A deep learning algorithm to translate and classify cardiac electrophysiology: From iPSC-CMs to adult cardiac cells

Exciting developments in both in vitro and in silico technologies have led to new ways to identify patient specific cardiac mechanisms. The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology and response to drugs. However, the iPSC-CM methodology is limited by the low throughput and high variability of resulting electrophysiological measurements. Moreover, the iPSC-CMs generate immature action potentials, and it is not clear if observations in the iPSC-CM model system can be confidently interpreted to reflect impact in human adults. There has been no demonstrated method to allow reliable translation of results from the iPSC-CM to a mature adult cardiac response. Here, we demonstrate a new computational approach intended to address the current shortcomings of the iPSC-CM platform by developing and deploying a multitask network that was trained and tested using simulated data and then applied to experimental data. We showed that a deep learning network can be applied to classify cells into the drugged and drug free categories and can be used to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CM action potential to the adult ventricular myocyte action potential. We validated the output of the model with experimental data. The method can be applied broadly across a spectrum of aging, but also to translate data between species.