Polarization and motility of one-dimensional multi-cellular trains

Ron, Jonathan E.; d'Alessandro, Joseph; Cellerin, Victor; Voituriez, Raphael; Ladoux, Benoit; Gov, Nir S.

doi:10.1016/j.bpj.2023.11.003

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…First, on circular micropatterns going from N = 2 to N = 8 revealed cell numberdependent rotational behaviours [27] (figure 9(B)). Secondly, a number of studies have investigated the behaviour of small clusters of cells on linear and circular microstripes [334][335][336][337][338][339] (figures 9(C) and (D)). For instance, in trains of keratocyte cells confined to 1D stripes, the speed of the train was shown to be independent of the number of cells in the axial direction, i.e.…”

Section: From Cell Pairs To Collective Migrationmentioning

confidence: 99%

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: From Cell Pairs To Collective Migrationmentioning

confidence: 99%

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

Brückner,

Broedersz

2024

Rep. Prog. Phys.

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Optogenetic generation of leader cells reveals a force–velocity relation for collective cell migration

Rossetti,

Grosser,

Abenza

et al. 2024

Nat. Phys.

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Optogenetic generation of leader cells reveals a force-velocity relation for collective cell migration

Rossetti,

Grosser,

Abenza

et al. 2024

Preprint

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The front of migratory cellular clusters during development, wound healing and cancer invasion is typically populated with highly protrusive cells that are called leader cells. Leader cells are thought to physically pull and direct their cohort of followers, but how leaders and followers are mechanically organized to migrate collectively remains controversial. One possibility is that the autonomous local action of a leader cell is sufficient to drive migration of the group. Yet another possibility is that a global mechanical organization is required for the group to move cohesively. Here we show that the effectiveness of leader-follower organization is proportional to the asymmetry of traction and tension within the cellular cluster. By combining hydrogel micropatterning and optogenetic activation of Rac1, we locally generate highly protrusive leaders at the edge of minimal cell groups. We find that the induced leader can robustly drag one follower but is generally unable to direct larger groups. By measuring traction forces and tension propagation in groups of increasing size, we establish a quantitative relationship between group velocity and the asymmetry of the traction and tension profiles. We propose a model of the motile cluster as an active polar fluid that explains this force-velocity relationship in terms of asymmetries in the distribution of active tractions. Our results challenge the notion of autonomous leader cells by showing that collective cell migration requires a global mechanical organization within the cluster.

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Polarization and motility of one-dimensional multi-cellular trains

Cited by 4 publications

References 38 publications

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

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

Optogenetic generation of leader cells reveals a force–velocity relation for collective cell migration

Optogenetic generation of leader cells reveals a force-velocity relation for collective cell migration

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