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      Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease

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          Abstract

          Metabolic profiling studies aim to achieve broad metabolome coverage in specific biological samples. However, wide metabolome coverage has proven difficult to achieve, mostly because of the diverse physicochemical properties of small molecules, obligating analysts to seek multiplatform and multimethod approaches. Challenges are even greater when it comes to applications to tissue samples, where tissue lysis and metabolite extraction can induce significant systematic variation in composition. We have developed a pipeline for obtaining the aqueous and organic compounds from diseased arterial tissue using two consecutive extractions, followed by a different untargeted UPLC-MS analysis method for each extract. Methods were rationally chosen and optimized to address the different physicochemical properties of each extract: hydrophilic interaction liquid chromatography (HILIC) for the aqueous extract and reversed-phase chromatography for the organic. This pipeline can be generic for tissue analysis as demonstrated by applications to different tissue types. The experimental setup and fast turnaround time of the two methods contributed toward obtaining highly reproducible features with exceptional chromatographic performance (CV % < 0.5%), making this pipeline suitable for metabolic profiling applications. We structurally assigned 226 metabolites from a range of chemical classes (e.g., carnitines, α-amino acids, purines, pyrimidines, phospholipids, sphingolipids, free fatty acids, and glycerolipids) which were mapped to their corresponding pathways, biological functions and known disease mechanisms. The combination of the two untargeted UPLC-MS methods showed high metabolite complementarity. We demonstrate the application of this pipeline to cardiovascular disease, where we show that the analyzed diseased groups ( n = 120) of arterial tissue could be distinguished based on their metabolic profiles.

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          Most cited references23

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          HMDB: a knowledgebase for the human metabolome

          The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.
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            Global metabolic profiling procedures for urine using UPLC-MS.

            The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.
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              Is Open Access

              LIPID MAPS online tools for lipid research

              The LIPID MAPS consortium has developed a number of online tools for performing tasks such as drawing lipid structures and predicting possible structures from mass spectrometry (MS) data. A simple online interface has been developed to enable an end-user to rapidly generate a variety of lipid chemical structures, along with corresponding systematic names and ontological information. The structure-drawing tools are available for six categories of lipids: (i) fatty acyls, (ii) glycerolipids, (iii) glycerophospholipids, (iv) cardiolipins, (v) sphingolipids and (vi) sterols. Within each category, the structure-drawing tools support the specification of various parameters such as chain lengths at a specific sn position, head groups, double bond positions and stereochemistry to generate a specific lipid structure. The structure-drawing tools have also been integrated with a second set of online tools which predict possible lipid structures from precursor-ion and product-ion MS experimental data. The MS prediction tools are available for three categories of lipids: (i) mono/di/triacylglycerols, (ii) glycerophospholipids and (iii) cardiolipins. The LIPID MAPS online tools are publicly available at www.lipidmaps.org/tools/.
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                Author and article information

                Journal
                Anal Chem
                Anal. Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                09 February 2015
                21 April 2015
                : 87
                : 8
                : 4184-4193
                Affiliations
                []Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.
                []Pharmaceutical Discovery and Life Sciences, Waters Corporations , Milford, Massachusetts 01757, United States
                [§ ]Academic Section of Vascular Surgery, Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London W6 8RF, U.K.
                []MRC-NIHR National Phenome Centre, IRDB Building, Imperial College London, Hammersmith Hospital , London W12 0NN, U.K.
                Author notes
                [* ]Tel: +44 (0)20 7594 3220. Fax: +44 (0) 20 759 43226. E-mail: elaine.holmes@ 123456imperial.ac.uk .
                Article
                10.1021/ac503775m
                4407508
                25664760
                85f5c625-9d33-49dc-8bbd-c35bc9247655
                Copyright © 2015 American Chemical Society

                This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.

                History
                : 08 October 2014
                : 09 February 2015
                Categories
                Article
                Custom metadata
                ac503775m
                ac-2014-03775m

                Analytical chemistry
                Analytical chemistry

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