ArticleJournal of autoimmunity2025
Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.
Article in Journal of autoimmunity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Insights into the pathogenesis of rheumatic and immune diseases from single-cell omics.Frontiers in immunology · 2026Pooled it
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Authors and funding
7 authors.
Funding
Abstract
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
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