Department of Neurology, Yale University School of Medicine, New Haven, CT, USA.
Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA.
Cell Rep Methods. 2023 Jan 12;3(1):100385. doi: 10.1016/j.crmeth.2022.100385. eCollection 2023 Jan 23.
The patch-clamp technique is the gold-standard methodology for analysis of excitable cells. However, throughput of manual patch-clamp is slow, and high-throughput robotic patch-clamp, while helpful for applications like drug screening, has been primarily used to study channels and receptors expressed in heterologous systems. We introduce an approach for automated high-throughput patch-clamping that enhances analysis of excitable cells at the channel and cellular levels. This involves dissociating and isolating neurons from intact tissues and patch-clamping using a robotic instrument, followed by using an open-source Python script for analysis and filtration. As a proof of concept, we apply this approach to investigate the biophysical properties of voltage-gated sodium (Nav) channels in dorsal root ganglion (DRG) neurons, which are among the most diverse and complex neuronal cells. Our approach enables voltage- and current-clamp recordings in the same cell, allowing unbiased, fast, simultaneous, and head-to-head electrophysiological recordings from a wide range of freshly isolated neurons without requiring culturing on coverslips.
膜片钳技术是分析可兴奋细胞的金标准方法。然而，手动膜片钳的通量较慢，而高通量机器人膜片钳虽然有助于药物筛选等应用，但主要用于研究异源系统中表达的通道和受体。我们引入了一种自动化高通量膜片钳的方法，可增强通道和细胞水平上可兴奋细胞的分析。这涉及从完整组织中分离和分离神经元，并使用机器人仪器进行膜片钳，然后使用开源 Python 脚本进行分析和过滤。作为概念验证，我们将此方法应用于研究背根神经节 (DRG) 神经元中电压门控钠 (Nav) 通道的生物物理特性，这些神经元是最多样化和最复杂的神经元细胞之一。我们的方法能够在同一细胞中进行电压和电流钳记录，允许从广泛的新鲜分离神经元中进行无偏、快速、同时和面对面的电生理记录，而无需在盖玻片上培养。
