SSBD:database

Key Organism Strain Cell Line Gene Symbol Protein name Protein tag Reporter Project ID Dataset ID Title Description Data Type Gene Ontology Biological Imaging Methods Method Summary Paper Information Medical Subject Headlines Person

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465-Ichita-VirtualStainPlant

https://ssbd.riken.jp/database/465-Ichita-VirtualStainPlant/

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ssbd-repos-000465

https://ssbd.riken.jp/repository/465/

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Image datasets for training and evaluating virtual staining of plant cell structures and cell viability classification

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.

2026-09-30

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CC BY 4.0

Image data based on Experiment

7 ( Image datasets: 7, Quantitative data datasets: 0 )

9.3 GB ( Image datasets: 9.3 GB, Quantitative data datasets: 0 bytes )

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Nicotiana tabacum, Egeria densa

TBY-2 cell

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plasma membrane nucleus, vacuole, plasma membrane, chloroplast

spinning disk confocal microscopy, Transmitted bright-field microscopy

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Takumi Higaki , Kumamoto University , Graduate School of Science and Technology , Graduate School of Science and Technology

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