Single-cell RNA-sequencing reveals widespread personalized, context-specific gene expression regulation in immune cells

Abstract

Gene expression and its regulation can be context-dependent. To dissect this, using samples from 120 individuals, we single-cell RNA-sequenced 1.3M peripheral blood mononuclear cells exposed to three different pathogens at two time points or left unexposed. This revealed thousands of cell type-specific expression changes (eQTLs) and pathogen-induced expression changes (response QTLs) that are influenced by genetic variation. In monocytes, the strongest responder to pathogen stimulations, genetics also affected co-expression of 71.4% of these eQTL genes. For example, the pathogen recognition receptor CLEC12A showed many such co-expression interactions, but only in monocytes after 3h pathogen stimulation. Further analysis linked this to interferon-regulating transcription factors, a finding that we recapitulated in an independent cohort of patients with systemic lupus erythematosus, a condition characterized by increased interferon activity. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

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

“…We revealed an opposite pattern of effect on gene expression between immune stimuli and dexamethasone in all cell types analyzed, which was evident both at the gene level and pathway level. These pathways with antagonistic patterns mainly contained immune-related pathways, such as IFN, response to lipopolysaccharide, cytokine-mediated signaling, and innate immune response, mostly shared across cell types, in line with previous findings [75]. Our study also highlighted the specialization in immune response of the different cell types as the majority of DEGs were identified within only one cell type.…”

“…Previous approaches have successfully mapped the genetic determinants of responses to infection, drugs, and other stimuli [2, 6, 10, 22, 32, 34, 42, 43, 44, 46, 56, 57, 59, 71, 73, 80, 88, 95, 112] using bulk RNA-seq. However, these genotype-by-environment (GxE) effects are also likely to be cell-type-specific [25], especially in the context of highly-specialized immune responses, as investigated in recent studies of response eQTL mapping using single cell technology in immune cells exposed to pathogens, bacteria and yeast [75], and influenza A [83]. While different immune stimuli have been studied at single cell resolution; the response to glucocorticoids has not been previously examined.…”

“…Studies in peripheral blood mononuclear cells [107], fibroblasts [72], tumor samples [54], and pluripotent and differentiating cells [13, 20, 37, 72] 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 [39, 51, 72], this likely overestimates the overall cell-type specificity of genetic effects on gene expression due to incomplete power of eQTL discovery in these datasets [75]. Previous approaches have successfully mapped the genetic determinants of responses to infection, drugs, and other stimuli [2, 6, 10, 22, 32, 34, 42, 43, 44, 46, 56, 57, 59, 71, 73, 80, 88, 95, 112] using bulk RNA-seq.…”

Genetic, parental and lifestyle factors influence telomere length

“…To study the relationships between telomere length variation with gene expression changes, we used single-cell RNA-sequencing (scRNA-seq) data generated on cryopreserved peripheral blood mononuclear cells (PBMCs) from 62 LLD donors, for which telomere length on six cell types was measured 49 . To classify cells, we combined the high resolution cell-type-annotations by Azimuth 50 to closely reflect the resolution of the Flow-FISH annotations (i.e.…”

“…To study gene expression changes with telomere length at the single-cell level, we used a subset of previously processed scRNA-seq data 49 on unstimulated PBMCs from 62 LLD donors for whom we collected Flow-FISH telomere length data for at least one cell type in the current study. This scRNA-seq data was generated 5 years after collection of the Flow-FISH telomere length data.…”