Biology subjects

Pitzalis, C.

Publications and source records attributed to Pitzalis, C..

17 recordsLinked to original sources

bioRxiv2026

cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution

Single-cell analysis has rapidly expanded to produce cell atlases encompassing all human tissues. However, computational methods to deconvolute bulk samples using single-cell reference data have failed to keep pace with the increasing data size. Here we present cellGeometry, which uses non-negative geometric deconvolution (NGD), an intuitive vector projection method featuring non-negative matrix regularisation. Using matrix operations, cellGeometry scales to massive datasets and is ultrafast. Benchmarked using simulations from single-cell RNA-Seq datasets with >3 million cells, cellGeometry is more accurate than existing methods and more robust against noise simulating different sequencing chemistries. It identifies outlying residual genes which may unveil pathogenic changes in gene expression and the presence of cell types absent from the reference. cellGeometrys flexible architecture allows merging of single-cell reference signatures to expand the range of cell types being deconvoluted. Validated against real bulk RNA blood and tissue samples, cellGeometry produces more accurate and realistic results.

bioinformatics↗

bioRxiv2026

Molecular and spatial profiling identifies immune endotypes for the stratification of OA patients

Osteoarthritis (OA) is a prevalent and heterogeneous joint disease in which synovial inflammation drives structural progression and pain. Despite the recognized heterogeneity of OA, the cellular and molecular organization of synovial tissue remains poorly characterized and defining distinct histological and immune endotypes could guide precision medicine and therapeutic targeting. We show that histologically defined synovial pathotypes are conserved across independent cohorts and correspond to distinct molecular immune endotypes. Integration of bulk and spatial transcriptomics with proteomics revealed niche-specific gene and protein signatures, reflecting the anatomical and functional diversity of OA synovium. The lympho-myeloid pathotype was characterized by mature ectopic lymphoid structures containing CD21+CD23+ follicular dendritic cells, spatially organized T and B cell zones, and clonally expanded T and B cells with shared immune cell receptor motifs, consistent with local adaptive immune activity correlating with radiological joint damage. These findings highlight how immune organization and cellular composition shape OA pathogenesis and provide a framework for endotype-guided stratification and therapeutic targeting.

immunology↗

bioRxiv2025

Discrete cytokine signaling networks instruct distinctsynovial pathotypes in inflammatory arthritis

Patients with rheumatoid arthritis (RA) display distinct patterns of synovitis. To define the inflammatory mechanisms driving this heterogeneity, we analyzed the inflamed synovium of wild-type (WT), Il6ra-/-, and Il27ra-/- mice with antigen-induced arthritis (AIA). Remarkably, each strain developed a joint pathology mirroring a major RA synovial pathotype: myeloid-rich (WT), fibroblast-rich/pauci-immune (Il6ra-/-), and lymphoid-rich (Il27ra-/-) synovitis. Histology confirmed minimal immune infiltration in Il6ra-/- joints, while WT and Il27ra-/- mice exhibited prominent immune involvement, including organized synovial lymphoid-like aggregates in Il27ra-/- mice. Transcriptomic and epigenomic profiling revealed both shared and distinct regulatory programs among genotypes. Il6ra-/- mice showed increased WNT, DKK, and AMPK signaling associated with fibroblast, chondrocyte, and osteoclast activation (e.g., Adamts19, Dkk1, Ecm1). Consistent with synovial ectopic lymphoid-like structures, Il27ra-/- mice showed enrichment of lymphocyte activation (e.g., Il17a, Il22, Bhlhe40). WT mice exhibited hallmarks of MAP kinase activation. These molecular signatures parallel those of fibroblast-, lymphoid-, and myeloid-rich synovitis in RA. Defining a STAT1-STAT3 regulatory interplay influencing transcriptional decisions in WT and Il27ra-/- mice, our findings offer insights into cytokine-driven disease heterogeneity. Together, these results establish a framework for mechanism-based classification of synovitis and introduce new mouse models to study the molecular drivers of synovial pathotypes and treatment response.

pathology↗

bioRxiv2025

multiDEGGs: a multi-omic differential network analysis package for biomarker discovery and predictive modeling

Modern clinical trials increasingly leverage high-throughput omic data for patient stratification and biomarker discovery. While traditional differential gene expression analysis disregards the networked nature of molecular entities and produces extensive gene lists with limited interpretability, differential network analysis has emerged as a crucial complementary analysis for comparative studies. Here we present multiDEGGs, a CRAN R package that enables differential network analysis in multi-omic scenarios. multiDEGGs uses a multi-layer graph framework to model omic data by leveraging an internal network of over 10 000 literature-validated biological interactions. For each data type, differential networks are generated, and the statistical significance of each link (p-values or adjusted p-values) is evaluated through robust linear regression with interaction terms. These networks are then integrated into a comprehensive visualisation that allows interactive exploration of cross-omic patterns. Beyond network visualization and exploration, multiDEGGs extends its utility to predictive modelling applications. The package facilitates seamless integration into cross-validation machine learning pipelines, serving as feature selection and augmentation tool. We validated multiDEGGs using two cohorts of rheumatoid arthritis patients who underwent tocilizumab and rituximab therapy, respectively. For each treatment group, multi-layer differential interactions were identified, and seven machine learning models were trained to predict treatment resistance using synovial RNA-seq data. We systematically compared multiDEGGs against five traditional feature selection methods. On average, AUC values obtained with multiDEGGs showed an improvement of 0.10 compared to conventional filters. KEY POINTSO_LITraditional gene expression analysis leaves researchers with hundreds of significant genes but no clear biological story. The multiDEGGs CRAN package shifts the focus: instead of asking which genes change, it asks which gene relationships change. C_LIO_LIIt can be used with single or multi-omic data: differential networks are calculated separately for each data type, with results integrated into a comprehensive, interactive view. C_LIO_LImultiDEGGs can be combined with the nestedcv CRAN package (nested cross-validation) to serve as feature selection and augmentation tool. C_LIO_LIIn comparative evaluations, machine learning models trained with multiDEGGs-selected features showed AUC improvements of 0.10 compared to other feature selection methods. C_LI

bioinformatics↗

bioRxiv2025

Spatial patterning of fibroblast TGFβ signaling underlies treatment resistance in rheumatoid arthritis

Treatment-refractory rheumatoid arthritis (RA) is a major unmet need, and the mechanisms driving treatment resistance are poorly understood. To identify molecular determinants of RA non-remission, we performed spatial transcriptomic profiling on pre- and post-treatment synovial tissue biopsies from treatment naive patients who received conventional DMARDs or adalimumab for 6 months. In the baseline biopsies of non-remission patients, we identified significant expansion of fibrogenic fibroblasts marked by high expression of COMP, a fibrosis-associated extracellular matrix protein. COMPhi fibroblasts localized to perivascular niches that, unexpectedly, served as transcriptional hubs for TGF{beta} activity. We identified endothelial-derived Notch signaling as an upstream regulator of fibroblast TGF{beta} signaling via its dual role in driving TGF{beta} isoform expression and suppressing TGF{beta} receptors, generating a proximal-distal gradient of TGF{beta} activity. Further, disruption of steady-state Notch signaling in vitro enabled fibrogenic fibroblast activation. Analysis of post-treatment biopsies revealed marked expansion of COMPhi fibroblasts in non-remission RA patients, despite evidence of successful immune cell depletion, suggesting a spatiotemporal process of fibrogenic remodeling linked to treatment resistance. Collectively, our data implicates targeting of TGF{beta} signaling to prevent exuberant synovial tissue fibrosis as a potential therapeutic strategy for refractory RA.

immunology↗

bioRxiv2024

IL-6 and IL-27 negatively regulate CRTAM-expressing CD4+ T-cells associated with lymphoid-driven synovitis.

ABSTRACT-Joint pathology in rheumatoid arthritis is heterogeneous, with histology providing evidence of fibroblast-driven, myeloid-driven, and lymphoid-driven synovitis. However, the immuno-modulatory pathways underlying their development remain unclear. Profiling synovial tissues from rheumatoid arthritis patients and mice with antigen-induced arthritis, we identified a subset of synovial infiltrating CD4+ T-cells expressing CRTAM (class-I MHC-restricted T-cell-associated molecule). In human synovial biopsies, CRTAM correlated with the expression of effector cytokines (IL21, IFNG), chemokine receptors (CXCR3, CXCR4, CCR5), granzymes (GZMA, GZMB, GZMK), and regulatory factors (TIGIT, EOMES, BATF) linked with T-cell-mediated immunity. Studies of antigen-induced arthritis showed that CRTAM+CD4+ T-cells accumulate in the inflamed synovium following disease onset. CRTAM+CD4+ T-cells were particularly abundant in synovial tissue from Il27ra-/- mice displaying ectopic lymphoid-like structures. CADM1 (cell adhesion molecule-1), the endogenous ligand for CRTAM, was also expressed in human synovitis and synovial tissues from wild-type, Il6ra-/-, and Il27ra-/- mice with antigen-induced arthritis. Cells expressing human CADM1 included synovial fibroblasts and subsets of monocytic and CD19+ cells. Considering the ex vivo regulation of CRTAM, we identified that activation of naive CD4+ T-cell increased CRTAM expression. This induction was blocked by IL-6 and IL-27, with further studies identifying a role for STAT3 in controlling the CRTAM transcriptional repressor, ZEB1. These results provide insights into the cytokine control of CRTAM on CD4+ T-cells and support the involvement of CRTAM+CD4+ T-cells in lymphoid-driven synovitis.

immunology↗

bioRxiv2023

Spatial mapping of rheumatoid arthritis synovial niches reveals specific macrophage networks associated with response to therapy

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease affecting peripheral joints and for which approximately 40% of the patients respond insufficiently to the available synthetic or biologic disease modifying anti-rheumatic drugs (DMARDs). The infiltration of the synovial membrane by lymphocytes and monocytes profoundly alters its homeostatic functions, leading to chronic joint inflammation and bone destruction. A better understanding of how DMARDs impact the complex synovial cell social network in relationship to response/ non-response remains an unmet need to design more targeted and active therapeutic strategies. Here, we used imaging mass cytometry (IMC) to comparatively profile more than 115,000 cells in the synovial tissue of healthy, low inflammatory osteoarthritis and matched active early treatment-naive RA patients at baseline and at 6 months after starting DMARDs treatment. We notably highlighted that tissue resident macrophages (LYVE1+CD206+) in perivascular synovial niches encompassing specific subsets of vascular cells, fibroblasts and immune cells vanished in active RA but were recovered in response to DMARDs treatment. Combined ligand-receptor analysis of single-cell RNA sequencing datasets identified that IL10, C-type lectin or TAM (TYRO3, AXL and MERTK) receptors were particularly involved in the restoration of these spatial cell interactions in the context of clinical remission. In addition to providing an unprecedented synovial spatial mapping, our work uncovered novel potential cellular and molecular targets for the development of therapies for RA. One Sentence SummarySingle-cell spatial profiling of rheumatoid arthritis synovium identifies specific cell states linked to treatment response

immunology↗

bioRxiv2023

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis

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 PAX5low naive 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.

genomics↗

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↗

bioRxiv2022

BBmix: a Bayesian Beta-Binomial mixture model for accurate genotyping from RNA-sequencing

MotivationWhile many pipelines have been developed for calling genotypes using RNA-sequencing data, they all have adapted DNA genotype callers that do not model biases specific to RNA-sequencing such as reference panel bias or allele specific expression. ResultsHere, we present BBmix, a Bayesian Beta-Binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than one hour is sufficient to call genotypes in a large number of samples. AvailabilityWe implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

genomics↗

bioRxiv2022

Cellular deconstruction of inflamed synovium defines diverse inflammatory phenotypes in rheumatoid arthritis

Rheumatoid arthritis (RA) is a prototypical autoimmune disease that causes destructive tissue inflammation in joints and elsewhere. Clinical challenges in RA include the empirical selection of drugs to treat patients, inadequate responders with incomplete disease remission, and lack of a cure. We profiled the full spectrum of cells in inflamed synovium from patients with RA with the goal of deconstructing the cell states and pathways characterizing pathogenic heterogeneity in RA. Our multicenter consortium effort used multi-modal CITE-seq, RNA-seq, and histology of synovial tissue from 79 donors to build a >314,000 single-cell RA synovial cell atlas with 77 cell states from T, B/plasma, natural killer, myeloid, stromal, and endothelial cells. We stratified tissue samples into six distinct cell type abundance phenotypes (CTAPs) individually enriched for specific cell states. These CTAPs demonstrate the striking diversity of RA synovial inflammation, ranging from marked enrichment of T and B cells (CTAP-TB) to a congregation of specific myeloid, fibroblast, and endothelial cells largely lacking lymphocytes (CTAP-EFM). Disease-relevant cytokines, histology, and serology metrics are associated with certain CTAPs. This comprehensive RA synovial atlas and molecular, tissue-based CTAP stratification reveal new insights into RA pathology and heterogeneity, which could lead to novel targeted-treatment approaches in RA.

genomics↗

bioRxiv2021

Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

genomics↗

bioRxiv2021

Single cell analysis of RA synovial B cells reveals a dynamic spectrum of ectopic lymphoid B cell activation and hypermutation characterized by NR4A nuclear receptor expression

Ectopic lymphoid structures (ELS) can develop in rheumatoid arthritis (RA) synovial tissue, but the precise pathways of B cell activation and selection are not well understood. Here, we identified a unique B cell population in the synovium characterized by co-expression of a family of orphan nuclear receptors, NR4A1 (also known as NUR77), NR4A2 (NURR1) and NR4A3 (NOR1), that is highly enriched at both early and late stages of RA. NR4A B cells are rare in healthy peripheral blood, RA blood, and SLE kidney, but share markers with blood transcriptomic signatures that peak during RA disease flare. Using combined single cell transcriptomics and B cell receptor (BCR) sequencing, we demonstrate that NR4A synovial B cells have an activated transcriptomic profile that significantly overlaps with germinal center (GC) light zone (LZ) B cells and an accrual of somatic hypermutation that correlates with loss of naive B cell status. NR4A B cells uniquely co-express lymphotoxin {beta} and IL6, supporting important functions in ELS promotion and pro-inflammatory cytokine production. Further, the presence of shared clones in this activated B cell state and NR4A expressing synovial plasma cells (PC) and the rapid up-regulation with BCR stimulation points to in situ differentiation. Taken together, we identified a dynamic progression of B cell activation in RA synovial ELS, with NR4A transcription factors having an important role in antigen activation and local adaptive immune responses. One sentence summaryB cells in the rheumatoid arthritis synovium undergo a spectrum of in situ activation, with the NR4A family of transcription factors having an important role in antigen stimulation, local adaptive immunity, and pathological B cell responses.

immunology↗

bioRxiv2019

Spectrum: Fast density-aware spectral clustering for single and multi-omic data

Clustering of single or multi-omic data is key to developing personalised medicine and identifying new cell types. We present Spectrum, a fast spectral clustering method for single and multi-omic expression data. Spectrum is flexible and performs well on single-cell RNA-seq data. The method uses a new density-aware kernel that adapts to data scale and density. It uses a tensor product graph data integration and diffusion technique to reveal underlying structures and reduce noise. We developed a powerful method of eigenvector analysis to determine the number of clusters. Benchmarking Spectrum on 21 datasets demonstrated improvements in runtime and performance relative to other state-of-the-art methods.\n\nContact: christopher.john@qmul.ac.uk

bioinformatics↗

bioRxiv2018

M3C: A Monte Carlo reference-based consensus clustering algorithm

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.

bioinformatics↗

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↗