Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats

• Published in: Metabolites Vol. 12; no. 11; p. 1130
• Main Authors: Kaufmann, Martin, Vaysse, Pierre-Maxence, Savage, Adele, Amgheib, Ala, Marton, András, Manoli, Eftychios, Fichtinger, Gabor, Pringle, Steven D, Rudan, John F, Heeren, Ron M A, Takáts, Zoltán, Balog, Júlia, Porta Siegel, Tiffany
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.11.2022; MDPI
• Subjects: Aerosols; ambient ionization mass spectrometry; Animal models; Classification; Data analysis; Discriminant analysis; Evaluation; Fatty acids; Food; Food sources; food-grade meat; Ionization energy; Laboratories; Liver; Management; Mass spectrometry; Mass spectroscopy; Meat industry; multi-site; reference material; Reference materials; REIMS; Reproducibility; Research centers; Scientific imaging; Statistical analysis; United Kingdom; Hungary; United Kingdom > UK; Ireland; ambient ionization mass spectrometry; REIMS; multi-site; reference material; food-grade meat
• ISSN: 2218-1989; 2218-1989
• DOI: 10.3390/metabo12111130

Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.