1932

Abstract

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites’ reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biochem-061516-044952
2017-06-20
2024-06-13
Loading full text...

Full text loading...

/deliver/fulltext/biochem/86/1/annurev-biochem-061516-044952.html?itemId=/content/journals/10.1146/annurev-biochem-061516-044952&mimeType=html&fmt=ahah

Literature Cited

  1. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C. 1.  et al. 2013. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res 41:D801–7 [Google Scholar]
  2. Wishart DS, Mandal R, Stanislaus A, Ramirez-Gaona M. 2.  2016. Cancer metabolomics and the Human Metabolome Database. Metabolites 6:10 [Google Scholar]
  3. Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S. 3.  et al. 2013. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31:419–25 [Google Scholar]
  4. Harkewicz R, Dennis EA. 4.  2011. Applications of mass spectrometry to lipids and membranes. Annu. Rev. Biochem. 80:301–25 [Google Scholar]
  5. Bennett BD, Yuan J, Kimball EH, Rabinowitz JD. 5.  2008. Absolute quantitation of intracellular metabolite concentrations by an isotope ratio-based approach. Nat. Protoc. 3:1299–311 [Google Scholar]
  6. Bennett BD, Kimball EH, Gao M, Osterhout R, Van Dien SJ, Rabinowitz JD. 6.  2009. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5:593–99 [Google Scholar]
  7. Park JO, Rubin SA, Xu YF, Amador-Noguez D, Fan J. 7.  et al. 2016. Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat. Chem. Biol. 12:482–89 [Google Scholar]
  8. Mashego MR, Rumbold K, De Mey M, Vandamme E, Soetaert W, Heijnen JJ. 8.  2007. Microbial metabolomics: past, present and future methodologies. Biotechnol. Lett. 29:1–16 [Google Scholar]
  9. van Gulik WM. 9.  2010. Fast sampling for quantitative microbial metabolomics. Curr. Opin. Biotechnol. 21:27–34 [Google Scholar]
  10. Van Gulik WM, Canelas AB, Taymaz-Nikerel H, Douma RD, de Jonge LP, Heijnen JJ. 10.  2012. Fast sampling of the cellular metabolome. Methods Mol. Biol. 881:279–306 [Google Scholar]
  11. Vuckovic D. 11.  2012. Current trends and challenges in sample preparation for global metabolomics using liquid chromatography–mass spectrometry. Anal. Bioanal. Chem. 403:1523–48 [Google Scholar]
  12. Martano G, Delmotte N, Kiefer P, Christen P, Kentner D. 12.  et al. 2015. Fast sampling method for mammalian cell metabolic analyses using liquid chromatography–mass spectrometry. Nat. Protoc. 10:1–11 [Google Scholar]
  13. Munger J, Bennett BD, Parikh A, Feng XJ, McArdle J. 13.  et al. 2008. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy. Nat. Biotechnol. 26:1179–86 [Google Scholar]
  14. Wittmann C, Kromer JO, Kiefer P, Binz T, Heinzle E. 14.  2004. Impact of the cold shock phenomenon on quantification of intracellular metabolites in bacteria. Anal. Biochem. 327:135–39 [Google Scholar]
  15. Palladino GW, Wood JJ, Proctor HJ. 15.  1980. Modified freeze clamp technique for tissue assay. J. Surg. Res. 28:188–90 [Google Scholar]
  16. Winder CL, Dunn WB, Schuler S, Broadhurst D, Jarvis R. 16.  et al. 2008. Global metabolic profiling of Escherichia coli cultures: an evaluation of methods for quenching and extraction of intracellular metabolites. Anal. Chem. 80:2939–48 [Google Scholar]
  17. Gonzalez B, Francois J, Renaud M. 17.  1997. A rapid and reliable method for metabolite extraction in yeast using boiling buffered ethanol. Yeast 13:1347–55 [Google Scholar]
  18. Maharjan RP, Ferenci T. 18.  2003. Global metabolite analysis: the influence of extraction methodology on metabolome profiles of Escherichia coli. Anal. Biochem. 313:145–54 [Google Scholar]
  19. Kimball E, Rabinowitz JD. 19.  2006. Identifying decomposition products in extracts of cellular metabolites. Anal. Biochem. 358:273–80 [Google Scholar]
  20. El Rammouz R, Letisse F, Durand S, Portais JC, Moussa ZW, Fernandez X. 20.  2010. Analysis of skeletal muscle metabolome: evaluation of extraction methods for targeted metabolite quantification using liquid chromatography tandem mass spectrometry. Anal. Biochem. 398:169–77 [Google Scholar]
  21. Geier FM, Want EJ, Leroi AM, Bundy JG. 21.  2011. Cross-platform comparison of Caenorhabditis elegans tissue extraction strategies for comprehensive metabolome coverage. Anal. Chem. 83:3730–36 [Google Scholar]
  22. Yuan M, Breitkopf SB, Yang X, Asara JM. 22.  2012. A positive/negative ion-switching, targeted mass spectrometry–based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7:872–81 [Google Scholar]
  23. Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G. 23.  et al. 2013. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protoc. 8:17–32 [Google Scholar]
  24. Rabinowitz JD, Kimball E. 24.  2007. Acidic acetonitrile for cellular metabolome extraction from Escherichia coli. Anal. Chem. 79:6167–73 [Google Scholar]
  25. Shryock JC, Rubio R, Berne RM. 25.  1986. Extraction of adenine nucleotides from cultured endothelial cells. Anal. Biochem. 159:73–81 [Google Scholar]
  26. Grob MK, O'Brien K, Chu JJ, Chen DD. 26.  2003. Optimization of cellular nucleotide extraction and sample preparation for nucleotide pool analyses using capillary electrophoresis. J. Chromatogr. B 788:103–11 [Google Scholar]
  27. Ritter JB, Genzel Y, Reichl U. 27.  2008. Simultaneous extraction of several metabolites of energy metabolism and related substances in mammalian cells: optimization using experimental design. Anal. Biochem. 373:349–69 [Google Scholar]
  28. Dietmair S, Timmins NE, Gray PP, Nielsen LK, Kromer JO. 28.  2010. Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal. Biochem. 404:155–64 [Google Scholar]
  29. Lorenz MA, Burant CF, Kennedy RT. 29.  2011. Reducing time and increasing sensitivity in sample preparation for adherent mammalian cell metabolomics. Anal. Chem. 83:3406–14 [Google Scholar]
  30. Ser Z, Liu X, Tang NN, Locasale JW. 30.  2015. Extraction parameters for metabolomics from cultured cells. Anal. Biochem. 475:22–28 [Google Scholar]
  31. Mackay GM, Zheng L, van den Broek NJF, Gottlieb E. 31.  2015. Analysis of cell metabolism using LC-MS and isotope tracers. Methods Enzymol 561:171–96 [Google Scholar]
  32. Siegel D, Permentier H, Reijngoud DJ, Bischoff R. 32.  2014. Chemical and technical challenges in the analysis of central carbon metabolites by liquid-chromatography mass spectrometry. J. Chromatogr. B 966:21–33 [Google Scholar]
  33. Gil A, Siegel D, Permentier H, Reijngoud DJ, Dekker F, Bischoff R. 33.  2015. Stability of energy metabolites—an often overlooked issue in metabolomics studies: a review. Electrophoresis 36:2156–69 [Google Scholar]
  34. Lu W, Kwon YK, Rabinowitz JD. 34.  2007. Isotope ratio-based profiling of microbial folates. J. Am. Soc. Mass Spectrom. 18:898–909 [Google Scholar]
  35. Kwon YK, Lu W, Melamud E, Khanam N, Bognar A, Rabinowitz JD. 35.  2008. A domino effect in antifolate drug action in Escherichia coli. Nat. Chem. Biol. 4:602–8 [Google Scholar]
  36. Overmyer KA, Thonusin C, Qi NR, Burant CF, Evans CR. 36.  2015. Impact of anesthesia and euthanasia on metabolomics of mammalian tissues: studies in a C57BL/6J mouse model. PLOS ONE 10:e0117232 [Google Scholar]
  37. Theodoridis G, Gika HG, Wilson ID. 37.  2011. Mass spectrometry–based holistic analysis approaches for metabolite profiling in systems biology studies. Mass. Spectrom. Rev. 30:884–906 [Google Scholar]
  38. Junot C, Fenaille F, Colsch B, Becher F. 38.  2014. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass. Spectrom. Rev. 33:471–500 [Google Scholar]
  39. Kuehnbaum NL, Britz-McKibbin P. 39.  2013. New advances in separation science for metabolomics: resolving chemical diversity in a post-genomic era. Chem. Rev. 113:2437–68 [Google Scholar]
  40. Milne SB, Mathews TP, Myers DS, Ivanova PT, Brown HA. 40.  2013. Sum of the parts: mass spectrometry–based metabolomics. Biochemistry 52:3829–40 [Google Scholar]
  41. Patti GJ, Yanes O, Siuzdak G. 41.  2012. Innovation: metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13:263–69 [Google Scholar]
  42. Crutchfield CA, Lu WY, Melamud E, Rabinowitz JD. 42.  2010. Mass spectrometry-based metabolomics of yeast. Methods Enzymol 470:393–426 [Google Scholar]
  43. Makarov A, Denisov E, Kholomeev A, Baischun W, Lange O. 43.  et al. 2006. Performance evaluation of a hybrid linear ion trap/Orbitrap mass spectrometer. Anal. Chem. 78:2113–20 [Google Scholar]
  44. Eliuk S, Makarov A. 44.  2015. Evolution of Orbitrap mass spectrometry instrumentation. Annu. Rev. Anal. Chem. 8:61–80 [Google Scholar]
  45. Makarov A. 45.  2000. Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis. Anal. Chem. 72:1156–62 [Google Scholar]
  46. Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G. 46.  2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78:779–87 [Google Scholar]
  47. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. 47.  2012. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84:5035–39 [Google Scholar]
  48. Mahieu NG, Genenbacher JL, Patti GJ. 48.  2016. A roadmap for the XCMS family of software solutions in metabolomics. Curr. Opin. Chem. Biol. 30:87–93 [Google Scholar]
  49. Patti GJ. 49.  2011. Separation strategies for untargeted metabolomics. J. Sep. Sci. 34:3460–69 [Google Scholar]
  50. Coulier L, Bas R, Jespersen S, Verheij E, van der Werf MJ, Hankemeier T. 50.  2006. Simultaneous quantitative analysis of metabolites using ion-pair liquid chromatography–electrospray ionization mass spectrometry. Anal. Chem. 78:6573–82 [Google Scholar]
  51. Luo B, Groenke K, Takors R, Wandrey C, Oldiges M. 51.  2007. Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography–mass spectrometry. J. Chromatogr. A 1147:153–64 [Google Scholar]
  52. Lu W, Bennett BD, Rabinowitz JD. 52.  2008. Analytical strategies for LC–MS-based targeted metabolomics. J. Chromatogr. B 871:236–42 [Google Scholar]
  53. Lu W, Clasquin MF, Melamud E, Amador-Noguez D, Caudy AA, Rabinowitz JD. 53.  2010. Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone Orbitrap mass spectrometer. Anal. Chem. 82:3212–21 [Google Scholar]
  54. Buescher JM, Moco S, Sauer U, Zamboni N. 54.  2010. Ultrahigh performance liquid chromatography–tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. Anal. Chem. 82:4403–12 [Google Scholar]
  55. Cubbon S, Bradbury T, Wilson J, Thomas-Oates J. 55.  2007. Hydrophilic interaction chromatography for mass spectrometric metabonomic studies of urine. Anal. Chem. 79:8911–18 [Google Scholar]
  56. Spagou K, Tsoukali H, Raikos N, Gika H, Wilson ID, Theodoridis G. 56.  2010. Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies. J. Sep. Sci. 33:716–27 [Google Scholar]
  57. Buszewski B, Noga S. 57.  2012. Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique. Anal. Bioanal. Chem. 402:231–47 [Google Scholar]
  58. Bajad SU, Lu WY, Kimball EH, Yuan J, Peterson C, Rabinowitz JD. 58.  2006. Separation and quantitation of water soluble cellular metabolites by hydrophilic interaction chromatography–tandem mass spectrometry. J. Chromatogr. A 1125:76–88 [Google Scholar]
  59. Pesek JJ, Matyska MT, Fischer SM, Sana TR. 59.  2008. Analysis of hydrophilic metabolites by high-performance liquid chromatography–mass spectrometry using a silica hydride-based stationary phase. J. Chromatogr. A 1204:48–55 [Google Scholar]
  60. Zhang T, Creek DJ, Barrett MP, Blackburn G, Watson DG. 60.  2012. Evaluation of coupling reversed phase, aqueous normal phase, and hydrophilic interaction liquid chromatography with Orbitrap mass spectrometry for metabolomic studies of human urine. Anal. Chem. 84:1994–2001 [Google Scholar]
  61. Xu YF, Lu W, Rabinowitz JD. 61.  2015. Avoiding misannotation of in-source fragmentation products as cellular metabolites in liquid chromatography–mass spectrometry–based metabolomics. Anal. Chem. 87:2273–81 [Google Scholar]
  62. Kanu AB, Dwivedi P, Tam M, Matz L, Hill HH Jr. 62.  2008. Ion mobility–mass spectrometry. J. Mass Spectrom. 43:1–22 [Google Scholar]
  63. Lanucara F, Holman SW, Gray CJ, Eyers CE. 63.  2014. The power of ion mobility–mass spectrometry for structural characterization and the study of conformational dynamics. Nat. Chem. 6:281–94 [Google Scholar]
  64. Taguchi R, Houjou T, Nakanishi H, Yamazaki T, Ishida M. 64.  et al. 2005. Focused lipidomics by tandem mass spectrometry. J. Chromatogr. B 823:26–36 [Google Scholar]
  65. Ross KL, Dalluge JJ. 65.  2009. Liquid chromatography/tandem mass spectrometry of glycolytic intermediates: deconvolution of coeluting structural isomers based on unique product ion ratios. Anal. Chem. 81:4021–26 [Google Scholar]
  66. Xu FG, Zou L, Liu Y, Zhang ZJ, Ong CN. 66.  2011. Enhancement of the capabilities of liquid chromatography–mass spectrometry with derivatization: general principles and applications. Mass. Spectrom. Rev. 30:1143–72 [Google Scholar]
  67. Struys EA, Jansen EEW, Verhoeven NM, Jakobs C. 67.  2004. Measurement of urinary d- and l-2-hydroxyglutarate enantiomers by stable-isotope-dilution liquid chromatography–tandem mass spectrometry after derivatization with diacetyl-l-tartaric anhydride. Clin. Chem. 50:1391–95 [Google Scholar]
  68. Gibson KM, ten Brink HJ, Schor DS, Kok RM, Bootsma AH. 68.  et al. 1993. Stable-isotope dilution analysis of d- and l-2-hydroxyglutaric acid: application to the detection and prenatal diagnosis of d - and l-2-hydroxyglutaric acidemias. Pediatr. Res. 34:277–80 [Google Scholar]
  69. Guo K, Li L. 69.  2009. Differential 12C-/13C-isotope dansylation labeling and fast liquid chromatography/mass spectrometry for absolute and relative quantification of the metabolome. Anal. Chem. 81:3919–32 [Google Scholar]
  70. Meyer TE, Fox SD, Issaq HJ, Xu X, Chu LW. 70.  et al. 2011. A reproducible and high-throughput HPLC/MS method to separate sarcosine from α- and β-alanine and to quantify sarcosine in human serum and urine. Anal. Chem. 83:5735–40 [Google Scholar]
  71. Chen J, Zhang J, Zhang WP, Chen ZL. 71.  2014. Sensitive determination of the potential biomarker sarcosine for prostate cancer by LC–MS with N,N′-dicyclohexylcarbodiimide derivatization. J. Sep. Sci. 37:14–19 [Google Scholar]
  72. Purwaha P, Silva LP, Hawke DH, Weinstein JN, Lorenzi PL. 72.  2014. An artifact in LC–MS/MS measurement of glutamine and glutamic acid: in-source cyclization to pyroglutamic acid. Anal. Chem. 86:5633–37 [Google Scholar]
  73. Annesley TM. 73.  2003. Ion suppression in mass spectrometry. Clin. Chem. 49:1041–44 [Google Scholar]
  74. Halket JM, Waterman D, Przyborowska AM, Patel RKP, Fraser PD, Bramley PM. 74.  2005. Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J. Exp. Bot. 56:219–43 [Google Scholar]
  75. Zimmermann D, Hartmann M, Moyer MP, Nolte J, Baumbach JI. 75.  2007. Determination of volatile products of human colon cell line metabolism by GC/MS analysis. Metabolomics 3:13–17 [Google Scholar]
  76. Grundy SM, Ahrens EH, Miettinen TA. 76.  1965. Quantitative isolation and gas–liquid chromatographic analysis of total fecal bile acids. J. Lipid Res. 6:397–410 [Google Scholar]
  77. Fiehn O, Wohlgemuth G, Scholz M, Kind T, Lee DY. 77.  et al. 2008. Quality control for plant metabolomics: reporting MSI‐compliant studies. Plant J 53:691–704 [Google Scholar]
  78. Roessner U, Wagner C, Kopka J, Trethewey RN, Willmitzer L. 78.  2000. Simultaneous analysis of metabolites in potato tuber by gas chromatography–mass spectrometry. Plant J 23:131–42 [Google Scholar]
  79. Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M. 79.  et al. 2009. FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81:10038–48 [Google Scholar]
  80. Stein SE. 80.  2014. The NIST 14 mass spectral library Natl. Inst. Stand. Technol. Gaithersburg, MD, updated Jan. 13, 2017. https://www.nist.gov/srd/nist-standard-reference-database-1a-v14 [Google Scholar]
  81. Koek MM, Jellema RH, van der Greef J, Tas AC, Hankemeier T. 81.  2011. Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics 7:307–28 [Google Scholar]
  82. Kaspar H, Dettmer K, Gronwald W, Oefner PJ. 82.  2008. Automated GC–MS analysis of free amino acids in biological fluids. J. Chromatogr. B 870:222–32 [Google Scholar]
  83. Kumari S, Stevens D, Kind T, Denkert C, Fiehn O. 83.  2011. Applying in-silico retention index and mass spectra matching for identification of unknown metabolites in accurate mass GC–TOF mass spectrometry. Anal. Chem. 83:5895–902 [Google Scholar]
  84. Abate S, Ahn YG, Kind T, Cataldi TRI, Fiehn O. 84.  2010. Determination of elemental compositions by gas chromatography/time‐of‐flight mass spectrometry using chemical and electron ionization. Rapid Commun. Mass Spectrom. 24:1172–80 [Google Scholar]
  85. Fiehn O. 85.  2016. Metabolomics by gas chromatography–mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114:30.4.1–32 [Google Scholar]
  86. Niehaus TD, Nguyen TND, Gidda SK, ElBadawi-Sidhu M, Lambrecht JA. 86.  et al. 2014. Arabidopsis and maize RidA proteins preempt reactive enamine/imine damage to branched-chain amino acid biosynthesis in plastids. Plant Cell 26:3010–22 [Google Scholar]
  87. Ward PS, Patel J, Wise DR, Abdel-Wahab O, Bennett BD. 87.  et al. 2010. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting α-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 17:225–34 [Google Scholar]
  88. de Raad M, Fischer CR, Northen TR. 88.  2016. High-throughput platforms for metabolomics. Curr. Opin. Chem. Biol. 30:7–13 [Google Scholar]
  89. Jonas M, LaMarr WA, Ozbal C. 89.  2009. Mass spectrometry in high throughput screening: a case study on acetyl-coenzyme a carboxylase using RapidFire®–mass spectrometry (RF-MS). Comb. Chem. High Throughput Screen. 12:752–59 [Google Scholar]
  90. Holt TG, Choi BK, Geoghagen NS, Jensen KK, Luo Q. 90.  et al. 2009. Label-free high-throughput screening via mass spectrometry: a single cystathionine quantitative method for multiple applications. Assay Drug Dev. Technol. 7:495–506 [Google Scholar]
  91. Lange HC, Eman M, van Zuijlen G, Visser D, van Dam JC. 91.  et al. 2001. Improved rapid sampling for in vivo kinetics of intracellular metabolites in Saccharomyces cerevisiae. Biotechnol. Bioeng. 75:406–15 [Google Scholar]
  92. Fuhrer T, Heer D, Begemann B, Zamboni N. 92.  2011. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection–time-of-flight mass spectrometry. Anal. Chem. 83:7074–80 [Google Scholar]
  93. Fuhrer T, Zamboni N. 93.  2015. High-throughput discovery metabolomics. Curr. Opin. Biotechnol. 31:73–78 [Google Scholar]
  94. Link H, Fuhrer T, Gerosa L, Zamboni N, Sauer U. 94.  2015. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat. Methods 12:1091–97 [Google Scholar]
  95. Holmes E, Foxall PJ, Spraul M, Farrant RD, Nicholson JK, Lindon JC. 95.  1997. 750 MHz 1H NMR spectroscopy characterisation of the complex metabolic pattern of urine from patients with inborn errors of metabolism: 2-hydroxyglutaric aciduria and maple syrup urine disease. J. Pharm. Biomed. Anal 151647–59 [Google Scholar]
  96. Holmes E, Foxall PJ, Nicholson JK, Neild GH, Brown SM. 96.  et al. 1994. Automatic data reduction and pattern recognition methods for analysis of 1H nuclear magnetic resonance spectra of human urine from normal and pathological states. Anal. Biochem. 220:284–96 [Google Scholar]
  97. Emwas AH. 97.  2015. The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol. Biol. 1277:161–93 [Google Scholar]
  98. Serkova NJ, Rose JC, Epperson LE, Carey HV, Martin SL. 98.  2007. Quantitative analysis of liver metabolites in three stages of the circannual hibernation cycle in 13-lined ground squirrels by NMR. Physiol. Genom. 31:15–24 [Google Scholar]
  99. Gronwald W, Klein MS, Kaspar H, Fagerer SR, Nurnberger N. 99.  et al. 2008. Urinary metabolite quantification employing 2D NMR spectroscopy. Anal. Chem. 80:9288–97 [Google Scholar]
  100. Klein MS, Almstetter MF, Schlamberger G, Nurnberger N, Dettmer K. 100.  et al. 2010. Nuclear magnetic resonance and mass spectrometry–based milk metabolomics in dairy cows during early and late lactation. J. Dairy Sci. 93:1539–50 [Google Scholar]
  101. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE. 101.  et al. 2008. BioMagResBank. Nucleic Acids Res 36:D402–8 [Google Scholar]
  102. Sud M, Fahy E, Cotter D, Azam K, Vadivelu I. 102.  et al. 2016. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res 44:D463–70 [Google Scholar]
  103. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM. 103.  2006. Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal. Chem. 78:4430–42 [Google Scholar]
  104. Cloarec O, Dumas ME, Craig A, Barton RH, Trygg J. 104.  et al. 2005. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal. Chem. 77:1282–89 [Google Scholar]
  105. Bingol K, Bruschweiler R. 105.  2014. Multidimensional approaches to NMR-based metabolomics. Anal. Chem. 86:47–57 [Google Scholar]
  106. Qiu F, McAlpine JB, Lankin DC, Burton I, Karakach T. 106.  et al. 2014. 2D NMR barcoding and differential analysis of complex mixtures for chemical identification: the Actaea triterpenes. Anal. Chem. 86:3964–72 [Google Scholar]
  107. Komatsu T, Ohishi R, Shino A, Kikuchi J. 107.  2016. Structure and metabolic-flow analysis of molecular complexity in a 13C-labeled tree by 2D and 3D NMR. Angew. Chem. Int. Ed. Engl. 55:6000–3 [Google Scholar]
  108. Schoenberger T, Menges S, Bernstein MA, Perez M, Seoane F. 108.  et al. 2016. Improving the performance of high-precision qNMR measurements by a double integration procedure in practical cases. Anal. Chem. 88:3836–43 [Google Scholar]
  109. Ravanbakhsh S, Liu P, Bjorndahl TC, Mandal R, Grant JR. 109.  et al. 2015. Accurate, fully-automated NMR spectral profiling for metabolomics. PLOS ONE 10:e0124219 [Google Scholar]
  110. Bruschweiler R, Zhang F. 110.  2004. Covariance nuclear magnetic resonance spectroscopy. J. Chem. Phys. 120:5253–60 [Google Scholar]
  111. Lewis IA, Schommer SC, Hodis B, Robb KA, Tonelli M. 111.  et al. 2007. Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H–13C NMR spectra. Anal. Chem. 79:9385–90 [Google Scholar]
  112. Giraudeau P. 112.  2017. Challenges and perspectives in quantitative NMR. Magn. Reson. Chem. 55:61–69 [Google Scholar]
  113. Weber M, Hellriegel C, Rück A, Sauermoser R, Wüthrich J. 113.  2013. Using high-performance quantitative NMR (HP-qNMR®) for certifying traceable and highly accurate purity values of organic reference materials with uncertainties <0.1%. Accredit. Qual. Assur. 18:91–98 [Google Scholar]
  114. Hu K, Westler WM, Markley JL. 114.  2011. Simultaneous quantification and identification of individual chemicals in metabolite mixtures by two-dimensional extrapolated time-zero 1H–13C HSQC (HSQC0). J. Am. Chem. Soc. 133:1662–65 [Google Scholar]
  115. Mauve C, Khlifi S, Gilard F, Mouille G, Farjon J. 115.  2016. Sensitive, highly resolved, and quantitative 1H–13C NMR data in one go for tracking metabolites in vegetal extracts. Chem. Commun. 52:6142–45 [Google Scholar]
  116. Klein MS, Oefner PJ, Gronwald W. 116.  2013. MetaboQuant: a tool combining individual peak calibration and outlier detection for accurate metabolite quantification in 1D 1H and 1H–13C HSQC NMR spectra. BioTechniques 54:251–56 [Google Scholar]
  117. Hu F, Furihata K, Kato Y, Tanokura M. 117.  2007. Nondestructive quantification of organic compounds in whole milk without pretreatment by two-dimensional NMR spectroscopy. J. Agric. Food Chem. 55:4307–11 [Google Scholar]
  118. Lewis IA, Schommer SC, Markley JL. 118.  2009. rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Magn. Reson. Chem. 47:Suppl. 1S123–26 [Google Scholar]
  119. Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J. 119.  et al. 2008. Metabolite identification via the Madison Metabolomics Consortium Database. Nat. Biotechnol. 26:162–64 [Google Scholar]
  120. Lewis IA, Karsten RH, Norton ME, Tonelli M, Westler WM, Markley JL. 120.  2010. NMR method for measuring carbon-13 isotopic enrichment of metabolites in complex solutions. Anal. Chem. 82:4558–63 [Google Scholar]
  121. Fan TW, Lane AN. 121.  2011. NMR-based stable isotope resolved metabolomics in systems biochemistry. J. Biomol. NMR 49:267–80 [Google Scholar]
  122. Lane AN, Fan TW, Higashi RM. 122.  2008. Isotopomer-based metabolomic analysis by NMR and mass spectrometry. Methods Cell Biol 84:541–88 [Google Scholar]
  123. Schrader MC, Eskey CJ, Simplaceanu V, Ho C. 123.  1993. A carbon-13 nuclear magnetic resonance investigation of the metabolic fluxes associated with glucose metabolism in human erythrocytes. Biochim. Biophys. Acta 1182:162–78 [Google Scholar]
  124. Delgado TC, Castro MM, Geraldes CF, Jones JG. 124.  2004. Quantitation of erythrocyte pentose pathway flux with [2-13C]glucose and 1H NMR analysis of the lactate methyl signal. Magn. Reson. Med. 51:1283–86 [Google Scholar]
  125. Lewis IA, Campanella ME, Markley JL, Low PS. 125.  2009. Role of band 3 in regulating metabolic flux of red blood cells. PNAS 106:18515–20 [Google Scholar]
  126. Tzika AA, Cheng LL, Goumnerova L, Madsen JR, Zurakowski D. 126.  et al. 2002. Biochemical characterization of pediatric brain tumors by using in vivo and ex vivo magnetic resonance spectroscopy. J. Neurosurg. 96:1023–31 [Google Scholar]
  127. Rothman DL, Sibson NR, Hyder F, Shen J, Behar KL, Shulman RG. 127.  1999. In vivo nuclear magnetic resonance spectroscopy studies of the relationship between the glutamate–glutamine neurotransmitter cycle and functional neuroenergetics. Philos. Trans. R. Soc. B 354:1165–77 [Google Scholar]
  128. Ardenkjaer-Larsen JH, Fridlund B, Gram A, Hansson G, Hansson L. 128.  et al. 2003. Increase in signal-to-noise ratio of >10,000 times in liquid-state NMR. PNAS 100:10158–63 [Google Scholar]
  129. Tennessen JM, Barry WE, Cox J, Thummel CS. 129.  2014. Methods for studying metabolism in Drosophila. Methods 68:105–15 [Google Scholar]
  130. Park J, Lee SB, Lee S, Kim Y, Song S. 130.  et al. 2006. Mitochondrial dysfunction in DrosophilaPINK1 mutants is complemented by parkin. Nature 441:1157–61 [Google Scholar]
  131. Zerez CR, Lee SJ, Tanaka KR. 131.  1987. Spectrophotometric determination of oxidized and reduced pyridine nucleotides in erythrocytes using a single extraction procedure. Anal. Biochem. 164:367–73 [Google Scholar]
  132. Wagner TC, Scott MD. 132.  1994. Single extraction method for the spectrophotometric quantification of oxidized and reduced pyridine nucleotides in erythrocytes. Anal. Biochem. 222:417–26 [Google Scholar]
  133. Jeon SM, Chandel NS, Hay N. 133.  2012. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature 485:661–65 [Google Scholar]
  134. Wu JT, Wu LH, Knight JA. 134.  1986. Stability of NADPH effect of various factors on the kinetics of degradation. Clin. Chem. 32:314–19 [Google Scholar]
  135. Hofmann D, Wirtz A, Santiago-Schubel B, Disko U, Pohl M. 135.  2010. Structure elucidation of the thermal degradation products of the nucleotide cofactors NADH and NADPH by nano-ESI-FTICR-MS and HPLC-MS. Anal. Bioanal. Chem. 398:2803–11 [Google Scholar]
  136. Rossi R, Milzani A, Dalle-Donne I, Giustarini D, Lusini L. 136.  et al. 2002. Blood glutathione disulfide: in vivo factor or in vitro artifact?. Clin. Chem. 48:742–53 [Google Scholar]
  137. Giustarini D, Dalle-Donne I, Milzani A, Fanti P, Rossi R. 137.  2013. Analysis of GSH and GSSG after derivatization with N-ethylmaleimide. Nat. Protoc. 8:1660–69 [Google Scholar]
  138. Giustarini D, Dalle-Donne I, Milzani A, Rossi R. 138.  2011. Low molecular mass thiols, disulfides and protein mixed disulfides in rat tissues: influence of sample manipulation, oxidative stress and ageing. Mech. Ageing Dev. 132:141–48 [Google Scholar]
  139. Kummel A, Panke S, Heinemann M. 139.  2006. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2:2006.0034 [Google Scholar]
  140. Henry CS, Broadbelt LJ, Hatzimanikatis V. 140.  2007. Thermodynamics-based metabolic flux analysis. Biophys. J. 92:1792–805 [Google Scholar]
  141. Fly R, Lloyd J, Krueger S, Fernie A, van der Merwe MJ. 141.  2015. Improvements to define mitochondrial metabolomics using nonaqueous fractionation. Methods Mol. Biol. 1305:197–210 [Google Scholar]
  142. Matuszczyk JC, Teleki A, Pfizenmaier J, Takors R. 142.  2015. Compartment-specific metabolomics for CHO reveals that ATP pools in mitochondria are much lower than in cytosol. Biotechnol. J. 10:1639–50 [Google Scholar]
  143. Chen WW, Freinkman E, Wang T, Birsoy K, Sabatini DM. 143.  2016. Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166:1324–37 [Google Scholar]
  144. Zhao Y, Jin J, Hu Q, Zhou HM, Yi J. 144.  et al. 2011. Genetically encoded fluorescent sensors for intracellular NADH detection. Cell Metab 14:555–66 [Google Scholar]
  145. Zhao Y, Wang A, Zou Y, Su N, Loscalzo J, Yang Y. 145.  2016. In vivo monitoring of cellular energy metabolism using SoNar, a highly responsive sensor for NAD+/NADH redox state. Nat. Protoc. 11:1345–59 [Google Scholar]
  146. Hou BH, Takanaga H, Grossmann G, Chen LQ, Qu XQ. 146.  et al. 2011. Optical sensors for monitoring dynamic changes of intracellular metabolite levels in mammalian cells. Nat. Protoc. 6:1818–33 [Google Scholar]
/content/journals/10.1146/annurev-biochem-061516-044952
Loading
/content/journals/10.1146/annurev-biochem-061516-044952
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error