E-MTAB-7143
Ontology highlight
ABSTRACT:
PROVIDER: E-MTAB-7143 | ExpressionAtlas | 2019-01-16
REPOSITORIES: ExpressionAtlas
Similar Datasets
Project description:RNA-seq expression profiling of humain MAIT cells in blood and liver as compared to mainstream TCD4+ and TCD8+ cells.
| PRJEB28200 | ENA
Project description:<p>Paired DNA and RNA profiling is increasingly employed in genomics research to uncover molecular mechanisms of disease and to explore personal genotype and phenotype correlations. We developed a novel simultaneous DNA and RNA sequencing approach (Simul-seq) that enables comprehensive genomic and transcriptomic profiles from small quantities of cells or tissues. In this study, Simul-seq was performed on patient-derived fibroblast cells as well as an esophageal adenocarcinoma tumor sample and compared with standard DNA and RNA-sequencing approaches.</p>
| phs001214 | dbGaP
Project description:Recent studies have revealed the importance of long noncoding RNAs (lncRNAs) as tissue-specific regulators of gene expression. There is ample evidence that distinct types of vasculature undergo tight transcriptional control to preserve their structure, identity, and functions. We determined, for the first time, the global lineage-specific lncRNAome of human dermal blood and lymphatic endothelial cells (BECs and LECs), combining RNA-Seq and CAGE-Seq. A subsequent genome-wide antisense oligonucleotide-knockdown profiling of two BEC- and two LEC-specific lncRNAs identified LETR1 as a critical gatekeeper of the global LEC transcriptome. Deep RNA-DNA and RNA-protein interaction studies, and phenotype rescue analyses revealed that LETR1 is a nuclear trans-acting lncRNA modulating, via key epigenetic factors, the expression of essential target genes governing the growth and migratory ability of LECs. Together, our study provides new evidence supporting the intriguing concept that every cell type expresses precise lncRNA signatures to control lineage-specific regulatory programs.
2020-12-21 | PXD018578 | Pride
Project description:<p>We generated primary cultures from mechanically isolated kidney glomeruli (filtration unit of the nephron) which are composed of podocytes and mesangial cells. In parallel, we generated primary kidney cortex tubule cell cultures, which are composed primarily of proximal tubule cells. Early passage cultures of these two cell types were subjected to chromatin accessibility profiling (DNase-Seq) and gene expression profiling (RNA-Seq). We found thousands of dynamically regulated enhancers in both cell types that potentially regulate nearby and distal target genes that are differentially expressed. These data will be useful for understanding the epigenomic regulation of gene transcription in key kidney cell types.</p>
| phs001720 | dbGaP
Project description:1. Evaluate the diagnostic value of long noncoding RNA (CCAT1) expression by RT-PCR in peripheral blood in colorectal cancer patients versus normal healthy control personal.
2. Evaluate the clinical utility of detecting long noncoding RNA (CCAT1) expression in diagnosis of colorectal cancer patients & its relation to tumor staging.
3. Evaluate the clinical utility of detecting long noncoding RNA (CCAT1) expression in precancerous colorectal diseases.
4. Compare long noncoding RNA (CCAT1) expression with traditional marker; carcinoembryonic antigen (CEA) and Carbohydrate antigen 19-9 (CA19-9) in diagnosis of colorectal cancer.
| 2330122 | ecrin-mdr-crc
Project description:This project was focused on transcriptome and secretome of pathogenic bacteria B. pertussis, which were cultivated on solid plates (charcoal agar) with/without blood and subsequently sub-cultured in liquid media. As a standard, Bordetella spp. strain are grown on agar plates supplemented with blood. The main aim of this project was to determine how the cells respond to blood exposure and how the blood treatment impacts on the gene expression profiles in cell inoculated from plates and subcultured in a blood-less medium. Gene expression profiles were analyzed by RNA-seq and proteins secreted by B. pertussis cells grown in liquid media were determined by LC-MS/MS technique. Our results indicate that B. pertussis cells exposed to blood secreted significantly higher amounts of several virulence factors including type III secretion system (T3SS), bifunctional hemolysin/adenylate cyclase CyaA and adhesin FhaS, compared to cells inoculated from plates without blood. Differences in secretion of T3SS needle complex, effector protein BopC and anti-sigma factor BtrA were at least an order of magnitude higher compared to the differences on the transcriptional level, suggesting that post-transcriptional regulation and/or regulation of the protein secretion plays an important role in modulating the function of T3SS in B. pertussis.
2021-09-09 | PXD022760 | Pride
Project description:<p>Variability in induced pluripotent stem cell (iPSC) lines remains a roadblock for disease modeling and regenerative medicine. Through linear mixed models we have described different sources of gene expression variability from RNA sequencing data in 317 human iPSC lines from 101 individuals. We found that ~50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele specific expression show that iPSCs retain a subject-specific gene expression pattern. Pathway enrichment and key driver analyses, based on predictive causal gene networks, found that Polycomb targets explain a significant part of the non-genetic variability present in iPSCs within and across individuals. These publically available iPSC lines and genetic datasets will be a resource to the scientific community and will open new avenues to reduce variability in iPSCs and improve their utility in disease modeling.</p> <p>SNP array data from individuals included in RNA-seq transcriptome profiling study of human induced pluripotent stem cells to characterize gene expression variation across individuals and within multiple iPSC lines from the same individual. Genotyping was performed on patient blood.</p> Data availability: <ul> <li>SNP-genotyping: dbGaP - current study</li> <li>RNA-seq counts: <a href="http://www.ncbi.nlm.nih.gov/geo/">GEO</a> - GSE79636</li> <li>FASTQ files: <a href="http://www.ncbi.nlm.nih.gov/sra">SRA</a> - SRP072417</li> </ul>
| phs001139 | dbGaP
Project description:We report the application of single-cell RNA sequencing (scRNA-seq) technology for single-cell level profiling of the transcriptional features of cells from two primary lesions, one liver metastasis, one normal liver tissue, and peripheral blood monocuclear cells in a patient with metastatic pNET. The transcriptomic profiles of a total of 24.544 cells were captured.
2021-08-24 | GSE162708 | GEO
Project description:With the aim of understanding how Treg cells in highly vascularized tissues are related to Treg cells in other organs, we performed RNA-seq analysis of bulk Treg and Tconv cells isolated from liver, blood, spleen, and the liver-draining portal lymph node. This revealed a clear separation of cell transcriptomes by both tissue and Treg/Tconv identity, with cells from the liver falling between blood- and spleen-derived cells. Compared to splenic Treg cells, hepatic Treg cells were enriched for genes related to proliferation and activation, and genes encoding chemokine and cytokine receptors.
2018-10-22 | GSE121480 | GEO
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
2022-11-16 | PXD029891 | JPOST Repository