Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings
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 simul...
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55571-6 |
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| author | Keivan Rahmani Yang Yang Ethan Paul Foster Ching-Ting Tsai Dhivya Pushpa Meganathan Diego D. Alvarez Aayush Gupta Bianxiao Cui Francesca Santoro Brenda L. Bloodgood Rose Yu Csaba Forro Zeinab Jahed |
| author_facet | Keivan Rahmani Yang Yang Ethan Paul Foster Ching-Ting Tsai Dhivya Pushpa Meganathan Diego D. Alvarez Aayush Gupta Bianxiao Cui Francesca Santoro Brenda L. Bloodgood Rose Yu Csaba Forro Zeinab Jahed |
| author_sort | Keivan Rahmani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-69b88371dee3421496f86352f17c18ea |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-69b88371dee3421496f86352f17c18ea2025-01-19T12:29:55ZengNature PortfolioNature Communications2041-17232025-01-0116111510.1038/s41467-024-55571-6Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordingsKeivan Rahmani0Yang Yang1Ethan Paul Foster2Ching-Ting Tsai3Dhivya Pushpa Meganathan4Diego D. Alvarez5Aayush Gupta6Bianxiao Cui7Francesca Santoro8Brenda L. Bloodgood9Rose Yu10Csaba Forro11Zeinab Jahed12Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoDepartment of Chemistry, Stanford UniversityDepartment of Chemistry, Stanford UniversityDepartment of Chemistry, Stanford UniversityAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoDepartment of Neurobiology, School of Biological Sciences, University of California San DiegoDepartment of Computer Science and Engineering, Jacobs School of Engineering, University of California San DiegoDepartment of Chemistry, Stanford UniversityCenter for Advanced Biomaterials for Healthcare, Istituto Italiano di TecnologiaDepartment of Neurobiology, School of Biological Sciences, University of California San DiegoDepartment of Computer Science and Engineering, Jacobs School of Engineering, University of California San DiegoDepartment of Chemistry, Stanford UniversityAiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San DiegoAbstract 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.https://doi.org/10.1038/s41467-024-55571-6 |
| spellingShingle | Keivan Rahmani Yang Yang Ethan Paul Foster Ching-Ting Tsai Dhivya Pushpa Meganathan Diego D. Alvarez Aayush Gupta Bianxiao Cui Francesca Santoro Brenda L. Bloodgood Rose Yu Csaba Forro Zeinab Jahed Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings Nature Communications |
| title | Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings |
| title_full | Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings |
| title_fullStr | Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings |
| title_full_unstemmed | Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings |
| title_short | Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings |
| title_sort | intelligent in cell electrophysiology reconstructing intracellular action potentials using a physics informed deep learning model trained on nanoelectrode array recordings |
| url | https://doi.org/10.1038/s41467-024-55571-6 |
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