Dataset Information

Collective motion of mammalian cell cohorts in 3D.

ABSTRACT: Collective cell migration is ubiquitous in biology, from development to cancer; it occurs in complex systems comprised of heterogeneous cell types, signals and matrices, and requires large scale regulation in space and time. Understanding how cells achieve organized collective motility is crucial to addressing cellular and tissue function and disease progression. While current two-dimensional model systems recapitulate the dynamic properties of collective cell migration, quantitative three-dimensional equivalent model systems have proved elusive. To establish such a model system, we study cell collectives by tracking individuals within cell cohorts embedded in three dimensional collagen scaffolding. We develop a custom algorithm to quantify the temporal and spatial heterogeneity of motion in cell cohorts during motility events. In the absence of external driving agents, we show that these cohorts rotate in short bursts, <2 hours, and translate for up to 6 hours. We observe, track, and analyze three dimensional motion of cell cohorts composed of 3-31 cells, and pave a path toward understanding cell collectives in 3D as a complex emergent system.

SUBMITTER: Sharma Y

PROVIDER: S-EPMC4823633 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Collective motion of mammalian cell cohorts in 3D.

Sharma Yasha Y Vargas Diego A DA Pegoraro Adrian F AF Lepzelter David D Weitz David A DA Zaman Muhammad H MH

Integrative biology : quantitative biosciences from nano to macro 20151109 12

Collective cell migration is ubiquitous in biology, from development to cancer; it occurs in complex systems comprised of heterogeneous cell types, signals and matrices, and requires large scale regulation in space and time. Understanding how cells achieve organized collective motility is crucial to addressing cellular and tissue function and disease progression. While current two-dimensional model systems recapitulate the dynamic properties of collective cell migration, quantitative three-dimen ...[more]

PMID: 26549557

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Project description:By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.

Dataset Information

Collective motion of mammalian cell cohorts in 3D.

Publications

Collective motion of mammalian cell cohorts in 3D.

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