Single-cell transcriptomics reveals peripheral immune responses in non-segmental vitiligo

Abstract

Background: Vitiligo is a common autoimmune depigmented dermatology due to destruction of melanocytes. Much evidence suggests that vitiligo is associated with systemic immune activation. Previous studies have focused on immune cell infiltration in and around lesion areas, but few studies have investigated the cell types and function of circulating immune cells in peripheral blood. Here, single cell RNA-sequencing (scRNA-seq) was used to investigate the mechanisms of peripheral immune responses in vitiligo patients.

Methods: Peripheral blood was collected from five patients with progressive non-segmental vitiligo and three healthy controls. Peripheral blood mononuclear cells (PBMCs) were obtained by Ficoll-Paque density gradient centrifugation, and scRNA-seq was performed on isolated cell populations to obtain single cell transcriptomes and characterize important genes and intracellular signaling pathways. The key findings were validated with qPCR and flow cytometry assays.

Results: We identified 10 major cell types by scRNA-seq. Among these cell types, neutrophils were specifically observed in our scRNA-seq data from PBMCs. Peripheral blood effector CD8+ T cells from vitiligo patients did not show significant differences at the transcriptome level compared with healthy controls, whereas regulatory T cells showed pro-inflammatory TH1-like properties. Innate immune cells, including natural killer cells and dendritic cells, showed increased antigen processing and presentation as well as upregulated interferon responses. B cells, monocytes, and neutrophils all showed activation. B cells, especially memory B cells, had upregulated expression of genes related to humoral immunity. Monocytes showed production of proinflammatory cytokines and chemokines. Neutrophils showed strong chemokine ligand-receptor (L-R) pair (CXCR8-CXCR2) autocrine signaling pathway.

Conclusion: This study revealed the genetic profile and signaling pathway characteristics of peripheral blood immune cells in vitiligo patients, providing new insights into its pathogenesis, which may facilitate identification of potential therapeutic targets.

Keywords: B cells; Tregs; immune cells; monocytes; neutrophils; single-cell RNA sequencing; vitiligo.

Figure 1

Single-cell landscape of circulating immune cells from PV patients and HCs. (A) Schematic flowchart of scRNA-seq experimental design of this study. (B) UMAP representation of 71,966 and 55,838 single cells from PV patients (n = 5) and HCs (n = 3), respectively, showing the formation of 10 clusters. (C) The fraction of cells for ten cell types in HCs and PV patients. (D) Bar graphs of each cell cluster population between HCs and PV patients. (E) Bubble plot of top5 gene expression in each cell cluster; the size of the bubble represents the percentage of expressed cells; the color represents the average expression of each gene in clusters: red means the high expression.

Figure 2

The heterogeneity and transcriptional features of NK&T cells in PV patients. (A) UMAP representation of 91,224 NK&T cells, showing the formation of nine clusters. (B) The fraction of cells for nine types in HCs and PV patients. (C) Bar graphs of each cell cluster population between HCs and PV patients. (D) Bubble plot of top5 gene expression in each cell cluster; the size of bubble represents the percentage of expressed cells; the color represents the average expression of each gene in clusters: red means the high expression. (E) GO analysis showing the biological process enriched in Treg NK and NKT of PV patients. (F) Heatmap showing the average expression of key regulatory TFs (estimated using SCENIC) between NK cells and T cell subsets in PV patients and HCs.

Figure 3

Analysis of CD8+ T cells differentiation trajectories; flow cytometry and qPCR validation of Tregs and Th1-like Tregs. (A) Heatmap showing the dynamic changes of gene expression along pseudotime. (B) The trajectory of six typical genes. Unsupervised transcriptional trajectory from Monocle, colored by pseudotime (C) and cell clusters (D). (E) Representative gating strategy of flow cytometry analysis for proportion of Tregs (CD4+CD25+CD127low/-) in CD4+ T cells and Th1-like Tregs (IFN-γ+ Treg) in Tregs. (F) Flow cytometry analysis of Tregs and Th1-like Tregs. (G) Analysis of FoxP3 and CTLA-4 mRNA expression by qPCR. Data are expressed as mean ± SEM and significance was set at *p ≤ 0.05, **p ≤ 0.01.

Figure 4

The heterogeneity and transcriptional features of B cells in PV patients. (A) UMAP representation of B cells, showing the formation of two clusters. (B) The fraction of cells for ten two types in HCs and PV patients. (C) Bar graphs of each cell cluster population between HCs and PV patients. (D) Bubble plot of top 5 gene expression in each cell cluster; the size of the bubble represents the percentage of expressed cells; the color represents the average expression of each gene in clusters: red means the high expression. (E) The violin diagram shows the expression levels of IGHA1, IGHA2 and IGHG1 in PV patients and HCs. (F) The mapplot of GO pathways of the DEGs in memory B cells of PV patients.

Figure 5

The heterogeneity and transcriptional features of monocytes in PV patients. (A) UMAP representation of monocytes, showing the formation of two clusters. (B) The fraction of cells for two types in HCs and PV patients. (C) Bubble plot of top5 gene expression in each cell cluster; the size of the bubble represents the percentage of expressed cells; the color represents the average expression of each gene in clusters: red means the high expression. (D) The violin diagram shows the expression levels of TNF, IL1B, DUSP2, S100A8, CCL3, CCL3L1, CCL4L2, CXCL, EGR1, FOS, JUN, and JUND in PV patients and HCs. (E) GO analysis showing the biological process enriched in non-classical monocytes and pDC of PV patients. (F) Heatmap showing the average expression of key regulatory TFs (estimated using SCENIC) between monocyte subsets in PV patients and HCs.

Figure 6

The heterogeneity and transcriptional features of neutrophils in PV patients. (A) UMAP representation of neutrophils, showing the formation of three clusters. (B) The fraction of cells for three types in HCs and PV patients. (C) Bar graphs of each cell cluster population between HCs and PV patients. (D) Bubble plot of top5 gene expression in each cell cluster; the size of the bubble represents the percentage of expressed cells; the color represents the average expression of each gene in clusters: red means the high expression. (E) UMAP plot of distribution of inflammatory response pathway in neutrophils. (F) The dotplot of GO pathways of the DEGs in Neutrophils_3 of PV patients.

Figure 7

Differential communication patterns in HCs and PV patients. (A) Scatter plot of incoming and outgoing interaction strength of each cell population in HCs and PV patients. (B) Significant signaling pathways were ranked based on differences in the overall information flow within the inferred networks between HCs and PV patients. The signaling pathways depicted in red are enriched in HCs, and those depicted in green were enriched in PV patients.

Figure 8

Complex intercellular communication network in the circulating immune cells in PV patients. (A) Hierarchical plot showing the inferred intercellular communication network for MIF signaling pathway. Circle sizes are proportional to the number of cells in each cell group and edge width represents the communication probability. (B) Heatmap showing the relative importance of each cell group for the four network centrality measures-based MIF signaling network. (C) Relative contribution of each ligand–receptor pair to the overall communication network of MIF signaling pathway. (D) The inferred CXCL signaling pathway network. (E) The computed network centrality measures of CXCL signaling. (F) Relative contribution of each CXCL ligand–receptor pair. (G) Analysis of CXCL8, CXCL9, CCL5 and CXCR2 mRNA expression by qPCR. Data are expressed as mean ± SEM and significance was set at *p ≤ 0.05, ***p ≤ 0.001.

Figure 9

A model of the complex and heterogeneous peripheral immune microenvironment of progressive non-segmental vitiligo.