Gene Expression-Based Identification of Antigen-Responsive CD8+ T Cells on a Single-Cell Level

Fuchs, Yannick F.; Sharma, Virag; Eugster, Anne; Kraus, Gloria; Morgenstern, Robert; Dahl, Andreas; Reinhardt, Susanne; Petzold, A.; Lindner, Annett; Löbel, Doreen; Bonifacio, Ezio

doi:10.3389/fimmu.2019.02568

Cited by 31 publications

(35 citation statements)

References 45 publications

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“…Exhaustion consensus signature list was derived by considering genes that were present in > 3 exhaustion signature datasets (31,32,(40)(41)(42)(43)(44)(45)(46). Genes that were present in cytotoxicity signatures (40,102) or viral activation signatures (65) were excluded from the consensus list (table S5). The R package fgsea was used to calculate the GSEA scores with the signal-to-noise ratio as a metric.…”

Section: Gene Set Enrichment Analysis and Signature Module Scoresmentioning

confidence: 99%

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 ⁺ T cells

et al. 2021

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The molecular properties of CD8+ T cells that respond to SARS-CoV-2 infection are not fully known. Here, we report on the single-cell transcriptomes of >80,000 virus-reactive CD8+ T cells, obtained using a modified Antigen-Reactive T cell Enrichment (ARTE) assay, from 39 COVID-19 patients and 10 healthy subjects. COVID-19 patients segregated into two groups based on whether the dominant CD8+ T cell response to SARS-CoV-2 was ‘exhausted’ or not. SARS-CoV-2-reactive cells in the exhausted subset were increased in frequency and displayed lesser cytotoxicity and inflammatory features in COVID-19 patients with mild compared to severe illness. In contrast, SARS-CoV-2-reactive cells in the dominant non-exhausted subset from patients with severe disease showed enrichment of transcripts linked to co-stimulation, pro-survival NF-κB signaling, and anti-apoptotic pathways, suggesting the generation of robust CD8+ T cell memory responses in patients with severe COVID-19 illness. CD8+ T cells reactive to influenza and respiratory syncytial virus from healthy subjects displayed polyfunctional features and enhanced glycolysis. Cells with such features were largely absent in SARS-CoV-2-reactive cells from both COVID-19 patients and healthy controls non-exposed to SARS-CoV-2. Overall, our single-cell analysis revealed substantial diversity in the nature of CD8+ T cells responding to SARS-CoV-2.

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Section: Gene Set Enrichment Analysis and Signature Module Scoresmentioning

confidence: 99%

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 ⁺ T cells

et al. 2021

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“…To further understand the immune characteristics of the two groups. The ssGSEA method was used to further evaluate the immune-cell infiltration status of TCGA colorectal cancer transcriptome, and the results suggested that neutrophils, macrophage M1 cells, T cells, and CD4 memory resting cells were enriched in the low-risk group, while M0 cells and T cell regulatory cells were more common in the high-risk group, numerous studies have shown that dense infiltration of T cells, especially cytotoxic CD8 T cells, and high density of M1 macrophages may be associated with acute inflammation, suggesting a good prognosis ( Fuchs et al, 2019 ; Marcelis et al, 2020 ). In contrast, in many malignancies, M2 macrophages (the major subtype of macrophages) are associated with chronic inflammation and contribute to tumor growth and the development of aggressive phenotypes and have been associated with adverse outcomes ( Mantovani et al, 2002 ; Yamaguchi et al, 2016 ), and it is noteworthy that our findings support these conclusions.…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer

Lin

Chen

et al. 2021

Front. Genet.

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Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC).Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC.Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB).Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

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“…Combining TCR sequencing (or selection based on autoantigen reactivity), with scRNAseq has the potential to give further insights into T cell function. This has not always been straightforward to demonstrate, for example analysis of IGRP-specific T cells from the peripheral blood did not show a distinctive gene expression (GEX) pattern in response to stimulation (25). Similarly scRNAseq of ZnT8 reactive cells from the peripheral blood of people with T1D showed similar GEX profiles to healthy controls, indicating that these peripheral T cells may not be playing a driving role in T1D, although T1D patients had higher expression of aryl hydrocarbon receptor (AHR) and aurora kinase A (AURKA) and lower expression of RORA (26).…”

Section: Phenotypes Of Antigen-specific T Cells: Combining Tcr Sequencing With Gene Expressionmentioning

confidence: 99%

Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity

et al. 2021

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In the past few years, huge advances have been made in techniques to analyse cells at an individual level using RNA sequencing, and many of these have precipitated exciting discoveries in the immunology of type 1 diabetes (T1D). This review will cover the first papers to use scRNAseq to characterise human lymphocyte phenotypes in T1D in the peripheral blood, pancreatic lymph nodes and islets. These have revealed specific genes such as IL-32 that are differentially expressed in islet –specific T cells in T1D. scRNAseq has also revealed wider gene expression patterns that are involved in T1D and can predict its development even predating autoantibody production. Single cell sequencing of TCRs has revealed V genes and CDR3 motifs that are commonly used to target islet autoantigens, although truly public TCRs remain elusive. Little is known about BCR repertoires in T1D, but scRNAseq approaches have revealed that insulin binding BCRs commonly use specific J genes, share motifs between donors and frequently demonstrate poly-reactivity. This review will also summarise new developments in scRNAseq technology, the insights they have given into other diseases and how they could be leveraged to advance research in the type 1 diabetes field to identify novel biomarkers and targets for immunotherapy.

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Gene Expression-Based Identification of Antigen-Responsive CD8+ T Cells on a Single-Cell Level

Cited by 31 publications

References 45 publications

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 ⁺ T cells

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 ⁺ T cells

Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer

Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity

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Gene Expression-Based Identification of Antigen-Responsive CD8+ T Cells on a Single-Cell Level

Cited by 31 publications

References 45 publications

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 + T cells

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 + T cells

Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer

Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity

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Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 ⁺ T cells

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 ⁺ T cells