Harmonization of Rapid Evaporative Ionization Mass Spectrometry Workflows across Four Sites and Testing Using Reference Material and Local Food-Grade Meats
Martin Kaufmann,
Pierre-Maxence Vaysse,
Adele Savage,
Ala Amgheib,
András Marton,
Eftychios Manoli,
Gabor Fichtinger,
Steven D. Pringle,
John F. Rudan,
Ron M. A. Heeren,
Zoltán Takáts,
Júlia Balog,
Tiffany Porta Siegel
Affiliations
Martin Kaufmann
Department of Surgery, Queen’s University, Kingston, ON K7L 2V7, Canada
Pierre-Maxence Vaysse
Maastricht MultiModal Molecular Imaging (M4i) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
Adele Savage
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
Ala Amgheib
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
András Marton
Waters Research Center, 1031 Budapest, Hungary
Eftychios Manoli
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
Gabor Fichtinger
School of Computing, Queen’s University, Kingston, ON K7L 2N8, Canada
Steven D. Pringle
Waters Corporation, Wilmslow SK9 4AX, UK
John F. Rudan
Department of Surgery, Queen’s University, Kingston, ON K7L 2V7, Canada
Ron M. A. Heeren
Maastricht MultiModal Molecular Imaging (M4i) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
Zoltán Takáts
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
Júlia Balog
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2BX, UK
Tiffany Porta Siegel
Maastricht MultiModal Molecular Imaging (M4i) Institute, Division of Imaging Mass Spectrometry, Maastricht University, 6229 ER Maastricht, The Netherlands
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 m/z 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.
