Stochastic Methods for Inferring States of Cell Migration
Cell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are necessary to identify and quantify the regulatory mechanisms that generate directed...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-07-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2020.00822/full |
| _version_ | 1818110197743222784 |
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| author | R. J. Allen C. Welch Neha Pankow Klaus M. Hahn Klaus M. Hahn Timothy C. Elston Timothy C. Elston |
| author_facet | R. J. Allen C. Welch Neha Pankow Klaus M. Hahn Klaus M. Hahn Timothy C. Elston Timothy C. Elston |
| author_sort | R. J. Allen |
| collection | DOAJ |
| description | Cell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are necessary to identify and quantify the regulatory mechanisms that generate directed cell movement. To address this need, we developed computational tools, based on stochastic modeling, to analyze time series data for the position of randomly migrating cells. Our approach allows parameters that characterize cell movement to be efficiently estimated from cell track data. We applied our methods to analyze the random migration of Mouse Embryonic Fibroblasts (MEFS) and HeLa cells. Our analysis revealed that MEFs exist in two distinct states of migration characterized by differences in cell speed and persistence, whereas HeLa cells only exhibit a single state. Further analysis revealed that the Rho-family GTPase RhoG plays a role in determining the properties of the two migratory states of MEFs. An important feature of our computational approach is that it provides a method for predicting the current migration state of an individual cell from time series data. Finally, we applied our computational methods to HeLa cells expressing a Rac1 biosensor. The Rac1 biosensor is known to perturb movement when expressed at overly high concentrations; at these expression levels the HeLa cells showed two migratory states, which correlated with differences in the spatial distribution of active Rac1. |
| first_indexed | 2024-12-11T02:43:19Z |
| format | Article |
| id | doaj.art-0bf369615a73480585d86af1d9a9b2be |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-11T02:43:19Z |
| publishDate | 2020-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-0bf369615a73480585d86af1d9a9b2be2022-12-22T01:23:30ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2020-07-011110.3389/fphys.2020.00822549021Stochastic Methods for Inferring States of Cell MigrationR. J. Allen0C. Welch1Neha Pankow2Klaus M. Hahn3Klaus M. Hahn4Timothy C. Elston5Timothy C. Elston6Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesComputational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesComputational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesCell migration refers to the ability of cells to translocate across a substrate or through a matrix. To achieve net movement requires spatiotemporal regulation of the actin cytoskeleton. Computational approaches are necessary to identify and quantify the regulatory mechanisms that generate directed cell movement. To address this need, we developed computational tools, based on stochastic modeling, to analyze time series data for the position of randomly migrating cells. Our approach allows parameters that characterize cell movement to be efficiently estimated from cell track data. We applied our methods to analyze the random migration of Mouse Embryonic Fibroblasts (MEFS) and HeLa cells. Our analysis revealed that MEFs exist in two distinct states of migration characterized by differences in cell speed and persistence, whereas HeLa cells only exhibit a single state. Further analysis revealed that the Rho-family GTPase RhoG plays a role in determining the properties of the two migratory states of MEFs. An important feature of our computational approach is that it provides a method for predicting the current migration state of an individual cell from time series data. Finally, we applied our computational methods to HeLa cells expressing a Rac1 biosensor. The Rac1 biosensor is known to perturb movement when expressed at overly high concentrations; at these expression levels the HeLa cells showed two migratory states, which correlated with differences in the spatial distribution of active Rac1.https://www.frontiersin.org/article/10.3389/fphys.2020.00822/fullcell migrationstochastic modelingRHOGRac1biosenormigration states |
| spellingShingle | R. J. Allen C. Welch Neha Pankow Klaus M. Hahn Klaus M. Hahn Timothy C. Elston Timothy C. Elston Stochastic Methods for Inferring States of Cell Migration Frontiers in Physiology cell migration stochastic modeling RHOG Rac1 biosenor migration states |
| title | Stochastic Methods for Inferring States of Cell Migration |
| title_full | Stochastic Methods for Inferring States of Cell Migration |
| title_fullStr | Stochastic Methods for Inferring States of Cell Migration |
| title_full_unstemmed | Stochastic Methods for Inferring States of Cell Migration |
| title_short | Stochastic Methods for Inferring States of Cell Migration |
| title_sort | stochastic methods for inferring states of cell migration |
| topic | cell migration stochastic modeling RHOG Rac1 biosenor migration states |
| url | https://www.frontiersin.org/article/10.3389/fphys.2020.00822/full |
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