CellMet: Extracting 3D shape metrics from cells and tissues

Abstract

During development and tissue repair, cells reshape and reconfigure to ensure organs take specific shapes. This process is inherently three-dimensional (3D). Yet, in part due to limitations in imaging and data analysis, cell shape analysis within tissues have been studied as a two-dimensional (2D) approximation, e.g., the Drosophila wing disc. With recent advances in imaging and machine learning, there has been significant progress in our understanding of 3D cell and tissue shape in vivo. However, even after gaining 3D segmentation of cells, it remains challenging to extract cell shape metrics beyond volume and surface area for cells within densely packed tissues. In order to extract 3D shape metrics, we have developed CellMet. This user-friendly tool enables extraction of quantitative shape information from 3D cell and tissue segmentation. It is developed for extracting cell scale information from densely packed tissues, such as cell face properties, cell twist, and cell rearrangements. Our method will improve the analysis of 3D cell shape and the understanding of cell organisation within tissues. Our tool is open source, available at https://github.com/TimSaundersLab/CellMet.