title: NetFCM: a semi-automated web-based method for flow cytometry data analysis.
abstract: Multi-parametric flow cytometry (FCM) represents an invaluable instrument to conduct single cell analysis and has significantly increased our understanding of the immune system. However, due to new techniques allowing us to measure an increased number of phenotypes within the immune system, FCM data analysis has become more complex and labor-intensive than previously. We have therefore developed a semi-automatic gating strategy (NetFCM) that uses clustering and principal component analysis (PCA) together with other statistical methods to mimic manual gating approaches. NetFCM is an online tool both for subset identification as well as for quantification of differences between samples. Additionally, NetFCM can classify and cluster samples based on multidimensional data. We tested the method using a data set of peripheral blood mononuclear cells collected from 23 HIV-infected individuals, which were stimulated with overlapping HIV Gag-p55 and CMV-pp65 peptides or medium alone (negative control). NetFCM clustered the virus-specific CD8+ T cells based on IFNγ and TNF responses into distinct compartments. Additionally, NetFCM was capable of identifying HIV- and CMV-specific responses corresponding to those obtained by manual gating strategies. These data demonstrate that NetFCM has the potential to identify relevant T cell populations by mimicking classical FCM data analysis and reduce the subjectivity and amount of time associated with such analysis. © 2014 International Society for Advancement of Cytometry.
authors.affiliation: Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, DK-2800, Kongens Lyngby, Denmark.
authors.forename: Juliet
authors.initials: J
authors.lastname: Frederiksen
authors.forename: Marcus
authors.initials: M
authors.lastname: Buggert
authors.forename: Annika C
authors.initials: AC
authors.lastname: Karlsson
authors.forename: Ole
authors.initials: O
authors.lastname: Lund
mesh_terms: D000328:Adult; D000465:Algorithms; D018791:CD4 Lymphocyte Count; D018414:CD8-Positive T-Lymphocytes / Q000166:cytology* / Q000276:immunology; D003627:Data Interpretation, Statistical; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D015685:Fusion Proteins, gag-pol / Q000494:pharmacology; D015658:HIV Infections; D006801:Humans; D007371:Interferon-gamma / Q000276:immunology; D020407:Internet; D007963:Leukocytes, Mononuclear / Q000166:cytology*; D008297:Male; D008875:Middle Aged; D010750:Phosphoproteins / Q000494:pharmacology; D025341:Principal Component Analysis; D016176:T-Lymphocyte Subsets / Q000166:cytology; D048069:Tumor Necrosis Factors / Q000276:immunology; D014763:Viral Matrix Proteins / Q000494:pharmacology
chemical_list: D015685:Fusion Proteins, gag-pol; D010750:Phosphoproteins; D048069:Tumor Necrosis Factors; D014763:Viral Matrix Proteins; C065418:cytomegalovirus matrix protein 65kDa; D007371:Interferon-gamma
keywords: cellular+; data analysis; flow cytometry; immunity; semi-automated; web-based