Data analysis as a source of variability of the HLA-peptide multimer assay: from manual gating to automated recognition of cell clusters
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Published on: September 6, 2017
Cécile Gouttefangeas1, Cliburn Chan, Sebastian Attig
1Department of Immunology, Institute for Cell Biology, Eberhard Karls University, Auf der Morgenstelle 15, 72076, Tübingen, Germany, cecile.gouttefangeas@uni-tuebingen.de.
Insights
Manual analysis of flow cytometry data for T cell detection introduces variability. An automated approach using statistical models can reduce this subjectivity and improve harmonization for antigen-specific T cell monitoring.
Area of Science:
- Immunology
- Computational Biology
Background:
- Multiparameter flow cytometry is crucial for T cell assessment in research and cancer immunotherapy.
- Variability in T cell immune monitoring results stems from sample processing, protocols, and subjective manual data analysis.
Purpose of the Study:
- To assess the contribution of manual data analysis to variability in antigen-specific CD8(+) T cell detection.
- To evaluate an automated analysis approach for reducing inter-laboratory variability in T cell frequency reporting.
Main Methods:
- Utilized a proficiency panel for detecting antigen-specific CD8(+) T cells via HLA-peptide multimer staining.
- Applied a hierarchical statistical mixture model for automated cell cluster identification and analysis.
Main Results:
- Manual data analysis was identified as a significant source of variation in T cell frequency outcomes.
- The automated analysis method successfully reduced variability despite challenges like non-standardized data and low cell frequencies.
Conclusions:
- Automated data analysis can overcome limitations of manual gating in flow cytometry.
- This automated approach offers broad applicability for harmonizing T cell assays across diverse experimental conditions.
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.
