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Image-Based Tracking of Heterogeneous Single-Cell Phenotypes.

Katherin Patsch1, Shannon M Mumenthaler1, Daniel Ruderman2

  • 1Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|February 25, 2018
PubMed
Summary

This study introduces a new image analysis pipeline to accurately track dynamic single-cell phenotypes in heterogeneous cell populations. The pipeline improves assessment of cellular heterogeneity by filtering erroneous tracks and enabling detailed analysis of cell behaviors.

Keywords:
HeterogeneityLive-cell imagingMitosisMotilityPhenotypesReceptor translocationTracking

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Area of Science:

  • Cell Biology
  • Bioimaging
  • Computational Biology

Background:

  • Cellular heterogeneity in motility, morphology, and signaling is crucial but challenging to analyze.
  • Existing image analysis pipelines struggle with small subpopulations and tracking errors.

Purpose of the Study:

  • To develop and validate a robust image analysis pipeline for tracking dynamic single-cell phenotypes.
  • To improve the assessment of cellular heterogeneity in live-cell imaging studies.

Main Methods:

  • A novel pipeline for live-cell imaging and single-cell tracking was developed.
  • Protocols were optimized for three distinct cell lines across two time scales.
  • Quality control steps were integrated to filter erroneous cell tracks.

Main Results:

  • The pipeline successfully captures intricate changes in small subpopulations.
  • Accurate tracking of dynamic phenotypes, including motility, nuclear receptor translocation, and mitosis, was demonstrated.
  • Improved assessment of cellular heterogeneity was achieved through robust quality control.

Conclusions:

  • The developed pipeline enhances the analysis of dynamic cellular phenotypes in heterogeneous populations.
  • This tool facilitates a deeper understanding of cell behavior and intercellular variability.
  • Recommendations for adapting the pipeline to custom datasets are provided.