Automated Electrophysiology Research

Background

The study of electrical properties of biological cells and tissues represents one of the most challenging studies due to the sensitive and delicate nature of these cells. 

New technological advancements have been introduced to this field and led to efficiency, precision, and throughput of electrophysiological research. The common feature of these advancements is the automation process of the experimental and analysis tools that are used in the field of electrophysiology research.  The latter has received a great deal of attention, and this was reflected in the number of studies that have been designed and published (Figure 1).

Although there was a steady increase in the number of these studies between 2000 to 2010, a noticeable surge was reported in the last five years (Figure 1). These studies have investigated crucial aspects related to neurons, cardiac and muscle cells using protocols that involve careful preparation of cells, precise execution of automated patch-clamp recordings, thorough data analysis, and stringent quality control measures. 

These benefits are particularly valuable in fields such as neuroscience, pharmacology, cardiology and physiotherapy.

Designs and protocols

Electrophysiology research has seen significant advancements due to the integration of robotics, microfluidics, and computational tools, which enhance the efficiency, precision, and throughput of electrophysiological experiments and making it possible to perform large-scale studies with high accuracy.

One of the most used protocols in this field is the automated patch-clamp techniques which allow precise measurement of ion currents across cell membranes (Figure 2).  In addition, high-throughput screening of ion channels and other electrophysiological properties which facilitate drug discovery and the study of genetic variations in ion channel function.

Similarly, advanced software and algorithms for data acquisition and analysis enable real-time monitoring and interpretation of electrophysiological data and improving the efficiency of research workflows (Figure 2). Furthermore, the use of robotics, microfluidics and multi-electrode array in electrophysiology allows for the precise manipulation of cells and solutions, reducing human error and increasing reproducibility (Figure 2).

Selected free full-text articles

• Perszyk RE, Yip MC, McConnell OL, Wang ET, Jenkins A, Traynelis SF, Forest CR. Automated Intracellular Pharmacological Electrophysiology for Ligand-Gated Ionotropic Receptor and Pharmacology Screening. Mol Pharmacol. 2021 Jul;100(1):73-82. doi: 10.1124/molpharm.120.000195. Epub 2021 May 6. PMID: 33958481; PMCID: PMC8274318. https://pubmed.ncbi.nlm.nih.gov/33958481/
• Oguntuyo K, Schuftan D, Guo J, Simmons D, Bhagavan D, Moreno JD, Kang PW, Miller E, Silva JR, Huebsch N. Robust, Automated Analysis of Electrophysiology in Induced Pluripotent Stem Cell-Derived Micro-Heart Muscle for Drug Toxicity. Tissue Eng Part C Methods. 2022 Sep;28(9):457-468. doi: 10.1089/ten.tec.2022.0053. Epub 2022 Aug 4. PMID: 35925789; PMCID: PMC9527045. https://pubmed.ncbi.nlm.nih.gov/35925789/
• Dallas ML, Bell D. Advances in ion channel high throughput screening: where are we in 2023? Expert Opin Drug Discov. 2024 Mar;19(3):331-337. doi: 10.1080/17460441.2023.2294948. Epub 2023 Dec 18. PMID: 38108110. https://pubmed.ncbi.nlm.nih.gov/38108110/
• Seibertz F, Rapedius M, Fakuade FE, Tomsits P, Liutkute A, Cyganek L, Becker N, Majumder R, Clauß S, Fertig N, Voigt N. A modern automated patch-clamp approach for high throughput electrophysiology recordings in native cardiomyocytes. Commun Biol. 2022 Sep 15;5(1):969. doi: 10.1038/s42003-022-03871-2. PMID: 36109584; PMCID: PMC9477872. https://pubmed.ncbi.nlm.nih.gov/36109584/
• Seibertz F, Rapedius M, Fakuade FE, Tomsits P, Liutkute A, Cyganek L, Becker N, Majumder R, Clauß S, Fertig N, Voigt N. A modern automated patch-clamp approach for high throughput electrophysiology recordings in native cardiomyocytes. Commun Biol. 2022 Sep 15;5(1):969. doi: 10.1038/s42003-022-03871-2. PMID: 36109584; PMCID: PMC9477872. https://pubmed.ncbi.nlm.nih.gov/36109584/