Literature DB >> 33958481

Automated Intracellular Pharmacological Electrophysiology for Ligand-Gated Ionotropic Receptor and Pharmacology Screening.

Riley E Perszyk1, Mighten C Yip1, Ona L McConnell1, Eric T Wang1, Andrew Jenkins2, Stephen F Traynelis2, Craig R Forest2.   

Abstract

Communication between neuronal cells, which is central to brain function, is performed by several classes of ligand-gated ionotropic receptors. The gold-standard technique for measuring rapid receptor response to agonist is manual patch-clamp electrophysiology, capable of the highest temporal resolution of any current electrophysiology technique. We report an automated high-precision patch-clamp system that substantially improves the throughput of these time-consuming pharmacological experiments. The patcherBotPharma enables recording from cells expressing receptors of interest and manipulation of them to enable millisecond solution exchange to activate ligand-gated ionotropic receptors. The solution-handling control allows for autonomous pharmacological concentration-response experimentation on adherent cells, lifted cells, or excised outside-out patches. The system can perform typical ligand-gated ionotropic receptor experimentation protocols autonomously, possessing a high success rate in completing experiments and up to a 10-fold reduction in research effort over the duration of the experiment. Using it, we could rapidly replicate previous data sets, reducing the time it took to produce an eight-point concentration-response curve of the effect of propofol on GABA type A receptor deactivation from likely weeks of recording to ∼13 hours of recording. On average, the rate of data collection of the patcherBotPharma was a data point every 2.1 minutes that the operator spent interacting with the patcherBotPharma The patcherBotPharma provides the ability to conduct complex and comprehensive experimentation that yields data sets not normally within reach of conventional systems that rely on constant human control. This technical advance can contribute to accelerating the examination of the complex function of ion channels and the pharmacological agents that act on them. SIGNIFICANCE STATEMENT: This work presents an automated intracellular pharmacological electrophysiology robot, patcherBotPharma, that substantially improves throughput and reduces human time requirement in pharmacological patch-clamp experiments. The robotic system includes millisecond fluid exchange handling and can perform highly efficient ligand-gated ionotropic receptor experiments. The patcherBotPharma is built using a conventional patch-clamp rig, and the technical advances shown in this work greatly accelerate the ability to conduct high-fidelity pharmacological electrophysiology.
Copyright © 2021 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2021        PMID: 33958481      PMCID: PMC8274318          DOI: 10.1124/molpharm.120.000195

Source DB:  PubMed          Journal:  Mol Pharmacol        ISSN: 0026-895X            Impact factor:   4.054


  24 in total

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Authors:  Ilya Kolb; Corey R Landry; Mighten C Yip; Colby F Lewallen; William A Stoy; John Lee; Amanda Felouzis; Bo Yang; Edward S Boyden; Christopher J Rozell; Craig R Forest
Journal:  J Neural Eng       Date:  2019-04-10       Impact factor: 5.379

Review 4.  An update on the advancing high-throughput screening techniques for patch clamp-based ion channel screens: implications for drug discovery.

Authors:  Alison Obergrussberger; Tom A Goetze; Nina Brinkwirth; Nadine Becker; Søren Friis; Markus Rapedius; Claudia Haarmann; Ilka Rinke-Weiß; Sonja Stölzle-Feix; Andrea Brüggemann; Michael George; Niels Fertig
Journal:  Expert Opin Drug Discov       Date:  2018-01-17       Impact factor: 6.098

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Authors:  I Kolb; W A Stoy; E B Rousseau; O A Moody; A Jenkins; C R Forest
Journal:  Sci Rep       Date:  2016-10-11       Impact factor: 4.379

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10.  Robotic Automation of In Vivo Two-Photon Targeted Whole-Cell Patch-Clamp Electrophysiology.

Authors:  Luca A Annecchino; Alexander R Morris; Caroline S Copeland; Oshiorenoya E Agabi; Paul Chadderton; Simon R Schultz
Journal:  Neuron       Date:  2017-08-30       Impact factor: 17.173

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