Geometry Adaptation of Protrusion and Polarity Dynamics in Confined Cell Migration

Brückner, David B.; Schmitt, Matthew; Fink, Alexandra; Flommersfeld, Johannes; Arlt, Nicolas; Hannezo, Édouard; Rädler, Joachim O.; Broedersz, Chase P.

doi:10.1103/physrevx.12.031041

Cited by 10 publications

(14 citation statements)

References 89 publications

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“…(J) Datasets of spatial fields of polarity cues can be treated with partial differential equation (PDE) inference to obtain reaction-diffusion models of cell polarity. (K) Protrusion tracking or actin flow measurements allow inference of nucleus-protrusion models [228]. (L) Cell trajectory data allows inference of underdamped equations of cell motion [19].…”

Section: Connecting Cell Dynamics To Mechanismsmentioning

confidence: 99%

“…Going further, an active gel or molecular clutch model could be mapped into an active particle model, and thereby indirectly linked to the inferable underdamped equation of motion for the cell. For instance, in [228], we provided a mapping between a model for the coupled dynamics of cell nucleus, protrusion and polarity and the underdamped equation of motion of the nuclear dynamics alone. Such mappings will be very useful in providing conceptual links between different models, and may help to test and constrain existing models, in particular when they are generalised to non-trivial external confinements.…”

Section: Bottom-up Models For Cell Migrationmentioning

confidence: 99%

“…In previous work, we extracted such protrusion trajectories from confined migrating cells, and inferred the coupled dynamics of cell nucleus and protrusion motion [228]. Interestingly, considering only nucleus and protrusion motion was not predictive of cell motion, and thus a time-correlated polarity-driven protrusion formation was required to capture the dynamics.…”

Section: Inference From Cellular Featuresmentioning

confidence: 99%

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Learning dynamical models of single and collective cell migration: a review

Brückner,

Broedersz

2024

Rep. Prog. Phys.

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Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasize how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.

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Section: Connecting Cell Dynamics To Mechanismsmentioning

confidence: 99%

Section: Bottom-up Models For Cell Migrationmentioning

confidence: 99%

Section: Inference From Cellular Featuresmentioning

confidence: 99%

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Learning dynamical models of single and collective cell migration: a review

Brückner,

Broedersz

2024

Rep. Prog. Phys.

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“…Cells confined to planar surfaces demonstrate adaptation of their shape to their confinement. [ 46 ] Upon release, the direction and velocity of migration depend on the initial confinement. [ 47 ] In the 3D‐confinement of a microfluidic channel, cell migration is affected by channel contractions, expansions, and junctions, when channel widths are in the size range of individual cells.…”

Section: Introductionmentioning

confidence: 99%

A Microfluidic Perfusion Culture Setup to Investigate Cell Migration in 3D Constrictions

Geiger,

Marsico,

Pensold

et al. 2024

Adv Materials Technologies

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Cell migration is a fundamental process underlying the morphological maturation of organs, but also in disease‐related conditions such as cancer. Cells are able to migrate through crowded space and a tight extracellular matrix (ECM). Passing through a constriction, a cell deforms strongly, including its nucleus. Such nuclear deformation can lead to changes in the 3D‐genomic architecture, and putatively, DNA methylation. However, the specific effects of deformation on cells are not well understood. It is highly desired to establish an ex vivo methodology to induce well‐defined cell deformation in complex geometrical constrictions. This study introduces a microfluidic system for the study of migrating cells in precisely controlled geometrical confinement. A procedure for coating, seeding of cerebellar granule cells, and perfusion culture is presented. By leveraging direct laser writing, channels with smooth, anisotropically curved surfaces on the cell‐scale can be fabricated. The system consists of constriction channels with a radius of 2 or 4 µm for the cells to pass through. This corresponds to a compression of the nucleus to 3.5% and 14.2% of its undeformed cross‐sectional area, respectively. The system can be used to investigate the influence of confinement geometry on the migration behavior and transcriptome of various cell types.

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“…T he movement of cells is essential for embryo development and wound healing. A study of individual human cells moving on a micropatterned surface reveals some of the basic principles governing this movement and shows how cells adapt their shape and behavior to the geometry of their surroundings [1]. The researchers developed a theoretical model, based on their experimental findings, that could be used to study and predict cell movement in more complex environments.…”

mentioning

confidence: 99%

How Cells Move through Narrow Spaces

Ball¹

2022

Physics

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Geometry Adaptation of Protrusion and Polarity Dynamics in Confined Cell Migration

Cited by 10 publications

References 89 publications

Learning dynamical models of single and collective cell migration: a review

Learning dynamical models of single and collective cell migration: a review

A Microfluidic Perfusion Culture Setup to Investigate Cell Migration in 3D Constrictions

How Cells Move through Narrow Spaces

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