Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques.

Journal: Cell reports methods
PMID: 38889687

Abstract

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

Authors

  • Andrew Kowalczewski
    Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA.
  • Shiyang Sun
    Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA; BioInspired Syracuse Institute for Material and Living Systems, Syracuse University, Syracuse, NY, USA.
  • Nhu Y Mai
    Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA; BioInspired Syracuse Institute for Material and Living Systems, Syracuse University, Syracuse, NY, USA.
  • Yuanhui Song
    Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA; BioInspired Syracuse Institute for Material and Living Systems, Syracuse University, Syracuse, NY, USA.
  • Plansky Hoang
    Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, New York, USA.
  • Xiyuan Liu
    Department of Mechanical & Aerospace Engineering, Syracuse University, Syracuse, NY, USA.
  • Huaxiao Yang
    Department of Biomedical Engineering, University of North Texas, Denton, TX, USA.
  • Zhen Ma
    Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA.

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