1932

Abstract

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-061318-114959
2019-06-12
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/ac/12/1/annurev-anchem-061318-114959.html?itemId=/content/journals/10.1146/annurev-anchem-061318-114959&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S et al. 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6:1060–83
    [Google Scholar]
  2. 2.
    Vermeersch KA, Styczynski MP 2013. Applications of metabolomics in cancer research. J. Carcinog. 12:9
    [Google Scholar]
  3. 3.
    Weckwerth W, Morgenthal K 2005. Metabolomics: from pattern recognition to biological interpretation. Drug Discov. Today 10:1551–58
    [Google Scholar]
  4. 4.
    Hao J, Liebeke M, Sommer U, Viant MR, Bundy JG, Ebbels TM 2016. Statistical correlations between NMR spectroscopy and direct infusion FT-ICR mass spectrometry aid annotation of unknowns in metabolomics. Anal. Chem. 88:2583–89
    [Google Scholar]
  5. 5.
    Geier FM, Want EJ, Leroi AM, Bundy JG 2011. Cross-platform comparison of Caenorhabditis elegans tissue extraction strategies for comprehensive metabolome coverage. Anal. Chem. 83:3730–36
    [Google Scholar]
  6. 6.
    Nevedomskaya E, Mayboroda OA, Deelder AM 2011. Cross-platform analysis of longitudinal data in metabolomics. Mol. Biosyst. 7:3214–22
    [Google Scholar]
  7. 7.
    Dark matter 2008. Nature 455:698 https://doi.org/10.1038/455698a
    [Crossref]
  8. 8.
    da Silva RR, Dorrestein PC, Quinn RA 2015. Illuminating the dark matter in metabolomics. PNAS 112:12549–50
    [Google Scholar]
  9. 9.
    Sumner LW, Amberg A, Barrett D, Beale MH, Beger R et al. 2007. Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211–21
    [Google Scholar]
  10. 10.
    Ding J, Sorensen CM, Jaitly N, Jiang H, Orton DJ et al. 2008. Application of the accurate mass and time tag approach in studies of the human blood lipidome. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 871:243–52
    [Google Scholar]
  11. 11.
    Koelmel JP, Ulmer CZ, Jones CM, Yost RA, Bowden JA 2017. Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1862:766–70
    [Google Scholar]
  12. 12.
    Lei Z, Huhman DV, Sumner LW 2011. Mass spectrometry strategies in metabolomics. J. Biol. Chem. 286:25435–42
    [Google Scholar]
  13. 13.
    Kuehnbaum NL, Britz-McKibbin P 2013. New advances in separation science for metabolomics: resolving chemical diversity in a post-genomic era. Chem. Rev. 113:2437–68
    [Google Scholar]
  14. 14.
    Deleted in proof
  15. 15.
    Fiehn O 2008. Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. Trends Anal. Chem. 27:261–69
    [Google Scholar]
  16. 16.
    Fiehn O 2016. Metabolomics by gas chromatography-mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114:30.4.1–.32
    [Google Scholar]
  17. 17.
    Haggarty J, Burgess KE 2017. Recent advances in liquid and gas chromatography methodology for extending coverage of the metabolome. Curr. Opin. Biotechnol. 43:77–85
    [Google Scholar]
  18. 18.
    Zhang W, Hankemeier T, Ramautar R 2017. Next-generation capillary electrophoresis-mass spectrometry approaches in metabolomics. Curr. Opin. Biotechnol. 43:1–7
    [Google Scholar]
  19. 19.
    Fenn J, Mann M, Meng C, Wong S, Whitehouse C 1989. Electrospray ionization for mass spectrometry of large biomolecules. Science 246:64–71
    [Google Scholar]
  20. 20.
    Kebarle P, Verkerk UH 2009. Electrospray: From ions in solution to ions in the gas phase, what we know now. Mass Spectrom. Rev. 28:898–917
    [Google Scholar]
  21. 21.
    Kebarle P 2000. A brief overview of the present status of the mechanisms involved in electrospray mass spectrometry. J. Mass Spectrom. 35:804–17
    [Google Scholar]
  22. 22.
    Pluskal T, Uehara T, Yanagida M 2012. Highly accurate chemical formula prediction tool utilizing high-resolution mass spectra, MS/MS fragmentation, heuristic rules, and isotope pattern matching. Anal. Chem. 84:4396–403
    [Google Scholar]
  23. 23.
    Brown M, Dunn WB, Dobson P, Patel Y, Winder CL et al. 2009. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 134:1322–32
    [Google Scholar]
  24. 24.
    Spagou K, Tsoukali H, Raikos N, Gika H, Wilson ID, Theodoridis G 2010. Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies. J. Sep. Sci. 33:716–27
    [Google Scholar]
  25. 25.
    Dunn W, Erban A, Weber RM, Creek D, Brown M et al. 2013. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9:44–66
    [Google Scholar]
  26. 26.
    Kind T, Fiehn O 2010. Advances in structure elucidation of small molecules using mass spectrometry. Bioanal. Rev. 2:23–60
    [Google Scholar]
  27. 27.
    Wishart DS 2011. Advances in metabolite identification. Bioanalysis 3:1769–82
    [Google Scholar]
  28. 28.
    Babushok V, Linstrom P 2004. On the relationship between Kováts and Lee retention indices. Chromatographia 60:725–28
    [Google Scholar]
  29. 29.
    Rostad CE, Pereira WE 1986. Kovats and Lee retention indices determined by gas chromatography/mass spectrometry for organic compounds of environmental interest. J. High Resolut. Chromatogr. 9:328–34
    [Google Scholar]
  30. 30.
    Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M 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]
  31. 31.
    Cao M, Fraser K, Huege J, Featonby T, Rasmussen S, Jones C 2015. Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics. Metabolomics 11:696–706
    [Google Scholar]
  32. 32.
    Creek DJ, Jankevics A, Breitling R, Watson DG, Barrett MP, Burgess KE 2011. Toward global metabolomics analysis with hydrophilic interaction liquid chromatography-mass spectrometry: improved metabolite identification by retention time prediction. Anal. Chem. 83:8703–10
    [Google Scholar]
  33. 33.
    Aicheler F, Li J, Hoene M, Lehmann R, Xu G, Kohlbacher O 2015. Retention time prediction improves identification in nontargeted lipidomics approaches. Anal. Chem. 87:7698–704
    [Google Scholar]
  34. 34.
    Bruderer T, Varesio E, Hopfgartner G 2017. The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 1071:3–10
    [Google Scholar]
  35. 35.
    Hagiwara T, Saito S, Ujiie Y, Imai K, Kakuta M et al. 2010. HPLC retention time prediction for metabolome analysis. Bioinformation 5:255–58
    [Google Scholar]
  36. 36.
    Navarro-Reig M, Ortiz-Villanueva E, Tauler R, Jaumot J 2017. Modelling of hydrophilic interaction liquid chromatography stationary phases using chemometric approaches. Metabolites 7:54
    [Google Scholar]
  37. 37.
    Kaliszan R 2007. QSRR:quantitative structure-(chromatographic) retention relationships. Chem. Rev. 107:3212–46
    [Google Scholar]
  38. 38.
    Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R 2012. Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791–810
    [Google Scholar]
  39. 39.
    Mauri ACV, Pavan M, Todeschini R 2006. Dragon software: an easy approach to molecular descriptor calculations. Match 56:237–48
    [Google Scholar]
  40. 40.
    Hong H, Xie Q, Ge W, Qian F, Fang H et al. 2008. Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J. Chem. Inf. Model. 48:1337–44
    [Google Scholar]
  41. 41.
    Yap CW 2011. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32:1466–74
    [Google Scholar]
  42. 42.
    Falchi F, Bertozzi SM, Ottonello G, Ruda GF, Colombano G et al. 2016. Kernel-based, partial least squares quantitative structure-retention relationship model for UPLC retention time prediction: a useful tool for metabolite identification. Anal. Chem. 88:9510–17
    [Google Scholar]
  43. 43.
    Gorynski K, Bojko B, Nowaczyk A, Bucinski A, Pawliszyn J, Kaliszan R 2013. Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds. Anal. Chim. Acta 797:13–19
    [Google Scholar]
  44. 44.
    Wolfer AM, Lozano S, Umbdenstock T, Croixmarie V, Arrault A, Vayer P 2015. UPLC–MS retention time prediction: a machine learning approach to metabolite identification in untargeted profiling. Metabolomics 12:8
    [Google Scholar]
  45. 45.
    Hall LM, Hill DW, Menikarachchi LC, Chen M-H, Hall LH, Grant DF 2015. Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data. Bioanalysis 7:939–55
    [Google Scholar]
  46. 46.
    Hall LM, Hall LH, Kertesz TM, Hill DW, Sharp TR et al. 2012. Development of Ecom50 and retention index models for nontargeted metabolomics: identification of 1,3-dicyclohexylurea in human serum by HPLC/mass spectrometry. J. Chem. Inf. Model. 52:1222–37
    [Google Scholar]
  47. 47.
    Hall LM, Hill DW, Bugden K, Cawley S, Hall LH et al. 2018. Development of a reverse phase HPLC retention index model for nontargeted metabolomics using synthetic compounds. J. Chem. Inf. Model. 58:591–604
    [Google Scholar]
  48. 48.
    Stein SE, Babushok VI, Brown RL, Linstrom PJ 2007. Estimation of Kovats retention indices using group contributions. J. Chem. Inf. Model. 47:975–80
    [Google Scholar]
  49. 49.
    Barnes BB, Wilson MB, Carr PW, Vitha MF, Broeckling CD et al. 2013. “Retention projection” enables reliable use of shared gas chromatographic retention data across laboratories, instruments, and methods. Anal. Chem. 85:11650–57
    [Google Scholar]
  50. 50.
    Stanstrup J, Neumann S, Vrhovsek U 2015. PredRet: prediction of retention time by direct mapping between multiple chromatographic systems. Anal. Chem. 87:9421–28
    [Google Scholar]
  51. 51.
    Johnson AR, Carlson EE 2015. Collision-induced dissociation mass spectrometry: a powerful tool for natural product structure elucidation. Anal. Chem. 87:10668–78
    [Google Scholar]
  52. 52.
    Doerr A 2015. DIA mass spectrometry. Nat. Methods 12:35
    [Google Scholar]
  53. 53.
    Bateman KP, Castro-Perez J, Wrona M, Shockcor JP, Yu K et al. 2007. MSE with mass defect filtering for in vitro and in vivo metabolite identification. Rapid Commun. Mass Spectrom. 21:1485–96
    [Google Scholar]
  54. 54.
    Kreimer S, Belov ME, Danielson WF, Levitsky LI, Gorshkov MV et al. 2016. Advanced precursor ion selection algorithms for increased depth of bottom-up proteomic profiling. J. Proteome Res. 15:3563–73
    [Google Scholar]
  55. 55.
    Broeckling CD, Hoyes E, Richardson K, Brown JM, Prenni JE 2018. Comprehensive tandem-mass-spectrometry coverage of complex samples enabled by data-set-dependent acquisition. Anal. Chem. 90:8020–27
    [Google Scholar]
  56. 56.
    Mullard G, Allwood JW, Weber R, Brown M, Begley P et al. 2015. A new strategy for MS/MS data acquisition applying multiple data dependent experiments on Orbitrap mass spectrometers in non-targeted metabolomic applications. Metabolomics 11:1068–80
    [Google Scholar]
  57. 57.
    Peterson AC, Russell JD, Bailey DJ, Westphall MS, Coon JJ 2012. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell. Proteom. 11:1475–88
    [Google Scholar]
  58. 58.
    Chen YH, Zhou Z, Yang W, Bi N, Xu J et al. 2017. Development of a data-independent targeted metabolomics method for relative quantification using liquid chromatography coupled with tandem mass spectrometry. Anal. Chem. 89:6954–62
    [Google Scholar]
  59. 59.
    Ferreira CR, Yannell KE, Mollenhauer B, Espy RD, Cordeiro FB et al. 2016. Chemical profiling of cerebrospinal fluid by multiple reaction monitoring mass spectrometry. Analyst 141:5252–55
    [Google Scholar]
  60. 60.
    Cordeiro FB, Ferreira CR, Sobreira TJP, Yannell KE, Jarmusch AK et al. 2017. Multiple reaction monitoring (MRM)-profiling for biomarker discovery applied to human polycystic ovarian syndrome. Rapid Commun. Mass Spectrom. 31:1462–70
    [Google Scholar]
  61. 61.
    Damen CWN, Isaac G, Langridge J, Hankemeier T, Vreeken RJ 2014. Enhanced lipid isomer separation in human plasma using reversed-phase UPLC with ion-mobility/high-resolution MS detection. J. Lipid Res. 55:1772–83
    [Google Scholar]
  62. 62.
    Guan SH, Marshall AG 1996. Stored waveform inverse Fourier transform (SWIFT) ion excitation in trapped-ion mass spectometry: theory and applications. Int. J. Mass Spectrom. 157:5–37
    [Google Scholar]
  63. 63.
    O'Connor PB, McLafferty FW 1995. High-resolution ion isolation with the ion-cyclotron resonance capacitively coupled open cell. J. Am. Soc. Mass Spectrom. 6:533–35
    [Google Scholar]
  64. 64.
    de Koning LJ, Nibbering NMM, van Orden SL, Laukien FH 1997. Mass selection of ions in a Fourier transform ion cyclotron resonance trap using correlated harmonic excitation fields (CHEF). Int. J. Mass Spectrom. 165:209–19
    [Google Scholar]
  65. 65.
    Li SZ, Park Y, Duraisingham S, Strobel FH, Khan N et al. 2013. Predicting network activity from high throughput metabolomics. PLOS Comp. Biol. 9:e1003123
    [Google Scholar]
  66. 66.
    Kind T, Tsugawa H, Cajka T, Ma Y, Lai ZJ et al. 2018. Identification of small molecules using accurate mass MS/MS search. Mass Spectrom. Rev. 37:513–32
    [Google Scholar]
  67. 67.
    Mandal R, Chamot D, Wishart DS 2018. The role of the Human Metabolome Database in inborn errors of metabolism. J. Inherit. Metab. Dis. 41:329–36
    [Google Scholar]
  68. 68.
    Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K et al. 2018. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46:D608–17
    [Google Scholar]
  69. 69.
    Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B et al. 2018. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90:3156–64
    [Google Scholar]
  70. 70.
    Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA et al. 2005. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27:747–51
    [Google Scholar]
  71. 71.
    Kind T, Liu KH, Lee DY, DeFelice B, Meissen JK, Fiehn O 2013. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 10:755–58
    [Google Scholar]
  72. 72.
    Mistrik R 2018. mzCLOUD: A spectral tree library for the identification of “unknown unknowns.”. Abstr. Pap. Am. Chem. Soc. 2018:255
    [Google Scholar]
  73. 73.
    Pringle SD, Giles K, Wildgoose JL, Williams JP, Slade SE et al. 2007. An investigation of the mobility separation of some peptide and protein ions using a new hybrid quadrupole/travelling wave IMS/oa-ToF instrument. Int. J. Mass Spectrom. 261:1–12
    [Google Scholar]
  74. 74.
    Kanu AB, Dwivedi P, Tam M, Matz L, Hill HH 2008. Ion mobility–mass spectrometry. J. Mass Spectrom. 43:1–22
    [Google Scholar]
  75. 75.
    Hu QZ, Cooks RG, Noll RJ 2007. Phase-enhanced selective ion ejection in an Orbitrap mass spectrometer. J. Am. Soc. Mass Spectrom. 18:980–83
    [Google Scholar]
  76. 76.
    Ausloos P, Clifton CL, Lias SG, Mikaya AI, Stein SE et al. 1999. The critical evaluation of a comprehensive mass spectral library. J. Am. Soc. Mass Spectrom. 10:287–99
    [Google Scholar]
  77. 77.
    Paglia G, Angel P, Williams JP, Richardson K, Olivos HJ et al. 2015. Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification. Anal. Chem. 87:1137–44
    [Google Scholar]
  78. 78.
    Yang XY, Neta P, Stein SE 2014. Quality control for building libraries from electrospray ionization tandem mass spectra. Anal. Chem. 86:6393–400
    [Google Scholar]
  79. 79.
    Wolf S, Schmidt S, Müller-Hannemann M, Neumann S 2010. In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinf 11:148
    [Google Scholar]
  80. 80.
    Ruttkies C, Schymanski EL, Wolf S, Hollender J, Neumann S 2016. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8:3
    [Google Scholar]
  81. 81.
    Dührkop K, Shen H, Meusel M, Rousu J, Böcker S 2015. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS 112:12580–85
    [Google Scholar]
  82. 82.
    Huan T, Tang CQ, Li RH, Shi Y, Lin GH, Li L 2015. MyCompoundID MS/MS search: metabolite identification using a library of predicted fragment-ion-spectra of 383,830 possible human metabolites. Anal. Chem. 87:10619–26
    [Google Scholar]
  83. 83.
    Witting M, Ruttkies C, Neumann S, Schmitt-Kopplin P 2017. LipidFrag: improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome. PLOS ONE 12:e0172311
    [Google Scholar]
  84. 84.
    Houjou T, Yamatani K, Imagawa M, Shimizu T, Taguchi R 2005. A shotgun tandem mass spectrometric analysis of phospholipids with normal-phase and/or reverse-phase liquid chromatography/electrospray ionization mass spectrometry. Rapid Commun. Mass Spectrom. 19:654–66
    [Google Scholar]
  85. 85.
    Kyle JE, Crowell KL, Casey CP, Fujimoto GM, Kim S et al. 2017. LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data. Bioinformatics 33:1744–46
    [Google Scholar]
  86. 86.
    Allen F, Pon A, Greiner R, Wishart D 2016. Computational prediction of electron ionization mass spectra to assist in GC/MS compound identification. Anal. Chem. 88:7689–97
    [Google Scholar]
  87. 87.
    Allen F, Greiner R, Wishart D 2015. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11:98–110
    [Google Scholar]
  88. 88.
    Menikarachchi LC, Cawley S, Hill DW, Hall LM, Hall L et al. 2012. MolFind: a software package enabling HPLC/MS-based identification of unknown chemical structures. Anal. Chem. 84:9388–94
    [Google Scholar]
  89. 89.
    Hu M, Muller E, Schymanski EL, Ruttkies C, Schulze T et al. 2018. Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS. Anal. Bioanal. Chem. 410:1931–41
    [Google Scholar]
  90. 90.
    Livanos AE, Greiner TU, Vangay P, Pathmasiri W, Stewart D et al. 2016. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat. Microbiol. 1:16140
    [Google Scholar]
  91. 91.
    Mairinger T, Causon TJ, Hann S 2018. The potential of ion mobility–mass spectrometry for non-targeted metabolomics. Curr. Opin. Chem. Biol. 42:9–15
    [Google Scholar]
  92. 92.
    Dodds JN, May JC, McLean JA 2017. Correlating resolving power, resolution, and collision cross section: unifying cross-platform assessment of separation efficiency in ion mobility spectrometry. Anal. Chem. 89:12176–84
    [Google Scholar]
  93. 93.
    Harper B, Neumann EK, Stow SM, May JC, McLean JA, Solouki T 2016. Determination of ion mobility collision cross sections for unresolved isomeric mixtures using tandem mass spectrometry and chemometric deconvolution. Anal. Chim. Acta 939:64–72
    [Google Scholar]
  94. 94.
    Groessl M, Graf S, Knochenmuss R 2015. High resolution ion mobility-mass spectrometry for separation and identification of isomeric lipids. Analyst 140:6904–11
    [Google Scholar]
  95. 95.
    Hofmann J, Hahm HS, Seeberger PH, Pagel K 2015. Identification of carbohydrate anomers using ion mobility–mass spectrometry. Nature 526:241–44
    [Google Scholar]
  96. 96.
    May JC, McLean JA 2015. Ion mobility-mass spectrometry: time-dispersive instrumentation. Anal. Chem. 87:1422–36
    [Google Scholar]
  97. 97.
    Cumeras R, Figueras E, Davis CE, Baumbach JI, Gràcia I 2015. Review on ion mobility spectrometry. Part 1: current instrumentation. Analyst 140:1376–90
    [Google Scholar]
  98. 98.
    Cumeras R, Figueras E, Davis CE, Baumbach JI, Gràcia I 2015. Review on ion mobility spectrometry. Part 2: hyphenated methods and effects of experimental parameters. Analyst 140:1391–410
    [Google Scholar]
  99. 99.
    Hines KM, Ross DH, Davidson KL, Bush MF, Xu L 2017. Large-scale structural characterization of drug and drug-like compounds by high-throughput ion mobility-mass spectrometry. Anal. Chem. 89:9023–30
    [Google Scholar]
  100. 100.
    Stow SM, Causon TJ, Zheng X, Kurulugama RT, Mairinger T et al. 2017. An interlaboratory evaluation of drift tube ion mobility–mass spectrometry collision cross section measurements. Anal. Chem. 89:9048–55
    [Google Scholar]
  101. 101.
    Bush MF, Hall Z, Giles K, Hoyes J, Robinson CV, Ruotolo BT 2010. Collision cross sections of proteins and their complexes: a calibration framework and database for gas-phase structural biology. Anal. Chem. 82:9557–65
    [Google Scholar]
  102. 102.
    Michelmann K, Silveira JA, Ridgeway ME, Park MA 2015. Fundamentals of trapped ion mobility spectrometry. J. Am. Soc. Mass Spectrom. 26:14–24
    [Google Scholar]
  103. 103.
    Silveira JA, Ridgeway ME, Park MA 2014. High resolution trapped ion mobility spectrometery of peptides. Anal. Chem. 86:5624–27
    [Google Scholar]
  104. 104.
    Giles K, Williams JP, Campuzano I 2011. Enhancements in travelling wave ion mobility resolution. Rapid Commun. Mass Spectrom. 25:1559–66
    [Google Scholar]
  105. 105.
    Forsythe JG, Petrov AS, Walker CA, Allen SJ, Pellissier JS et al. 2015. Collision cross section calibrants for negative ion mode traveling wave ion mobility-mass spectrometry. Analyst 140:6853–61
    [Google Scholar]
  106. 106.
    Hines KM, May JC, McLean JA, Xu L 2016. Evaluation of collision cross section calibrants for structural analysis of lipids by traveling wave ion mobility-mass spectrometry. Anal. Chem. 88:7329–36
    [Google Scholar]
  107. 107.
    Gelb AS, Jarratt RE, Huang Y, Dodds ED 2014. A study of calibrant selection in measurement of carbohydrate and peptide ion-neutral collision cross sections by traveling wave ion mobility spectrometry. Anal. Chem. 86:11396–402
    [Google Scholar]
  108. 108.
    Chai M, Young MN, Liu FC, Bleiholder C 2018. A transferable, sample-independent calibration procedure for trapped ion mobility spectrometry (TIMS). Anal. Chem. 90:9040–47
    [Google Scholar]
  109. 109.
    Bush MF, Campuzano IDG, Robinson CV 2012. Ion mobility mass spectrometry of peptide ions: effects of drift gas and calibration strategies. Anal. Chem. 84:7124–30
    [Google Scholar]
  110. 110.
    Zhou Z, Shen X, Tu J, Zhu Z-J 2016. Large-scale prediction of collision cross-section values for metabolites in ion mobility-mass spectrometry. Anal. Chem. 88:11084–91
    [Google Scholar]
  111. 111.
    Zhou Z, Xiong X, Zhu Z-J 2017. MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics. Bioinformatics 33:2235–37
    [Google Scholar]
  112. 112.
    Soper-Hopper MT, Petrov AS, Howard JN, Yu SS, Forsythe JG et al. 2017. Collision cross section predictions using 2-dimensional molecular descriptors. Chem. Commun. 53:7624–27
    [Google Scholar]
  113. 113.
    Campuzano I, Bush MF, Robinson CV, Beaumont C, Richardson K et al. 2012. Structural characterization of drug-like compounds by ion mobility mass spectrometry: comparison of theoretical and experimentally derived nitrogen collision cross sections. Anal. Chem. 84:1026–33
    [Google Scholar]
  114. 114.
    Kyle JE, Zhang X, Weitz KK, Monroe ME, Ibrahim YM et al. 2016. Uncovering biologically significant lipid isomers with liquid chromatography, ion mobility spectrometry and mass spectrometry. Analyst 141:1649–59
    [Google Scholar]
  115. 115.
    Kliman M, May JC, McLean JA 2011. Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1811:935–45
    [Google Scholar]
  116. 116.
    Goodwin CR, Fenn LS, Derewacz DK, Bachmann BO, McLean JA 2012. Structural mass spectrometry: rapid methods for separation and analysis of peptide natural products. J. Nat. Prod. 75:48–53
    [Google Scholar]
  117. 117.
    May JC, Goodwin CR, Lareau NM, Leaptrot KL, Morris CB et al. 2014. Conformational ordering of biomolecules in the gas phase: nitrogen collision cross sections measured on a prototype high resolution drift tube ion mobility-mass spectrometer. Anal. Chem. 86:2107–16
    [Google Scholar]
  118. 118.
    Clendinen CS, Stupp GS, Wang B, Garrett TJ, Edison AS 2016. 13C metabolomics: NMR and IROA for unknown identification. Curr. Metab. 4:116–20
    [Google Scholar]
  119. 119.
    Clendinen CS, Stupp GS, Ajredini R, Lee-McMullen B, Beecher C, Edison AS 2015. An overview of methods using 13C for improved compound identification in metabolomics and natural products. Front. Plant Sci. 6:611
    [Google Scholar]
  120. 120.
    Clendinen CS, Pasquel C, Ajredini R, Edison AS 2015. 13C NMR metabolomics: INADEQUATE network analysis. Anal. Chem. 87:5698–706
    [Google Scholar]
  121. 121.
    Clendinen CS, Lee-McMullen B, Williams CM, Stupp GS, Vandenborne K et al. 2014. 13C NMR metabolomics: applications at natural abundance. Anal. Chem. 86:9242–50
    [Google Scholar]
  122. 122.
    Nemutlu E, Juranic N, Zhang S, Ward LE, Dutta T et al. 2012. Electron spray ionization mass spectrometry and 2D 31P NMR for monitoring 18O/16O isotope exchange and turnover rates of metabolic oligophosphates. Anal. Bioanal. Chem. 403:697–706
    [Google Scholar]
  123. 123.
    Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE et al. 2008. BioMagResBank. Nucleic Acids Res 36:D402–8
    [Google Scholar]
  124. 124.
    Wishart DS, Knox C, Guo AC, Eisner R, Young N et al. 2009. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–10
    [Google Scholar]
  125. 125.
    Giraudeau P, Frydman L 2014. Ultrafast 2D NMR: an emerging tool in analytical spectroscopy. Annu. Rev. Anal. Chem. 7:129–61
    [Google Scholar]
  126. 126.
    Edison AS, Schroeder FC 2010. NMR spectroscopy of small molecules and analysis of complex mixtures. Comprehensive Natural Products II. Chemistry and Biology L Mander, L Hung-Wen 169–96 Oxford, UK: Elsevier
    [Google Scholar]
  127. 127.
    Tredwell GD, Behrends V, Geier FM, Liebeke M, Bundy JG 2011. Between-person comparison of metabolite fitting for NMR-based quantitative metabolomics. Anal. Chem. 83:8683–87
    [Google Scholar]
  128. 128.
    Srinivasan J, Kaplan F, Ajredini R, Zachariah C, Alborn HT et al. 2008. A blend of small molecules regulates both mating and development in Caenorhabditis elegans. Nature 454:1115–18
    [Google Scholar]
  129. 129.
    Edison AS, Clendinen CS, Ajredini R, Beecher C, Ponce FV, Stupp GS 2015. Metabolomics and natural-products strategies to study chemical ecology in nematodes. Integr. Comp. Biol. 55:478–85
    [Google Scholar]
  130. 130.
    Choe A, Chuman T, von Reuss SH, Dossey AT, Yim JJ et al. 2012. Sex-specific mating pheromones in the nematode Panagrellus redivivus. PNAS 109:20949–54
    [Google Scholar]
  131. 131.
    Wolfender J-L, Bohni N, Ndjoko-Ioset K, Edison AS 2017. Advanced spectroscopic detectors for identification and quantification: nuclear magnetic resonance. Liquid Chromatography: Fundamentals and Instrumentation S Fanali, PR Haddad, CF Poole, F Schoenmakers, D Lloyd 349–84 Amsterdam: Elsevier
    [Google Scholar]
  132. 132.
    Schroeder FC, Taggi AE, Gronquist M, Malik RU, Grant JB et al. 2008. NMR-spectroscopic screening of spider venom reveals sulfated nucleosides as major components for the brown recluse and related species. PNAS 105:14283–87
    [Google Scholar]
  133. 133.
    Pungaliya C, Srinivasan J, Fox BW, Malik RU, Ludewig AH et al. 2009. A shortcut to identifying small molecule signals that regulate behavior and development in Caenorhabditis elegans. PNAS 106:7708–13
    [Google Scholar]
  134. 134.
    Robinette SL, Brüschweiler R, Schroeder FC, Edison AS 2012. NMR in metabolomics and natural products research: two sides of the same coin. Acc. Chem. Res. 45:288–97
    [Google Scholar]
  135. 135.
    Bingol K, Brüschweiler-Li L, Yu C, Somogyi A, Zhang F, Brüschweiler R 2015. Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures. Anal. Chem. 87:3864–70
    [Google Scholar]
  136. 136.
    Wang C, He L, Li DW, Brüschweiler-Li L, Marshall AG, Brüschweiler R 2017. Accurate identification of unknown and known metabolic mixture components by combining 3D NMR with Fourier transform ion cyclotron resonance tandem mass spectrometry. J. Proteome Res. 16:3774–86
    [Google Scholar]
  137. 137.
    Wang B, Dossey AT, Walse SS, Edison AS, Merz KM Jr. 2009. Relative configuration of natural products using NMR chemical shifts. J. Nat. Prod. 72:709–13
    [Google Scholar]
  138. 138.
    Blaženović I, Kind T, Ji J, Fiehn O 2018. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites 8:E31
    [Google Scholar]
/content/journals/10.1146/annurev-anchem-061318-114959
Loading
/content/journals/10.1146/annurev-anchem-061318-114959
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