Semblance of heterogeneity in collective cell migration

Abstract

Cell population heterogeneity is increasingly a focus of inquiry in biological research. For example, cell migration studies have investigated the heterogeneity of invasiveness and taxis in development, wound healing, and cancer. However, relatively little effort has been devoted to explore when heterogeneity is mechanistically relevant and how to reliably measure it. Statistical methods from the animal movement literature offer the potential to analyse heterogeneity in collections of cell tracking data. A popular measure of heterogeneity, which we use here as an example, is the distribution of delays in directional cross-correlation. Using a suitably generic, yet minimal, model of collective cell movement in three dimensions, we show how using such measures to quantify heterogeneity in tracking data can result in the inference of heterogeneity where there is none. Our study highlights a potential pitfall in the statistical analysis of cell population heterogeneity, and we argue this can be mitigated by the appropriate choice of null models. Highlights• groups of identical cells appear heterogeneous due to limited sampling and experimental repeatability• heterogeneity bias increases with attraction/repulsion between cells • movement in confined environments decreases apparent heterogeneity • hypothetical applications in neural crest and in vitro cancer systems In BriefWe use a mathematical model to show how cell populations can appear heterogeneous in their migratory characteristics, even though they are made up of identically-behaving individual cells. This has important consequences for the study of collective cell migration in areas such as embryo development or cancer invasion.

When Seeing Isn't Believing: How Math Can Guide Our Interpretation of Measurements and Experiments

“…As powerful new technologies allow us to probe single-cell dynamics in high throughput, are we at risk of reaching the wrong conclusions? An intriguing paper by Schumacher, Maini and Baker in this issue of Cell Systems uses a mathematical model of collective cell migration-a 21 st -century computational thought experiment-to examine the potential pitfalls of measuring heterogeneity (Schumacher et al, 2017). In the process, they highlight the potential for mathematical modeling to help us better understand the meaning and limits of experimental measurements.…”

“…(See the supplementary material in Macklin et al (2012) for more on the mathematics underlying this thought experiment.) This homogeneous cell 2017) used a mathematical model of collective cell motion to generate synthetic cell tracks (inset, modified here from Figure 3b in Schumacher et al (2017)) and analyze them for heterogeneity. Simulations with completely identical cells (blue, modified and recolored from their Figure 4a) could be fitted to match available experimental datasets, just as well as simulations with 10% leader cells and 90% followers (red, modified and recolored from their Figure 4c).…”