Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States.
Washington University in St. Louis, St. Louis, United States.
Elife. 2021 Jul 2;10:e68335. doi: 10.7554/eLife.68335.
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.
诱导多能干细胞衍生心肌细胞（iPSC-CMs）的发展是研究患者特异性生理学、病理生理学和药理学的一个重要的体外进展。我们设计了一种新的深度学习多任务网络方法，旨在解决 iPSC-CM 平台的低通量、高变异性和不成熟表型的问题。之所以结合翻译和分类任务，是因为我们在这里描述的深度学习技术最有可能的应用是在施加扰动后翻译 iPSC-CMs。该深度学习网络使用模拟动作电位 (AP) 数据进行训练，并应用于将细胞分类为无药物和有药物两类，并预测电生理扰动对从幼稚 iPSC-CMs 到成年心室肌细胞的整个衰老过程的影响。由于膜电阻急剧上升而对扰动极其敏感的 AP 相位包含了成功进行网络多任务所需的关键信息。我们还通过对后者预测实验性药物对成年心肌细胞 AP 的影响，成功地对实验和模拟 iPSC-CM AP 数据进行了翻译，验证了我们的网络。
