Summary Cellular morphology has the capacity to serve as a surrogate for cellular state and functionality. However, primary cardiomyocytes, the standard model in cardiovascular research, are highly heterogeneous cells and therefore impose methodological challenges to analysis. Hence, we aimed to devise a robust methodology to deconvolute cardiomyocyte morphology on a single-cell level: C-MORE (cellular morphology recognition) is a workflow from bench to data analysis tailored for heterogeneous primary cells using our R package cmoRe. We demonstrate its utility in proof-of-principle applications such as modulation of canonical hypertrophy pathways and linkage of genotype-phenotype in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). In our pilot study, exposure of cardiomyocytes to blood plasma prior to versus after aortic valve replacement allows identification of a disease fingerprint and reflects partial reversibility following therapeutic intervention. C-MORE is a valuable tool for cardiovascular research with possible fields of application in basic research and personalized medicine.
Graphical abstract
Highlights C-MORE deconvolutes cardiomyocyte morphology on single-cell level C-MORE could be utilized for high-throughput pharmacological screens C-MORE detects pathological phenotypes caused by genetic alterations C-MORE liquid biopsy setup reflects disease state in individuals with aortic stenosis
Furkel et al. present an integrative single-cell morphology-based strategy (C-MORE) to detect cardiomyocyte activation status and response to pharmacological treatment. Deconvolution of disease state in cardiomyocyte cultures exposed to liquid biopsies and in genetically engineered models by C-MORE may pave the way for development of next-generation personalized cardiovascular medicine.