Investigating tunable experiment variable effects on hiPSC-CMs maturation via unsupervised learning

Cardiomyocytes (CMs) are heart cells responsible for heart contraction and relaxation. CMs can be derived from human induced pluripotent stem cells (hiPSCs) with high yield and purity. Mature CMs can potentially replace dead and dysfunctional cardiac tissue and be used for screening cardiac drugs and toxins. However, hiPSCs-derived CMs (hiPSC-CMs) are immature, which limits their utilization. Therefore, it is crucial to understand how experimental variables, especially tunable ones, of hiPSC expansion and differentiation phases affect the hiPSC-CM maturity stage. This study applied clustering algorithms to day 30 cardiac differentiation data to investigate if any maturity-related cell features could be related to the experimental variables. The best models were obtained using k-means and Gaussian mixture model clustering algorithms based on the evaluation metrics. They grouped the cells based on eccentricity and elongation. The cosine similarity between the clustering results and the experimental parameters revealed that the Gaussian mixture model results have strong similarities of 0.88, 0.94, and 0.93 with axial ratio, diameter, and cell concentration.