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Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy
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Stochastic Methods for Inferring States of Cell Migration.

R J Allen1, C Welch1, Neha Pankow1

  • 1Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Frontiers in Physiology
|August 6, 2020
PubMed
Summary
This summary is machine-generated.

Computational tools analyzing cell migration data reveal distinct migratory states in Mouse Embryonic Fibroblasts (MEFs), influenced by RhoG. HeLa cells show a single state, but perturbing Rac1 reveals two states, correlating with Rac1 activity.

Keywords:
RHOGRac1biosenorcell migrationmigration statesstochastic modeling

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Area of Science:

  • Cell biology
  • Computational biology
  • Biophysics

Background:

  • Cell migration is crucial for biological processes and requires precise spatiotemporal regulation of the actin cytoskeleton.
  • Understanding the regulatory mechanisms driving directed cell movement necessitates robust computational analysis of cell track data.

Purpose of the Study:

  • To develop and apply computational tools for analyzing cell migration patterns.
  • To identify and quantify parameters characterizing cell movement from time-series position data.
  • To investigate the migratory behaviors of Mouse Embryonic Fibroblasts (MEFs) and HeLa cells.

Main Methods:

  • Stochastic modeling was employed to create computational tools for analyzing cell track data.
  • The developed methods were used to estimate parameters characterizing cell movement.
  • The approach was applied to time-series data of randomly migrating MEFs and HeLa cells.

Main Results:

  • MEFs exhibit two distinct migration states (speed and persistence), while HeLa cells display a single state.
  • The Rho-family GTPase RhoG was identified as a factor influencing the two migratory states in MEFs.
  • Computational methods predicted individual cell migration states from time-series data.
  • HeLa cells expressing a Rac1 biosensor at high concentrations showed two migratory states correlated with Rac1 spatial distribution.

Conclusions:

  • The developed computational approach efficiently estimates cell movement parameters and predicts migration states.
  • RhoG plays a significant role in regulating MEF migratory behavior.
  • Rac1 activity spatial distribution is linked to migratory state plasticity in HeLa cells.