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.
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.
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.
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.
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.
Project description:The progression to AIDS is influenced by changes in the biology of heterogeneous monocyte subsets. Classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16++) monocytes may represent progressive stages of monocyte maturation or disparate myeloid lineages with different turnover rates and function. To investigate the relationship between monocyte subsets and the response to SIV infection, we performed microarray analysis of monocyte subsets in rhesus macaques at three timepoints: prior to SIV infection, 26 days post-infection, and necropsy with AIDS. Genes with a 2-fold change between monocyte subsets (2023 genes) or infection timepoints (424 genes) were selected. We identify 172 genes differentially expressed among monocyte subsets in both uninfected and SIV-infected animals. Classical monocytes express genes associated with inflammatory responses and cell proliferation. Nonclassical monocytes express genes associated with activation, immune effector functions, and cell cycle inhibition. The classical and intermediate subsets are most similar at all timepoints, and transcriptional similarity between intermediate and nonclassical monocytes increases with AIDS. Cytosolic sensors of nucleic acids, restriction factors, and interferon-stimulated genes are induced in all three subsets with AIDS. We conclude that SIV infection alters the transcriptional relationship between monocyte subsets and that the innate immune response to SIV infection is conserved across monocyte subsets.
Project description:In this study gene expression of human blood classical monocytes (CD14++CD16-), CD16 positive monocytes (consisting of non-classical CD14+16++ and intermediate CD14++CD16+ monocytes) and CD1c+ CD19- dendritic cells from healthy subjects were investigated. Keywords: expression profiling by array
Project description:Monocytes are a critical component of the cellular innate immune system, and can be subdivided into classical, intermediate and non-classical subsets on the basis of surface CD14 and CD16 expression. Classical monocytes play the canonical role of phagocytosis, and account for the majority of circulating cells. Intermediate and non-classical cells are known to exhibit varying levels of phagocytosis and cytokine secretion, and are differentially expanded in certain pathological states. Characterisation of cell surface proteins expressed by each subset is informative not only to improve understanding of phenotype, but also to provide biological insight into function. Here we use highly multiplexed Tandem-Mass-Tag (TMT)-based mass spectrometry with selective cell surface biotinylation to characterise the classical monocyte surface proteome, then interrogate the phenotypic differences between each monocyte subset to identify novel protein markers.
Project description:Tuberculosis (TB) is responsible for the majority of mortality and morbidity associated with infectious diseases worldwide. The characterization of exact molecular components of immune response associated with protection against TB may help design more effective therapeutic interventions. In this study, we aimed to characterize the immune signature of monocyte subsets associated with active versus latent infection with Mycobacterium tuberculosis. Transcriptomic profiling using RNA sequencing was performed on classical (CD14+CD16-), intermediate (CD14+CD16+) and non-classical (CD14-CD16+) monocytes isolated from individuals with active TB (at diagnosis and 2 months post treatment), latent TB, as well as from TB negative healthy controls. Overall, we found specific gene signatures for each monocyte subset that could successfully discriminate between individuals with active TB at diagnosis, treated active TB, latent TB and healthy controls.
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