Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SMART-Seq analysis to understand the differences between WT and SRC-3-/- Tregs transcriptome

ABSTRACT: SMART-Seq analysis to understand the differences between WT and SRC-3-/- Tregs transcriptome

PROVIDER: PRJNA818314 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR18399490_1.fastq.gz	Fastqsanger.gz
	SRR18399490_2.fastq.gz	Fastqsanger.gz
	SRR18399491_1.fastq.gz	Fastqsanger.gz
	SRR18399491_2.fastq.gz	Fastqsanger.gz
	SRR18399492_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 16

Similar Datasets

SMART-Seq analysis to understand the differences between WT and SRC-3-/- Tregs transcriptome

Project description:SMART-seq analysis is used for single cells or for a small amount of purified total RNA from low number of cells. The goal of this study to compare the quantitative differences between the SRC-3+ and SRC-3-/- Tregs cell transcriptomes. Method: Tregs were isolated from FOXP3cre SRC-3f/f (SRC-3KO) and FOXP3 cre monogenic (WT) mice and sorted for GFP expression and the expression of cre

2025-03-19 | GSE199085 | GEO

Steroid receptor coactivator 3 is a key modulator of regulatory T cell–mediated tumor evasionT Cells

2023-05-30 | GSE216931 | GEO

Transcriptomic differences in CCR10+ versus CCR10- regulatory T cells (Tregs)

Project description:Transcriptomic differences in CCR10+ versus CCR10- regulatory T cells (Tregs)

| PRJNA872344 | ENA

IL-1β–driven osteoclastogenic Tregs accelerate bone erosion in arthritis

Project description:Purpose: The goal of this study is to evaluate Il1rn-/- Tregs features by bulk RNA sequencing (RNA-seq) of Tregs from WT and Il1rn-/- lymph nodes. Methods: mRNA profiles of isolated CD4+Foxp3eGFP+ from lymph nodes of three 35-day-old wild-type (WT) and three Il1rn-/- mice were generated by deep sequencing. RNA was extracted from sorted cells (5X10^4) using a Qiagen RNeasy Micro Kit. RNA-seq was performed using the Smart-Seq2 platform. Smart-Seq2 libraries were prepared by the Broad Technology Labs and sequenced by the Joint Biology Consortium– associated Broad Genomics Platform. Transcripts were quantified by the Broad computational pipeline using Cuffquant version 2.2.1. Results: WT and Il1rn-/- Tregs showed similar expression of multiple genes associated with suppressor function, including Foxp3, Il2ra, Il7r, and Ebi3 (a component of the suppressive cytokine IL-35), and transcription factors including E_o_m_e_s_, Tbx21 (encoding Tbet), and Irf4. Tregs from WT and Il1rn-/- _mice showed similar expression of genes encoding molecules involved in Treg localization and trafficking, including CXCR6, CXCR3, and CCR10. However, gene set enrichment analysis (GSEA) identified enhanced expression of genes encoding proteins involved in fatty acid metabolism (for example, NTHL1 and GABARAPL1) in Il1rn-/- Tregs and oxidative phosphorylation (OxPhos; for example, ATP5H and NDUFB3) in WT Tregs. Conclusions: Il1rn-/- Foxp3+ Tregs exhibited greater glycolytic capacity and reserve that WT Tregs. This finding is consistent with the superiority of Il1rn-/- Tregs with respect to suppression of T cell proliferation, a capacity associated with glycolysis

2021-09-06 | GSE183254 | GEO

Transcriptome analysis of naïve Tregs, memory Tregs and CD161+ Tregs

Project description:Transcriptome analysis of naïve Tregs, memory Tregs and CD161+ Tregs

| PRJNA489005 | ENA

Proteomic analysis of rest and activated Tregs

Project description:The aim of the project is to do proteomic analysis of total cell lysate from rest and activated mouse Tregs. The proteomic data were mapped to the Gene Ontology Cellular Component (GOCC)database to obtain the list of membrane proteins on activated Tregs. The set of membrane proteins identified were compared with the potential Siglec-1 counter-receptors on activated Tregs.

2021-04-28 | PXD022259 | Pride

Transcriptomic differences in CCR10+ versus CCR10- regulatory T cells (Tregs)

Project description:We identified decreases in circulating CCR10+ Tregs in human hypertension. In order to better determine the phenotype of these cells, we performed bulk RNA sequencing of flow sorted human CCR10+ versus CCR10- Tregs from healthy control individuals. Results revealed evidence of increased T cell activation in CCR10+ Tregs.

2023-05-02 | GSE211844 | GEO

ATAC-seq data from naïve Tregs, memory Tregs and CD161+ Tregs

Project description:ATAC-seq data from naïve Tregs, memory Tregs and CD161+ Tregs

| PRJNA489004 | ENA

Gene expression profiling of murine WT and DNR Tregs

Project description:Donminant negative transform growth factor receptor II (DNR) mice were served as a murine primary biliary cirrhrosis model. CD4+Foxp3+ Regulatory T cells (Tregs) play a critical role in self-tolerance and in regulating PBC. In order to determine whether DNR mice derived Tregs processed defective function compared with WT Tregs, CD4+Foxp3+ Treg cells were sorted from DNR and WT mice, respectively, then gene expression analysis was performed by using the Affymetrix GeneChip Mouse Genome 430 2.0 arrays CD4+Foxp3+ Tregs were sorted from the spleen of 10-week-old DNR mice and B6 wild-type mice, respectively. RNA of each sample was then extracted and hybridized on Affymetrix microarrays to detail differences between DNR Tregs and WT Tregs in gene expression.

2014-11-05 | E-GEOD-62964 | biostudies-arrayexpress

TIGIT+ Tregs

Project description:TIGIT+ Tregs suppress Th1 and Th17 responses while sparing Th2 responses. Analysis of global gene expression of TIGIT+ vs. TIGIT- Tregs from naive mice reveled that TIGIT+ Tregs display an activated phenotype and are enriched for Treg signature genes including the Treg effector molecule Fgl2 which enables them to selectively spare Th2 responses. TIGIT+ and TIGIT- Tregs were sorted from naïve Foxp3-GFP KI mice (pooled spleen and lymph nodes) TIGIT: T cell immunoreceptor with Ig and ITIM domains

2014-12-12 | E-GEOD-56299 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data