High throughput image processing software for analyzing hiPSC-CM sarcomere physiology and cardiac fibroblast activation.

Abstract

Heart disease is the leading cause of death worldwide and discovering therapies that treat its underlying cause is paramount to improving survivability and patient health. Since heart disease often results in detrimental remodeling of heart tissue, high-throughput screening (HTS) using cardiomyocytes and cardiac fibroblasts is frequently used for drug discovery. A major challenge in analyzing HTS data is ensuring that analysis is both fast and minimally biased. In many forms of heart disease and remodeling, cardiomyocytes will suffer from loss of sarcomeres and sarcomere disarray, while fibroblasts will often express increased α-smooth muscle actin (α-SMA) stress fibers when activated into myofibroblasts. Two custom MATLAB HTS image processing scripts named ‘Tamarack’ and ‘Porcupine’ were developed for quantifying human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) sarcomere morphology and cardiac fibroblast activation, respectively. Both scripts use wavelet transforms to detect subcellular structures that are of interest for analyzing phenotypes at the cellular level. Tamarack enables quantification of sarcomere metrics, including sarcomere count, length, orientation, and α-actinin rich areas. Porcupine enables quantification of fibroblast activation metrics, including α-SMA stress fiber count, length, orientation, nuclei and α-SMA overlap, and F-actin and α-SMA overlap. Both scripts enable analysis of other cellular features, such as nuclei count and stain intensity/protein expression. Tamarack obtained statistically significant results when comparing control hiPSC-CMs with hiPSC-CMs subjected to siRNA knockdowns of genes of interest. Porcupine was validated by analyzing fibroblasts with increasing concentrations of TGF-β which induced increasing activation. By developing technology for analyzing cardiomyocyte and fibroblast imaged-based screens, drug candidates can be efficiently evaluated without bias on cell types that are relevant to human heart disease. This research may help expand drug candidate portfolios and improve the number of treatments available to patients worldwide.