Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.

Sjogren's Disease (SjD) is an autoimmune disorder characterized by salivary and lacrimal gland dysfunction and immune cell infiltration leading to gland inflammation and destruction. Although SjD is a common disease, its pathogenesis is not fully understood. In this study, we conducted a single-cell transcriptome analysis of peripheral blood mononuclear cells (PBMC) from patients with SjD and symptomatic non-SjD controls to identify cell types and functional changes involved in SjD pathogenesis. All PBMCs populations showed marked differences in gene expression between SjD patients and controls, particularly an increase in interferon (IFN) signaling gene signatures. T and B cells of SjD patients displayed a depletion of ribosomal gene expression and pathways linked to protein translation. SjD patients had increased frequencies of naive B cells, which featured a unique gene expression profile (GEP) distinct from controls and had hallmarks of B cell hyperactivation. Non-negative matrix factorization (NMF) also identified several non-overlapping GEPs in CD4 and CD8 T cells with differential usage in SjD patients and controls. Of these, only the Th1 activation GEP was enriched in T cells of SjD patients whereas the other two GEPs were depleted in T cells, emphasizing the important role of Th1 cells in SjD. Our study provides evidence for aberrant and unique gene expression patterns in both B and T lymphocytes of SjD patients that point to their altered activation states and may provide new insights into the pathogenesis of SjD.