scholarly journals Single-Cell Transcriptome Analysis of Chronic Antibody-Mediated Rejection After Renal Transplantation

2022 ◽  
Vol 12 ◽  
Author(s):  
Fanhua Kong ◽  
Shaojun Ye ◽  
Zibiao Zhong ◽  
Xin Zhou ◽  
Wei Zhou ◽  
...  
Keyword(s):  
T Cells ◽  
Renal Transplantation ◽  
B Cells ◽  
Western Blot ◽  
Single Cell ◽  
Signaling Pathways ◽  
T Cell Subsets ◽  
T And B Cells ◽  
Term Survival ◽  
Antibody Mediated Rejection

Renal transplantation is currently the most effective treatment for end-stage renal disease. However, chronic antibody-mediated rejection (cABMR) remains a serious obstacle for the long-term survival of patients with renal transplantation and a problem to be solved. At present, the role and mechanism underlying immune factors such as T- and B- cell subsets in cABMR after renal transplantation remain unclear. In this study, single-cell RNA sequencing (scRNA-seq) of peripheral blood monocytes (PBMCs) from cABMR and control subjects was performed to define the transcriptomic landscape at single-cell resolution. A comprehensive scRNA-seq analysis was performed. The results indicated that most cell types in the cABMR patients exhibited an intense interferon response and release of proinflammatory cytokines. In addition, we found that the expression of MT-ND6, CXCL8, NFKBIA, NFKBIZ, and other genes were up-regulated in T- and B-cells and these genes were associated with pro-inflammatory response and immune regulation. Western blot and qRT-PCR experiments also confirmed the up-regulated expression of these genes in cABMR. GO and KEGG enrichment analyses indicated that the overexpressed genes in T- and B-cells were mainly enriched in inflammatory pathways, including the TNF, IL-17, and Toll-like receptor signaling pathways. Additionally, MAPK and NF-κB signaling pathways were also involved in the occurrence and development of cABMR. This is consistent with the experimental results of Western blot. Trajectory analysis assembled the T-cell subsets into three differentiation paths with distinctive phenotypic and functional prog rams. CD8 effector T cells and γδ T cells showed three different differentiation trajectories, while CD8_MAI T cells and naive T cells primarily had two differentiation trajectories. Cell-cell interaction analysis revealed strong T/B cells and neutrophils activation in cABMR. Thus, the study offers new insight into pathogenesis and may have implications for the identification of novel therapeutic targets for cABMR.

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2019 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2020 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals Single-cell RNA-Seq reveals transcriptional heterogeneity and immune subtypes associated with disease activity in human myasthenia gravis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wanlin Jin ◽  
Qi Yang ◽  
Yuyao Peng ◽  
Chengkai Yan ◽  
Yi Li ◽  
...  
Keyword(s):  
T Cells ◽  
B Cells ◽  
Myasthenia Gravis ◽  
Disease Activity ◽  
Single Cell ◽  
Immune Cell ◽  
Large Cell ◽  
Cellular Heterogeneity ◽  
T Cell Subsets ◽  
The Impact

AbstractMyasthenia gravis (MG) is a rare autoimmune disease. Although the impact of immune cell disorder in MG has been extensively studied, little is known about the transcriptomes of individual cells. Here, we assessed the transcriptional profiles of 39,243 cells by single-cell sequencing and identified 13 major cell clusters, along with 39 subgroups of cells derived from patients with new-onset myasthenia gravis and healthy controls. We found that B cells, CD4+ T cells, and monocytes exhibited more heterogeneity in MG patients. CD4+ T cells were expanded in MG patients. We reclustered B cells and CD4+ T cells, and predict their essential regulators. Further analyses demonstrated that B cells in MG exhibited higher transcriptional activity towards plasma cell differentiation, CD4+ T cell subsets were unbalanced, and inflammatory pathways of monocytes were highly activated. Notably, we discovered a disease-relevant subgroup, CD180− B cells. Increased CD180− B cells in MG are indicative of a high IgG composition and were associated with disease activity and the anti-AChR antibody. Together, our data further the understanding of the cellular heterogeneity involved in the pathogenesis of MG and provide large cell-type-specific markers for subsequent research.

scholarly journals Exploring and analysing immune single cell multi-omics data with VDJView

2019 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

AbstractBackgroundSingle cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.ResultsWe developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.ConclusionsVDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.

HLA-E restricted CD8+ T cell subsets are phenotypically altered in multiple sclerosis patients

2013 ◽  
Vol 20 (7) ◽  
pp. 790-801 ◽  
Author(s):  
Kim Pannemans ◽  
Bieke Broux ◽  
An Goris ◽  
Bénédicte Dubois ◽  
Tom Broekmans ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
T Cells ◽  
T Cell ◽  
B Cells ◽  
T Cell Subsets ◽  
Regulatory Role ◽  
T And B Cells ◽  
Autoreactive T Cell ◽  
Multiple Sclerosis Patients

Background: The importance of Qa-1 restricted CD8+ T cells in regulating autoreactive T cell responses has been demonstrated in animal models for autoimmune disorders, including multiple sclerosis (MS). Objective: We hypothesize that their human variant, HLA-E restricted CD8+ T cells, fulfills a similar regulatory role in man and that these cells are of importance in MS. Methods: A large cohort of MS patients and healthy controls was genotyped for the two known HLA-E polymorphisms. Flow cytometry was used to determine HLA-E expression kinetics and to phenotype HLA-E restricted CD8+ T cells. Immunohistochemistry was performed to investigate HLA-E expression in the central nervous system (CNS) of MS patients. Results: HLA-E is upregulated on immune cells upon in vitro activation and this upregulation is polymorphism-dependent for T and B cells. T and B cells in lesions of MS patients show enhanced HLA-E expression. Furthermore, NKG2C+CD8+ T cells of MS patients have a significantly lower Foxp3 expression, while NKG2A+CD8+ T cells of MS patients produce higher levels of pro-inflammatory cytokines compared to those of healthy individuals. Conclusion: Our study indicates that the HLA-E system is altered in MS and could play a regulatory role in disease.

scholarly journals Divergent Frequencies of IGF-I Receptor-Expressing Blood Lymphocytes in Monozygotic Twin Pairs Discordant for Graves’ Disease: Evidence for a Phenotypic Signature Ascribable to Nongenetic Factors

2009 ◽  
Vol 94 (5) ◽  
pp. 1797-1802 ◽  
Author(s):  
Raymond S. Douglas ◽  
Thomas H. Brix ◽  
Catherine J. Hwang ◽  
Laszlo Hegedüs ◽  
Terry J. Smith
Keyword(s):  
T Cells ◽  
B Cells ◽  
Surface Display ◽  
Monozygotic Twins ◽  
Monozygotic Twin ◽  
T Cell Subsets ◽  
Graves Disease ◽  
T And B Cells ◽  
Blood Lymphocytes ◽  
Igf I

Abstract Context: Graves’ disease (GD) is an autoimmune process of the thyroid and orbital connective tissues. The fraction of T and B cells expressing IGF-I receptor (IGF-IR) is increased in GD. It is a potentially important autoantigen in GD. Susceptibility to GD arises from both genetic and acquired factors. Objective: The aim of the study was to determine whether the increased frequency of IGF-IR-expressing T and B cells in GD results from genetic or nongenetic factors. Design/Setting/Participants: Display of IGF-IR was assessed on blood lymphocytes from 18 pairs of monozygotic twins in the Danish Twin Registry, including seven discordant pairs, four pairs concordant for GD, and seven healthy pairs. Main Outcome Measures: Subjects underwent physical examination and laboratory analysis. Surface display of IGF-IR on T and B cells was analyzed by flow cytometry. Results: Twins with GD display increased IGF-IR-expressing CD3+ T cells and T cell subsets including total CD4+, CD4+ naive, CD4+ memory, and CD8+ cells (P < 0.0001, P = 0.0001, P = 0.0003, P = 0.01, and P = 0.02, respectively) compared to healthy twins. The frequency of IGF-IR-expressing B cells from affected twins was increased relative to healthy controls (P = 0.009). In pairs discordant for GD, affected twins exhibited increased frequency of IGF-IR+ CD3+, CD4+, and CD4+ naive T cells (P < 0.05, P = 0.03, and P = 0.03, respectively) compared to their healthy twin. Conclusion: Our findings suggest that more frequent IGF-IR+ T cells in GD cannot be attributed to genetic determinants. Rather, this skew appears to be acquired. These results underscore the potential role of nongenetic, acquired factors in genetically susceptible individuals.

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2020 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2019 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

Sign in / Sign up

Export Citation Format

Share Document