Identifying the mechanism for superdiffusivity in mouse fibroblast motility

TL;DR

This study investigates the superdiffusive motility of mouse fibroblasts on 2D substrates, developing models and tools to identify the underlying mechanisms, revealing a hybrid model with heterogeneous noise best explains short-timescale behaviors.

Contribution

The paper introduces an automated toolkit for comparing cell trajectory data to different models, and develops a hybrid model that better captures short-timescale behaviors in cell motility.

Findings

Ensemble-averaged metrics fit multiple models equally well.

Neither simple model captures short-timescale displacement distributions.

A hybrid model with heterogeneous noise accurately matches short-timescale behaviors.

Abstract

We seek to characterize the motility of mouse fibroblasts on 2D substrates. Utilizing automated tracking techniques, we find that cell trajectories are super-diffusive, where displacements scale faster than t^(1/2) in all directions. Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with L\'{e}vy-distributed run times, and ensembles of cells with heterogeneous speed and rotational noise. We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model. However, neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution. We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs, which correctly matches the short-timescale behaviors and indicates that the run times are not L\'{e}vy distributed. The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments.