Theis Sophie, Mendieta-Serrano Mario A, Chapa-Y-Lazo Bernardo, Chen Juliet, Saunders Timothy E
Centre for Mechanochemical Cell Biology, Warwick Medical School, University of Warwick, Coventry, United Kingdom.
London Centre for Nanotechnology, Department of Cell and Developmental Biology, UCL, London, United Kingdom.
PLoS Comput Biol. 2025 Jul 30;21(7):e1013260. doi: 10.1371/journal.pcbi.1013260. eCollection 2025 Jul.
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 has largely been studied in two-dimensions (2D), 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. To address the challenge of extracting 3D cell shape metrics from dense tissues, we have developed the Python package CellMet. This user-friendly tool enables extraction of quantitative shape information from 3D cell and tissue segmentations, including cell face properties, cell twist, and cell rearrangements in 3D. 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.
在发育和组织修复过程中，细胞会重塑和重新配置，以确保器官呈现特定形状。这个过程本质上是三维的（3D）。然而，部分由于成像和数据分析的限制，组织内的细胞形状分析很大程度上是在二维（2D）层面进行研究的，例如果蝇翅芽。随着成像技术和机器学习的最新进展，我们对体内三维细胞和组织形状的理解取得了重大进展。然而，即使获得了细胞的三维分割，对于密集组织中的细胞，要提取除体积和表面积之外的细胞形状指标仍然具有挑战性。为了应对从密集组织中提取三维细胞形状指标的挑战，我们开发了Python包CellMet。这个用户友好的工具能够从三维细胞和组织分割中提取定量形状信息，包括细胞面属性、细胞扭曲以及三维空间中的细胞重排。我们的方法将改进对三维细胞形状的分析以及对组织内细胞组织的理解。我们的工具是开源的，可在https://github.com/TimSaundersLab/CellMet获取。
