Tracking collective cell motion by topological data analysis

TL;DR

This paper combines a calibrated active vertex model with topological data analysis to simulate and analyze collective cell motion, revealing insights into tissue spreading, cell segregation, and interface dynamics in biological systems.

Contribution

It introduces a novel integration of topological data analysis with cell motion modeling to automatically classify and track cellular interfaces in large datasets.

Findings

Spreading tests show finger formation and swirl patterns consistent with experiments.

Cell segregation depends on junction tensions and cell preferences for mixing or segregation.

Topological data analysis effectively classifies and tracks interface dynamics in simulations and experimental data.

Abstract

By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments, spreading tests exhibit finger formation in the moving interfaces, swirls in the velocity field, and the polar order parameter and correlation and swirl lengths increase with time. Cells inside the tissue have smaller area than those at the interface, as observed in recent experiments. In antagonistic migration assays, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to numerical simulations. We use time series of numerical simulation data to automatically group, track and classify advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These topological data analysis techniques are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.