Abstract
Untargeted metabolomics has rapidly become a profiling method of choice in many areas of research, including mitochondrial biology. Most commonly, untargeted metabolomics is performed with liquid chromatography/mass spectrometry because it enables measurement of a relatively wide range of physiochemically diverse molecules. Specifically, to assess energy pathways that are associated with mitochondrial metabolism, hydrophilic interaction liquid chromatography (HILIC) is often applied before analysis with a high-resolution accurate mass instrument. The workflow produces large, complex data files that are impractical to analyze manually. Here, we present a protocol to perform untargeted metabolomics on biofluids such as plasma, urine, and cerebral spinal fluid with a HILIC separation and an Orbitrap mass spectrometer. Our protocol describes each step of the analysis in detail, from preparation of solvents for chromatography to selecting parameters during data processing.
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References
Oliver SG, Winson MK, Kell DB, Baganz F (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16:373–378
Cho K, Mahieu NG, Johnson SL, Patti GJ (2014) After the feature presentation: technologies bridging untargeted metabolomics and biology. Curr Opin Biotechnol 28:143–148
Wishart DS (2019) Metabolomics for investigating physiological and pathophysiological processes. Physiol Rev 99:1819–1875
Tebani A, Bekri S (2019) Paving the way to precision nutrition through metabolomics. Front Nutr 6:41
Nikolskiy I, Siuzdak G, Patti GJ (2015) Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry. Bioinformatics 31:2017–2023
Patti GJ (2011) Separation strategies for untargeted metabolomics. J Sep Sci 34:3460–3469
Ivanisevic J, Want EJ (2019) From samples to insights into metabolism: uncovering biologically relevant information in LC-HRMS metabolomics data. Meta 9(12):308
Patti GJ, Yanes O, Siuzdak G (2012) Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13:263–269
Dunn WB, Bailey NJC, Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130:606–625
Kind T, Tsugawa H, Cajka T, Ma Y, Lai Z, Mehta SS, Wohlgemuth G, Barupal DK, Showalter MR, Arita M, Fiehn O (2017) Identification of small molecules using accurate mass MS/MS search. Mass Spectrom Rev 37(4):513–532
Dunn WB, Ellis DI (2005) Metabolomics: current analytical platforms and methodologies. Trends Anal Chem 24(4):285–294
Mahieu NG, Genenbacher JL, Patti GJ (2016) A roadmap for the XCMS family of software solutions in metabolomics. Curr Opin Chem Biol 30:87–93
Olivon F, Grelier G, Roussi F, Litaudon M, Touboul D (2017) MZmine 2 data-preprocessing to enhance molecular networking reliability. Anal Chem 89:7836–7840
Lommen A, Kools HJ (2012) MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware. Metabolomics 8:719–726
Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL (2011) Systems level studies of mammalian metabolomes: the role of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40:387–426
Uppal K, Soltow QA, Strobel FH, Pittard WS, Gernert KM, Yu T, Jones DP (2013) xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinformatics 14:15
Schwaiger M, Schoeny H, El Abiead Y, Hermann G, Rampler E, Koellensperger K (2019) Merging metabolomics and lipidomics into one analytical run. Analyst 144:220–229
Naser FJ, Mahieu NG, Wang L, Spalding JL, Johnson SL, Patti GJ (2018) Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome. Anal Bioanal Chem 410(4):1287–1297
Contrepois K, Jiang L, Snyder M (2015) Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry. Mol Cell Proteomics 14(6):1684–1695
Ivanisevic J, Zhu ZJ, Plate L, Tautenhahn R, Chen S, O’Brien PJ, Johnson CH, Marletta MA, Patti GJ, Siuzdak G (2013) Toward ‘omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal Chem 85(14):6876–6884
Roberts LD, Souza AL, Gerszten RE, Clish CB (2012) Targeted metabolomics. Curr Protoc Mol Biol 98(1):1–34
Snyder NW, Khezam M, Mesaros CA, Worth A, Blair IA (2013) Untargeted metabolomics from biological sources using ultraperformance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). J Vis Exp 75:1–8
Want EJ (2018) LC-MS untargeted analysis. In: Theodoridis AG, Gika HG, Wilson ID (eds) Metabolic profiling, methods and protocols. Humana, New York, NY, pp 99–116
University of California San Diego. Metabolomics workbench, general protocols. https://www.metabolomicsworkbench.org/protocols/general.php. Accessed 12 Jan 2019
European Molecular Biology Laboratory (2019) Metabolomics core facility, protocols used for LC-MS analysis. https://www.embl.de/mcf/metabolomics-core-facility/protocols/. Accessed 12 Jan 2019
Saigusa D, Okamura Y, Motoike IN, Katoh Y, Kurosawa Y, Saijyo R, Koshiba S, Yasuda J, Motohashi H, Sugawara J, Tanabe O, Kinoshita K, Yamamoto M (2016) Establishment of protocols for global metabolomics by LC-MS for biomarker discovery. PLoS One 11(8):e0160555
Gika HG, Zisi C, Theodoridis G, Wilson ID (2016) Protocol for quality control in metabolic profiling of biological fluids by U(H)PLC-MS. J Chromatogr B Anal Technol Biomed Life Sci 1008:15–25
Knee JM, Rzezniczak TZ, Barsch A, Guo KZ, Merritt TJS (2013) A novel ion pairing LC/MS metabolomics protocol for study of a variety of biologically relevant polar metabolites. J Chromatogr B Anal Technol Biomed Life Sci 936:63–73
Esterhuizen K, van der Westhuizen FH, Louw R (2017) Metabolomics of mitochondrial disease. Mitochondrion 35:97–110
Barshop BA (2004) Metabolomic approaches to mitochondrial disease: correlation of urine organic acids. Mitochondrion 4:521–527
Shaham O, Slate NG, Goldberger O, Xu Q, Ramanathan A, Souza AL, Clish CB, Sims KB, Mootha VK (2010) A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells. Proc Natl Acad Sci U S A 107(4):1571–1575
Leoni V, Strittmatter L, Zorzi G, Zibordi F, Dusi S, Garavaglia B, Venco P, Caccia C, Souza AL, Deik A, Clish CB, Rimoldi M, Ciusani E, Bertini E, Nardocci N, Mootha VK, Tiranti V (2012) Metabolic consequences of mitochondrial coenzyme A deficiency in patients with PANK2 mutations. Mol Genet Metab 105(3):463–471
Yao CH, Wang R, Wang Y, Kung CP, Weber JD, Patti GJ (2019) Mitochondrial fusion supports increased oxidative phosphorylation during cell proliferation. Elife 8:pii:e41351
Mandal R, Chamot D, Wishart DS (2018) The role of the Human Metabolome Database in inborn errors of metabolism. J Inherit Metab Dis 41:329–336
Feuchtbaum L, Carter J, Dowray S, Currier RJ, Lorey F (2012) Birth prevalence of disorders detectable through newborn screening by race/ethnicity. Genet Med 14:937–945
Thermo Fisher Scientific, Exactive Series Operating Manual, BRE0012255, Revision A, April 2017. https://assets.thermofisher.com/TFS-Assets/CMD/manuals/man-bre0012255-exactive-series-manbre0012255-en.pdf. Accessed 16 Feb 2019
Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, Kanazawa M, VanderGheynst J, Fiehn O, Arita M (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12:523–526
Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, Wishart DS, Xia J (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46:W486–W494
Mahieu NG, Spalding J, Patti GJ (2015) Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis. Bioinformatics 32:268–275
Blazenovic I, Kind T, Ji J, Fiehn O (2018) Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Meta 8(2):31
Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathe E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thevenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S (2019) The metaRbolomics toolbox in bioconductor and beyond. Meta 9(10):200
Souza A, Tautenhan R (2018) Features or compounds? A data reduction strategy for untargeted metabolomics to generate meaningful data (Report no. TN65204-EN 0418S). Thermo Fisher Scientific. https://assets.thermofisher.com/TFS-Assets/CMD/Technical-Notes/tn-65204-lc-ms-untargeted-metabolomics-tn65204-en.pdf. Accessed 16 Feb 2020
Mahieu NG, Patti GJ (2017) Systems-level annotation of a metabolomics data set reduces 25000 features to fewer than 1000 unique metabolites. Anal Chem 89:10397–10406
Fiehn O, Robertson D, Griffin J, van der Werf M, Nikolau B, Morrison N, Sumner LW, Goodacre R, Hardy NW, Taylor C, Fostel J, Kristal B, Kaddurah-Daouk R, Mendes P, van Ommen B, Lindon JC, Sansone S (2007) The metabolomics standards initiative (MSI). Metabolomics 3:175–178
Sumner LW, Amberg A, Barrett D, Beale MH, Beger RD, Daykin CA, Fan T, Fiehn O, Goodacre R, Griffin JL, Hankemeier T, Hardy NW, Harnly J, Higashi RM, Kopka J, Lane AN, Lindon JC, Marriott P, Nicholls AW, Reily MD, Thaden J, Viant M (2007) Proposed minimum reporting standards for chemical analysis. Metabolomics 3:231–241
Souza A, Ntai I, Tautenhan R (2018) Accelerated unknown compound annotation with confidence: from spectra to structure in untargeted metabolomics experiments (Report no. AN65362-EN 1218M). Thermo Fisher Scientific. https://assets.thermofisher.com/TFS-Assets/CMD/Application-Notes/an-65362-ms-compound-annotation-an65362-en.pdf. Accessed 16 Feb 2020
Acknowledgments
The authors would like to thank Dr. Clary Clish (Broad Institute of Harvard and MIT) for discussion and insights that helped develop this protocol.
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Souza, A.L., Patti, G.J. (2021). A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing. In: Weissig, V., Edeas, M. (eds) Mitochondrial Medicine . Methods in Molecular Biology, vol 2276. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-1266-8_27
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DOI: https://doi.org/10.1007/978-1-0716-1266-8_27
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