Landscape of stimulation-responsive chromatin across diverse human immune cells

Abstract

The immune system is controlled by a balanced interplay among specialized cell types transitioning between resting and stimulated states. Despite its importance, the regulatory landscape of this system has not yet been fully characterized. To address this gap, we collected ATAC-seq and RNA-seq data under resting and stimulated conditions for 25 immune cell types from peripheral blood of four healthy individuals, and seven cell types from three fetal thymus samples. We found that stimulation caused widespread chromatin remodeling, including a large class of response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the critical importance of these cell states in autoimmunity. Use of allele-specific read-mapping identified thousands of variants that alter chromatin accessibility in particular conditions. Notably, variants associated with changes in stimulation-specific chromatin accessibility were not enriched for associations with gene expression regulation in whole blood -a tissue commonly used in eQTL studies. Thus, large-scale maps of variants associated with gene regulation lack a condition important for understanding autoimmunity. As a proof-of-principle we identified variant rs6927172, which links stimulated T cell-specific chromatin dysregulation in the TNFAIP3 locus to ulcerative colitis and rheumatoid arthritis. Overall, our results provide a broad resource of chromatin landscape dynamics and highlight the need for large-scale characterization of effects of genetic variation in stimulated cells.

Mapping gene regulatory networks of primary CD4+T cells using single-cell genomics and genome engineering

“…Because CD4 + T cells dynamically migrate to and egress from tissues through circulation, cells isolated from PBMCs likely represent a functionally diverse population of cells reflective of the specific immunological state of an individual. This is supported by previous functional genomic analyses of activated primary CD4 + cells demonstrating marked heterogeneity within and variability between individuals in the chromatin and expression profiles that overlap signatures from multiple sorted populations including Th 1 s, Th 2 s, and Th 17 s (18,22,33).…”

“…While the functionalization of CD4 + T cells is predominantly determined by the polarization of naive T cells (T naive ), recent results have suggested a high degree of variation within and plasticity between canonical subtypes (17). Indeed, we (18,19) and others (20)(21)(22) have shown that human circulating CD4 + T cells are composed of a mixture of canonical and non-canonical populations with significant interindividual variability in both the proportion and gene expression of each population (18).…”

Clonal replacement of tumor-specific T cells following PD-1 blockade

“…1d). Cell types were annotated using three parallel approaches: 1) correlation between aggregated cluster expression profiles and bulk RNA expression profiles from reference populations 21 , 2) differential gene expression, and 3) known marker gene expression (Methods, Supplementary Fig. 2a-c).…”

“…Clusters were also confirmed by identifying differentially expressed marker genes for each cluster and comparing to known cell type marker genes. Finally, we downloaded bulk RNA-seq count data from sorted immune cell populations from Calderon, et al 21 and compared bulk gene expression to pseudo-bulk expression profiles from single cell clusters. UMI counts were summed for all cells in each cluster to generate pseudo-bulk profiles.…”

“…All malignant cells were treated as one cluster. (b) Correlation between immune cell type clusters identified in BCC TME and sorted bulk population from Calderon, et al 21 . UMI counts were summed for each BCC cluster to create pseudo-bulk gene expression profiles and Pearson correlation calculated between each bulk population based on variable genes from bulk profiles.…”