Disentangling the behavioural variability of confined cell migration

David B. Brückner, Alexandra Fink, Joachim O. Rädler, Chase P. Broedersz

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Cell-to-cell variability is inherent to numerous biological processes, including cell migration. Quantifying and characterizing the variability of migrating cells is challenging, as it requires monitoring many cells for long time windows under identical conditions. Here, we observe the migration of single human breast cancer cells (MDA-MB-231) in confining two-state micropatterns. To describe the stochastic dynamics of this confined migration, we employ a dynamical systems approach. We identify statistics to measure the behavioural variance of the migration, which significantly exceeds that predicted by a population-averaged stochastic model. This additional variance can be explained by the combination of an 'ageing' process and population heterogeneity. To quantify population heterogeneity, we decompose the cells into subpopulations of slow and fast cells, revealing the presence of distinct classes of dynamical systems describing the migration, ranging from bistable to limit cycle behaviour. Our findings highlight the breadth of migration behaviours present in cell populations.
Original languageEnglish
Article number0689
JournalJournal of the Royal Society. Interface
Volume17
Issue number163
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

Funding

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 by a DFG fellowship within the Graduate School of Quantitative Biosciences Munich (QBM) and by the Joachim Herz Stiftung.

FundersFunder number
Graduate School of Quantitative Biosciences Munich
QBM
Joachim Herz Stiftung
Deutsche Forschungsgemeinschaft201269156 - SFB 1032, B12

    Keywords

    • Cell migration
    • Cell-to-cell variability
    • Dynamical systems theory
    • Nonlinear dynamics

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