Introduction:
Analyzing cardiac action potential (AP) properties has been a crucial tool in arrhythmia research and drug development for decades. In recent years, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have become an emerging field in cardiovascular research. The spontaneous electrical activity of hiPSC-CMs allows to simultaneously investigate parameters from both individual APs and the collective recording of multiple APs such as beating frequency or arrhythmogenicity in vitro. However, due to the diverse morphologies of hiPSC-CM APs, manual extraction of standard AP parameters is time-consuming and depends on the operator's subjective judgment which hampers reproducible, quantitative comparisons between various types of CMs. Here, we introduce a consistent, fast, reproducible and operator-independent algorithm, which extracts quantitative AP parameters from recordings of various CM types.
Methods:
Given the variability and noise in hiPSC-CM AP recordings, classical mathematical tools or thresholding cannot be directly applied. Instead, APs are identified as sequences characterized by a potential rise exceeding 20 mV within a time frame of 25 ms. Markers for the start of an AP, maximum upstroke velocity, peak potential and maximum diastolic potential are derived automatically (for further details, see https://gitlab.kit.edu/kit/ibt-public/analysis-of-ap-parameters). The analyzed AP parameters are complemented by the AP duration (APD), AP amplitude, cycle length, frequency, area of APD90 and APD corrected by Bazett's formula for varying beating frequencies.
Results:
The algorithm was tested on 1607 AP recordings from hiPSC-CMs, stimulated isolated native CMs, HL-1 cells and computational electrophysiological simulations. In the absence of a ground truth, our algorithm was validated against manual expert inspection. The markers placed by the algorithm were considered correct in over 99% of cases, computed in 0.4s for a 60s AP recording.
Conclusion:
This algorithm enables standardized, reproducible and operator-independent high-throughput analyses of diverse AP recordings, facilitating quantitative comparisons of electrophysiological properties in various types of CMs.