Machine learning to optimize geometrically confined cardiac organoids
2024
Syracuse University, Syracuse, USA
Stem cell organoids are powerful models for studying organ development, disease modelling, 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, micropatterning techniques were utilized to create a designer library of 230 cardiac organoids with 7 geometric designs. Single organoid heterogeneity was analysed based on 10 physiological parameters using manifold learning techniques. The cardiac orgnoids were clustered and refined based on their functional similarity using unsupervised machine learning approaches, thereby, elucidating unique functionalities associated with geometric designs. Furthermore, the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results was highlighted. 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.
Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques
Zhen Ma
Added on: 07-08-2024
[1] https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(24)00154-1
