Convergent extension by intercalation without mediolaterally fixed cell motion.

Area of Science:

• Biophysics
• Developmental Biology
• Computational Biology

Background:

• Convergent extension is a key morphogenetic process driving tissue elongation.
• Existing models often require specific cell alignment or external cues.
• Understanding the minimal biophysical requirements for convergent extension remains a challenge.

Purpose of the Study:

• To develop and implement a minimal stochastic model of convergent extension.
• To identify the essential cell behaviors and tissue composition for this process.
• To reproduce emergent tissue architectures not seen in previous models.

Main Methods:

• Constructed a stochastic model incorporating cell volume conservation, random motion, and adhesion.
• Included a novel assumption of internal pressure from cytoskeletal polymerization maintaining cell convexity.
• Simulated tissue behavior under two key conditions: cell-specific aspect ratio regulation and a mixture of cell types with differing shape preferences.

Main Results:

• The model successfully simulated convergent extension without requiring explicit cell alignment.
• A novel stacked arrangement of elongated cells, one cell wide, was achieved.
• This arrangement mimics structures observed in ascidian notochords.

Conclusions:

• Convergent extension can be driven by intrinsic cell properties and tissue composition.
• A combination of cell shape regulation and a mix of elongated and round cells is sufficient for tissue elongation.
• The model provides a new framework for understanding tissue morphogenesis and emergent structures.