Biology subjects

Boyle, D. L.

Publications and source records attributed to Boyle, D. L..

6 recordsLinked to original sources

bioRxiv2025

Spatial transcriptomics reveals distinct inflammatory and adaptive immune properties of rheumatoid arthritis synovial fibroblasts

ObjectiveRheumatoid arthritis (RA) synovium displays cellular heterogeneity, with gene expression driving pathogenesis. Prior transcriptomic studies relied on disaggregated tissue, which causes cell loss and induction. We applied spatial transcriptomics to investigate synovial lining and sublining fibroblasts and macrophages from RA and osteoarthritis (OA) patients. MethodsFresh frozen synovial tissues from 7 RA and 8 OA patients were analyzed using the NanoString GeoMX DSP Whole Transcriptome Assay. Lining and sublining regions were segmented into fibroblasts and macrophages. Principal component analysis separated samples by cell type and disease. Differentially expressed genes (DEG) were analyzed by linear mixed models (p-value<0.05 and |log2 fold change|>0.5) and Reactome pathway (FDR<0.02) analysis. ResultsDEG analysis of RA compared to OA revealed distinct gene signatures across regions and cell types. RA lining fibroblasts exhibited a strong pro-inflammatory and matrix-destructive signature, while RA sublining fibroblasts showed an unexpected role in antigen presentation and adaptive immunity. RA lining macrophages exhibited enrichment for interleukin signaling, extracellular matrix organization, and translation-related pathways. In contrast, sublining macrophages showed minimal transcriptional differences between RA and OA, suggesting limited pathogenic involvement. Comparison between lining and sublining within RA showed that lining fibroblasts display a higher activated phenotype than sublining cells. ConclusionSpatial transcriptomic analysis uncovers distinct region- and cell-type-specific transcriptional profiles in RA synovium. Lining fibroblasts are highly activated and destructive, and sublining fibroblasts contribute surprisingly to adaptive immunity. This data provides clues to region-cell-type-specific functions that could be exploited to identify novel therapeutic targets.

immunology↗

bioRxiv2025

Multi-lineage transcriptional and cell communication signatures define pathways in individuals at-risk for developing rheumatoid arthritis that initiate and perpetuate disease

Rheumatoid arthritis (RA) is a systemic autoimmune disease arising from loss of tolerance and autoantibody development in at-risk individuals. Targeted therapies yield variable responses and current preventive strategies delay but do not stop disease onset. We propose that a set of transcription factors (TFs) and their downstream pathways regulate inflammatory cell communication networks in at-risk populations and RA. These networks enable multiple pathogenic cell types and mediators and could account for variable responses to targeted agents. To test this hypothesis, we identified anti-citrullinated protein antibody (ACPA)-positive at-risk individuals, patients with early and established RA and healthy controls. Single cell chromatin accessibility and transcriptomic profiles from blood mononuclear r cells were integrated and identified share pathogenic mechanisms, especially SUMOylation, RUNX2, YAP1, NOTCH3, and {beta}-Catenin Pathways. Surprisingly, this signature was found in multiple cell types. Individualized gene expression patterns were then confirmed in RA synovium. Cell communication analysis revealed that multiple lineages can deliver a core set of pro-inflammatory mediators to receiver cells. Longitudinal analysis showed that the signature cell types evolve in individual at-risk participants. Cell-type-specific signature pathways could contribute to the differing clinical responses to targeted therapies. This study describes how a common clinical phenotype could arise from multiple pathogenic mechanisms.

systems biology↗

bioRxiv2024

Epigenetic trajectory predicts development of clinical rheumatoid arthritis in ACPA+ individuals: Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA)

The presence of anti-citrullinated protein antibodies (ACPAs) in the absence of clinically-apparent inflammatory arthritis (IA) identifies individuals "at-risk" for developing future clinical rheumatoid arthritis (RA). However, it is unclear why some ACPA+ individuals convert to clinical RA while others do not. We explored the possibility that epigenetic remodeling is part of the trajectory from an at-risk state to clinical disease. Cross-sectional differential methylation analysis at baseline revealed DMLs that distinguish the Pre-RA methylome from ACPA+ Non-converters. Genes overlapping these DMLs correspond to aberrant NOTCH signaling and DNA repair pathways in B cells. Longitudinal analysis showed that ACPA-Control and ACPA+ Non-converter methylomes are relatively constant. In contrast, the Pre-RA methylome remodeled along a dynamic "RA methylome trajectory" characterized by epigenetic changes in active regulatory elements. Machine learning revealed individual loci predictive of RA conversion. DNA methylation is a dynamic process in ACPA+ individuals at-risk for developing RA that later transition to clinical disease. In contrast, non-converters and controls have stable methylomes. The accumulation of epigenetic marks over time prior to conversion to clinical RA conforms to pathways that are associated with immunity and can be used to identify potential pathogenic pathways for therapeutic targeting and/or use as prognostic biomarkers.

immunology↗

bioRxiv2023

Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium

Rheumatoid arthritis (RA) is an autoimmune disease initiated by antigen-specific T cells and B cells, which promote synovial inflammation through a complex set of interactions with innate immune and stromal cells. To better understand the phenotypes and clonal relationships of synovial T and B cells, we performed single-cell RNA and repertoire sequencing on paired synovial tissue and peripheral blood samples from 12 donors with seropositive RA ranging from early to chronic disease. Paired transcriptomic-repertoire analyses highlighted 3 clonally distinct CD4 T cells populations that were enriched in RA synovium: T peripheral helper (Tph) and T follicular helper (Tfh) cells, CCL5+ T cells, and T regulatory cells (Tregs). Among these cells, Tph cells showed a unique transcriptomic signature of recent T cell receptor (TCR) activation, and clonally expanded Tph cells expressed an elevated transcriptomic effector signature compared to non-expanded Tph cells. CD8 T cells showed higher oligoclonality than CD4 T cells, and the largest CD8 T cell clones in synovium were highly enriched in GZMK+ cells. TCR analyses revealed CD8 T cells with likely viral-reactive TCRs distributed across transcriptomic clusters and definitively identified MAIT cells in synovium, which showed transcriptomic features of TCR activation. Among B cells, non-naive B cells including age-associated B cells (ABC), NR4A1+ activated B cells, and plasma cells, were enriched in synovium and had higher somatic hypermutation rates compared to blood B cells. Synovial B cells demonstrated substantial clonal expansion, with ABC, memory, and activated B cells clonally linked to synovial plasma cells. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate RA synovium.

immunology↗

bioRxiv2018

Defining Inflammatory Cell States in Rheumatoid Arthritis Joint Synovial Tissues by Integrating Single-cell Transcriptomics and Mass Cytometry

To define the cell populations in rheumatoid arthritis (RA) driving joint inflammation, we applied single-cell RNA-seq (scRNA-seq), mass cytometry, bulk RNA-seq, and flow cytometry to sorted T cells, B cells, monocytes, and fibroblasts from 51 synovial tissue RA and osteoarthritis (OA) patient samples. Utilizing an integrated computational strategy based on canonical correlation analysis to 5,452 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics together revealed cell states expanded in RA synovia: THY1+HLAhigh sublining fibroblasts (OR=33.8), IL1B+ pro-inflammatory monocytes (OR=7.8), CD11c+T-bet+ autoimmune-associated B cells (OR=5.7), and PD-1+Tph/Tfh (OR=3.0). We also defined CD8+ T cell subsets characterized by GZMK+, GZMB+, and GNLY+ expression. Using bulk and single-cell data, we mapped inflammatory mediators to source cell populations, for example attributing IL6 production to THY1+HLAhigh fibroblasts and naive B cells, and IL1B to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

immunology↗

bioRxiv2018

High dimensional analyses of cells dissociated from cryopreserved synovial tissue

BackgroundDetailed molecular analyses of cells from rheumatoid arthritis (RA) synovium hold promise in identifying cellular phenotypes that drive tissue pathology and joint damage. The Accelerating Medicines Partnership (AMP) RA/SLE network aims to deconstruct autoimmune pathology by examining cells within target tissues through multiple high-dimensional assays. Robust standardized protocols need to be developed before cellular phenotypes at a single cell level can be effectively compared across patient samples.\n\nMethodsMultiple clinical sites collected cryopreserved synovial tissue fragments from arthroplasty and synovial biopsy in a 10%-DMSO solution. Mechanical and enzymatic dissociation parameters were optimized for viable cell extraction and surface protein preservation for cell sorting and mass cytometry, as well as for reproducibility in RNA sequencing (RNA-seq). Cryopreserved synovial samples were collectively analyzed at a central processing site by a custom-designed and validated 35-marker mass cytometry panel. In parallel, each sample was flow sorted into fibroblast, T cell, B cell, and macrophage suspensions for bulk population RNA-seq and plate-based single cell CEL-Seq2 RNA-seq.\n\nResultsUpon dissociation, cryopreserved synovial tissue fragments yielded a high frequency of viable cells, comparable to samples undergoing immediate processing. Optimization of synovial tissue dissociation across six clinical collection sites with [~]30 arthroplasty and [~]20 biopsy samples yielded a consensus digestion protocol using 100{micro}g/mL of Liberase TL enzyme. This protocol yielded immune and stromal cell lineages with preserved surface markers and minimized variability across replicate RNA-seq transcriptomes. Mass cytometry analysis of cells from cryopreserved synovium distinguished: 1) diverse fibroblast phenotypes, 2) distinct populations of memory B cells and antibody-secreting cells, and 3) multiple CD4+ and CD8+ T cell activation states. Bulk RNA sequencing of sorted cell populations demonstrated robust separation of synovial lymphocytes, fibroblasts, and macrophages. Single cell RNA-seq produced transcriptomes of over 1000 genes/cell, including transcripts encoding characteristic lineage markers identified.\n\nConclusionWe have established a robust protocol to acquire viable cells from cryopreserved synovial tissue with intact transcriptomes and cell surface phenotypes. A centralized pipeline to generate multiple high-dimensional analyses of synovial tissue samples collected across a collaborative network was developed. Integrated analysis of such datasets from large patient cohorts may help define molecular heterogeneity within RA pathology and identify new therapeutic targets and biomarkers.

immunology↗