Cancer Immunology, Immunotherapy

, Volume 64, Issue 5, pp 585–598

Data analysis as a source of variability of the HLA-peptide multimer assay: from manual gating to automated recognition of cell clusters

  • Cécile Gouttefangeas
  • Cliburn Chan
  • Sebastian Attig
  • Tania T. Køllgaard
  • Hans-Georg Rammensee
  • Stefan Stevanović
  • Dorothee Wernet
  • Per thor Straten
  • Marij J. P. Welters
  • Christian Ottensmeier
  • Sjoerd H. van der Burg
  • Cedrik M. Britten
Original Article

DOI: 10.1007/s00262-014-1649-1

Cite this article as:
Gouttefangeas, C., Chan, C., Attig, S. et al. Cancer Immunol Immunother (2015) 64: 585. doi:10.1007/s00262-014-1649-1

Abstract

Multiparameter flow cytometry is an indispensable method for assessing antigen-specific T cells in basic research and cancer immunotherapy. Proficiency panels have shown that cell sample processing, test protocols and data analysis may all contribute to the variability of the results obtained by laboratories performing ex vivo T cell immune monitoring. In particular, analysis currently relies on a manual, step-by-step strategy employing serial gating decisions based on visual inspection of one- or two-dimensional plots. It is therefore operator dependent and subjective. In the context of continuing efforts to support inter-laboratory T cell assay harmonization, the CIMT Immunoguiding Program organized its third proficiency panel dedicated to the detection of antigen-specific CD8+ T cells by HLA-peptide multimer staining. We first assessed the contribution of manual data analysis to the variability of reported T cell frequencies within a group of laboratories staining and analyzing the same cell samples with their own reagents and protocols. The results show that data analysis is a source of variation in the multimer assay outcome. To evaluate whether an automated analysis approach can reduce variability of proficiency panel data, we used a hierarchical statistical mixture model to identify cell clusters. Challenges for automated analysis were the need to process non-standardized data sets from multiple centers, and the fact that the antigen-specific cell frequencies were very low in most samples. We show that this automated method can circumvent difficulties inherent to manual gating strategies and is broadly applicable for experiments performed with heterogeneous protocols and reagents.

Keywords

HLA-peptide multimer Proficiency panel Data analysis Flow cytometry gating Automated analysis 

Abbreviations

CIC

Cancer Immunotherapy Consortium

CIP

CIMT Immunoguiding Program

CIMT

Association for Cancer Immunotherapy

HCMV

Human cytomegalovirus

CV

Coefficient of variation

FCM

Flow cytometry

FCS file

Flow Cytometry Standard format for data files

FMO

Fluorescence minus one

HLA

Human leukocyte antigen

ICS

Intracellular cytokine staining

IFNγ

Interferon gamma

mAb

Monoclonal antibody

PBMC

Peripheral blood mononuclear cells

RT

Room temperature

TCR

T cell receptor

Supplementary material

262_2014_1649_MOESM1_ESM.pdf (2.5 mb)
Supplementary material 1 (PDF 2587 kb)

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Cécile Gouttefangeas
    • 1
  • Cliburn Chan
    • 2
  • Sebastian Attig
    • 1
    • 8
  • Tania T. Køllgaard
    • 3
    • 9
  • Hans-Georg Rammensee
    • 1
  • Stefan Stevanović
    • 1
  • Dorothee Wernet
    • 4
  • Per thor Straten
    • 3
  • Marij J. P. Welters
    • 5
  • Christian Ottensmeier
    • 6
  • Sjoerd H. van der Burg
    • 5
  • Cedrik M. Britten
    • 7
  1. 1.Department of Immunology, Institute for Cell BiologyEberhard Karls UniversityTübingenGermany
  2. 2.Department of Biostatistics and BioinformaticsDuke University Medical CenterDurhamUSA
  3. 3.Center for Cancer Immune TherapyCopenhagen University HospitalHerlevDenmark
  4. 4.Center for Clinical Transfusion MedicineUniversity HospitalTübingenGermany
  5. 5.Department of Clinical OncologyLeiden University Medical CenterLeidenThe Netherlands
  6. 6.Cancer Sciences DivisionSouthampton University HospitalsSouthamptonUK
  7. 7.Translational Oncology, University Medical CenterJohannes Gutenberg University (TRON gGmbH)MainzGermany
  8. 8.TRON gGmbH, University Medical CenterJohannes Gutenberg University MainzMainzGermany
  9. 9.Institute for Inflammation Research (IIR)Copenhagen University HospitalCopenhagenDenmark

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