Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants

Abstract

The Genotype-Tissue Expression (GTEx) resource has provided insights into the regulatory impact of genetic variation on gene expression across human tissues; however, thus far has not considered how variation acts at the resolution of the different cell types. Here, using gene expression signatures obtained from mouse cell types, we deconvolute bulk RNA-seq samples from 28 GTEx tissues to quantify cellular composition, which reveals striking heterogeneity across these samples. Conducting eQTL analyses for GTEx liver and skin samples using cell composition estimates as interaction terms, we identify thousands of genetic associations that are cell-type-associated. The skin cell-type associated eQTLs colocalize with skin diseases, indicating that variants which influence gene expression in distinct skin cell types play important roles in traits and disease. Our study provides a framework to estimate the cellular composition of GTEx tissues enabling the functional characterization of human genetic variation that impacts gene expression in cell-type-specific manners.

A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders

“…As our cortex and SN atlases have been generated from the same individuals through the same process, this difference in glial proportion likely reflects genuine variation in the cellular composition of different brain regions. The observed proportions of different cell populations in our SN atlas were consistent with histopathological studies, reporting ODCs as the most frequent glial cell population (45-75% across all brain regions, 62% for SN) 9 and recent bioinformatics predictions of tissue cell-type composition 10 . Despite significant variation in the numbers of cell types captured between inter-and intra-individual samples ( Supplementary Table 3b), we observed consistent clustering by cell type between replicates, across samples and regions ( Supplementary Fig.…”

Gene expression variability in human and chimpanzee populations share common determinants

“… ( A ) Cell type proportion deconvolution ( Donovan et al, 2020 ) for left ventricle and atrial appendage tissue sections from matched individuals. Height of colored bars represents estimates of the proportion of each heart cell type.…”

“…To investigate the extent to which the cell composition differences that drive dispersion are biologically relevant, as opposed to technical differences in tissue dissection and sample preparation, we turned to cell deconvolution profiles of samples from anatomically different heart samples from matched individuals from GTEx ( Donovan et al, 2020 ). We reasoned that if intentionally anatomically different heart sections (left ventricle, versus atrial appendage) from the same individual correlate better than matched tissue samples from different individuals, then the cell type composition differences across our chimpanzee samples are also likely driven by individual level differences, rather than technical differences in sample acquisition.…”

“…It is important to account for cellular composition not only in comparative studies of variation in gene expression, but also in studies that focus on a single species. Indeed, cellular heterogeneity may itself have a genetic component, which will be confounded with regulatory differences within a cell type ( Donovan et al, 2020 ; Marderstein et al, 2020 ).…”

Single-cell genomics to understand disease pathogenesis

“…Further utilization of single-cell genomics analysis using clinical samples to dissect pathology is advancing, but the importance of bulk sample analysis, which does not require specialized equipment and rigorous cell isolation and enables the processing of many samples, will be maintained. There are algorithms to characterize cell-type composition across subjects from bulk RNA-seq data using single-cell RNA-seq profiles as references [95][96][97]. Wang et al developed multi-subject single-cell deconvolution to characterize cell-type composition from bulk RNA-seq data of the kidney and revealed that the proportion of distal convoluted tubule cells increases with disease progression [95].…”