Assessing the role of spatial correlations during collective cell spreading

Abstract

Spreading cell fronts are essential features of development, repair and disease processes. Many mathematical models used to describe the motion of cell fronts, such as Fisher's equation, invoke a mean–field assumption which implies that there is no spatial structure, such as cell clustering, present. Here, we examine the presence of spatial structure using a combination of in vitro circular barrier assays, discrete random walk simulations and pair correlation functions. In particular, we analyse discrete simulation data using pair correlation functions to show that spatial structure can form in a spreading population of cells either through sufficiently strong cell–to–cell adhesion or sufficiently rapid cell proliferation. We analyse images from a circular barrier assay describing the spreading of a population of MM127 melanoma cells using the same pair correlation functions. Our results indicate that the spreading melanoma cell populations remain very close to spatially uniform, suggesting that the strength of cell–to–cell adhesion and the rate of cell proliferation are both sufficiently small so as not to induce any spatial patterning in the spreading populations.

Modelling the movement of interacting cell populations: A moment dynamics approach

“…First, our moment dynamics model approximately incorporates clustering and correlation, which are implicitly neglected in previous PDE-based descriptions. This is important since clustering and correlation effects are often observed in cell biology experiments (Treloar et al, 2014). We note that the clustering incorporated is not due to explicit cell-to-cell adhesion but from the nature of the proliferation mechanism.…”

“…In all cases we always assume that the influence of cell-to-cell adhesion and cell-to-substrate adhesion is negligible. While these assumptions are relevant for certain types of cells, it is well known that other types of cells, such as glioma and melanoma, exhibit significant adhesion (Khain et al, 2012;Treloar and Simpson, 2013;Treloar et al, 2014). Therefore, it is of interest to examine how to incorporate the effects of cell-to-cell adhesion and cell-tosubstrate adhesion in our moment dynamics framework.…”

Spatial moment dynamics for collective cell movement incorporating a neighbour-dependent directional bias

“…For instance, time-lapse imaging of in vitro cell migration assays, such as circular barrier assays [48] and scratch assays [50], generates data in two spatial dimensions. Image analysis techniques can then be employed to measure the distances between cell pairs and these data used to calculate a PCF.…”

A fractal analysis of the spatial distribution of tumoral mast cells in lymph nodes and bone marrow

“…The spatial distribution of the cells, however, is of particular significance from the point of view of the biological processes occurring in tumor tissues. In fact, the formation of a cell pattern is the result of a complex set of cell-cell and cell-matrix interactions [21] and the analysis of its morphology can provide valuable information to generate hypotheses on the essential cell behaviors leading to the observed tissue appearance and to validate (or refuse) models of the occurring physiological or pathological mechanisms [22]. Among others, examples of this approach were provided in studies concerning the endothelial cell self-organization under anti-or pro-angiogenic stimuli [23] or the role of astrocytes in diseases and injuries of the retina [24].…”