Functional Optimization of Designer Cardiac Organoids Enabled by Machine Learning Techniques

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 principle for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs (Circle 200, Circle 600, Circle 1000, Rectangle 1:1, Rectangle 1:4, Star 1:1, and Star 1:4). We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We successfully clustered and refined our 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 rising time in distinguishing organoids based on geometric patterns and clustering results. This innovative 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.