Image-based parameter inference for epithelial mechanics.

Area of Science:

• Biophysics
• Developmental Biology
• Cell Biology

Background:

• In vivo measurement of mechanical parameters in tissues is crucial for understanding morphogenesis.
• Existing methods for measuring cell-cell junction mechanics in vivo are technically challenging.

Purpose of the Study:

• To develop and validate an image-based statistical approach for estimating mechanical parameters of epithelial cells in vivo.
• To investigate the mechanical feedback between junction tension and shrinkage during cell rearrangement.
• To elucidate how tissue polarity and stretching influence tension anisotropy.

Main Methods:

• Formulated an image-based statistical approach using force-cell shape correlations from image data.
• Constructed candidate mechanical models and used force-balance equations at cell vertices.
• Employed a least-squares method for parameter estimation and model selection.
• Validated the method using synthetic data.

Main Results:

• The method accurately estimated mechanical parameters and selected appropriate models using synthetic data.
• Found correlations between junction tension-shrinkage feedback, spring constant, tension, and myosin-II levels during cell rearrangement in Drosophila.
• Clarified the effects of altered tissue polarity and stretching on tension parameter anisotropy.

Conclusions:

• The developed image-based statistical method offers a novel approach for in vivo mechanical parameter estimation in epithelial cells.
• This method provides new insights into the mechanisms governing epithelial morphogenesis and cell dynamics.