Inferring active and passive mechanical drivers of epithelial convergent extension

Area of Science:

• Developmental Biology
• Biophysics
• Cell Biology

Background:

• Tissue morphogenesis, the process of tissue shaping, is driven by complex mechanical forces.
• Convergent extension (CE) is a key developmental process involving tissue narrowing and lengthening.
• Distinguishing between active cellular processes and passive responses to external forces in CE remains challenging.

Purpose of the Study:

• To develop a computational framework for analyzing dynamic cell shape changes to identify underlying morphogenetic mechanisms.
• To differentiate between passive and active cellular processes driving convergent extension (CE).
• To quantify and compare the contributions of various CE mechanisms across different model systems.

Main Methods:

• Construction of a computational model for passive and active epithelial cell CE.
• Simulation of CE through active anisotropic processes (crawling, contraction, capture).
• Development of an image analysis pipeline integrating mechanical and statistical approaches.
• Creation of a MEchanism Index (MEI) to quantify similarity between live and simulated cells.

Main Results:

• Simulated mechanisms uniquely contribute to tissue morphology and force transmission coordination.
• Analysis of *Xenopus* neural CE revealed a combination of passive motion and active forces.
• Spatial variations in inferred mechanisms were observed across the neural plate.
• Distinct active modes showed varying prevalences in frog, mouse, and fly CE models.

Conclusions:

• Dynamic cell shape analysis provides insights into the mechanical basis of tissue morphogenesis.
• The MEI effectively quantifies contributions of different cellular mechanisms to CE.
• This framework aids in understanding how mechanics influences tissue and organ development across species.