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Abstract Number: 2629

Deciphering Salivary Gland Inflammation in Sjögren’s Syndrome Reveals Shared and Autoantibody-Specific Immune Cell Heterogeneity

Jun Inamo1, Masaru Takeshita2, Katsuya Suzuki2, Kazuyuki Tsunoda2, Satoshi Usuda2, Junko Kuramoto2, Tsutomu Takeuchi3 and Yuko Kaneko4, 1University of Colorado School of Medicine, Aurora, CO, 2Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan, 3Department of Internal Medicine, Keio University, Tokyo, Tokyo, Japan, 4Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, JP, Shinjuku-ku, Tokyo, Japan

Meeting: ACR Convergence 2024

Keywords: Bioinformatics, genomics, Sjögren's syndrome

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Session Information

Date: Monday, November 18, 2024

Title: Abstracts: Sjögren's Syndrome – Basic & Clinical Science

Session Type: Abstract Session

Session Time: 3:00PM-4:30PM

Background/Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by progressive immune cell-mediated destruction of the exocrine glands. SS patients have distinct clinical phenotypes based on their autoantibody profiles; anti-SSA positive patients often present with hypergammaglobulinemia, more severe glandular dysfunction, and extraglandular manifestations, whereas anti-centromere positive patients show a higher incidence of Raynaud’s phenomenon and sclerodactyly. Despite these clinical differences and the common presence of salivary gland destruction, the underlying pathogenic mechanisms driving salivary gland destruction remain understood.

Methods: To elucidate the complex cellular interplay driving salivary gland inflammation in SS, we performed an integrative analysis using multimodal single-cell technologies including RNA sequencing (scRNA-seq) together with T-cell receptor (TCR) and B-cell receptor (BCR) repertoire analysis on salivary gland samples from SS patients stratified by their autoantibody profiles (SSA+, n=8; CENT+, n=4; SSA+CENT+, n=4; Sicca, n=8). Furthermore, we analyzed spatial transcriptomic data of salivary gland samples from SS patients (SSA+, n=6; CENT+, n=5; SSA+CENT+, n=4; Sicca, n=5) to characterize pathogenic cell types in the inflammation.

Results: We identified fine-scaled and heterogeneous cell types in salivary glands based on the gene expression pattern (Fig. 1A-D). Within the T cell clusters, GZMB+ and GZMK+ CD8 T cells were clonally expanded independently, regardless of disease status (Fig. 2A-B). In SS samples, increased GZMK+ CD8 T, Tph/Tfh, and Treg were shared features (Fig. 2C-D), with CD8 subsets more common in SSA+SS and CD4 subsets more common in CENT+SS (Fig. 2E-F). Both TCR and BCR diversity was higher in CENT+ phenotypes, reflecting that the autoantigens targeted by anti-centromere antibodies are more diverse than those of anti-SSA antibodies. Furthermore, unsupervised deconstruction analysis of spatial transcriptome data revealed a disease related signature (factor 1) at the focus area (Fig. 3A-B). This signature was composed of complement-mediated cell-cell interactions by inflammatory fibroblasts, which are assumed to amplify inflammation in the salivary gland lesion (Fig. 3C-E).

Conclusion: Our findings provide a deep understanding of the cellular landscape and intercellular communication within the salivary glands, shared and varying by autoantibody profile. This work advances our understanding of how autoimmunity is orchestrated at the cellular level in SS and opens new avenues for the identification of potential therapeutic targets in this complex autoimmune disorder.

Supporting image 1

Fig. 1: Cell-type-specific clustering analysis revealed distinct cell states in the inflamed salivary glands of Sjögren’s syndrome. Cells in UMAP are colored by cell clusters. A, T cell subsets. B, B/plasma cell subsets. C, Tissue cell subsets. D, lymphocytes other than T cells and B/plasma cells.

Supporting image 2

Fig. 2: Expansion of T cell subsets in the salivary glands. A, Clonal expansion of T cells. B. T cell clusters in the top 20 expanded clones by autoantibody status. C, Differential abundance analysis comparing all SS (SSA+, CENT+, and SSA+CENT+) and Sicca. Cells in UMAP are colored in red (expansion) or blue (depletion) and p-value is shown as well. D, Distributions of cell neighborhood correlations by T cell clusters. Error bars represent 95% confidence intervals. E, Differential abundance analysis comparing SSA+ SS and CENT+ SS. Cells in UMAP are colored in red (expansion) or blue (depletion) and p-value is shown as well. F, Distributions of cell neighborhood correlations by T cell clusters. Error bars represent 95% confidence intervals.

Supporting image 3

Fig. 3: Spatial transcriptomic analysis reveals inflammatory fibroblast phenotypes as core players in inflamed site or lesion. A, Microscopic images of representative samples used for spatial transcriptome analysis. B. Unsupervised deconstruction of spatial transcriptome data by underlying latent factors. Factor 1 value is shown. C, Genes with top weights of Factor 1. D, Expression of genes with top weights of Factor 1 in tissue clusters in scRNA-seq data. E, Sender and receiver signals of C3-C3AR1 pathway.


Disclosures: J. Inamo: None; M. Takeshita: None; K. Suzuki: None; K. Tsunoda: None; S. Usuda: None; J. Kuramoto: None; T. Takeuchi: AbbVie, 4, 6, Astellas, 2, 6, Eisai, 4, 6, Eli Lilly Japan, 2, 6, Gilead, 2, 6, Pfizer Japan, 6; Y. Kaneko: None.

To cite this abstract in AMA style:

Inamo J, Takeshita M, Suzuki K, Tsunoda K, Usuda S, Kuramoto J, Takeuchi T, Kaneko Y. Deciphering Salivary Gland Inflammation in Sjögren’s Syndrome Reveals Shared and Autoantibody-Specific Immune Cell Heterogeneity [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/deciphering-salivary-gland-inflammation-in-sjogrens-syndrome-reveals-shared-and-autoantibody-specific-immune-cell-heterogeneity/. Accessed .
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