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Development, characterization and comparisons of targeted and non-targeted metabolomics methods
Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry.
Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. University of the Basque Country (UPV/EHU), Spain.
Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry.
Number of Authors: 32018 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 11, article id e0207082Article in journal (Refereed) Published
Abstract [en]

The potential of a metabolomics method to detect statistically significant perturbations in the metabolome of an organism is enhanced by excellent analytical precision, unequivocal identification, and broad metabolomic coverage. While the former two metrics are usually associated with targeted metabolomics and the latter with non-targeted metabolomics, a systematic comparison of the performance of both approaches has not yet been carried out. The present work reports on the development and performance evaluation of separate targeted and non-targeted metabolomics methods. The targeted approach facilitated determination of 181 metabolites (quantitative analysis of 18 amino acids, 11 biogenic amines, 5 neurotransmitters, 5 nucleobases and semi-quantitative analysis of 50 carnitines, 83 phos-phatidylcholines, and 9 sphingomyelins) using ultra-performance liquid chromatography - tandem mass spectrometry (UPLC-MS/MS) and flow injection-tandem mass spectrometry (Fl-MS/MS). Method accuracy and/or precision were assessed using replicate samples of NIST SRM1950 as well as fish liver and brain tissue from Gilthead Bream (Sparus aurata). The non-target approach involved UPLC-high resolution (Orbitrap) mass spectrometry (UPLC-HRMS). Testing of ionization mode and stationary phase revealed that a combination of positive electrospray ionization and HILIC chromatography produced the largest number of chromatographic features during non-target analysis. Furthermore, an evaluation of 4 different sequence drift correction algorithms, and combinations thereof, revealed that batchCorr produced the best precision in almost every test. However, even following correction of non-target data for signal drift, the precision of targeted data was better, confirming our existing assumptions about the strengths of targeted metabolomics. Finally, the accuracy of the online MS2-library mzCloud was evaluated using reference standards for 38 different metabolites. This is among the few studies that have systematically evaluated the performance of targeted and non-targeted metabolomics and provides new insight into the advantages and disadvantages of each approach.

Place, publisher, year, edition, pages
2018. Vol. 13, no 11, article id e0207082
National Category
Earth and Related Environmental Sciences Chemical Sciences
Identifiers
URN: urn:nbn:se:su:diva-162786DOI: 10.1371/journal.pone.0207082ISI: 000450254000034PubMedID: 30439966OAI: oai:DiVA.org:su-162786DiVA, id: diva2:1274042
Available from: 2018-12-27 Created: 2018-12-27 Last updated: 2018-12-27Bibliographically approved

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