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

The measurement of cytoskeletal features can provide valuable insights into cell biology. In recent years, digital image analysis of cytoskeletal features has become an important research tool for quantitative evaluation of cytoskeleton organization. In this study, we examined the utility of a supervised machine learning approach with digital image analysis to distinguish different cellular organizational patterns. We focused on the jigsaw puzzle-shaped pavement cells of Arabidopsis thaliana. Measurements of three features of cortical microtubules in these cells (parallelness, density, and the coefficient of variation of the intensity distribution of fluorescently labeled cytoskeletons [as an indicator of microtubule bundling]) were obtained from microscopic images. A random forest machine learning model was then used with these images to differentiate mutant and wild type, and Taxol-treated and control cells. Using these three metrics, we were able to distinguish wild type from bpp125 triple mutant cells, with approximately 80% accuracy; classification accuracy was 88% for control and Taxol-treated cells. Different features contributed most to the classification, namely, coefficient of variation for the wild-type/mutant cells and parallelness for the Taxol-treated/control cells. The random forest method used enabled quantitative evaluation of the contribution of features to the classification, and partial dependence plots showed the relationships between metric values and classification accuracy. While further improvements to the method are needed, our small-scale analysis shows the potential for this approach in large-scale screening analyses.