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      Development of a rapid profiling method for the analysis of polar analytes in urine using HILIC–MS and ion mobility enabled HILIC–MS

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          Abstract

          Introduction

          As large scale metabolic phenotyping is increasingly employed in preclinical studies and in the investigation of human health and disease the current LC–MS/MS profiling methodologies adopted for large sample sets can result in lengthy analysis times, putting strain on available resources. As a result of these pressures rapid methods of untargeted analysis may have value where large numbers of samples require screening.

          Objectives

          To develop, characterise and evaluate a rapid UHP-HILIC-MS-based method for the analysis of polar metabolites in rat urine and then extend the capabilities of this approach by the addition of IMS to the system.

          Methods

          A rapid untargeted HILIC LC–MS/MS profiling method for the analysis of small polar molecules has been developed. The 3.3 min separation used a Waters BEH amide (1 mm ID) analytical column on a Waters Synapt G2-Si Q-Tof enabled with ion mobility spectrometry (IMS). The methodology, was applied to the metabolic profiling of a series of rodent urine samples from vehicle-treated control rats and animals administered tienilic acid. The same separation was subsequently linked to IMS and MS to evaluate the benefits that IMS might provide for metabolome characterisation.

          Results

          The rapid HILIC–MS method was successfully applied to rapid analysis of rat urine and found, based on the data generated from the data acquired for the pooled quality control samples analysed at regular intervals throughout the analysis, to be robust. Peak area and retention times for the compounds detected in these samples showed good reproducibility across the batch. When used to profile the urine samples obtained from vehicle-dosed control and those administered tienilic acid the HILIC-MS method detected 3007 mass/retention time features. Analysis of the same samples using HILIC–IMS–MS enabled the detection of 6711 features. Provisional metabolite identification for a number of compounds was performed using the high collision energy MS/MS information compared against the Metlin MS/MS database and, in addition, both calculated and measured CCS values from an experimentally derived CCS database.

          Conclusion

          A rapid metabolic profiling method for the analysis of polar metabolites has been developed. The method has the advantages of speed and both reducing sample and solvent consumption compared to conventional profiling methods. The addition of IMS added an additional dimension for feature detection and the identification of metabolites.

          Electronic supplementary material

          The online version of this article (10.1007/s11306-019-1474-9) contains supplementary material, which is available to authorized users.

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          Most cited references37

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          Toward global metabolomics analysis with hydrophilic interaction liquid chromatography-mass spectrometry: improved metabolite identification by retention time prediction.

          Metabolomics is an emerging field of postgenomic biology concerned with comprehensive analysis of small molecules in biological systems. However, difficulties associated with the identification of detected metabolites currently limit its application. Here we demonstrate that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography (HILIC)-high-resolution mass spectrometry metabolomics platform. A quantitative structure retention relationship (QSRR) model, incorporating six physicochemical variables in a multiple-linear regression based on 120 authentic standard metabolites, shows good predictive ability for retention times of a range of metabolites (cross-validated R(2) = 0.82 and mean squared error = 0.14). The predicted retention times improved metabolite identification by removing 40% of the false identifications that occurred with identification by accurate mass alone. The importance of this procedure was demonstrated by putative identification of 690 metabolites in extracts of the protozoan parasite Trypanosoma brucei, thus allowing identified metabolites to be mapped onto an organism-wide metabolic network, providing opportunities for future studies of cellular metabolism from a global systems biology perspective.
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            XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization.

            Mass spectrometry based metabolomics represents a new area for bioinformatics technology development. While the computational tools currently available such as XCMS statistically assess and rank LC-MS features, they do not provide information about their structural identity. XCMS(2) is an open source software package which has been developed to automatically search tandem mass spectrometry (MS/MS) data against high quality experimental MS/MS data from known metabolites contained in a reference library (METLIN). Scoring of hits is based on a "shared peak count" method that identifies masses of fragment ions shared between the analytical and reference MS/MS spectra. Another functional component of XCMS(2) is the capability of providing structural information for unknown metabolites, which are not in the METLIN database. This "similarity search" algorithm has been developed to detect possible structural motifs in the unknown metabolite which may produce characteristic fragment ions and neutral losses to related reference compounds contained in METLIN, even if the precursor masses are not the same.
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              Ion Mobility-Derived Collision Cross Section As an Additional Measure for Lipid Fingerprinting and Identification

              Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules’ rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., “shotgun” lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.
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                Author and article information

                Contributors
                adam_king@waters.com
                i.wilson@imperial.ac.uk
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (New York )
                1573-3882
                1573-3890
                22 January 2019
                22 January 2019
                2019
                : 15
                : 2
                : 17
                Affiliations
                [1 ]Waters Corporation, SK9 4AX Wilmslow, Cheshire UK
                [2 ]ISNI 0000 0004 0436 6763, GRID grid.1025.6, Separations Science and Metabolomics laboratory, , Murdoch University, ; South Street, 6150 Murdoch, WA Australia
                [3 ]ISNI 0000 0004 0580 039X, GRID grid.433801.d, Waters Corporation, ; 01757 Milford, MA USA
                [4 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, , Imperial College London, ; London, UK
                [5 ]Discovery Safety, Drug Safety and Metabolism, IMED Biotech Unit, AstraZeneca, 1 Francis Crick Avenue, CB2 0RE Cambridge, UK
                [6 ]ISNI 0000 0004 0436 6763, GRID grid.1025.6, Medical and Molecular Sciences, School of Veterinary and Life Sciences, , Murdoch University, ; South Street, 6150 Murdoch, WA Australia
                Author information
                http://orcid.org/0000-0002-0819-2874
                http://orcid.org/0000-0001-5560-933X
                http://orcid.org/0000-0002-8558-7394
                http://orcid.org/0000-0002-1380-9285
                http://orcid.org/0000-0003-3513-364X
                Article
                1474
                10.1007/s11306-019-1474-9
                6342856
                30830424
                1e30b102-1c7c-405e-b998-d998da4a1fc3
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 7 August 2018
                : 20 December 2018
                Categories
                Original Article
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2019

                Molecular biology
                hydrophilic interaction chromatography,metabolic phenotyping,ims,lc–ms/ms
                Molecular biology
                hydrophilic interaction chromatography, metabolic phenotyping, ims, lc–ms/ms

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