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

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.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbab039 ◽

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.

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

Frontiers in Immunology ◽

10.3389/fimmu.2021.599512 ◽

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.

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

Blood ◽

10.1182/blood-2020-134929 ◽

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.

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Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

10.1101/2021.12.17.472326 ◽

2021 ◽

Author(s):

Zhibin Li ◽

chengcheng Sun ◽

Fei Wang ◽

Xiran Wang ◽

Jiacheng Zhu ◽

...

Keyword(s):

Single Cell ◽

Immune Cells ◽

Evolutionary Biology ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Mononuclear Cells ◽

Immune Cell ◽

Cell Types ◽

Genetic Regulatory Networks ◽

Peripheral Blood Mononuclear

Background: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. Methods: In this study, we compared the single-cell transcriptomes of 77 957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. Results: The results revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular cross-talks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. Conclusions: This study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders

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Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽

10.7554/elife.49501 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 17

Author(s):

Prashant Rajbhandari ◽

Douglas Arneson ◽

Sydney K Hart ◽

In Sook Ahn ◽

Graciel Diamante ◽

...

Keyword(s):

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Single Cell Analysis ◽

Metabolic Response ◽

Cell Types ◽

Specific Cell ◽

Beneficial Effects ◽

Thermogenic Adipocytes ◽

And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

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Immune cell composition in normal human kidneys

Scientific Reports ◽

10.1038/s41598-020-72821-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jun-Gyu Park ◽

Myeongsu Na ◽

Min-Gang Kim ◽

Su Hwan Park ◽

Hack June Lee ◽

...

Keyword(s):

T Cells ◽

Immune Cells ◽

Immune Cell ◽

Kidney Diseases ◽

Myeloid Cells ◽

Human Kidney ◽

Effector Memory ◽

Immune Cell Subsets ◽

Immunological Mechanisms ◽

Normal Human

Abstract An understanding of immunological mechanisms in kidney diseases has advanced using mouse kidneys. However, the profiling of immune cell subsets in human kidneys remains undetermined, particularly compared with mouse kidneys. Normal human kidneys were obtained from radically nephrectomised patients with urogenital malignancy (n = 15). Subsequently, human kidney immune cell subsets were analysed using multicolor flow cytometry and compared with subsets from C57BL/6 or BALB/c mice under specific pathogen-free conditions. Twenty kidney sections from healthy kidney donors or subjects without specific renal lesions were additionally analysed by immunohistochemistry. In human kidneys, 47% ± 12% (maximum 63%) of immune cells were CD3+ T cells. Kidney CD4+ and CD8+ T cells comprised 44% and 56% of total T cells. Of these, 47% ± 15% of T cells displayed an effector memory phenotype (CCR7− CD45RA− CD69−), and 48% ± 19% were kidney-resident cells (CCR7− CD45RA− CD69+). However, the proportions of human CD14+ and CD16+ myeloid cells were approximately 10% of total immune cells. A predominance of CD3+ T cells and a low proportion of CD14+ or CD68+ myeloid cells were also identified in healthy human kidney sections. In mouse kidneys, kidney-resident macrophages (CD11blow F4/80high) were the most predominant subset (up to 50%) but the proportion of CD3+ T cells was less than 20%. These results will be of use in studies in which mouse results are translated into human cases under homeostatic conditions or with disease.

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Applications of Single-Cell Omics to Dissect Tumor Microenvironment

Frontiers in Genetics ◽

10.3389/fgene.2020.548719 ◽

2020 ◽

Vol 11 ◽

Author(s):

Tingting Guo ◽

Weimin Li ◽

Xuyu Cai

Keyword(s):

Tumor Microenvironment ◽

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Cell Types ◽

Immune Checkpoint Blockade ◽

Lineage Tracing ◽

Great Success ◽

Novel Technologies ◽

Single Cell Sequencing

The recent technical and computational advances in single-cell sequencing technologies have significantly broaden our toolkit to study tumor microenvironment (TME) directly from human specimens. The TME is the complex and dynamic ecosystem composed of multiple cell types, including tumor cells, immune cells, stromal cells, endothelial cells, and other non-cellular components such as the extracellular matrix and secreted signaling molecules. The great success on immune checkpoint blockade therapy has highlighted the importance of TME on anti-tumor immunity and has made it a prime target for further immunotherapy strategies. Applications of single-cell transcriptomics on studying TME has yielded unprecedented resolution of the cellular and molecular complexity of the TME, accelerating our understanding of the heterogeneity, plasticity, and complex cross-interaction between different cell types within the TME. In this review, we discuss the recent advances by single-cell sequencing on understanding the diversity of TME and its functional impact on tumor progression and immunotherapy response driven by single-cell sequencing. We primarily focus on the major immune cell types infiltrated in the human TME, including T cells, dendritic cells, and macrophages. We further discuss the limitations of the existing methodologies and the prospects on future studies utilizing single-cell multi-omics technologies. Since immune cells undergo continuous activation and differentiation within the TME in response to various environmental cues, we highlight the importance of integrating multimodal datasets to enable retrospective lineage tracing and epigenetic profiling of the tumor infiltrating immune cells. These novel technologies enable better characterization of the developmental lineages and differentiation states that are critical for the understanding of the underlying mechanisms driving the functional diversity of immune cells within the TME. We envision that with the continued accumulation of single-cell omics datasets, single-cell sequencing will become an indispensable aspect of the immune-oncology experimental toolkit. It will continue to drive the scientific innovations in precision immunotherapy and will be ultimately adopted by routine clinical practice in the foreseeable future.

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Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression

Cell Discovery ◽

10.1038/s41421-020-00214-5 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Guohe Song ◽

Yang Shi ◽

Meiying Zhang ◽

Shyamal Goswami ◽

Saifullah Afridi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

T Cell ◽

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

T Cell Subsets ◽

M2 Macrophage ◽

Advanced Hcc ◽

Immune Cell Subsets

AbstractDiverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

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Multi-modal single cell analysis reveals brain immune landscape plasticity during aging and gut microbiota dysbiosis

10.1101/2020.07.15.205377 ◽

2020 ◽

Author(s):

Samantha M. Golomb ◽

Ian H. Guldner ◽

Anqi Zhao ◽

Qingfei Wang ◽

Bhavana Palakurthi ◽

...

Keyword(s):

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Cell Types ◽

Cell Plasticity ◽

Tissue Microenvironment ◽

Gut Dysbiosis ◽

Resolution Single ◽

The Brain ◽

Gut Microbiota Dysbiosis

ABSTRACTThe brain contains a diverse array of immune cell types. The phenotypic and functional plasticity of brain immune cells collectively contribute to brain tissue homeostasis and disease progression. Immune cell plasticity is profoundly influenced by local tissue microenvironment cues and systemic factors. Yet, the transcriptional mechanism by which systemic stimuli, such as aging and gut microbiota dysbiosis, reshape brain immune cell plasticity and homeostasis has not been fully delineated. Using Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), we analyzed compositional and transcriptional changes of the brain immune landscape in response to aging and gut dysbiosis. We first examined the discordance between canonical surface marker-defined immune cell types (Cell-ID) and their transcriptome signatures, which suggested transcriptional plasticity among immune cells despite sharing the same cell surface markers. Specifically, inflammatory and patrolling Ly6C+ monocytes were shifted predominantly to a pro-inflammatory transcriptional program in the aged brain, while brain ILCs shifted toward an ILC2 transcriptional profile. Finally, aging led to an increase of ILC-like cells expressing a T memory stemness (Tscm) signature in the brain. Antibiotics (ABX)-induced gut dysbiosis reduced the frequency of ILCs exhibiting Tscm-like properties in the aged mice, but not in the young mice. Enabled by high-resolution single-cell molecular phenotyping, our study revealed that systemic changes due to aging and gut dysbiosis prime the brain environment for an increased propensity for neuroinflammation, which provided insights into gut dysbiosis in age-related neurological diseases.Manuscript SummaryGolomb et al. performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) on immune cells from the brains of young and aged mice with and without antibiotics-induced gut dysbiosis. High resolution, single cell immunophenotyping enabled the dissection of extensive transcriptional plasticity of canonically identified monocytes and innate lymphoid cells (ILCs) in the aged brain. Through differential gene expression and trajectory inference analyses, the authors revealed tissue microenvironment-dependent cellular responses influenced by aging and gut dysbiosis that may potentiate neuroinflammatory diseases.Graphical Abstract

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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

10.21203/rs.3.rs-73533/v1 ◽

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.

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