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Multi‐parametric cytometry from a complex cellular sample: Improvements and limits of manual versus computational‐based interactive analyses
Author(s) -
GondoisRey F.,
Granjeaud S.,
Rouillier P.,
Rioualen C.,
Bidaut G.,
Olive D.
Publication year - 2016
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22850
Subject(s) - computer science , parametric statistics , gating , software , sample (material) , cytometry , identification (biology) , data mining , pattern recognition (psychology) , nonparametric statistics , process (computing) , artificial intelligence , flow cytometry , biology , mathematics , statistics , physics , physiology , genetics , botany , programming language , thermodynamics , operating system
Abstract The wide possibilities opened by the developments of multi‐parametric cytometry are limited by the inadequacy of the classical methods of analysis to the multi‐dimensional characteristics of the data. While new computational tools seemed ideally adapted and were applied successfully, their adoption is still low among the flow cytometrists. In the purpose to integrate unsupervised computational tools for the management of multi‐stained samples, we investigated their advantages and limits by comparison to manual gating on a typical sample analyzed in immunomonitoring routine. A single tube of PBMC, containing 11 populations characterized by different sizes and stained with 9 fluorescent markers, was used. We investigated the impact of the strategy choice on manual gating variability, an undocumented pitfall of the analysis process, and we identified rules to optimize it. While assessing automatic gating as an alternate, we introduced the Multi‐Experiment Viewer software (MeV) and validated it for merging clusters and annotating interactively populations. This procedure allowed the finding of both targeted and unexpected populations. However, the careful examination of computed clusters in standard dot plots revealed some heterogeneity, often below 10%, that was overcome by increasing the number of clusters to be computed. MeV facilitated the identification of populations by displaying both the MFI and the marker signature of the dataset simultaneously. The procedure described here appears fully adapted to manage homogeneously high number of multi‐stained samples and allows improving multi‐parametric analyses in a way close to the classic approach. © 2016 International Society for Advancement of Cytometry