Abstract Body: Background: Heart failure (HF) is a leading cause of global mortality driven by both genetic and environmental factors, yet the role of cardiac cell-type composition (CCC), the relative proportions of distinct cell types in the heart, in HF pathogenesis remains poorly understood. Single-cell sequencing, widely used to study cell composition in other organs, is severely limited in the heart by the large size and variable nuclearity of cardiomyocytes (CMs) and remains cost-prohibitive at the scale needed for population-level genetic studies. Elucidating the genetic architecture of CCC could open transformative new avenues for understanding and treating HF.

Methods and Results: We profiled 425 mice from 71 strains of the Collaborative Cross (a genetic reference population engineered to capture genetic diversity comparable to that of human populations) treated with isoproterenol (ISO; 30 µg/g/day) via osmotic minipump for three weeks to model HF. Bulk cardiac transcriptomes were deconvolved using an algorithmic pipeline that integrated single-nucleus RNA sequencing data to estimate proportions of five major cardiac cell types.

CCC varied substantially across the panel. CM RNA comprised 79% (±5%) of cardiac RNA at baseline, but after ISO treatment, some strains exhibited up to a 25% reduction in CM RNA and a striking 7.2-fold surge in fibroblast RNA. A genome-wide association study on CCC phenotypes identified significant loci (P<2×10^-5) for every cell type examined, including five loci regulating baseline CM abundance. A notable locus on chromosome 11 (P=9×10^-6) overlaps Adamts2, a metalloproteinase our group previously identified as a major driver of HF phenotypes. Crucially, baseline CM abundance was negatively correlated with ISO-induced cardiac hypertrophy (R=−0.35, P=3×10^-4), suggesting that CCC in the healthy heart may influence future susceptibility to HF.

Conclusion: Our findings establish CCC as a heritable, complex trait shaped by known HF-associated genes. This work demonstrates that bulk transcriptomic deconvolution at population scale is a powerful approach for uncovering novel genetic regulators and mechanistic underpinnings of CCC and HF pathology.