Mean-cluster approach indicates cell sorting time scales are determined by collective dynamics

Area of Science:

• Biophysics
• Computational Biology
• Cell Biology

Background:

• Cell migration is crucial for tissue formation, wound healing, and tumor evolution.
• The precise factors governing cell clustering time scales in mixtures remain unclear.
• Existing models of cluster growth deviate from experimental observations in cell segregation.

Purpose of the Study:

• To develop an analytic model for cell segregation incorporating finite-size corrections.
• To investigate the influence of cell-cell interaction mechanisms on segregation dynamics.
• To compare analytic predictions with active matter simulations and experimental data.

Main Methods:

• Developed a power law model linking diffusion constant and cluster mass.
• Incorporated finite-size corrections into the analytic approach.
• Compared model results with active matter simulations and literature data, considering differential adhesion and different velocities hypotheses.

Main Results:

• Simulations demonstrated normal diffusion for clusters over long time intervals.
• Cluster evolution approaches a scaling regime, with deviations only at finite sizes.
• Collective cell behavior significantly enhances cluster diffusion, potentially making it size-independent.

Conclusions:

• The scaling exponent for cluster growth is determined by the mass-diffusion relationship, not local mechanisms.
• Enhanced cell sorting speed due to collective behavior has implications for biological evolution.
• The findings are applicable to active matter systems, offering insights into cell segregation.