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Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and

Kyle C Nguyen1, Carter D Jameson2, Scott A Baldwin3

  • 1Biomathematics Graduate Program, North Carolina State University, Raleigh, NC 27607, USA; Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27607, USA.

Mathematical Biosciences
|February 19, 2024
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Summary
This summary is machine-generated.

Confluent fibroblasts exhibit collective motion, moving in anti-parallel directions, a phenomenon termed "fluidization." This collective cell behavior, analyzed using machine learning and topological data analysis, suggests repulsion forces drive fluidization patterns.

Keywords:
Agent-based ModelingApproximate Bayesian ComputationCell migrationDeep learningTopological data analysis

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Area of Science:

  • Cell biology
  • Biophysics
  • Complex systems

Background:

  • Confluent fibroblasts typically display parallel alignment.
  • Cellular collective motion is a key aspect of tissue development and disease.

Purpose of the Study:

  • To investigate the collective motion of confluent fibroblasts.
  • To characterize the phenomenon of cell population "fluidization".

Main Methods:

  • Acquisition and analysis of live-cell microscopy movies.
  • Machine learning-based cell tracking.
  • Topological Data Analysis (TDA) for analyzing cell track data.
  • Bayesian parameter estimation for the D'Orsogna agent-based model (ABM).

Main Results:

  • Confluent fibroblasts exhibit motility and collective anti-parallel movement ("fluidization").
  • TDA revealed significant topological information content driven by fluidization.
  • Parameter estimation for the D'Orsogna ABM yielded consistent results across movies.

Conclusions:

  • Fluidization in confluent fibroblast populations is driven by collective anti-parallel cell movement.
  • Inter-cellular repulsion at close range may be a key mechanism underlying fluidization.
  • TDA is a powerful tool for analyzing complex collective cell behaviors and informing agent-based models.