Cellular immunological assays are important tools for the monitoring of responses to T-cell-inducing vaccine candidates. As these bioassays are often technically complex and require considerable experience, careful technology transfer between laboratories is critical if high quality, reproducible data that allows comparison between sites, is to be generated. The aim of this study, funded by the European Union Framework Program 7-funded TRANSVAC project, was to optimise Standard Operating Procedures and the technology transfer process to maximise the reproducibility of three bioassays for interferon-gamma responses: enzyme-linked immunosorbent assay (ELISA), ex-vivo enzyme-linked immunospot and intracellular cytokine staining. We found that the initial variability in results generated across three different laboratories reduced following a combination of Standard Operating Procedure harmonisation and the undertaking of side-by-side training sessions in which assay operators performed each assay in the presence of an assay 'lead' operator. Mean inter-site coefficients of variance reduced following this training session when compared with the pre-training values, most notably for the ELISA assay. There was a trend for increased inter-site variability at lower response magnitudes for the ELISA and intracellular cytokine staining assays. In conclusion, we recommend that on-site operator training is an essential component of the assay technology transfer process and combined with harmonised Standard Operating Procedures will improve the quality, reproducibility and comparability of data produced across different laboratories. These data may be helpful in ongoing discussions of the potential risk/benefit of centralised immunological assay strategies for large clinical trials versus decentralised units.
Assay optimisation and technology transfer for multi-site immuno-monitoring in vaccine trials.
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作者:Smith Steven G, Harris Stephanie A, Satti Iman, Bryan Donna, Walker K Barry, Dockrell Hazel M, McShane Helen, Ho Mei Mei
| 期刊: | PLoS One | 影响因子: | 2.600 |
| 时间: | 2017 | 起止号: | 2017 Oct 11; 12(10):e0184391 |
| doi: | 10.1371/journal.pone.0184391 | ||
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