Intelligent In-Cell Electrophysiology: Reconstructing Action Potentials from Nanoelectrode Data Using Physics-Informed Deep Learning.

Abstract

Intracellular electrophysiology is utilized across different scientific and medical disciplines, including neuroscience, cardiology, and pharmacology, due to its pivotal role in exploring and comprehending the electrical properties of cells within diverse biological systems. Traditional methods for intracellular electrophysiology, such as patch-clamp, are highly precise but suffer from being low-throughput and invasive. In contrast, Nanoelectrode Arrays (NEAs) present a promising alternative, enabling simultaneous intracellular action potential (iAP) and extracellular action potential (eAP) recordings with high throughput. However, accessing intracellular potentials with NEAs remains a challenge. This study introduces a technique for intracellular electrophysiology supported by artificial intelligence (AI) that capitalizes on thousands of synchronous eAP and iAP pairs collected from stem-cell-derived cardiomyocytes on NEAs. Our analysis on this unique dataset uncovered strong correlations between specific features of eAP and iAP waveforms, such as amplitude and spiking velocity, that were not detected before, suggesting that extracellular signals could be reliable indicators of intracellular signals. By developing a physics-informed deep learning model trained on these datasets, we show successful reconstruction of iAP waveforms from extracellular recordings. We then demonstrate our model’s potential for non-invasive, long-term, and high-throughput assessments of drug cardiotoxicity. Although we show one specific application of the AI-based model, it opens the door for future research in electrophysiology, with the promise of expanding the dataset to encompass various electrogenic cell types and drug interactions.