+ Abstract
Drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms (DiHS/DRESS) is a potentially fatal multiorgan inflammatory disease associated with herpesvirus reactivation and subsequent onset of autoimmune diseases1,2,3,4. Pathophysiology remains elusive and therapeutic options are limited. Cases refractory to corticosteroid therapy pose a clinical challenge1,5 and approximately 30% of patients with DiHS/DRESS develop complications, including infections and inflammatory and autoimmune diseases1,2,5. Progress in single-cell RNA sequencing (scRNA-seq) provides an opportunity to dissect human disease pathophysiology at unprecedented resolutions6, particularly in diseases lacking animal models, such as DiHS/DRESS. We performed scRNA-seq on skin and blood from a patient with refractory DiHS/DRESS, identifying the JAK–STAT signaling pathway as a potential target. We further showed that central memory CD4+ T cells were enriched with DNA from human herpesvirus 6b. Intervention via tofacitinib enabled disease control and tapering of other immunosuppressive agents. Tofacitinib, as well as antiviral agents, suppressed culprit-induced T cell proliferation in vitro, further supporting the roles of the JAK–STAT pathway and herpesviruses in mediating the adverse drug reaction. Thus, scRNA-seq analyses guided successful therapeutic intervention in the patient with refractory DiHS/DRESS. scRNA-seq may improve our understanding of complicated human disease pathophysiology and provide an alternative approach in personalized medicine.
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Data availability
Source data for Figs. 1–4 and Extended Data Figs. 1, 2 and 4 are provided with the paper. The matrix and raw data for scRNA-seq reported in this paper have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO series accession number GSE132802. All other data are available from the corresponding author upon reasonable request.
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Acknowledgements
We thank the NIAMS flow cytometry and sequencing cores for technical support, the patient and volunteers, V. Pillai, M. Taylor and B. Higgins, for their contributions. This research was supported by the Intramural Research Program of NIAMS, National Cancer Institute (contract HHSN261200800001E) and NIAID, NIH.
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K.N. conceived the study and designed experiments with D.K. D.K. performed experiments with assistance from T.K., B.V., J.H.J., M.K., K.S., S.P.J., S.P., J.C.A. and F.P.D. H.B.P., J.L.N., J.H.M. and S.D.R. provided clinical data. I.D.I. and G.A.F. performed viral analyses. H.H.K. assisted tissue acquisition. A.F.F. provided patient care. D.K. and K.N. wrote the manuscript.
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Extended data
Extended Data Fig. 1 Infiltration of T cells in DiHS/DRESS skin.
a, scRNAseq tSNE analysis of DiHS/DRESS and HV skin cells, color-coded based on origin (n = 18,218 cells). b, Heatmap of the top-five genes marking clusters of skin cells (x-axis; from Fig. 1b). Keratinocyte (KC); smooth muscle (SM). c, Frequencies of cells in each cluster, color-coded based on origin. d, Fourteen clusters in Fig. 1b were grouped into 7 major subsets based on cell types (DiHS/DRESS cells, n = 4,676; HVs cells, n = 13,542). Numbers of differentially expressed genes (nDEGs) between DiHS/DRESS- and HV cells within each cell type projected onto a tSNE map. DEG: |log fold change| > 0.5, adjusted p-value < 0.05, Wilcoxon rank sum test. e, Immunohistochemical staining for CD3, CD4, and CD8 in DiHS/DRESS lesional skin. Scale bars, 100 μm. Representatives of six serial sections from one sample. f, tSNE plot for the lymphocyte subcluster, color-coded based on origin (DiHS/DRESS cells, n = 589; HV cells, n = 1,148). g, tSNE projections of selected genes (n = 1,737 cells). h, Frequency of cells from (f) expressing the displayed genes. i, Immunofluorescence staining with anti-CD3 (green) and anti-CCR10 (red) antibodies or rabbit IgG isotype in DiHS/DRESS lesional skin. Dotted lines denote the boundary between the epidermis (Ep) and dermis. Scale bar, 50 μm. j, Immunofluorescence staining with anti-CD3 (green) and anti-JAK3 (red) in DiHS/DRESS lesional skin. Staining in atopic dermatitis and HV skin sections are shown as comparison. Nuclear labeling with DAPI (blue). Scale bar, 50 μm. k, Hematoxylin and eosin staining (top; H&E) and immunohistochemical staining for phosphorylated STAT1 (pSTAT1) in DiHS/DRESS and HV skin. Scale bar, 20 μm. i-k, Representative of 3 independent experiments.
+Source data +
Extended Data Fig. 2 Characterization of DiHS/DRESS T cells in blood.
a, scRNAseq tSNE analysis of DiHS/DRESS and HV PBMCs, color-coded based on origin (n = 14,932 cells). b, Frequencies of cells in each cluster, color-coded based on origin. c, Heatmap of the top-five genes marking PBMC clusters (from Fig. 2a). d, Violin plots show the distribution of the normalized expression levels of selected genes in each lymphocyte cluster (CD4(1), n = 2,322 cells; CD4(2), n = 1,649 cells; CD4(3), n = 1,318 cells; CD8(1), n = 2,350 cells; CD8(2), n = 850 cells; CD8(3), n = 335 cells; mitotic, n = 525 cells; Treg, n = 245 cells). e, Frequencies of top-20 common T cell receptor (TCR) clonotypes in PBMCs from HV and in PBMCs and skin from DiHS/DRESS patient as determined by single-cell TCR V(D)J gene sequencing. f, Frequent clonotypes, defined as cells expressing common TCR combinations that were shared by more than 10 cells (DiHS/DRESS, n = 2 clonotypes in 22 cells; HV, n = 21 clonotypes in 1,466 cells), were mapped onto the tSNE plot. g. Trajectory analysis of cells in the CD4 T cell clusters (n = 6,059 cells), colored by pseudotime (top) and clusters (bottom). h, Pseudo-temporal single cell expression of indicated genes, colored by cluster. i, Pathways upregulated in DiHS/DRESS lymphoid clusters with high transcriptomic changes (CD4(3), CD8(1), and mitotic clusters, n = 4,193 cells). P-value, hypergeometric test.
+Source data +
Extended Data Fig. 3 Reduction of circulating CD8+ effector memory T cells after tofacitinib.
Flow cytometry analysis for chemokine receptor expression and memory phenotype in DiHS/DRESS peripheral blood CD3+ CD8+ T cells pre- and 2 weeks post-treatment with tofacitinib. Right panels: Naïve, central memory (CM), and effector memory (EM) CD8+ T cells. Representatives of 2 technical replicates.
Extended Data Fig. 4 Pathway analysis of transcriptomic changes induced in CD4+ T cells and myeloid cells during LTT.
a, scRNAseq tSNE analysis of PBMCs after a 4-day culture with or without SMX/TMP, color-coded based on origin (n = 5,881 cells). b, Pathways upregulated in CD4(1) cluster (from Fig. 4b–d, n = 1,486) after SMX/TMP treatment. c, Volcano plot of up- (red) and down-regulated (blue) genes differentially expressed in SMX/TMP treated CD4(1) cells (|log fold change| > 0.5), highlighting relevant genes associated with pathways from (b). With SMX-TMP, n = 418 CD4(1) cells; without SMX-TMP, n = 1,068 CD4(1) cells. Full list of differentially expressed genes from the three CD4 T cell clusters (n = 3,869 cells) observed in LTT (see Fig. 4b, c) is provided in Supplementary Table 2. d, Pathways upregulated in myeloid cells after SMX/TMP treatment. e. Volcano plot of differentially expressed genes in myeloid cells (n = 513) by SMX/TMP treatment, labeling relevant genes in pathways from (d). f, Frequencies of top-20 common T cell receptor (TCR) clonotypes in PBMCs after a 4-day culture with or without SMX/TMP as determined by single-cell TCR V(D)J gene sequencing. g, h, scRNAseq tSNE analysis on SMX/TMP-treated PBMCs with or without tofacitinib (n = 2,068 cells) colour-coded based on cluster (g, top) and origin (h). tSNE projections of selected marker genes (g, bottom). i, Pathways downregulated in CD4 lymphocytes of tofacitinib-treated PBMC, and j, Volcano plot with labeling of relevant downregulated genes. Tofacitinib-treated CD4 cells, n = 825; untreated CD4 cells, n = 508. P-values in b,d,i, hypergeometric test; p-values in c, e, j, Wilcoxon rank sum test.
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Kim, D., Kobayashi, T., Voisin, B. et al. Targeted therapy guided by single-cell transcriptomic analysis in drug-induced hypersensitivity syndrome: a case report. + Nat Med 26, 236–243 (2020). https://doi.org/10.1038/s41591-019-0733-7
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DOI: https://doi.org/10.1038/s41591-019-0733-7
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