Single-Cell RNA Sequencing Reveals Compromised Immune Microenvironment in Precursor Stages of Multiple Myeloma

Abstract Introduction: In multiple myeloma (MM), despite well-characterized precursor states such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), there is a lack of sufficient biomarkers to predict mechanisms of disease progression. Most genomic analyses have sought biomarkers by study of the malignant plasma cells, however, cancers form a complex ecosystem with the immune and stromal microenvironment. Thus, to characterize the cellular composition and transcriptional programs of each component of the tumor and microenvironment at different stages of MM progression, we employed a single-cell RNA sequencing on a cohort of 22 patients and 9 healthy donors. Methods: We performed 10X droplet-based single-cell RNA sequencing using CD138-expressing plasma cells and microenvironmental populations isolated from bone marrow (BM) aspirates of patients with MGUS (n=6), low-risk SMM (n=3), high-risk SMM (n=13), newly diagnosed MM (n=8) and from 9 healthy donors (NBM). We collected a total of ~88.8K cells, comprising ~48K CD138+ cells (~36.4 from MM stages) and ~40.8K CD45+/CD138- cells (~30.8 from MM stages).Raw read data was processed using the Cell Ranger pipeline to obtain a gene-by-cell expression matrix, which was used to identify cell types and transcriptional programs by clustering and non-negative matrix factorization. Results: Expression profiles of plasma cells revealed clear tumor-specific differences including known oncogenic drivers in MM (MMSET/FGFR3, CCND1 and MAFB) as well as Lysosome-associated Membrane Protein 5 (LAMP5),Histone Cluster 1 H1 Family Member C (HIST1H1C) and Amphiregulin (AREG) distinguishing them from healthy plasma cells. We identified a subset of cycling plasma cells, observing a range of proliferative activity of the malignant fraction. Furthermore, our approach allowed a unique head-to-head comparison of gene expression changes in normal and malignant plasma cells in the MGUS and SMM patients within an individual, excluding inter-individual variation. We were able to discriminate malignant from non-malignant plasma cells and identify transcriptional alterations including known drivers, genes related to immune modulation (NKBIA) or controlling transcription and differentiation (EID1).Some alterations were patient-specific, while others, such as MHC I overexpression and CD27 loss, were recurrently observed across subsets of the cohort. Analysis of BM microenvironment in several stages of MM progression demonstrated a striking shift in the composition of immune cells with significant infiltration of natural killer cells, non-classical monocytes/macrophages, and T cells, enriched even in the earliest stages of the disease. Further investigation revealed significant upregulation of HLA expression at the mRNA level in CD14+ monocytes/macrophages. Intriguingly, comparison of healthy and patient samples by CyTOF showed downregulation of surface MHC II representation in the corresponding cell type, and moreover, co-culture with MM cell lines induced a sharp decrease of extracellular MHC II. This provided strong evidence for compromised antigen presentation by macrophages in the disease setting, hinting at a mechanism of immune evasion. Additionally, expression signatures in cytotoxic T-cells indicated a substantial skewing towards either granzyme B/H- or granzyme K-expressing memory cell-like transcriptional program. In a subgroup of patients, we found a strong simultaneous enrichment of the anti-viral/anti-bacterial gene expression signature for interferon type-1 activated genes in CD14+ monocytes/macrophages and T cells. Together, our results provide a comprehensive view at the complex interplay of the immune and malignant cells in different stages of the disease. We, for the first time, demonstrate the immune response beginning in premalignant conditions to be heterogeneous, including compromised antigen presentation as well as alterations in cellular composition and signaling. Consideration of the type of immunological response may prove valuable in determination of progression risk, as well as open up potential strategies for therapy. Disclosures Bustoros: Dava Oncology: Honoraria. Ghobrial:Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Takeda: Consultancy.

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Analysis of Deficiency of Adenosine Deaminase 2 Pathogenesis Based on Single Cell RNA Sequencing of Monocytes

Blood ◽

10.1182/blood-2019-123859 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 2317-2317

Author(s):

Naoki Watanabe ◽

Shouguo Gao ◽

Sachiko Kajigaya ◽

Carrie Diamond ◽

Lemlem Alemu ◽

...

Keyword(s):

Gene Expression ◽

Bone Marrow ◽

Immune Response ◽

Single Cell ◽

Adenosine Deaminase ◽

Rna Sequencing ◽

Healthy Donors ◽

Single Cell Rna Sequencing ◽

Adenosine Deaminase 2 ◽

Upregulated Genes

Deficiency of adenosine deaminase 2 (DADA2) is a rare autosomal recessive disease caused by loss-of-function mutations in the ADA2 gene. DADA2 typically presents in childhood and is characterized by vasculopathy, stroke, inflammation, and immunodeficiency as well as hematologic manifestations, such as bone marrow failure and lymphoproliferation. The ADA2 protein is predominantly expressed in stimulated monocytes, dendritic cells and macrophages. ADA2 increases in the setting of inflammation and/or infection conditions. ADA2 has been reported to have a critical role in maintaining the balance between M1 (pro-inflammatory) and M2 (anti-inflammatory) macrophages. Macrophages of DADA2 patients are polarized towards M1 subset., DADA2 pathogenesis is not well characterized. To elucidate molecular mechanisms in DADA2 deficiency, we analyzed a gene expression profile of CD14+ monocytes derived from peripheral blood using single cell RNA sequencing (scRNA-seq). Blood was collected from DADA2 patients and age- and sex-matched healthy donors; all patients were studied in a registered research protocol (clinicaltrials.gov NCT00071045). Samples were obtained from 14 DADA2 patients and 6 healthy donors; median age of the DADA2 patients was 23 years old (range, 5 - 57 years). Among the 14 patients, 7 had hematological phenotypes: 5 lymphopenia, 3 neutropenia, 3 thrombocytopenia, and 2 with hypocellular bone marrow histology. Low serum immunoglobulins and cutaneous findings were frequent. Nine of the 14 patients had been treated with TNF inhibitors (etanercept and adalimumab). Mutations were distributed throughout the ADA2 gene; although two siblings had the same mutation, even they showed poor genotype-phenotype correlation. Monocytes were isolated by immunomagnetic positive selection with the EasySep™ positive CD14 selection kit Ⅱ, then subjected to scRNA-seq using Single Cell 3' Reagent Kits v2 (10X Genomics). Libraries for scRNA-seq were sequenced on the HiSeq-3000 instrument. Based on scRNA-seq data, we could classify monocytes into three populations by conventional flow cytometric criteria using cell surface protein expression imputed from scRNA-seq: CD14++CD16- classical, CD14++CD16+ intermediate, and CD14+CD16++ nonclassical monocytes (Figure A). CD16 expression was higher in DADA2 patients than in healthy donors (Figure B). A proportion of nonclassical monocytes among total monocytes were significantly higher in DADA2 patients compared to healthy donors (Figure C). On comparison of gene expression of each monocyte subtypes in DADA2 patients with that of healthy donors, there were 215, 237, and 267 differentially expressed upregulated genes in classical, intermediate, and nonclassical monocytes, respectively (at a threshold avg_logFC > 0.2). Approximately 35% of upregulated genes were overlapped among the three monocyte subtypes of DADA2 patients, including immune response genes such as IFITM1, IFITM2, IFITM3, and C3AR1 (Figure D). Common gene pathways were associated with immune function, such as interferon alpha/beta signaling and interferon gamma signaling. Specific genes to classical and intermediate monocytes were less than 10% of all the upregulated genes. Distinctively, the NF-κB pathway was upregulated in nonclassical monocytes, this might contribute to the pathogenesis of DADA2 as inflammatory disease. Overall, each monocyte subtype of DADA2 patients showed upregulation of immune response gene sets compared to controls. DADA2 patients have increased numbers of nonclassical monocytes which may contribute the immune dysregulation and increased inflammation observed in the disease. Figure Disclosures No relevant conflicts of interest to declare.

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Single-cell RNA sequencing reveals heterogeneous tumor and immune cell populations in early-stage lung adenocarcinomas harboring EGFR mutations

Oncogene ◽

10.1038/s41388-020-01528-0 ◽

2020 ◽

Cited By ~ 1

Author(s):

Di He ◽

Di Wang ◽

Ping Lu ◽

Nan Yang ◽

Zhigang Xue ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Single Cell ◽

Tumor Cells ◽

Rna Sequencing ◽

Early Stage ◽

Cell Types ◽

Egfr Mutations ◽

Advanced Tumor ◽

Single Cell Rna Sequencing

Abstract Lung adenocarcinoma (LUAD) harboring EGFR mutations prevails in Asian population. However, the inter-patient and intra-tumor heterogeneity has not been addressed at single-cell resolution. Here we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II LUAD samples harboring EGFR mutations and five tumor-adjacent lung tissues. We identified diverse cell types within the tumor microenvironment (TME) in which myeloid cells and T cells were the most abundant stromal cell types in tumors and adjacent lung tissues. Within tumors, accompanied by an increase in CD1C+ dendritic cells, the tumor-associated macrophages (TAMs) showed pro-tumoral functions without signature gene expression of defined M1 or M2 polarization. Tumor-infiltrating T cells mainly displayed exhausted and regulatory T-cell features. The adenocarcinoma cells can be categorized into different subtypes based on their gene expression signatures in distinct pathways such as hypoxia, glycolysis, cell metabolism, translation initiation, cell cycle, and antigen presentation. By performing pseudotime trajectory, we found that ELF3 was among the most upregulated genes in more advanced tumor cells. In response to secretion of inflammatory cytokines (e.g., IL1B) from immune infiltrates, ELF3 in tumor cells was upregulated to trigger the activation of PI3K/Akt/NF-κB pathway and elevated expression of proliferation and anti-apoptosis genes such as BCL2L1 and CCND1. Taken together, our study revealed substantial heterogeneity within early-stage LUAD harboring EGFR mutations, implicating complex interactions among tumor cells, stromal cells and immune infiltrates in the TME.

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Impact of sequencing depth and read length on single cell RNA sequencing data: lessons from T cells

10.1101/134130 ◽

2017 ◽

Cited By ~ 2

Author(s):

Simone Rizzetto ◽

Auda A. Eltahla ◽

Peijie Lin ◽

Rowena Bull ◽

Andrew R. Lloyd ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Depth ◽

Read Length ◽

Single Cell Rna Sequencing

ABSTRACTSingle cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.We studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCRαβ were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

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OP0032 SINGLE CELL ANALYSIS OF SPONDYLOARTHRITIS REGULATORY T CELLS IDENTIFIES DISTINCT SYNOVIAL GENE EXPRESSION PATTERNS AND CLONAL FATES

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.4278 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 17.2-18

Author(s):

D. Simone ◽

F. Penkava ◽

A. Ridley ◽

S. Sansom ◽

H. Al Mossawi ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Synovial Fluid ◽

Regulatory T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Gene Expression Analysis ◽

Clonal Expansion ◽

Transcriptional Profile ◽

Single Cell Rna Sequencing

Background:Regulatory T cells (Tregs) play an important role in controlling inflammation and limiting autoimmunity, but their phenotypes at inflammatory sites in human disease are poorly understood. Whilst the phenotype and transcriptional profile of Tregs have been studied in some immune mediated conditions, they have been little studied (especially at the single cell level) in synovial fluid in the course of inflammatory arthritis. In Spondyloarthritis (SpA), in particular, where pathogenesis and inflammation is driven by dysregulated effector immunity, the role of the regulatory arm of immunity is largely unknown.Objectives:We aimed to draw an atlas of Tregs in the context of SpA joint inflammation using single cell RNA sequencing of blood and SF Tregs of patients with Ankylosing Spondylitis (AS) and Psoriatic Arthritis (PsA). Functionally distinct specialised Treg subtypes, and specific changes in transcriptional profile occurring in synovial fluid Tregs, providing an insight on Treg adaptation during inflammation. Furthermore, by coupling gene expression analysis with TCR sequencing, we aimed to describe clonally expanded and likely antigen-driven Tregs in the SF.Methods:Fluorescent activated cell sorting (FACS) was used to isolate 13,400 memory CD3+ CD45RA-ve CD25 + 127low Tregs from the blood and synovial fluid (SF) of 2 patients with HLA-B27+ AS presenting with active knee arthritis. Single-cell RNA sequencing (scRNA-seq) using 5’ V(D)J 10x Genomics technology allowed both transcriptional definition of Tregs, and exploration of their immune TCR repertoire. Findings were compared to >3,000 SF and blood Tregs from 3 patients with olygoarticular PsA 1. Multicolor flow cytometry and in vitro cell-based assays using patient-derived cells were used to confirm and expand, at protein and functional level, the findings that emerged from the gene expression analysis.Results:We report a large scRNAseq dataset (approx. 17,000 cells) comparing Tregs from SpA blood and joints. We identify multiple Treg clusters with distinct transcriptomic profiles, including, among others, a regulatory CD8+ subset expressing cytotoxic markers/genes, and a Th17-like RORC+ Treg subset characterized by IL-10 and LAG-3 expression. Synovial Tregs show upregulation of interferon signature and TNF receptor superfamily genes, and marked clonal expansion, consistent with tissue adaptation and antigen contact respectively. Individual synovial Treg clones map to different clusters indicating cell fate divergence. Finally, we demonstrate that LAG-3 directly inhibits IL-12/23 and TNF secretion by patient-derived monocytes, a mechanism with translational potential in SpA.Conclusion:Our detailed characterization of Tregs at an important inflammatory site illustrates the marked specialization of Treg subpopulations and identifies a broad transcriptional profile upregulated across all synovial regulatory cells. Our TCR analysis provides evidence of Treg clonal expansion, which may be driven by antigen, and confirms functional specialisation of individual clones. We also propose a new insight into a Treg functional mechanism through LAG-3 that suggests a novel therapeutic approach to immune-driven diseases.References:[1]Penkava et al., Nature Communications, 2020Disclosure of Interests:Davide Simone: None declared, Frank Penkava: None declared, Anna Ridley: None declared, Stephen Sansom: None declared, Hussein Al Mossawi Employee of: UCB, Paul Bowness Grant/research support from: Regeneron, Celgene/BMS and GSK

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Single-Cell Transcriptomic and Proteomic Diversity in Multiple Myeloma

Blood ◽

10.1182/blood-2019-131005 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 5531-5531

Author(s):

Reyka G Jayasinghe ◽

Yige Wu ◽

Ying Zhu ◽

Ruiyang Liu ◽

Mark A. Fiala ◽

...

Keyword(s):

Multiple Myeloma ◽

T Cells ◽

Single Cell ◽

Board Of Directors ◽

Rna Sequencing ◽

Research Funding ◽

Plasma Cells ◽

Malignant Cells ◽

Advisory Committees ◽

Equity Ownership

Multiple myeloma (MM) is a disease defined by clonal proliferation of abnormal plasma cells from B-cells. Improved treatments for MM have led to improving overall lifespan, but still remains incurable due to acquired resistance to therapy and tumor heterogeneity. Single-cell RNA sequencing studies (scRNA-seq) of MM patients have highlighted the significant inter-individual heterogeneity and subclonal architecture of the malignant plasma cell populations, emphasizing the importance of developing personalized therapies specific to a patients molecular pathogenesis. In this study, we have integrated scRNA-seq with single-cell proteomics (sc-Prot) for 10 plasma cells and CD4+ T cells to validate and prioritize driver events in malignant cells and evaluate the tumor microenvironment. This effort will be expanded to another 10 cases to further integrate scRNA-seq, snATAC-seq, whole exome sequencing and bulk RNA-sequencing on a fraction of the cells isolated from bone marrow. The remaining cells will be sorted using FACS to select for specific malignant and immune cells including 40 plasma cells, 15 CD4+ T and 15 CD8+ T cells. These sorted cells will be profiled with a scProt technology (BASIL nanoPOTS) to illuminate their cell-to-cell heterogeneity. In our pilot study comparing bulk and single-cell proteomic data of a single patient's plasma cells (CD138+) for 400 representative proteins, while a majority of expression signatures are concurrent between the two methods, some signaling pathways including translation and apoptotic cleavage are discordant. Our findings stress the importance of interrogating subpopulations of immune and malignant cells at the single-cell level to further refine the transcriptomic and proteomic heterogeneity of MM in a cell type specific manner. With the aid of single-cell technology, we have assessed the heterogeneity of malignant and immune cell types to evaluate transcriptomic and proteomic changes contributing to altering the interplay between the immune environment and tumor cells. Disclosures Fiala: Incyte: Research Funding. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. O'Neal:Wugen: Patents & Royalties: Patent Pending; WashU: Patents & Royalties: Patent Pending. DiPersio:WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Macrogenics: Research Funding, Speakers Bureau; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Magenta Therapeutics: Equity Ownership; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; Amphivena Therapeutics: Consultancy, Research Funding; Bioline Rx: Research Funding, Speakers Bureau. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding.

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Molecular determinants and heterogeneity of tissue-resident memory CD8+ T lymphocytes revealed by single-cell RNA sequencing

10.1101/2020.03.02.973578 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nadia S. Kurd ◽

Zhaoren He ◽

J. Justin Milner ◽

Kyla D. Omilusik ◽

Tiani L. Louis ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Microbial Infection ◽

Molecular Determinants ◽

Single Cell Rna Sequencing

AbstractDuring an immune response to microbial infection, CD8+ T cells give rise to distinct classes of cellular progeny that coordinately mediate clearance of the pathogen and provide long-lasting protection against reinfection, including a subset of non-circulating tissue-resident memory (TRM) cells that mediate potent protection within non-lymphoid tissues. Here, we utilized single-cell RNA-sequencing to examine the gene expression patterns of individual CD8+ T cells in the spleen and small intestine intraepithelial lymphocyte (siIEL) compartment throughout the course of their differentiation in response to viral infection. These analyses revealed previously unknown transcriptional heterogeneity within the siIEL CD8+ T cell population at several states of differentiation, representing functionally distinct TRM cell subsets as well as a subset of TRM cell precursors within the tissue early in infection. Taken together, these findings may inform strategies to optimize CD8+ T cell responses to protect against microbial infection and cancer.One sentence summaryHere, we applied single-cell RNA-sequencing to elucidate the gene expression patterns of individual CD8+ T cells differentiating throughout the course of infection in the spleen and small intestinal epithelium, which revealed previously unidentified molecular determinants of tissue-resident T cell differentiation as well as functional heterogeneity within the tissue-resident CD8+ T cell population.

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Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Nature Communications ◽

10.1038/s41467-021-24730-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

David S. Fischer ◽

Meshal Ansari ◽

Karolin I. Wagner ◽

Sebastian Jarosch ◽

Yiqi Huang ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Ex Vivo ◽

Severe Disease ◽

Human Leukocyte ◽

Cell Receptor ◽

Single Cell Rna Sequencing

AbstractThe in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

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