A deep learning algorithm to translate and classify cardiac electrophysiology.
Journal:
eLife
PMID:
34212860
Abstract
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 pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.
Authors
Keywords
Action Potentials
Algorithms
Cell Differentiation
Computer Simulation
Deep Learning
Electrophysiologic Techniques, Cardiac
Electrophysiological Phenomena
ERG1 Potassium Channel
Gene Expression Regulation
Humans
Induced Pluripotent Stem Cells
Models, Biological
Myocytes, Cardiac
Phenethylamines
Sulfonamides