High-throughput combined voltage-clamp/current-clamp analysis of freshly isolated neurons

被引:9
|
作者
Ghovanloo, Mohammad -Reza [1 ,2 ,3 ]
Tyagi, Sidharth [1 ,2 ,3 ,4 ]
Zhao, Peng [1 ,2 ,3 ]
Kiziltug, Emre [1 ]
Estacion, Mark [1 ,2 ,3 ]
Dib-Hajj, Sulayman D. [1 ,2 ,3 ]
Waxman, Stephen G. [1 ,2 ,3 ]
机构
[1] Yale Univ, Sch Med, Dept Neurol, New Haven, CT 06510 USA
[2] Yale Univ, Ctr Neurosci & Regenerat Res, West Haven, CT 06520 USA
[3] Vet Affairs Connecticut Healthcare Syst, Neurorehabil Res Ctr, West Haven, CT 06516 USA
[4] Yale Univ, Sch Med, Med Scientist Training Program, New Haven, CT USA
来源
CELL REPORTS METHODS | 2023年 / 3卷 / 01期
基金
美国国家卫生研究院; 加拿大健康研究院;
关键词
DORSAL-ROOT GANGLION; GATED SODIUM-CHANNELS; SLOW INACTIVATION; CURRENTS; EXPRESSION; PAIN; RAT; ELECTROPHYSIOLOGY; NA(V)1.7; RECORDINGS;
D O I
10.1016/j.crmeth.2022.100385
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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 het-erologous 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 cur-rent-clamp recordings in the same cell, allowing unbiased, fast, simultaneous, and head-to-head electro-physiological recordings from a wide range of freshly isolated neurons without requiring culturing on coverslips.
引用
收藏
页数:17
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