Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording
Abstract The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole‐cell recordings. The framework integrates mac...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202404166 |
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| author | Shengjie Yang Jiaqi Xue Ziqi Li Shiqing Zhang Zhang Zhang Zhifeng Huang Ken Kin Lam Yung King Wai Chiu Lai |
| author_facet | Shengjie Yang Jiaqi Xue Ziqi Li Shiqing Zhang Zhang Zhang Zhifeng Huang Ken Kin Lam Yung King Wai Chiu Lai |
| author_sort | Shengjie Yang |
| collection | DOAJ |
| description | Abstract The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole‐cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi‐class classification. The anomaly detection excludes recordings that are incompatible with ion channel behavior. The multi‐class classification combined a 1D convolutional neural network, bidirectional long short‐term memory, and an attention mechanism to capture the spatiotemporal patterns of the recordings. The framework achieves an accuracy of 97.58% in classifying 124 test datasets into six categories based on ion channel kinetics. The utility of the novel framework is demonstrated in two applications: Alzheimer's disease drug screening and nanomatrix‐induced neuronal differentiation. In drug screening, the framework illustrates the inhibitory effects of memantine on endogenous channels, and antagonistic interactions among potassium, magnesium, and calcium ion channels. For nanomatrix‐induced differentiation, the classifier indicates the effects of differentiation conditions on sodium and potassium channels associated with action potentials, validating the functional properties of differentiated neurons for Parkinson's disease treatment. The proposed framework is promising for enhancing the efficiency and accuracy of ion channel kinetics analysis in electrophysiological research. |
| format | Article |
| id | doaj-art-1a9ae4dc59a744c09957a10e63f6e8e8 |
| institution | DOAJ |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-1a9ae4dc59a744c09957a10e63f6e8e82025-08-20T02:49:46ZengWileyAdvanced Science2198-38442025-03-011212n/an/a10.1002/advs.202404166Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp RecordingShengjie Yang0Jiaqi Xue1Ziqi Li2Shiqing Zhang3Zhang Zhang4Zhifeng Huang5Ken Kin Lam Yung6King Wai Chiu Lai7Department of Biomedical Engineering City University of Hong Kong Tat Chee Avenue, Kowloon Tong Kowloon Hong Kong SAR ChinaDepartment of Biomedical Engineering City University of Hong Kong Tat Chee Avenue, Kowloon Tong Kowloon Hong Kong SAR ChinaDepartment of Biomedical Engineering City University of Hong Kong Tat Chee Avenue, Kowloon Tong Kowloon Hong Kong SAR ChinaJNU‐HKUST Joint Laboratory for Neuroscience and Innovative Drug Research College of Pharmacy Jinan University 601 West Huangpu Road, Tianhe Guangzhou 510632 ChinaSchool of Public Health Guangzhou Medical University Xinzao, Panyu Guangzhou 511436 ChinaDepartment of Chemistry Chinese University of Hong Kong Shatin New Territories Hong Kong SAR ChinaDepartment of Science and Environmental Studies Education University of Hong Kong 10 Lo Ping Road Tai Po New Territories Hong Kong SAR ChinaDepartment of Biomedical Engineering City University of Hong Kong Tat Chee Avenue, Kowloon Tong Kowloon Hong Kong SAR ChinaAbstract The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole‐cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi‐class classification. The anomaly detection excludes recordings that are incompatible with ion channel behavior. The multi‐class classification combined a 1D convolutional neural network, bidirectional long short‐term memory, and an attention mechanism to capture the spatiotemporal patterns of the recordings. The framework achieves an accuracy of 97.58% in classifying 124 test datasets into six categories based on ion channel kinetics. The utility of the novel framework is demonstrated in two applications: Alzheimer's disease drug screening and nanomatrix‐induced neuronal differentiation. In drug screening, the framework illustrates the inhibitory effects of memantine on endogenous channels, and antagonistic interactions among potassium, magnesium, and calcium ion channels. For nanomatrix‐induced differentiation, the classifier indicates the effects of differentiation conditions on sodium and potassium channels associated with action potentials, validating the functional properties of differentiated neurons for Parkinson's disease treatment. The proposed framework is promising for enhancing the efficiency and accuracy of ion channel kinetics analysis in electrophysiological research.https://doi.org/10.1002/advs.202404166deep learningelectrophysiologyion channelspatch clampwhole‐cell recording |
| spellingShingle | Shengjie Yang Jiaqi Xue Ziqi Li Shiqing Zhang Zhang Zhang Zhifeng Huang Ken Kin Lam Yung King Wai Chiu Lai Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording Advanced Science deep learning electrophysiology ion channels patch clamp whole‐cell recording |
| title | Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording |
| title_full | Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording |
| title_fullStr | Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording |
| title_full_unstemmed | Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording |
| title_short | Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording |
| title_sort | deep learning based ion channel kinetics analysis for automated patch clamp recording |
| topic | deep learning electrophysiology ion channels patch clamp whole‐cell recording |
| url | https://doi.org/10.1002/advs.202404166 |
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