Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
Kanagawa Institute of Industrial Science and Technology, 3-2-1 Sakado, Takatsu-ku, Kawasaki-shi, Kanagawa, 213-0012, Japan.
Biosens Bioelectron. 2023 Oct 1;237:115490. doi: 10.1016/j.bios.2023.115490. Epub 2023 Jun 25.
This paper describes a novel signal processing method to characterize the activity of ion channels on a lipid bilayer system in a real-time and quantitative manner. Lipid bilayer systems, which enable single-channel level recordings of ion channel activities against physiological stimuli in vitro, are gaining attention in various research fields. However, the characterization of ion channel activities has heavily relied on time-consuming analyses after recording, and the inability to return the quantitative results in real time has long been a bottleneck to incorporating the system into practical products. Herein, we report a lipid bilayer system that integrates real-time characterization of ion channel activities and real-time response based on the characterization result. Unlike conventional batch processing, an ion channel signal is divided into short segments and processed during the recording. After optimizing the system to maintain the same characterization accuracy as conventional operation, we demonstrated the usability of the system with two applications. One is quantitative control of a robot based on ion channel signals. The velocity of the robot was controlled every second, which was around tens of times faster than the conventional operation, in proportion to the stimulus intensity estimated from changes in ion channel activities. The other is the automation of data collection and characterization of ion channels. By constantly monitoring and maintaining the functionality of a lipid bilayer, our system enabled continuous recording of ion channels over 2 h without human intervention, and the time of manual labor has been reduced from conventional 3 h to 1 min at a minimum. We believe the accelerated characterization and response in the lipid bilayer systems presented in this work will facilitate the transformation of lipid bilayer technology toward a practical level, finally leading to its industrialization.
本文描述了一种新颖的信号处理方法，能够实时、定量地描述脂质双层系统中离子通道的活性。脂质双层系统能够在体外对离子通道活性进行单通道水平记录，在各个研究领域中受到关注。然而，离子通道活性的特征化在很大程度上依赖于记录后的耗时分析，并且无法实时返回定量结果一直是将该系统纳入实际产品的瓶颈。在此，我们报告了一种脂质双层系统，该系统将离子通道活性的实时特征化和基于特征化结果的实时响应相结合。与传统的批量处理不同，离子通道信号被分成短片段并在记录过程中进行处理。在优化系统以保持与传统操作相同的特征化精度之后，我们通过两个应用展示了该系统的可用性。一个应用是基于离子通道信号对机器人进行定量控制。机器人的速度每秒控制一次，与从离子通道活动变化中估计的刺激强度成正比，比传统操作快数十倍左右。另一个应用是离子通道数据收集和特征化的自动化。通过不断监测和维护脂质双层的功能，我们的系统能够在无人干预的情况下连续记录离子通道 2 小时以上，并且人工劳动时间已从传统的 3 小时减少到最短 1 分钟。我们相信，本文中提出的脂质双层系统的加速特征化和响应将促进脂质双层技术向实用化的转变，最终实现其产业化。
Zotero 插件