Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.

Détails

Ressource 1Télécharger: 25908275_BIB_A190F236B1C2.pdf (3270.58 [Ko])
Etat: Serval
Version: Final published version
ID Serval
serval:BIB_A190F236B1C2
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.
Périodique
Cytometry. Part A
Auteur(s)
Lin L., Frelinger J., Jiang W., Finak G., Seshadri C., Bart P.A., Pantaleo G., McElrath J., DeRosa S., Gottardo R.
ISSN
1552-4930 (Electronic)
ISSN-L
1552-4922
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
87
Numéro
7
Pages
675-682
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
An important aspect of immune monitoring for vaccine development, clinical trials, and research is the detection, measurement, and comparison of antigen-specific T-cells from subject samples under different conditions. Antigen-specific T-cells compose a very small fraction of total T-cells. Developments in cytometry technology over the past five years have enabled the measurement of single-cells in a multivariate and high-throughput manner. This growth in both dimensionality and quantity of data continues to pose a challenge for effective identification and visualization of rare cell subsets, such as antigen-specific T-cells. Dimension reduction and feature extraction play pivotal role in both identifying and visualizing cell populations of interest in large, multi-dimensional cytometry datasets. However, the automated identification and visualization of rare, high-dimensional cell subsets remains challenging. Here we demonstrate how a systematic and integrated approach combining targeted feature extraction with dimension reduction can be used to identify and visualize biological differences in rare, antigen-specific cell populations. By using OpenCyto to perform semi-automated gating and features extraction of flow cytometry data, followed by dimensionality reduction with t-SNE we are able to identify polyfunctional subpopulations of antigen-specific T-cells and visualize treatment-specific differences between them.
Mots-clé
Adolescent, Algorithms, Antigens/immunology, Computational Biology/methods, Cytokines/analysis, Epitopes/immunology, Flow Cytometry/methods, Humans, Leukocytes, Mononuclear, Staining and Labeling, T-Lymphocytes/classification, T-Lymphocytes/immunology
Pubmed
Web of science
Open Access
Oui
Création de la notice
20/07/2015 11:06
Dernière modification de la notice
08/05/2019 22:58
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