://github.com/phue/models/tree/aradeepopsis_manuscript/research/deeplab ) and ground-truth annotations ( https://doi.org/10.5281/zenodo.3946393 ) we deposited for that purposeOpen asset ↗phue/modelslines:134-141Code / 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 · Crossref · Europe PMC · checked 9 Sept 2026
BACKGROUND: (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. RESULTS: The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. CONCLUSIONS: DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しているため、方法開発が中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe authors explicitly state their DAPD normalization and segmentation code is publicly available on GitHub.Code · publicOur code and a Python notebook come with a friendly user manual detailing how to use it, and they are available at https://github.com/diloc/DAPD_Normalization.git .Open asset ↗diloc/DAPD_Normalizationlines:73-78Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Optical Projection Tomography (OPT) is a powerful three-dimensional imaging technique used for the observation of millimeter-scaled biological samples, compatible with bright-field and fluorescence contrast. OPT is affected by spatially variant artifacts caused by the fact that light diffraction is not taken into account by the straight-light propagation models used for reconstruction. These artifacts hinder high-resolution imaging with OPT. In this work we show that, by using a multiview imaging approach, a 3D reconstruction of the bright-field contrast can be obtained without the diffraction artifacts typical of OPT, drastically reducing the amount of acquired data, compared to previously reported approaches. The method, purely based on bright-field contrast of the unstained sample, provides a comprehensive picture of the sample anatomy, as demonstrated in vivo on Arabidopsis thaliana and zebrafish embryos. Furthermore, this bright-field reconstruction can be implemented on practically any multi-view light-sheet fluorescence microscope without complex hardware modifications or calibrations, complementing the fluorescence information with tissue anatomy.
Why it matches plant phenotyping methods多視点取得と画像融合による3D明視野再構成法を開発し、Arabidopsisの解剖学的形態を実証しているため、植物形態の取得手法が中心です。
abstractIn this work we show that, by using a multiview imaging approach, a 3D reconstruction of the bright-field contrast can be obtained without the diffraction artifacts typical of OPT
Reproduction assets foundThe paper's authors state that the Python sample code implementing their bright-field multi-view reconstruction (used for the Arabidopsis thaliana and zebrafish phenotyping/imaging analysis) is publicly available on GitHub under the authors' account. The GitHub URL in the text contains formatting artifacts and does notCode · publicData processing was performed in Python; a sample code is available on GitHubOpen asset ↗pdf-page:7 lines:1-55Code / 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 confirmedEurope PMC · checked 9 Sept 2026
ArabidopsisGrowth chamberLeafRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / development / phenologyLeaf traitsRoot system architecture
Background Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform, GrowScreen - Agar , for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium. Results The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78 Arabidopsis accessions from the 1001 genomes project. The chosen concept 'plant-to-sensor' is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow Arabidopsis plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately. Conclusions Because the GrowScreen - Agar system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.
Why it matches plant phenotyping methods自動画像計測による根・シュート形質の非破壊取得プラットフォームを開発・検証しており、表現型取得法が研究の中心である。
abstractThe phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min.
Reproduction assets foundThe paper's phenotypic datasets (root/shoot trait measurements of 78 Arabidopsis accessions and experiments 1-2) are publicly deposited in the e!DAL research data publication system. The analysis software is only available upon request from the corresponding author, so it is not a public asset. AraPheno and cited worksDataset · publicThe datasets generated and analysed during the current study are available in the e!DAL research data publication system, https://doi.org/10.25622/FZJ/2020/0 .Open asset ↗e!DAL research data publication system · 10.25622/FZJ/2020/0lines:157-166Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 9 Sept 2026
Abstract Background Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. Results The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.
Why it matches plant phenotyping methods植物表現型ハイスループット測定の時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しており、方法が研究の中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe paper's DAPD normalization and segmentation analysis code is explicitly stated to be publicly available on the authors' GitHub repository, matching an allowed URL. No phenotype dataset or image deposit is stated; the in-house dataset is not publicly shared.Code · publicOur code is available for reuse at https://github.com/diloc/DAPD_Normalization.git.Open asset ↗diloc/DAPD_Normalizationpdf-page:14 lines:1-67Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.
Why it matches plant phenotyping methodsHTPプラットフォームによる時系列画像取得と機械学習解析が、放射線処理の成長表現型を定量化する中心的方法として明示され、別研究での検証も行われている。
abstractIrradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms.
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。Supplement · publicTable S3. Summary of all phenotyping data from preliminary, main, and validation studies.Open asset ↗lines:77-105Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
SUMMARY The broad host range necrotrophic fungus Sclerotinia sclerotiorum is a devastating pathogen of many oil and vegetable crops. Plant genes conferring complete resistance against S. sclerotiorum have not been reported. Instead, plant populations challenged by S. sclerotiorum exhibit a continuum of partial resistance designated as quantitative disease resistance (QDR). Because of their complex interplay and their small phenotypic effect, the functional characterization of QDR genes remains limited. How broad host range necrotrophic fungi manipulate plant programmed cell death is for instance largely unknown. Here, we designed a time‐resolved automated disease phenotyping pipeline enabling high‐throughput disease lesion measurement with high resolution, low footprint at low cost. We could accurately recover contrasted disease responses in several pathosystems using this system. We used our phenotyping pipeline to assess the kinetics of disease symptoms caused by seven S. sclerotiorum isolates on six A. thaliana natural accessions with unprecedented resolution. Large effect polymorphisms common to the most resistant A. thaliana accessions identified highly divergent alleles of the nucleotide‐binding site leucine‐rich repeat gene LAZ5 in the resistant accessions Rubezhnoe and Lip‐0. We show that impaired LAZ5 expression in laz5.1 mutant lines and in A. thaliana Rub natural accession correlate with enhanced QDR to S. sclerotiorum . These findings illustrate the value of time‐resolved image‐based phenotyping for unravelling the genetic bases of complex traits such as QDR. Our results suggest that S. sclerotiorum manipulates plant sphingolipid pathways guarded by LAZ5 to trigger programmed cell death and cause disease.
Why it matches plant phenotyping methods時間分解型の自動画像フェノタイピングパイプラインを設計し、病斑を高解像度・高スループットで測定して複数の病原系で検証・適用しており、表現型取得法が研究の中心である。
abstractwe designed a time‐resolved automated disease phenotyping pipeline enabling high‐throughput disease lesion measurement with high resolution, low footprint at low cost.
Reproduction assets foundThe paper's INFEST image-analysis pipeline (the computational core of the Navautron phenotyping system) is publicly available on GitHub, along with a tutorial repository containing example phenotyping pictures and grid layout files. No raw phenotype dataset from this study's Sclerotinia experiments appears to be postedCode · publice. Characteristic values are the duration of latency phase and the lesion doubling time (LDT). Data shown correspond to values collected on five leaves of A. thaliana Col‐0, the red curve shows fitted average.
Box 1
Installation guide for INFEST, the Navautron image analysis tools
A complete tutorial and updates can be found at https://github.com/A02l01/INFEST . Infest requires python and conda installed on your machine. Major steps of the installation procedure are:
1
Clone the infest repository
$ git clone https://github.com/A02l01/INFEST.git
2
Create and activate the INFEST conda environment using the yaml file
$ conda env create ‐n INFEST ‐f env_Infest.yml
3
Analyze pictures contOpen asset ↗A02l01/INFESTlines:36-86Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 14 Sept 2026
Abstract Linking plant phenotype to genotype, i.e., identifying genetic determinants of phenotypic traits, is a common goal of both plant breeders and geneticists. While the ever-growing genomic resources and rapid decrease of sequencing costs have led to enormous amounts of genomic data, collecting phenotypic data for large numbers of plants remains a bottleneck. Many phenotyping strategies rely on imaging plants, which makes it necessary to extract phenotypic measurements from these images rapidly and robustly. Common image segmentation tools for plant phenotyping mostly rely on color information, which is error-prone when either background or plant color deviate from the underlying expectations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ara deep opsis was built around the deep-learning model DeepLabV3+ that was re-trained for segmentation of Arabidopsis thaliana rosettes. It uses semantic segmentation to classify leaf tissue into up to three categories: healthy, anthocyanin-rich, and senescent. This makes ara deep opsis particularly powerful at quantitative phenotyping from early to late developmental stages, of mutants with aberrant leaf color and/or phenotype, and of plants growing in stressful conditions where leaf color may deviate from green. Using our tool on a panel of 210 natural Arabidopsis accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to map known loci related to anthocyanin production and early necrosis using the ara deep opsis output in genome-wide association analyses. Our pipeline is able to handle images of diverse origins, image quality, and background composition, and could even accurately segment images of a distantly related Brassicaceae. Because it can be deployed on virtually any common operating system and is compatible with several high-performance computing environments, ara deep opsis can be used independently of bioinformatics expertise and computing resources. ara deep opsis is available at https://github.com/Gregor-Mendel-Institute/aradeepopsis .
Why it matches plant phenotyping methods植物画像から葉組織の状態と表現型測定値を抽出するオープンソース画像解析パイプラインの開発が中心であり、植物フェノタイピング手法に該当する。
abstractWe have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner.
Reproduction assets foundThe paper's phenotyping pipeline (Nextflow workflow), its trained-model training code with annotated training datasets, a Docker container, and the GWA analysis tool are all explicitly released at public author URLs.Code · publicwas applied and decayed according to a polynomial
function after a burn-in period of 2,000 training steps. Images were randomly cropped to
321x321 pixels, and training was stopped after 75,000 iterations.
Implementation
Based on the trained models, an image analysis pipeline was implemented in Nextflow [16]
and is available at https://github.com/Gregor-Mendel-Institute/aradeepopsis. The workflow is
outlined in Fig 3. Nextflow allows external pipeline dependencies to be packaged in a Docker
container, which we provide at https://hub.docker.com/r/beckerlab/aradeepopsis/. The
container can be run using Docker [34], podman [35], or Singularity [36]. Alternatively,
dependencies can be automaticalOpen asset ↗Gregor-Mendel-Institute/aradeepopsispdf-layout-page:16 lines:1-55Code · publicr manual image annotation; custom
scripts were used to produce annotation masks from the XML output. The publicly available
DeepLabV3+ [14,15] code was modified to enable model training on our own annotated
training sets. The code used for training as well as download links for our annotated training
datasets is available here:
https://github.com/phue/models/tree/aradeepopsis_manuscript/research/deeplab.
For model evaluation, we split the annotated sets 80:20: 80% of the images were used to train
the model and 20% for its evaluation.
A transfer learning strategy was employed by using a model checkpoint based on the
xception65 architecture [7] that has been pretrained on the ImageNet dataset Open asset ↗phue/models · aradeepopsis_manuscriptpdf-layout-page:16 lines:1-55Code · publicssociate phenotype and single nucleotide
polymorphisms, we used the 1,135 genotype SNP matrix and the corresponding kinship
matrix, subset to those accessions where we had trait information. We screened the results
for interesting trait-date combinations and followed these up using Arabidopsis-specific tools
developed in-house (https://github.com/Gregor-Mendel-Institute/gwaR). The analysis is
detailed in Supplemental File 1.
Acknowledgements
We thank James M. Watson for critical reading of the manuscript and valuable comments.
Karina Weiser Lobão helped in the manual image annotation. We thank Dario Galanti and
Oliver Bossdorf for providing images of T. arvense. Klaus Schlaeppi and Selma CadOpen asset ↗Gregor-Mendel-Institute/gwaRpdf-layout-page:17 lines:1-47Code / 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 · OpenAlex · checked 9 Sept 2026
The local environment of the geographical origin of plants shaped their genetic variations through environmental adaptation. While the characteristics of the local environment correlate with the genotypes and other genomic features of the plants, they can also be indicative of genotype-phenotype associations providing additional information relevant to environmental dependence. In this study, we investigate how the geoclimatic features from the geographical origin of the Arabidopsis thaliana accessions can be integrated with genomic features for phenotype prediction and association analysis using advanced canonical correlation analysis (CCA). In particular, we propose a novel method called hierarchical canonical correlation analysis (HCCA) to combine mutations, gene expressions, and DNA methylations with geoclimatic features for informative coprojections of the features. HCCA uses a condition number of the cross-covariance between pairs of datasets to infer a hierarchical structure for applying CCA to combine the data. In the experiments on Arabidopsis thaliana data from 1001 Genomes and 1001 Epigenomes projects and climatic, atmospheric, and soil environmental variables combined by CLIMtools, HCCA provided a joint representation of the genomic data and geoclimate data for better prediction of the special flowering time at 10°C (FT10) of Arabidopsis thaliana . We also extended HCCA with information from a protein-protein interaction (PPI) network to guide the feature learning by imposing network modules onto the genomic features, which are shown to be useful for identifying genes with more coherent functions correlated with the geoclimatic features. The findings in this study suggest that environmental data comprise an important component in plant phenotype analysis. HCCA is a useful data integration technique for phenotype prediction, and a better understanding of the interactions between gene functions and environment as more useful functional information is introduced by coprojections of multiple genomic datasets.
Why it matches plant phenotyping methods植物の開花期という表現型を予測するための新規CCA手法を開発し、ゲノム・環境データ統合による予測性能を評価しており、計算的表現型推定が研究の中心である。
abstractwe propose a novel method called hierarchical canonical correlation analysis (HCCA) to combine mutations, gene expressions, and DNA methylations with geoclimatic features for informative coprojections of the features.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' source code and data for the HCCA phenotype-prediction analysis in a public GitHub repository, matching an allowed URL.Code · publicAll the source code and data are available at https://github.com/kuanglab/HCCA .Open asset ↗kuanglab/HCCAlines:178-192Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Autophagy is the main catabolic process in eukaryotes and plays a key role in cell homeostasis. In vivo measurement of autophagic activity (flux) is a powerful tool for investigating the role of the pathway in organism development and stress responses. Here we describe a significant optimization of the tandem tag assay for detection of autophagic flux in planta in epidermal root cells of Arabidopsis thaliana seedlings. The tandem tag consists of TagRFP and mWasabi fluorescent proteins fused to ATG8a, and is expressed in wildtype or autophagy-deficient backgrounds to obtain reporter and control lines, respectively. Upon autophagy activation, the TagRFP-mWasabi-ATG8a fusion protein is incorporated into autophagosomes and delivered to the lytic vacuole. Ratiometric quantification of the low pH-tolerant TagRFP and low pH-sensitive mWasabi fluorescence in the vacuoles of control and reporter lines allows for a reliable estimation of autophagic activity. We provide a step by step protocol for plant growth, imaging and semi-automated data analysis. The protocol presents a rapid and robust method that can be applied for any studies requiring in planta quantification of autophagic flux.
Why it matches plant phenotyping methods植物体内のオートファジー活性という生理状態を、蛍光イメージングと半自動解析で定量する手法を最適化し、プロトコルとして提示しているため。
abstractHere we describe a significant optimization of the tandem tag assay for detection of autophagic flux in planta in epidermal root cells of Arabidopsis thaliana seedlings.
Reproduction assets foundThe paper's semi-automated autophagic flux analysis pipeline (ImageJ macros and R scripts) is publicly available in the authors' AuTToFlux GitHub repository, which also contains demo data for validating the analysis.Code · public20-22 °C, 50-70% humidity, 150 µM light
Confocal Laser Scanning Microscope (CLSM; Zeiss, LSM 800)
Software
Fiji, the version of ImageJ with included set of plugins ( https://fiji.sc/ , for this study, we utilized versions 1.51s and 2.0.0-rc-69/1.52i).
AuTToFlux repository containing three ImageJ macro and three R script files ( https://github.com/jonasoh/AuTToFlux/archive/master.zip ):
CalibrateThreshold.ijm
ImageProcessor.ijm
FluorescenceIntensity.ijm
EvaluateCalibration.R
Control-vs-Reporter.R
Flux-vs-Time.R
R ( https://www.r-project.org , we used 3.5.2 and 3.5.1)
RStudio ( https://www.rstudio.com/ , we used versions 1.1.453 and 1.2.1186).
Git ( https://git-scm.com/downloads , we usedOpen asset ↗jonasoh/AuTToFluxlines:121-191Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Background Seed size and number are important plant traits from an ecological and horticultural/agronomic perspective. However, in small-seeded species such as Arabidopsis thaliana, research on seed size and number is limited by the absence of suitable high throughput phenotyping methods. Results We report on the development of a high throughput method for counting seeds and measuring individual seed sizes. The method uses a large-particle flow cytometer to count individual seeds and sort them according to size, allowing an average of 12,000 seeds/hour to be processed. To achieve this high throughput, post harvested seeds are first separated from remaining plant material (dust and chaff) using a rapid sedimentation-based method. Then, classification algorithms are used to refine the separation process in silico. Accurate identification of all seeds in the samples was achieved, with relative errors below 2%. Conclusion The tests performed reveal that there is no single classification algorithm that performs best for all samples, so the recommended strategy is to train and use multiple algorithms and use the median predictions of seed size and number across all algorithms. To facilitate the use of this method, an R package (SeedSorter) that implements the methodology has been developed and made freely available. The method was validated with seed samples from several natural accessions of Arabidopsis thaliana, but our analysis pipeline is applicable to any species with seed sizes smaller than 1.5 mm.
Why it matches plant phenotyping methods種子数と種子サイズという植物形質を高スループットに取得するフローサイトメトリー法を開発・検証し、解析用Rパッケージも提供しているため、植物フェノタイピング手法が研究の中心である。
abstractWe report on the development of a high throughput method for counting seeds and measuring individual seed sizes.
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2件を確認しました。Code · publicTo facilitate the use of this method, all necessary computations have been implemented into an R package ( SeedSorter ) that is freely available online at https://github.com/aleMorales/SeedSorter .Open asset ↗aleMorales/SeedSorterlines:76-82Code · publicThe R scripts and data required to reproduce these results can be obtained at https://github.com/aleMorales/SeedSorterPaper .Open asset ↗aleMorales/SeedSorterPaperlines:120-135Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vascular plants, lignin is deposited during morphogenesis but also under stress conditions. Assessing the degree of stress-induced lignin deposition is complicated because it occurs locally and irregularly in plant tissues. In this study, we developed a macro program, LigninJ, for the open-source software ImageJ to automatically and efficiently determine areas and levels of lignification after Wiesner (phloroglucinol-HCl) staining. We used the CIELAB color space for detection of red color following the Wiesner reaction. In addition, LigninJ has a function for adjusting the background level and its white balance to reduce biases that are inherent to individual color images. Furthermore, LigninJ can be used for batch analyses of multiple images, taking about 2 s per image. In this study, we analyzed wound-induced lignin deposition in cotyledons of the Arabidopsis thaliana ecotypes Landsberg erecta and Columbia and assessed ectopic lignin depositions in roots of lignescence ( lig ) mutants of Arabidopsis . Our results confirmed that this method is efficient for evaluating the degree of stress-induced lignin deposition.
Why it matches plant phenotyping methods植物組織のリグニン沈着量を画像から自動定量するImageJマクロを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed a macro program, LigninJ, for the open-source software ImageJ to automatically and efficiently determine areas and levels of lignification
Reproduction assets foundThe paper's authors publicly distribute the LigninJ ImageJ macro program, an Excel macro file, and sample microscopic images via their lab website, directly supporting this paper's lignin-deposition image analysis.Code · publicas in the a * stack image, measurement and record of selected areas, and mean values of L *, a *, and b * components. A description of the practical application of LigninJ is shown below. Samples of microscopic pictures, the LigninJ macro program file and the macro file of Microsoft Excel are provided from the author’s website (http://bio.sci.ehime-u.ac.jp/morphol/SatoLab).
Save a set of color images (e.g., in JPEG or TIFF format) at the same magnification in one working directory.
Open an image of a microscale or an image including a scale bar at the same magnification as the sample pictures in ImageJ, and calculate the length of a known distance in pixels. For instance, use the “straight lOpen asset ↗lines:85-97Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and counting. Here, we show how different phenotyping traits can be extracted simultaneously from plant images, using Multi-Task Learning (MTL). MTL leverages information contained in the training images of related tasks to improve overall generalization and learns models with fewer labels. We present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification. We adopted a modified ResNet50 as a feature extractor, trained end-to-end to predict multiple traits. We also leverage MTL to show that through learning from more easily obtainable annotations (such as PLA and genotype) we can predict a better leaf count (harder to obtain annotation). We evaluate our findings on several publicly available datasets of top-view images of Arabidopsis thaliana. Experimental results show that the proposed MTL method improves the leaf count Mean Squared Error (MSE) by more than 40 %, compared to a single task network on the same dataset. We also show that our MTL framework can be trained with up to 75 % fewer leaf count annotations without significantly impacting performance, whereas a single task model shows a steady decline when fewer annotations are available.
Why it matches plant phenotyping methods植物画像から複数形質を同時推定するマルチタスク深層学習手法の開発・評価が中心であり、明確な植物フェノタイピング方法論研究である。
abstractWe present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification.
Reproduction assets foundThe paper's authors provide public analysis code (MTL phenotyping framework) on GitHub, and the study analyzes publicly available CVPPP plant image datasets (Ara2013, A1, A4) hosted on plant-phenotyping.org. Both are paper-specific, public, and actionable.Code · publicCode available at https://github.com/andobrescu/Multi_task_plant_phenotyping .Open asset ↗andobrescu/Multi_task_plant_phenotypinglines:224-295Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017-challenge .Open asset ↗lines:607-694Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The micro-morphology of leaf epidermises is valuable for the study of leaf development and function, as well as the classification of plant species. There have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis. Here, four specimen preparation methods were used to investigate the leaf epidermis morphology of Arabidopsis , radish, cucumber, wheat, rice, and maize, under an inverted basic light microscope (LM), a laser scanning confocal microscope (LSCM), or a scanning electron microscope (SEM). Optical microscope specimens were obtained using either the direct isolation method or the chloral hydrate-based clearing method. SEM images were obtained using a standard stage for conventional dehydrated samples or a Coolstage for fresh tissue. Different parts of epidermis peels were well focused under the LM. Investigation of samples cleared by chloral hydrate is convenient and autofluorescence of cell walls can be detected in rice. The resolution of images of conventional SEM leaf samples was generally higher than the Coolstage images at the same magnification, whereas local collapse and shrinkage were observed in leaves with high water content when using the conventional method. However, stomatal apparatuses of Arabidopsis , cucumber, radish, and maize deformed and showed poor appearance when using the Coolstage. Moreover, we usually used glutaraldehyde as an SEM fixative when using t-butanol for freeze-drying, though methanol is considered a better fixative in recent studies. In addition, fresh samples were not stable on the Coolstage. Thus, we compared four different t-butanol freeze-drying methods and two Coolstage methods. The dimension and morphology of tissues were compared using the six different methods. The results indicate that methanol fixative obviously reduced shrinkage of SEM samples compared with glutaraldehyde and formaldehyde alcohol acetic acid (FAA) fixatives. The use of methanol and a graded series of steps improved the preservation of samples. Preparing samples with optimal cutting temperature compound and observing at -30°C helped to increase the stability of Coolstage samples. In summary, our results provide an overview of the shortcomings and merits of four different methods, and might provide some information about choosing an optimal method for visualizing epidermal morphology.
Why it matches plant phenotyping methods葉表皮形態の可視化について、複数の試料調製法・顕微鏡法を比較し、組織形態の保存性や画像品質を評価しており、植物形質取得法が研究の中心である。
abstractThere have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis.
Reproduction assets foundThe paper reports LM/LSCM/SEM imaging of leaf epidermises and shrinkage/stability measurements. No author analysis code, trained models, or external repository deposit is mentioned. The only paper-specific public asset is the article's Supplementary Material, which the authors state contains all data generated or analySupplement · publicgy Project of Henan Province (182102110234).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.00133/full#supplementary-material
Click here for additional data file.
Abbreviations
SEM, scanning electron microscope; LSCM, laser scanning confocal microscope; LM, light microscope; DIC, differential interference contrast; CPD, critical point drying; OCT, optimum cutting temperature.
References
Bailes E. J. GlovOpen asset ↗lines:293-368Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
Why it matches plant phenotyping methods低コストの生育光環境、IMAGING-PAMのスループット向上、植物画像の半自動セグメンテーションとデータ解析を開発・検証しており、植物表現型取得手法が中心である。
abstractWe have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets.
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。Dataset · publicThe scripts described in the text can be downloaded from https://github.com/CougPhenomics/ImagingPAMProcessing and the accompanying 11 day dataset can be downloaded from https://doi.org/10.17605/OSF.IO/P32AY .Open asset ↗OSF · 10.17605/OSF.IO/P32AYlines:160-190Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Enzyme biosensors are useful tools that can monitor rapid changes in metabolite levels in real-time. However, current approaches are largely constrained to metabolites within a limited chemical space. With the rising development of artificial metalloenzymes (ArM), a unique opportunity exists to design biosensors from the ground-up for metabolites that are difficult to detect using current technologies. Here we present the design and development of the ArM ethylene probe (AEP), where an albumin scaffold is used to solubilize and protect a quenched ruthenium catalyst. In the presence of the phytohormone ethylene, cross metathesis can occur to produce fluorescence. The probe can be used to detect both exogenous- and endogenous-induced changes to ethylene biosynthesis in fruits and leaves. Overall, this work represents an example of an ArM biosensor, designed specifically for the spatial and temporal detection of a biological metabolite previously not accessible using enzyme biosensors.
Why it matches plant phenotyping methods植物組織中のエチレンを空間・時間的に検出する新規バイオセンサーの設計・開発が中心であり、植物の生理状態を測定する方法論的貢献に該当する。
abstractHere we present the design and development of the ArM ethylene probe (AEP)
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。Dataset · publicSource data are provided as a Source Data file.Open asset ↗lines:95-101Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ABSTRACT An increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named “Neighbor GWAS”. Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype from when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN ( https://cran.rproject.org/package=rNeighborGWAS ).
Why it matches plant phenotyping methods植物の食害表現型を対象に、近隣遺伝子型を組み込むGWAS手法を開発し、実データで検証した研究である。Rパッケージとしても提供されており、表現型解析手法が中心である。
abstractwe incorporated neighbor genotypic identity into a regression model, named “Neighbor GWAS”.
Reproduction assets foundThe paper's field herbivory phenotype data (leaf damage scores), accession list, simulation code, and R scripts are publicly available on the authors' GitHub repository, and the neighbor GWAS method is released as an R package on CRAN. Both are paper-specific, public, and actionable.Code · publice) scores, with the
451 option “--gene-definition undownstream10000,” “--min-genes 20,” and
452 “--mode gene.” The GO.db package (Carlson et al. 2018) and the latest
453 TAIR AGI code annotation were used to build input files. The R source
454 codes, accession list, and phenotype data are available at the GitHub
455 repository (https://github.com/naganolab/NeighborGWAS).
456
457 R package, “rNeighborGWAS”
458 To increase the availability of the new method, we have developed the
p. 21Open asset ↗naganolab/NeighborGWASpdf-layout-page:21 lines:1-55Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 15 Sept 2026
The worldwide rise in heatwave frequency poses a threat to plant survival and productivity. Determining the new marker phenotypes that show reproducible response to heat stress and contribute to heat stress tolerance is becoming a priority. In this study, we describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system. Heat stress exposure resulted in an acute reduction of quantum yield of photosystem II and increased leaf angle. In the longer term, exposure to heat also affected plant growth and morphology. By tracking the recovery period of WT and mutants impaired in thermotolerance (hsp101), we observed that the difference in maximum quantum yield, quenching, rosette size, and morphology. By examining the correlation across the traits throughout time, we observed that early changes in photochemical quenching corresponded with the rosette size at later stages, which suggests the contribution of quenching to overall heat tolerance. We also determined that 6h of heat stress provides the most informative insight in plant responses to heat, as it shows a clear separation between treated and non-treated plants as well as WT and hsp101. Our work streamlines future discoveries by providing an experimental protocol, data analysis pipeline and new phenotypes that could be used as targets in thermotolerance screenings.
Why it matches plant phenotyping methods自動化・非破壊フェノタイピングシステムを用いた形態・光合成表現型の取得プロトコル、データ解析パイプライン、新規表現型を中心的に提示しており、耐暑性スクリーニングへの再利用可能な方法論である。
abstractwe describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system.
Reproduction assets foundThe paper publicly deposits its authors' analysis code: an R-notebook for data analysis and a Jupyter notebook for machine learning, both on Zenodo. No phenotype dataset or image deposit is stated in the supplied blocks.Code · public5
statistical analysis using ggpubr. Machine learning classification was implemented using
1
Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
2
publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
3
the Jupyter notebook containing the command lines used for machine learning
4
(http://doi.org/10.5281/zenodo.3534148).5
6
3. Results
7
8
3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
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size and photosynthetic efficiency
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To assess whether high-throughput phenotyping cOpen asset ↗zenodo · 10.5281/zenodo.3534239pdf-raw-page:5 lines:1-56Code · publicng ggpubr. Machine learning classification was implemented using
1
Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
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publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
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the Jupyter notebook containing the command lines used for machine learning
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(http://doi.org/10.5281/zenodo.3534148).5
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3. Results
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3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
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size and photosynthetic efficiency
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To assess whether high-throughput phenotyping can capture significant alterations in plant
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physiology caused by exposure to heat stress, we exposed three weeks old ArabidopsisOpen asset ↗zenodo · 10.5281/zenodo.3534148pdf-raw-page:5 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Premise When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible. Methods and results Twenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in Arabidopsis thaliana leaves. Conclusions The digital imaging-based nature of this protocol makes it a low-cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large-scale, cost-effective systems for monitoring plant health.
Why it matches plant phenotyping methods植物アントシアニン量という生理状態をデジタル画像から推定する回帰モデルと非侵襲的モニタリング手法の開発が中心であり、植物フェノタイピング手法に該当する。
abstractTwenty-two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data.
Reproduction assets foundThe paper's authors publicly deposited all R code used for leaf image processing, regression training, and accuracy evaluation in a GitHub repository, explicitly stated in the DATA AVAILABILITY section. This is a paper-specific, publicly actionable analysis code asset for the anthocyanin phenotyping method.Code · publicments; B.C.A. and W.S.L. developed
the methods used for data analysis; and B.C.A. and J.K. wrote the
manuscript.All authors read and approved the final manuscript.
DATA AVAILABILITY
All R code used to process and sort the leaf images, train the regres-
sions, and evaluate the accuracy of the regressions can be down-
loaded from https://github.com/bryceaskey/anthocyanin_accum
ulation.
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Drought stress induces anthocyanin accumulation.
Side view (A) and overhead (B) photos of wild-type Arabidopsis
thaliana (Col-0) under either well-watered (control) or watOpen asset ↗bryceaskey/anthocyanin_accumpdf-raw-page:6 lines:1-82Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
ArabidopsisStem / branchMorphology / geometry measurementGrowth / development / phenology
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low-quality images produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets not restricted to Arabidopsis ( Arabidopsis thaliana ). Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user.
Why it matches plant phenotyping methods幼苗胚軸長を画像から高速・高スループットに推定する深層学習手法の開発であり、植物形質取得が研究の中心です。
abstractTo make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method.
Reproduction assets foundThe paper's authors publicly released their full analysis code (U-Net-based hypocotyl segmentation/measurement pipeline) on GitHub and the training images used for phenotyping on Kaggle. Trained models are only available upon request and are therefore not listed as public assets.Code · publicThe code is fully open source and available at GitHub ( https://github.com/biomag-lab/hypocotyl-UNet ).Open asset ↗biomag-lab/hypocotyl-UNetlines:134-142Dataset · publicImages used for training are also available at https://www.kaggle.com/tivadardanka/plant-segmentation .Open asset ↗tivadardanka/plant-segmentationlines:268-326Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · bioRxiv · checked 14 Sept 2026
Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5 , confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.
Why it matches plant phenotyping methods低コストの生育環境、IMAGING-PAMのスループット向上、植物画像の半自動セグメンテーションとデータ解析を開発・検証しており、植物表現型取得手法が中心です。
abstractWe have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets.
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。Dataset · publicThe scripts described in the text can be downloaded from
https://github.com/CougPhenomics/ImagingPAMProcessing and the accompanying 11 day
dataset can be downloaded from https://doi.org/10.17605/OSF.IO/P32AYOpen asset ↗OSF · 10.17605/OSF.IO/P32AYpdf-page:13 lines:1-51Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
ABSTRACT Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.
Why it matches plant phenotyping methodsトウモロコシの葉数という植物形質を対象に、合成・実画像データセットとCNNによる画像ベース計測手法を開発・評価しており、フェノタイピング手法が中心である。
abstractTwo sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait.
Reproduction assets foundThe paper deposits its phenotyping analysis scripts/source code in a public GitHub repository and used a public Zooniverse project to crowd-score the real-world maize leaf-count images; both are paper-specific, public, and actionable.Code · publicAdditional Information
The scripts and source code employed in this study have been deposited at https://github.com/freemao/MaizeLeafCounting.Images and annotations used in this study have been deposited with CyVerse [27].
The authors declare no competing interests.
References
1. Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. reviews genetics 11, 855 (2010).
2. Furbank, R. T. & Tester, M. Phenomics–technologies to relieve the phenotyping bottOpen asset ↗freemao/MaizeLeafCounting.Imagespdf-raw-page:9 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 9 Sept 2026
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has been developed from using rulers and millimeter papers to the assessment of digitized images, yet it remained a labour-intensive, monotonous and time consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low quality images, produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets, not restricted to Arabidopsis thaliana . Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet , but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user. One-sentence summary A deep learning-based algorithm, providing an adaptable tool for determining hypocotyl or coleoptile length of different plant species.
Why it matches plant phenotyping methods幼植物の表現型である胚軸・子葉鞘長を画像から高スループットに推定する深層学習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。
abstractTo make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method.
Reproduction assets foundThe paper explicitly states that the full open-source analysis code (U-Net-based hypocotyl segmentation/measurement pipeline) is available on GitHub and that the training images (annotated Arabidopsis, Sinapis, Brachypodium seedling images) are publicly available on Kaggle. Both are paper-specific, public, and directlyCode · publicThe code is fully open source and available at GitHub
(https://github.com/biomag-lab/hypocotyl-UNet).Open asset ↗biomag-lab/hypocotyl-UNetpdf-page:9 lines:1-56Dataset · publicImages used for training are also available
at https://www.kaggle.com/tivadardanka/plant-segmentation.Open asset ↗pdf-page:9 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
RNA-based silencing functions as an important antiviral immunity mechanism in plants. Plant viruses evolved to encode viral suppressors of RNA silencing (VSRs) that interfere with the function of key components in the silencing pathway. As effectors in the RNA silencing pathway, ARGONAUTE (AGO) proteins are targeted by some VSRs, such as that encoded by Turnip crinkle virus (TCV). A VSR-deficient TCV mutant was used to identify AGO proteins with antiviral activities during infection. A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green, and blue imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation. This process captured and analyzed growth and leaf color of Arabidopsis (Arabidopsis thaliana) plants in response to virus infection over time. By combining this quantitative phenotypic data with molecular assays to detect local and systemic virus accumulation, AGO2, AGO3, and AGO7 were shown to play antiviral roles during TCV infection. In leaves, AGO2 and AGO7 functioned as prominent nonadditive, anti-TCV effectors, whereas AGO3 played a minor role. Other AGOs were required to protect inflorescence tissues against TCV. Overall, these results indicate that distinct AGO proteins have specialized, modular roles in antiviral defense across different tissues, and demonstrate the effectiveness of image-based phenotyping to quantify disease progression.
Why it matches plant phenotyping methodsPlantCVを用いた画像ベースの色・成長形質解析とセグメンテーションによる病害進行の定量化が明示され、感染植物の表現型取得・解析が主要な方法的貢献として扱われている。
abstractA quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green, and blue imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation.
Reproduction assets foundThe paper explicitly deposits its PlantCV/R image-analysis code on GitHub and its raw input images, analyzed output images, and analysis results on Figshare, both with public URLs stated in the Image Analysis section.Code · publicThe PlantCV and R analysis code and result files are available at GitHub ( https://github.com/carringtonlab/tcv-image-analysis ).Open asset ↗carringtonlab/tcv-image-analysislines:178-192Dataset · publicThe raw input images, the analyzed output images, and analysis results are available at Figshare ( https://doi.org/10.6084/m9.figshare.7599923 ).Open asset ↗10.6084/m9.figshare.7599923lines:178-192Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages and experimental conditions. When comparing the data obtained from this software with that of manual root measurements, we found a high correlation between both methods (R 2 = 0.997). When compared with previous developed software with similar features (BRAT and EZ-Rhizo), MyROOT offered an improved accuracy for root length measurements. Therefore, MyROOT will be of great use to the plant science community by permitting high-throughput root length measurements while saving both labor and time.
Why it matches plant phenotyping methods根長を画像から半自動抽出するソフトウェアを開発し、手動測定および既存ソフトウェアと比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates.
Reproduction assets foundThe authors deposited the MyROOT standalone executable application together with the root-length datasets generated in the study (Figures 3, 5 and S4) in a Zenodo repository with an explicit public DOI, making it a paper-specific, publicly actionable asset.Dataset · publiclable to the plant sciences community through the Plant Image Analysis website (plant‐image‐analysis.org; Lobet et al ., 2013 ) as a standalone executable application. The executable application together with the datasets generated during the current study (from Figures 3 , 5 and S4 ) are available in the [Zenodo] repository, [ https://doi.org/10.5281/zenodo.2552250 ].
Conflict of Interest
The authors declare no conflicts of interest.
Author Contributions
AIC‐D conceived the idea. AG and XS developed the algorithms for the method. AG, XS, IB‐P and DB‐E performed the validation experiments. IB‐P and DB‐E acquired the dataset. XS and AIC‐D designed and supervised the study. IB‐P, AG, XS andOpen asset ↗Zenodo · 10.5281/zenodo.2552250lines:139-168Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
We present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges. We introduce a novel approach to edge classification, which forms an important part of a method to both count the leaves and establish the leaf area of a growing plant from images obtained in a high-throughput phenotyping system. Our technique uses a relatively shallow convolutional neural network to classify image edges as background, plant edge, leaf-on-leaf edge or internal leaf noise. The edges themselves were found using the Canny edge detector and the classified edges can be used with simple image processing techniques to generate a region-based segmentation in which the leaves are distinct. This approach is strong at distinguishing occluding pairs of leaves where one leaf is largely hidden, a situation which has proved troublesome for plant image analysis systems in the past. In addition, we introduce the publicly available plant image dataset that was used for this work.
Why it matches plant phenotyping methods葉画像から葉数・葉面積を抽出するエッジ分類および画像分割手法を開発し、ハイスループット表現型解析で評価するとともにデータセットも公開しており、植物フェノタイピング手法が中心である。
abstractWe present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges.
Reproduction assets foundThe paper introduces the Aberystwyth Leaf Evaluation Dataset (ALED), a public Zenodo deposit containing the Arabidopsis top-down plant images, hand-annotated ground truth, and segmentation evaluation software used directly in this work's leaf segmentation experiments.Dataset · publicArabidopsis plant image dataset
As part of this work we have generated a dataset of several thousand top
down images of growing Arabidopsis plants.
The data has been made available to the wider community as the Aberystwyth Leaf Evaluation Dataset (ALED)
1 1
1
The dataset is hosted at https://zenodo.org and it can be found from
https://doi.org/10.5281/zenodo.168158 .
The images were obtained
using the Photon Systems Instruments (PSI) PlantScreen plant scanner
[ 22 ] at the National Plant Phenomics Centre.
Alongside the images themselves are some ground truth hand-annotations and
software to evaluate leaf level segmentation against this ground truth.Open asset ↗zenodo · 10.5281/zenodo.168158lines:101-172Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana . Results A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. Conclusions HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.
Why it matches plant phenotyping methods植物の表現型取得を目的とする低コスト・オープンソースのハイパースペクトル画像システムを開発し、植物ストレス実験で信頼性と精度を検証しており、手法が研究の中心である。
abstractwe have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner.
Reproduction assets foundThe paper's supporting datasets (growth-environment and hyperspectral reflectance data from the Arabidopsis drought/salt stress experiments) are publicly available in the authors' Cyverse repository; the authors' Ardupy control/analysis software is public on GitHub and archived on Zenodo; and the 3D model files are on Dataset · publicThe datasets supporting the conclusions of this article are available in the Cyverse repository ( https://de.cyverse.org/de/?type=data&folder=/iplant/home/elytas/experiment_repository ).Open asset ↗Cyverselines:379-449Code · publicThese tools, named Ardupy, have been made publicly available on the University of Wisconsin EnSpec organization’s Github page ( https://github.com/EnSpec/Plant_CNC_Controller ) as well as on Zenodo ( https://doi.org/10.5281/zenodo.1406721 )Open asset ↗GitHub · EnSpec/Plant_CNC_Controllerlines:138-145Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .
Why it matches plant phenotyping methods種子形態と放出粘液を画像から同時測定する高スループット手法およびImageJツールを開発・適用しており、植物表現型取得が研究の中心である。
abstractHere, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters.
Reproduction assets foundThe paper's MuSeeQ ImageJ macro (the authors' phenotyping analysis code) is explicitly stated to be freely available, with video tutorials, at the authors' dedicated public website http://MuSeeQ.free.fr, which appears in the allowed URLs.Code · publicThe script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .Open asset ↗MuSeeQ.free.frlines:1-73Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.
Why it matches plant phenotyping methods植物集団の代謝表現型を対象に、メタボロミクス、データマイニング、代謝経路再構築を統合して分類・予測する手法の適用可能性を検証しており、単なる生物学的測定ではない。
abstractTo test the applicability of metabolomics studies for a characterization of plant populations in the field
Reproduction assets foundThe paper's supplementary material, publicly hosted at the Frontiers article page, contains paper-specific phenotyping assets: example plant images of the sampled Arabidopsis populations (Data Sheet S1), metabolomics analysis outputs (PCA loadings of GC-MS/LC-MS metabolites, Jacobian entry tables), and SNP-enriched-genSupplement · publicof the department-associated greenhouse facility for their support and advice.
1
http://www.1001genomes.org
2
http://www.freizeitkarte-osm.de/de/oesterreich.html
3
https://www.rdocumentation.org/packages/stats/versions/3.5.1/topics/hclust
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2018.01556/full#supplementary-material
Figure S1
PCA analysis of primary metabolites.
Click here for additional data file.
Table S1
PCA loadings of GC-MS and LC-MS metabolites.
Click here for additional data file.
Table S2
Table of Jacobian entries and their associated metabolite, pathway and enzyme reaction (EC numberOpen asset ↗lines:91-238Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Microcracks in materials reflect their mechanical properties. The quantification of the number or orientation of such cracks is thus essential in many fields, including engineering and geology. In biology, cracks in soft tissues can reflect adhesion defects, and the analysis of their pattern can help to deduce the magnitude and orientation of tensions in organs and tissues. Here, we describe a semi-automatic method amenable to analyze cell separations occurring in the epidermis of Arabidopsis thaliana seedlings. Our protocol is applicable to any image exhibiting small cracks, and thus also adapted to the analysis of emerging cracks in animal tissues and materials.
Why it matches plant phenotyping methodsArabidopsis幼苗の表皮に生じる細胞分離・亀裂を画像から半自動定量する方法が論文の中心であり、植物組織の形態状態を測定するフェノタイピング手法に該当する。
abstractHere, we describe a semi-automatic method amenable to analyze cell separations occurring in the epidermis of Arabidopsis thaliana seedlings.
Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination (δ 13 C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and δ 13 C further suggests that this variation has consequences on water-use efficiency. Genome wide association analyses indicate a complex genetic architecture underlying not only variation in stomatal patterning but also to its covariation with carbon uptake parameters. Yet, we report two novel QTL affecting δ 13 C independently of stomatal patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and covariation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favour the evolution of drought avoidance mechanisms over drought escape strategies.
Why it matches plant phenotyping methods自動化共焦点顕微鏡による気孔サイズ・密度の表現型取得法を開発し、330系統へ大規模適用しているため、植物表現型計測が中心である。
abstractWe developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully sequenced Arabidopsis thaliana accessions collected throughout the European range of the species.
Reproduction assets foundThe paper's Data Accessibility statement explicitly deposits raw confocal image data, image analysis scripts, and phenotypic data in a Dryad repository, uploads genotypic phenotype means to AraPheno, and provides authors' GWAS and MTMM analysis scripts on GitHub. All are paper-specific, public, and actionable.Dataset · publicRaw image data and image analysis scripts are stored in a Dryad repository ( https://doi.org/10.5061/dryad.n068q74 ). Phenotypic data are provided as supplemental material and included in the Dryad repository.Open asset ↗Dryad · 10.5061/dryad.n068q74lines:153-213Code · publicGWAS scripts are available at https://github.com/arthurkorte/GWAS .Open asset ↗GitHub · arthurkorte/GWASlines:153-213Code · publicMTMM scripts are available at https://github.com/Gregor-Mendel-Institute/mtmm .Open asset ↗GitHub · Gregor-Mendel-Institute/mtmmlines:153-213Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Background High resolution and high throughput genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. More recently, CNNs have been used for plant classification and phenotyping, using individual static images of the plants. On the other hand, dynamic behavior of the plants as well as their growth has been an important phenotype for plant biologists, and this motivated us to study the potential of LSTMs in encoding these temporal information for the accession classification task, which is useful in automation of plant production and care. Methods In this paper, we propose a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for automatic joint feature and classifier learning, compared to using hand-crafted features. In addition, we leverage the potential of LSTMs to study the growth of the plants and their dynamic behaviors as important discriminative phenotypes for accession classification. Moreover, we collected a dataset of time-series image sequences of four accessions of Arabidopsis, captured in similar imaging conditions, which could be used as a standard benchmark by researchers in the field. We made this dataset publicly available. Conclusion The results provide evidence of the benefits of our accession classification approach over using traditional hand-crafted image analysis features and other accession classification frameworks. We also demonstrate that utilizing temporal information using LSTMs can further improve the performance of the system. The proposed framework can be used in other applications such as in plant classification given the environment conditions or in distinguishing diseased plants from healthy ones.
Why it matches plant phenotyping methods植物の時系列画像から成長・動態という表現型を抽出するCNN-LSTM手法と、ベンチマーク用データセットの開発が中心であるため。
abstractwe propose a CNN-LSTM framework for plant classification of various genotypes.
Reproduction assets foundThe paper's authors publicly released their Arabidopsis time-series image dataset used for the CNN-LSTM accession classification, with explicit availability statements and two public URLs (figshare and ANU PhenoCam).Dataset · publicData is publicly available at https://figshare.com/s/e18a978267675059578f or http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zipOpen asset ↗figsharelines:403-478Dataset · publicData is publicly available at https://figshare.com/s/e18a978267675059578f or http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zipOpen asset ↗lines:403-478Code / dataset availability confirmedOpenAlex · arXiv · checked 10 Sept 2026
Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We train a state-of-the-art deep learning segmentation architecture (Mask-RCNN) with a combination of real and synthetic images of Arabidopsis plants. Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC). Our approach also achieves 81% mean performance over all five test datasets.
Why it matches plant phenotyping methods植物画像から個葉を自動セグメンテーションする手法を開発・評価しており、植物表現型抽出のための方法が中心的です。
abstractAutomated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their generated synthetic Arabidopsis dataset (10,000 top-down images with 2D segmentation labels) used for training their leaf segmentation models, hosted on the CSIRO robotics databases page. The Matterport Mask_RCNN repository is a generic third-party library, and the CodaLab L5Dataset · publicOur generated synthetic dataset is publicly available at 3 3
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https://research.csiro.au/robotics/databases . The synthetic dataset contains 10,000 top down images of synthetic Arabidopsis plants and their corresponding 2D segmentation labels.Open asset ↗lines:242-290Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The model species Arabidopsis thaliana has extensive resources to investigate intraspecific trait variability and the genetic bases of ecologically relevant traits. However, the cost of equipment and software required for high-throughput phenotyping is often a bottleneck for large-scale studies, such as mutant screening or quantitative genetics analyses. Simple tools are needed for the measurement of fitness-related traits, like relative growth rate and fruit production, without investment in expensive infrastructures. Here, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera. We developed two models to predict plant dry mass and fruit number from the parameters extracted with the analysis of rosette and inflorescence images. Predictive models were trained by sacrificing growing individuals for dry mass estimation, and manually measuring a fraction of individuals for fruit number at maturity. Using a cross-validation approach, we showed that quantitative parameters extracted from image analysis predicts more 90% of both plant dry mass and fruit number. When used on 451 natural accessions, the method allowed modeling growth dynamics, including relative growth rate, throughout the life cycle of various ecotypes. Estimated growth-related traits had high heritability (0.65 < H 2 < 0.93), as well as estimated fruit number ( H 2 = 0.68). In addition, we validated the method for estimating fruit number with rev5 , a mutant with increased flower abortion. The method we propose here is an application of automated computerization of plant images with ImageJ, and subsequent statistical modeling in R. It allows plant biologists to measure growth dynamics and fruit number in hundreds of individuals with simple computing steps that can be repeated and adjusted to a wide range of laboratory conditions. It is thus a flexible toolkit for the measurement of fitness-related traits in large populations of a model species.
Why it matches plant phenotyping methods画像から植物乾物重・果実数・成長動態を推定する手法を開発し、交差検証と変異体で検証しており、表現型取得・推定法が研究の中心である。
abstractHere, we describe methods that enable the estimation of biomass accumulation and fruit number from the analysis of rosette and inflorescence images taken with a regular camera.
Reproduction assets foundThe paper's availability statement explicitly links authors' code on GitHub and the phenotypic dataset on Dryad; supplementary files also contain the ImageJ macros and R code used for the phenotyping analysis.Code · publicCodes are available on Github ( https://github.com/fvasseur ), and phenotypic data are available on the Dryad repository ( https://doi.org/10.5061/dryad.343bd84 ) [ 43 ].Open asset ↗github.com/fvasseurlines:312-394Dataset · publicphenotypic data are available on the Dryad repository ( https://doi.org/10.5061/dryad.343bd84 ) [ 43 ].Open asset ↗Dryad · 10.5061/dryad.343bd84lines:312-394Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Motivation The fundamental challenge of modern genetic analysis is to establish gene-phenotype correlations that are often found in the large-scale publications. Because lexical features of gene are relatively regular in text, the main challenge of these relation extraction is phenotype recognition. Due to phenotypic descriptions are often study- or author-specific, few lexicon can be used to effectively identify the entire phenotypic expressions in text, especially for plants. Results We have proposed a pipeline for extracting phenotype, gene and their relations from biomedical literature. Combined with abbreviation revision and sentence template extraction, we improved the unsupervised word-embedding-to-sentence-embedding cascaded approach as representation learning to recognize the various broad phenotypic information in literature. In addition, the dictionary- and rule-based method was applied for gene recognition. Finally, we integrated one of famous information extraction system OLLIE to identify gene-phenotype relations. To demonstrate the applicability of the pipeline, we established two types of comparison experiment using model organism Arabidopsis thaliana. In the comparison of state-of-the-art baselines, our approach obtained the best performance (F1-Measure of 66.83%). We also applied the pipeline to 481 full-articles from TAIR gene-phenotype manual relationship dataset to prove the validity. The results showed that our proposed pipeline can cover 70.94% of the original dataset and add 373 new relations to expand it. Availability and implementation The source code is available at http://www.wutbiolab.cn: 82/Gene-Phenotype-Relation-Extraction-Pipeline.zip. Supplementary information Supplementary data are available at Bioinformatics online.
Why it matches plant phenotyping methods植物の表現型情報を文献から認識・抽出し、遺伝子との関係を構築する計算パイプライン自体が研究の中心であり、Arabidopsisで性能評価も行っている。
abstractWe have proposed a pipeline for extracting phenotype, gene and their relations from biomedical literature.
Reproduction assets foundThe paper's authors explicitly state that the source code for their gene–phenotype relationship extraction pipeline (the computational analysis of this paper) is publicly available for download at their lab's URL (wutbiolab.cn). No paper-specific phenotype datasets, images, or trained model checkpoints are described asCode · publicThe source code is available at http://www.wutbiolab.cn: 82/Gene-Phenotype-Relation-Extraction-Pipeline.zip .Open asset ↗Gene-Phenotype-Relation-Extraction-Pipelinelines:1-33Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water-loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination ({delta}13C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and {delta}13C further suggests that this variation has consequences on water-use efficiency. Genome-wide association analyses indicate a complex genetic architecture underlying not only variation in stomata patterning but also to its co-variation with carbon uptake parameters. Yet, we report two novel QTL affecting {delta}13C independently of stomata patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and co-variation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favor the evolution of drought avoidance mechanisms over drought escape strategies.
Why it matches plant phenotyping methods自動共焦点顕微鏡法を開発し、330系統で気孔サイズ・密度という植物形質を大規模に測定しており、表現型取得法が研究の中心です。
abstractWe developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species.
Reproduction assets foundThe data accessibility statement lists public, paper-specific assets: phenotypic (stomata/δ13C) data to be deposited in AraPheno with a public URL, and the authors' GWAS and MTMM analysis scripts on GitHub. Raw images and image-analysis scripts are only available upon request (Dryad deposit pending acceptance), so theyCode · public1001genomes.org, (Seren et
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al., 2017) and stored in a Dryad repository upon acceptance. Additionally, we provide an R
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corresponding R code used to create the figures and statistics in the supplemental material.
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GWAS scripts are available at https://github.com/arthurkorte/GWAS. MTMM scripts are
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Genomic data used is publicly available in the 1001 genomes database (Alonso-Blanco et al.,
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JdM, AK, and HD conceived the study. HD conducted the experiment and produced
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phenotypic data for stomaOpen asset ↗arthurkorte/GWASpdf-raw-page:35 lines:1-46Code · publicory upon acceptance. Additionally, we provide an R
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corresponding R code used to create the figures and statistics in the supplemental material.
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GWAS scripts are available at https://github.com/arthurkorte/GWAS. MTMM scripts are
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Genomic data used is publicly available in the 1001 genomes database (Alonso-Blanco et al.,
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JdM, AK, and HD conceived the study. HD conducted the experiment and produced
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provideOpen asset ↗Gregor-Mendel-Institute/mtmm.925pdf-raw-page:35 lines:1-46Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementObject detectionTrackingRoot system architecture
Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging however, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom-up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages. Upon comparing the data obtained using this software with that of manual root measurements, we found that there are no significant differences (t-test, p-value < 0.05). Thus, MyROOT will be of great aid to the plant science community by permitting high-throughput root length measurements while saving on both labor and time.
Why it matches plant phenotyping methods根長という植物形質の半自動画像計測ソフトウェアを開発し、手動測定との比較で検証しており、フェノタイピング手法が研究の中心です。
abstractwe have developed MyROOT, a novel software for the semi-automatic quantification of root growth of seedlings growing directly in agar plates.
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 · publicMyROOT software is available at https://www.cragenomica.es/research-Open asset ↗pdf-page:3 lines:1-45Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The formation of crossovers during meiosis is pivotal for the redistribution of traits among the progeny of sexually reproducing organisms. In plants the molecular mechanisms underlying the formation of crossovers have been well established, but relatively little is known about the factors that determine the exact location and the frequency of crossover events in the genome. In the model plant species Arabidopsis , research on these factors has been greatly facilitated by reporter lines containing linked fluorescence marker genes under control of promoters active in seeds or pollen, allowing for the visualization of crossover events by fluorescence microscopy. However, the usefulness of these reporter lines to screen for novel modulators of crossover frequency in a high throughput manner relies on the availability of programs that can accurately count fluorescent seeds. Such a program was previously not available in scientific literature. Results Here we present MeioSeed, a novel CellProfiler-based program that accurately counts GFP and RFP fluorescent Arabidopsis seeds with adjustable detection thresholds for fluorescence intensity, making use of a robust seed classifier which was trained by machine learning in Ilastik. Using the previously published reporter line Col3-4/20 as an example, we explain the use of MeioSeed and the steps taken to optimize the thresholding settings of the program to fit the published model for recombination frequency and transgene segregation. The use of MeioSeed is illustrated by investigating salt stress as a novel abiotic trigger for changes in crossover frequency in Col3-4/20 (♂) × Ler-0 (♀) F 1 hybrids. Salt stress was found to trigger increases in crossover frequency between the marker genes of up to 70% compared to the control treatment without salt stress. Genotyping of control and salt treated populations revealed that the changes in crossover frequency were not limited to the region between the marker genes, but that fluctuations in crossover frequency are likely to occur genome-wide after treatment with high salt concentrations. Conclusions MeioSeed allows for the high throughput recognition and counting of fluorescent Arabidopsis seeds and can facilitate the screening for novel abiotic and biotic modulators of crossover frequency using reporter lines in Arabidopsis .
Why it matches plant phenotyping methods蛍光種子を機械学習・画像解析で高スループットに認識・計数し、交差頻度という植物の遺伝的状態を推定するソフトウェア開発が中心であるため。
abstractHere we present MeioSeed, a novel CellProfiler-based program that accurately counts GFP and RFP fluorescent Arabidopsis seeds with adjustable detection thresholds for fluorescence intensity, making use of a robust seed classifier which was trained by machine learning in Ilastik.
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 MeioSeed package is available at http://cellprofiler.org/examples/published_pipelines .Open asset ↗lines:44-51Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Most effective nematicides for the control of root-knot nematodes are banned, which demands a better understanding of the plant-nematode interaction. Understanding how gene expression in the nematode-feeding sites relates to morphological features may assist a better characterization of the interaction. However, nematode-induced galls resulting from cell-proliferation and hypertrophy hinders such observation, which would require tissue sectioning or clearing. We demonstrate that a method based on the green auto-fluorescence produced by glutaraldehyde and the tissue-clearing properties of benzyl-alcohol/benzyl-benzoate preserves the structure of the nematode-feeding sites and the plant-nematode interface with unprecedented resolution quality. This allowed us to obtain detailed measurements of the giant cells’ area in an Arabidopsis line overexpressing CHITINASE-LIKE-1 (CTL1) from optical sections by confocal microscopy, assigning a role for CTL1 and adding essential data to the scarce information of the role of gene repression in giant cells. Furthermore, subcellular structures and features of the nematodes body and tissues from thick organs formed after different biotic interactions, i.e., galls, syncytia, and nodules, were clearly distinguished without embedding or sectioning in different plant species (Arabidopsis, cucumber or Medicago). The combination of this method with molecular studies will be valuable for a better understanding of the plant-biotic interactions.
Why it matches plant phenotyping methods根こぶ線虫摂食部位の構造を共焦点画像から高解像度に取得し、巨大細胞面積を測定する植物フェノタイピング法の開発が中心である。
titleA Phenotyping Method of Giant Cells from Root-Knot Nematode Feeding Sites by Confocal Microscopy Highlights a Role for CHITINASE-LIKE 1 in Arabidopsis
Reproduction assets foundThe paper describes a confocal-microscopy phenotyping method for nematode-induced giant cells. The only paper-specific public asset referenced is the authors' supplementary material (hosted at MDPI), which per the text contains Table S1 (gene filtering results) and Videos S6–S9 of the confocal optical sections used forSupplement · public(PEII-2014-020-P to Carmen Fenoll). Javier Cabrera is supported by a Cytema-Santander contract from Universidad de Castilla-La Mancha. Christian Hermans is a research associate from Fonds de la Recherche Scientifique—National Fund for Scientific Research (Belgium).
Supplementary Materials
Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/2/429/s1 and www.mdpi.com/1422-0067/19/2/429/s2 .
Click here for additional data file.
Click here for additional data file.
Author Contributions
Javier Cabrera, Rocio Olmo, Virginia Ruiz-Ferrer, and Christian Hermans conceived and designed the experiments; Javier Cabrera, Rocio Olmo, Virginia Ruiz-Ferrer, Christian Hermans, and Isabel Open asset ↗lines:51-66Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Results Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way. Conclusions Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.
Why it matches plant phenotyping methods植物の画像から複数の成長形質を自動抽出するソフトウェアの開発・評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractwe present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application
Reproduction assets foundThe authors explicitly state that the Leaf-GP software package, source code, executables, the 4.3 GB raw image datasets, processed images, and phenotypic CSV trait measurements for the Arabidopsis and wheat case studies are freely available at the public GitHub releases URL.Dataset · publicAll the 4.3 GB image datasets as well as The Leaf-GP software package and source code are freely available from our online repository https://github.com/Crop-Phenomics-Group/Leaf-GP/releasesOpen asset ↗Crop-Phenomics-Group/Leaf-GPlines:367-436Code / 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 confirmedarXiv · OpenAlex · checked 15 Sept 2026
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Why it matches plant phenotyping methods植物表現型解析用の合成画像生成ネットワークを開発し、葉数を条件付けたデータセットを作成・評価しており、表現型取得・解析ワークフローの技術的貢献が中心である。
abstractWe propose a new Ax dataset of artificial plants images, obtained by our ARIGAN.
Reproduction assets foundThe paper's authors publicly released the Ax dataset of 57 synthetic Arabidopsis plant images generated by ARIGAN (with leaf-count annotations in a CSV), which directly reproduces the paper's phenotyping data contribution. The CVPPP 2017 LCC dataset is the training input but is cited prior work, not a paper-specific.Dataset · publicr quantitative experiments show that the extension of the training dataset with the images in Ax improved the testing error and reduced overfitting. We run a 4-fold cross validation experiment on A4 dataset. Evaluation metrics of our experiments are reported in Table 1 . Our synthetic dataset Ax is available to download at \url http://www.valeriogiuffrida.academy/ax.
Acknowledgements
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, and also by the BBSRC grant BB/P023487/1.
References
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F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron,
N. Bouchard, and Y. Bengio.
Theano: new features and speed improvements.
Deep LearninOpen asset ↗Axlines:101-153Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 15 Sept 2026
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DC-GAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128 x 128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Why it matches plant phenotyping methods植物フェノタイピング用の合成画像生成手法を開発し、葉数条件付き生成とデータセットの評価を行っており、表現型データ取得・解析基盤が研究の中心です。
abstractWe propose a new Ax dataset of artificial plants images, obtained by our ARIGAN.
Reproduction assets foundThe paper's authors publicly released their synthetic Ax dataset of 57 GAN-generated Arabidopsis plant images, with an explicit download URL stated in the paper. This is a paper-specific, publicly available asset directly tied to this paper's phenotyping analysis. No code or trained model availability is stated.Dataset · publicBatch normalization: Accelerating
Evaluation metrics of our experiments are reported in Ta- deep network training by reducing internal covariate shift.
In F. Bach and D. Blei, editors, Proceedings of the 32nd In-
ble 1. Our synthetic dataset Ax is available to download at
ternational Conference on Machine Learning, volume 37 of
http://www.valeriogiuffrida.academy/ax. Proceedings of Machine Learning Research, pages 448–456,
Lille, France, 07–09 Jul 2015. PMLR.
Acknowledgements [14] Y. LeCunn. The MNIST database of handwritten digits,
http://yann.lecun.com/exdb/mnist/.
This work was supported by The Alan Turing Institute un- [15] A. L. Maas, A. Y. Hannun, and A. Y. Ng. Rectifier non-
der theOpen asset ↗pdf-layout-page:7 lines:1-56Code / 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 · OpenAlex · bioRxiv · checked 15 Sept 2026
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Results Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively. Conclusions Leaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases .
Why it matches plant phenotyping methods植物の画像から複数の成長形質を自動抽出するソフトウェアと解析ワークフローが研究の中心であり、異なる画像装置での評価も行っているため。
abstractwe present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application
Reproduction assets foundThe paper's authors publicly released the Leaf-GP phenotyping software (source code and executables) together with the 4.3 GB raw image datasets and experimental results used in the Arabidopsis and wheat growth studies, via their GitHub releases repository.Code · publich Park, Norwich UK
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2
John Innes Centre, Norwich Research Park, Norwich UK
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3
University of East Anglia, Norwich Research Park, Norwich UK
648
649
Availability of data and materials
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All the 4.3 GB image datasets as well as The Leaf-GP software package and source code are freely
651
available from our online repository https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.652
653
Open Access
654
The software is distributed under the terms of the Creative Commons Attribution 4.0 International
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License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
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reproduction in any medium, provided you give appropriate credit to the originOpen asset ↗https://github.com/Crop-Phenomics-Group/Leaf-GP/releasespdf-raw-page:14 lines:1-97Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Guard cells dynamically adjust their shape in order to regulate photosynthetic gas exchange, respiration rates and defend against pathogen entry. Cell shape changes are determined by the interplay of cell wall material properties and turgor pressure. To investigate this relationship between turgor pressure, cell wall properties and cell shape, we focused on kidney-shaped stomata and developed a biomechanical model of a guard cell pair. Treating the cell wall as a composite of the pectin-rich cell wall matrix embedded with cellulose microfibrils, we show that strong, circumferentially oriented fibres are critical for opening. We find that the opening dynamics are dictated by the mechanical stress response of the cell wall matrix, and as the turgor rises, the pectinaceous matrix stiffens. We validate these predictions with stomatal opening experiments in selected Arabidopsis cell wall mutants. Thus, using a computational framework that combines a 3D biomechanical model with parameter optimization, we demonstrate how to exploit subtle shape changes to infer cell wall material properties. Our findings reveal that proper stomatal dynamics are built on two key properties of the cell wall, namely anisotropy in the form of hoop reinforcement and strain stiffening.
Why it matches plant phenotyping methods3D生体力学モデルとパラメータ最適化により、気孔の形状変化から細胞壁の物性を推定する方法を開発し、変異体実験で検証しており、表現型取得・推定が研究の中心である。
abstractusing a computational framework that combines a 3D biomechanical model with parameter optimization, we demonstrate how to exploit subtle shape changes to infer cell wall material properties
Reproduction assets foundThe paper's authors explicitly state that all simulation, data-processing, and graphing scripts for their guard cell biomechanical model are freely available in a public GitHub repository (stomasimulator), which is a paper-specific, publicly actionable code asset. No separate public phenotype dataset deposit is stated;Code · publicAll of the scripts used to run these simulations, process the data and generate the graphs are freely available at https://github.com/woolfeh/stomasimulator .Open asset ↗https://github.com/woolfeh/stomasimulator · stomasimulatorlines:410-476Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 14 Sept 2026
Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana .
Why it matches plant phenotyping methods植物フェノタイピング向けのオープンソース深層学習ツールを開発し、複数のベンチマークで性能評価しているため、表現型取得・解析手法が研究の中心です。
abstractwe introduce an open-source deep learning tool called Deep Plant Phenomics.
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 · publicDeep Plant Phenomics is available for download at https://github.com/usaskdapper/deepplantphenomics . Detailed documentation describing installation and usage of the platform is available in the software repository.Open asset ↗usaskdapper/deepplantphenomicslines:49-57Code / 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 · Crossref · OpenAlex · checked 14 Sept 2026
Background Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling development in the context of a high-throughput chemical screen. We have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored under conditions where the phenotypic effects of large numbers of small molecules can be assessed. Results The 'Microphenotron' platform uses 96-well microtitre plates to deliver chemical treatments to seedlings of Arabidopsis thaliana L. and is based around four components: (a) the 'Phytostrip', a novel seedling growth device that enables chemical treatments to be combined with the automated capture of images of developing roots and shoots; (b) an illuminated robotic platform that uses a commercially available robotic manipulator to capture images of developing shoots and roots; (c) software to control the sequence of robotic movements and integrate these with the image capture process; (d) purpose-made image analysis software for automated extraction of quantitative phenotypic data. Imaging of each plate (representing 80 separate assays) takes 4 min and can easily be performed daily for time-course studies. As currently configured, the Microphenotron has a capacity of 54 microtitre plates in a growth room footprint of 2.1 m 2 , giving a potential throughput of up to 4320 chemical treatments in a typical 10 days experiment. The Microphenotron has been validated by using it to screen a collection of 800 natural compounds for qualitative effects on root development and to perform a quantitative analysis of the effects of a range of concentrations of nitrate and ammonium on seedling development. Conclusions The Microphenotron is an automated screening platform that for the first time is able to combine large numbers of individual chemical treatments with a detailed analysis of whole-seedling development, and particularly root system development. The Microphenotron should provide a powerful new tool for chemical genetics and for wider chemical biology applications, including the development of natural and synthetic chemical products for improved agricultural sustainability.
Why it matches plant phenotyping methodsロボット撮像、画像解析、定量的形質抽出を統合した植物表現型解析プラットフォームの開発・検証が中心である。
abstractWe have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored
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 · publicAutoRoot, the software for automated analysis of the images [ 19 ], is Open Source and can be downloaded from https://zenodo.org/ , and the Phytostrips are available to purchase by contacting the corresponding author.Open asset ↗zenodolines:107-110Code / 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:94-155Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
ArabidopsisLeafGrowth / time-series analysisGrowth / development / phenology
Background The analysis of circadian leaf movement rhythms is a simple yet effective method to study effects of treatments or gene mutations on the circadian clock of plants. Currently, leaf movements are analysed using time lapse photography and subsequent bioinformatics analyses of leaf movements. Programs that are used for this purpose either are able to perform one function (i.e. leaf tip detection or rhythm analysis) or their function is limited to specific computational environments. We developed a leaf movement analysis tool-PALMA-that works in command line and combines image extraction with rhythm analysis using Fast Fourier transformation and non-linear least squares fitting. Results We validated PALMA in both simulated time series and in experiments using the known short period mutant sensitivity to red light reduced 1 ( srr1 - 1 ). We compared PALMA with two established leaf movement analysis tools and found it to perform equally well. Finally, we tested the effect of reduced iron conditions on the leaf movement rhythms of wild type plants. Here, we found that PALMA successfully detected period lengthening under reduced iron conditions. Conclusions PALMA correctly estimated the period of both simulated and real-life leaf movement experiments. As a platform-independent console-program that unites both functions needed for the analysis of circadian leaf movements it is a valid alternative to existing leaf movement analysis tools.
Why it matches plant phenotyping methodsPALMAは、植物の葉運動という表現型を画像から抽出し、周期を解析するソフトウェアとして開発・検証されており、方法論が研究の中心です。
abstractWe developed a leaf movement analysis tool-PALMA-that works in command line and combines image extraction with rhythm analysis using Fast Fourier transformation and non-linear least squares fitting.
Reproduction assets foundThe paper's authors publicly deposited the PALMA leaf movement analysis code (Additional file 2) on SourceForge; the URL matches an allowed entry. No phenotype datasets or images were separately deposited.Code · publicThe PALMA code has been provided as a supplemental file (Additional file 2 ) and can be found on Sourceforge.net ( https://sourceforge.net/projects/palma-leafmov/ ).Open asset ↗Sourceforge.net · palma-leafmovlines:144-160Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plants are permanently exposed to rapidly changing environments, therefore it is evident that they had to evolve mechanisms enabling them to dynamically adapt to such fluctuations. Here we study how plants can be trained to enhance their photoprotection and elaborate on the concept of the short-term illumination memory in Arabidopsis thaliana. By monitoring fluorescence emission dynamics we systematically observe the extent of non-photochemical quenching (NPQ) after previous light exposure to recognise and quantify the memory effect. We propose a simplified mathematical model of photosynthesis that includes the key components required for NPQ activation, which allows us to quantify the contribution to photoprotection by those components. Due to its reduced complexity, our model can be easily applied to study similar behavioural changes in other species, which we demonstrate by adapting it to the shadow-tolerant plant Epipremnum aureum. Our results indicate that a basic mechanism of short-term light memory is preserved. The slow component, accumulation of zeaxanthin, accounts for the amount of memory remaining after relaxation in darkness, while the fast one, antenna protonation, increases quenching efficiency. With our combined theoretical and experimental approach we provide a unifying framework describing common principles of key photoprotective mechanisms across species in general, mathematical terms.
Why it matches plant phenotyping methodsNPQ蛍光動態を用いた植物の光防御状態の定量と、他種にも適用可能な数学モデルの開発が研究の中心であり、単なる生物学的 routine 測定ではない。
abstractBy monitoring fluorescence emission dynamics we systematically observe the extent of non-photochemical quenching (NPQ) after previous light exposure to recognise and quantify the memory effect.
Reproduction assets foundThe authors explicitly provide open-source code (the npqmodel repository) that reproduces all figures in the paper, including the simulations of the PAM fluorescence measurements. No separate public phenotype dataset deposit is stated (data extraction is referenced only vaguely as 'the database').Code · publiccode (available from https://github.com/QTB-HHU/npqmodel), with chlorophyll fluorescence quenching in spinach thylakoids from light treated orOpen asset ↗QTB-HHU/npqmodelpdf-page:9 lines:1-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
A major challenge in plant systems biology is the development of robust, predictive multiscale models for organ growth. In this context it is important to bridge the gap between the, rather well-documented molecular scale and the organ scale by providing quantitative methods to study within-organ growth patterns. Here, we describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs that does not require adding markers at the organ surface. The method allows for the simultaneous analysis of root and hypocotyl growth, provides spatio-temporal information on curvature, growth anisotropy and relative elemental growth rate and can cope with complex organ movements. We demonstrate the performance of the method by documenting previously unsuspected complex growth patterns within the growing hypocotyl of the model species Arabidopsis thaliana during normal growth, after treatment with a growth-inhibiting drug or in a mechano-sensing mutant. The method is freely available as an intuitive and user-friendly Matlab application called KymoRod.
Why it matches plant phenotyping methods植物器官の成長パターン、曲率、成長異方性、相対元素成長率を自動抽出する手法とソフトウェアを開発しており、植物表現型取得が研究の中心です。
abstractwe describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs
Reproduction assets foundThe paper's KymoRod Matlab application for automated kinematic analysis of rod-shaped plant organs is explicitly stated to be freely available on the authors' public GitHub repository (ijpb/KymoRod). This is the authors' analysis code implementing the paper's phenotyping method. No public phenotype dataset or image de-Code · publicwe have presented a simple and robust
method for the analysis of sub-organ growth patterns in
plant seedlings. Its performance exceeds that of previous
methods that are mostly too laborious for the analysis of
large numbers of samples. The method is packaged in
KymoRod, a user-friendly application freely available on
internet (http://github.com/ijpb/KymoRod), which should
facilitate the study of the cellular basis of organ growth for
non-specialist users.
EXPERIMENTAL PROCEDURES
Plant growth, image acquisition and pre-treatment
Arabidopsis seeds (genotypes Col-0 and fer-4; Duan et al., 2010)
were surface sterilized (Santoni et al., 1994), plated on Arabidop-
sis medium (Santoni etOpen asset ↗ijpb/KymoRodpdf-raw-page:6 lines:1-205Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract With the implementation of novel automated, high throughput methods and facilities in the last years, plant phenomics has developed into a highly interdisciplinary research domain integrating biology, engineering and bioinformatics. Here we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress). The result is a comprehensive dataset of images and extracted phenotypical features. Such datasets require detailed documentation, standardized description of experimental metadata as well as sustainable data storage and publication in order to ensure the reproducibility of experiments, data reuse and comparability among the scientific community. Therefore the here presented dataset has been annotated using the standardized ISA-Tab format and considering the recently published recommendations for the semantical description of plant phenotyping experiments.
Why it matches plant phenotyping methods画像解析による高スループット植物フェノタイピング実験の画像・抽出形質データセットを提示し、再現性とデータ再利用のための標準化記述も扱うため、方法論的役割が中心です。
abstractHere we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress).
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 IAP software is an open-source project and is available at https://github.com/OpenImageAnalysisGroup/IAP. For the presented analysis version 2.0.2 was used.Open asset ↗html-lines:43-61Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
I show that Arabidopsis leaf growth can be described with good precision by a conformal map, where expansion is locally isotropic (the same in all directions) but the amount of expansion can vary with position. Data obtained by tracking leaf growth over time can be reproduced with almost 90% accuracy by such a map. The growth follows a Möbius transformation, which is a type of conformal map that would arise if there were an underlying linear gradient of growth rate. From the data one can derive the parameters that describe this linear gradient and show how it changes over time. Such a rule has the property of maintaining the flatness of a leaf.
Why it matches plant phenotyping methods葉の経時追跡データから成長を定量化し、成長勾配パラメータを推定する数理モデルが研究の中心であり、植物成長表現型の抽出手法に該当する。
abstractData obtained by tracking leaf growth over time can be reproduced with almost 90% accuracy by such a map.
Reproduction assets foundThe paper's analysis is built on publicly available Arabidopsis leaf bead-tracking data (from Remmler & Rolland-Lagan 2012 and Rolland-Lagan et al. 2014), which the authors state are deposited with a package of Matlab leaf-shape analysis programs at a public repository handle. This is a paper-specific, publicly and行动可及Dataset · publicthe Discussion that re-
conciles the anisotropy of clones with the conformal model.
A.1. Data and data-analysis programs
The two papers from Prof. Rolland-Lagan's laboratory, (Re-
mmler and Rolland-Lagan, 2012; Rolland-Lagan et al., 2014), give a
thorough description of growth patterns and point the reader to-
wards a website (http://hdl.handle.net/10393/30401) where the
bead data for individual leaves are available, and also a package of
Matlab programs for leaf shape analysis. Leaves are referred to by
pot and plant numbers in their data sets, and Table A1 shows how
this relates to the numbering used here.
A.2. Complex functions and their derivatives
Let f be a complex analytic funOpen asset ↗pdf-raw-page:9 lines:1-233