scholarly journals Single-cell transcriptional atlas of the Chinese horseshoe bat (Rhinolophus sinicus) provides insight into the cellular mechanisms which enable bats to be viral reservoirs

2020 ◽  
Author(s):  
Lili Ren ◽  
Chao Wu ◽  
Li Guo ◽  
Jiacheng Yao ◽  
Conghui Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Cell Biology ◽  
Expression Patterns ◽  
Cell Types ◽  
Transcriptional Analysis ◽  
Effector Memory ◽  
Viral Reservoirs ◽  
Isolated Lung ◽  
Horseshoe Bat

AbstractBats are a major “viral reservoir” in nature and there is a great interest in not only the cell biology of their innate and adaptive immune systems, but also in the expression patterns of receptors used for cellular entry by viruses with potential cross-species transmission. To address this and other questions, we created a single-cell transcriptomic atlas of the Chinese horseshoe bat (Rhinolophus sinicus) which comprises 82,924 cells from 19 organs and tissues. This atlas provides a molecular characterization of numerous cell types from a variety of anatomical sites, and we used it to identify clusters of transcription features that define cell types across all of the surveyed organs. Analysis of viral entry receptor genes for known zoonotic viruses showed cell distribution patterns similar to that of humans, with higher expression levels in bat intestine epithelial cells. In terms of the immune system, CD8+ T cells are in high proportion with tissue-resident memory T cells, and long-lived effector memory nature killer (NK) T-like cells (KLRG1, GZMA and ITGA4 genes) are broadly distributed across the organs. Isolated lung primary bat pulmonary fibroblast (BPF) cells were used to evaluate innate immunity, and they showed a weak response to interferon β and tumor necrosis factor-α compared to their human counterparts, consistent with our transcriptional analysis. This compendium of transcriptome data provides a molecular foundation for understanding the cell identities, functions and cellular receptor characteristics for viral reservoirs and zoonotic transmission.

scholarly journals Intra-tumoral injection of caerin 1.1 and 1.9 peptides in vaccinated TC-1 tumour bearing mice with PD-1 blockade modulates macrophage heterogeneity and the activation of CD8+ T cells in the tumour microenvironment

2020 ◽  
Author(s):  
Guoying Ni ◽  
Xiaolian Wu ◽  
Ying Liu ◽  
Hejie Li ◽  
Shu Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Cd8 T Cells ◽  
Cell Function ◽  
Cell Types ◽  
Tumour Microenvironment ◽  
Effector Memory ◽  
Proteomic Profiling ◽  
T Subsets ◽  
Cell Transcriptome

Abstract Development of a vaccine formula that alters the tumour-infiltrating lymphocytes to be more immune active against a tumour is key to the improvement of clinical responses to immunotherapy. Here, we demonstrate that, in conjunction with E7 antigen specific immunotherapy, and IL-10 and PD-1 blockade, intra-tumoral administration of caerin 1.1 and 1.9 peptides further improves the tumour microenvironment (TME) when compared with injection of a control peptide. We used single cell transcriptomics and mass spectrometry-based proteomics to quantify changes in cellular activity across different cell types within the TME. We show that the injection of caerin 1.1/1.9 increases immune activating macrophages and NK cells, while reducing immunosuppressive macrophages with M2 phenotype, and increased numbers of activated CD8+ T cells with higher populations of memory and effector-memory CD8+ T subsets. Proteomic profiling demonstrated activation of Stat1 modulated apoptosis and production of nitric oxide. Further, computational integration of the proteome with the single cell transcriptome was consistent with deactivation of immune suppressive B cell function following caerin 1.1 and 1.9 treatment.

scholarly journals Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

2020 ◽  
Author(s):  
Xuan Liu ◽  
Sara J.C. Gosline ◽  
Lance T. Pflieger ◽  
Pierre Wallet ◽  
Archana Iyer ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Effector Memory ◽  
Immune Cell Population ◽  
Fine Grained ◽  
Knowledge Based ◽  
Comparable Performance

AbstractSingle-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

scholarly journals Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data

2021 ◽  
Author(s):  
Xuan Liu ◽  
Sara J C Gosline ◽  
Lance T Pflieger ◽  
Pierre Wallet ◽  
Archana Iyer ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Effector Memory ◽  
Fine Grained ◽  
Knowledge Based ◽  
Learning Classifier ◽  
Comparable Performance ◽  
Difficult Challenge

Abstract Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

scholarly journals FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms

2020 ◽  
Author(s):  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Xiang Wan ◽  
Can Yang ◽  
Angela Ruohao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
Developmental Trajectories ◽  
Expression Patterns ◽  
Cell Types ◽  
Original Structure ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) has now been used extensively to discover novel cell types and reconstruct developmental trajectories by measuring mRNA expression patterns of individual cells. However, datasets collected using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms, are difficult to compare because of their heterogeneity. Each platform has unique advantages, and integration of these datasets would provide deeper insights into cell biology and gene regulation. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple platforms. We applied FIRM to numerous scRNA-seq datasets generated using SS2 and 10X from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the-art methods. The integrated datasets generated using FIRM show accurate mixing of shared cell type identities and superior preservation of original structure for each dataset. FIRM not only generates robust integrated datasets for downstream analysis, but is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a versatile and indispensable tool for scRNA-seq analysis.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Single-cell multi-omics analysis of the immune response in COVID-19

2021 ◽  
Author(s):  
Emily Stephenson ◽  
◽  
Gary Reynolds ◽  
Rachel A. Botting ◽  
Fernando J. Calero-Nieto ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Single Cell ◽  
Mononuclear Cells ◽  
Severe Disease ◽  
Effector Memory ◽  
Peripheral Blood Mononuclear ◽  
Cross Sectional ◽  
Hematopoietic Stem ◽  
Symptomatic Disease

AbstractAnalysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy.

scholarly journals Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lei He ◽  
Quan Zhang ◽  
Yue Zhang ◽  
Yixian Fan ◽  
Fahu Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Trial Registration ◽  
T Cell Subsets ◽  
Disease Group ◽  
Communication Analysis ◽  
Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Elizabeth Chavez ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Expression Patterns ◽  
The Other ◽  
Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

scholarly journals Early Response of CD8+ T Cells in COVID-19 Patients

2021 ◽  
Vol 11 (12) ◽  
pp. 1291
Author(s):  
Deni Ramljak ◽  
Martina Vukoja ◽  
Marina Curlin ◽  
Katarina Vukojevic ◽  
Maja Barbaric ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
T Cells ◽  
Mrna Expression ◽  
Cd8 T Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Effector Memory ◽  
Healthy Controls ◽  
Ifn Γ

Healthy and controlled immune response in COVID-19 is crucial for mild forms of the disease. Although CD8+ T cells play important role in this response, there is still a lack of studies showing the gene expression profiles in those cells at the beginning of the disease as potential predictors of more severe forms after the first week. We investigated a proportion of different subpopulations of CD8+ T cells and their gene expression patterns for cytotoxic proteins (perforin-1 (PRF1), granulysin (GNLY), granzyme B (GZMB), granzyme A (GZMA), granzyme K (GZMK)), cytokine interferon-γ (IFN-γ), and apoptotic protein Fas ligand (FASL) in CD8+ T cells from peripheral blood in first weeks of SARS-CoV-2 infection. Sixteen COVID-19 patients and nine healthy controls were included. The absolute counts of total lymphocytes (p = 0.007), CD3+ (p = 0.05), and CD8+ T cells (p = 0.01) in COVID-19 patients were significantly decreased compared to healthy controls. In COVID-19 patients in CD8+ T cell compartment, we observed lower frequency effector memory 1 (EM1) (p = 0.06) and effector memory 4 (EM4) (p < 0.001) CD8+ T cells. Higher mRNA expression of PRF1 (p = 0.05) and lower mRNA expression of FASL (p = 0.05) at the fifth day of the disease were found in COVID-19 patients compared to healthy controls. mRNA expression of PRF1 (p < 0.001) and IFN-γ (p < 0.001) was significantly downregulated in the first week of disease in COVID-19 patients who progressed to moderate and severe forms after the first week, compared to patients with mild symptoms during the entire disease course. GZMK (p < 0.01) and FASL (p < 0.01) mRNA expression was downregulated in all COVID-19 patients compared to healthy controls. Our results can lead to a better understanding of the inappropriate immune response of CD8+ T cells in SARS-CoV2 with the faster progression of the disease.

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