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Morphodynamical cell state description via live-cell imaging trajectory embedding.

Jeremy Copperman1, Sean M Gross2, Young Hwan Chang2,3

  • 1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA. copperma@ohsu.edu.

Communications Biology
|May 4, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces trajectory embedding for analyzing cell behavior over time, offering a more dynamic and comprehensive view than traditional snapshot methods. This approach enhances the quantitative analysis of cell morphology and responses in live-cell imaging.

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Area of Science:

  • Cell biology
  • Quantitative imaging
  • Bioinformatics

Background:

  • Quantitative analysis of dynamic cellular responses via time-lapse imaging is challenging.
  • Traditional methods often analyze single time points (snapshots) rather than continuous cellular behavior.

Purpose of the Study:

  • To develop and apply a novel trajectory embedding method for analyzing cellular morphological changes over time.
  • To quantitatively model cell state transitions and ligand-induced responses in live-cell imaging.

Main Methods:

  • Exploited "trajectory embedding" to analyze morphological feature trajectory histories across multiple time points simultaneously.
  • Applied the method to live-cell images of MCF10A mammary epithelial cells treated with microenvironmental perturbagens.
  • Constructed a shared cell state landscape to reveal ligand-specific regulation of cell state transitions.

Main Results:

  • Morphodynamical trajectory embedding enables quantitative and descriptive models of single-cell trajectories.
  • Incorporating trajectories improves systematic characterization of cell state dynamics and phenotype separation.
  • The analysis provides more descriptive models of ligand-induced cellular differences compared to snapshot-based approaches.

Conclusions:

  • Morphodynamical trajectory embedding offers a powerful, broadly applicable approach for quantitative analysis of cell responses in live-cell imaging.
  • This method advances the understanding of dynamic cellular behaviors and their regulation by microenvironmental factors.
  • The technique is valuable for various biological and biomedical applications requiring detailed analysis of cell dynamics.