Learning the dynamics of cell-cell interactions in confined cell migration

TL;DR

This study develops a data-driven stochastic model to describe and predict the contact-mediated interaction behaviors of different cell types during confined migration, revealing distinct interaction mechanisms for non-cancerous and cancerous cells.

Contribution

The paper introduces a novel framework for inferring stochastic interaction equations from experimental cell collision data, distinguishing behaviors of non-cancerous and cancerous cells.

Findings

Non-cancerous cells exhibit repulsion and friction interactions.

Cancerous cells show attraction and anti-friction interactions.

The model accurately predicts observed cell collision behaviors.

Abstract

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell-cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following and sliding past each other upon collision. Capitalizing on this large experimental data set of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting non-cancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and anti-friction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.