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Single-cell RNA sequencing reveals clonally expanded CD4+ tissue-resident memory T cells in histidyl-tRNA synthetase–induced myositis
Decheng Li, Daniel P. Reay, Iago Pinal-Fernandez, Maria Casal-Dominguez, Andrew L. Mammen, Sarah L. Gaffen, Timothy B. Oriss, Dana P. Ascherman
Decheng Li, Daniel P. Reay, Iago Pinal-Fernandez, Maria Casal-Dominguez, Andrew L. Mammen, Sarah L. Gaffen, Timothy B. Oriss, Dana P. Ascherman
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Research Article Immunology

Single-cell RNA sequencing reveals clonally expanded CD4+ tissue-resident memory T cells in histidyl-tRNA synthetase–induced myositis

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Abstract

The precise mechanisms underlying the pathogenesis of idiopathic inflammatory myopathy (IIM) remain undefined. However, there has been increasing recognition that tissue-resident memory T cells (TRMs) play an important role in the pathogenesis of systemic autoimmune disease. In IIM, TRM-associated transcriptional signatures have been reported but on a very limited basis. By using multimodal single-cell RNA-sequencing analysis in our established murine model of histidyl-tRNA synthetase–induced myositis, we identified a prominent population of CD4+ TRMs in inflamed skeletal muscle. Muscle CD4+ TRMs exhibited high expression of genes encoding Cd69, Cxcr6, Runx3, and Prdm1, alongside low expression of Klf2, Ccr7, Sell, S1pr1, and Tcf7 — a profile that is generally consistent with previous reports of TRM gene signature and that we validate through comparison with transcriptomic profiles of human muscle tissue. Detailed pathway analysis in our model indicates that muscle CD4+ TRMs contribute to innate immune regulatory pathways enriched for TNF and IFN-γ signaling. Furthermore, analysis of TCR clonotype distribution and CDR3 sequence similarity revealed pronounced clonal expansion of CD4+ TRMs relative to other T cell subsets — a pattern that remained stable from 2 to 6 weeks after immunization. Collectively, these results suggest a potential role for CD4+ TRMs in the pathogenesis of autoimmune myositis.

Authors

Decheng Li, Daniel P. Reay, Iago Pinal-Fernandez, Maria Casal-Dominguez, Andrew L. Mammen, Sarah L. Gaffen, Timothy B. Oriss, Dana P. Ascherman

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Figure 4

Functional characterization of TRMs and interactions between TRMs and other cells.

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Functional characterization of TRMs and interactions between TRMs and ot...
(A and B) Pathway analysis based on DEGs of CD4+ TRMs in T/NK subsets. Pathways generated from GO were selected from the top 40 pathways identified (A), and pathways generated from KEGG were selected from the top 70 (B). (C and D) Interaction strength between CD4+ TRMs and all clusters, visualized by network and scatter plots. Interaction strength represents ligand-receptor–mediated intercellular communication probability, measured by CellChat. Vertex size is proportional to cell counts in each cluster (C). In scatter plots displaying sources and targets across all clusters (D), dot size is proportional to the number of inferred outgoing and incoming links associated with each cell cluster. (E) Heatmap visualizing TNF and IFN-γ signaling networks across different clusters. Horizontal and longitudinal axes indicate incoming and outgoing signals, respectively. (F and G) Interaction strength between CD4+ TRMs and other subsets of T/NK cells, again depicted as network and scatter plots with parameters as described for plots shown in C and D. (H) Heatmap visualizing TNF signaling network across different subsets within T/NK cells. Horizontal and longitudinal axes indicate respective incoming and outgoing signals.

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