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Quantitatively characterizing drug‐induced arrhythmic contractile motions of human stem cell‐derived cardiomyocytes
Author(s) -
Hoang Plansky,
Huebsch Nathaniel,
Bang Shin Hyuk,
Siemons Brian A.,
Conklin Bruce R.,
Healy Kevin E.,
Ma Zhen,
Jacquir Sabir
Publication year - 2018
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.26709
Subject(s) - induced pluripotent stem cell , computer science , toolbox , waveform , cardiac cycle , motion (physics) , computational model , drug discovery , biomedical engineering , artificial intelligence , biological system , neuroscience , biology , cardiology , medicine , bioinformatics , biochemistry , radar , embryonic stem cell , gene , programming language , telecommunications
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.