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Front Immunol. 2017 Jul 26;8:858. doi: 10.3389/fimmu.2017.00858. eCollection 2017.

Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells.

Author information

1
Division of Immunology and Vaccinology, Veterinary Institute, Technical University of Denmark, Copenhagen, Denmark.
2
Department of Immunology, Interfaculty Institute for Cell Biology, University of Tuebingen, Tuebingen, Germany.
3
Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States.
4
David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, United States.
5
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States.
6
Human Longevity Inc., San Diego, CA, United States.
7
Immudex Aps, Copenhagen, Denmark.
8
Department of Pathology, University of California, San Diego, La Jolla, CA, United States.

Abstract

Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (<0.1% of live, single lymphocytes), SWIFT outperformed the other tools. As used in this study, none of the algorithms offered a completely automated pipeline for identification of MHC multimer populations, as varying degrees of human interventions were needed to complete the analysis. In this study, we demonstrate the feasibility of using automated analysis pipelines for assessing and identifying even rare populations of antigen-responsive T cells and discuss the main properties, differences, and advantages of the different methods tested.

KEYWORDS:

antigen-specific T cells; automated gating; computational analysis; flow cytometry; major histocompatibility complex dextramers; major histocompatibility complex multimers

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