CellMet: Extracting 3D shape and topology metrics from confluent cells within tissues

Area of Science:

• Developmental Biology
• Cell Biology
• Bioinformatics

Background:

• Cellular reshaping is fundamental to organ development and tissue repair, occurring in three-dimensions (3D).
• Traditional 2D analyses limit understanding of complex 3D cellular and tissue dynamics.
• Extracting detailed 3D cell shape metrics from dense tissues remains a significant challenge.

Purpose of the Study:

• To develop a computational tool for quantitative analysis of 3D cell shape within densely packed tissues.
• To enable extraction of advanced shape metrics beyond simple volume and surface area.
• To facilitate a deeper understanding of cell organization and tissue morphogenesis.

Main Methods:

• Development of the open-source Python package CellMet.
• Utilizes 3D cell and tissue segmentation data as input.
• Implements algorithms for calculating cell face properties, cell twist, and cell rearrangements in 3D.

Main Results:

• CellMet successfully extracts quantitative 3D cell shape metrics from complex tissue data.
• The package provides insights into cell face geometry, topological rearrangements, and tissue dynamics.
• Demonstrates improved analysis capabilities for 3D cellular organization.

Conclusions:

• CellMet addresses the limitations in analyzing 3D cell shape within dense biological tissues.
• The tool enhances the study of cellular behavior during development and repair.
• Open-source availability promotes wider adoption and advancement in the field.