Abstract P1021: Estimating Cardiac Cell Composition In Gtex With Single-nucleus Reference Profiles.

Abstract

Heart failure (HF) is a heterogeneous condition defined by an inability of the heart to meet the circulatory needs of the body. HF is the result of a maladaptive response to chronic stressors during which the heart undergoes structural and cellular remodeling. For example, cardiomyocytes undergo apoptosis after chronic overstimulation while fibroblasts proliferate to fill gaps left by the loss of larger cells. Although changes to the cellular makeup of the heart are known to mediate HF pathology, estimates of basal cardiac cellular composition vary widely. Moreover, despite our understanding of HF as a complex condition that is influenced by environmental and genetic factors, most studies on heart composition only examine small groups of individuals or inbred models. Recent efforts have used single-nucleus RNAseq (snRNAseq) to tally nuclear proportions as a proxy for cellular proportions. However, cellular dissociation biases for certain cell types and skews the resulting estimates. Furthermore, snRNAseq studies are expensive, making them infeasible and underpowered for population-scale analyses. To avoid these issues, we developed a method to estimate cellular composition of bulk RNAseq using cell-type-specific references defined in snRNAseq. While most deconvolution tools are designed for single-cell RNAseq inputs, our method considers the technical and biological differences of snRNAseq. It also allows for the inclusion of additional reference datasets, utilizes simulated controls, and leverages an existing deconvolution tool, MuSiC. We apply our method to the Genome-Tissue-Expression project (GTEx), a multi-tissue database with bulk RNAseq from hundreds of subjects and snRNAseq from a small subset of those subjects. We estimated left ventricular cellular composition in 386 individuals, revealing considerable heterogeneity. We found significant correlations between subject phenotypes and discrete cell populations. This analysis demonstrates our ability to leverage existing, public datasets to accurately map the understudied variability of cardiac cell type variation across human populations. We now look to incorporate genomic datasets to find genetic markers that link composition estimates to population-level variation.