Abstract 14293: Automated Arrhythmia Detection in HiPSC-Derived Cardiac Cell Sheet Models for Drug Testing and Disease Modeling

• Published in: Circulation (New York, N.Y.) N.Y.), 2021-11, Vol.144 (Suppl_1), p.A14293-A14293
• Main Authors: Landesberg, Michal, Huber, Irit, Gepstein, Amira, Arbel, Gil, Gepstein, Lior
• Format: Article
• Language: English
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.144.suppl_1.14293

IntroductionThe use of human-induced pluripotent stem cells (hiPSC) derived cardiomyocytes may bring a unique value for drug testing and disease modeling applications. In this project, we developed an automatic and robust arrhythmia detection tool that enables recognition of early-after-depolarizations (EADs) and reentrant activity (spiral waves) in hiPSC-derived cardiac tissue models. Methods and ResultshiPSCs were differentiated into cardiomyocytes, seeded as large-scale hiPSC-derived cardiac cell-sheets (hiPSC-CCSs), loaded with a voltage-sensitive dye, and optically mapped. Arrhythmias were induced either spontaneously, using pacing interventions, or following drug applications. We examined two algorithms to detect spiral wave arrhythmiasoptical flow (OF) and dominant frequency (DF). OF enables flow detection through spatial analysis of local velocity vectors (Fig.1 a-d). DF maps depict the frequency with the highest energy in each pixel (Fig.1 e-f). Spiral waves were readily detected by both approaches by the presence of significant diversity in both the frequencies maps (DF) and in the velocity angles (OF). Moreover, in DF maps, the core of the spiral wave (singularity point) could be identified as pixels displaying high dominant frequency as compared to values in other parts of the specimen. (Figure 1). Finally, EADs could be detected using a curve-fitting approach (Fig.1g). Figure 1(a-d) Consecutive optical map frames (a, c) and velocity vector maps (b, d) of normal (a, b) and a spiral wave (c-d) propagations. (e-f ) DF maps of normal (e) and spiral wave (f) propagations. (g) EAD detection in red. ConclusionAutomatic algorithms were developed to enable robust detection of arrhythmic activity (EADs and spiral waves) in a hiPSC-based cardiac tissue model. This novel approach may bring a unique value to arrhythmia mechanistic studies, high throughput drug screening, and disease modeling.