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

David B. Brückner, Nicolas Arlt, Alexandra Fink, Pierre Ronceray, Joachim O. Rädler*, Chase P. Broedersz

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Article numbere2016602118
Pages (from-to)1-9
Number of pages9
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number7
DOIs
Publication statusPublished - 16 Feb 2021

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. We thank Ricard Alert, Edouard Hannezo, and Joris Messelink for inspiring discussions. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 201269156–SFB 1032 (Projects B01 and B12). D.B.B. is supported in part by a DFG fellowship within the Graduate School of Quantitative Biosciences Munich and by the Joachim Herz Stiftung. P.R. is supported by a Center for the Physics of Biological Function fellowship (NSF Grant PHY-1734030).

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

ACKNOWLEDGMENTS. We thank Ricard Alert, Edouard Hannezo, and Joris Messelink for inspiring discussions. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 201269156–SFB 1032 (Projects B01 and B12). D.B.B. is supported in part by a DFG fellowship within the Graduate School of Quantitative Biosciences Munich and by the Joachim Herz Stiftung. P.R. is supported by a Center for the Physics of Biological Function fellowship (NSF Grant PHY-1734030).

FundersFunder number
Graduate School of Quantitative Biosciences Munich
National Science FoundationPHY-1734030
Directorate for Mathematical and Physical Sciences1734030
Joachim Herz Stiftung
Deutsche ForschungsgemeinschaftB12, 201269156–SFB 1032

    Keywords

    • Cell migration | cell–cell interactions | contact inhibition of locomotion | stochastic inference

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