scholarly journals Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture across the human body

2021 ◽  
Author(s):  
Conde C Domínguez ◽  
T Gomes ◽  
LB Jarvis ◽  
C Xu ◽  
SK Howlett ◽  
...  
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Γδ T Cells ◽  
Cell Types ◽  
Cell Receptor ◽  
Lymphoid Tissues ◽  
Cell Type ◽  
Variable Regions ◽  
Tissue Specific

AbstractDespite their crucial role in health and disease, our knowledge of immune cells within human tissues, in contrast to those circulating in the blood, remains limited. Here, we surveyed the immune compartment of lymphoid and non-lymphoid tissues of six adult donors by single-cell RNA sequencing, including alpha beta T-cell receptor (αβ TCR), gamma delta (γδ) TCR and B-cell receptor (BCR) variable regions. To aid systematic cell type identification we developed CellTypist, a tool for automated and accurate cell type annotation. Using this approach combined with manual curation, we determined the tissue distribution of finely phenotyped immune cell types and cell states. This revealed tissue-specific features within cell subsets, such as a subtype of activated dendritic cells in the airways (expressing CSF2RA, GPR157, CRLF2), ITGAD-expressing γδ T cells in spleen and liver, and ITGAX+ splenic memory B cells. Single cell paired chain TCR analysis revealed cell type-specific biases in VDJ usage, and BCR analysis revealed characteristic patterns of somatic hypermutation and isotype usage in plasma and memory B cell subsets. In summary, our multi-tissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis and antigen receptor sequencing.

scholarly journals Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

2021 ◽  
Author(s):  
Congmin Xu ◽  
Junkai Yang ◽  
Astrid Kosters ◽  
Benjamin R Babcock ◽  
Peng Qiu ◽  
...  
Keyword(s):  
Immune System ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Receptor ◽  
Surface Proteins ◽  
Cell Type ◽  
Human Immune System ◽  
Receptor Repertoire ◽  
Human Immune

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissue and disease contexts. Still, deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in our multi-omics single-cell datasets, we developed SuPERR-seq, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery and characterization of previously hidden cell subsets. We show that by incorporating information from cell-surface proteins and immunoglobulin transcript counts, we accurately remove cell doublets and prevent widespread cell-type misclassification. This approach uniquely improves the identification of heterogeneous cell types in the human immune system, including a novel subset of antibody-secreting cells in the bone marrow.

scholarly journals Single Cell Atlas of Human Dura Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response

2021 ◽  
Author(s):  
Anthony Z Wang ◽  
Jay Bowman-Kirigin ◽  
Rupen Desai ◽  
Pujan Patel ◽  
Bhuvic Patel ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Copy Number Variant ◽  
Cell Types ◽  
Cell Receptor ◽  
Immune Microenvironment ◽  
Clinical Models ◽  
Insight Into

Recent investigation of the meninges, specifically the dura layer, has highlighted its importance in CNS immune surveillance beyond a purely structural role. However, most of our understanding of the meninges stems from the use of pre-clinical models rather than human samples. In this study, we use single cell RNA-sequencing to perform the first characterization of both non-tumor-associated human dura and meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, through T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. We also identify a functionally heterogeneous population of non-immune cell types and report copy-number variant heterogeneity within our meningioma samples. Our comprehensive investigation of both the immune and non-immune cell landscapes of human dura and meningioma at a single cell resolution provide new insight into previously uncharacterized roles of human dura.

Flexible migration program regulates γδ T-cell involvement in humoral immunity

Blood ◽  
2003 ◽  
Vol 102 (10) ◽  
pp. 3693-3701 ◽  
Author(s):  
Marlène Brandes ◽  
Katharina Willimann ◽  
Alois B. Lang ◽  
Ki-Hoan Nam ◽  
Chenggang Jin ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cell ◽  
Humoral Immunity ◽  
Γδ T Cells ◽  
Cell Receptor ◽  
Lymphoid Tissues ◽  
Migration Program ◽  
And Function

Abstractγδ T cells are inadequately defined both in terms of their migration potential and contribution to antimicrobial immunity. Here, we have examined the migration profile of human blood γδ T cells and related cell lines and correlated these findings with their distribution in secondary lymphoid tissues and their function in B-cell cocultures. We find that resting γδ T cells are characterized by an inflammatory migration program similar to cells of the innate immune system. However, T-cell receptor (TCR) triggering resulted in the rapid but transient induction of a lymph node (LN)-homing program, as evidenced by functional CCR7 expression and concomitant reduction in expression and function of CCR5 and, to a lesser degree, CCR2. Moreover, the LN-homing program was reflected by the presence of γδ T cells in gastrointestinal lymphoid tissues, notably in clusters within germinal centers of B-cell follicles. In line with these findings, VγVδ-TCR triggering resulted in prominent expression of essential B-cell costimulatory molecules, including CD40L, OX40, CD70, and ICOS. Furthermore, γδ T cells were shown to provide potent B-cell help during in vitro antibody production. Collectively, our findings agree with a role for γδ T cells in humoral immunity during the early phase of antimicrobial responses. (Blood. 2003; 102:3693-3701)

scholarly journals Immune Cell Profiling of COVID-19 Patients in the Recovery Stage by Single-Cell Sequencing

2020 ◽  
Author(s):  
Wen Wen ◽  
Wenru Su ◽  
Hao Tang ◽  
Wenqing Le ◽  
Xiaopeng Zhang ◽  
...  
Keyword(s):  
T Cells ◽  
B Cells ◽  
B Cell ◽  
Single Cell ◽  
Hospital Discharge ◽  
Immune Cell ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Early Recovery ◽  
Recovery Stage

AbstractCOVID-19, caused by SARS-CoV-2, has recently affected over 300,000 people and killed more than 10,000. The manner in which the key immune cell subsets change and their states during the course of COVID-19 remain unclear. Here, we applied single-cell technology to comprehensively characterize transcriptional changes in peripheral blood mononuclear cells during the recovery stage of COVID-19. Compared with healthy controls, in patients in the early recovery stage (ERS) of COVID-19, T cells decreased remarkably, whereas monocytes increased. A detailed analysis of the monocytes revealed that there was an increased ratio of classical CD14++ monocytes with high inflammatory gene expression as well as a greater abundance of CD14++IL1B+ monocytes in the ERS. CD4+ and CD8+ T cells decreased significantly and expressed high levels of inflammatory genes in the ERS. Among the B cells, the plasma cells increased remarkably, whereas the naïve B cells decreased. Our study identified several novel B cell-receptor (BCR) changes, such as IGHV3-23 and IGHV3-7, and confirmed isotypes (IGHV3-15, IGHV3-30, and IGKV3-11) previously used for virus vaccine development. The strongest pairing frequencies, IGHV3-23-IGHJ4, indicated a monoclonal state associated with SARS-CoV-2 specificity. Furthermore, integrated analysis predicted that IL-1β and M-CSF may be novel candidate target genes for inflammatory storm and that TNFSF13, IL-18, IL-2 and IL-4 may be beneficial for the recovery of COVID-19 patients. Our study provides the first evidence of an inflammatory immune signature in the ERS, suggesting that COVID-19 patients are still vulnerable after hospital discharge. Our identification of novel BCR signaling may lead to the development of vaccines and antibodies for the treatment of COVID-19.Highlights-The immune response was sustained for more than 7 days in the early recovery stage of COVID-19, suggesting that COVID-19 patients are still vulnerable after hospital discharge.-Single-cell analysis revealed a predominant subset of CD14++ IL1β+ monocytes in patients in the ERS of COVID-19.-Newly identified virus-specific B cell-receptor changes, such as IGHV3-23, IGHV3-7, IGHV3-15, IGHV3-30, and IGKV3-11, could be helpful in the development of vaccines and antibodies against SARS-CoV-2.-IL-1β and M-CSF were discovered as novel mediators of inflammatory cytokine storm, and TNFSF13, IL-2, IL-4, and IL-18 may be beneficial for recovery.

scholarly journals Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq

2018 ◽  
Author(s):  
Santiago J Carmona ◽  
David Gfeller
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Adaptive Immune System ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Recent Developments ◽  
Species Specific ◽  
Development And Validation ◽  
Whole Transcriptome

Single-cell RNA-seq is revolutionizing our understanding of cell type heterogeneity in many fields of biology, ranging from neuroscience to cancer to immunology. In Immunology, one of the main promises of this approach is the ability to define cell types as clusters in the whole transcriptome space (i.e., without relying on specific surface markers), thereby providing an unbiased classification of immune cell types. So far, this technology has been mainly applied in mouse and human. However, technically it could be used for immune cell-type identification in any species without requiring the development and validation of species-specific antibodies for cell sorting. Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish (Danio rerio). We advocate that single-cell RNA-seq technology is likely to provide key insights into our understanding of the evolution of the adaptive immune system.

scholarly journals Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq

2018 ◽  
Author(s):  
Santiago J Carmona ◽  
David Gfeller
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Adaptive Immune System ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Recent Developments ◽  
Species Specific ◽  
Development And Validation ◽  
Whole Transcriptome

Single-cell RNA-seq is revolutionizing our understanding of cell type heterogeneity in many fields of biology, ranging from neuroscience to cancer to immunology. In Immunology, one of the main promises of this approach is the ability to define cell types as clusters in the whole transcriptome space (i.e., without relying on specific surface markers), thereby providing an unbiased classification of immune cell types. So far, this technology has been mainly applied in mouse and human. However, technically it could be used for immune cell-type identification in any species without requiring the development and validation of species-specific antibodies for cell sorting. Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish (Danio rerio). We advocate that single-cell RNA-seq technology is likely to provide key insights into our understanding of the evolution of the adaptive immune system.

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

scholarly journals ACTINN: automated identification of cell types in single cell RNA sequencing

2019 ◽  
Author(s):  
Feiyang Ma ◽  
Matteo Pellegrini
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Types ◽  
Mouse Cell ◽  
Supplementary Information ◽  
Cell Type ◽  
Human T Cell ◽  
Single Cell Rna Sequencing

Abstract Motivation Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

2019 ◽  
Author(s):  
Samuel A Danziger ◽  
David L Gibbs ◽  
Ilya Shmulevich ◽  
Mark McConnell ◽  
Matthew WB Trotter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Immune Cell ◽  
De Novo ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Data Set ◽  
Rnaseq Data

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

scholarly journals Leveraging Single-Cell RNA-seq Data to Uncover the Association Between Cell Type and Chronic Liver Diseases

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiangyu Ye ◽  
Julong Wei ◽  
Ming Yue ◽  
Yan Wang ◽  
Hongbo Chen ◽  
...  
Keyword(s):  
B Cell ◽  
Single Cell ◽  
Liver Diseases ◽  
Nk Cell ◽  
Cell Types ◽  
Integrative Analysis ◽  
Chronic Liver ◽  
Cell Type ◽  
Chronic Liver Diseases ◽  
Related Cell

BackgroundComponents of liver microenvironment is complex, which makes it difficult to clarify pathogenesis of chronic liver diseases (CLD). Genome-wide association studies (GWASs) have greatly revealed the role of host genetic background in CLD pathogenesis and prognosis, while single-cell RNA sequencing (scRNA-seq) enables interrogation of the cellular diversity and function of liver tissue at unprecedented resolution. Here, we made integrative analysis on the GWAS and scRNA-seq data of CLD to uncover CLD-related cell types and provide clues for understanding on the pathogenesis.MethodsWe downloaded three GWAS summary data and three scRNA-seq data on CLD. After defining the cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate the GWAS and scRNA-seq. In addition, we analyzed one scRNA-seq data without association to CLD to validate the specificity of our findings.ResultsAfter processing the scRNA-seq data, we obtain about 19,002–32,200 cells and identified 10–17 cell types. For the HCC analysis, we identified the association between B cell and HCC in two datasets. RolyPoly also identified the association, when we integrated the two scRNA-seq datasets. In addition, we also identified natural killer (NK) cell as HCC-associated cell type in one dataset. In specificity analysis, we identified no significant cell type associated with HCC. As for the cirrhosis analysis, we obtained no significant related cell type.ConclusionIn this integrative analysis, we identified B cell and NK cell as HCC-related cell type. More attention and verification should be paid to them in future research.

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