[Single-cell RNA sequencing of B cells reveals molecular typing in Sjögren syndrome].

OBJECTIVE: To establish a molecular classification framework for Sjögren syndrome (SS) by stratifying patients into distinct subtypes through unsupervised clustering of B cell single-cell RNA sequencing (scRNA-seq). This study characterizes subtype-specific gene signatures to construct protein-protein interaction (PPI) networks, thereby elucidating core regulatory mechanisms and potential therapeutic targets. Concurrently, it defines the clinical heterogeneity of SS by profiling autoantibodies and B-cell subset distributions across subtypes.

METHODS: The scRNA-seq data from 24 SS patients and 4 healthy controls were obtained from the Gene Expression Omnibus (GEO) database. We constructed a B cell atlas and identified differential gene expression profiles between SS and healthy controls B cells. Unsupervised clustering was applied to stratify SS patients into different molecular subtypes. Functional enrichment analysis of subtype-specific gene signatures was performed to infer associated biological processes/pathways. PPI networks were constructed using the STRING database and Cytoscape software to identify core functions and potential therapeutic targets for subtype-specific genes. The prevalence of autoantibodies and proportions of B cell subsets were statistically analyzed across subtypes.

RESULTS: The B cells were classified into eight subsets: transitional B cell, naïve B cell, memory B cell, double negative 1 (DN1) B cell, double negative 2 (DN2) B cell, VAV3+IRF1+ B cell, GP9+ B cell, and plasma cell. The FindAllMarkers function identified 792 differentially expressed genes (DEGs) between the SS patients and healthy controls. Unsupervised clustering stratified patients into three subtypes: (1) Inter-feron-dominant subtype characterized by enrichment in type Ⅰ/Ⅱ interferon and non-canonical nuclear factor kappa-B (NF-κB) signaling pathways. This subtype showed the highest proportions of naïve B cells and transitional B cells, along with the highest anti-Sjögren syndrome antigen A (SSA)/Sjögren syndrome antigen B (SSB) positivity. (2) B cell activation subtype characterized by enrichment in Fc receptor and B cell receptor signaling pathways. This subtype exhibited the highest proportions of memory B cells and DN1 B cells. (3) Endoplasmic reticulum stress subtype characterized by enrichment in protein folding and endoplasmic reticulum-associated degradation pathways. This subtype was marked by the highest proportion of VAV3+IRF1+ B cells. PPI networks identified subtype-specific hub genes regulating these core functions.

CONCLUSION: Stratification of SS patients through clustering of B cell DEGs successfully defined three molecular subtypes (interferon-dominant, B cell activation, and endoplasmic reticulum stress subtypes). Each subtype exhibits distinct autoantibody profiles and B cell subset distributions. This molecular typing framework advances our understanding of SS heterogeneity and provides actionable insights for targeted therapy development.