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.
Highlights
To examine the feasibility of using mouse-derived gene expression signatures to deconvolute human tissues, we compare cellular composition estimates of Genotype-Tissue Expression (GTEx) liver and GTEx skin samples generated using human scRNA-seq to those generated using the Tabula Muris scRNA-seq resource
We used previously defined cell types from Tabula Muris mouse liver cells11, and to be consistent, we used the Tabula Muris annotation approach to analyze existing human liver scRNA-seq data6
Human and mouse scRNA-seq from liver captured several shared cell types, including hepatocytes, endothelial cells, and various immune cells (Kuppfer cells, B cells, and natural killer (NK) cells) (Fig. 1b–e), we noted that there were many more distinct cell types for human liver. This was due to the fact that cell type resolution can be influenced by: [1] the number of cells captured and subjected to scRNA-seq, which may influence the proportion of observed common or rare cell types13; and [2] how the tissue was sampled, which may enrich for selected populations or capture how populations are distinguished by spatial location
Summary
To examine the feasibility of using mouse-derived gene expression signatures to deconvolute human tissues, we compare cellular composition estimates of GTEx liver and GTEx skin samples generated using human scRNA-seq to those generated using the Tabula Muris scRNA-seq resource. We show that the human and mouse single-cell data capture many overlapping cell populations and that using either humanderived or mouse-derived gene signatures to deconvolute the 175 GTEx liver samples and the 860 GTEx skin samples resulted in highly correlated estimated cellular compositions. Our study demonstrates two major principles: [1] mouse-derived signature genes can be used to deconvolute the cellular composition of human tissues; and [2] the estimation of cellular heterogeneity by deconvolution enhances the genetic insights yielded from the GTEx resource
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