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Tracking collective cell motion by topological data analysis

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dc.affiliation.institutoUC3M. Instituto Universitario sobre Modelización y Simulación en Fluidodinámica, Nanociencia y Matemática Industrial Gregorio Millán Barbanyes
dc.contributor.authorLópez Bonilla, Luis Francisco
dc.contributor.authorCarpio, Ana
dc.contributor.authorTrenado, Carolina
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2023-07-07T11:52:52Z
dc.date.available2023-07-07T11:52:52Z
dc.date.issued2020-12-23
dc.description.abstractBy 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.en
dc.description.sponsorshipThis work has been supported by the FEDER/Ministerio de Ciencia, Innovacion y Universidades -- Agencia Estatal de Investigacion grant MTM2017-84446-C2-2-R (CT and LLB); by the FEDER/Ministerio de Ciencia, Innovacion y Universidades -- Agencia Estatal de Investigacion grant MTM2017-84446-C2-1-R (AC), and by the Programa Propio de la Universidad Carlos III de Madrid (CT).en
dc.format.extent43
dc.identifier.bibliographicCitationBonilla, L. L., Carpio, A., & Trenado, C. (2020). Tracking collective cell motion by topological data analysis. PLOS Computational Biology, 16(12), e1008407.en
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1008407
dc.identifier.issn1553-734X
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue12, e1008407
dc.identifier.publicationlastpage43
dc.identifier.publicationtitlePLoS Computational Biologyen
dc.identifier.publicationvolume16
dc.identifier.urihttps://hdl.handle.net/10016/37785
dc.identifier.uxxiAR/0000027794
dc.language.isoeng
dc.publisherPLOS
dc.relation.projectIDGobierno de España. MTM2017-84446-C2-2-Res
dc.relation.projectIDGobierno de España. MTM2017-84446-C2-1-Res
dc.rights© 2020 Bonilla et al.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaFísicaes
dc.subject.ecienciaMatemáticases
dc.subject.ecienciaQuímicaes
dc.titleTracking collective cell motion by topological data analysisen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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