OP0359 Exploration of t-cell signatures following tcr stimulation using single cell rna-seq to inform treatment response studies in rheumatoid arthritis

Despite recent developments in the therapy of chronic lymphocytic leukemia (CLL), Richter's transformation (RT), an aggressive lymphoma, remains a clinical challenge. Immune checkpoint inhibitor (ICI) therapy has shown promise in selective lymphoma types, however, only 30-40% RT patients respond to anti-PD1 pembrolizumab; while the underlying CLL failed to respond and 10% CLL patients progress rapidly within 2 months of treatment. Studies indicate pre-existing T cells in tumor biopsies are associated with a greater anti-PD1 response, hence we hypothesized that pre-existing T cell subset characteristics and regulation in anti-PD1 responders differed from those who progressed in CLL. We used mass cytometry (CyTOF) to analyze T cell subsets isolated from peripheral blood mononuclear cells (PBMCs) from 19 patients with who received pembrolizumab as a single agent. PBMCs were obtained baseline(pre-therapy) and within 3 months of therapy initiation. Among this cohort, 3 patients had complete or partial response (responders), 2 patients had rapid disease progression (progressors) (Fig. A), and 14 had stable disease (non-responders) within the first 3 months of therapy. CyTOF analysis revealed that Treg subsets in responders as compared with progressors or non-responders (MFI -55 vs.30, p=0.001) at both baseline and post-therapy were increased (Fig. B). This quantitative analysis indicated an existing difference in Tregs and distinct molecular dynamic changes in response to pembrolizumab between responders and progressors. To delineate the T cell characteristics in progressors and responders, we performed single-cell RNA-seq (SC-RNA-seq; 10X Genomics platform) using T (CD3+) cells enriched from PBMCs derived from three patients (1 responder: RS2; 2 progressors: CLL14, CLL17) before and after treatment. A total of ~10000 cells were captured and an average of 1215 genes was detected per cell. Using a clustering approach (Seurat V3.1.5), we identified 7 T cell clusters based on transcriptional signature (Fig.C). Responders had a larger fraction of Tregs (Cluster 5) as compared with progressors (p=0.03, Fig. D), and these Tregs showed an IFN-related gene signature (Fig. E). To determine any changes in the cellular circuitry in Tregs between responders and progressors, we used FOXP3, CD25, and CD127 as markers for Tregs in our SC-RNA-seq data. We saw a greater expression of FOXP3, CD25, CD127, in RS2 in comparison to CLL17 and CLL14. Gene set enrichment analysis (GSEA) revealed the upregulation of genes involved in lymphocyte activation and FOXP3-regulated Treg development-related pathways in the responder's Tregs (Fig.F). Together, the greater expression of genes involved in Treg activation may reduce the suppressive functions of Tregs, which led to the response to anti-PD1 treatment seen in RS2 consistent with Tregs in melanoma. To delineate any state changes in T cells between progressors and responder, we performed trajectory analysis using Monocle (R package tool) and identified enrichment of MYC/TNF/IFNG gene signature in state 1 and an effector T signature in state 3 For RS2 after treatment (p=0.003), indicating pembrolizumab induced proliferative and functional T cell signatures in the responder only. Further, our single-cell results were supported by the T cell receptor (TCR beta) repertoire analysis (Adaptive Biotechnology). As an inverse measure of TCR diversity, productive TCR clonality in CLL14 and CLL17 samples was 0.638 and 0.408 at baseline, respectively. Fifty percent of all peripheral blood T cells were represented by one large TCR clone in CLL14(progressor) suggesting tumor related T-cell clone expansion. In contrast, RS2(responder) contained a profile of diverse T cell clones with a clonality of 0.027 (Fig. H). Pembrolizumab therapy did not change the clonality of the three patients during the treatment course (data not shown). In summary, we identified enriched Treg signatures delineating responders from progressors on pembrolizumab treatment, paradoxical to the current understanding of T cell subsets in solid tumors. However, these data are consistent with the recent observation that the presence of Tregs suggests a better prognosis in Hodgkin lymphoma, Follicular lymphoma, and other hematological malignancies. Figure 1 Disclosures Kay: Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Sunesis: Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; MEI Pharma: Research Funding. Ansell:AI Therapeutics: Research Funding; Takeda: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; ADC Therapeutics: Research Funding. Ding:Astra Zeneca: Research Funding; Abbvie: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; DTRM: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding. OffLabel Disclosure: pembrolizumab

Download Full-text

Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation

Nature Communications ◽

10.1038/s41467-017-02659-x ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 98

Author(s):

William Stephenson ◽

Laura T. Donlin ◽

Andrew Butler ◽

Cristina Rozo ◽

Bernadette Bracken ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Single Cell ◽

Synovial Tissue ◽

Low Cost ◽

Rheumatoid Arthritis Synovial Tissue ◽

Rna Seq

Download Full-text

Single-cell RNA-seq unveils critical regulators of human FOXP3+ regulatory T cell stability

Science Bulletin ◽

10.1016/j.scib.2020.01.002 ◽

2020 ◽

Vol 65 (13) ◽

pp. 1114-1124

Author(s):

Gang Yi ◽

Yi Zhao ◽

Feng Xie ◽

Fuxiang Zhu ◽

Ziyun Wan ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Regulatory T Cell ◽

Rna Seq ◽

Cell Stability

Download Full-text

Single Cell RNA Sequencing and V(D)J Profiling of T-Cell Large Granular Lymphocytosis

Blood ◽

10.1182/blood-2018-99-115166 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2404-2404

Author(s):

Shouguo Gao ◽

Zhijie Wu ◽

Carrie Diamond ◽

Bradley Arnold ◽

Valentina Giudice ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Single Cell ◽

Research Funding ◽

Clonal Expansion ◽

Rna Seq ◽

Healthy Donors ◽

Tcr Repertoire ◽

Before And After ◽

Repertoire Diversity

Abstract Introduction . T-cell large granular lymphocytosis (T-LGL) is a low grade lymphoproliferative disorder, often clinically manifest as bone marrow failure. Treatment with immunosuppressive therapies is effective, but the dominant clone may persist even in responding patients. The pathogenesis of T-LGL has not been fully elucidated. In this study, we performed single cell RNA sequencing (sc-RNA seq) and V(D)J profiling to discern clonotypes and gene expression patterns of T lymphocytes from T-LGL patients who were sampled before and after treatment. Methods. Blood was obtained from patients participating in a phase 2 protocol of alemtuzumab as second line therapy (NCT00345345; Dumitriu B et al, Lancet Haematol 2016). Leukapheresis was performed in 13 patients (M/F 7/6; median age 51 years, range 26-85) before and after 3-6 months alemtuzumab administration and in 7 age-matched healthy donors. Cryopreserved blood was enriched for T cells with the EasySep Human T cell Isolation Kit (Stem cell). sc-RNA seq was performed on the 10XGenomics Chromium Single Cell V(D)J + 5' Gene Expression platform, and sequencing obtained on the HiSeq3000 Platform. Barcode assignment, alignment, unique molecular index counting and T cell receptor sequence assembly were performed using Cell Ranger 2.1.1. Results. Four hundred fifty thousand cells from 13 patients and 107,000 cells from 7 healthy donors were profiled. We measured productive TCR chains (which fully span the V and J regions, with a recognizable start codon in the V region and lacking a stop codon in the V-J region, thus potentially generating a protein). We detected at least one productive TCR α-chain in 50%, one productive TCR β-chain in 69% and paired productive αβ-chains in 47% of all cells. There was loss of TCR repertoire diversity in patients which was quantified by Simpson's diversity index; most patients showed oligoclonal or, less frequently, monoclonal expansion of the TCR repertoire (Fig. A). Regardless of clinical response, alemtuzumab treatment did not correct the low TCR repertoire diversity. TCR repertoires can be classified as "public", when they express identical TCR sequences across multiple individuals, or "private", when each individual displays distinct TCR clonotypes. No TCRA or TCRB CDR3 homology among patients was observed: most TCR clonotypes appeared to be private. Our data suggests that T-LGL is etiologically heterogenous disease, consistent with T cell expansion in response to a variety antigens, in diverse HLA contexts, or randomly. Despite differences of TCR among patients and healthy donors, and the presence of large clones in patients, distribution of TCR diversity followed the power law distribution in healthy donors and patients (Fig. B, showing the negative linear relationship between logarithmic expression of clone frequency and clone size). The observed distribution is consistent with a somatic evolution model, in which cell fitness depends on cellular receptor response to specific antigens and stimulation of cells by cytokine and other signals from the environment; fitted clones have higher birth-death ratios and thus expand (Desponds J et al, PNAS 2016). CD4 and CD8 T cells can be virtually separated by imputation from their transcriptomes (Fig. C). Comparison of gene expression between patients and healthy donors showed dysregulation of genes involved in pathways related to the immune response and cell apoptosis, consistent with a pathophysiology of T cell clonal expansion. We used diffusion mapping, which localizes datapoints to their eigen components in low-dimesional space, to characterize sources contributing to the gene expression phenotype: the first component was mainly from T cell activation and the second was associated with TCR expression. In LGL the T cell transcriptome appeared to be shaped by both lineage development and TCR rearrangement. Conclusion. We describe at the single cell level T clonal expansion profiles in T-LGL, pre- and post-treatment. Single cell analysis allows accurate recovery of paired α and β chains in the same cell and demonstrates a continuum of cell lineage differentiation. We found a range of differences in transcriptome and TCR repertoires across patients. Transcriptome data, coupled with detailed TCR-based lineage information, provides a rich resource for understanding of the pathology of T-LGL and has implications for prognosis, treatment, and monitoring in the clinic. Figure. Figure. Disclosures Young: GlaxoSmithKline: Research Funding; CRADA with Novartis: Research Funding; National Institute of Health: Research Funding.

Download Full-text

Single Cell RNA-Seq Analysis Identifies Differentially Expressed Genes of Treg Cell in Early Treatment-Naive Rheumatoid Arthritis By Arsenic Trioxide

Frontiers in Pharmacology ◽

10.3389/fphar.2021.656124 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunling Li ◽

Tianshu Chu ◽

Zhiyi Zhang ◽

Yue Zhang

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Amino Acids ◽

Single Cell ◽

Signaling Pathway ◽

Early Treatment ◽

Cell Apoptosis ◽

Treg Cells ◽

Rna Seq ◽

Treatment Naïve

Objective: Early treatment-naïve rheumatoid arthritis (RA) has defective regulatory T (Treg) cells and increased inflammation response. In this study, we aim to illustrate the regulation of Treg cells in pathogenesis of early rheumatoid arthritis by arsenic trioxide (As2O3).Methods: We studied the effects of As2O3 on gene expression in early treatment-naïve RA Treg cells with single cell RNA-seq (scRNA-seq). Treg cells were sorted from peripheral blood mononuclear cells (PBMCs) and purified by fluorescence-activated cell sorting (FACS) and cultured with or without As2O3 (at 0.1 µM) for 24 h. Total RNA was isolated and sequenced, and functional analysis was performed against the Gene Ontology (GO) database. Results for selected genes were confirmed with RT-qPCR.Results: As2O3 exerts no significant effect on CD4+ T-cell apoptosis under physical condition, and selectively modulate CD4+ T cells toward Treg cells not Th17 cells under special polarizing stimulators. As2O3 increased the expression of 200 and reduced that of 272 genes with fold change (FC) 2.0 or greater. Several genes associated with inflammation, Treg-cell activation and differentiation as well as glucose and amino acids metabolism were among the most strongly affected genes. GO function analysis identified top ten ranked significant biological process (BPs), molecular functions (MFs), and cell components (CCs) in treatment and nontreatment Treg cells. In GO analysis, genes involved in the immunoregulation, cell apoptosis and cycle, inflammation, and cellular metabolism were enriched among the significantly affected genes. The KEGG pathway enrichment analysis identified the forkhead box O (FoxO) signal pathway, apoptosis, cytokine–cytokine receptor interaction, cell cycle, nuclear factor-kappa B (NF-κB) signaling pathway, tumor necrosis factor α (TNF-α), p53 signaling pathway, and phosphatidylinositol 3′-kinase (PI3K)-Akt signaling pathway were involved in the pathogenesis of early treatment-naïve RA.Conclusion: This is the first study investigating the genome-wide effects of As2O3 on the gene expression of treatment-naïve Treg cells. In addition to clear anti-inflammatory and immunoregulation effects, As2O3 affect amino acids and glucose metabolism in Treg cells, an observation that might be particularly important in the metabolic phenotype of treatment-naïve RA.

Download Full-text

An Efficient Single-Cell RNA-Seq Approach to Identify Neoantigen-Specific T Cell Receptors

Molecular Therapy ◽

10.1016/j.ymthe.2017.10.018 ◽

2018 ◽

Vol 26 (2) ◽

pp. 379-389 ◽

Cited By ~ 31

Author(s):

Yong-Chen Lu ◽

Zhili Zheng ◽

Paul F. Robbins ◽

Eric Tran ◽

Todd D. Prickett ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

T Cell Receptors ◽

Rna Seq ◽

Cell Receptors

Download Full-text

Reference Mapping Pediatric Leukemia Using Single Cell Multiomic Atlas of Pediatric Hematopoiesis

Blood ◽

10.1182/blood-2021-153428 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3265-3265

Author(s):

Jason Xu ◽

Changya Chen ◽

Tiffaney Vincent ◽

Elizabeth Li ◽

Yusha Sun ◽

...

Keyword(s):

Bone Marrow ◽

T Cell ◽

Single Cell ◽

Research Funding ◽

Leukemic Cells ◽

Patient Specific ◽

Rna Seq ◽

Pediatric Leukemia ◽

Hematopoietic Development ◽

Reference Map

Abstract Acute lymphoblastic leukemia is the most common pediatric cancer and leading cause of cancer related mortality in pediatric populations. A key challenge to bridge better therapies to patients that fail conventional therapy are to understand their tumor landscape and aberrations in cell signaling, particularly in relation to normal hematopoietic development. To address this gap, we produced a unified reference map of pediatric T, B, and myeloid cell development from the HSPC using single cell RNA-seq and single-cell ATAC-seq on healthy pediatric bone marrow and thymus. We employed 6 different FACS sorting strategies in order to capture rare, but informative, progenitor cell states, including those of the CCR9+ CD34+ CD1A- CD4- CD8- early-T-cell precursor, CD34+ CD1A- CD4- CD8- pro-T cell, and CD34+ CD1A+ CD4- CD8- pre-T cell and Lin-CD34+CD38- multipotent, lymphoid, and myeloid progenitors from the bone marrow. We mapped leukemic cells from patients from 4 different subtypes of pediatric leukemia (T-ALL, ETP-ALL, MPAL, AML) to our healthy reference and found that our reference map can distinguish between subtle differences in transcriptome and epigenome that were undetectable using surface marker or canonical gene expression. Notably, using trajectories inferred from our healthy reference map, we discovered a large amount of inter-tumoral and intra-tumoral heterogeneity, with leukemic blasts from different patients and different populations within any one patient projecting to different cell states along normal development. Finally, we mapped engrafted leukemic cells from patient derived xenografts (PDX) back to our healthy reference. While we observed patient-specific transcriptomic shifts in engrafted versus primary leukemic blasts, we found that the overall transcriptomic hierarchy is maintained in the most PDX, with engrafted cells projecting to near-identical stages of arrest along our healthy hematopoietic trajectory. Interestingly, for PDX that projected to different areas in development compared to primary sample, we discovered alterations in expression of key transcription factors that regulate hematopoietic development. Our single cell multi-omic reference map of pediatric hematopoiesis serves as a valuable reference for mapping RNA-seq and ATAC-seq data back to nearest healthy precursors in normal hematopoietic development. On-going analysis is utilizing single cell transcriptomic, chromatin accessibility data from additional leukemic patients, including patients with B-ALL, to determined key genes and regulators that are altered in comparison to nearest healthy cell-types. In addition, population level signatures learned from healthy reference are being tested in bulk-transcriptomic ALL datasets. We are eager to present the results of these analyses at ASH. *CC and JX, as well as, DTT and KT contributed equally to this work Figure 1 Figure 1. Disclosures Teachey: Janssen: Consultancy; NeoImmune Tech: Research Funding; Sobi: Consultancy; BEAM Therapeutics: Consultancy, Research Funding.

Download Full-text

Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq

PLoS ONE ◽

10.1371/journal.pone.0258029 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0258029

Author(s):

Ying Yao ◽

Łukasz Wyrozżemski ◽

Knut E. A. Lundin ◽

Geir Kjetil Sandve ◽

Shuo-Wang Qiao

Keyword(s):

T Cells ◽

Celiac Disease ◽

T Cell ◽

Single Cell ◽

Differential Expression ◽

T Cell Receptor ◽

Cell Receptor ◽

Effector Memory ◽

Fatty Acid Transport ◽

Rna Seq

Gluten-specific CD4+ T cells drive the pathogenesis of celiac disease and circulating gluten-specific T cells can be identified by staining with HLA-DQ:gluten tetramers. In this first single-cell RNA-seq study of tetramer-sorted T cells from untreated celiac disease patients blood, we found that gluten-specific T cells showed distinct transcriptomic profiles consistent with activated effector memory T cells that shared features with Th1 and follicular helper T cells. Compared to non-specific cells, gluten-specific T cells showed differential expression of several genes involved in T-cell receptor signaling, translational processes, apoptosis, fatty acid transport, and redox potentials. Many of the gluten-specific T cells studied shared T-cell receptor with each other, indicating that circulating gluten-specific T cells belong to a limited number of clones. Moreover, the transcriptional profiles of cells that shared the same clonal origin were transcriptionally more similar compared with between clonally unrelated gluten-specific cells.

Download Full-text

High dimensional analyses of cells dissociated from cryopreserved synovial tissue

10.1101/284844 ◽

2018 ◽

Cited By ~ 2

Author(s):

Laura T. Donlin ◽

Deepak A. Rao ◽

Kevin Wei ◽

Kamil Slowikowski ◽

Mandy J. McGeachy ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

Synovial Tissue ◽

High Dimensional ◽

Integrated Analysis ◽

Rna Seq ◽

Mass Cytometry ◽

Viable Cells ◽

Cellular Phenotypes

AbstractBackgroundDetailed 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.MethodsMultiple 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.ResultsUpon 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 100µg/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.ConclusionWe 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.

Download Full-text

Integrated Single Cell and Spatial Transcriptomics Reveal Autoreactive Differentiated B Cells in Joints of Early Rheumatoid Arthritis

10.1101/2021.08.09.21260015 ◽

2021 ◽

Author(s):

Uta Hardt ◽

Konstantin Carlberg ◽

Erik af Klint ◽

Peter Sahlstrom ◽

Ludvig Larsson ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

T Cell ◽

B Cells ◽

Single Cell ◽

Plasma Cell ◽

Plasma Cells ◽

Early Time ◽

Joint Tissue ◽

Lymphoid Structures ◽

Patient Subgroups

Rheumatoid Arthritis (RA) is a prevalent autoimmune disease characterized by inflammation of peripheral joints. Patients can be subdivided by the presence or absence of Rheumatoid Factor and anti-citrullinated protein antibodies (ACPA) in their circulation. Inflammation of the joint tissue is associated with infiltration of leukocytes from the blood, which can result in generation of lymphoid structures composed of B and T cells. Previous studies have shown that both memory B cells and antibody-secreting plasma cells populate the rheumatic joint tissue when captured from established and often long-standing disease. However, it has remained unclear, whether these cells are autoreactive and whether the associated lymphoid structures are present at the site of inflammation already at the time of diagnosis. Here, we used an integrated single cell and spatial transcriptomic approach to study B and plasma cells in synovial tissue of ACPA- and ACPA+ RA patients at this early time point. We found evidence for T cell help to B cells and presence of memory B and plasma cell pools in ACPA- as well as in ACPA+ RA. Our results demonstrated common supportive microenvironments in both patient subgroups, clonal relationships between the memory B and plasma cell pools and autoreactivity within the plasma cell compartment. These findings challenge our understanding of the dynamics of local adaptive immune responses in the RA joint of ACPA- and ACPA+ patients at the time of diagnosis, with direct implications for B and T cell targeting therapies for both patient subgroups.

Download Full-text