{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Burgermeister S"],"funding":["Swiss National Science Foundation","NIAID NIH HHS"],"pagination":["530"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12108741"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(5)"],"pubmed_abstract":["Follicles (Fs)/Germinal Centers (GCs) in tonsils and lymph nodes are dynamic microenvironments where diverse immune cell populations interact for the development of antibody responses against pathogens. The accurate in situ phenotypic analysis of these immune cells is a prerequisite for the comphehensive understanding of GC development. In this study, we explore unsupervised clustering approaches for distinguishing cell populations within F/GCs using marker expression data. We evaluate multiple clustering algorithms and find that k-means clustering provides the most effective separation of distinct cell subsets. Additionally, we investigate the predictive potential of common GC markers (CD3, CD4, CD20 and BCL6) for PD-1 expression, an important immune checkpoint regulator. Our analysis dem"],"journal":["Biology"],"pubmed_title":["Unsupervised Clustering of Cell Populations in Germinal Centers Using Multiplexed Immunofluorescence."],"pmcid":["PMC12108741"],"funding_grant_id":["UM1 AI164561","310030","SNF, 310030_204226","204226"],"pubmed_authors":["Lindsay H","Brenna C","Orfanakis M","Gottardo R","Petrovas C","Burgermeister S","Georgakis S","Fenwick C","Pantaleo G"],"additional_accession":[]},"is_claimable":false,"name":"Unsupervised Clustering of Cell Populations in Germinal Centers Using Multiplexed Immunofluorescence.","description":"Follicles (Fs)/Germinal Centers (GCs) in tonsils and lymph nodes are dynamic microenvironments where diverse immune cell populations interact for the development of antibody responses against pathogens. The accurate in situ phenotypic analysis of these immune cells is a prerequisite for the comphehensive understanding of GC development. In this study, we explore unsupervised clustering approaches for distinguishing cell populations within F/GCs using marker expression data. We evaluate multiple clustering algorithms and find that k-means clustering provides the most effective separation of distinct cell subsets. Additionally, we investigate the predictive potential of common GC markers (CD3, CD4, CD20 and BCL6) for PD-1 expression, an important immune checkpoint regulator. Our analysis dem","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 May","modification":"2026-05-28T03:08:04.449Z","creation":"2026-05-28T03:05:33.851Z"},"accession":"S-EPMC12108741","cross_references":{"pubmed":["40427719"],"doi":["10.3390/biology14050530"]}}