Collective cell motion in an epithelial sheet can be quantitatively described by a stochastic interacting particle model.

Modelling the displacement of thousands of cells that move in a collective way is required for the simulation and the theoretical analysis of various biological processes. Here, we tackle this question in the controlled setting where the motion of Madin-Darby Canine Kidney (MDCK) cells in a confluent epithelium is triggered by the unmasking of free surface. We develop a simple model in which cells are described as point particles with a dynamic based on the two premises that, first, cells move in a stochastic manner and, second, tend to adapt their motion to that of their neighbors. Detailed comparison to experimental data show that the model provides a quantitatively accurate description of cell motion in the epithelium bulk at early times. In addition, inclusion of model "leader" cells with modified characteristics, accounts for the digitated shape of the interface which develops over the subsequent hours, providing that leader cells invade free surface more easily than other cells and coordinate their motion with their followers. The previously-described progression of the epithelium border is reproduced by the model and quantitatively explained.

Automated velocity mapping of migrating cell populations (AVeMap).

Characterizing the migration of a population of cells remains laborious and somewhat subjective. Advances in genetics and robotics allow researchers to perform many experiments in parallel, but analyzing the large sets of data remains a bottleneck. Here we describe a rapid, fully automated correlation-based method for cell migration analysis, compatible with standard video microscopy. This method allows for the computation of quantitative migration parameters via an extensive dynamic mapping of cell displacements.