Event Abstract

Back to Event

CardioMeaSignatures: A MATLAB software package for automated extraction of 2D and 3D cardiac electrophysiological signatures recorded on high-density MEAs

Alicia J. Kaestli1*, Michele Fiscella1, 2, Jan Müller1, 2, Urs Frey1, 2, Firat Duru3, 4 and Andreas Hierlemann1
  • 1 ETH Zurich, Department of Biosystems Science and Engineering, Switzerland
  • 2 MaxWell Biosystems, Switzerland
  • 3 Universitäres Herzzentrum Zürich, Department of Cardiology, Switzerland
  • 4 University of Zurich, Center for Integrative Human Physiology, Switzerland

Motivation: Studying human-induced-pluripotent-stem-cell (hiPSC)-derived cardiomyocyte electrophysiological activity is of increasing importance for understanding both the underlying causes of cardiac diseases and the cardiotoxicity of novel pharmaceutical compounds in drug development. Microelectrode arrays (MEA) can be used to model the electrophysiology of cardiac diseases, such as cardiomyopathies. Further, the Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative was established to improve the understanding of in vitro iPSC-derived cardiomyocyte model systems, including readouts using MEAs. The initiative is supported by many partners, including the US FDA, EMA, and Japan NIHS. Nevertheless, there is currently a lack of open-source software that can be used to extract specific cardiac electrophysiological signatures from high-density (HD) MEA recordings [1]. Purpose: The purpose of this study was to develop an open-source software package for automated analysis of cardiomyocyte HD-MEA recordings. Method: The CardioMeaSignatures MATLAB software package can be used to automatically extract cardiomyocyte signatures from multi-day recordings. Both 2D and 3D hiPSC-derived cardiomyocyte tissues were cultured on HD-MEAs. The signal propagation across a cardiomyocyte tissue was measured using a HD-MEA with a temporal and spatial resolution of 20 kHz and 17.5 µm, respectively [2]. The HD-MEA permits the simultaneous measurement of 1024 electrodes, selected arbitrarily from 26’400 electrodes, placed at a center-to-center distance of 17.5 µm on a 8 mm2 surface. Electrode activity was filtered with a low-pass filter. R and S peaks in the signal were detected using a k-means clustering algorithm. Once spikes had been detected, the spatiotemporal characteristics of the QRS complex and T wave were extracted from each electrode. The software package then compiled videos of the wave amplitude and speed across the array over time. Parameters from multi-day experiments were automatically summarized into single graphs. Result: We used the CardioMeaSignatures software package to measure the acute and chronic effects of the antiarrhythmic drug flecainide acetate and the steroid hormone testosterone. The compounds were measured over five days and across four orders of magnitude in concentration. The percentage of active electrodes, wave amplitude, and wave speed were measured. Approximately 1000 cardio peaks were automatically extracted from each tissue over twenty-five 3-minute recordings. As anticipated, the wave speed, the interbeat (RR) interval, and the percentage of active electrodes for the media negative control remained constant over the course of the experiment. When the concentration of the antiarrhythmic drug flecainide was increased gradually from 10 nM to 10 uM over four days, the R wave speed increased by 83%, and the percentage active electrodes decreased by 22%. Testosterone was recently found to be associated with poor outcome in the rare cardiac disease arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) [3]. However, surprisingly, we found that testosterone was cardioprotective for the hiPSC-derived in vitro cultures. Despite an increase in testosterone from 1 nM to 1000 nM, the wave speed and the percentage of active electrodes of the cultures were all stabilized at baseline levels. Conclusion: We designed an open-source software package for the automated analysis of cardiac electrophysiological signatures from HD-MEA recordings. We used the software package to successfully study the effects of two compounds on hiPSC-derived cardiomyocyte cultures.

Acknowledgements

A.J. Kaestli, M. Fiscella, J. Müller, U. Frey, and A. Hierlemann are supported by the European Research Council Advanced Grant 694829 “neuroXscales.” F. Duru is supported by the Georg and Bertha Schwyzer-Winiker Foundation Zurich and the Baugarten Foundation Zurich.

References

[1] C.D. Sanchez-Bustamante, U. Frey, J.M. Kelm, A. Hierlemann, and M. Fussenegger. “Modulation of cardiomyocyte electrical properties using regulated bone morphogenetic protein-2 expression.” Tissue Engineering Part A, 2008, 14(12), 1969-1988.

[2] J. Müller, M. Ballini, P. Livi, Y. Chen, M. Radivojevic, A. Shadmani, V. Viswam, I. L. Jones, M. Fiscella, R. Diggelmann, A. Stettler, U. Frey, D. J. Bakkum, A. Hierlemann, “High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels”, Lab Chip, 2015, 15, 2767-2780.

[3] D. Akdis, A.M. Saguner, K. Shah, C. Wei, A. Medeiros-Domingo, A. von Eckardstein, T.F. Lüscher, C. Brunckhorst, H.S.V. Chen, and F. Duru. “Sex hormones affect outcome in arrhythmogenic right ventricular cardiomyopathy/dysplasia: from a stem cell derived cardiomyocyte-based model to clinical biomarkers of disease outcome.” European Heart Journal, 2017, 38(19), 1498-1508.

Keywords: IPSC-derived cardiomyocytes, data analysis, high-density MEA, Drug testing, 3D tissue culture

Conference: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays, Reutlingen, Germany, 4 Jul - 6 Jul, 2018.

Presentation Type: Oral Presentation

Topic: Stem cell-derived applications

Citation: Kaestli AJ, Fiscella M, Müller J, Frey U, Duru F and Hierlemann A (2019). CardioMeaSignatures: A MATLAB software package for automated extraction of 2D and 3D cardiac electrophysiological signatures recorded on high-density MEAs. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00105

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 27 Mar 2018; Published Online: 17 Jan 2019.

* Correspondence: Ms. Alicia J Kaestli, ETH Zurich, Department of Biosystems Science and Engineering, Basel, 4058, Switzerland, alicia.kaestli@bsse.ethz.ch