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Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph.

Saeed Mohammadzadeh1, Emma Lejeune2

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This summary is machine-generated.

Researchers enhanced the SarcGraph tool to analyze cardiomyocyte structure, improving analysis of immature cells and enabling new insights into cardiac cell organization and disease modeling.

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Area of Science:

  • Cardiovascular Biology
  • Computational Biology
  • Biophysics

Background:

  • Cardiomyocyte structural organization indicates cell maturity and health.
  • Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are vital models but often lack quantifiable structure.
  • Existing computational tools struggle with the structural complexity of hiPSC-CMs.

Purpose of the Study:

  • To enhance the SarcGraph framework for improved structural analysis of hiPSC-CMs, particularly immature and diseased cells.
  • To develop novel computational methods for quantifying cardiomyocyte structural features.
  • To enable more accurate assessment of hiPSC-CM structural integrity and function.

Main Methods:

  • Extended the SarcGraph framework with a deep learning-based z-disc classifier.
  • Introduced a novel ensemble graph-scoring approach for improved sarcomere detection.
  • Applied the enhanced framework to analyze the Allen Institute for Cell Science dataset.

Main Results:

  • Significantly reduced false positive sarcomere detections in immature hiPSC-CMs.
  • Improved detection of longer myofibrils in mature hiPSC-CMs.
  • Successfully extracted key structural features to predict expert scores and identify scoring bias.
  • Developed an unsupervised learning approach for explainable clustering of cardiomyocyte structures.

Conclusions:

  • The modified SarcGraph algorithm effectively extracts biologically meaningful structural features from hiPSC-CMs.
  • This enhanced framework provides deeper insights into hiPSC-CM structural integrity and disease modeling.
  • The open-source release of the code and tools aims to advance cardiac research and computational analysis.