Disentangling the behavioural variability of confined cell migration

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

doi:10.1098/rsif.2019.0689

Cited by 27 publications

(26 citation statements)

References 85 publications

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“…Contact Acceleration Maps Reveal Dynamics of Cell-Cell Interactions Here, we aim to describe the underlying interaction dynamics that capture the full stochastic long-timescale behavior of repeatedly colliding cell pairs. The dynamics of single migrating cells are well described by an equation of motion that is second order in time (24)(25)(26)(27)(28)(29), making accelerations the natural quantity to describe cell motility. Specifically, we previously showed that the migration dynamics of single cells in confinement can be described by the average acceleration as a function of cell position x and velocity v = dx /dt, given by the conditional aver- .…”

Section: Mcf10a and Mda-mb-231 Cells Exhibit Distinct Collision Behaviormentioning

confidence: 99%

“…, wherev = dv /dt(27)(28)(29). To uncover the general structure of the cell-cell interactions in our experiments, we therefore first focus on the observed cellular accelerations upon contact as a function of the distance and relative velocity of the cells.…”

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confidence: 99%

“…Achieving a quantitative understanding of the stochastic migratory dynamics of cells at the behavioral level could yield key insights into both the underlying molecular mechanisms ( 22 , 23 ) and the biological functions ( 10 ) associated to these behaviors. For noninteracting, single migrating cells, data-driven approaches have revealed quantitative frameworks to describe the behavior of free unconstrained migration ( 24 – 26 ) and confined migration in structured environments ( 27 – 29 ). However, it is still poorly understood how the migratory dynamics of cells are affected by cell–cell interactions and a quantitative formalism for the emergent behavioral dynamics of interacting cells is still lacking ( 30 ).…”

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confidence: 99%

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

Brückner

Arlt

Fink

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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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.

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Section: Mcf10a and Mda-mb-231 Cells Exhibit Distinct Collision Behaviormentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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

Brückner

Arlt

Fink

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Additionally, cell micropatterning has been utilized to examine dynamic cancer cell processes including migration. A two-state micropatterning with two large rectangular regions connected by a narrow bridge was developed to determine the heterogeneity in migration for established cancer cell lines (Brückner et al, 2020). The migration of MDA-MB-231 cells seeded on one side of the pattern and crossing the small bridge to the second region was recorded and assessed with statistical analysis to reveal potential subpopulations with distinct mechanical and migration capabilities.…”

Section: Substrate Surface Patternsmentioning

confidence: 99%

The Role of Microenvironmental Cues and Mechanical Loading Milieus in Breast Cancer Cell Progression and Metastasis

Riehl

Kim

Bouzid

et al. 2021

Front. Bioeng. Biotechnol.

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Cancer can disrupt the microenvironments and mechanical homeostatic actions in multiple scales from large tissue modification to altered cellular signaling pathway in mechanotransduction. In this review, we highlight recent progresses in breast cancer cell mechanobiology focusing on cell-microenvironment interaction and mechanical loading regulation of cells. First, the effects of microenvironmental cues on breast cancer cell progression and metastasis will be reviewed with respect to substrate stiffness, chemical/topographic substrate patterning, and 2D vs. 3D cultures. Then, the role of mechanical loading situations such as tensile stretch, compression, and flow-induced shear will be discussed in relation to breast cancer cell mechanobiology and metastasis prevention. Ultimately, the substrate microenvironment and mechanical signal will work together to control cancer cell progression and metastasis. The discussions on breast cancer cell responsiveness to mechanical signals, from static substrate and dynamic loading, and the mechanotransduction pathways involved will facilitate interdisciplinary knowledge transfer, enabling further insights into prognostic markers, mechanically mediated metastasis pathways for therapeutic targets, and model systems required to advance cancer mechanobiology.

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“…When observing freely migrating cells (i.e., not guided by an external gradient of any kind), one is often struck by the large variation in their migration patterns [1][2][3][4][5], both for a single cell over time, and for a (seemingly identical) cell population. The origin of the large cell-to-cell variability (CCV), or phenotypic, population heterogeneity, observed during cell migration is not understood [6], and is usually ascribed to the inherent noise of cellular systems [7,8].…”

Section: Introductionmentioning

confidence: 99%

One-dimensional cell motility patterns

Ron

Monzo

Gauthier

et al. 2020

Phys. Rev. Research

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During migration, cells exhibit a rich variety of seemingly random migration patterns, which makes unraveling the underlying mechanisms that control cell migration a difficult challenge. For efficient migration, cells require a mechanism for polarization, so that traction forces are produced in the direction of motion, while adhesion is released to allow forward migration. To simplify the study of this process, cells have been studied when placed along one-dimensional tracks, where single cells exhibit both smooth and stick-slip migration modes. The stick-slip motility mode is characterized by protrusive motion at the cell front, coupled with a slow elongation of the cell, which is followed by a rapid retraction of the cell rear. In this study, we explore a minimal physical model that couples the force applied on the adhesion bonds to the length variations of the cell and to the traction forces applied by the polarized actin retrograde flow. We show that the rich spectrum of cell migration patterns emerges from this model as different deterministic dynamical phases. This result suggests a source for the large cell-tocell variability (CCV) in cell migration patterns observed in single cells over time and within cell populations: fluctuations in the cellular components, such as adhesion strength or polymerization activity, can shift the cells from one migration mode to another, due to crossing the dynamical phase transition lines. Temporal noise is shown to drive random changes in the cellular polarization direction, which is enhanced during the stick-slip migration mode. The model contains an emergent critical length for cell polarization, whereby cells that retract below this length loose polarity, and are prone to making direction changes in migration. These results offer a new framework to explain experimental observations of migrating cells, resulting from noisy switching between underlying deterministic migration modes.

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Disentangling the behavioural variability of confined cell migration

Cited by 27 publications

References 85 publications

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

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

The Role of Microenvironmental Cues and Mechanical Loading Milieus in Breast Cancer Cell Progression and Metastasis

One-dimensional cell motility patterns

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