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Related Experiment Video

Updated: Sep 4, 2025

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction
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A strategy to quantify myofibroblast activation on a continuous spectrum.

Alexander Hillsley1, Matthew S Santoso1, Sean M Engels1

  • 1McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA.

Scientific Reports
|July 19, 2022
PubMed
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This study introduces a novel machine learning approach to quantify myofibroblast activation on a continuous scale, moving beyond traditional binary classifications for better wound healing and fibrosis research.

Area of Science:

  • Cell Biology
  • Biotechnology
  • Computational Biology

Background:

  • Myofibroblasts are key in wound healing and fibrosis.
  • Traditional classification of myofibroblasts is binary (activated/quiescent).
  • A continuous spectrum of myofibroblast activation exists but lacks quantification methods.

Purpose of the Study:

  • To develop a quantitative, imaging-based method for myofibroblast activation.
  • To establish a continuous scale for myofibroblast activation.
  • To validate the method using cardiac fibroblasts and transcriptomic data.

Main Methods:

  • Microscopy imaging of over 1000 cardiac fibroblasts.
  • Extraction and analysis of morphological features.
  • Application of dimensionality reduction and self-supervised machine learning.

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Last Updated: Sep 4, 2025

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  • Comparison with single-cell RNA sequencing data.

    • Main Results:

    • Morphological features accurately predict myofibroblast activation state.
    • A continuous activation scale was successfully generated from imaging data.
    • The imaging-based scale correlates with transcriptomic profiles.

    Conclusions:

    • Myofibroblast activation exists on a continuous spectrum.
    • An imaging and machine learning strategy can quantify this spectrum.
    • This method offers a new tool for studying fibrosis and wound healing.