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Abstract
Cardiovascular disease remains one of the most dangerous categories of diseases in the United States and worldwide. The fatality of these conditions is mainly due to the lack of proliferative ability of mature cardiomyocytes, and any loss of these cells results in the transformation of heart tissue into a fibrotic scar. One strategy which researchers believe will provide a more effective treatment is cell-based therapy for myocardial repair. In this methodology, cardiomyocytes are generated from human pluripotent stem cells (hPSCs) and then used for transplant into the patient's heart. Many roadblocks still need to be overcome before we achieve the safe and scalable manufacture of cardiomyocytes. Persistence of an immature phenotype is one of the major problems, meaning a high ratio of immature mononucleated cardiomyocytes are generated in the manufacturing process. To optimize the cardiomyocyte manufacture protocol, it is critical to improve our understanding of cardiomyocyte differentiation and maturation and identify effective markers which provide strategies to better monitor cell state and stage of differentiation. Recent advances in microscopy and sequencing means that we now have greater capabilities to study epigenetic changes during cellular development with greater resolution. Deep learning tools also became more powerful and precise in the analyses of cellular substructures. Applying these state-of-the-art methodologies, we 1) revealed major mesoscale and locus-level epigenetic shifts during cardiomyocyte differentiation, maturation and senescence; 2) developed a deep learning pipeline for the non-invasive monitoring of live cardiomyocyte maturation in cell culture.