ArticleMetabolomics : Official journal of the Metabolomic Society2020
Can we trust biomarkers identified using different non-targeted metabolomics platforms? Multi-platform, inter-laboratory comparative metabolomics profiling of lettuce cultivars via UPLC-QTOF-MS.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrated Metabolomics and Selection Signal Analysis Provide Insights Into the Selection for Flavonol Biosynthesis Associated With Lettuce Quality Improvement.Plant biotechnology journal · 2026Article
- Evaluation of the Efficacy of a Lactobacilli-Based Teat Detergents for the Microbiota of Cows Teats Using an Untargeted Metabolomics Approach.Journal of microbiology and biotechnology · 2024Article
- Multiplatform untargeted metabolomics.Magnetic resonance in chemistry : MRC · 2023Review
- Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Advances in Microbial NMR Metabolomics.Advances in experimental medicine and biology · 2023Article
- Article
- Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data.Metabolites · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
introductionData analysis during UPLC-MS non-targeted metabolomics introduces variation as different manufacturers use specific algorithms for data treatment and this makes untargeted metabolomics an application for the discovery of new biomarkers with low confidence in the reproducibility of the results under the use of different metabolomics platforms.
objectivesThis study compared the ability of two platforms (Agilent UPLC-ESI-QTOF-MS and Waters UPLC-IMS-QTOF-MS) to identify biomarkers in butterhead and romaine lettuce cultivars.
methodsTwo case studies by different metabolomics platforms: (1) Waters and Agilent datasets processed by the same data pre-processing software (Progenesis QI), and (2) Datasets processed by different data pre-processing software.
resultsA higher number of candidate biomarkers shared between sample groups in case 2 (101) than in case 1 (26) was found. Thirteen metabolites were common to both cases. Romaine lettuce was characterised by phenolic compounds including flavonoids, hydroxycinnamate derivatives, and 9-undecenal, while Butterhead showed sesquiterpene lactones and xanthosine. This study demonstrates that high percentages of the most discriminatory entities can be obtained by using the manufacturers' embedded pre-processing software and following the recommended processing data guidelines using commercial software to normalise the data matrix.
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