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From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

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Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 19, 1005 Lausanne, Switzerland
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Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
*
Authors to whom correspondence should be addressed.
Metabolites 2019, 9(12), 308; https://doi.org/10.3390/metabo9120308
Received: 4 November 2019 / Revised: 9 December 2019 / Accepted: 12 December 2019 / Published: 17 December 2019
Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies. View Full-Text
Keywords: untargeted metabolomics; liquid chromatography–mass spectrometry (LC-MS); metabolism; experimental design; sample preparation; data processing; metabolite identification; univariate and multivariate statistics; metabolic pathway and network analysis untargeted metabolomics; liquid chromatography–mass spectrometry (LC-MS); metabolism; experimental design; sample preparation; data processing; metabolite identification; univariate and multivariate statistics; metabolic pathway and network analysis
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MDPI and ACS Style

Ivanisevic, J.; Want, E.J. From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites 2019, 9, 308.

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