Background: The proliferative potential in the adult heart is very limited, therefore, adaptation to stressors is achieved through cardiomyocyte (CM) plasticity, e.g. hypertrophy. Cellular morphology has the capacity to serve as a surrogate for cellular state and functionality and thereby displays an important tool to characterize cardiomyocytes in cardiac disease. However, primary cardiomyocytes, the standard model in cardiovascular research, are highly heterogeneous cells and therefore, impose methodological challenges to morphological analysis. Hence, we aimed to devise a robust methodology to deconvolute cardiomyocyte morphology on a single-cell level.

Methods:  After preparation neonatal rat cardiomyocytes (NRCMs) were transduced with a NFAT-GFP adenovirus serving as a reporter for activation of the calcineurin-NFAT pathway. The cells were then incubated with a treatment (pharmocological drug and/or patient plasma) for 48h, fixated, stained and images were acquired using the INCell Analyzer 2200 microscope (3 channels: Desmin - TexasRed - cytosceleton; DAPI - nucleus; GFP/FITC - NFAT localization). Morphological features were extracted using CellProfiler (e.g. intensity, shape and texture); data preprocessing and data analysis were conducted using our R package cmoRe. Plates of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) (healthy and MYBPC3 hypertrophic cardiomyopathy genotype) were kindly provided as previously published by Seeger et al. 

Results: Herein we present C-MORE (cellular morphology recognition) which is a workflow for image-based morphological phenotyping of heterogeneous primary cells from bench to data analysis.  For this we use our R package cmoRe specifically tailored for highly heterogeneous cells: We implement an in silico filter to only include vital, attached cardiomyocyte cell type and determine cell cycle status and reporter status using the cmoRe thresholding function. Using linear mixed models and crossvalidation ensures robustness while increasing sensitivity. We demonstrate C-MORE's utility in proof-of-principle applications such as modulation of canonical hypertrophy pathways and linkage of genotype-phenotype in 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.

Conclusion: We present C-MORE as an open-source tool to identify morphological patterns in basic research and cardiovascular disease in translational approaches. C-MORE is compatible with both primary cell types used most frequently in cardiac research and comprises preprocessing steps and data analysis suited for extracting relevant information out of data with a low signal-to-noise ratio. The demonstrated versatility of C-MORE makes it ideal for quick and easy application in drug screening and in-depth characterization of single cells for investigating CM biology. Future applications of C-MORE may include investigation of disease mechanisms and drug screening in CMs, and might also be applicable to other primary cell types beyond the niche of CMs. In translational medicine, C-MORE shows applicability for material from affected individuals with potential to support clinical decision-making in the frame of personalized medicine.