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Purpose: To develop a novel method to quantify the amount of fibrosis in the salivary gland and to investigate the relationship between fibrosis and specific symptoms associated with Sjögren's syndrome (SS) using this method. Materials and Methods: Paraffin-embedded labial salivary gland (LSG) slides from 20 female SS patients and their clinical and LSG pathology data were obtained from the Sjögren's International Collaborative Clinical Alliance. Relative interstitial fibrosis area (RIFA) in Masson's trichrome-stained LSG sections was quantified from digitally scanned slides and used for correlation analysis. Gene expression levels were assessed by microarray analysis. Core promoter accessibility for RIFA-correlated genes was determined using DNase I hypersensitive sites sequencing analysis. Results: RIFA was significantly correlated with unstimulated whole saliva flow rate in SS patients. Sixteen genes were significantly and positively correlated with RIFA. In a separate analysis, a group of differentially expressed genes was identified by comparing severe and moderate fibrosis groups. This combined set of genes was distinct from differentially expressed genes identified in lung epithelium from idiopathic pulmonary fibrosis patients compared with controls. Single-cell RNA sequencing analysis of salivary glands suggested most of the RIFA-correlated genes are expressed by fibroblasts in the gland and are in a permissive chromatin state. Conclusion: RIFA quantification is a novel method for assessing interstitial fibrosis and the impact of fibrosis on SS symptoms. Loss of gland function may be associated with salivary gland fibrosis, which is likely to be driven by a unique set of genes that are mainly expressed by fibroblasts. [ABSTRACT FROM AUTHOR]

BMP6 is a central cytokine in the induction of Sjögren's syndrome-associated (SS-associated) secretory hypofunction. However, the upstream initiation leading to the production of this cytokine in SS is unknown. In this study, RNA ISH on salivary gland sections taken from patients with SS indicated monocytic lineage cells as a cellular source of BMP6. RNA-Seq data on human salivary glands suggested that TLR4 signaling was an upstream regulator of BMP6, which was confirmed by in vitro cell assays and single-cell transcriptomics of human PBMCs. Further investigation showed that HSP70 was an endogenous natural TLR4 ligand that stimulated BMP6 expression in SS. Release of HSP70 from epithelial cells could be triggered by overexpression of lysosome-associated membrane protein 3 (LAMP3), a protein also associated with SS in several transcriptome studies. In vitro studies supported the idea that HSP70 was released as a result of lysosomal exocytosis initiated by LAMP3 expression, and reverse transcription PCR on RNA from minor salivary glands of patients with SS confirmed a positive correlation between BMP6 and LAMP3 expression. BMP6 expression could be experimentally induced in mice by overexpression of LAMP3, which developed an SS-like phenotype. The newly identified LAMP3/HSP70/BMP6 axis provided an etiological model for SS gland dysfunction and autoimmunity. [ABSTRACT FROM AUTHOR]

Sjögren's Disease (SjD) is a systemic autoimmune disease without a clear etiology or effective therapy. Utilizing unbiased single-cell and spatial transcriptomics to analyze human minor salivary glands in health and disease we developed a comprehensive understanding of the cellular landscape of healthy salivary glands and how that landscape changes in SjD patients. We identified novel seromucous acinar cell types and identified a population of PRR4+CST3+WFDC2 - seromucous acinar cells that are particularly targeted in SjD. Notably, GZMK +CD8 T cells, enriched in SjD, exhibited a cytotoxic phenotype and were physically associated with immune-engaged epithelial cells in disease. These findings shed light on the immune response's impact on transitioning acinar cells with high levels of secretion and explain the loss of this specific cell population in SjD. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's Disease.

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications., Competing Interests: Conflict of interest: The authors had access to the study data and reviewed and approved the final manuscript. Although the authors view each of these as noncompeting financial interests, KMB, QTE, BFM, and BMW are all active members of the Human Cell Atlas; furthermore, KMB is a scientific advisor at Arcato Laboratories. All other authors declare no competing interests.