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Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq.

ABSTRACT: Gluten-specific CD4+ T cells drive the pathogenesis of celiac disease and circulating gluten-specific T cells can be identified by staining with HLA-DQ:gluten tetramers. In this first single-cell RNA-seq study of tetramer-sorted T cells from untreated celiac disease patients blood, we found that gluten-specific T cells showed distinct transcriptomic profiles consistent with activated effector memory T cells that shared features with Th1 and follicular helper T cells. Compared to non-specific cells, gluten-specific T cells showed differential expression of several genes involved in T-cell receptor signaling, translational processes, apoptosis, fatty acid transport, and redox potentials. Many of the gluten-specific T cells studied shared T-cell receptor with each other, indicating that circulating gluten-specific T cells belong to a limited number of clones. Moreover, the transcriptional profiles of cells that shared the same clonal origin were transcriptionally more similar compared with between clonally unrelated gluten-specific cells.

SUBMITTER: Yao Y

PROVIDER: S-EPMC8496852 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Differential expression profile of gluten-specific T cells identified by single-cell RNA-seq

Project description:Gluten-specific CD4+ T cells drive the pathogenesis of celiac disease and circulating gluten-specific T cells can be identified by staining with HLA-DQ:gluten tetramers. In this first single-cell RNA-seq study of tetramer-sorted T cells from untreated celiac disease patients blood, we found that gluten-specific T cells showed distinct transcriptomic profiles consistent with activated effector memory T cells that shared features with Th1 and follicular helper T cells. Compared to non-specific cells, gluten-specific T cells showed differential expression of several genes involved in T-cell receptor signaling, translational processes, apoptosis, fatty acid transport, and redox potentials. Many of the gluten-specific T cells studied shared T-cell receptor with each other, indicating that circulating gluten-specific T cells belong to a limited number of clones. Moreover, the transcriptional profiles of cells that shared the same clonal origin were transcriptionally more similar compared with between clonally unrelated gluten-specific cells.

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The long non-coding RNA expression profile of Coxsackievirus A16 infected RD cells identified by RNA-seq.

Project description:Coxsackievirus A16 (CVA16) is one of major pathogens of hand, foot and mouth disease (HFMD) in children. Long non-coding RNAs (IncRNAs) have been implicated in various biological processes, but they have not been associated with CVA16 infection. In this study, we comprehensively characterized the landscape of IncRNAs of normal and CVA16 infected rhabdomyosarcoma (RD) cells using RNA-Seq to investigate the functional relevance of IncRNAs. We showed that a total of 760 IncRNAs were upregulated and 1210 IncRNAs were downregulated. Out of these dysregulated IncRNAs, 43.64% were intergenic, 22.31% were sense, 15.89% were intronic, 8.67% were bidirectional, 5.59% were antisense, 3.85% were sRNA host IncRNAs and 0.05% were enhancer. Six dysregulated IncRNAs were validated by quantitative PCR assays and the secondary structures of these IncRNAs were projected. Moreover, we conducted a bioinformatics analysis of an IncRNAs (ENST00000602478) to elucidate the diversity of modification and functions of IncRNAs. In summary, the current study compared the dysregulated IncRNAs profile upon CVA16 challenge and illustrated the intricate relationship between coding and IncRNAs transcripts. These results may not only provide a complete picture of transcription in CVA16 infected cells but also provide novel molecular targets for treatments of HFMD.

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Two-phase differential expression analysis for single cell RNA-seq.

Project description:Motivation:Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of 'differential expression'. However, most computational methods still stick with the old mentality of viewing differential expression as a simple 'up or down' phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations. Results:We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes. Availability and implementation:SC2P is implemented as an open source R package publicly available at https://github.com/haowulab/SC2P. Supplementary information:Supplementary data are available at Bioinformatics online.

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Differential expression analysis of RNA-seq data at single-base resolution.

Project description:RNA-sequencing (RNA-seq) is a flexible technology for measuring genome-wide expression that is rapidly replacing microarrays as costs become comparable. Current differential expression analysis methods for RNA-seq data fall into two broad classes: (1) methods that quantify expression within the boundaries of genes previously published in databases and (2) methods that attempt to reconstruct full length RNA transcripts. The first class cannot discover differential expression outside of previously known genes. While the second approach does possess discovery capabilities, statistical analysis of differential expression is complicated by the ambiguity and variability incurred while assembling transcripts and estimating their abundances. Here, we propose a novel method that first identifies differentially expressed regions (DERs) of interest by assessing differential expression at each base of the genome. The method then segments the genome into regions comprised of bases showing similar differential expression signal, and then assigns a measure of statistical significance to each region. Optionally, DERs can be annotated using a reference database of genomic features. We compare our approach with leading competitors from both current classes of differential expression methods and highlight the strengths and weaknesses of each. A software implementation of our method is available on github (https://github.com/alyssafrazee/derfinder).

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Single-cell RNA-Seq analysis identified kidney progenitor cells from human urine.

Project description: Not available

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IDEAS: individual level differential expression analysis for single-cell RNA-seq data.

Project description:We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms.

| S-EPMC8784862 | biostudies-literature

Corrigendum: Differential expression analysis of RNA-seq data at single-base resolution

Project description: Not available

| S-EPMC4059467 | biostudies-literature

B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data.

Project description:Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.

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DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.

Project description:MOTIVATION:Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. RESULTS:We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. AVAILABILITY AND IMPLEMENTATION:The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

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RNA-Seq reveals differential gene expression in Staphylococcus aureus with single-nucleotide resolution.

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