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Abstract Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells’ electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer a promising alternative by enabling simultaneous intracellular and extracellular action potential (iAP and eAP) recordings with high throughput. However, accessing intracellular potentials with NEAs remains challenging. This study presents an AI-supported technique that leverages thousands of synchronous eAP and iAP pairs from stem-cell-derived cardiomyocytes on NEAs. Our analysis revealed strong correlations between specific eAP and iAP features, such as amplitude and spiking velocity, indicating that extracellular signals could be reliable indicators of intracellular activity. We developed a physics-informed deep learning model to reconstruct iAP waveforms from extracellular recordings recorded from NEAs and Microelectrode arrays (MEAs), demonstrating its potential for non-invasive, long-term, high-throughput drug cardiotoxicity assessments. This AI-based model paves the way for future electrophysiology research across various cell types and drug interactions. 

Abstract The brain integrates activity across networks of interconnected neurons to generate behavioral outputs. Several physiological and imaging-based approaches have been previously used to monitor responses of individual neurons. While these techniques can identify cellular responses greater than the neuron’s action potential threshold, less is known about the events that are smaller than this threshold or are localized to subcellular compartments. Here we use NEAs to obtain temporary intracellular access to neurons allowing us to record information-rich data that indicates action potentials, and sub-threshold electrical activity. We demonstrate these recordings from primary hippocampal neurons, induced pluripotent stem cell-derived (iPSC) neurons, and iPSC-derived brain organoids. Moreover, our results show that our arrays can record activity from subcellular compartments of the neuron. We suggest that these data might enable us to correlate activity changes in individual neurons with network behavior, a key goal of systems neuroscience.