Dataset Information

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

ABSTRACT: Drug-induced cardiotoxicity is the leading cause of drug attrition and withdrawal from the market. Therefore, using appropriate preclinical cardiac safety assessment models is a critical step during drug development. Currently, cardiac safety assessment is still highly dependent on animal studies. However, animal models are plagued by poor translational specificity to humans due to species-specific differences, particularly in terms of cardiac electrophysiological characteristics. Thus, there is an urgent need to develop a reliable, efficient, and human-based model for preclinical cardiac safety assessment. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as an invaluable in vitro model for drug-induced cardiotoxicity screening and disease modeling. hiPSC-CMs can be obtained from individuals with diverse genetic backgrounds and various diseased conditions, making them an ideal surrogate to assess drug-induced cardiotoxicity individually. Therefore, methodologies to comprehensively investigate the functional characteristics of hiPSC-CMs need to be established. In this protocol, we detail various functional assays that can be assessed on hiPSC-CMs, including the measurement of contractility, field potential, action potential, and calcium handling. Overall, the incorporation of hiPSC-CMs into preclinical cardiac safety assessment has the potential to revolutionize drug development.

SUBMITTER: Zhao SR

PROVIDER: S-EPMC9668591 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Zhao Shane Rui SR Mondéjar-Parreño Gema G Li Dong D Shen Mengcheng M Wu Joseph C JC

Journal of visualized experiments : JoVE 20220804 186

Drug-induced cardiotoxicity is the leading cause of drug attrition and withdrawal from the market. Therefore, using appropriate preclinical cardiac safety assessment models is a critical step during drug development. Currently, cardiac safety assessment is still highly dependent on animal studies. However, animal models are plagued by poor translational specificity to humans due to species-specific differences, particularly in terms of cardiac electrophysiological characteristics. Thus, there is ...[more]

PMID: 35993757

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Project description:Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as "spike sorting." For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

Dataset Information

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Publications

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

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