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

Lin Lin, Jacob Frelinger, Wenxin Jiang, Greg Finak, Chetan Seshadri, Pierre Alexandre Bart, Giuseppe Pantaleo, Julie Mcelrath, Steve Derosa, Raphael Gottardo

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)675-682
Number of pages8
JournalCytometry Part A
Issue number7
StatePublished - Jul 1 2015

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology


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