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Clin Immunol. 2015 Apr;157(2):249-60. doi: 10.1016/j.clim.2014.12.009. Epub 2015 Jan 7.

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

Author information

1
Systems Biology Lab, Institut Pasteur, Paris, France; Laboratory of Analysis Geometry and Modeling, Department of Mathematics, University of Cergy-Pontoise, Ile de France, France.
2
Center for Human Immunology, Institut Pasteur, Paris France.
3
Center for Human Immunology, Institut Pasteur, Paris France; INSERM U818, France; Laboratory of Dendritic Cell Immunobiology, Department of Immunology, Institut Pasteur, Paris France.
4
Center for Bioinformatics, Institut Pasteur, Paris France.
5
Laboratory of Immunoregulation, Department of Immunology, Institut Pasteur, Paris France.
6
University of Cergy-Pontoise, France; CLMA, ENS-Cachan, France.
7
Center for Human Immunology, Institut Pasteur, Paris France; Laboratory of Immunoregulation, Department of Immunology, Institut Pasteur, Paris France.
8
Unit of Human Evolutionary Genetics, Department of Genomes & Genetics, Institut Pasteur, Paris, France; CNRS URA3012, France.
9
Center for Human Immunology, Institut Pasteur, Paris France; INSERM U818, France; Laboratory of Dendritic Cell Immunobiology, Department of Immunology, Institut Pasteur, Paris France; INSERM UMS20, France. Electronic address: albertm@pasteur.fr.
10
Systems Biology Lab, Institut Pasteur, Paris, France. Electronic address: benno@pasteur.fr.

Abstract

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.

KEYWORDS:

Algorithms;; Automation;; Flow cytometry;; Multidimensional analysis;; Population-based cohort;; Standardization;

PMID:
25576660
DOI:
10.1016/j.clim.2014.12.009
[Indexed for MEDLINE]
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