Discovering single-cell eQTLs from scRNA-seq data only

Abstract

eQTL studies are essential for understanding genomic regulation. Effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a unique powerful tool for utilizing the huge scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.

DualGCN: a dual graph convolutional network model to predict cancer drug response

“…Transferring knowledge studied from in vitro cancer cell lines to single-cell and clinical data tends to be an important direction [14]. However, it is unreliable to call SNVs from clinical and single-cell tumor data covering all candidate loci [17][18][19]. In addition, recent evidence shows that whole tumors collectively act on drugs [12].…”

“…Similarly, detecting reliable SNVs covering all hotspots simultaneously from single-cell data is unattainable. Both sequencing coverage and sequencing depth in single-cell data are too low to detect SNVs completely from the data [18,19]. Second, current methods encode gene features as separate units.…”

The Power of Single-Cell RNA Sequencing in eQTL Discovery

“…Most of the studies thus far have used the 10× Genomics Chromium platform that sequences the 3′- or 5′-end of mRNA and does not allow the identification of splicing QTLs for isoform detection or deep intronic QTLs. These issues can be addressed by the full-length sequencing approaches such as SMART-seq [ 15 ] which, however, comes with a higher cost per cells. High technical noise arising from ribosomal or mitochondrial contamination is another challenge in scRNA-seq data.…”

“…eQTL analysis using scRNA-seq is a relatively new approach and only a dozen studies are available [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. These studies show diverse applications of scRNA-seq in identification of the quantitative effects of genetic variants or loci using purified cell types [ 4 , 5 ], induced pluripotent stem cells (iPSCs) [ 6 , 9 , 11 , 13 ] or whole organisms [ 10 ] and to study population ancestry and cell type specific response to an environmental stimulus such as viral infection [ 12 ].…”

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution

“…Indeed, multiple studies have leveraged scRNA-seq technologies to map cell-type-specific eQTLs across the different subpopulations comprising complex tissues. Studies in peripheral blood mononuclear cells [119], fibroblasts [78], tumor samples [59], and pluripotent and differentiating cells [15, 23, 42, 78] found several eQTLs that would have been missed in bulk sequencing approaches due to being active in only one or a few cell types. While in many single-cell studies most of the detected eQTLs were only found in a single cell type [44, 56, 78], this likely overestimates the overall cell-type specificity of genetic effects on gene expression due to incomplete power of eQTL discovery in these datasets [81].…”