A statistical-inference approach to reconstruct inter-cellular interactions in cell-migration experiments

TL;DR

This paper introduces a statistical-inference method to detect inter-cellular interactions in cell migration experiments, successfully distinguishing between collective and individual cell behaviors in different biological contexts.

Contribution

The paper presents a novel inference approach for identifying cell-cell interactions from migration data, validated on synthetic and real experimental datasets.

Findings

Detected cell-cell interactions in wound-healing migration

Found no evidence of interactions in chemokine gradient migration

Predicted intercellular contacts consistent with experimental observations

Abstract

Migration of cells can be characterized by two, prototypical types of motion: individual and collective migration. We propose a statistical-inference approach designed to detect the presence of cell-cell interactions that give rise to collective behaviors in cell-motility experiments. Such inference method has been first successfully tested on synthetic motional data, and then applied to two experiments. In the first experiment, cell migrate in a wound-healing model: when applied to this experiment, the inference method predicts the existence of cell-cell interactions, correctly mirroring the strong intercellular contacts which are present in the experiment. In the second experiment, dendritic cells migrate in a chemokine gradient. Our inference analysis does not provide evidence for interactions, indicating that cells migrate by sensing independently the chemokine source. According to this prediction, we speculate that mature dendritic cells disregard inter-cellular signals that could otherwise delay their arrival to lymph vessels.