Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA252845 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Monocytes are a heterogeneous cell population with subset-specific functions and phenotypes. The differential expression of CD14 and CD16 distinguishes classical CD14++CD16-, intermediate CD14++CD16+ and non-classical CD14+CD16++ monocytes. However, CD14++CD16+ monocytes remain the most poorly characterized subset so far. Therefore we analyzed the transcriptomes of the three monocyte subsets using SuperSAGE in combination with high-throughput sequencing. Analysis of 5,487,603 tags revealed unique identifiers of CD14++CD16+ monocytes, delineating these cells from the two other monocyte subsets. CD14++CD16+ monocytes were linked to antigen processing and presentation (e.g. CD74, HLA-DR, IFI30, CTSB), to inflammation and monocyte activation (e.g. TGFB1, AIF1, PTPN6), and to angiogenesis (e.g. TIE2, CD105). Therefore we provide genetic evidence for a distinct role of CD14++CD16+ monocytes in human immunity. Human monocyte subsets (CD14++CD16-, CD14++CD16+, CD14+CD16++) were isolated from 12 healthy volunteers based on MACS technology. Total RNA from monocyte subsets was isolated and same aliquots from each donor and monocyte subset were matched for SuperSAGE. Three SuperSAGE libraries (CD14++CD16-, CD14++CD16+ and CD14+CD16++) were generated.
2011-07-24 | E-GEOD-30811 | biostudies-arrayexpress
Project description:Monocytes are a heterogeneous cell population with subset-specific functions and phenotypes. The differential expression of CD14 and CD16 distinguishes classical CD14++CD16-, intermediate CD14++CD16+ and non-classical CD14+CD16++ monocytes. However, CD14++CD16+ monocytes remain the most poorly characterized subset so far. Therefore we analyzed the transcriptomes of the three monocyte subsets using SuperSAGE in combination with high-throughput sequencing. Analysis of 5,487,603 tags revealed unique identifiers of CD14++CD16+ monocytes, delineating these cells from the two other monocyte subsets. CD14++CD16+ monocytes were linked to antigen processing and presentation (e.g. CD74, HLA-DR, IFI30, CTSB), to inflammation and monocyte activation (e.g. TGFB1, AIF1, PTPN6), and to angiogenesis (e.g. TIE2, CD105). Therefore we provide genetic evidence for a distinct role of CD14++CD16+ monocytes in human immunity.
2011-07-24 | GSE30811 | GEO
Project description:Human peripheral monocytes have been categorized into three subsets based on differential expression levels of CD14 and CD16. However, the factors that influence the distribution of monocyte subsets and the roles which each subset plays in autoimmunity are not well studied. To compare the gene expression profiling 1) on intermediate monocytes CD14++CD16+ monocytes between healthy donors and autoimmune uveitis patients and 2) among 3 monocyte subsets in health donors, here we purified circulating intermediate CD14++CD16+ monocytes from 5 patients with autoimmune uveitis (labeled as P1-5) and 4 healthy donors (labeled as HD1-4) by flow cytometry and isolated total RNA to proceed microarray assay. In addition, we also purified CD14+CD16++ (non-classical monocytes) and CD14++CD16- (classical monocytes) from 4 healthy donors to do microarray. We demonstrate that CD14++CD16+ monocytes from patients and healthy control donors share a similar gene expression profile. The CD14+CD16++ cells (non-classical monocytes) display the most distinctive gene expression profiling when compared to intermediate CD14++CD16+ monocytes and classical CD14++CD16- monocytes.
2015-03-17 | GSE66936 | GEO
Project description:On the basis of the cell-surface molecule expression, CD16+ monocytes are likely comprised of distinct subpopulations of monocytes rather than a continuum of CD14+ monocytes with differing levels of cell activation. To better study this, we used gene array analysis that compared overall gene expression profiles of CD16+ subpopulations (CD14+CD16+ and CD16+) with that of CD14+CD16-. Gene expression in three FACS-sorted monocyte subsets was assessed by Affymetrix rhesus macaque oligonucleotide gene arrays that contain 52,024 probe sets covering 47,000 monkey genes. There were 29,361 probe sets that expressed in at least one subpopulation (raw array signal intensity > 32). Raw data were processed using robust multi-array average. To identify the most strongly, differentially expressed genes in each subpopulation, we only selected transcripts with consistently greater than four-fold difference (P < .05). In comparison to CD14+CD16- monocyte subset, a large number of genes (9098/29361, 30.9%) were differentially expressed in both CD14+CD16+ and CD16+ subsets: 1999 genes down-regulated; and 7099 genes up-regulated. Altogether, we observed large-scale gene expression differences between the CD14+CD16- subset and the two CD16+ subsets (CD14+CD16+ and CD16+), demonstrating transcriptional heterogeneity. The differential gene expression between CD16- and CD16+ monocytes underscore the fundamental differences between these cells. Comparisons between CD14+CD16+ and CD16+ were made to identify the genes that distinguish between these two CD16+ subpopulations. A relatively small number of genes were specifically associated with each subpopulation. Thirty-one genes were expressed strongly in CD14+CD16+ subset compared to CD16+ subset, and 94 genes were expressed strongly in CD16+ subset compared to CD14+CD16+ subset. A small set of genes that were expressed differentially between the two CD16+ subpopulations highlights similarity between the two cell types, but differentially expressed genes of function observed in each subset suggest different roles that these two subpopulations may play in vivo.
2009-01-31 | GSE14482 | GEO
Project description:SuperSAGE evidence for CD14++CD16+ monocytes as a third monocyte subset
| PRJNA144215 | ENA
Project description:The new official nomenclature subdivides human monocytes into three subsets, classical (CD14++CD16-), intermediate (CD14++CD16+) and nonclassical (CD14+CD16+). Here, we comprehensively define relationships and unique characteristics of the three human monocyte subsets using microarray and flow cytometry analysis. Our analysis revealed that the intermediate and nonclassical monocyte subsets were most closely related. For the intermediate subset, majority of genes and surface markers were expressed at an intermediary level between the classical and nonclassical subset. There features therefore indicate a close and direct lineage relationship between the intermediate and nonclassical subset. From gene expression profiles, we define unique characteristics for each monocyte subset. Classical monocytes were functionally versatile, due to the expression of a wide range of sensing receptors and several members of the AP-1 transcription factor family. The intermediate subset was distinguished by high expression of MHC class II associated genes. The nonclassical subset were most highly differentiated and defined by genes involved in cytoskeleton rearrangement that explains their highly motile patrolling behavior in vivo. Additionally, we identify unique surface markers, CLEC4D, IL-13RA1 for classical, GFRA2, CLEC10A for intermediate and GPR44 for nonclassical. Our study hence defines the fundamental features of monocyte subsets necessary for future research on monocyte heterogeneity. Three human monocyte subsets, the CD14++CD16- classical, the CD14++CD16+ intermediate and CD14+CD16+ nonclassical subsets were purified using fluorescence activated cell sorting from peripheral blood mononuclear cells. RNA was processed from the three monocyte subsets from 4 individual donors in duplicates, giving a total of 24 samples.
2011-05-01 | E-GEOD-25913 | biostudies-arrayexpress
Project description:Gene expression profiling of the classical (CD14++CD16-), intermediate (CD14++CD16+) and nonclassical (CD14+CD16+) human monocyte subsets
| PRJNA135861 | ENA
Project description:On the basis of the cell-surface molecule expression, CD16+ monocytes are likely comprised of distinct subpopulations of monocytes rather than a continuum of CD14+ monocytes with differing levels of cell activation. To better study this, we used gene array analysis that compared overall gene expression profiles of CD16+ subpopulations (CD14+CD16+ and CD16+) with that of CD14+CD16-. Gene expression in three FACS-sorted monocyte subsets was assessed by Affymetrix rhesus macaque oligonucleotide gene arrays that contain 52,024 probe sets covering 47,000 monkey genes. There were 29,361 probe sets that expressed in at least one subpopulation (raw array signal intensity > 32). Raw data were processed using robust multi-array average. To identify the most strongly, differentially expressed genes in each subpopulation, we only selected transcripts with consistently greater than four-fold difference (P < .05). In comparison to CD14+CD16- monocyte subset, a large number of genes (9098/29361, 30.9%) were differentially expressed in both CD14+CD16+ and CD16+ subsets: 1999 genes down-regulated; and 7099 genes up-regulated. Altogether, we observed large-scale gene expression differences between the CD14+CD16- subset and the two CD16+ subsets (CD14+CD16+ and CD16+), demonstrating transcriptional heterogeneity. The differential gene expression between CD16- and CD16+ monocytes underscore the fundamental differences between these cells. Comparisons between CD14+CD16+ and CD16+ were made to identify the genes that distinguish between these two CD16+ subpopulations. A relatively small number of genes were specifically associated with each subpopulation. Thirty-one genes were expressed strongly in CD14+CD16+ subset compared to CD16+ subset, and 94 genes were expressed strongly in CD16+ subset compared to CD14+CD16+ subset. A small set of genes that were expressed differentially between the two CD16+ subpopulations highlights similarity between the two cell types, but differentially expressed genes of function observed in each subset suggest different roles that these two subpopulations may play in vivo. To identify differentially expressed genes in subpopulations of monkey monocytes, three monocyte subsets from two normal uninfected rhesus macaques were FACS sorted based on their CD14 and CD16 expression. RNA purification and labeling, hybridization, array scanning, and image quantification were performed according to the manufacturerâs instructions. Briefly, FACS-isolated monocytes were spun down and lysed in Trizol reagents (Invitrogen), and total RNA was prepared using PureLink Micro-to-Midi Total RNA Purification system (Invitrogen). Quality of RNA was determined by 2100 Bioanalyzer RNA LabChip (Agilent Technologies). One hundred ng of high-quality total RNA was subjected to Affymetrix 1-cycle or 2-cycle synthesis amplification, fluorescent labeling, and hybridization to Affymetrix Rhesus Genome Arrays. Expression data was obtained from two aligned replicates using an Affymetrix GSC3000 scanner and processed by GCOS software (Affymetrix). Partek Genomic Suite System was used for downstream analysis of GCOS processed data. Signals from all probe sets were normalized using Rhesus Array Normalization Controls.
2010-05-06 | E-GEOD-14482 | biostudies-arrayexpress
Project description:Synovial fluid (SF)-derived monocyte–macrophage lineage (MON–Mϕ) cells in knee osteoarthritis (KOA) remain poorly understood. This lineage consists of four subpopulations with considerable interindividual variability that correlates with the distribution and activation of other immune cells. The most abundant subpopulation was that of CD11b+CD14−CD16− myeloid dendritic cells (mDCs; type cDC2), which were inactive and exhibited low cytokine production, low T-lymphocyte stimulation, high migratory ability comparing to other MON–Mϕ cells. Other major subpopulations included CD11b+CD14+CD16− monocyte-like cells and CD11b+CD14+CD16+ macrophages. A subpopulation of CD11b−CD14−CD16− mDCs (type cDC1) was less common. To confirm the identity of MON–Mϕ subpopulations characterised through flow cytometry, we performed bulk RNA-seq analysis on sorted CD11b+CD14−CD16−, CD11b+CD14+CD16− and CD11b+CD14+CD16+ MON–Mϕ subpopulations from the SF of four patients with KOA with a predominance of MON–Mϕ lineage cells. The CD11b+CD14+CD16− and CD11b+CD14+CD16+ cells shared a similar transcriptomic profile. However, CD11b+CD14−CD16− cells were remarkably distinct from other CD11b+ populations. The dataset is part of a study published by Mikulkova et al. Cell Reports.
2024-10-25 | GSE279915 | GEO
Project description:Psoriasis patients exhibit an increased risk of death by cardiovascular disease (CVD) and have elevated levels of circulating intermediate (CD14++CD16+) monocytes. This elevation could represent evidence of monocyte dysfunction in psoriasis patients at risk of CVD, as increases in circulating CD14++CD16+ monocytes are predictive of myocardial infarction and death. An elevation in the CD14++CD16+ cell population has been previously reported in patients with psoriatic disease, which has been confirmed in the cohort of our human psoriasis patients. CD16 expression was induced in CD14++CD16neg classical monocytes following plastic adhesion, which also elicited enhanced β2 but not β1 integrin surface expression, suggesting increased adhesive capacity. Indeed, we found that psoriasis patients have increased monocyte aggregation among circulating PBMCs which is recapitulated in the KC-Tie2 murine model of psoriasis. Visualization of human monocyte aggregates using imaging cytometry revealed that classical CD14++CD16neg monocytes are the predominant cell type participating in these aggregate pairs. Many of these pairs also included CD16+ monocytes, which could account for apparent elevations of intermediate monocytes. Additionally, intermediate monocytes and monocyte aggregates were the predominant cell type to adhere to TNF-α and IL-17A-stimulated dermal endothelium. Ingenuity Pathway Analysis (IPA) demonstrated that monocyte aggregates have a distinct transcriptional profile from singlet monocytes and monocytes following plastic adhesion, suggesting that circulating monocyte responses to aggregation are not fully accounted for by homotypic adhesion, and that further factors influence their functionality. qRT-PCR Gene Expression Profiling - 30 Samples Analyzed, 10 biological replicates, 10 Control Samples, 20 Test Samples
2015-06-27 | E-GEOD-70327 | biostudies-arrayexpress