{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ahrazoglu T"],"funding":["Deutsche Forschungsgemeinschaft","Susanne-Bunnenberg-Stiftung at the Düsseldorf Heart Center"],"pagination":["1251"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11506830"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(10)"],"pubmed_abstract":["Human monocytes can be subdivided into phenotypically and functionally different classical, intermediate and non-classical monocytes according to the cell surface expression of CD14 and CD16. A precise identification and characterisation of monocyte subsets is necessary to unravel their role in inflammatory diseases. Here, we compared three different flow cytometric strategies (A-C) and found that strategy C, which included staining against CD11b, HLA-DR, CD14 and CD16, followed by several gating steps, most reliably identified monocyte subtypes in blood samples from healthy volunteers and from patients with stable coronary heart disease (CHD) or ST-elevation myocardial infarction (STEMI). Additionally, we established a fixation and permeabilisation protocol to enable the analysis of intracellular markers. We investigated the phagocytosis of lipid nanoparticles, the uptake of 2-NBD-glucose and the intracellular levels of CD74 and HLA-DM. This revealed that classical and intermediate monocytes from patients with STEMI showed the highest uptake of 2-NBD-glucose, whereas classical and intermediate monocytes from patients with CHD took up the largest amounts of lipid nanoparticles. Interestingly, intermediate monocytes had the highest expression level of HLA-DM. Taken together, we present a robust flow cytometric approach for the identification and functional characterisation of monocyte subtypes in healthy humans and patients with diseases."],"journal":["Biomolecules"],"pubmed_title":["Design of a Robust Flow Cytometric Approach for Phenotypical and Functional Analysis of Human Monocyte Subsets in Health and Disease."],"pmcid":["PMC11506830"],"funding_grant_id":["397484323","none","236177352"],"pubmed_authors":["Temme S","Nienhaus FT","Kluczny JI","Bonner F","Irschfeld LM","Ahrazoglu T","Kleimann P","Gerdes N"],"additional_accession":[]},"is_claimable":false,"name":"Design of a Robust Flow Cytometric Approach for Phenotypical and Functional Analysis of Human Monocyte Subsets in Health and Disease.","description":"Human monocytes can be subdivided into phenotypically and functionally different classical, intermediate and non-classical monocytes according to the cell surface expression of CD14 and CD16. A precise identification and characterisation of monocyte subsets is necessary to unravel their role in inflammatory diseases. Here, we compared three different flow cytometric strategies (A-C) and found that strategy C, which included staining against CD11b, HLA-DR, CD14 and CD16, followed by several gating steps, most reliably identified monocyte subtypes in blood samples from healthy volunteers and from patients with stable coronary heart disease (CHD) or ST-elevation myocardial infarction (STEMI). Additionally, we established a fixation and permeabilisation protocol to enable the analysis of intracellular markers. We investigated the phagocytosis of lipid nanoparticles, the uptake of 2-NBD-glucose and the intracellular levels of CD74 and HLA-DM. This revealed that classical and intermediate monocytes from patients with STEMI showed the highest uptake of 2-NBD-glucose, whereas classical and intermediate monocytes from patients with CHD took up the largest amounts of lipid nanoparticles. Interestingly, intermediate monocytes had the highest expression level of HLA-DM. Taken together, we present a robust flow cytometric approach for the identification and functional characterisation of monocyte subtypes in healthy humans and patients with diseases.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2025-04-04T03:04:07.732Z","creation":"2025-04-04T03:04:07.732Z"},"accession":"S-EPMC11506830","cross_references":{"pubmed":["39456184"],"doi":["10.3390/biom14101251"]}}