CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses

Introduction: In recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, despite technological advancements of HD-MEAs, there is a lack of effective data-analysis platforms that are capable of processing and analyzing the data, particularly in the context of cardiac arrhythmias and drug testing. Methods: To address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSCCMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks. Results and discussion: We demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, userfriendly platform for studying cardiac diseases and assessing drug effects.

Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2024-10-30
DOI
https://doi.org/10.3929/ethz-b-000705999
Primary Topic
Neuroscience and Neural Engineering
Type
article
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article

CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses

Repository for Publications and Research Data (ETH Zurich)
Neuroscience and Neural Engineering
article

CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses

article en

Abstract

Introduction: In recent years, high-density microelectrode arrays (HD-MEAs) have emerged as a valuable tool in preclinical research for characterizing the electrophysiology of human induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CMs). HD-MEAs enable the capturing of both extracellular and intracellular signals on a large scale, while minimizing potential damage to the cell. However, despite technological advancements of HD-MEAs, there is a lack of effective data-analysis platforms that are capable of processing and analyzing the data, particularly in the context of cardiac arrhythmias and drug testing. Methods: To address this need, we introduce CardioMEA, a comprehensive data-analysis platform designed specifically for HD-MEA data that have been obtained from iPSCCMs. CardioMEA features scalable data processing pipelines and an interactive web-based dashboard for advanced visualization and analysis. In addition to its core functionalities, CardioMEA incorporates modules designed to discern crucial electrophysiological features between diseased and healthy iPSC-CMs. Notably, CardioMEA has the unique capability to analyze both extracellular and intracellular signals, thereby facilitating customized analyses for specific research tasks. Results and discussion: We demonstrate the practical application of CardioMEA by analyzing electrophysiological signals from iPSC-CM cultures exposed to seven antiarrhythmic drugs. CardioMEA holds great potential as an intuitive, userfriendly platform for studying cardiac diseases and assessing drug effects.

Repository for Publications and Research Data (ETH Zurich)
Openalex Percentile: Top 55%
Neuroscience and Neural Engineering
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CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses · Repository for Publications and Research Data (ETH Zurich) (2024) | TGRS Research Map | TGRS