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

Data analysis as a source of variability of the HLA-peptide multimer assay: from manual gating to automated recognition of cell clusters
Data analysis as a source of variability of the HLA-peptide multimer assay: from manual gating to automated recognition of cell clusters
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.
0340-7004
585-598
Gouttefangeas, Cécile
e06644af-a88f-4d60-b11c-debfaffefd0d
Chan, Cliburn
94f483be-8475-4ff8-aeb2-ff47d4bcd05d
Attig, Sebastian
98de59db-81c0-4612-8876-107c0a166af2
Køllgaard, Tania T.
aa47386e-b874-43fe-8b26-216c0bf55d24
Rammensee, Hans-Georg
d88e7677-da87-4fa3-b899-5384d5ff24bd
Stevanović, Stefan
e09af639-b73f-459b-b111-55a6c722b03d
Wernet, Dorothee
66ee0f90-7604-4cac-9f67-5d369ebd1200
thor Straten, Per
8496b4a5-cd9c-4daa-a1b4-6b05257fd39c
Welters, Marij J.P.
ece995e7-8d4c-431d-83a9-fc0a24f6fdf4
Ottensmeier, Christian
42b8a398-baac-4843-a3d6-056225675797
van der Burg, Sjoerd H.
1e880617-4966-4046-b70c-0ef451c2c2ab
Britten, Cedrik M.
a303deac-ffba-4a55-9a7e-fa5e158c0f8d
Gouttefangeas, Cécile
e06644af-a88f-4d60-b11c-debfaffefd0d
Chan, Cliburn
94f483be-8475-4ff8-aeb2-ff47d4bcd05d
Attig, Sebastian
98de59db-81c0-4612-8876-107c0a166af2
Køllgaard, Tania T.
aa47386e-b874-43fe-8b26-216c0bf55d24
Rammensee, Hans-Georg
d88e7677-da87-4fa3-b899-5384d5ff24bd
Stevanović, Stefan
e09af639-b73f-459b-b111-55a6c722b03d
Wernet, Dorothee
66ee0f90-7604-4cac-9f67-5d369ebd1200
thor Straten, Per
8496b4a5-cd9c-4daa-a1b4-6b05257fd39c
Welters, Marij J.P.
ece995e7-8d4c-431d-83a9-fc0a24f6fdf4
Ottensmeier, Christian
42b8a398-baac-4843-a3d6-056225675797
van der Burg, Sjoerd H.
1e880617-4966-4046-b70c-0ef451c2c2ab
Britten, Cedrik M.
a303deac-ffba-4a55-9a7e-fa5e158c0f8d

Gouttefangeas, Cécile, Chan, Cliburn, Attig, Sebastian, Køllgaard, Tania T., Rammensee, Hans-Georg, Stevanović, Stefan, Wernet, Dorothee, thor Straten, Per, Welters, Marij J.P., Ottensmeier, Christian, van der Burg, Sjoerd H. and Britten, Cedrik M. (2015) 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, 64 (5), 585-598. (doi:10.1007/s00262-014-1649-1). (PMID:25854580)

Record type: Article

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.

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More information

Accepted/In Press date: 18 December 2014
e-pub ahead of print date: 18 February 2015
Published date: May 2015
Organisations: Cancer Sciences

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Local EPrints ID: 396340
URI: http://eprints.soton.ac.uk/id/eprint/396340
ISSN: 0340-7004
PURE UUID: b9a12751-e1bf-4173-9d36-cbae333f38b4

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Date deposited: 08 Jun 2016 13:34
Last modified: 15 Mar 2024 00:51

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Contributors

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

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