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SIRPG, a primate-specific type 1 transmembrane protein in the Signal Regulatory Protein (SIRP) family, is predominantly expressed in T cells. It contains a short cytoplasmic domain, which does not contain any known signaling motif, and its only known ligand is CD47. Several genetic variations in SIRPG , including the V263A (rs6043409) polymorphism, linked to increased type 1 diabetes risk, highlight its potential importance. However, its expression and physiological role remain largely unclear due to its absence in rodents. Here, we demonstrate that SIRPG and GzmB exhibit near mutually exclusive expression in resting peripheral CD8+ T cells. We further show that SIRPG serves as a valuable marker for GzmK-expressing CD8+ T cells in peripheral blood and that its expression in both CD4+ and CD8+ T cells is upregulated by anti-CD3 stimulation, with further enhancement by the TNFα inhibitor adalimumab, but not certolizumab. While SIRPG ablation minimally affects T cell activation and IFNγ/TNFα production, it impairs the expression of mitosis-regulating genes like UBE2C and TOP2A , leading to reduced proliferation, and alters the expression of certain activation-induced surface molecules, including CRTAM. Notably, SIRPG-mediated proliferation and CRTAM expression are cell-autonomous and CD47-independent. Structural and functional analyses reveal that SIRPG-driven proliferation is independent of its extracellular D1 domain, not significantly affected by the V263 variant, but dependent on its cytoplasmic domain. Collectively, our findings offer novel insights into the expression, function, and mechanism of action of SIRPG in T cells.

Purpose of Review Rheumatoid arthritis is one of the most common rheumatic and autoimmune diseases. While it can affect many different organ systems, RA primarily involves inflammation in the synovium, the tissue that lines joints. Patients with RA exhibit significant clinical heterogeneity in terms of presence or absence of autoantibodies, degree of permanent deformities, and most importantly, treatment response. These clinical characteristics point to heterogeneity in the cellular and molecular pathogenesis of RA, an area that several recent studies have begun to address. Recent Findings Single-cell RNA-sequencing initiatives and deeper focused studies have revealed several RA-associated cell populations in synovial tissues, including peripheral helper T cells, autoimmunity-associated B cells (ABCs), and NOTCH3 + sublining fibroblasts. Recent large transcriptional studies and translational clinical trials present frameworks to capture cellular and molecular heterogeneity in RA synovium. Technological developments, such as spatial transcriptomics and machine learning, promise to further elucidate the different types of RA synovitis and the biological mechanisms that characterize them, key elements of precision medicine to optimize patient care and outcomes in RA. Summary This review recaps the findings of those recent studies and puts our current knowledge and future challenges into scientific and clinical perspective.

Juvenile idiopathic arthritis (JIA) is the most prevalent chronic inflammatory arthritis of childhood, yet the spatial organization in the synovium remains poorly understood. Here, we perform subcellular-resolution spatial transcriptomic profiling of synovial tissue from patients with active JIA. We identify diverse immune and stromal cell populations and reconstruct spatially defined cellular niches. Applying a newly developed spatial colocalization analysis pipeline, we uncover microanatomical structures, including endothelial-fibroblast interactions mediated by NOTCH signaling, and a CXCL9/CXCR3 signaling axis between inflammatory macrophages and CD8+ T cells, alongside the characterization of other resident macrophage subsets. We also detect and characterize tertiary lymphoid structures marked by CXCL13/CXCR5 and CCL19-mediated signaling from Tph cells and immunoregulatory DCs, analogous to those observed in other autoimmune diseases. Finally, comparative analysis with rheumatoid arthritis reveals JIA-enriched cell states, including NOTCH3+ and CXCL12+ sublining fibroblasts, suggesting potentially differential inflammatory programs in pediatric versus adult arthritis. These findings provide a spatially resolved molecular framework of JIA synovitis and introduce a generalizable computational pipeline for spatial colocalization analysis in tissue inflammation.

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches typically make restrictive assumptions such as representing tissue regions by local abundances of manually typed, discrete cell types, or representing samples in terms of abundances of manually called, discrete spatial structures; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to extract numerical \"fingerprints\" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of data-dependent \"microniches\" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then meta-analyzes across the autoencoders to identify microniches whose abundance correlates with case-control status while controlling for multiple testing. We show in simulations that VIMA is well calibrated. We then apply VIMA to spatial datasets spanning three different diseases and spatial modalities: a 7-marker immunofluorescence (IF) microscopy dataset in rheumatoid arthritis (RA), a 52-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 140-gene spatial transcriptomics dataset in dementia. In each case, we recapitulate known biology and identify novel spatial features of disease that were not discoverable with current state-of-the-art methods.

Natural killer (NK) cells are innate immune lymphocytes that react to cells lacking self-MHC (major histocompatibility complex) class I molecules, such as transformed or virally infected host cells and allogeneic bone marrow. This reactivity is mediated by inhibitory receptors for MHC class I that block the ability of activation receptors to stimulate NK cells. Since many NK cells lack receptors that recognize self-MHC, the inhibitory receptors also mediate a second function, termed NK cell licensing, to protect against autoreactivity. To become licensed, i.e. functionally competent to be triggered through its activation receptors, an NK cell must engage host MHC class I via at least one of its MHC class I-specific inhibitory receptors, which in mice belong to the Ly49 family of receptors. However, many properties of this process remain unclear. To explore potential determinants of NK cell licensing on a single Ly49 receptor, we have investigated the relative licensing impacts of the b, d, k, q, r, and s H2 haplotypes on Ly49A+ NK cells. In ex vivo stimulation assays, some Ly49A-MHC class I haplotype combinations produced an intermediate licensing phenotype, indicating that licensing is not a binary phenomenon. Comparisons of these data with soluble Ly49A tetramer binding assays indicate that licensing is essentially analog but is saturated by moderate-binding MHC class I ligands. Interestingly, licensing exhibited a strong inverse correlation with Ly49A surface accessibility, a measure of cis engagement of Ly49A with MHC class I expressed on the same cell. Finally, Ly49A-mediated effector inhibition was found to be more sensitive to MHC class I engagement than licensing of Ly49A+ NK cells, suggesting that licensing establishes a margin of safety against NK cell autoreactivity.

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches often rely on hand-crafted features such as local abundances of manually annotated, discrete cell types, which may overlook important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA uses a variational autoencoder to extract numerical \"fingerprints\" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of \"microniches\" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status. We show in simulations that VIMA is well calibrated and more powerful and accurate than other approaches. We then apply VIMA to a 140-gene spatial transcriptomics dataset in Alzheimer's dementia, a 54-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 7-marker immunohistochemistry dataset in rheumatoid arthritis (RA), in each case recapitulating known biology and identifying novel spatial features of disease.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/yakirr/vima* https://github.com/yakirr/vimasim* https://github.com/yakirr/vimapaper

Fibroblasts play critical roles in tissue homeostasis, but in pathologic states can drive fibrosis, inflammation, and tissue destruction. In the joint synovium, fibroblasts provide homeostatic maintenance and lubrication. Little is known about what regulates the homeostatic functions of fibroblasts in healthy conditions. We performed RNA sequencing of healthy human synovial tissue and identified a fibroblast gene expression program characterized by enhanced fatty acid metabolism and lipid transport. We found that fat-conditioned media reproduces key aspects of the lipid-related gene signature in cultured fibroblasts. Fractionation and mass spectrometry identified cortisol in driving the healthy fibroblast phenotype, confirmed using glucocorticoid receptor gene ( ) deleted cells. Depletion of synovial adipocytes in mice resulted in loss of the healthy fibroblast phenotype and revealed adipocytes as a major contributor to active cortisol generation via β expression. Cortisol signaling in fibroblasts mitigated matrix remodeling induced by TNFα- and TGFβ, while stimulation with these cytokines repressed cortisol signaling and adipogenesis. Together, these findings demonstrate the importance of adipocytes and cortisol signaling in driving the healthy synovial fibroblast state that is lost in disease.

Synovial tissue inflammation is the hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. We measured genome-wide open chromatin at single cell resolution from 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identified 24 chromatin classes and predicted their associated transcription factors, including a + + class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating an RA tissue transcriptional atlas, we found that the chromatin classes represented 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

Rheumatoid arthritis (RA) is a systemic autoimmune disease with currently no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in 'At-Risk' populations prior to clinical disease onset is crucial to establishing effective prevention strategies. Here, we applied mass cytometry to deeply characterize the immunophenotypes in blood from At-Risk individuals identified through the presence of serum antibodies to citrullinated protein antigens (ACPA) and/or first-degree relative (FDR) status (n=52), as compared to established RA (n=67), and healthy controls (n=48). We identified significant cell expansions in At-Risk individuals compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5 naïve B cell population was expanded in ACPA-positive FDRs. Further, we developed an \"RA immunophenotype score\" classification method based on the degree of enrichment of cell states relevant to established RA patients. This score significantly distinguished At-Risk individuals from controls. In all, we systematically identified activated lymphocyte phenotypes in At-Risk individuals, along with immunophenotypic differences among both ACPA+ and ACPA-FDR At-Risk subpopulations. Our classification model provides a promising approach for understanding RA pathogenesis with the goal to further improve prevention strategies and identify novel therapeutic targets.

T cells acquire a regulatory phenotype when their T cell receptors (TCRs) experience an intermediate-high affinity interaction with a self-peptide presented on MHC. Using TCR sequences from FACS-sorted human cells, we identified TCR features that shape affinity to these self-peptide-MHC complexes, finding that 1) CDR3β hydrophobicity and 2) certain TRBV genes promote Treg fate. We developed a scoring system for TCR-intrinsic regulatory potential (TiRP) and found that within the tumor microenvironment clones exhibiting Treg-Tconv plasticity had higher TiRP than expanded clones maintaining the Tconv phenotype. To elucidate drivers of these predictive TCR features, we examined the two elements of the Treg TCR ligand separately: the self-peptide via murine Tregs, and the human MHC II molecule via human memory Tconvs. These analyses revealed that CDR3β hydrophobicity promotes reactivity to self-peptides, while TRBV gene usage shapes the TCRs general propensity for MHC II restricted activation. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://clients.adaptivebiotech.com/pub/seay-2016-jciinsight * https://clients.adaptivebiotech.com/pub/thornton-2019-eji * https://clients.adaptivebiotech.com/pub/soto-2020-cr * https://clients.adaptivebiotech.com/pub/emerson-2017-natgen * https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE123814 * https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158769 * https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114724 * https://clients.adaptivebiotech.com/pub/peakman-2017-naturecommunications