Article open access publication

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

Cancer Immunology, Immunotherapy, Springer Nature, ISSN 1432-0851

Volume 64, 5, 2015

DOI:10.1007/s00262-014-1649-1, Dimensions: pub.1002414378, PMC: PMC4528367, PMID: 25854580,



  1. (1) Department of Immunology, Institute for Cell Biology, Eberhard Karls University, Auf der Morgenstelle 15, 72076, Tübingen, Germany
  2. (2) Duke Medical Center, grid.414179.e
  3. (3) TRON gGmbH, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
  4. (4) Institute for Inflammation Research (IIR), Copenhagen University Hospital, Copenhagen, Denmark
  5. (5) Herlev Hospital, grid.411900.d, Capital Region
  6. (6) Universitätsklinikum Tübingen, grid.411544.1
  7. (7) Leiden University Medical Center, grid.10419.3d
  8. (8) Southampton General Hospital, grid.123047.3
  9. (9) Translational Oncology, University Medical Center, Johannes Gutenberg University (TRON gGmbH), Mainz, Germany


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.


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