Automated detection and analysis of depolarization events in human cardiomyocytes using MaDEC

Abstract

Optical imaging-based methods for assessing the membrane electrophysiology of in vitro human cardiac cells allow for non-invasive temporal assessment of the effect of drugs and other stimuli. Automated methods for detecting and analyzing the depolarization events (DEs) in image-based data allow quantitative assessment of these different treatments. In this study, we use 2-photon microscopy of fluorescent voltage-sensitive dyes (VSDs) to capture the membrane voltage of actively beating human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). We built a custom and freely available Matlab software, called MaDEC, to detect, quantify, and compare DEs of hiPS-CMs treated with the β-adrenergic drugs, propranolol and isoproterenol. The efficacy of our software is quantified by comparing detection results against manual DE detection by expert analysts, and comparing DE analysis results to known drug-induced electrophysiological effects. The software accurately detected DEs with true positive rates of 98–100% and false positive rates of 1–2%, at signal-to-noise ratios (SNRs) of 5 and above. The MaDEC software was also able to distinguish control DEs from drug-treated DEs both immediately as well as 10 min after drug administration.

Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter

“…MMiCE was tested under 3 paradigms: (1) simulated somatic calcium imaging data (groundtruth available), (2) experimentally recorded simultaneous somatic fMCI and patch-clamp data (ground-truth available), as well as (3) experimentally recorded, from now on referred to as real, somatic fMCI and dendritic spine fMCI data (no ground-truth available). Although only tested on fMCI data here, due to its data-driven design, the algorithm is easily generalizable to other fluorescent neural imaging modalities, such as voltage-sensitive-dye imaging (Szymanska et al, 2015). In order to ease implementation and applicability hurdles, MMiCE was developed in Matlab with an intuitive graphical user interface.…”