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 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 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 Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Konstantin Carlberg ◽  
Marina Korotkova ◽  
Ludvig Larsson ◽  
Anca I. Catrina ◽  
Patrik L. Ståhl ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Immune Cell ◽  
Memory T Cells ◽  
Inflammatory Diseases ◽  
Adaptive Immune Response ◽  
Cell Types ◽  
Effector Memory ◽  
Cellular Mechanisms ◽  
Chronic Inflammatory Diseases

AbstractLately it has become possible to analyze transcriptomic profiles in tissue sections with retained cellular context. We aimed to explore synovial biopsies from rheumatoid arthritis (RA) and spondyloarthritis (SpA) patients, using Spatial Transcriptomics (ST) as a proof of principle approach for unbiased mRNA studies at the site of inflammation in these chronic inflammatory diseases. Synovial tissue biopsies from affected joints were studied with ST. The transcriptome data was subjected to differential gene expression analysis (DEA), pathway analysis, immune cell type identification using Xcell analysis and validation with immunohistochemistry (IHC). The ST technology allows selective analyses on areas of interest, thus we analyzed morphologically distinct areas of mononuclear cell infiltrates. The top differentially expressed genes revealed an adaptive immune response profile and T-B cell interactions in RA, while in SpA, the profiles implicate functions associated with tissue repair. With spatially resolved gene expression data, overlaid on high-resolution histological images, we digitally portrayed pre-selected cell types in silico. The RA displayed an overrepresentation of central memory T cells, while in SpA effector memory T cells were most prominent. Consequently, ST allows for deeper understanding of cellular mechanisms and diversity in tissues from chronic inflammatory diseases.

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 Profiles of Immune Cell Infiltration in Carotid Artery Atherosclerosis Based on Gene Expression Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Long Wang ◽  
Beibei Gao ◽  
Mingyue Wu ◽  
Wei Yuan ◽  
Ping Liang ◽  
...  
Keyword(s):  
T Cells ◽  
Carotid Artery ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Th2 Cells ◽  
Effector Memory ◽  
T Helper ◽  
Carotid Artery Atherosclerosis ◽  
Helper Type

Since immune infiltration is closely associated with the progression and prognosis of atherosclerosis, we aimed to describe the abundance of 24 immune cell types within atherosclerotic tissues. In the current study, we used the Immune Cell Abundance Identifier (ImmuCellAI), a web-based tool, to estimate the abundance of 24 immune cells based on the microarray profiles of atherosclerotic carotid artery samples to analyze the proportions and the dysregulation of immune cell types within carotid atherosclerosis. We found that atherosclerotic immune cells had a diverse landscape dominated by T cells and myeloid cells and that macrophages and dendritic cells (DCs) showed different abundance in normal and atherosclerotic tissues. Moreover, the expression of macrophages was closely related to the level of the expression of DCs and of exhausted T cells, while the expression of T-helper type 1 (Th1) cells was strongly correlated with the expression of T-helper type 2 (Th2) cells and effector memory cells. Our data confirm a distinct profile of atherosclerosis-infiltrating immune cell subpopulations, which may inspire an immunological direction for research on atherosclerosis.

scholarly journals A Single-Cell Transcriptome Profiling of Anterior Kidney Leukocytes From Nile Tilapia (Oreochromis niloticus)

2021 ◽  
Vol 12 ◽  
Author(s):  
Liting Wu ◽  
Along Gao ◽  
Lan Li ◽  
Jianlin Chen ◽  
Jun Li ◽  
...  
Keyword(s):  
T Cells ◽  
B Cells ◽  
B Cell ◽  
Single Cell ◽  
Nile Tilapia ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Transcriptome ◽  
B Cell Subsets ◽  
Single Cell Transcriptome

Teleost fish anterior kidney (AK) is an important hematopoietic organ with multifarious immune cells, which have immune functions comparable to mammalian bone marrow. Myeloid and lymphoid cells locate in the AK, but the lack of useful specific gene markers and antibody-based reagents for the cell subsets makes the identification of the different cell types difficult. Single-cell transcriptome sequencing enables single-cell capture and individual library construction, making the study on the immune cell heterogeneity of teleost fish AK possible. In this study, we examined the transcriptional patterns of 11,388 AK leukocytes using 10× Genomics single-cell RNA sequencing (scRNA-seq). A total of 22 clusters corresponding to five distinct immune cell subsets were identified, which included B cells, T cells, granulocytes, macrophages, and dendritic cells (DCs). However, the subsets of myeloid cells (granulocytes, macrophages, and DCs) were not identified in more detail according to the known specific markers, even though significant differences existed among the clusters. Thereafter, we highlighted the B-cell subsets and identified them as pro/pre B cells, immature/mature B cells, activated B/plasmablasts, or plasma cells based on the different expressions of the transcription factors (TFs) and cytokines. Clustering of the differentially modulated genes by pseudo-temporal trajectory analysis of the B-cell subsets showed the distinct kinetics of the responses of TFs to cell conversion. Moreover, we classified the T cells and discovered that CD3+CD4−CD8−, CD3+CD4+CD8+, CD4+CD8−, and CD4−CD8+ T cells existed in AK, but neither CD4+CD8− nor CD4−CD8+ T cells can be further classified into subsets based on the known TFs and cytokines. Pseudotemporal analysis demonstrated that CD4+CD8− and CD4−CD8+ T cells belonged to different states with various TFs that might control their differentiation. The data obtained above provide a valuable and detailed resource for uncovering the leukocyte subsets in Nile tilapia AK, as well as more potential markers for identifying the myeloid and lymphoid cell types.

scholarly journals Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immunity

2016 ◽  
Author(s):  
Santiago J. Carmona ◽  
Sarah A. Teichmann ◽  
Lauren Ferreira ◽  
Iain C. Macaulay ◽  
Michael J.T. Stubbington ◽  
...  
Keyword(s):  
T Cells ◽  
Membrane Proteins ◽  
Nk Cells ◽  
Single Cell ◽  
Immune Cell ◽  
Mammalian Species ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Cell Populations ◽  
Vertebrate Species

AbstractThe immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analysed gene conservation of all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared to other immune cell populations, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA-Sequencing of lck:GFP cells in zebrafish to obtain the first transcriptome of specific immune cell types in a non-mammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T-cells, a novel type of NK-like cells and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune cell specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed a higher gene turnover and lower conservation of trans-membrane proteins in NK cells compared to T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

scholarly journals Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rongqun Guo ◽  
Mengdie Lü ◽  
Fujiao Cao ◽  
Guanghua Wu ◽  
Fengcai Gao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Significant Difference ◽  
Cell Phenotypes ◽  
Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

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.

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