Machine learning and feature analysis of the cortical microtubule organization of Arabidopsis cotyledon pavement cells

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

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

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

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.

Machine Learning Accelerates Screening of Osteoclast Differentiation Inhibitors from Natural Products

Natural products that inhibit osteoclast differentiation are promising therapeutic and preventive agents for osteoporosis. Conventionally, identifying osteoclast differentiation involves visual inspection of the microscope images of stained osteoclasts. In this study, a supervised machine learning model was developed to classify bright-field microscope images of osteoclasts without staining. The model was used to screen a compound library, and osteoclast differentiation inhibitors were identified, demonstrating the validity of our method. Next, an in-house library of fungal extracts was screened, and pinolidoxin was revealed as an inhibitor of osteoclast differentiation. Our machine learning method enabled accurate, objective, and high-throughput evaluation of osteoclast differentiation and efficient screening of the inhibitors from natural product extracts. This study represents the first machine learning classification developed to evaluate the inhibitory activity of natural products in osteoclast differentiation.

Comprehensive and quantitative analysis of intracellular structure polarization at the apical-basal axis in elongating Arabidopsis zygotes

A comprehensive and quantitative evaluation of multiple intracellular structures or proteins is a promising approach to provide a deeper understanding of and new insights into cellular polarity. In this study, we developed an image analysis pipeline to obtain intensity profiles of fluorescent probes along the apical-basal axis in elongating Arabidopsis thaliana zygotes based on two-photon live-cell imaging data. This technique showed the intracellular distribution of actin filaments, mitochondria, microtubules, and vacuolar membranes along the apical-basal axis in elongating zygotes from the onset of cell elongation to just before asymmetric cell division. Hierarchical cluster analysis of the quantitative data on intracellular distribution revealed that the zygote may be compartmentalized into two parts, with a boundary located 43.6% from the cell tip, immediately after fertilization. To explore the biological significance of this compartmentalization, we examined the positions of the asymmetric cell divisions from the dataset used in this distribution analysis. We found that the cell division plane was reproducibly inserted 20.5% from the cell tip. This position corresponded well with the midpoint of the compartmentalized apical region, suggesting a potential relationship between the zygote compartmentalization, which begins with cell elongation, and the position of the asymmetric cell division.