A deep learning algorithm to translate and classify cardiac electrophysiology
- University of California Davis, United States
- Washington University in St. Louis, United States
- University of Shizuoka, Japan
- National Institute of Health Sciences, Japan
- University California Davis, United States
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.
Data availability
Since we used simulated data, we have made all drugged and drug-free iPSC-CM and adult-CM AP data used for training and testing the multitask network publicly available at Clancy lab Github.(https://github.com/ClancyLabUCD/Multitask_network/tree/master/data). In addition, we have illustrated training and test dataset in Figure1 and Figure5.We have also shared the jupyter notebook for preparing clean and organized data for training the network at Clancy lab Github (https://github.com/ClancyLabUCD/Multitask_network/tree/master/jupyter).We also made experimental data used for the model validation publicly available at Clancy lab Github.(https://github.com/ClancyLabUCD/Multitask_network/blob/master/data/clean_data/experiments.csv ). Figure 7 illustrates the experimental data we used to validate the network.
Article and author information
Author details
Yasunari Kanda Ph.D.
Colleen E Clancy Ph.D.
Funding
Common Fund (OT2OD026580)
- Igor Vorobyov
- Colleen E Clancy Ph.D.
Texas Advanced Computing Center Leadership Resource Allocation (MCB20010)
- Igor Vorobyov
- Colleen E Clancy Ph.D.
Oracle cloud for research allocation
- Igor Vorobyov
- Colleen E Clancy Ph.D.
Common Fund (OT2OD025308‐01S2)
- Parya Aghasafari Ph.D.
American Heart Association (19CDA34770101)
- Igor Vorobyov
National Heart, Lung, and Blood Institute (R01HL152681)
- Igor Vorobyov
- Colleen E Clancy Ph.D.
National Heart, Lung, and Blood Institute (R01HL128170)
- Colleen E Clancy Ph.D.
National Heart, Lung, and Blood Institute (U01HL126273)
- Colleen E Clancy Ph.D.
Department of Physiology and Membrane Biology Research Partnership Fund
- Igor Vorobyov
- Colleen E Clancy Ph.D.
Extreme Science and Engineering Discovery Environment (MCB170095)
- Igor Vorobyov
- Colleen E Clancy Ph.D.
National Center for Supercomputing Applications Blue Waters Broadening Participation Allocation
- Igor Vorobyov
- Colleen E Clancy Ph.D.
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Thomas Hund, The Ohio State University, United States
Version history
- Received: March 11, 2021
- Accepted: June 29, 2021
- Accepted Manuscript published: July 2, 2021 (version 1)
- Version of Record published: July 15, 2021 (version 2)
Copyright
© 2021, Aghasafari et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A deep learning algorithm to translate and classify cardiac electrophysiology
eLife 10:e68335.
https://doi.org/10.7554/eLife.68335
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