Ontology highlight
ABSTRACT:
PROVIDER: PRJNA814427 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR18286485.fastq.gz | Fastqsanger.gz | |||
| SRR18286486.fastq.gz | Fastqsanger.gz | |||
| SRR18286487.fastq.gz | Fastqsanger.gz | |||
| SRR18286488.fastq.gz | Fastqsanger.gz | |||
| SRR18286489.fastq.gz | Fastqsanger.gz |
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Project description:Human naïve CD4+ T cells (CD4+ CD45RA+ CD25- CD45RO- CD8- CD14- CD15- CD16- CD19- CD34- CD36- CD56- CD123- TCRγ/δ- HLA-DR- and CD235a-) were magnetically negatively isolated from peripheral blood. Cells were stimulated with anti-CD3/anti-CD28 antibodies plus IL-2, and samples were taken at 6h, 24h, 48h and 6d of stimulation. Mock stimulation control cells (sample group G02) received no further compounds, whereas induced regulatory T cells (iTregs) were either differentiated under addition of TGF-b (sample group G03) or TGF-b + retinoic acid + rapamycin (sample group G05). As control, naïve CD4+ T cells were left unstimulated (0h; sample group G01). Ex vivo isolated CD25-high cells were included as positive control for the Treg signature (“nTreg”; sample group G07). Tregs were defined by expression of FOXP3, the “master” transcription factor of Tregs. Samples from 3 male healthy donors (age 34 to 38 years) were prepared with the Qiagen Allprep kit and protein precipitate was solubilized (5 min, 95°C) in freshly prepared buffer containing 4% (w/v) SDS, 25 mM HEPES pH 7.6, 1mM DTT. Samples were prepared using the FASP assay and peptides were labeled with TMT 10-plex reagents and MS data acquired on a Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer.
Phenotyping, stability and functional analyses for iTregs induced under these conditions are available in Schmidt A et al., PLoSONE 2016, PMID: 26886923). In the publication associated to this dataset, the time-course proteomic profiling during human Treg differentiation is presented and integrated with RNA-Seq data from the same cells (including additional iTreg culture conditions and 2h time points for RNA-Seq). The data underwent clustering, network analysis and disease enrichment, which revealed many known regulators of Tregs along with novel candidate genes putatively involved in FOXP3 induction, the biological importance of which was validated with a targeted shRNA screen.
2018-04-10 | PXD005703 | JPOST Repository
Project description:<p>RNA sequencing (RNAseq) of peripheral blood lymphocytes was used to develop a means to assess immune function in a way that can be used in discovery science and applied to patients individually in clinical settings. The premise is that profiles of RNA present in immune cells is reflective of the combined influence of genetic and environmental variation on immune potential of individuals and that this potential can be tapped to understand human immunity in a variety of biological contexts. CD4+ cells were isolated from fresh whole blood via positive magnetic bead selection and cell lysates were prepared using Qiazol (QIAGEN) and stored at -80ºC for 3 to 8 months. RNA was extracted in batches for cDNA library preparation and RNA-Seq. For this study, we developed standard operating procedures for handling human blood samples and determined: a) the best way to enrich for CD4+ T cells from whole blood and yield high quality RNA, b) the sensitivity of this RNA profiling strategy, and c) the reproducibility of generated immune profiles from healthy subjects. We then developed bioinformatics processes to establish immune response signatures and immune response phenotypes within cohorts of individuals.</p>
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Project description:We performed ChIP-Seq for hallmark TFs (Ets1, Runx1), histone modification marks (H3K4me1, H3K4me2, H3K4me3, H3K27me3, H3K36me3), total RNA Pol II, short RNA-Seq as well as nucleosome mapping mainly in murine Rag2 -/- thymocytes. We also performed ChIP-Seq for E47 as well as nucleosome mapping, gene expression microarray analysis in CD4+ CD8+ DP thymocytes. Overall, we find a key role for the transcription factor Ets1, contributing towards alpha beta T cell lineage commitment via differential transactivation of stage-specific genes orchestrated by dynamic, co-association -mediated chromatin remodeling, as well as transcription dependent generation of a specialized chromatin structure at the TCR beta locus. Genome-wide analysis via ChIP-Seq for Ets1, Runx1, total RNA Pol II binding, H3K4me1, H3K4me2, H3K4me3, H3K27me3, H3K36me3, short RNA-Seq, Mnase-Seq in murine Rag2 -/- thymocytes, ChIP-Seq for E47, Mnase-Seq and gene expression microarray analysis in DP thymocytes This Series represents ChIP-Seq data.
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Project description:TMT 10-plex proteome analysis of murine CD4+ T cells differentiated towards a Th17 lineage in the presence of a SMAC mimetic
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Project description:Single cell RNA-seq of CDH5+ Rela fl/fl murine hepatic CD4+ T-cells
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Project description:RNA-seq of murine CD4 T cells after pentanoate treatment compared to untreated control
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Project description:Naïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. We used quantitative proteomics, bulk RNA-seq and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to generate a detailed map of cytokine-regulated gene expression programs. We demonstrated that cytokine response differs substantially between naïve and memory T cells and showed that memory cells are unable to differentiate into the Th2 phenotype. Moreover, memory T cells acquire a Th17-like phenotype in response to iTreg polarization. At the single-cell level, we demonstrated that T cells form a continuum which progresses from naïve to effector memory T cells. This continuum is accompanied by a gradual increase in the expression levels of chemokines and cytokines and thus represents an effectorness gradient. Finally, we found that T cell cytokine responses are determined by where the cells lie in the effectorness gradient and identified genes whose expression is controlled by cytokines in an effectorness-dependent manner. Our results shed light on the heterogeneity of T cells and their responses to cytokines, provide insight into immune disease inflammation and could inform drug development.
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