Bridging the gap between single-cell migration and collective dynamics

Abstract

Motivated by the wealth of experimental data recently available, we present a cellular-automaton-based modeling framework focussing on high-level cell functions and their concerted effect on cellular migration patterns. Specifically, we formulate a coarse-grained description of cell polarity through self-regulated actin organization and its response to mechanical cues. Furthermore, we address the impact of cell adhesion on collective migration in cell cohorts. The model faithfully reproduces typical cell shapes and movements down to the level of single cells, yet allows for the efficient simulation of confluent tissues. In confined circular geometries, we find that specific properties of individual cells (polarizability; contractility) influence the emerging collective motion of small cell cohorts. Finally, we study the properties of expanding cellular monolayers (front morphology; stress and velocity distributions) at the level of extended tissues.

Quasi-periodic migration of single cells on short microlanes

“…On the other hand, if the cell loses contacts with the substrate (retraction), then 285 intracellular signaling weakens the polarization field and therefore increases the 286 likelihood of further retractions. In particular, the polarization field decays with a rate 287 for all contact sites that are surrounded by more retractions than protrusions 288 within a fixed signaling range [30,36]:…”

“…For all other contact sites between the cell and the substrate, the polarization field 290 approaches a 'rest state' [30,36]: detailed description of the parameters is provided in the Supporting Information S1. 307 We then simulated individual cells with fixed parameters and constant average area 308 on stripe-shaped microlanes.…”

“…In a first advance to rigorously test physical 27 models of cell migration, we find that the statistics of the cell migration is 28 recapitulated by a Cellular Potts model with a minimal description of cytoskeleton 29 dynamics. Using LifeAct-GFP transfected cells and microlanes with differently 30 shaped ends, we show that the local deformation of the leading cell edge in response 194 We further quantify the quasi-periodic migration of cells. First, we determine the 195 overall distribution of absolute cell velocities (Fig 3a).…”

“…Detailed spatiotemporal models that account for cell 58 shape changes, in response to the formation of Rho GTPase patterns and their 59 regulation of the cytoskeleton, were found to reproduce front-rear polarization of cells 60 [15,22,23]. The biophysical principles that underlie the coupling between 61 polarization and migration of cells and determine their shape have been explored by a 62 variety of successful conceptual approaches [24][25][26][27][28][29][30]. To rigorously test these models, 63 it is necessary to employ experimental techniques that are capable of studying the 64 3 shape, migration and internal chemistry of cells in a well-controlled and high-65 throughput fashion.…”

“…and 245 determine its distribution for four different microlane lengths (Fig 4b). We note that spatiotemporal dynamics of cells in 2D [30,36]. In particular, we model the 264 spatiotemporal dynamics of the contact area of a cell with a planar substrate, which is 265 described by a set of discrete adhesion sites on a 2D lattice.…”