CardioShift: Resolving cell-to-cell functional heterogeneity in hiPSC-derived cardiomyocytes from high-throughput single-cell imaging

Abstract

High-content screening of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is widely used for disease modeling, yet standard pipelines rely on whole-well averages that obscure cell-to-cell heterogeneity. Moving from well-averages to single-cell analysis reveals hidden variations, but may introduce a pseudoreplication bias if cells are analysed as independent data points. We present CardioShift, an open-source bioimage informatics framework designed to map single-cell distributions by properly accounting for this nested variance structure. Applying this framework to 32,818 Ca2+ transients and 10,719 contractility-lipid profiles, we show that cell-to-cell variability represents the largest source of variance for most functional parameters, demonstrating how traditional well averages mask condition-dependent phenotypes. Furthermore, unsupervised clustering identifies condition-dependent shifts in functional subpopulations, including a distinct cellular state that accumulates lipids but maintains baseline contraction kinetics that was not detected using well- and single-cell averaging approaches. CardioShift provides a robust analytical approach to resolve hidden single-cell distributions.