Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

IF 14.3 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY Advanced Science Pub Date : 2024-12-31 DOI:10.1002/advs.202404166
Shengjie Yang, Jiaqi Xue, Ziqi Li, Shiqing Zhang, Zhang Zhang, Zhifeng Huang, Ken Kin Lam Yung, King Wai Chiu Lai
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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.

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膜片钳技术是研究离子通道动力学和电生理特性的基本工具。本研究首次提出了表征全细胞记录的多种离子通道动力学的人工智能框架。该框架整合了异常检测的机器学习和多类分类的深度学习。异常检测排除了不符合离子通道行为的记录。多类分类结合了一维卷积神经网络、双向长短期记忆和注意力机制,以捕捉记录的时空模式。该框架根据离子通道动力学将 124 个测试数据集分为六类,准确率达到 97.58%。新框架的实用性在两个应用中得到了证明:阿尔茨海默病药物筛选和纳米基质诱导的神经元分化。在药物筛选中,该框架说明了美金刚对内源性通道的抑制作用,以及钾、镁和钙离子通道之间的拮抗相互作用。在纳米基质诱导的分化中,分类器显示了分化条件对与动作电位相关的钠和钾通道的影响,验证了分化神经元的功能特性,可用于帕金森病的治疗。所提出的框架有望提高电生理研究中离子通道动力学分析的效率和准确性。
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来源期刊
Advanced Science
Advanced Science CHEMISTRY, MULTIDISCIPLINARYNANOSCIENCE &-NANOSCIENCE & NANOTECHNOLOGY
CiteScore
18.90
自引率
2.60%
发文量
1602
审稿时长
1.9 months
期刊介绍: Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.
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