Automated flow cytometric analysis across large numbers of samples and cell types

Automated flow cytometric analysis across large numbers of samples and cell types

Copyright © 2015. Published by Elsevier Inc.

Bibliographische Detailangaben
Veröffentlicht in:Clinical immunology (Orlando, Fla.). - 1999. - 157(2015), 2 vom: 21. Apr., Seite 249-60
1. Verfasser: Chen, Xiaoyi (VerfasserIn)
Weitere Verfasser: Hasan, Milena, Libri, Valentina, Urrutia, Alejandra, Beitz, Benoît, Rouilly, Vincent, Duffy, Darragh, Patin, Étienne, Chalmond, Bernard, Rogge, Lars, Quintana-Murci, Lluis, Albert, Matthew L, Schwikowski, Benno, Milieu Intérieur Consortium
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2015
Zugriff auf das übergeordnete Werk:Clinical immunology (Orlando, Fla.)
Schlagworte:Journal Article Algorithms; Automation; Flow cytometry; Multidimensional analysis; Population-based cohort; Standardization;
Beschreibung
Zusammenfassung:Copyright © 2015. Published by Elsevier Inc.
Multi-parametric flow cytometry is a key technology for characterization of immune cell phenotypes. However, robust high-dimensional post-analytic strategies for automated data analysis in large numbers of donors are still lacking. Here, we report a computational pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-based approach was utilized for initial clustering, with the number of clusters determined using Bayesian Information Criterion. Meta-clustering in a reference donor permitted automated identification of 24 cell types across four panels. Cluster labels were integrated into FCS files, thus permitting comparisons to manual gating. Cell numbers and coefficient of variation (CV) were similar between FlowGM and conventional gating for lymphocyte populations, but notably FlowGM provided improved discrimination of "hard-to-gate" monocyte and dendritic cell (DC) subsets. FlowGM thus provides rapid high-dimensional analysis of cell phenotypes and is amenable to cohort studies
Beschreibung:Date Completed 29.06.2015
Date Revised 27.04.2015
published: Print-Electronic
Citation Status MEDLINE
ISSN:1521-7035
DOI:10.1016/j.clim.2014.12.009