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. 2022 May 26;5(1):506.
doi: 10.1038/s42003-022-03430-9.

Chromatin accessibility analysis reveals regulatory dynamics and therapeutic relevance of Vogt-Koyanagi-Harada disease

Wen Shi  1   2   3 , Jinguo Ye  1 , Zhuoxing Shi  1 , Caineng Pan  1 , Qikai Zhang  1 , Yuheng Lin  1 , Yuanting Luo  1 , Wenru Su  1 , Yingfeng Zheng  4   5   6 , Yizhi Liu  1   2   3
Affiliations

Chromatin accessibility analysis reveals regulatory dynamics and therapeutic relevance of Vogt-Koyanagi-Harada disease

Wen Shi et al. Commun Biol. .

Abstract

The barrier to curing Vogt-Koyanagi-Harada disease (VKH) is thought to reside in a lack of understanding in the roles and regulations of peripheral inflammatory immune cells. Here we perform a single-cell multi-omic study of 166,149 cells in peripheral blood mononuclear cells from patients with VKH, profile the chromatin accessibility and gene expression in the same blood samples, and uncover prominent cellular heterogeneity. Immune cells in VKH blood are highly activated and pro-inflammatory. Notably, we describe an enrichment of transcription targets for nuclear factor kappa B in conventional dendritic cells (cDCs) that governed inflammation. Integrative analysis of transcriptomic and chromatin maps shows that the RELA in cDCs is related to disease complications and poor prognosis. Ligand-receptor interaction pairs also identify cDC as an important predictor that regulated multiple immune subsets. Our results reveal epigenetic and transcriptional dynamics in auto-inflammation, especially the cDC subtype that might lead to therapeutic strategies in VKH.

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Conflict of interest statement

The authors declare no Competing interests.

Figures

Fig. 1
Fig. 1. The single-cell multiomic experimental design.
a Schematic representation of the single-cell profiling of PBMCs from healthy controls (n = 12) and VKH disease patients (n = 12) in this study, sequencing experiments and downstream bioinformatic analyses. All data are aligned and annotated to hg38 reference genome.
Fig. 2
Fig. 2. Single-cell chromatin landscape of health human peripheral immune cell subsets.
a UMAP projection of 74,510 scATAC-seq profiles of peripheral blood immune cell types from 12 healthy controls. Dots represent individual cells, and colors indicate immune cell types (labeled on the right). Bar plot indicates the number of scATAC-seq profiles in each cell types. b Bar plot of annotated DAR locations for each cell type. c Heatmap of Z-scores of 75,654 cis-regulatory elements in scATAC-seq peripheral blood cell types derived from Fig. 2a. Gene labels indicate the nearest gene to each regulatory element. d UMAP projection colored by gene activity scores for the annotated lineage-defining genes in HC group of scATAC-seq dataset. The minimum and maximum gene activity scores are shown in each panel. e Heatmap representation of chromVAR bias-corrected deviations in the most variable TFs across all healthy immune cell types. f TF footprints with motifs in the indicated scATAC-seq healthy immune cell types. The Tn5 insertion bias track is shown below. g UMAP projection of scATAC-seq peripheral blood profiles colored by chromVAR TF motif bias-corrected deviations for the indicated factors. All data are aligned and annotated to hg38 reference genome.
Fig. 3
Fig. 3. Overview of the immune-cell epigenetic and transcriptional landscape of PBMCs from VKH patients and healthy human.
a Schematic for Multi-omics integration strategy for processing the scATAC-seq dataset and scRNA-seq dataset. b Dot plots of gene activity scores (left) and gene expression (right) of the marker genes in scATAC-seq and scRNA-seq dataset. The dot size indicates the percentage of the cells in each cluster in which the gene of interest. The standardized gene activity score level (left) and gene expression level (right) were indicated by color intensity. c Cis-regulatory architecture at the following GWAS loci and cell types in PBMCs: IL23R and HLA-DQA1. Only connections originating in the loci with peak-to-gene accessibility above 0.2 are shown. d ChromVAR deviation enrichment of the peakset of human tissues (including eyes and skins) from ATAC-seq and CHIP-seq dataset from HCs against the scATAC-seq dataset from healthy peripheral blood cell populations. e Dot plots of the expression level of the differential genes between normal and VKH CD4+T cells, CD8+T cells, nature killer cells, B cells, monocytes and dendritic cells in scRNA-seq dataset. All data are aligned and annotated to hg38 reference genome.
Fig. 4
Fig. 4. Epigenomic and transcriptional signatures of T cell subsets in VKH patients.
a Subclustering UMAP of 3,182 CD4+ Treg. Dots represent individual cells, and colors indicate immune cell types (labeled on the below). b UMAP projection of CD4+ Treg colored by gene activity scores to the indicated gene. c Differences in the proportions of rTreg and eTreg among HC (n = 12) and VKH groups (n = 12). The adjusted p values were calculated using two-sided pairwise Wilcoxon test. d Heatmap of Z-scores of DARs in Th1, Th17, Treg, CD8TEM, and MAIT from HC and VKH. e Representative GO terms and KEGG pathways enriched in the nearest genes of upregulated DARs of Th1, Th17, Treg, CD8 TEM, and MAIT cells in the VKH/HC comparison group. f Comparison of aggregate TF footprints for RELA and NFKB1 in MAIT cells from HC and VKH. All data are aligned and annotated to hg38 reference genome.
Fig. 5
Fig. 5. Epigenomic and transcriptional signatures of CD14+ monocytes subsets in VKH patients.
a Subclustering UMAP of 20,054 CM cells. Dots represent individual cells, and colors indicate immune cell types (labeled on the below). b Genome browser tracks showing single-cell chromatin accessibility in the IL1B and HLA-DQA1 locus. c TF motif enrichment analysis of cluster-specific sequences. d UMAP showing the lineage trajectory of CM ordered based on pro-inflammatory, HLA+ and rest states. Pseudotime values were overlaid on the UMAP embedding; the smoothed line and arrow represent the visualization of the trajectory path from the spline fit. e Heatmaps of the ordered TF motif accessibility across pseudotime in the CM (see Fig. 5d). The TF motif accessibilities are indicated by chromVAR TF-motif bias-corrected deviation. f chromVAR bias-corrected deviation scores for the indicated TFs across CM pseudotime. Each dot represents the deviation score in an individual pseudotime-ordered scATAC-seq profile. The line represents the smoothed fit across pseudotime and chromVAR deviation scores. g Comparison of aggregate TF footprints for HIF1A in CM subsets. h Genome browser tracks showing single-cell chromatin accessibility in the IL1B and TNF locus. i Dot plots of the expression level of the differential genes between normal and VKH in CM in RNA-seq dataset. j Comparison of aggregate TF footprints for NFKB1 and RELA in CM from HC and VKH. All data are aligned and annotated to hg38 reference genome.
Fig. 6
Fig. 6. Epigenomic and transcriptional signatures of cDC subsets in VKH patients.
a Dot plots of the expression level of the differential genes between normal and VKH in cDCs in scRNA-seq dataset. b Genome browser tracks showing single-cell chromatin accessibility in the CCR7 and LAMP3 locus. c Box plot of inflammatory signature score in all cells of each group. All p values were calculated using Kruskal-Wallis test. d Enrichment of biological processes associated with nearest genes of DARs in VKH compared to HC regions. e Visualization of TF binding motif enrichment analysis results for DARs in VKH compared to HC regions by using CIS-BP database from chromVAR. f Comparison of aggregate TF footprints for NFKB1 and RELA in cDC cells from HC and VKH. g TF regulatory network showing the NF-κB family and its potential target genes in VKH. The width of an edge indicates the peak to gene linkage correlation. h Kaplan–Meier curve for patients with VKH (n = 89) stratified by putative RELA-target genes (n = 328); average z score log2(expression) (log-rank test p < 0.001). All data are aligned and annotated to hg38 reference genome.
Fig. 7
Fig. 7. The cDC-centric cellular communications of peripheral immune cells in HC and VKH.
a Network plot showing the changes in ligand-receptor interaction events between cDCs and indicated immune cell types in the HC group. Cell-cell communication and the number of ligands and receptors are indicated by the connected line. b Network plot showing the changes in ligand-receptor interaction events between cDCs and indicated immune cell types in the VKH group. Cell-cell communication and the number of ligands and receptors are indicated by the connected line. c Dot plot of predicted interactions between cDCs and indicated immune cell types in HC and VKH. Circle sizes indicated p values. The expression levels of the interacted genes were indicated by colors, scales on the right. All data are aligned and annotated to hg38 reference genome.
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