Evaluating chemical effects on human neural cells through calcium imaging and deep learning
Ray Yueh Ku,
Ankush Bansal,
Dipankar J. Dutta,
Satoshi Yamashita,
John Peloquin,
Diana N. Vu,
Yubing Shen,
Tomoki Uchida,
Masaaki Torii,
Kazue Hashimoto-Torii
Affiliations
Ray Yueh Ku
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA
Ankush Bansal
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA; Corresponding author
Dipankar J. Dutta
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA
Satoshi Yamashita
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA
John Peloquin
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA
Diana N. Vu
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA
Yubing Shen
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA
Tomoki Uchida
Novel Business Development Department, Suntory Global Innovation Center Limited, 8-1-1 Seikadai, Seika-cho, Soraku-gun, Kyoto 619-0284, Japan
Masaaki Torii
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA; Department of Pediatrics, Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20052, USA; Corresponding author
Kazue Hashimoto-Torii
Center for Neuroscience Research, Children’s Research Institute, Children’s National Hospital, Washington, DC 20010, USA; Department of Pediatrics, Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20052, USA; Corresponding author
Summary: New substances intended for human consumption must undergo extensive preclinical safety pharmacology testing prior to approval. These tests encompass the evaluation of effects on the central nervous system, which is highly sensitive to chemical substances. With the growing understanding of the species-specific characteristics of human neural cells and advancements in machine learning technology, the development of effective and efficient methods for the initial screening of chemical effects on human neural function using machine learning platforms is anticipated. In this study, we employed a deep learning model to analyze calcium dynamics in human-induced pluripotent stem cell-derived neural progenitor cells, which were exposed to various concentrations of four representative chemicals. We report that this approach offers a reliable and concise method for quantitatively classifying the effects of chemical exposures and predicting potential harm to human neural cells.
