CD4 T cell subsets stratified by complement receptor type 2 (CR2) expression
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Arcadio Rubio GarcÃa
PROVIDER: E-MTAB-4834 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Peripheral Blood Mononuclear Cells (PBMCs) were isolated from a buffy coat (Australian Blood Bank) using Ficoll methodology. CD4+ T cells were isolated using Dynal Beads kit. Pure CD4+ T cells were then stained using a cocktail of monoclonal antobodies (mAbs), including: anti-CD4PE, CD45RO ECD, CD62L APC-Cy7, CD25 APC, CD127 Pacific Blue. After incubation, cells were washed twice in PBS/FCS (0.2%), and sorted into five different cell subsets: CD4+CD25+CD127low CD62L+CD45RO- (naive regulatory T cells), CD4+CD25+CD127low CD62L+/- CD45RO+ (activated regulatory T cells), CD4+CD25+CD127hi CD62L+/- CD45RO+ (memory T cells), CD4+CD25-CD127low CD62L+/- CD45RO+ (effector T cells) and CD4+CD25-CD127hi CD62L+ CD45RO- (naive T cells).
2011-02-10 | E-MEXP-2578 | biostudies-arrayexpress
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2021-10-20 | E-MTAB-9574 | biostudies-arrayexpress
Project description:Define,(a) intrinsic differences and (b) changes occuring upon TCR activation in genetic profiles of naive CD4+ and CD8+ T cells from young and old animals, to identify candidate genes altered in old T cells involved in impairement of immune response in order to restore immune function in the old. Activation dependant time series on sorted naive (CD62Lhi/CD44lo) CD4 and CD8 T cells from young and old animals-Each time point contains RNA pooled from 4 independent sortings
2011-03-24 | E-GEOD-28165 | biostudies-arrayexpress
Project description:In this study, we determined the signature of CD4+Foxp3- effector T cells and CD4+Foxp3+ Treg cells in naive animals and following LCMV WE infection. In addition, transcriptional signatures were determined in CXCR3+ CD4+Foxp3+ Treg cells arising in Th1 settings following LCMV infection.
2020-03-11 | E-MTAB-8861 | biostudies-arrayexpress
Project description:This project focuses on examining the differences in the global CD4+ T cell proteome between cells lacking the negative regulators of eIF4E( eIf4E-Binding Proteins 1/2) and WT naive CD4+ T cells both prior and following TCR stimulation.
2023-05-20 | PXD039635 | Pride
Project description:Naive CD4+ T cells are the common precursors of multiple effector and memory T cell subsets and possess a high plasticity in terms of differentiation potential. This stem-cell like character is important for cell therapies aiming at regeneration of specific immunity. Cell surface proteins are crucial for recognition and response to signals mediated by other cells or environmental changes. Knowledge of cell surface proteins of human naive CD4+ T cells and their changes during the early phase of T cell activation is urgently needed for a guided differentiation of naive T cells and may support the selection of pluripotent cells for cell therapy.<br>Periodate oxidation and aniline-catalyzed oxime ligation (PAL) technology was applied with subsequent quantitative LC-MS/MS (PAL-qLC-MS/MS) to generate a dataset describing the surface proteome of human naive CD4+ T cells and to monitor dynamic changes during the early phase of activation. This led to the identification of 173 N-glycosylated surface proteins, of which 24 were previously not known to be expressed on human naive CD4+ T cells or have no defined role within T cell activation. To independently confirm the proteomic dataset and to analyse the cell surface by an alternative technique a systematic phenotypic expression analysis of surface antigens via flow cytometry was performed. This screening expanded the previous dataset, resulting in 229 surface proteins which are expressed on naive unstimulated and activated CD4+ T cells. Furthermore, we generated a surface expression atlas based on transcriptome data, experimental annotation and predicted subcellular localization, and correlated the proteomics result with this transcriptional dataset.<br>This extensive surface atlas provides an overall naive CD4+ T cell surface resource and will enable future studies aiming at a deeper understanding of mechanisms of T cell biology allowing the identification of novel immune targets usable for the development of therapeutic treatments.
2015-05-27 | PXD001432 | Pride
Project description:H3K36me3 ChIP sequencing performed on circulating ex vivo isolated CD4+ Naive T cells and Activated CD4+ Naive T cells
2021-10-20 | E-MTAB-9572 | biostudies-arrayexpress
Project description:Different cytokines and drugs were administered to naive CD4+ T helper cells in vitro for 5 days
2019-06-10 | E-MTAB-8000 | biostudies-arrayexpress
Project description:To increase the dataset of our proteomic naive CD4+ T cell surface atlas, we applied whole genome microarray expression analysis. The NCBI RefSeq accession number of all transcripts which were detected in naive and stimulated CD4+ T cells (aCD3/aCD28 for 3h) of four different donors, was mapped to the corresponding UniProtKB accession number. For these UniProtKB ACs, we extracted the subcellular localization (UniProt_SL) annotation from the UniProtKB/Swiss-Prot database or if not available the subcellular localization was predicted unsing LocTree3 and PolyPhobius. This led to the identification of 908 genes coding for proteins located on the surface of human naive and/or activated CD4+ T cells. Gene expression in human naive T cells and naive T cells which were stimulated with aCD3/aCD28 for 3h was measured. Naive T cells from four different human blood donors were analyzed.
2014-11-01 | E-GEOD-61983 | biostudies-arrayexpress
Project description:Gene expression profiling of human CD8+ CD161hi and CD161lo central and effector memory and naïve T cell subsets. The mechanisms by which IL-17 secreting cells are regulated have not been completely elucidated. We previously identified a population of rhodamine-effluxing memory CD8+ T cells with high expression of CD161 that contributes to immune reconstitution after lymphopenia-inducing chemotherapy. Here we find that CD161hi CD8+ T cells share transcriptional programming with Th17 cells, but most do not secrete IL-17 or proliferate to stimulation through the T cell receptor (TCR). Transcriptional analysis of subsets identified by expression of CD161 and CD62L revealed a novel mechanism of TCR signaling pathway regulation in CD161hi CD8+ T cells that is distinct from that described in anergic or tolerant cells and renders them functionally dependent on costimulation through innate cytokine receptors or CD28. CD161hi CD8+ T cells, induced to proliferate by a TCR signal delivered with costimulation, demonstrated plasticity that was dependent on the nature of costimulation and resulted in expansion of IL-17 secreting cells that could not proliferate to a TCR signal alone or differentiation to Tc1-like cells that proliferated to TCR stimulation in the absence of costimulation. The data show an association between TCR signaling pathway downregulation and type 17 programming in CD161hi CD8+ T cells, whose dysregulation could mediate IL-17 dependent inflammatory diseases. T cell subsets were sort-purified from healthy adults and analyzed by gene expression profiling. Isolation: Effluxing CD161hi and non-effluxing CD161lo CD8+ TCM and TEM subsets were purified using magnetic bead separation and cell sorting to achieve >98% purity, as previously described (35). CD8+ T cells were positively selected using CD8 Microbeads (Miltenyi Biotec, Germany), loaded with Rh123 (Sigma, St. Louis, MO) and cultured for 30 min to allow Rh123 efflux, then labeled with fluorochrome-conjugated antibodies to CD4, CD16, TCRγδ, Vα24, CD8, CD95, CD62L and CD161. CD161hi and CD161lo TCM and TEM subsets were sort-purified on a FACS ARIA equipped with 405 nm, 488 nm and 633 nm lasers (BD Biosciences). CD161hi TCM and TEM subsets were identified as CD62L+/Rh123lo/CD161hi and CD62L-/Rh123lo/CD161hi events, respectively, in the CD4-/CD16-/TcRγδ-/Vα24-/CD8+/CD95+ population. CD161lo TCM and TEM subsets were identified as CD62L+/Rh123hi/CD161int/neg and CD62L-/Rh123hi/CD161int/neg events, respectively, in the CD4-/CD16-/TcRγδ-/Vα24-/CD8+/CD95+ population. Gene expression profiling: RNA was extracted from sort-purified subsets from 6 healthy individuals using the RNeasy Plus Minikit (Qiagen, Valencia, CA) and biotinylated, followed by generation of amplified cRNA for array hybridization using the Illumina TotalPrep RNA Amplification Kit (Applied Biosystems, Foster City, CA). Amplified biotinylated cRNA was then purified before quality control to ensure high quality cRNA was available for hybridization. Labeled cRNA was hybridized to Illumina WG-6 Expression BeadChips v3.0 (Illumina, San Diego, CA), which quantitate expression of 48,802 transcripts derived from the NCBI RefSeq (Build 36.2, Release 22) and UniGene databases (Build 199). BeadChips were washed before reading and image extraction, and then scanned on an Illumina BeadArray Reader. The resulting TIFF images were processed using GenomeStudio Gene Expression Module (GEM) software. Data quality was assessed using the Control Summary feature in GenomeStudio GEM. For a given analysis set, a GenomeStudio Probe-level Final Report was generated by combining the Sample Probe Profile and Control Probe Profile tables. The Final Report comprising the full dataset was initially processed using the Bioconductor package lumi by employing a background correction estimate and quantile normalization. A small adjustment (i.e. 20 counts) was made to the entire dataset to make all intensity signals non-negative and these values were log2-transformed. The dataset was initially filtered using the ‘shorth’ function of the Bioconductor package genefilter, resulting in retention of 31,084 of 48,802 original probes. Each pairwise comparison was further filtered by discarding probes whose signal intensity was less than a defined signal “noise floor” across all arrays in the data subset. This was achieved by calculating the median of the ‘negative control’ probe signals for each array, averaging these values, and setting the noise floor as 3 times this average. Differential gene expression was then determined using the Bioconductor package limma, and a false discovery rate (FDR) method applied to correct for multiple testing. Significant differential gene expression was defined by a log2 ratio > 0.585 (± 1.5-fold) and FDR (adjusted p value) < 0.05.
2011-07-31 | E-GEOD-23663 | biostudies-arrayexpress