Single Cell RNA Sequencing Driven Characterization of Rare B/Myeloid and T/Myeloid Mixed Phenotype Acute Leukemia

Abstract Introduction: Pediatric mixed phenotype acute leukemia (MPAL), a rare subgroup of leukemia, contains features of both myeloid and lymphoid lineage blasts, which makes the disease more difficult to diagnose/treat. More information is needed to understand the origins of the major pediatric MPAL subtypes, B/Myeloid (B-MPAL) and T/Myeloid (T-MPAL), and how they relate to other leukemias. Single-cell RNA sequencing (scRNA-seq) analysis of bone marrow (BM) can provide in-depth information about the leukemia microenvironment and reveal differences/similarities between MPAL subtypes and other types of leukemia that could be exploited to develop novel diagnostics/therapies. Methods: We analyzed ~16,000 cells from five pediatric MPAL BM samples to generate a transcriptomic landscape of B-MPAL and T-MPAL blasts and associated microenvironment cells. Samples collected at the time of diagnosis (Dx) were used to generate scRNA-Seq data using a droplet-based barcoding technique (Panigrahy et al. JCI 2019, Tellechea et al. JID, 2020). After data normalization, cell clusters were identified using principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) approach (Becht et al. Nat. Biotech 2018). Meta-analysis was performed using single cell samples from ongoing studies in the Bhasin lab (Bhasin, et al. Blood 2020 (ASH), Thomas et al. Blood 2020 (ASH)) and publicly available single cell data from GEO biorepository. Unsupervised analysis using UMAP and PCA was performed to determine the overall relationship among B-MPAL, T-MPAL and other leukemias (acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell ALL (T-ALL)). Supervised differentially expressed gene (DEG) analysis was performed to identify B- and T- MPAL blast cell signatures (P value < 0.001 and log2 FC > 0.5). Transcriptomic profiles in MPAL samples and normal BM stem and immune cells were compared using data from the Human Cell Atlas Data Portal (humancellatlas.org). Gene set enrichment analysis (GSEA) was performed, and significantly enriched pathways were compared in MPAL subtypes (P value < 0.001). Results: PCA analysis showed transcriptome similarity between B-MPAL and both B-ALL and AML, while T-MPAL transcriptome correlated with T-ALL and AML (Fig. 1A). B- and T-MPAL subtype blasts clustered separately from each other in UMAP analyses, with T-MPAL blasts clustering with T-ALL blasts, and B-MPAL somewhat overlapping with B-ALL blasts. Subtype DEG analysis of leukemia blasts and healthy BM revealed distinct significantly upregulated gene signatures in B-MPAL (YBX3, SOCS2, BCL11A, and HIST1H1C) and T-MPAL (ITM2A, HPGD, PDLIM1, and TRDC) blasts (Fig. 1B). Pathway analysis showed upregulated gene activity related to TNFA signaling via NFKB, B-cell survival, and the AP1, FRA, and NGF transcription factors in B-MPAL blasts. In contrast, IL-17 and IL-12, T-cell apoptosis, and Stathmin pathways were upregulated in T-MPAL blasts (Fig. 1C). T-MPAL T-cells also expressed higher levels of T-cell exhaustion markers compared to T-cells in B-MPAL samples and healthy bone marrow. After filtering out genes that are significantly expressed in immune cells, we identified genes that are differentially expressed at diagnosis in MPAL blasts from patients that relapsed after treatment (Dx1) versus patients that achieved remission (Dx2). These genes are potential prognostic markers for B-MPAL and T-MPAL relapse/remission. These include MDM2 and NEIL1 from Dx1 and FOSL2 and CDKN1A in Dx2 B-MPAL blasts. In T-MPAL, expression of HES4 and SPINK2 is associated with Dx1 blasts and GNAQ and ITGA4 with Dx2 blasts. Pathway enrichment analysis on B-MPAL blasts revealed upregulation of interferon gamma and PD-1 signaling in Dx1 samples and increased HSP27 and Cell Cycle pathways in the Dx2 subset. T-MPAL Dx1 associated pathways included prostaglandin synthesis and IL-17, while cell-cell junction and extracellular matrix interactions were increased in T-MPAL Dx2 samples (Fig. 1D). Conclusion: Single-cell profiling was used to characterize the molecular landscapes of MPAL blasts and the bone marrow microenvironment and identified gene signatures and pathways that are specifically enriched in B- and T-MPAL subtypes. Figure 1 Figure 1. Disclosures DeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

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Clonal Hematopoiesis-Driver DNMT3A Mutations Alter Immune Cells in Heart Failure

Circulation Research ◽

10.1161/circresaha.120.317104 ◽

2020 ◽

Cited By ~ 1

Author(s):

Wesley T Abplanalp ◽

Sebastian Cremer ◽

David John ◽

Jedrzej Hoffmann ◽

Bianca Schuhmacher ◽

...

Keyword(s):

Heart Failure ◽

T Cell ◽

Chronic Heart Failure ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cells ◽

Driver Mutations ◽

Clonal Hematopoiesis ◽

Mutation Carriers ◽

Single Cell Rna Sequencing

Rationale: Clonal hematopoiesis (CH) driven by mutations of DNA methyltransferase 3a (DNMT3A) is associated with increased incidence of cardiovascular disease and poor prognosis of patients with chronic heart failure (HF) and aortic stenosis. Although experimental studies suggest that DNMT3A CH-driver mutations may enhance inflammation, specific signatures of inflammatory cells in humans are missing. Objective: To define subsets of immune cells mediating inflammation in humans using single-cell RNA-sequencing. Methods and Results: Transcriptomic profiles of peripheral blood mononuclear cells were analysed in N=6 HF patients harboring DNMT3A CH-driver mutations and N=4 patients with HF and no DNMT3A mutations by single-cell RNA-sequencing. Monocytes of HF patients carrying DNMT3A mutations demonstrated a significantly increased expression of inflammatory genes compared to monocytes derived from HF patients without DNMT3A mutations. Among the specific up-regulated genes were the prototypic inflammatory interleukin (IL) IL1B, IL6, IL8, the inflammasome NLRP3, and the macrophage inflammatory proteins CCL3 and CCL4 as well as resistin, which augments monocyte-endothelial adhesion. Silencing of DNMT3A in monocytes induced a paracrine pro-inflammatory activation and increased adhesion to endothelial cells. Furthermore, the classical monocyte subset of DNMT3A mutation carriers showed increased expression of T-cell stimulating immunoglobulin superfamily members CD300LB, CD83, SIGLEC12, as well as the CD2 ligand and cell adhesion molecule CD58, all of which may be involved in monocyte-T cell interactions. DNMT3A mutation carriers were further characterized by increased expression of the T-cell alpha receptor constant chain and Th1, Th2, Th17, CD8+ effector, CD4+ memory and Treg specific signatures. Conclusions: This study demonstrates that circulating monocytes and T-cells of HF patients harboring CH-driver mutations in DNMT3A exhibit a highly inflamed transcriptome, which may contribute to the aggravation of chronic heart failure.

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Thymus Regeneration Is Dependent on Distinct Mesenchymal Stromal Cell Populations

Blood ◽

10.1182/blood-2019-126708 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 586-586

Author(s):

Karin Gustafsson ◽

Nikolas Barkas ◽

Ninib Baryawno ◽

Elizabeth W Scadden ◽

Nicolas Severe ◽

...

Keyword(s):

Bone Marrow ◽

T Cell ◽

Board Of Directors ◽

Stromal Cells ◽

Stromal Cell ◽

Cell Recovery ◽

Advisory Committees ◽

Single Cell Rna Sequencing ◽

Equity Ownership ◽

Thymus Regeneration

Background The regenerative ability of the thymus is an important factor in determining the outcome of bone marrow transplantation. However, the currently employed cytoreductive regimens invariably damage the thymic stroma, thus impeding recovery of T lymphopoiesis. Additionally, the thymic niche is poorly defined. Thymic epithelial cells have been extensively characterized, but our understanding of how other stromal cell types contribute to T lymphopoiesis is limited. We therefore set out to further define the thymic niche under homeostasis and regeneration. Results Using single-cell RNA-sequencing, we demonstrated that the thymic stromal cell compartment is composed of 10 stromal cell subsets. A specific subset of periostin expressing mesenchymal stromal cells (Postn+ MSCs) were found to be enriched in T cell promoting factors such as BMP2, BMP4, Ccl19 and Flt3 ligand (Fig. 1A). To elucidate the functional role of Postn+ MSCs in thymus regeneration, thymic stromal cells were isolated 3 days post-irradiation and transplantation and sequenced. Although the subsets classified as MSC generally persist following irradiation, the Postn+ MSCs were significantly reduced at a time when thymus seeding progenitors typically enter the tissue (Fig 1B). The secretion of chemokines and cytokines was also found to be faulty in the Postn+ MSC subset following transplantation, including significant reductions in Bmp2 and Cxcl14 (Fig 1C). In addition, there was a significant increase in a separate class of pro-adipogenic MSCs (Fig 1B), suggesting that the slow regeneration of the thymus after a transplantation could in part be due to this imbalance in MSC subtypes. Testing this hypothesis, thymic MSC subsets were adoptively transferred into irradiated and transplanted hosts. Specific subsets increased influx of thymocyte progenitors and aided in endothelial cell recovery (Fig 1D) consistent with regeneration of the thymic microenvironment. Furthermore, the transferred MSCs persisted and improved T cell numbers in the circulation up to 16 weeks post-transplantation (Fig 1E). To further investigate the clinical relevance of the MSC compartment, single-cell RNA-sequencing was performed on thymus stromal cells from human samples. Similarly, to what was observed in the murine tissue, human Postn+ MSC were found to express high levels of CCL19 and BMP4. Conclusion These data indicate that specific mesenchymal cell subsets in the thymus are important mediators of thymus regeneration. Moreover, adoptive transfer of MSC subsets may enable improved T cell recovery in the setting of bone marrow transplantation and perhaps other settings of T cell deficiency. Disclosures Scadden: Novartis: Other: Sponsored research; Bone Therapeutics: Consultancy; Magenta Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Editas Medicine: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Fog Pharma: Consultancy; Red Oak Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Fate Therapeutics: Consultancy, Equity Ownership; Clear Creek Bio: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; LifeVaultBio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

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Single-Cell RNA Sequencing Reveals B Cells Are Important Regulators in Fracture Healing

Frontiers in Endocrinology ◽

10.3389/fendo.2021.666140 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hao Zhang ◽

Renkai Wang ◽

Guangchao Wang ◽

Bo Zhang ◽

Chao Wang ◽

...

Keyword(s):

Bone Marrow ◽

B Cells ◽

Fracture Healing ◽

B Cell ◽

Bone Regeneration ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cells ◽

Bone Homeostasis ◽

Single Cell Rna Sequencing

The bone marrow microenvironment is composed primarily of immune and stromal cells that play important roles in fracture healing. Although immune cells have been identified in mouse bone marrow, variations in their numbers and type during the fracture healing process remain poorly defined. In this study, single-cell RNA sequencing was used to identify immune cells in fracture tissues, including neutrophils, monocytes, T cells, B cells, and plasma cells. The number of B cells decreased significantly in the early stage of fracture healing. Furthermore, B cells in mice fracture models decreased significantly during the epiphyseal phase and then gradually returned to normal during the epiphyseal transformation phase of fracture healing. The B-cell pattern was opposite to that of bone formation and resorption activities. Notably, B-cell–derived exosomes inhibited bone homeostasis in fracture healing. In humans, a decrease in the number of B cells during the epiphyseal phase stimulated fracture healing. Then, as the numbers of osteoblasts increased during the callus reconstruction stage, the number of B cells gradually recovered, which reduced additional bone regeneration. Thus, B cells are key regulators of fracture healing and inhibit excessive bone regeneration by producing multiple osteoblast inhibitors.

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Mapping the Immune Landscape of Clear Cell Renal Cell Carcinoma by Single-Cell RNA-seq

10.1101/824482 ◽

2019 ◽

Cited By ~ 3

Author(s):

Ajaykumar Vishwakarma ◽

Nicholas Bocherding ◽

Michael S. Chimenti ◽

Purshottam Vishwakarma ◽

Kenneth Nepple ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

T Cell ◽

Single Cell ◽

T Cell Receptor ◽

Immune Cells ◽

Immune Cell ◽

Clear Cell ◽

Cell Receptor ◽

Cell Renal Cell Carcinoma ◽

Single Cell Rna Sequencing

AbstractThe immune cells within the tumor microenvironment are considered key determinants of response to cancer immunotherapy. Immune checkpoint blockade (ICB) has transformed the treatment of clear cell renal cell carcinoma (ccRCC), although the role of specific immune cell states remains unclear. To characterize the tumor microenvironment (TME) of ccRCC, we applied single-cell RNA sequencing (scRNA-seq) along with paired T cell receptor sequencing to map the transcriptomic heterogeneity of 24,904 individual CD45+ lymphoid and myeloid cells in matched tumor and blood from patients with ccRCC. We identified multiple distinct immune cell phenotypes for B and T (CD4 and CD8) lymphocytes, natural kill (NK) cells, and myeloid cells. Evaluation of T cell receptor (TCR) sequences revealed limited shared clonotypes between patients, whereas tumor-infiltrating T cell clonotypes were frequently found in peripheral blood, albeit in lower abundance. We further show that the circulating CD4+ T cell clonality is far less diverse than peripheral CD8+. Evaluation of myeloid subsets revealed unique gene programs defining monocytes, dendritic cells and tumor-associated macrophages. In summary, here we have leveraged scRNA-seq to refine our understanding of the relative abundance, diversity and complexity of the immune landscape of ccRCC. This report represents the first characterization of ccRCC immune landscape using scRNA-seq. With further characterization and functional validation, these findings may identify novel sub-populations of immune cells amenable to therapeutic intervention.One Sentence SummarySingle-cell RNA-sequencing reveals unique lymphoid and myeloid gene programs with putative functions in clear cell renal cancer patients

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Direct Detection of Expressed Mutations in AML Cells Using Single Cell RNA-Sequencing, and Its Impact on Defining Sources of Expression Heterogeneity

Blood ◽

10.1182/blood-2018-99-119075 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1314-1314

Author(s):

Allegra A Petti ◽

Stephen R Williams ◽

Christopher A Miller ◽

Ian T Fiddes ◽

David Chen ◽

...

Keyword(s):

Gene Expression ◽

Bone Marrow ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

The Other ◽

Employment Equity ◽

Clonal Heterogeneity ◽

Single Cell Rna Sequencing ◽

Equity Ownership

Abstract Background. Acute Myeloid Leukemia (AML) is genetically and epigenetically heterogeneous. Most AML samples display clonal heterogeneity at presentation, which evolves with therapeutic interventions. To better understand the epigenetic consequences of clonal heterogeneity, we are using single-cell RNA-sequencing (scRNA-seq) to characterize expression heterogeneity in AML. To date, scRNA-seq has had limited utility in applications where it is essential to link transcriptional heterogeneity to genetic variation, because it has been difficult to identify specific mutations in individual cells using scRNA-seq data alone. To address this limitation, we developed an approach to use scRNA-seq data to identify expressed mutations in individual AML cells, and link these variants to the expression heterogeneity in the same samples. Methods. We generated duplicate cDNA libraries for each of 5 cryopreserved bone marrow samples from adult patients with de novo AML, using the 10x Genomics Chromium Single Cell 5' Gene Expression workflow for Single Cell RNA Sequencing. Single cell libraries were sequenced to yield a median of 20,474 cells per sample, and 192,427 reads per cell. Transcript alignment, counting, and inter-library normalization were performed using the Cell Ranger pipeline (10x Genomics). The Seurat R package was used for further normalization, filtering, principal component analysis, clustering, and t-SNE visualization. A nearest-neighbor algorithm was developed to assign each cell in the data set to the most transcriptionally similar hematopoietic lineage. For each case, we performed whole genome sequencing (WGS) to identify germline and somatic variants, and define clonal architecture. We then developed bioinformatic methods to determine which cells harbor these mutations, assign those cells to mutationally-defined subclones, and link mutations to defined expression clusters. Results. WGS identified 25-56 coding mutations per sample; we were able to identify 22%-46% of these mutations in at least one cell in the scRNA-seq data, including point mutations (e.g. DNMT3A, U2AF1, TP53, IDH1, IDH2, SRSF2, CEBPA, and others) and indels (e.g. FLT3-ITD, NPMc). Although the libraries were 5' biased, expressed mutations could be identified at long distances from the 5' end of transcripts; for example, an expressed DNMT3AR882H mutation (2.646 Kb from the initiating codon) was easily detected (Fig 1c). The frequency of detected mutations in the single-cell data varied widely (range: 1-1564 cells; median: 11 cells), and as expected, depended heavily on the expression level of the gene, and the size of the clone containing the mutation. Regardless, a median of 1378 cells (6.7%) had at least one identifiable mutation in the 5 samples. Using these data, we were able to 1) distinguish AML cells from normal cells in bone marrow samples (Fig 1a/b), 2) identify major subclones within the AML samples (Fig 1c/d), and 3) identify mutation-specific and subclone-specific expression profiles. In 2 samples with mutationally-defined subclones (one with a CEBPAR142fs mutation, and the other with a GATA2R361C mutation), subclone-specific gene expression profiles were clearly detected in the scRNA-seq data, and could be directly associated with cells containing the mutant transcription factors. In the case with the subclonal GATA2R361C mutation, cells with that mutation were restricted to a subset of expression clusters (Fig 1d). In this subset, we identified an expression signature that is supported by pre-existing knowledge of the GATA2/SPI1 transcriptional regulatory circuit. In addition, we observed that expression heterogeneity frequently occurs independent of mutations defined by specific subclones. For instance, the GATA2R361C subclone contained additional heterogeneity (5 independent expression clusters) that could not be accounted for by mutations (Fig 1a/d). Moreover, the other 3 cases exhibited extensive expression heterogeneity within the AML cells that was not explained by genetically defined subclones. In sum, scRNA-seq data, when adapted to detect mutations, has dramatically improved our understanding of the expression heterogeneity of AML, which arises from two main sources: 1) cell-type composition of the sample, and 2) expression variation among the AML cells themselves (caused by both mutation-associated and mutation-independent factors). Disclosures Williams: 10x Genomics: Employment, Equity Ownership. Fiddes:10x Genomics: Employment, Equity Ownership. Church:10x Genomics: Employment, Equity Ownership.

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Integrative single cell RNA-sequencing descrambles a substantial divergence of adaptive immune cell identities and transcriptional programs in mouse and human atherosclerosis

European Heart Journal ◽

10.1093/eurheartj/ehab724.3411 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

H Horstmann ◽

N Anto Michel ◽

X S Sheng ◽

S Hansen ◽

A Lindau ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cells ◽

Immune Cell ◽

Atherosclerotic Plaques ◽

Cell Repertoire ◽

Single Cell Rna Sequencing ◽

Human Atherosclerosis ◽

Human Immune

Abstract Aims The distinct function of immune cells in human atherosclerosis has been mostly defined by preclinical mouse studies. Contrastingly, the immune cell composition of human atherosclerotic plaques and their contribution to disease progression is only poorly understood. It remains uncertain whether genetic animal models allow for valuable translational approaches. Methods and results We performed single cell RNA-sequencing (scRNAseq) to define the immune cell landscape in human carotid atherosclerotic plaques. The human immune cell repertoire was dominated by T cells with a considerable inter-patient variability and an unexpected heterogeneity. We performed bioinformatical integration with 7 mouse data sets and discovered a total of 38 cellular identities, of which some were not conserved between species and exclusively found in mice or humans. Locations, frequencies, and transcriptional programs of immune cells in preclinical mouse models did not resemble the immune cell landscape in human atherosclerosis. In contrast to mice, human plaques were not myeloid- and B cell-dominated and instead contained several T cell phenotypes with hallmarks of T cell memory, dysregulation, exhaustion, and activation. Human immune cells were predominantly enriched for transcriptional programs of hypoxia, glucose, and autoimmunity. In a validation cohort of 43 patients activated immune cell subsets defined by multi-colour flow cytometry associated with cerebral ischemia and coronary artery disease. Conclusion Here, we uncover yet undefined immune cell types associating with clinical disease. This leukocyte atlas of human atherosclerosis builds the conceptual basis for subsequent identification of cellular targets for clinical immunomodulatory therapies and risk prediction. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): ERC Starting Grant

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Mapping the immune environment in clear cell renal carcinoma by single-cell genomics

Communications Biology ◽

10.1038/s42003-020-01625-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Nicholas Borcherding ◽

Ajaykumar Vishwakarma ◽

Andrew P. Voigt ◽

Andrew Bellizzi ◽

Jacob Kaplan ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Immune Cells ◽

Clear Cell ◽

Cell Receptor ◽

Renal Tissue ◽

High Response Rate ◽

Cell Renal Cell Carcinoma ◽

Tumor Mutational Burden ◽

Macrophage Populations

AbstractClear cell renal cell carcinoma (ccRCC) is one of the most immunologically distinct tumor types due to high response rate to immunotherapies, despite low tumor mutational burden. To characterize the tumor immune microenvironment of ccRCC, we applied single-cell-RNA sequencing (SCRS) along with T-cell-receptor (TCR) sequencing to map the transcriptomic heterogeneity of 25,688 individual CD45+ lymphoid and myeloid cells in matched tumor and blood from three patients with ccRCC. We also included 11,367 immune cells from four other individuals derived from the kidney and peripheral blood to facilitate the identification and assessment of ccRCC-specific differences. There is an overall increase in CD8+ T-cell and macrophage populations in tumor-infiltrated immune cells compared to normal renal tissue. We further demonstrate the divergent cell transcriptional states for tumor-infiltrating CD8+ T cells and identify a MKI67 + proliferative subpopulation being a potential culprit for the progression of ccRCC. Using the SCRS gene expression, we found preferential prediction of clinical outcomes and pathological diseases by subcluster assignment. With further characterization and functional validation, our findings may reveal certain subpopulations of immune cells amenable to therapeutic intervention.

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IMMU-27. SINGLE CELL RNA-SEQUENCING IDENTIFIES NOVEL BONE MARROW DERIVED MYELOID CELLS IN GLIOBLASTOMA ASSOCIATED WITH TUMOR AGGRESSION

Neuro-Oncology ◽

10.1093/neuonc/noaa215.457 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii110-ii110

Author(s):

Christina Jackson ◽

Christopher Cherry ◽

Sadhana Bom ◽

Hao Zhang ◽

John Choi ◽

...

Keyword(s):

Bone Marrow ◽

Single Cell ◽

Tumor Cells ◽

Rna Sequencing ◽

Metabolic Pathways ◽

Myeloid Cells ◽

Tumor Grade ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Two Populations

Abstract BACKGROUND Glioma associated myeloid cells (GAMs) can be induced to adopt an immunosuppressive phenotype that can lead to inhibition of anti-tumor responses in glioblastoma (GBM). Understanding the composition and phenotypes of GAMs is essential to modulating the myeloid compartment as a therapeutic adjunct to improve anti-tumor immune response. METHODS We performed single-cell RNA-sequencing (sc-RNAseq) of 435,400 myeloid and tumor cells to identify transcriptomic and phenotypic differences in GAMs across glioma grades. We further correlated the heterogeneity of the GAM landscape with tumor cell transcriptomics to investigate interactions between GAMs and tumor cells. RESULTS sc-RNAseq revealed a diverse landscape of myeloid-lineage cells in gliomas with an increase in preponderance of bone marrow derived myeloid cells (BMDMs) with increasing tumor grade. We identified two populations of BMDMs unique to GBMs; Mac-1and Mac-2. Mac-1 demonstrates upregulation of immature myeloid gene signature and altered metabolic pathways. Mac-2 is characterized by expression of scavenger receptor MARCO. Pseudotime and RNA velocity analysis revealed the ability of Mac-1 to transition and differentiate to Mac-2 and other GAM subtypes. We further found that the presence of these two populations of BMDMs are associated with the presence of tumor cells with stem cell and mesenchymal features. Bulk RNA-sequencing data demonstrates that gene signatures of these populations are associated with worse survival in GBM. CONCLUSION We used sc-RNAseq to identify a novel population of immature BMDMs that is associated with higher glioma grades. This population exhibited altered metabolic pathways and stem-like potentials to differentiate into other GAM populations including GAMs with upregulation of immunosuppressive pathways. Our results elucidate unique interactions between BMDMs and GBM tumor cells that potentially drives GBM progression and the more aggressive mesenchymal subtype. Our discovery of these novel BMDMs have implications in new therapeutic targets in improving the efficacy of immune-based therapies in GBM.

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Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

Cell & Bioscience ◽

10.1186/s13578-021-00613-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Gen Zou ◽

Jianzhang Wang ◽

Xinxin Xu ◽

Ping Xu ◽

Libo Zhu ◽

...

Keyword(s):

Dendritic Cells ◽

Single Cell ◽

Natural Killer ◽

Rna Sequencing ◽

Peritoneal Cavity ◽

Immune Cells ◽

Peritoneal Fluid ◽

Control Sample ◽

Immune Dysfunction ◽

Single Cell Rna Sequencing

Abstract Background Endometriosis is a refractory and recurrent disease and it affects nearly 10% of reproductive-aged women and 40% of infertile patients. The commonly accepted theory for endometriosis is retrograde menstruation where endometrial tissues invade into peritoneal cavity and fail to be cleared due to immune dysfunction. Therefore, the comprehensive understanding of immunologic microenvironment of peritoneal cavity deserves further investigation for the previous studies mainly focus on one or several immune cells. Results High-quality transcriptomes were from peritoneal fluid samples of patients with endometriosis and control, and firstly subjected to 10 × genomics single-cell RNA-sequencing. We acquired the single-cell transcriptomes of 10,280 cells from endometriosis sample and 7250 cells from control sample with an average of approximately 63,000 reads per cell. A comprehensive map of overall cells in peritoneal fluid was first exhibited. We unveiled the heterogeneity of immune cells and discovered new cell subtypes including T cell receptor positive (TCR+) macrophages, proliferating macrophages and natural killer dendritic cells in peritoneal fluid, which was further verified by double immunofluorescence staining and flow cytometry. Pseudo-time analysis showed that the response of macrophages to the menstrual debris might follow the certain differentiation trajectory after endometrial tissues invaded into the peritoneal cavity, that is, from antigen presentation to pro-inflammation, then to chemotaxis and phagocytosis. Our analyses also mirrored the dysfunctions of immune cells including decreased phagocytosis and cytotoxic activity and elevated pro-inflammatory and chemotactic effects in endometriosis. Conclusion TCR+ macrophages, proliferating macrophages and natural killer dendritic cells are firstly reported in human peritoneal fluid. Our results also revealed that immune dysfunction happens in peritoneal fluid of endometriosis, which may be responsible for the residues of invaded menstrual debris. It provided a large-scale and high-dimensional characterization of peritoneal microenvironment and offered a useful resource for future development of immunotherapy.

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The order and logic of CD4 versus CD8 lineage choice and differentiation in mouse thymus

Nature Communications ◽

10.1038/s41467-020-20306-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Mohammad M. Karimi ◽

Ya Guo ◽

Xiaokai Cui ◽

Husayn A. Pallikonda ◽

Veronika Horková ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Single Cell ◽

Signal Strength ◽

Gene Activity ◽

Cytotoxic T Cell ◽

Individual Selection ◽

Cell Lineages ◽

Single Cell Rna Sequencing ◽

Tcr Signals

AbstractCD4 and CD8 mark helper and cytotoxic T cell lineages, respectively, and serve as coreceptors for MHC-restricted TCR recognition. How coreceptor expression is matched with TCR specificity is central to understanding CD4/CD8 lineage choice, but visualising coreceptor gene activity in individual selection intermediates has been technically challenging. It therefore remains unclear whether the sequence of coreceptor gene expression in selection intermediates follows a stereotypic pattern, or is responsive to signaling. Here we use single cell RNA sequencing (scRNA-seq) to classify mouse thymocyte selection intermediates by coreceptor gene expression. In the unperturbed thymus, Cd4+Cd8a- selection intermediates appear before Cd4-Cd8a+ selection intermediates, but the timing of these subsets is flexible according to the strength of TCR signals. Our data show that selection intermediates discriminate MHC class prior to the loss of coreceptor expression and suggest a model where signal strength informs the timing of coreceptor gene activity and ultimately CD4/CD8 lineage choice.

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