Polarity-JaM: An image analysis toolbox for cell polarity, junction and morphology quantification

Cell polarity involves the asymmetric distribution of cellular components such as signaling molecules and organelles within a cell, asymmetries of a cell’s shape as well as contacts with neighboring cells. Advances in fluorescence microscopy combined with deep learning algorithms for image segmentation open up a wealth of possibilities to understand cell polarity behavior in health and disease. We have therefore developed the open-source package Polarity-JaM, which offers versatile methods for performing reproducible exploratory image analysis. Multi-channel single-cell segmentation is performed using a flexible and user-friendly interface to state-of-the-art deep learning algorithms. Interpretable single-cell features are automatically extracted, including cell and organelle orientation, cell-cell contact morphology, signaling molecule gradients, as well as collective orientation, tissue-wide size, and shape variation. Circular statistics of cell polarity, including polarity indices, confidence intervals, and circular correlation analysis, can be computed using our web application. We demonstrate our investigations on image data from endothelial cells (ECs). ECs line the inside of blood vessels and are essential for vessel formation and repair, as well as for various cardiovascular diseases, cancer, and inflammation. However, the general architecture of the software will allow it to be applied to other cell types and image modalities. The package is built in Python, allowing researchers to seamlessly integrate Polarity-JaM into their image and data analysis workflows, see . In addition, a web application for statistical analysis, available at , and a Napari plugin are available, each with a graphical user interface to facilitate exploratory analysis.