Promises and pitfalls of untargeted metabolomics

Metabolomics
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Abstract

Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi-quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC-MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre-analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post-analytical (data processing).

Notes

Compliance with ethical standards

Conflict of interest

Ilya Gertsman and Bruce A. Barshop declare that they have no conflict of interest.

References

  1. Alvarez-Sanchez B, Priego-Capote F, Luque de Castro MD (2010) Metabolomics analysis. I. Selection of biological samples and practical aspects preceding sample preparation. Trends Anal Chem 29:111–119Google Scholar
  2. Aragon AD, Quinones GA, Allen C et al (2006) An automated, pressure-driven sampling device for harvesting from liquid cultures for genomic and biochemical analyses. J Microbiol Methods 65:357–360CrossRefPubMedGoogle Scholar
  3. Barker M, Rayens W (2003) Partial least squares for discrimination. J Chemom 17:166–173CrossRefGoogle Scholar
  4. Barri T, Dragsted LO (2013) UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. Anal Chim Acta 768:118–128CrossRefPubMedGoogle Scholar
  5. Belanger MP, Askin N, Wittnich C (2002) Multiple in vivo liver biopsies using a freeze-clamping technique. J Investig Surg 15:109–112CrossRefGoogle Scholar
  6. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57:289–300Google Scholar
  7. Benton HP, Wong DM, Trauger SA et al (2008) XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 80:6382-6389Google Scholar
  8. Benton HP, Ivanisevic J, Mahieu NG et al (2015) Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem 87:884–891CrossRefPubMedGoogle Scholar
  9. Blank LM, Kuepfer L, Sauer U (2005) Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome Biol 6:R49CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911–917CrossRefPubMedGoogle Scholar
  11. Bylesjö M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J (2006) OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemom 20:341–351CrossRefGoogle Scholar
  12. Canelas AB, ten Pierick A, Ras C et al (2009) Quantitative evaluation of intracellular metabolite extraction techniques for yeast metabolomics. Anal Chem 81:7379–7389Google Scholar
  13. Caspi R, Altman T, Billington R et al (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 42:D459–D471CrossRefPubMedGoogle Scholar
  14. Chapman AG, Miller AL, Atkinson DE (1976) Role of the adenylate deaminase reaction in regulation of adenine nucleotide metabolism in Ehrlich ascites tumor cells. Cancer Res 36:1144–1150PubMedGoogle Scholar
  15. Chen S, Hoene M, Li J et al (2013) Simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether from a single small tissue sample for ultra-high performance liquid chromatography/mass spectrometry. J Chromatogr A 1298:9–16CrossRefPubMedGoogle Scholar
  16. Csató E, Fülöp N, Szabó G (1990) Preparation and comparison of a pentafluorophenyl stationary phase for reversed-phase liquid chromatography. J Chromatogr A 511:79–88CrossRefGoogle Scholar
  17. Denery JR, Nunes AA, Dickerson TJ (2011) Characterization of differences between blood sample matrices in untargeted metabolomics. Anal Chem 83:1040–1047CrossRefPubMedGoogle Scholar
  18. Dietmair S, Timmins NE, Gray PP, Nielsen LK, Kromer JO (2010) Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem 404:155–164CrossRefPubMedGoogle Scholar
  19. Dietz C, Ehret F, Palmas F, et al (2017) Applications of high-resolution magic angle spinning MRS in biomedical studies II-human diseases. NMR Biomed 30:e3784Google Scholar
  20. Dorn GW 2nd (2013) MicroRNAs and the butterfly effect. Cell cycle (Georgetown, Tex) 12:707–708CrossRefGoogle Scholar
  21. Dudzik D, Barbas-Bernardos C, Garcia A, Barbas C (2017) Quality assurance procedures for mass spectrometry untargeted metabolomics. A review. J Pharma Biomed Anal 147:149-173Google Scholar
  22. Dunn OJ (1961) Multiple Comparisons among Means. J Am Stat Assoc 56:52CrossRefGoogle Scholar
  23. Dunn WB, Broadhurst D, Begley P 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 6:1060–1083CrossRefPubMedGoogle Scholar
  24. Fan TW, Lane AN (2016) Applications of NMR spectroscopy to systems biochemistry. Prog Nucl Magn Reson Spectrosc 92-93:18–53CrossRefPubMedPubMedCentralGoogle Scholar
  25. Fan TW, Lane AN, Higashi RM et al (2009) Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer 8:41CrossRefPubMedPubMedCentralGoogle Scholar
  26. FDA (2001) Guidance for Industry: Bioanalytical Method Validation (Fda.Gov/cder/guidance/index.htm) Google Scholar
  27. Fiehn O, Robertson D, Griffin J et al (2007) The metabolomics standards initiative (MSI). Metabolomics 3:175–178CrossRefGoogle Scholar
  28. Fischer EH (2013) Cellular regulation by protein phosphorylation. Biochem Biophys Res Commun 430:865–867CrossRefPubMedGoogle Scholar
  29. Folch J, Lees M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem 226:497–509PubMedGoogle Scholar
  30. Fuhrer T, Heer D, Begemann B, Zamboni N (2011) High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Anal Chem 83:7074–7080CrossRefPubMedGoogle Scholar
  31. Genovese C, Wasserman L (2002) Operating characteristics and extensions of the false discovery rate procedure. J Roy Stat Soc B 64:499–517CrossRefGoogle Scholar
  32. Gertsman I, Gangoiti J, Barshop B (2014) Validation of a dual LC–HRMS platform for clinical metabolic diagnosis in serum, bridging quantitative analysis and untargeted metabolomics. Metabolomics 10:312–323CrossRefPubMedGoogle Scholar
  33. Gertsman I, Gangoiti JA, Nyhan WL, Barshop BA (2015) Perturbations of tyrosine metabolism promote the indolepyruvate pathway via tryptophan in host and microbiome. Mol Genet Metab 114:431–437CrossRefPubMedGoogle Scholar
  34. Golland P, Fischl B (2003) Permutation tests for classification: towards statistical significance in image-based studies. Inf Process Med Imaging 18:330–341CrossRefPubMedGoogle Scholar
  35. Guillarme D, Ruta J, Rudaz S, Veuthey JL (2010) New trends in fast and high-resolution liquid chromatography: a critical comparison of existing approaches. Anal Bioanal Chem 397:1069–1082CrossRefPubMedGoogle Scholar
  36. Hill C, Drolet J, Kellogg MD, Tolstikov V, Narain NR, Kiebish MA (2017) Blood sampled through dried blood spots (DBS) exhibits diminished ex vivo metabolism compared to whole blood through use of a kinetic isotope-labeling metabolomics approach. Biochem Anal Biochem 6:325–330Google Scholar
  37. Horai H, Arita M, Kanaya S et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45:703–714CrossRefPubMedGoogle Scholar
  38. Jandera P, Janas P (2017) Recent advances in stationary phases and understanding of retention in hydrophilic interaction chromatography. A review. Anal Chim Acta 967:12–32CrossRefPubMedGoogle Scholar
  39. Jensen SK (2008) Improved Bligh and dyer extraction procedure. Lipid Technol 20:280–281CrossRefGoogle Scholar
  40. Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci 374:20150202CrossRefPubMedPubMedCentralGoogle Scholar
  41. Kind T, Fiehn O (2007) Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8:105CrossRefPubMedPubMedCentralGoogle Scholar
  42. Kolarovic L, Fournier NC (1986) A comparison of extraction methods for the isolation of phospholipids from biological sources. Anal Biochem 156:244–250CrossRefPubMedGoogle Scholar
  43. Kouassi Nzoughet J, Bocca C, Simard G et al (2017) A nontargeted UHPLC-HRMS metabolomics pipeline for metabolite identification: application to cardiac remote ischemic preconditioning. Anal Chem 89:2138–2146Google Scholar
  44. Koulman A, Prentice P, Wong MCY et al (2014) The development and validation of a fast and robust dried blood spot based lipid profiling method to study infant metabolism. Metabolomics 10:1018–1025CrossRefPubMedPubMedCentralGoogle Scholar
  45. Kummel A, Panke S, Heinemann M (2006) Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2:0034CrossRefPubMedGoogle Scholar
  46. Leon Z, Garcia-Canaveras JC, Donato MT, Lahoz A (2013) Mammalian cell metabolomics: experimental design and sample preparation. Electrophoresis 34:2762–2775PubMedGoogle Scholar
  47. Li L, Ren W, Kong H, et al (2017) An alignment algorithm for LC-MS-based metabolomics dataset assisted by MS/MS information. Analytica Chimica Acta 990:96-102Google Scholar
  48. Lommen A (2009) MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81:3079–3086CrossRefPubMedGoogle Scholar
  49. Lorenz MA, Burant CF, Kennedy RT (2011) Reducing time and increasing sensitivity in sample preparation for adherent mammalian cell metabolomics. Anal Chem 83:3406–3414CrossRefPubMedPubMedCentralGoogle Scholar
  50. Madalinski G, Godat E, Alves S et al (2008) Direct introduction of biological samples into a LTQ-Orbitrap hybrid mass spectrometer as a tool for fast metabolome analysis. Anal Chem 80:3291–3303CrossRefPubMedGoogle Scholar
  51. Martano G, Delmotte N, Kiefer P et al (2015) Fast sampling method for mammalian cell metabolic analyses using liquid chromatography-mass spectrometry. Nat Protoc 10:1–11CrossRefPubMedGoogle Scholar
  52. McCalley DV (2017) Understanding and manipulating the separation in hydrophilic interaction liquid chromatography-a review. J Chromatogr A 1523:49-71Google Scholar
  53. McCloskey D, Gangoiti JA, King ZA et al (2014) A model-driven quantitative metabolomics analysis of aerobic and anaerobic metabolism in E. Coli K-12 MG1655 that is biochemically and thermodynamically consistent. Biotechnol Bioeng 111:803–815CrossRefPubMedGoogle Scholar
  54. Metabolon (2017) Sample preparation and shipping guidelines.www.metabolon.comGoogle Scholar
  55. Miller MJ, Kennedy AD, Eckhart AD et al (2015) Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J Inherit Metab Dis 38:1029–1039CrossRefPubMedPubMedCentralGoogle Scholar
  56. Overmyer KA, Thonusin C, Qi NR, Burant CF, Evans CR (2015) Impact of anesthesia and euthanasia on metabolomics of mammalian tissues: studies in a C57BL/6J mouse model. PLoS One 10:e0117232CrossRefPubMedPubMedCentralGoogle Scholar
  57. Perneger TV (1998) What's wrong with Bonferroni adjustments. Brit Med J 316:1236–1238CrossRefPubMedPubMedCentralGoogle Scholar
  58. Reaves ML, Rabinowitz JD (2011) Metabolomics in systems microbiology. Curr Opin Biotechnol 22:17–25CrossRefPubMedGoogle Scholar
  59. Righi V, Tugnoli V, Mucci A, Bacci A, Bonora S, Schenetti L (2012) MRS study of meningeal hemangiopericytoma and edema: a comparison with meningothelial meningioma. Oncol Rep 28:1461–1467CrossRefPubMedGoogle Scholar
  60. Rojas-Cherto M, Peironcely JE, Kasper PT et al (2012) Metabolite identification using automated comparison of high-resolution multistage mass spectral trees. Anal Chem 84:5524–5534CrossRefPubMedGoogle Scholar
  61. Rose HG, Oklander M (1965) Improved procedure for the extraction of lipids from human erythrocytes. J Lipid Res 6:428–431PubMedGoogle Scholar
  62. Salek RM, Steinbeck C, Viant MR, Goodacre R, Dunn WB (2013) The role of reporting standards for metabolite annotation and identification in metabolomic studies. Gigascience 2:13CrossRefPubMedPubMedCentralGoogle Scholar
  63. Salerno S Jr, Mehrmohamadi M, Liberti MV et al (2017) RRmix: a method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards. PLoS One 12:e0179530CrossRefPubMedPubMedCentralGoogle Scholar
  64. Sellick CA, Hansen R, Stephens GM, Goodacre R, Dickson AJ (2011) Metabolite extraction from suspension-cultured mammalian cells for global metabolite profiling. Nat Protoc 6:1241–1249CrossRefPubMedGoogle Scholar
  65. Shi H, Enriquez A, Rapadas M et al (2017) NAD deficiency, congenital malformations, and niacin supplementation. N Engl J Med 377:544–552CrossRefPubMedGoogle Scholar
  66. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100:9440–9445CrossRefPubMedPubMedCentralGoogle Scholar
  67. Sud M, Fahy E, Cotter D 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–D470CrossRefPubMedGoogle Scholar
  68. Sud M, Fahy E, Cotter D, Dennis EA, Subramaniam S (2012) LIPID MAPS-nature Lipidomics gateway: an online resource for students and educators interested in lipids. J Chem Educ 89:291–292CrossRefPubMedGoogle Scholar
  69. Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics 3:211–221Google Scholar
  70. Sysi-Aho M, Katajamaa M, Yetukuri L, Oresic M (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics 8:93CrossRefPubMedPubMedCentralGoogle Scholar
  71. Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012a) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30:826–828CrossRefPubMedPubMedCentralGoogle Scholar
  72. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012b) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84:5035–5039CrossRefPubMedPubMedCentralGoogle Scholar
  73. Teng Q, Huang W, Collette TW, Ekman DR, Tan C (2009) A direct cell quenching method for cell-culture based metabolomics. Metabolomics 5:199–208CrossRefGoogle Scholar
  74. Thonusin C, IglayReger HB, Soni T, Rothberg AE, Burant CF, Evans CR (2017) Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data. J Chromatogr A 1523:265-274Google Scholar
  75. Tognarelli JM, Dawood M, Shariff MI et al (2015) Magnetic resonance spectroscopy: principles and techniques: lessons for clinicians. Journal of clinical and experimental hepatology 5:320–328CrossRefPubMedPubMedCentralGoogle Scholar
  76. Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemom 16:119–128CrossRefGoogle Scholar
  77. Tugizimana F, Steenkamp PA, Piater LA, Dubery IA (2016) A conversation on sata mining strategies in LC-MS untargeted metabolomics: pre-processing and pre-treatment steps. Metabolites 6:40Google Scholar
  78. Vaniya A, Fiehn O (2015) Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics. Trends Analyt Chem 69:52–61CrossRefPubMedPubMedCentralGoogle Scholar
  79. Vuckovic D (2012) Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Anal Bioanal Chem 403:1523–1548CrossRefPubMedGoogle Scholar
  80. Wang M, Carver JJ, Phelan VV et al (2016) Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat Biotechnol 34:828–837CrossRefPubMedPubMedCentralGoogle Scholar
  81. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37:W623–W633CrossRefPubMedPubMedCentralGoogle Scholar
  82. Want EJ, Cravatt BF, Siuzdak G (2005) The expanding role of mass spectrometry in metabolite profiling and characterization. Chembiochem: a European journal of chemical biology 6:1941–1951Google Scholar
  83. Want EJ, Masson P, Michopoulos F et al (2013) Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 8:17–32CrossRefPubMedGoogle Scholar
  84. Want EJ, Nordstrom A, Morita H, Siuzdak G (2007) From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. J Proteome Res 6:459–468CrossRefPubMedGoogle Scholar
  85. Warrack BM, Hnatyshyn S, Ott KH et al (2009) Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Anal Technol Biomed Life Sci 877:547–552CrossRefGoogle Scholar
  86. Watson DG (2013) A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans. Comput Struct Biotechnol J 4:e201301005CrossRefPubMedPubMedCentralGoogle Scholar
  87. Wehrens R, Hageman JA, van Eeuwijk F et al (2016) Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12:88CrossRefPubMedPubMedCentralGoogle Scholar
  88. Westerhuis JA, Hoefsloot HCJ, Smit S et al (2008) Assessment of PLSDA cross validation. Metabolomics 4:81–89CrossRefGoogle Scholar
  89. Wheelock AM, Wheelock CE (2013) Trials and tribulations of ‘omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine. Mol BioSyst 9:2589–2596CrossRefPubMedGoogle Scholar
  90. Wikoff WR, Gangoiti JA, Barshop BA, Siuzdak G (2007) Metabolomics identifies perturbations in human disorders of propionate metabolism. Clin Chem 53:2169–2176CrossRefPubMedGoogle Scholar
  91. Williams A, Tkachenko V (2014) The Royal Society of Chemistry and the delivery of chemistry data repositories for the community. J Comput Aided Mol Des 28:1023–1030CrossRefPubMedGoogle Scholar
  92. Wishart DS, Knox C, Guo AC et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–D610CrossRefPubMedGoogle Scholar
  93. Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14 10 11–14 10 91Google Scholar
  94. Yanes O, Tautenhahn R, Patti GJ, Siuzdak G (2011) Expanding coverage of the metabolome for global metabolite profiling. Anal Chem 83:2152–2161CrossRefPubMedPubMedCentralGoogle Scholar
  95. Yin P, Lehmann R, Xu G (2015) Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem 407:4879–4892CrossRefPubMedPubMedCentralGoogle Scholar
  96. Zhou J, Liu H, Liu Y, Liu J, Zhao X, Yin Y (2016) Development and evaluation of a parallel reaction monitoring strategy for large-scale targeted metabolomics quantification. Anal Chem 88:4478–4486Google Scholar

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© SSIEM 2018

Authors and Affiliations

  1. 1.Biochemical Genetics and Metabolomics Laboratory, Department of PediatricsUniversity of California San DiegoCAUSA

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