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

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 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 if 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.

Validation of Immunomonitoring Methods for Application in Clinical Studies: The HLA‐Peptide Multimer Staining Assay

“…3) yielding CVs at par with similar studies (<10%) (24,25). Interestingly we observed a general trend of increased variability when low frequency cells were quantified, which is well in-line with results of proficiency panels that have shown an increased inter-laboratory variation in the reported frequencies of low frequency T cells (<0.1% of the CD8 (1) subset) (11,35).…”

“…Manual analysis is a known source of variability in flow cytometry assays (34)(35)(36)(37)(38). We therefore addressed the contribution of data analysis to the variability of the multimer assay.…”

Computational flow cytometry: helping to make sense of high-dimensional immunology data

“…Manual analysis of flow cytometry data has been shown to constitute one of the major sources of variability in flow cytometry analyses [10][11][12][13] . Manual gating is often hard to reproduce mainly owing to two reasons.…”

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

“…Manual analysis of cytometry data is a simple and intuitive one. However, it constitutes a big source of variability and it is time consuming when a large number of samples and markers are analyzed [2][3][4]. Moreover, experts are typically looking for specific and expected cell types, excluding other cells from the analysis [5].…”