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

Natasja Wulff Pedersen, P. Anoop Chandran, Yu Qian, Jonathan Rebhahn, Nadia Viborg Petersen, Mathilde Dalsgaard Hoff, Scott White, Alexandra J. Lee, Rick Stanton, Charlotte Halgreen, Kivin Jakobsen, Tim Mosmann, Cécile Gouttefangeas, Cliburn Chan, Richard H. Scheuermann, Sine Reker Hadrup

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    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 (
    Original languageEnglish
    Article number858
    JournalFrontiers in Immunology
    Volume8
    Number of pages12
    ISSN1664-3224
    DOIs
    Publication statusPublished - 2017

    Keywords

    • Antigen-specific T cells
    • Automated gating
    • Computational analysis
    • Flow cytometry
    • Major histocompatibility complex dextramers
    • Major histocompatibility complex multimers

    Cite this

    Pedersen, Natasja Wulff ; Chandran, P. Anoop ; Qian, Yu ; Rebhahn, Jonathan ; Petersen, Nadia Viborg ; Hoff, Mathilde Dalsgaard ; White, Scott ; Lee, Alexandra J. ; Stanton, Rick ; Halgreen, Charlotte ; Jakobsen, Kivin ; Mosmann, Tim ; Gouttefangeas, Cécile ; Chan, Cliburn ; Scheuermann, Richard H. ; Hadrup, Sine Reker. / Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells. In: Frontiers in Immunology. 2017 ; Vol. 8.
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    title = "Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells",
    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 (",
    keywords = "Antigen-specific T cells, Automated gating, Computational analysis, Flow cytometry, Major histocompatibility complex dextramers, Major histocompatibility complex multimers",
    author = "Pedersen, {Natasja Wulff} and Chandran, {P. Anoop} and Yu Qian and Jonathan Rebhahn and Petersen, {Nadia Viborg} and Hoff, {Mathilde Dalsgaard} and Scott White and Lee, {Alexandra J.} and Rick Stanton and Charlotte Halgreen and Kivin Jakobsen and Tim Mosmann and C{\'e}cile Gouttefangeas and Cliburn Chan and Scheuermann, {Richard H.} and Hadrup, {Sine Reker}",
    year = "2017",
    doi = "10.3389/fimmu.2017.00858",
    language = "English",
    volume = "8",
    journal = "Frontiers in Immunology",
    issn = "1664-3224",
    publisher = "Frontiers Research Foundation",

    }

    Pedersen, NW, Chandran, PA, Qian, Y, Rebhahn, J, Petersen, NV, Hoff, MD, White, S, Lee, AJ, Stanton, R, Halgreen, C, Jakobsen, K, Mosmann, T, Gouttefangeas, C, Chan, C, Scheuermann, RH & Hadrup, SR 2017, 'Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells', Frontiers in Immunology, vol. 8, 858. https://doi.org/10.3389/fimmu.2017.00858

    Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells. / Pedersen, Natasja Wulff; Chandran, P. Anoop; Qian, Yu; Rebhahn, Jonathan; Petersen, Nadia Viborg; Hoff, Mathilde Dalsgaard; White, Scott; Lee, Alexandra J.; Stanton, Rick; Halgreen, Charlotte; Jakobsen, Kivin; Mosmann, Tim; Gouttefangeas, Cécile; Chan, Cliburn; Scheuermann, Richard H.; Hadrup, Sine Reker.

    In: Frontiers in Immunology, Vol. 8, 858, 2017.

    Research output: Contribution to journalJournal articleResearchpeer-review

    TY - JOUR

    T1 - Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells

    AU - Pedersen, Natasja Wulff

    AU - Chandran, P. Anoop

    AU - Qian, Yu

    AU - Rebhahn, Jonathan

    AU - Petersen, Nadia Viborg

    AU - Hoff, Mathilde Dalsgaard

    AU - White, Scott

    AU - Lee, Alexandra J.

    AU - Stanton, Rick

    AU - Halgreen, Charlotte

    AU - Jakobsen, Kivin

    AU - Mosmann, Tim

    AU - Gouttefangeas, Cécile

    AU - Chan, Cliburn

    AU - Scheuermann, Richard H.

    AU - Hadrup, Sine Reker

    PY - 2017

    Y1 - 2017

    N2 - 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 (

    AB - 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 (

    KW - Antigen-specific T cells

    KW - Automated gating

    KW - Computational analysis

    KW - Flow cytometry

    KW - Major histocompatibility complex dextramers

    KW - Major histocompatibility complex multimers

    U2 - 10.3389/fimmu.2017.00858

    DO - 10.3389/fimmu.2017.00858

    M3 - Journal article

    C2 - 28798746

    VL - 8

    JO - Frontiers in Immunology

    JF - Frontiers in Immunology

    SN - 1664-3224

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    ER -