An Open Platform for Large Scale LC-MS-Based Metabolomics

Metabolomics utilising liquid chromatography mass spectrometry (LC-MS) offers biomedical researchers a powerful means of assessing and comparing human phenotypes via measurement of the metabolome in biological samples. Platforms for LC-MS-based global profiling quantify hundreds or thousands of small molecule metabolites and/or lipids using combinations of distinct methods and analyses to develop broad coverage of the metabolome with high analytical sensitivity and specificity. However, the breadth of coverage provided by global profiling assays still outpaces efforts to characterise them by annotating profile signals with their respective metabolite identities. Fully realising the utility of metabolomics in biomedical research requires closing this gap by more accurately defining the finite metabolome coverage provided by common LC-MS-based global profiling methods. To date, method characterisation activities have progressed in the absence of broadly accepted standard LC methods as parallel efforts at building in-house libraries. While methodological diversity is a natural consequence of different design constraints and priorities observed across laboratories, it does tend to relegate in-house libraries to silos of information and investment that fail to advance the broader metabolomics community. Here, the National Phenome Centre’s established platform for LC-MS-based global profiling of small molecule metabolites and lipids is made open in its entirety. Complete and detailed protocols for reversed-phase and hydrophilic interaction liquid chromatography LC-MS methods are offered alongside discussion of the rationale for their design specifics. In addition to the formal protocols used routinely within the Centre, the reader is provided with notes for replication and adaptation of the methodology, as well as guidance on the preparation of biofluid samples to ensure their suitability for the analytical platform. The Centre’s accompanying open-source software for data extraction and pre-processing is also reviewed, and finally the method-specific identity of more than 700 small molecule and lipid species is disclosed. We hope that the substantial annotation information is useful to metabolomics practitioners of all experience levels and promotes the subsequent disclosure and constructive comparison (e.g. for validation and collective growth) of other in-house libraries and their associated methods. For interdisciplinary research teams looking to introduce LC-MS based metabolomics to their biomedical research programmes, we offer the open platform as a turnkey solution and welcome the growth in collective knowledge that may arise from its implementation in others’ hands.