A high-performance extracellular field potential analyzer for iPSC-derived cardiomyocytes

Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have emerged as a pivotal model for research. Specialized devices can generate Extracellular Field Potential (EFP) measurements from these cells, analogous to the ventricular complex of the electrocardiogram. However, electrophysiological analysis can be complex and requires specialized expertise, posing a barrier to broader adoption in non-specialized labs. We present the EFP-Analyzer (EFPA), a semi-automized analyzer for EFP traces, which identifies and averages beats, identifies landmarks, and calculates intervals. We demonstrate an analysis of 358 EFP traces from 22 patient-derived lines. We analyzed spontaneously beating iPSC-CMs and optically paced iPSC-CMs through channelrhodopsin. We developed stringent quality criteria and measured EFP intervals, including Field Potential Duration (FPD). We further analyzed the usability and data replicability of EFPA through an inter-intra observer analysis. Correlation coefficient for inter-reader tangent and threshold measurements for these FPD ranged between r: 0.93-1.00. Bland-Altman plots comparing inter observer results for spontaneously beating and paced iPSC-CMs showed 95% limits of agreement (- 13.6 to 19.4 ms and - 13.2 to 15.3 ms, respectively). EFPA could accurately detect FPD prolongation due to drug (moxifloxacin) or pathogenic loss of function mutations (CACNA1C N639T). This program and instructions are available for download at https://github.com/kroncke-lab/EFPA .

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Publication Details

Journal
Scientific Reports
Published
2025-03-15
DOI
https://doi.org/10.1038/s41598-025-88946-w
Citations
1
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.91

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article

A high-performance extracellular field potential analyzer for iPSC-derived cardiomyocytes

Alex Shen, Andrew M. Glazer, Brett M. Kroncke, Loren Vanags et al.
1 citations
Scientific Reports
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0.91
article

A high-performance extracellular field potential analyzer for iPSC-derived cardiomyocytes

Alex Shen, Andrew M. Glazer, Brett M. Kroncke, Loren Vanags, Björn C. Knollmann, Mingyu Yang, Nidhi Patel, Matthew Ku, Devyn Mitchell, Yuko Wada, Joe‐Elie Salem, William Morris, Suah Woo, Marcia Blair, Kundivy Dauda
article en
1 citations

Abstract

Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have emerged as a pivotal model for research. Specialized devices can generate Extracellular Field Potential (EFP) measurements from these cells, analogous to the ventricular complex of the electrocardiogram. However, electrophysiological analysis can be complex and requires specialized expertise, posing a barrier to broader adoption in non-specialized labs. We present the EFP-Analyzer (EFPA), a semi-automized analyzer for EFP traces, which identifies and averages beats, identifies landmarks, and calculates intervals. We demonstrate an analysis of 358 EFP traces from 22 patient-derived lines. We analyzed spontaneously beating iPSC-CMs and optically paced iPSC-CMs through channelrhodopsin. We developed stringent quality criteria and measured EFP intervals, including Field Potential Duration (FPD). We further analyzed the usability and data replicability of EFPA through an inter-intra observer analysis. Correlation coefficient for inter-reader tangent and threshold measurements for these FPD ranged between r: 0.93-1.00. Bland-Altman plots comparing inter observer results for spontaneously beating and paced iPSC-CMs showed 95% limits of agreement (- 13.6 to 19.4 ms and - 13.2 to 15.3 ms, respectively). EFPA could accurately detect FPD prolongation due to drug (moxifloxacin) or pathogenic loss of function mutations (CACNA1C N639T). This program and instructions are available for download at https://github.com/kroncke-lab/EFPA .

Scientific ReportsVol. 15(1)
Inserm (FR), Vanderbilt University (US), Sorbonne Université (FR), Vanderbilt University Medical Center (US)
Fondation Leducq, National Institutes of Health
Zero hunger
Openalex Percentile: Top 30%
Neuroscience and Neural Engineering
0.91
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