1932

Abstract

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-071015-041734
2016-06-12
2024-04-22
Loading full text...

Full text loading...

/deliver/fulltext/anchem/9/1/annurev-anchem-071015-041734.html?itemId=/content/journals/10.1146/annurev-anchem-071015-041734&mimeType=html&fmt=ahah

Literature Cited

  1. Aebersold R, Mann M. 1.  2003. Mass spectrometry-based proteomics. Nature 422:198–207 [Google Scholar]
  2. Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M. 2.  et al. 2014. Mass-spectrometry-based draft of the human proteome. Nature 509:582–87 [Google Scholar]
  3. Kim M-S, Pinto SM, Getnet D, Nirujogi RS, Manda SS. 3.  et al. 2014. A draft map of the human proteome. Nature 509:575–81 [Google Scholar]
  4. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P. 4.  et al. 2015. Tissue-based map of the human proteome. Science 347:1260419 [Google Scholar]
  5. Uhlén M, Ponten F. 5.  2005. Antibody-based proteomics for human tissue profiling. Mol. Cell. Proteomics 4:384–93 [Google Scholar]
  6. Marx V. 6.  2015. Mapping proteins with spatial proteomics. Nat. Methods 12:815–19 [Google Scholar]
  7. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC. 7.  et al. 2007. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35:D521–26 [Google Scholar]
  8. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C. 8.  et al. 2013. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res. 41:D801–7 [Google Scholar]
  9. Junot C, Fenaille F, Colsch B, Bécher F. 9.  2014. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. Mass Spectrom. Rev. 33:471–500 [Google Scholar]
  10. Yetukuri L, Ekroos K, Vidal-Puig A, Oresic M. 10.  2008. Informatics and computational strategies for the study of lipids. Mol. BioSyst. 4:121–27 [Google Scholar]
  11. Hood L, Heath JR, Phelps ME, Lin B. 11.  2004. Systems biology and new technologies enable predictive and preventative medicine. Science 306:640–43 [Google Scholar]
  12. Nicholson JK, Wilson ID. 12.  2003. Understanding ‘global' systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2:668–76 [Google Scholar]
  13. Feng X, Liu X, Luo Q, Liu B-F. 13.  2008. Mass spectrometry in systems biology: an overview. Mass Spectrom. Rev. 27:635–60 [Google Scholar]
  14. Graessel A, Hauck SM, von Toerne C, Kloppmann E, Goldberg T. 14.  et al. 2015. A combined omics approach to generate the surface atlas of human naive CD4+ T cells during early T-cell receptor activation. Mol. Cell. Proteomics 14:2085–102 [Google Scholar]
  15. Bohacek RS, McMartin C, Guida WC. 15.  1996. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16:3–50 [Google Scholar]
  16. Peironcely JE, Reijmers T, Coulier L, Bender A, Hankemeier T. 16.  2011. Understanding and classifying metabolite space and metabolite-likeness. PLOS ONE 6e28966
  17. Gurard-Levin ZA, Scholle MD, Eisenberg AH, Mrksich M. 17.  2011. High-throughput screening of small molecule libraries using SAMDI mass spectrometry. ACS Comb. Sci. 13:347–50 [Google Scholar]
  18. de Rond T, Danielewicz M, Northen T. 18.  2015. High throughput screening of enzyme activity with mass spectrometry imaging. Curr. Opin. Biotechnol. 31:1–9 [Google Scholar]
  19. Mardis ER. 19.  2008. The impact of next-generation sequencing technology on genetics. Trends Genet. 24:133–41 [Google Scholar]
  20. Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C. 20.  et al. 2015. Big data: astronomical or genomical?. PLOS Biol. 13:e1002195 [Google Scholar]
  21. Laney D. 21.  2001. 3D data management: controlling data volume, velocity, and variety File 949. Application Delivery Strategies Stamford, CT: META Group
  22. Lusher SJ, McGuire R, van Schaik RC, Nicholson CD, de Vlieg J. 22.  2014. Data-driven medicinal chemistry in the era of big data. Drug Discov. Today 19:859–68 [Google Scholar]
  23. Askenazi M, Webber JT, Marto JA. 23.  2011. mzServer: web-based programmatic access for mass spectrometry data analysis. Mol. Cell. Proteomics 10:M110.003988 [Google Scholar]
  24. Bird I. 24.  2011. Computing for the Large Hadron Collider. Annu. Rev. Nuclear Part. Sci. 61:99–118 [Google Scholar]
  25. Reymond J-L, Ruddigkeit L, Blum L, van Deursen R. 25.  2012. The enumeration of chemical space. Wiley Interdiscip. Rev. Comp. Mol. Sci. 2:717–33 [Google Scholar]
  26. Fuller RB, Marks RW. 26.  1973. The Dymaxion World of Buckminster Fuller Garden City, NY: Anchor Press/Doubleday
  27. Wang L. 27.  2015. Chemical Abstract Service marks multiple milestones. Chemical and Engineering News July 1. American Chemical Society
  28. Bolton EE, Wang Y, Thiessen PA, Bryant SH. 28.  2008. Chapter 12 - PubChem: integrated platform of small molecules and biological activities. Annu. Rep. Comput. Chem. 4:217–41 [Google Scholar]
  29. 29. PubChem Substance Database https://pubchem.ncbi.nlm.nih.gov. Accessed September 28, 2015
  30. 30. PubChem Compound Database https://pubchem.ncbi.nlm.nih.gov. Accessed September 28, 2015
  31. Pence HE, Williams A. 31.  2010. ChemSpider: an online chemical information resource. J. Chem. Educ. 87:1123–24 [Google Scholar]
  32. 32. NIST Chemistry WebBook 2015. NIST Standard Reference Database Number 69, eds. PJ Linstrom, WG Mallard. http://webbook.nist.gov./ Accessed September 28, 2015
  33. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J. 33.  et al. 2014. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42:D1083–90 [Google Scholar]
  34. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC. 34.  et al. 2014. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42:D1091–97 [Google Scholar]
  35. Lipinski C, Hopkins A. 35.  2004. Navigating chemical space for biology and medicine. Nature 432:855–61 [Google Scholar]
  36. Virshup AM, Contreras-García J, Wipf P, Yang W, Beratan DN. 36.  2013. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J. Am. Chem. Soc. 135:7296–303 [Google Scholar]
  37. Bars I, Terning J. 37.  2010. Extra Dimensions in Space and Time New York: Springer-Verlag
  38. Ruddigkeit L, Blum LC, Reymond J-L. 38.  2013. Visualization and virtual screening of the Chemical Universe Database GDB-17. J. Chem. Inf. Model. 53:56–65 [Google Scholar]
  39. Reymond J-L. 39.  2015. The Chemical Space Project. Acc. Chem. Res. 48:722–30 [Google Scholar]
  40. Oprea TI, Gottfries J. 40.  2001. Chemography: the art of navigating in chemical space. J. Comb. Chem. 3:157–66 [Google Scholar]
  41. Engel T. 41.  2006. Basic overview of chemoinformatics. J. Chem. Inform. Model. 46:2267–77 [Google Scholar]
  42. Varnek A, Baskin II. 42.  2011. Chemoinformatics as a theoretical chemistry discipline. Mol. Inform. 30:20–32 [Google Scholar]
  43. 43. Scifinder 2015. Columbus, OH:Chem. Abstr. Serv https://scifinder.cas.org/. Accessed September 28, 2015
  44. Jinha AE. 44.  2010. Article 50 million: an estimate of the number of scholarly articles in existence. Learned Publ. 23:258–63 [Google Scholar]
  45. Powell JR. 45.  2008. The quantum limit to Moore's law. Proc. IEEE 96:1247–48 [Google Scholar]
  46. Walter C. 46.  2005. Kryder's law. Sci. Am. 293:32–22 [Google Scholar]
  47. Eldering CA, Sylla ML, Eisenach JA. 47.  1999. Is there a Moore's law for bandwidth?. Commun. Mag. IEEE 37:117–21 [Google Scholar]
  48. Sobel D. 48.  2010. Longitude: The True Story of a Lone Genius Who Solved the Greatest Scientific Problem of His Time London: Bloomsbury
  49. Misura KMS, Chivian D, Rohl CA, Kim DE, Baker D. 49.  2006. Physically realistic homology models built with Rosetta can be more accurate than their templates. PNAS 103:5361–66 [Google Scholar]
  50. Kaufmann KW, Lemmon GH, DeLuca SL, Sheehan JH, Meiler J. 50.  2010. Practically useful: what the Rosetta protein modeling suite can do for you. Biochemistry 49:2987–98 [Google Scholar]
  51. Khatib F, Cooper S, Tyka MD, Xu K, Makedon I. 51.  et al. 2011. Algorithm discovery by protein folding game players. PNAS 108:18949–53 [Google Scholar]
  52. Khatib F, DiMaio F, Cooper S, Kazmierczyk M, Gilski M. 52.  et al. 2011. Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nat. Struct. Mol. Biol. 18:1175–77 [Google Scholar]
  53. Gilski M, Kazmierczyk M, Krzywda S, Zabranska H, Cooper S. 53.  et al. 2011. High-resolution structure of a retroviral protease folded as a monomer. Acta Crystallogr. D 67:907–14 [Google Scholar]
  54. Eiben CB, Siegel JB, Bale JB, Cooper S, Khatib F. 54.  et al. 2012. Increased diels-alderase activity through backbone remodeling guided by Foldit players. Nat. Biotechnol. 30:190–92 [Google Scholar]
  55. Bradley JC, Lancashire R, Lang A, Williams A. 55.  2009. The Spectral Game: leveraging Open Data and crowdsourcing for education. J. Cheminform. 1:9 [Google Scholar]
  56. Du L, Robles AJ, King JB, Powell DR, Miller AN. 56.  et al. 2014. Crowdsourcing natural products discovery to access uncharted dimensions of fungal metabolite diversity. Angew. Chem. Int. Ed. 53:804–9 [Google Scholar]
  57. Martin SF, Falkenberg H, Dyrlund TF, Khoudoli GA, Mageean CJ, Linding R. 57.  2013. PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development. J. Proteomics 88:41–46 [Google Scholar]
  58. Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. 58.  2014. Critical assessment of methods of protein structure prediction (CASP)—round x. Proteins Struct. Funct. Bioinform. 82:1–6 [Google Scholar]
  59. Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ. 59.  et al. 2012. Wisdom of crowds for robust gene network inference. Nat. Methods 9:796–804 [Google Scholar]
  60. Bishop CM. 60.  2006. Pattern Recognition and Machine Learning New York: Springer
  61. Smalheiser NR. 61.  2002. Informatics and hypothesis-driven research. EMBO Rep. 3:702–2 [Google Scholar]
  62. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Ullah Khan S. 62.  2015. The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47:98–115 [Google Scholar]
  63. Chen T, Zhao J, Ma J, Zhu Y. 63.  2015. Web resources for mass spectrometry-based proteomics. Genom. Proteom. Bioinform. 13:36–39 [Google Scholar]
  64. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. 64.  2012. XCMS online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84:5035–39 [Google Scholar]
  65. Rinehart D, Johnson CH, Nguyen T, Ivanisevic J, Benton HP. 65.  et al. 2014. Metabolomic data streaming for biology-dependent data acquisition. Nat. Biotechnol. 32:524–27 [Google Scholar]
  66. Rübel O, Greiner A, Cholia S, Louie K, Bethel EW. 66.  et al. 2013. OpenMSI: a high-performance web-based platform for mass spectrometry imaging. Anal. Chem. 85:10354–61 [Google Scholar]
  67. Fischer CR, Ruebel O, Bowen BP. 67.  2016. An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses. Arch. Biochem. Biophys. 589:18–26 [Google Scholar]
  68. Malm E, Srivastava V, Sundqvist G, Bulone V. 68.  2014. APP: an Automated Proteomics Pipeline for the analysis of mass spectrometry data based on multiple open access tools. BMC Bioinform. 15:441 [Google Scholar]
  69. Mohammed Y, Mostovenko E, Henneman AA, Marissen RJ, Deelder AM, Palmblad M. 69.  2012. Cloud parallel processing of tandem mass spectrometry based proteomics data. J. Proteome Res. 11:5101–8 [Google Scholar]
  70. Muth T, Peters J, Blackburn J, Rapp E, Martens L. 70.  2013. ProteoCloud: a full-featured open source proteomics cloud computing pipeline. J. Proteomics 88:104–8 [Google Scholar]
  71. Deutsch EW, Mendoza L, Shteynberg D, Slagel J, Sun Z, Moritz RL. 71.  2015. Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteom. Clin. Appl. 9:745–54 [Google Scholar]
  72. Slagel J, Mendoza L, Shteynberg D, Deutsch EW, Moritz RL. 72.  2015. Processing shotgun proteomics data on the Amazon Cloud with the Trans-Proteomic Pipeline. Mol. Cell. Proteom. 14:399–404 [Google Scholar]
  73. Riffle M, Eng JK. 73.  2009. Proteomics data repositories. Proteomics 9:4653–63 [Google Scholar]
  74. Perez-Riverol Y, Alpi E, Wang R, Hermjakob H, Vizcaíno JA. 74.  2015. Making proteomics data accessible and reusable: current state of proteomics databases and repositories. Proteomics 15:930–50 [Google Scholar]
  75. Karger BL, Snyder LR, Horvath C. 75.  1973. Introduction to Separation Science New York: Wiley
  76. Giddings JC. 76.  1984. Two-dimensional separations: concept and promise. Anal. Chem. 56:1258A–70A [Google Scholar]
  77. Frahm JL, Howard BE, Heber S, Muddiman DC. 77.  2006. Accessible proteomics space and its implications for peak capacity for zero-, one- and two-dimensional separations coupled with FT-ICR and TOF mass spectrometry. J. Mass Spectrom. 41:281–88 [Google Scholar]
  78. Barner-Kowollik C, Gruendling T, Falkenhagen J, Weidner S. 78.  2012. Mass Spectrometry in Polymer Chemistry New York: Wiley
  79. Canterbury JD, Yi X, Hoopmann MR, MacCoss MJ. 79.  2008. Assessing the dynamic range and peak capacity of nanoflow LC−FAIMS−MS on an ion trap mass spectrometer for proteomics. Anal. Chem. 80:6888–97 [Google Scholar]
  80. Schneider B, Nazarov E, Covey T. 80.  2012. Peak capacity in differential mobility spectrometry: effects of transport gas and gas modifiers. Int. J. Ion Mobil. Spectrom. 15:141–50 [Google Scholar]
  81. Merenbloom SI, Bohrer BC, Koeniger SL, Clemmer DE. 81.  2007. Assessing the peak capacity of IMS−IMS separations of tryptic peptide ions in He at 300 K. Anal. Chem. 79:515–22 [Google Scholar]
  82. May JC, McLean JA. 82.  2013. The influence of drift gas composition on the separation mechanism in traveling wave ion mobility spectrometry: insight from electrodynamic simulations. Int. J. Ion Mobil. Spectrom. 16:85–94 [Google Scholar]
  83. May JC, McLean JA. 83.  2015. Ion mobility-mass spectrometry: time-dispersive instrumentation. Anal. Chem. 87:1422–36 [Google Scholar]
  84. Causon TJ, Hann S. 84.  2015. Theoretical evaluation of peak capacity improvements by use of liquid chromatography combined with drift tube ion mobility-mass spectrometry. J. Chromatogr. A 1416:47–56 [Google Scholar]
  85. McLean JA, Ruotolo BT, Gillig KJ, Russell DH. 85.  2005. Ion mobility-mass spectrometry: a new paradigm for proteomics. Int. J. Mass Spectrom. 240:301–15 [Google Scholar]
  86. Neue UD. 86.  2005. Theory of peak capacity in gradient elution. J. Chromatogr. A 1079:153–61 [Google Scholar]
  87. Neue UD. 87.  2008. Peak capacity in unidimensional chromatography. J. Chromatogr. A 1184:107–30 [Google Scholar]
  88. Moore AW, Jorgenson JW. 88.  1995. Comprehensive three-dimensional separation of peptides using size exclusion chromatography/reversed phase liquid chromatography/optically gated capillary zone electrophoresis. Anal. Chem. 67:3456–63 [Google Scholar]
  89. Tia S, Herr AE. 89.  2009. On-chip technologies for multidimensional separations. Lab. Chip 9:2524–36 [Google Scholar]
  90. Bruce JE, Anderson GA, Wen J, Harkewicz R, Smith RD. 90.  1999. High-mass-measurement accuracy and 100 sequence coverage of enzymatically digested bovine serum albumin from an ESI-FTICR mass spectrum. Anal. Chem. 71:2595–99 [Google Scholar]
  91. Prentice BM, Chumbley CW, Caprioli RM. 91.  2015. High-speed MALDI MS/MS imaging mass spectrometry using continuous raster sampling. J. Mass Spectrom. 50:703–10 [Google Scholar]
  92. Stauber J, MacAleese L, Franck J, Claude E, Snel M. 92.  et al. 2010. On-tissue protein identification and imaging by MALDI–ion mobility mass spectrometry. J. Am. Soc. Mass Spectrom. 21:338–47 [Google Scholar]
  93. Trede D, Schiffler S, Becker M, Wirtz S, Steinhorst K. 93.  et al. 2012. Exploring three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry data: three-dimensional spatial segmentation of mouse kidney. Anal. Chem. 84:6079–87 [Google Scholar]
  94. Kind T, Fiehn O. 94.  2010. Advances in structure elucidation of small molecules using mass spectrometry. Bioanal. Rev. 2:23–60 [Google Scholar]
  95. Kind T, Fiehn O. 95.  2007. Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinform. 8:1–20 [Google Scholar]
  96. Annesley TM. 96.  2003. Ion suppression in mass spectrometry. Clin. Chem. 49:1041–44 [Google Scholar]
  97. Kim S, Rodgers RP, Marshall AG. 97.  2006. Truly “exact” mass: elemental composition can be determined uniquely from molecular mass measurement at ∼0.1 mDa accuracy for molecules up to ∼500 Da. Int. J. Mass Spectrom. 251:260–65 [Google Scholar]
  98. Savory JJ, Kaiser NK, McKenna AM, Xian F, Blakney GT. 98.  et al. 2011. Parts-per-billion Fourier transform ion cyclotron resonance mass measurement accuracy with a “walking” calibration equation. Anal. Chem. 83:1732–36 [Google Scholar]
  99. Scheltema RA, Kamleh A, Wildridge D, Ebikeme C, Watson DG. 99.  et al. 2008. Increasing the mass accuracy of high-resolution LC-MS data using background ions—a case study on the LTQ-Orbitrap. Proteomics 8:4647–56 [Google Scholar]
  100. Green FM, Gilmore IS, Seah MP. 100.  2011. Mass spectrometry and informatics: distribution of molecules in the PubChem database and general requirements for mass accuracy in surface analysis. Anal. Chem. 83:3239–43 [Google Scholar]
  101. Nefedov AV, Mitra I, Brasier AR, Sadygov RG. 101.  2011. Examining troughs in the mass distribution of all theoretically possible tryptic peptides. J. Proteome Res. 10:4150–57 [Google Scholar]
  102. Yergey AL, Edmonds CG, Lewis IAS, Vestal ML. 102.  2013. Liquid Chromatography/Mass Spectrometry: Techniques and Applications New York: Springer
  103. Norris JL, Caprioli RM. 103.  2013. Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. Chem. Rev. 113:2309–42 [Google Scholar]
  104. Watrous JD, Dorrestein PC. 104.  2011. Imaging mass spectrometry in microbiology. Nat. Rev. Microbiol. 9:683–94 [Google Scholar]
  105. Lanekoff I, Burnum-Johnson K, Thomas M, Cha J, Dey S. 105.  et al. 2015. Three-dimensional imaging of lipids and metabolites in tissues by nanospray desorption electrospray ionization mass spectrometry. Anal. Bioanal. Chem. 407:2063–71 [Google Scholar]
  106. Calligaris D, Caragacianu D, Liu X, Norton I, Thompson CJ. 106.  et al. 2014. Application of desorption electrospray ionization mass spectrometry imaging in breast cancer margin analysis. PNAS 111:15184–89 [Google Scholar]
  107. Giles K, Pringle SD, Worthington KR, Little D, Wildgoose JL, Bateman RH. 107.  2004. Applications of a traveling wave-based radio-frequency-only stacked ring ion guide. Rapid Commun. Mass Spectrom. 18:2401–14 [Google Scholar]
  108. Baker ES, Clowers BH, Li F, Tang K, Tolmachev AV. 108.  et al. 2007. Ion mobility spectrometry—mass spectrometry performance using electrodynamic ion funnels and elevated drift gas pressures. J. Am. Soc. Mass Spectrom. 18:1176–87 [Google Scholar]
  109. Sleno L. 109.  2012. The use of mass defect in modern mass spectrometry. J. Mass Spectrom. 47:2226–36 [Google Scholar]
  110. Kendrick E. 110.  1963. A mass scale based on CH2=14.0000 for high resolution mass spectrometry of organic compounds. Anal. Chem. 35:2146–54 [Google Scholar]
  111. Hughey CA, Hendrickson CL, Rodgers RP, Marshall AG, Qian K. 111.  2001. Kendrick mass defect spectrum: a compact visual analysis for ultrahigh-resolution broadband mass spectra. Anal. Chem. 73:4676–81 [Google Scholar]
  112. Marshall AG, Rodgers RP. 112.  2004. Petroleomics: the next grand challenge for chemical analysis. Acc. Chem. Res. 37:53–59 [Google Scholar]
  113. Lerno LA, German JB, Lebrilla CB. 113.  2010. Method for the identification of lipid classes based on referenced Kendrick mass analysis. Anal. Chem. 82:4236–45 [Google Scholar]
  114. Senko MW, Beu SC, McLafferty FW. 114.  1995. Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions. J. Am. Soc. Mass Spectrom. 6:229–33 [Google Scholar]
  115. Wu CH, Yeh L-SL, Huang H, Arminski L, Castro-Alvear J. 115.  et al. 2003. The Protein Information Resource. Nucleic Acids Res. 31:345–47 [Google Scholar]
  116. Yao X, Diego P, Ramos AA, Shi Y. 116.  2008. Averagine-scaling analysis and fragment ion mass defect labeling in peptide mass spectrometry. Anal. Chem. 80:7383–91 [Google Scholar]
  117. Toumi ML, Desaire H. 117.  2010. Improving mass defect filters for human proteins. J. Proteome Res. 9:5492–95 [Google Scholar]
  118. Nefedov AV, Mitra I, Brasier AR, Sadygov RG. 118.  2011. Examining troughs in the mass distribution of all theoretically possible tryptic peptides. J. Proteome Res. 10:4150–57 [Google Scholar]
  119. Mitra I, Nefedov AV, Brasier AR, Sadygov RG. 119.  2012. Improved mass defect model for theoretical tryptic peptides. Anal. Chem. 84:3026–32 [Google Scholar]
  120. Cuyckens F, Hurkmans R, Castro-Perez JM, Leclercq L, Mortishire-Smith RJ. 120.  2009. Extracting metabolite ions out of a matrix background by combined mass defect, neutral loss and isotope filtration. Rapid Commun. Mass Spectrom. 23:327–32 [Google Scholar]
  121. Zhang H, Zhang D, Ray K, Zhu M. 121.  2009. Mass defect filter technique and its applications to drug metabolite identification by high-resolution mass spectrometry. J. Mass Spectrom. 44:999–1016 [Google Scholar]
  122. Zhu M, Ma L, Zhang D, Ray K, Zhao W. 122.  et al. 2006. Detection and characterization of metabolites in biological matrices using mass defect filtering of liquid chromatography/high resolution mass spectrometry data. Drug Metab. Dispos. 34:1722–33 [Google Scholar]
  123. Li X, Brownawell BJ. 123.  2009. Analysis of quaternary ammonium compounds in estuarine sediments by LC−TOF-MS: very high positive mass defects of alkylamine ions as powerful diagnostic tools for identification and structural elucidation. Anal. Chem. 81:7926–35 [Google Scholar]
  124. Nagy K, Sandoz L, Craft BD, Destaillats F. 124.  2011. Mass-defect filtering of isotope signatures to reveal the source of chlorinated palm oil contaminants. Food Addit. Contam. A 28:1492–500 [Google Scholar]
  125. Mason EA, McDaniel EW. 125.  1988. Transport Properties of Ions in Gases New York: Wiley
  126. Lietz CB, Yu Q, Li L. 126.  2014. Large-scale collision cross-section profiling on a traveling wave ion mobility mass spectrometer. J. Am. Soc. Mass Spectrom. 25:2009–19 [Google Scholar]
  127. Paglia G, Williams JP, Menikarachchi L, Thompson JW, Tyldesley-Worster R. 127.  et al. 2014. Ion mobility derived collision cross sections to support metabolomics applications. Anal. Chem. 86:3985–93 [Google Scholar]
  128. Wyttenbach T, Pierson NA, Clemmer DE, Bowers MT. 128.  2014. Ion mobility analysis of molecular dynamics. Annu. Rev. Phys. Chem. 65:175–96 [Google Scholar]
  129. Zhong Y, Hyung S-J, Ruotolo BT. 129.  2012. Ion mobility–mass spectrometry for structural proteomics. Expert Rev. Proteom. 9:47–58 [Google Scholar]
  130. Lapthorn C, Pullen F, Chowdhry BZ. 130.  2012. Ion mobility spectrometry–mass spectrometry (IMS-MS) of small molecules: separating and assigning structures to ions. Mass Spectrom. Rev. 32:43–71 [Google Scholar]
  131. Lanucara F, Holman SW, Gray CJ, Eyers CE. 131.  2014. The power of ion mobility-mass spectrometry for structural characterization and the study of conformational dynamics. Nat. Chem. 6:281–94 [Google Scholar]
  132. Berant Z, Karpas Z. 132.  1989. Mass-mobility correlation of ions in view of new mobility data. J. Am. Chem. Soc. 111:3819–24 [Google Scholar]
  133. Fenn LS, Kliman M, Mahsut A, Zhao SR, McLean JA. 133.  2009. Characterizing ion mobility-mass spectrometry conformation space for the analysis of complex biological samples. Anal. Bioanal. Chem. 394:235–44 [Google Scholar]
  134. Harvey D, Crispin M, Bonomelli C, Scrivens J. 134.  2015. Ion mobility mass spectrometry for ion recovery and clean-up of MS and MS/MS spectra obtained from low abundance viral samples. J. Am. Soc. Mass Spectrom. 26:1754–67 [Google Scholar]
  135. Li H, Bendiak B, Siems W, Gang D, Hill H Jr. 135.  2013. Ion mobility-mass correlation trend line separation of glycoprotein digests without deglycosylation. Int. J. Ion Mobil. Spectrom. 16:105–15 [Google Scholar]
  136. May JC, Goodwin CR, Lareau NM, Leaptrot KL, Morris CB. 136.  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]
  137. May JC, McLean JA. 137.  2014. Lipid map. Anal. Sci. 0814. https://theanalyticalscientist.com/issues/0814/data-visualization-infographics/
  138. Ceglarek U, Leichtle A, Brügel M, Kortz L, Brauer R. 138.  et al. 2009. Challenges and developments in tandem mass spectrometry based clinical metabolomics. Mol. Cell. Endocrinol. 301:266–71 [Google Scholar]
  139. Benton HP, Ivanisevic J, Mahieu NG, Kurczy ME, Johnson CH. 139.  et al. 2015. Autonomous metabolomics for rapid metabolite identification in global profiling. Anal. Chem. 87:884–91 [Google Scholar]
  140. Patti GJ, Yanes O, Siuzdak G. 140.  2012. Innovation: metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13:263–69 [Google Scholar]
  141. Johnson CH, Ivanisevic J, Benton HP, Siuzdak G. 141.  2015. Bioinformatics: the next frontier of metabolomics. Anal. Chem. 87:147–56 [Google Scholar]
  142. Patti GJ, Tautenhahn R, Rinehart D, Cho K, Shriver LP. 142.  et al. 2013. A view from above: cloud plots to visualize global metabolomic data. Anal. Chem. 85:798–804 [Google Scholar]
  143. Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP. 143.  et al. 2014. Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal. Chem. 86:6931–39 [Google Scholar]
  144. Goodacre R, Neal MJ, Kell DB. 144.  1996. Quantitative analysis of multivariate data using artificial neural networks: a tutorial review and applications to the deconvolution of pyrolysis mass spectra. Zentralbl. Bakteriol. 284:516–39 [Google Scholar]
  145. Franceschi P, Wehrens R. 145.  2014. Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets. Proteomics 14:853–61 [Google Scholar]
  146. Patterson AD, Li H, Eichler GS, Krausz KW, Weinstein JN. 146.  et al. 2008. UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation. Anal. Chem. 80:665–74 [Google Scholar]
  147. Goodwin CR, Sherrod SD, Marasco CC, Bachmann BO, Schramm-Sapyta N. 147.  et al. 2014. Phenotypic mapping of metabolic profiles using self-organizing maps of high-dimensional mass spectrometry data. Anal. Chem. 86:6563–71 [Google Scholar]
  148. Goodwin CR, Covington BC, Derewacz DK, McNees CR, Wikswo JP. 148.  et al. 2015. Structuring microbial metabolic responses to multiplexed stimuli via self-organizing metabolomics maps. Chem. Biol. 22:661–70 [Google Scholar]
  149. Sherrod SD, McLean JA. 149.  2016. Systems-wide high dimensional data acquisition and informatics using structural mass spectrometry strategies. Clin. Chem. 62:77–83 [Google Scholar]
/content/journals/10.1146/annurev-anchem-071015-041734
Loading
/content/journals/10.1146/annurev-anchem-071015-041734
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