Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA238978 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:miRNA gene expression of human blood cells from healthy donors
| PRJNA386329 | ENA
Project description:To elucidate the difference between CD161-negative TCR Vdelta1 and CD161-positive TCR Vdelta1 cells, we performed GeneChip analysis of these two types of cells in peripheral blood mononuclear cells from healthy donors. Such analysis identified 192 genes that were expressed at two-fold or higher levels on CD161-positive TCR Vdelta1 cells than on CD161-negative TCR Vdelta1 cells. Expression of three genes (CCL3, CCL4 and CD161) from this signature was quantified in the same RNA samples by real-time PCR. CD161-negative and CD161-positive TCR Vdelta1cells of three healthy volunteers were isolated from freshly peripheral blood mononuclear cells.
2014-01-09 | E-GEOD-43402 | biostudies-arrayexpress
Project description:RANK-positive and RANK-negative luminal progenitor cells were isolated by FACS from histologically normal human breast tissue from wild-type human donors. RNA-seq gene expression profiling was used to find differentially expressed genes between the RANK-positive and RANK-negative cell populations.
2016-06-20 | GSE71203 | GEO
Project description:<p>Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies.</p> <p>Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression.</p>
| phs000815 | dbGaP
Project description:To elucidate the difference between CD161-negative TCR Vdelta1 and CD161-positive TCR Vdelta1 cells, we performed GeneChip analysis of these two types of cells in peripheral blood mononuclear cells from healthy donors. Such analysis identified 192 genes that were expressed at two-fold or higher levels on CD161-positive TCR Vdelta1 cells than on CD161-negative TCR Vdelta1 cells. Expression of three genes (CCL3, CCL4 and CD161) from this signature was quantified in the same RNA samples by real-time PCR.
2014-01-09 | GSE43402 | GEO
Project description:The study was completed to compare expression profiles of primary human beta cells (in the form of adult human islets), to the expression profile of hESC-derived beta-like cells. A HES3 line modified by homologous recombination to express GFP under the insulin promoter allowed us to FACS sort the hESC-derived cells into purified insulin-positive (presumably beta-like cells), and insulin-negative populations. Expression profile of adult human islets from cadaveric donors is compared to insulin-positive and insulin-negative populations of hESC-derived beta-like cells
2012-09-09 | E-GEOD-40709 | biostudies-arrayexpress
Project description:CITE-sequencing and TCR-sequencing of sorted activated, unactivated, and mock T cells derived from blood. T cells from five healthy human donors were stimulated with a peptide pool (CEFX, JPT) and anti-CD28/CD49d co-stimulation. Cells were then sorted based on activation (OX40 and PD-L1 for CD4s, CD137 for CD8s) and labeled with corresponding hashing antibodies prior to sequencing. In total, this dataset encompasses 42,370 cells (12,743 AIM-positive, 15,369 AIM-negative, 14,258 Mock).
2025-06-01 | GSE297814 | GEO
Project description:RNA-Seq of conventional and regulatory CD4+ T cells of healthy human donors
| PRJEB36933 | ENA
Project description:Coexpression of CD49b and LAG-3 identifies human and mouse T regulatory type 1 cells
| PRJNA232183 | ENA
Project description:Human CD4+ T-cell miRNAs were profiled by OpenArray after either positive or negative cytoselection CD4+ T-cells were obtained from blood of three human donors by positive and negative immunoselection methods. The miRNA complement of positively and negatively selected cells was determined by OpenArray.
2013-10-25 | E-GEOD-51659 | biostudies-arrayexpress