Analytical pitfalls and challenges in clinical metabolomics

Bioanalysis

Volumn 8, Issue 14, 2016, Pages 1509-1532

  Kohler, Isabelle ^a   Verhoeven, Aswin ^a   Derks, Rico JE ^a   Giera, Martin ^a  

^a LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

capillary electrophoresis; clinical metabolomics; data analysis; GC; LC; lipidomics; MS; NMR

Indexed keywords

ANALYTICAL ERROR; CHROMATOGRAPHY; CLINICAL METABOLOMICS; CLINICAL RESEARCH; DATA ANALYSIS; DIAGNOSIS; ELECTROPHORESIS; EXPERIMENT; HUMAN; MASS SPECTROMETRY; METABOLITE; METABOLOMICS; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PATHOPHYSIOLOGY; PERSONALIZED MEDICINE; PROGNOSIS; QUALITY CONTROL; REVIEW; SAMPLING; STORAGE; STUDY DESIGN; TISSUE PREPARATION; WORKFLOW; ANIMAL; CAPILLARY ELECTROPHORESIS; DIAGNOSTIC PROCEDURE; LIQUID CHROMATOGRAPHY; PROCEDURES;

ANIMALS; CHROMATOGRAPHY, LIQUID; DIAGNOSTIC TECHNIQUES AND PROCEDURES; ELECTROPHORESIS, CAPILLARY; HUMANS; MAGNETIC RESONANCE SPECTROSCOPY; MASS SPECTROMETRY; METABOLOMICS; PRECISION MEDICINE;

EID: 84977080419     PISSN: 17576180     EISSN: 17576199     Source Type: Journal    
DOI: 10.4155/bio-2016-0090     Document Type: Review

References (152)

1. Gates SC, Sweeley CC. Quantitative metabolic profiling based on gas chromatography. Clin. Chem. 24(10), 1663-1673 (1978).
2. Dalgliesh CE, Horning EC, Horning MG, Knox KL, Yarger K. A gas-liquid-chromatographic procedure for separating a wide range of metabolites occurring in urine or tissue extracts. Biochem. J. 101(3), 792-810 (1966).
3. Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16(9), 373-378 (1998).
4. Fiehn O. Metabolomics-The link between genotypes and phenotypes. Plant Mol. Biol. 48(1-2), 155-171 (2002).
5. Nicholson JK, Lindon JC, Holmes E. ?Metabonomics: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11), 1181-1189 (1999).
6. Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13(4), 263-269 (2012).
7. Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal. Chem. 88(1), 524-545 (2016).
8. Naz S, Vallejo M, Garcia A, Barbas C. Method validation strategies involved in non-Targeted metabolomics. J. Chromatogr. A 1353, 99-105 (2014).
9. Tzoulaki I, Ebbels TM, Valdes A, Elliott P, Ioannidis JP. Design and analysis of metabolomics studies in epidemiologic research: a primer on-omic technologies. Am. J. Epidemiol. 180(2), 129-139 (2014).
10. Dettmer K, Aronov PA, Hammock BD. Mass spectrometrybased metabolomics. Mass Spectrom. Rev. 26(1), 51-78 (2007).
11. Sands BE. Biomarkers of inflammation in inflammatory bowel disease. Gastroenterology 149(5), 1275-1285 (2015).
12. Kell DB. Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7(3), 296-307 (2004).
13. Viswanathan CT, Bansal S, Booth B et al. Quantitative bioanalytical methods validation and implementation: best practices for chromatographic and ligand binding assays. Pharm. Res. 24(10), 1962-1973 (2007).
14. Rozet E, Marini RD, Ziemons E, Boulanger B, Hubert P. Advances in validation, risk and uncertainty assessment of bioanalytical methods. J. Pharm. Biomed. Anal. 55(4), 848-858 (2011).
15. Food and Drug Administration. Guidance for Industry, Bioanalytical Method Validation. www.fda.gov/downloads/Drugs/Guidances/ucm070107.pdf
16. European Medicine Agency. Guideline on Bioanalytical Method Validation. www.ema.europa.eu
17. International Conference on Harmonisation. Validation of Analytical Procedures: Text and Methodology Q2(R1). www.ich.org
18. Thompson M, Ellison SLR, Wood R. Harmonized guidelines for single-laboratory validation of methods of analysis (IUPAC technical report). Pure Appl. Chem. 74(5), 835-855 (2002).
19. Dunn WB, Broadhurst D, Begley P et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6(7), 1060-1083 (2011).
20. Zhu ZJ, Schultz AW, Wang J et al. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat. Protoc. 8(3), 451-460 (2013).
21. Yuan M, Breitkopf SB, Yang X, Asara JM. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7(5), 872-881 (2012).
22. Want EJ, Wilson ID, Gika H et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5(6), 1005-1018 (2010).
23. Want EJ, Masson P, Michopoulos F et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protoc. 8(1), 17-32 (2013).
24. Dunn WB, Wilson ID, Nicholls AW, Broadhurst D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4(18), 2249-2264 (2012).
25. Goodacre R, Broadhurst D, Smilde AK et al. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3, 231-241 (2007).
26. Sumner LW, Amberg A, Barrett D et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3(3), 211-221 (2007).
27. Beisken S, Eiden M, Salek RM. Getting the right answers: understanding metabolomics challenges. Expert Rev. Mol. Diagn. 15(1), 97-109 (2015).
28. Hyotylainen T, Oresic M. Bioanalytical techniques in nontargeted clinical lipidomics. Bioanalysis 8(4), 351-364 (2016).
29. Johnson CH, Gonzalez FJ. Challenges and opportunities of metabolomics. J. Cell. Physiol. 227(8), 2975-2981 (2012).
30. Arkin CF, Wachtel MS. How many patiends are necessary to assess test performance. JAMA 263, 275-278 (1990).
31. Bando K, Kawahara R, Kunimatsu T et al. Influences of biofluid sample collection and handling procedures on GCMS based metabolomic studies. J. Biosci. Bioeng. 110(4), 491-499 (2010).
32. Liu L, Aa J, Wang G et al. Differences in metabolite profile between blood plasma and serum. Anal. Biochem. 406(2), 105-112 (2010).
33. Gil A, Siegel D, Permentier H, Reijngoud DJ, Dekker F, Bischoff R. Stability of energy metabolites-An often overlooked issue in metabolomics studies: a review. Electrophoresis 36(18), 2156-2169 (2015).
34. Siegel D, Permentier H, Bischoff R. Controlling detrimental effects of metal cations in the quantification of energy metabolites via ultrahigh pressure-liquid chromatographyelectrospray-Tandem mass spectrometry by employing acetylacetone as a volatile eluent modifier. J. Chromatogr. A 1294, 87-97 (2013).
35. Mirnaghi FS, Caudy AA. Challenges of analyzing different classes of metabolites by a single analytical method. Bioanalysis 6(24), 3393-3416 (2014).
36. Wisniewski CA, White JA, Michel CE et al. Optimal method of collection of first-void urine for diagnosis of Chlamydia trachomatis infection in men. J. Clin. Microbiol. 46(4), 1466-1469 (2008).
37. Delanghe J, Speeckaert M. Preanalytical requirements of urinalysis. Biochem. Med. (Zagreb) 24(1), 89-104 (2014).
38. Gika HG, Wilson ID, Theodoridis GA. LC-MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 966, 1-6 (2014).
39. Vigor C, Bertrand-Michel J, Pinot E et al. Non-enzymatic lipid oxidation products in biological systems: assessment of the metabolites from polyunsaturated fatty acids. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 964, 65-78 (2014).
40. Kanani H, Chrysanthopoulos PK, Klapa MI. Standardizing GC-MS metabolomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871(2), 191-201 (2008).
41. Giera M, Lingeman H, Niessen WM. Recent advancements in the LC-And GC-based analysis of malondialdehyde (MDA): a brief overview. Chromatographia 75(9-10), 433-440 (2012).
42. Bernini P, Bertini I, Luchinat C, Nincheri P, Staderini S, Turano P. Standard operating procedures for pre-Analytical handling of blood and urine for metabolomic studies and biobanks. J. Biomol. NMR 49(3-4), 231-243 (2011).
43. Gika HG, Theodoridis GA, Wilson ID. Liquid chromatography and ultra-performance liquid chromatography-mass spectrometry fingerprinting of human urine: sample stability under different handling and storage conditions for metabonomics studies. J. Chromatogr. A 1189(1-2), 314-322 (2008).
44. Lauridsen M, Hansen SH, Jaroszewski JW, Cornett C. Human urine as test material in 1H NMR-based metabonomics: recommendations for sample preparation and storage. Anal. Chem. 79(3), 1181-1186 (2007).
45. Van De Merbel NC, De Vries R. Aging of biological matrices and its effect on bioanalytical method performance. Bioanalysis 5(19), 2393-2407 (2013).
46. Roux A, Thevenot EA, Seguin F, Olivier MF, Junot C. Impact of collection conditions on the metabolite content of human urine samples as analyzed by liquid chromatography coupled to mass spectrometry and nuclear magnetic resonance spectroscopy. Metabolomics 11(5), 1095-1105 (2015).
47. Saude EJ, Sykes BD. Urine stability for metabolomics studies: effects of preparation and storage. Metabolomics 3(1), 19-27 (2007).
48. Khamis MM, Adamko DJ, El-Aneed A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom. Rev. doi:10.1002/mas.21455 (2015) (Epub ahead of print).
49. Kohler I, Schappler J, Rudaz S. Microextraction techniques combined with capillary electrophoresis in bioanalysis. Anal. Bioanal. Chem. 405(1), 125-141 (2013).
50. Raterink RJ, Lindenburg PW, Vreeken RJ, Ramautar R, Hankemeier T. Recent developments in sample pre-Treatment techniques for mass spectrometry-based metabolomics. TrAC 61, 157-167 (2014).
51. Bruce SJ, Tavazzi I, Parisod V, Rezzi S, Kochhar S, Guy PA. Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal. Chem. 81(9), 3285-3296 (2009).
52. Want EJ, O?maille G, Smith CA et al. Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal. Chem. 78(3), 743-752 (2006).
53. Fernandez-Peralbo MA, Luque De Castro MD. Preparation of urine samples prior to targeted or untargeted metabolomics mass-spectrometry analysis. TrAC 41, 75-85 (2012).
54. Kohler I, Derks RJ, Giera M. The rise of hydrophilic interaction chromatography in untargeted clinical metabolomics. LCGC Eur. 30(1), 60-75 (2016).
55. Kohler I, Schappler J, Rudaz S. Highly sensitive capillary electrophoresis-mass spectrometry for rapid screening and accurate quantitation of drugs of abuse in urine. Anal. Chim. Acta 780, 101-109 (2013).
56. Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D. Lipid extraction by methyl-Tert-butyl ether for high-Throughput lipidomics. J. Lipid Res. 49(5), 1137-1146 (2008).
57. Sarafian MH, Gaudin M, Lewis MR et al. Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography-mass spectrometry. Anal. Chem. 86(12), 5766-5774 (2014).
58. Van Eeckhaut A, Lanckmans K, Sarre S, Smolders I, Michotte Y. Validation of bioanalytical LC-MS/MS assays: evaluation of matrix effects. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 877(23), 2198-2207 (2009).
59. Krumpochova P, Bruyneel B, Molenaar D et al. Amino acid analysis using chromatography-mass spectrometry: an inter platform comparison study. J. Pharm. Biomed. Anal. 114, 398-407 (2015).
60. Fang M, Ivanisevic J, Benton HP et al. Thermal degradation of small molecules: a global metabolomic investigation. Anal. Chem. 87(21), 10935-10941 (2015).
61. Borman S. Heated dispute over analytical method. C&EN 93(42), 25-26 (2015).
62. Koek MM, Jellema RH, Van Der Greef J, Tas AC, Hankemeier T. Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics 7(3), 307-328 (2011).
63. Mastrangelo A, Ferrarini A, Rey-Stolle F, Garcia A, Barbas C. From sample treatment to biomarker discovery: a tutorial for untargeted metabolomics based on GC-(EI)-QMS. Anal. Chim. Acta 900, 21-35 (2015).
64. Naz S, Moreira Dos Santos DC, Garcia A, Barbas C. Analytical protocols based on LC-MS, GC-MS and CEMS for nontargeted metabolomics of biological tissues. Bioanalysis 6(12), 1657-1677 (2014).
65. Kloos DP, Gay E, Lingeman H et al. Comprehensive gas chromatography-electron ionisation mass spectrometric analysis of fatty acids and sterols using sequential one-pot silylation: quantification and isotopologue analysis. Rapid Commun. Mass Spectrom. 28(13), 1507-1514 (2014).
66. Fiehn O, Kopka J, Trethewey RN, Willmitzer L. Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Anal. Chem. 72(15), 3573-3580 (2000).
67. Kloos D, Lingeman H, Mayboroda OA, Deelder AM, Niessen WM, Giera M. Analysis of biologically-Active, endogenous carboxylic acids based on chromatography-mass spectrometry. TrAC 61, 17-28 (2014).
68. Halket JM, Zaikin VG. Derivatization in mass spectrometry-1. Silylation. Eur. J. Mass. Spectrom (Chichester, Eng) 9(1), 1-21 (2003).
69. Villas-Boas SG, Smart KF, Sivakumaran S, Lane GA Alkylation or silylation for analysis of amino and nonamino organic acids by GC-MS? Metabolites 1 1 3-20 2011).
70. Spagou K, Tsoukali H, Raikos N, Gika H, Wilson ID, Theodoridis G. Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies. J. Sep. Sci. 33(6-7), 716-727 (2010).
71. Denoroy L, Zimmer L, Renaud B, Parrot S. Ultra high performance liquid chromatography as a tool for the discovery and the analysis of biomarkers of diseases: a review. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 927, 37-53 (2013).
72. Fekete S, Veuthey JL, Guillarme D. Comparison of the most recent chromatographic approaches applied for fast and high resolution separations: theory and practice. J. Chromatogr. A 1408, 1-14 (2015).
73. Fekete S, Kohler I, Rudaz S, Guillarme D. Importance of instrumentation for fast liquid chromatography in pharmaceutical analysis. J. Pharm. Biomed. Anal. 87, 105-119 (2014).
74. Kloos DP, Lingeman H, Niessen WM, Deelder AM, Giera M, Mayboroda OA. Evaluation of different column chemistries for fast urinary metabolic profiling. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 927, 90-96 (2013).
75. Hemstrom P, Irgum K. Hydrophilic interaction chromatography. J. Sep. Sci. 29(12), 1784-1821 (2006).
76. Buszewski B, Noga S. Hydrophilic interaction liquid chromatography (HILIC)-A powerful separation technique. Anal. Bioanal. Chem. 402(1), 231-247 (2012).
77. Novakova L, Havlikova L, Vlckova H. Hydrophilic interaction chromatography of polar and ionizable compounds by UHPLC. TrAC 63, 55-64 (2014).
78. Periat A, Kohler I, Veuthey JL, Guillarme D. Advances in hydrophilic interaction liquid chromatography for pharmaceutical analysis. LCGC Eur. 26(5), 17-22 (2013).
79. Barbas C, Moraes EP, Villasenor A. Capillary electrophoresis as a metabolomics tool for non-Targeted fingerprinting of biological samples. J. Pharm. Biomed. Anal. 55(4), 823-831 (2011).
80. Ramautar R, De Jong GJ. Recent developments in liquidphase separation techniques for metabolomics. Bioanalysis 6(7), 1011-1026 (2014).
81. Zenobi R. Single-cell metabolomics: analytical and biological perspectives. Science 342(6163), 1243259 (2013).
82. Wuethrich A, Haddad PR, Quirino JP. Chiral capillary electromigration techniques-mass spectrometry-hope and promise. Electrophoresis 35(1), 2-11 (2014).
83. Britz-Mckibbin P. Capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS)-based metabolomics. Methods Mol. Biol. 708, 229-246 (2011).
84. Lindenburg PW, Haselberg R, Rozing G, Ramautar R. Developments in interfacing designs for CE-MS: towards enabling tools for proteomics and metabolomics. Chromatographia 78, 367-377 (2015).
85. Ramautar R, Somsen GW, De Jong GJ. CE-MS for metabolomics: developments and applications in the period 2012-2014. Electrophoresis 36(1), 212-224 (2015).
86. Hirayama A, Wakayama M, Soga T. Metabolome analysis based on capillary electrophoresis-mass spectrometry. TrAC 61, 215-222 (2014).
87. Harrison AG. Chemical Ionization Mass Spectrometry (2nd Edition). CRC Press LLC, Boca Raton, FL, USA (1992).
88. Quehenberger O, Armando AM, Dennis EA. High sensitivity quantitative lipidomics analysis of fatty acids in biological samples by gas chromatography-mass spectrometry. Biochim. Biophys. Acta 1811(11), 648-656 (2011).
89. Wilm M. Principles of electrospray ionization. Mol. Cell. Proteomics 10(7), M111 009407 (2011).
90. Matuszewski BK, Constanzer ML, Chavez-Eng CM. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Anal. Chem. 75(13), 3019-3030 (2003).
91. Jonasdottir HS, Nicolardi S, Jonker W et al. Detection and structural elucidation of esterified oxylipids in human synovial fluid by electrospray ionization-fourier transform ion-cyclotron mass spectrometry and liquid chromatographyion trap-MS(3): detection of esterified hydroxylated docosapentaenoic acid containing phospholipids. Anal. Chem. 85(12), 6003-6010 (2013).
92. Pacchiarotta T, Derks RJ, Hurtado-Fernandez E et al. Online spectral library for GC-Atmospheric pressure chemical ionization-ToF MS. Bioanalysis 5(12), 1515-1525 (2013).
93. Hommerson P, Khan AM, De Jong GJ, Somsen GW. Ionization techniques in capillary electrophoresis-mass spectrometry: principles, design, and application. Mass Spectrom. Rev. 30(6), 1096-1120 (2011).
94. Li DX, Gan L, Bronja A, Schmitz OJ. Gas chromatography coupled to atmospheric pressure ionization mass spectrometry (GC-API-MS): review. Anal. Chim. Acta 891, 43-61 (2015).
95. Dahinden CA, Clancy RM, Hugli TE. Stereospecificity of leukotriene B4 and structure-function relationships for chemotaxis of human neutrophils. J. Immunol. 133(3), 1477-1482 (1984).
96. Jonasdottir HS, Papan C, Fabritz S et al. Differential mobility separation of leukotrienes and protectins. Anal. Chem. 87(10), 5036-5040 (2015).
97. Chouinard CD, Wei MS, Beekman CR, Kemperman RH, Yost RA. Ion mobility in clinical analysis: current progress and future perspectives. Clin. Chem. 62(1), 124-133 (2016).
98. Baker PR, Armando AM, Campbell JL, Quehenberger O, Dennis EA. Three-dimensional enhanced lipidomics analysis combining UPLC, differential ion mobility spectrometry, and mass spectrometric separation strategies. J. Lipid Res. 55(11), 2432-2442 (2014).
99. Basit A, Pontis S, Piomelli D, Armirotti A. Ion mobility mass spectrometry enhances low-Abundance species detection in untargeted lipidomics. Metabolomics 12, 50 (2016).
100. Lintonen TP, Baker PR, Suoniemi M et al. Differential mobility spectrometry-driven shotgun lipidomics. Anal. Chem. 86(19), 9662-9669 (2014).
101. Kowlgi NG, Chhabra L. D-lactic acidosis: an underrecognized complication of short bowel syndrome. Gastroenterol. Res. Pract. 2015, 476215 (2015).
102. Struys EA. 2-Hydroxyglutarate is not a metabolite; D-2-hydroxyglutarate and L-2-hydroxyglutarate are! Proc. Natl Acad. Sci. USA 110(51), E4939 (2013).
103. Armstrong DW, Zhang B. Chiral stationary phases for HPLC. Anal. Chem. 73(19), A557-A561 (2001).
104. Westerhuis JA, Hoefsloot HC, Smit S et al. Assessment of PLSDA cross validation. Metabolomics 4, 81-89 (2008).
105. Hoye TR, Jeffrey CS, Shao F. Mosher ester analysis for the determination of absolute configuration of stereogenic (chiral) carbinol carbons. Nat. Protoc. 2(10), 2451-2458 (2007).
106. Daykin CA, Corcoran O, Hansen SH et al. Application of directly coupled HPLC NMR to separation and characterization of lipoproteins from human serum. Anal. Chem. 73(6), 1084-1090 (2001).
107. Akira K, Mitome H, Imachi M, Shida Y, Miyaoka H, Hashimoto T. LC-NMR identification of a novel taurine-related metabolite observed in (1)H NMR-based metabonomics of genetically hypertensive rats. J. Pharm. Biomed. Anal. 51(5), 1091-1096 (2010).
108. Psychogios N, Hau DD, Peng J et al. The human serum metabolome. PLoS ONE 6(2), e16957 (2011).
109. Bouatra S, Aziat F, Mandal R et al. The human urine metabolome. PLoS ONE 8(9), e73076 (2013).
110. Tomasi G, Van Den Berg F, Andersson C. Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. J. Chemometrics 18(5), 231-241 (2004).
111. Savorani F, Tomasi G, Engelsen SB. icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J. Magn. Reson. 202(2), 190-202 (2010).
112. Veselkov KA, Lindon JC, Ebbels TM et al. Recursive segment-wise peak alignment of biological (1)h NMR spectra for improved metabolic biomarker recovery. Anal. Chem. 81(1), 56-66 (2009).
113. Staab JM, O'connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics 11, 123 (2010).
114. De Meyer T, Sinnaeve D, Van Gasse B et al. NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal. Chem. 80(10), 3783-3790 (2008).
115. Anderson PE, Mahle DA, Doom TE, Reo NV, Delraso NJ, Raymer ML. Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data. Metabolomics 7(2), 179-190 (2011).
116. Davis RA, Charlton AJ, Godward J, Jones SA, Harrison M, Wilson J. Adaptive binning: an improved binning method for metabolomics data using the undecimated wavelet transform. J. Chemolab. 85, 144-154 (2007).
117. Hao J, Astle W, De Iorio M, Ebbels TM. BATMAN-An R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 28(15), 2088-2090 (2012).
118. Wishart DS, Tzur D, Knox C et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521-D526 (2007).
119. Ulrich EL, Akutsu H, Doreleijers JF et al. BioMagResBank. Nucleic Acids Res. 36, D402-D408 (2008).
120. Cui Q, Lewis IA, Hegeman AD et al. Metabolite identification via the Madison Metabolomics Consortium Database. Nat. Biotechnol. 26(2), 162-164 (2008).
121. Fonville JM, Maher AD, Coen M, Holmes E, Lindon JC, Nicholson JK. Evaluation of full-resolution J-resolved 1H NMR projections of biofluids for metabonomics information retrieval and biomarker identification. Anal. Chem. 82(5), 1811-1821 (2010).
122. Lewis IA, Schommer SC, Markley JL. rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Magn. Reson. Chem. 47(Suppl. 1), S123-S126 (2009).
123. Xia J, Bjorndahl TC, Tang P, Wishart DS. MetaboMinersemi-Automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics 9, 507 (2008).
124. Van QN, Issaq HJ, Jiang Q et al. Comparison of 1D and 2D NMR spectroscopy for metabolic profiling. J. Proteome Res. 7(2), 630-639 (2008).
125. Cloarec O, Dumas ME, Craig A et al. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal. Chem. 77(5), 1282-1289 (2005).
126. Smith R, Ventura D, Prince JT. LC-MS alignment in theory and practice: a comprehensive algorithmic review. Brief. Bioinform. 16(1), 104-117 (2015).
127. Lange E, Tautenhahn R, Neumann S, Gropl C. Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics 9, 375 (2008).
128. Warrack BM, Hnatyshyn S, Ott KH et al. Normalization strategies for metabonomic analysis of urine samples. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 877(5-6), 547-552 (2009).
129. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78(13), 4281-4290 (2006).
130. Chen Y, Shen G, Zhang R et al. Combination of injection volume calibration by creatinine and MS signals? normalization to overcome urine variability in LC-MS-based metabolomics studies. Anal. Chem. 85(16), 7659-7665 (2013).
131. Van Den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, Van Der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).
132. Teahan O, Gamble S, Holmes E et al. Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal. Chem. 78(13), 4307-4318 (2006).
133. Kjeldahl K, Ramsus B. Some common misunderstandings in chemometrics. J. Chemometrics 24, 558-564 (2010).
134. Worley B, Powers R. Multivariate analysis in metabolomics. Curr. Metabol. 1(1), 92-107 (2013).
135. Christin C, Hoefsloot HC, Smilde AK et al. A critical assessment of feature selection methods for biomarker discovery in clinical proteomics. Mol. Cell. Proteomics 12(1), 263-276 (2013).
136. Leichtle AB, Dufour JF, Fiedler GM. Potentials and pitfalls of clinical peptidomics and metabolomics. Swiss Med. Wkly 143, w13801 (2013).
137. Lin D, Hollander Z, Meredith A, Mcmanus BM. Searching for omic biomarkers. Can. J. Cardiol. 25(Suppl. A), A9-A14 (2009).
138. Pepe MS, Fan J, Seymour CW, Li C, Huang Y, Feng Z. Biases introduced by choosing controls to match risk factors of cases in biomarker research. Clin. Chem. 58(8), 1242-1251 (2012).
139. Dona AC, Coffey S, Figtree G. Translational and emerging clinical applications of metabolomics in cardiovascular disease diagnosis and treatment. Eur. J. Prev. Cardiol. doi:10.1177/2047487316645469 (2016) (Epub ahead of print).
140. Abu Bakar MH, Sarmidi MR, Cheng KK et al. Metabolomics-The complementary field in systems biology: a review on obesity and Type 2 diabetes. Mol. Biosyst. 11(7), 1742-1774 (2015).
141. Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and Type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol. 2(1), 65-75 (2014).
142. Priori R, Scrivo R, Brandt J et al. Metabolomics in rheumatic diseases: the potential of an emerging methodology for improved patient diagnosis, prognosis, and treatment efficacy. Autoimmun. Rev. 12(10), 1022-1030 (2013).
143. Bhargava P, Calabresi PA. Metabolomics in multiple sclerosis. Mult. Scler. 22(4), 451-460 (2016).
144. Haas CT, Roe JK, Pollara G, Mehta M, Noursadeghi M. Diagnostic omics for active tuberculosis. BMC Med. 14, 37 (2016).
145. Hassan-Smith G, Wallace GR, Douglas MR, Sinclair AJ. The role of metabolomics in neurological disease. J. Neuroimmunol. 248(1-2), 48-52 (2012).
146. Gentile G, Remuzzi G. Novel biomarkers for renal diseases? None for the moment (but One). J. Biomol. Screen. doi:10.1177/1087057116629916 (2016) (Epub ahead of print).
147. Trezzi JP, Vlassis N, Hiller K. The role of metabolomics in the study of cancer biomarkers and in the development of diagnostic tools. Adv. Exp. Med. Biol. 867, 41-57 (2015).
148. Mordente A, Meucci E, Martorana GE, Silvestrini A. Cancer biomarkers discovery and validation: state of the art, problems and future perspectives. Adv. Exp. Med. Biol. 867, 9-26 (2015).
149. Shackleton CH. Role of a disordered steroid metabolome in the elucidation of sterol and steroid biosynthesis. Lipids 47(1), 1-12 (2012).
150. Lynn Henty N, Hayes DF. Cancer biomarkers. Mol. Oncol. 6(2), 140-146 (2012).
151. Duffy MJ, Crown J. A personalized approach to cancer treatment: how biomarkers can help. Clin. Chem. 54(11), 1770-1779 (2008).
152. Poinsignon V, Mercier L, Nakabayashi K et al. Quantitation of isocitrate dehydrogenase (IDH)-induced D and L enantiomers of 2-hydroxyglutaric acid in biological fluids by a fully validated liquid tandem mass spectrometry method, suitable for clinical applications. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 1022, 290-297 (2016).