Virtual staining from bright-field microscopy for label-free quantitative analysis of plant cell structures
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Abstract
The applicability of a deep learning model for the virtual staining of plant cell structures using bright-field microscopy was investigated. The training dataset consisted of microscopy images of tobacco BY-2 cells with the plasma membrane stained with the fluorescent dye PlasMem Bright Green and the cell nucleus labeled with Histone-red fluorescent protein. The trained models successfully detected the expansion of cell nuclei upon aphidicolin treatment and a decrease in the cell aspect ratio upon propyzamide treatment, demonstrating its utility in cell morphometry. The model also accurately documented the shape of Arabidopsis pavement cells in both wild type and the bpp125 triple mutant, which has an altered pavement cell phenotype. Metrics such as cell area, circularity, and solidity obtained from virtual staining analyses were highly correlated with those obtained by manual measurements of cell features from microscopy images. Furthermore, the versatility of virtual staining was highlighted by its application to track chloroplast movement in Egeria densa . The method was also effective for classifying live and dead BY-2 cells using texture-based machine learning, suggesting that virtual staining can be applied beyond typical segmentation tasks. Although this method still has some limitations, its non-invasive nature and efficiency make it highly suitable for label-free, dynamic, and high-throughput analyses in quantitative plant cell biology.
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When virtual staining of the plasma membrane was applied to these images, live cells displayed clear and intact cell contours, while dead cells exhibited shrunken and collapsed internal cell membranes (Fig. 7a)
This is a cool application of virtual staining that would eliminate the need for a live-dead stain. Did you confirm the brightfield morphologies with a viability stain?
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Circularity was defined as 4πSL–2, where S and L represent the cell area and perimeter, respectively. The highest value of 1 indicates a perfect circle, whereas the lowest value of 0 indicates a highly complex shape
Examining the cell's eccentricity may be interesting if it captures something not reflected in the circularity calculation.
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These training and test images are publicly available
Were all images acquired on the same day, or were these compiled across multiple imaging days? Given the day-to-day variability that imaging experiments can have (sample prep and microscope performance), it would be worth noting the number of images acquired per day that ended up in the dataset. How was the splitting of images into training and test datasets determined?
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561
Was 561 nm the optimal excitation for chloroplasts in this sample? I would expect the optimal excitation to be either between 400-460 nm or 600-750 nm.
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UPlanXApo ×20; Olympus
A more standard way to report the objective would be "Olympus UPlanApo 20x"
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