Quantitatively characterizing drug‐induced arrhythmic contractile motions of human stem cell‐derived cardiomyocytes. Issue 8 (27th April 2018)

• Record Type: Journal Article
• Authors: Hoang, Plansky; Huebsch, Nathaniel; Bang, Shin Hyuk; Siemons, Brian A.; Conklin, Bruce R.; Healy, Kevin E.; Ma, Zhen; Jacquir, Sabir
• Is Part Of: Biotechnology and bioengineering. Volume 115:Issue 8(2018)
• Page Start: 1958
• Page End: 1970
• Publication Date: 2018-04-27
• Subjects: arrhythmia -- biosignal processing -- cardiac motion -- phase space reconstruction -- optical flow; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.26709
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug‐induced arrhythmias (i.e., Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC‐CMs). However, the amplitude and frequency analysis of contractile motion waveforms does not produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion data from beating hiPSC‐CMs using motion tracking software based on optical flow analysis, and then implemented a computational algorithm, phase space reconstruction (PSR), to derive parameters (embedding, regularity, and fractal dimensions) to further characterize the dynamic nature of the cardiac contractile motions. Application of drugs known to cause cardiac arrhythmia induced significant changes to these resultant dimensional parameters calculated from PSR analysis. Integrating this new computational algorithm with the existing analytical toolbox of cardiac contractile motions will allow us to expand current assessments of cardiac tissue physiology into an automated, high‐throughput, and quantifiable manner which will allow more objective assessments of drug‐induced proarrhythmias. Abstract : In this work, we have utilized optical flow‐based motion tracking to evaluate contractile motion exhibited by human induced pluripotent stem cell derived cardiomyocytes (hiPSC‐CMs). We then integrated a computational algorithm based on phase space reconstruction (PSR) to derive parameters from contractile motion information that allowed us to quantify drug‐induced arrhythmic motion. This analytical toolbox can be used to quantify and classify drug‐induced arrhythmic potential across a wide range of standard assays of contractile physiology.