TOPIC

Using Automated Patch Clamp for High Throughput Characterization of Sodium and Potassium Channels in Human-Induced Pluripotent Stem Cell-Derived Sensory Neurons

Journal

BPS 2022, ICMS 2023, Canadian Pain Society 2024

Author(s)

Mei Zhang, Vincent Truong, Daniel R Sauter, Patrick Walsh, Kyle Jensen

Year

2022

Dorsal root ganglion (DRG) neurons transfer sensory signals from the peripheral to the central nervous system. Understanding the electrophysiological properties of DRG neurons has a significant application for pain research and potential drug development. Given the limited availability of human DRG neurons, the development of human-induced pluripotent stem cell (hiPSC)-derived sensory neurons (SNs) that contain DRG neuron electrophysiological properties provides a promising way to perform in vitro pain research. In addition, the latest expansion of automated patch clamp (APC) into a 384-well format provides the possibility of a high throughput screening (HTS) for thousands of compounds. In this study, RealDRG™ hiPSC-derived SNs were produced at massive scale and consistency to meet the demands of high-throughput APC studies. These SNs bear similarities to human DRG from a whole-transcriptome perspective and have been previously shown to possess functional voltage and ligand-gated channels important for nociception via manual patch clamp. To show the utility of nociceptors in HTS APC, we investigated the electrophysiological properties of hiPSC derived SNs on the Qube 384 APC system over time spans of 9-, 14-, 21-, and 28-day cultures. Using an optimized cell dissociation protocol to obtain healthy cell membranes for patch-clamp, we obtained a whole-cell success rate of 40.69-53.19%. Among these cells, 90.54-97.22% expressed KV currents, and 59.76-77.48% expressed Nav currents. For the Nav current group, 30.7-37.5% of cells carried a detectable TTX-resistant component. Furthermore, under current clamp mode, action potential firings were recorded from 60.12-66.76% of the cells that passed the success filter criteria.

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