bioRxivGenomicsPreprint
Single-cell RNA sequencing of peripheral blood defines two immunological subtypes of Sjögren's disease
G. UrbanskiiD, Kimberly E. TayloriD, Emily FlynniD, Armita Norouzi, Catherine Chu, Brittany R. DavidsoniD, Arnab Ghosh, Joanne NitithamiD, Ravi K. PateliD, Annie W. Poon, Gabriela K. FragiadakisiD, Alexis J. CombesiD, Walter L. EckalbariD, Lindsey A. CriswelliD, Chun YeiD, Caroline H. ShiboskiiD
Abstract
Sjogrens disease (SjD) is a heterogeneous autoimmune disorder with significant clinical and molecular diversity. While anti-SSA antibodies serve as a hallmark serological biomarker, nearly half of patients lack them, raising fundamental questions about distinct pathogenic mechanisms. To address this, we performed single-cell RNA sequencing with surface protein profiling on 1.5 million PBMCs from 333 participants in the Sjogrens International Collaborative Clinical Alliance cohort, comparing those with and without SjD and stratifying them by anti-SSA status. Our analysis identified two immunologically distinct subtypes of SjD, with SSA-positive patients exhibiting a dominant and persistent IFN-I signature, profoundly impacting immune cell maturation. Notably, transitional B cells were disproportionately affected, displaying signs of premature maturation, reduced BCR diversity, and shorter CDR3 regions, reinforced interactions with proinflammatory cells, thereby fostering an environment conducive to autoreactivity. By contrast, SSA-negative SjD participants lacked an upregulated IFN-I signature, challenging prevailing pathogenic models and suggesting alternative immune dysregulation pathways. These findings support a two-disease model of SjD and highlight transitional B cells as both a key biomarker and a potential therapeutic target. Targeting IFN-I signaling and transitional B cell maturation may represent a novel therapeutic avenue to modulate immune dysregulation and prevent autoreactivity in SjD.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 2
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