Learning the dynamics of cell–cell interactions in confined cell migration

Abstract

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.

Cell migration guided by long-lived spatial memory

“…The cytoskeleton spatio-temporal dynamics is controlled by complex regulatory networks 4 , and can be characterized by both deterministic and stochastic components [5][6][7][8] . The integration over time of this complex intracellular dynamics determines the large scale properties of cell trajectories, which can, in turn, be used as accessible read-outs to infer intracellular properties [9][10][11] , as well as cell interactions with the environment [12][13][14][15] or with neighbouring cells [16][17][18] . In vivo, cells interact with various extra-cellular environments with a broad range of biochemical and biomechanical properties 2 .…”

“…In such in vitro set-ups, and especially in 1D settings, the reduced dimensionality of the cellular environment allows for an extensive quantitative analysis of the phase space roamed by migrating cells. In particular, such 1D assays have revealed striking deterministic features in cell motility patterns, while cell paths in higher dimensions remain seemingly random 8,18,22,27,28 . Moreover, many of the cell migration features on a 1D substrate can mimic cell behaviour in 3D matrix 22 .…”

Modelling Active Non-Markovian Oscillations

“…Fitting the data to the model, we have calculated that the power consumption by the hair bundle during its spontaneous motion requires the consumption of at least ten ATP molecules per oscillation cycle. We expect that our model could be applied to decipher the energetics of other relevant active oscillations observed in living systems, such as confined cell migration [64], neuronal networks [65], and actomyosin gels [66].…”

Optical Cellular Micromotion: A New Paradigm to Measure Tumour Cells Invasion in 3D Tumour Environments

“…image segmentation). However, cell-cell interactions play a significant role in the migration of cells through tissues 60, 61 and refinement of the method to enable such complex measurement could further strengthen its significance.…”