Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT.

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.

Inhibition of JAK-STAT pathway corrects salivary gland inflammation and interferon driven immune activation in Sjögren's Disease.

Objectives: Inflammatory cytokines that signal through the JAK- STAT pathway, especially interferons (IFNs), are implicated in Sjögren's Disease (SjD). Although inhibition of JAKs is effective in other autoimmune diseases, a systematic investigation of IFN-JAK-STAT signaling and effect of JAK inhibitor (JAKi) therapy in SjD-affected human tissues has not been reported. Methods: Human minor salivary glands (MSGs) and peripheral blood mononuclear cells (PBMCs) were investigated using bulk or single cell (sc) RNA sequencing (RNAseq), immunofluorescence microscopy (IF), and flow cytometry. Ex vivo culture assays on PBMCs and primary salivary gland epithelial cell (pSGEC) lines were performed to model changes in target tissues before and after JAKi. Results: RNAseq and IF showed activated JAK-STAT pathway in SjD MSGs. Elevated IFN-stimulated gene (ISGs) expression associated with clinical variables (e.g., focus scores, anti-SSA positivity). scRNAseq of MSGs exhibited cell-type specific upregulation of JAK-STAT and ISGs; PBMCs showed similar trends, including markedly upregulated ISGs in monocytes. Ex vivo studies showed elevated basal pSTAT levels in SjD MSGs and PBMCs that were corrected with JAKi. SjD-derived pSGECs exhibited higher basal ISG expressions and exaggerated responses to IFNß, which were normalized by JAKi without cytotoxicity. Conclusions: SjD patients' tissues exhibit increased expression of ISGs and activation of the JAK-STAT pathway in a cell type-dependent manner. JAKi normalizes this aberrant signaling at the tissue level and in PBMCs, suggesting a putative viable therapy for SjD, targeting both glandular and extraglandular symptoms. Predicated on these data, a Phase Ib/IIa randomized controlled trial to treat SjD with tofacitinib was initiated.

Sclerosing Sialadenitis Is Associated With Salivary Gland Hypofunction and a Unique Gene Expression Profile in Sjögren's Syndrome.

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.

Correction to: ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference.

Following the publication of this article [1], the authors informed us of the following typographical errors in the Results section (the changes are marked in bold).

ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference.

BACKGROUND: Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data. RESULTS: We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads. CONCLUSIONS: We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.