Anisotropic persistent random walk model simulates T-cells migration over curved landscapes.

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Cell migration is crucial for tissue development and implicated in diseases.
• Substrate curvature influences cell movement, but mechanisms remain unclear.
• Understanding T-cell migration on complex landscapes is vital.

Purpose of the Study:

• To investigate the impact of substrate curvature on T-cell migration persistence.
• To model T-cell movement on corrugated landscapes using an anisotropic persistent random walk model.
• To elucidate the role of cell heterogeneity and anisotropic randomness in migration guidance.

Main Methods:

• Developed an anisotropic persistent random walk model incorporating cell heterogeneity.
• Simulated T-cell migration on various corrugated topographies.
• Compared model-generated trajectories with in vitro T-cell migration data.

Main Results:

• The model accurately replicates T-cell trajectories on both flat and curved surfaces.
• Observed a directional bias of T-cells towards concave regions.
• Demonstrated superdiffusive behavior and enhanced movement efficiency on curved surfaces compared to flat ones.
• Identified anisotropic randomness as key to shaping T-cell persistence and concave-directed migration.

Conclusions:

• Anisotropic randomness is critical for T-cell migration persistence and guidance.
• Curvature influences T-cell movement, promoting more efficient migration in concave areas.
• The developed model provides a robust framework for studying cell migration on complex terrains.