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

By Lin Lin, J. Frelinger, Wenxin Jiang, Greg Finak, C. Seshadri, P. Bart, G. Pantaleo, J. McElrath, Steve DeRosa, Raphael Gottardo. 2015

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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. © 2015 The Authors. Published by Wiley Periodicals, Inc.

Subfields: Biophysics, Immunology, Molecular Biology

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Citations in the corpus

Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets (2018, background): “…finding initial poor t-SNE visualizations from large datasets, researchers often resort to either subsampling their data to the very limit of detection of rare populations 12 or to exporting specific populations from their dataset, thus compromising the ‘unbiased’ data analysis approach 13,14 .”
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets (2017, methodology): “## [1] "pNFkB" "pp38" "pStat5" "pAkt" "pStat1" "pSHP2" "pZap70" ## [8] "pStat3" "pSlp76" "pBtk" "pPlcg2" "pErk" "pLat" "pS6"”