Excited to share our paper in @NatureComms where we present the 1M-scBloodNL study in which we profiled 1.3M immune cells from 120 healthy individuals, longitudinally exposed to 3 different pathogens doi.org/10.1038/s41467…@UMCGgenetica#scRNA 1/8
We mapped differential expression, cis-eQTLs and response-QTLs in 6 major immune cell types and 10 subcell types. This indicated that the genetic control of the gene expression response upon pathogen stimulation was more cell-type- than pathogen-specific. 2/8
Always wondered how and in which context genetics controls downstream gene expression, but without having to test dozens of specific stimuli to figure it out? Here we show how co-expression QTL analysis on whole pathogen-stimulated immune cells can do so! 3/8
In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes (i.e. co-expression QTL). 4/8
We delved deeper in two such context-specific co-expression QTLs: effects on CLEC12A and ZFAND2A were driven by changes in IFN signaling and pH levels, likely affecting the binding affinity for the associated eQTL loci of IRF and HSF1 transcription factors, respectively. 5/8
We expect that increasing the number of individuals and cells/individual doi.org/10.1101/2022.0… and single-cell multi-omics approaches will further aid in uncovering the underlying mechanisms of gene regulation driven by genetics in the future. 6/8
1M-scBloodNL gene expression count matrices can be found on our website (eqtlgen.org/sc/datasets/1m…) and allows requesting access to raw single-cell gene expression and genotype data. 7/8