Quantitative videomicroscopy reveals latent control of cell-pair rotations <i>in vivo</i>

Kozak, Eva L.; Miranda-Rodríguez, Jerónimo R.; Borges, Augusto; Dierkes, Kai; Mineo, Alessandro; Pinto-Teixeira, Filipe; Viader-Llargués, Oriol; Solon, Jérôme; Chara, Osvaldo; López‐Schier, Hernán

doi:10.1242/dev.200975

Cited by 5 publications

(1 citation statement)

References 65 publications

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“…Many systems that are controlled by cell-cell interactions rely on the concerted dynamics of small groups of cells, including the dynamics of pairs of cells [204,321] and migration of small clusters [322,323] in developmental systems, as well as migrating tumour clusters of up to eight cells [324,325]. Studying cell-cell interactions at fixed total number of cells N in simplified in vitro systems provides a major simplification, as the dynamics of cell proliferation can be neglected, and allows building complexity step-by-step.…”

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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Collective mechanics of small migrating cell groups

Shi,

Gupta,

Copos

et al. 2025

Seminars in Cell & Developmental Biology

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ForSys: non-invasive stress inference from time-lapse microscopy

Borges,

Miranda-Rodríguez,

Ceccarelli

et al. 2024

Preprint

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During tissue development and regeneration, cells interpret and exert mechanical forces that are challenging to measure in vivo. Therefore, stress inference algorithms have emerged as powerful tools to estimate tissue stresses. However, how to incorporate tissue dynamics effectively into the inference remains elusive. Here, we present ForSys, a Python-based software that estimates intercellular stresses and intracellular pressures using time-lapse microscopy. We validated ForSys in silico and in vivo using the well-characterized mucociliary epithelium of the Xenopus embryo. We applied ForSys to study the migrating zebrafish lateral line primordium. We found that stress increases during cell rounding just before cell division and predicted the onset of epithelial rosettogenesis with high accuracy. Finally, we analyzed the development of the zebrafish neuromast and inferred mechanical asymmetries in a cell type-specific adhesion pattern. The versatility and simplicity of ForSys enhance the toolkit for studying spatiotemporal patterns of mechanical forces during tissue morphogenesis in vivo.

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Quantitative videomicroscopy reveals latent control of cell-pair rotations in vivo

Cited by 5 publications

References 65 publications

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

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

Collective mechanics of small migrating cell groups

ForSys: non-invasive stress inference from time-lapse microscopy

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