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

Details

Ressource 1Download: 25908275_BIB_A190F236B1C2.pdf (3270.58 [Ko])
State: Public
Version: Final published version
Serval ID
serval:BIB_A190F236B1C2
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.
Journal
Cytometry. Part A
Author(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
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
87
Number
7
Pages
675-682
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
Keywords
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
Yes
Create date
20/07/2015 11:06
Last modification date
20/08/2019 16:07
Usage data