Cell-scale biophysical determinants of cell competition in epithelia

Abstract

How cells with different genetic makeups compete in tissues is an outstanding question in developmental biology and cancer research. Studies in recent years have revealed two fundamental mechanisms of cell competition, driven by short-range biochemical signalling or by long-range mechanical stresses within the tissue. In both scenarios, the outcome of cell competition has generally been characterised using population-scale metrics. However, the underlying strategies for competitive interactions at the single-cell level remain elusive.Here, we develop a cell-based computational model for competition assays informed by highthroughput timelapse imaging experiments. By integrating physical cell interactions with cellular automata rules for proliferation and apoptosis, we find that the emergent modes of cell competition are determined by a trade-off between entropic and energetic properties of the mixed tissue. While biochemical competition is strongly sensitive to local tissue organisation, mechanical competition is largely driven by the difference in homeostatic pressures of the two competing cell types. These findings suggest that competitive cell interactions arise when the local tissue free energy is high, and proceed until free energy is minimised. RESULTS Cell-based model for competition.Our cell-based model for competition consists of two distinct computational layers that simulate:(i) mechanical interactions between cells and the underlying substrate, and (ii) a cellular automaton that makes decisions for cell growth, mitosis and apoptosis ( Figure 1A). Physical interactions at the cell-cell and cell-substrate interfaces are simulated using the Cellular Potts Model [21] (Methods).This implementation was preferred to the less computationally costly vertex model [22], because we calibrate our model to our in vitro competition experiments [15] that start from a sub-confluent state. In the Potts model, each cell type is assigned a value of adhesion energy with other cells and the substrate, as well as a preferred (target) area, A T , and a compressibility modulus, λ. While the balance of forces between adhesion and elasticity determines equilibrium cell shapes, changes in cell size during growth, division and apoptosis are controlled by a second computational layer comprising cell automata rules. It is in this layer that cellular decision-making is implemented at

FGF21 Induced by the ASK1-p38 Pathway Promotes Mechanical Cell Competition by Attracting Cells

“…As reported before, we confirmed that, when cocultured with wild-type (WT) cells, GFP-expressing scrib KD cells were outcompeted by cell competition in a tetracycline (Tet)-dependent manner (Figure S1A). [7][8][9][10] We also observed that the density of scrib KD cells was remarkably increased after tetracycline addition compared to that of WT cells in cell competition, suggesting that scrib KD cells were compacted (Figure S1B). [7][8][9] A previous report showed that, when WT and scrib KD colonies come into contact, both colonies move synchronously, with scrib KD colony always followed by WT colony, suggesting that this migratory behavior promotes the compression and elimination of scrib KD cells in cell competition.…”

“…[7][8][9][10] We also observed that the density of scrib KD cells was remarkably increased after tetracycline addition compared to that of WT cells in cell competition, suggesting that scrib KD cells were compacted (Figure S1B). [7][8][9] A previous report showed that, when WT and scrib KD colonies come into contact, both colonies move synchronously, with scrib KD colony always followed by WT colony, suggesting that this migratory behavior promotes the compression and elimination of scrib KD cells in cell competition. 7 However, the precise mechanism by which the migratory behavior of WT and scrib KD colonies is induced remains unelucidated.…”

The Field of Cell Competition Comes of Age: Semantics and Technological Synergy

“…Looking to previous work, mathematical modeling has played a critical role in understanding the evolutionary pressures that drive clonal competition in cancer ( Gatenby and Vincent, 2003 ; Vermeulen et al, 2013 ). Indeed, computational approaches using live-cell imaging data have been successful at disentangling cellular factors involved in 2-dimensional mechanical competition ( Gradeci et al, 2019 ). Additionally, modeling of intestinal stem cell clonality over time initially predicted that the progressive clonal takeover of intestinal crypts fit well with a stochastic model, indicating that neutral drift was the driver of clonality in non-oncogenic situations ( Snippert et al, 2010 ).…”

A Stiff Extracellular Matrix Favors the Mechanical Cell Competition that Leads to Extrusion of Bacterially-Infected Epithelial Cells

“…This competition between losers and winners is essential for tissue homeostasis but it also emerges during tissue development and can play a role in various pathologies including tumor development ( Meyer et al, 2014 ; Moreno et al, 2019 ). Although the chemical signals driving the battle between two different cell populations are relatively more explored, an increasing number of studies showcases that mechanical signals such as differential sensitivity to compression during cell crowding are also crucial in driving such interactions ( Gradeci et al, 2019 ; Matamoro-Vidal and Levayer, 2019 ; Moreno et al, 2019 ; Bastounis et al, 2021b ).…”