bioRxivBiophysics
Collective chemotaxis in a Voronoi model for confluent clusters
Abstract
Collective chemotaxis, where single cells cannot climb a biochemical signaling gradient but clusters of cells can, has been observed in different biological contexts, including confluent tissues where there are no gaps or overlaps between cells. Although particle-based models have been developed that predict important features of collective chemotaxis, the mechanisms in those models depend on particle overlaps, and so it remains unclear if they can explain behavior in confluent systems. Here, we develop an open-source code that couples a 2D Voronoi simulation for confluent cell mechanics to a dynamic chemical signal that can diffuse, advect, and/or degrade, and use the code to study potential mechanisms for collective chemotaxis in cellular monolayers. We first study the impact of advection on collective chemotaxis, and delineate a regime where advective terms are important. Next, we investigate two possible chemotactic mechanisms, contact inhibition of locomotion and heterotypic interfacial tension, and demonstrate that both can drive collective chemotaxis in certain parameter regimes. We further demonstrate that the scaling behavior of cluster motion is well-captured by simple analytic theories. 1 Statement of SignificanceThe ability of cell collectives to respond to biochemical signals, called collective chemotaxis, is crucial for many important processes including embryonic development and wound healing. We developed an open-source computational model that couples biochemical signaling gradients to confluent cell layers, where there are no gaps between cells. Our model demonstrates that two experimentally observed local cell behaviors - neighbor-induced changes to interfacial tension or a tendency of cells to repel one another after they come into contact - can drive collective chemotaxis. We also highlight a regime in which the motion of migrating cells can alter the gradient.
- assay: spatial transcriptomics
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
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