Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016 (2016), Article ID 9210408, 13 pages
http://dx.doi.org/10.1155/2016/9210408
Review Article

Mass Spectrometry-Based Metabolomic and Proteomic Strategies in Organic Acidemias

1IRCCS SDN, 80143 Naples, Italy
2CEINGE Biotecnologie Avanzate s.c.a.r.l., 80145 Naples, Italy
3Dipartimento di Scienze Motorie e del Benessere, Università di Napoli “Parthenope”, 80133 Naples, Italy
4Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli “Federico II”, 80121 Naples, Italy
5Associazione Culturale DiSciMuS RCF, Casoria, 80026 Naples, Italy

Received 19 February 2016; Accepted 15 May 2016

Academic Editor: Hai-Teng Deng

Copyright © 2016 Esther Imperlini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Garrod, “The Croonian lectures on inborn errors of metabolism, lecture II: alkaptonuria,” The Lancet, vol. 2, pp. 73–79, 1908. View at Google Scholar
  2. J. M. Saudubray and C. Charpentier, “Clinical phenotypes: diagnosis/algorithms,” in The Metabolic and Molecular Bases of Inherited Disease, C. R. Scriver, A. L. Beaudet, W. S. Sly, and D. Valle, Eds., pp. 1327–1403, McGraw-Hill, New York, NY, USA, 8th edition, 2001. View at Google Scholar
  3. B. Lanpher, N. Brunetti-Pierri, and B. Lee, “Inborn errors of metabolism: the flux from Mendelian to complex diseases,” Nature Reviews Genetics, vol. 7, no. 6, pp. 449–460, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Scriver, A. Beaudet, E. S. Sly et al., The Metabolic and Molecular Bases of Inherited Disease, McGraw-Hill, New York, NY, USA, 8th edition, 2001.
  5. S. Kölker, P. Burgard, S. W. Sauer, and J. G. Okun, “Current concepts in organic acidurias: understanding intra- and extracerebral disease manifestation,” Journal of Inherited Metabolic Disease, vol. 36, no. 4, pp. 635–644, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. I. Knerr, N. Weinhold, J. Vockley, and K. M. Gibson, “Advances and challenges in the treatment of branched-chain amino/keto acid metabolic defects,” Journal of Inherited Metabolic Disease, vol. 35, no. 1, pp. 29–40, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Spada, P. L. Calvo, A. Brunati et al., “Liver transplantation in severe methylmalonic acidemia: the sooner, the better,” Journal of Pediatrics, vol. 167, no. 5, p. 1173, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. J. L. Sloan, I. Manoli, and C. P. Venditti, “Liver or combined liver-kidney transplantation for patients with isolated methylmalonic acidemia: who and when?” The Journal of Pediatrics, vol. 166, no. 6, pp. 1346–1350, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Kölker and J. G. Okun, “Methylmalonic acid—an endogenous toxin?” Cellular and Molecular Life Sciences, vol. 62, no. 6, pp. 621–624, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. W. A. Fenton, R. A. Gravel, and D. S. Rosenblatt, “Disorders of propionate and methylmalonate metabolism,” in The Metabolic and Molecular Bases of Inherited Disease, C. R. Scriver, W. S. Sly, B. Childs, A. L. Beaudet, D. Valle, K. W. Kinzler et al., Eds., pp. 2165–2192, McGraw-Hill, New York, NY, USA, 8th edition, 2001. View at Google Scholar
  11. Y. De Keyzer, V. Valayannopoulos, J.-F. Benoist et al., “Multiple OXPHOS deficiency in the liver, kidney, heart, and skeletal muscle of patients with methylmalonic aciduria and propionic aciduria,” Pediatric Research, vol. 66, no. 1, pp. 91–95, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Hörster, M. R. Baumgartner, C. Viardot et al., “Long-term outcome in methylmalonic acidurias is influenced by the underlying defect (mut0, mut-, cblA, cblB),” Pediatric Research, vol. 62, no. 2, pp. 225–230, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. E. R. Baumgarter and C. Viardot, “Long-term follow-up of 77 patients with isolated methylamalonic acidaemia,” Journal of Inherited Metabolic Disease, vol. 18, no. 2, pp. 138–142, 1995. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Nicolaides, J. Leonard, and R. Surtees, “Neurological outcome of methylmalonic acidaemia,” Archives of Disease in Childhood, vol. 78, no. 6, pp. 508–512, 1998. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Dionisi-Vici, F. Deodato, W. Röschinger, W. Rhead, and B. Wilcken, “‘Classical’ organic acidurias, propionic aciduria, methylmalonic aciduria and isovaleric aciduria: long-term outcome and effects of expanded newborn screening using tandem mass spectrometry,” Journal of Inherited Metabolic Disease, vol. 29, no. 2-3, pp. 383–389, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. D. S. Froese and R. A. Gravel, “Genetic disorders of vitamin B12 metabolism: eight complementation groups—eight genes,” Expert Reviews in Molecular Medicine, vol. 12, article e37, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Gherasim, M. Lofgren, and R. Banerjee, “Navigating the B12 road: assimilation, delivery, and disorders of cobalamin,” Journal of Biological Chemistry, vol. 288, no. 19, pp. 13186–13193, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. D. S. Froese, J. Kopec, F. Fitzpatrick et al., “Structural insights into the MMACHC-MMADHC protein complex involved in vitamin B12 trafficking,” The Journal of Biological Chemistry, vol. 290, no. 49, pp. 29167–29177, 2015. View at Publisher · View at Google Scholar
  19. D. Watkins and D. S. Rosenblatt, “Inborn errors of cobalamin absorption and metabolism,” American Journal of Medical Genetics, Part C: Seminars in Medical Genetics, vol. 157, no. 1, pp. 33–44, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Martinelli, F. Deodato, and C. Dionisi-Vici, “Cobalamin C defect: natural history, pathophysiology, and treatment,” Journal of Inherited Metabolic Disease, vol. 34, no. 1, pp. 127–135, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. S. G. Oliver, M. K. Winson, D. B. Kell, and F. Baganz, “Systematic functional analysis of the yeast genome,” Trends in Biotechnology, vol. 16, no. 9, pp. 373–378, 1998. View at Publisher · View at Google Scholar · View at Scopus
  22. J. K. Nicholson and J. C. Lindon, “Systems biology: metabonomics,” Nature, vol. 455, no. 7216, pp. 1054–1056, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. R. J. Williams, “Individual metabolic patterns and human disease: an exploratory study utilizing predominantly paper chromatographic methods,” in Introduction, General Discussion and Tentative Conclusions, University Texas Publication no. 5109, Biochemical Institute Studies IV, pp. 7–21, The University of Texas, Austin, Tex, USA, 1951. View at Google Scholar
  24. E. C. Horning and M. G. Horning, “Human metabolic profiles obtained by GC and GC/MS,” Journal of Chromatographic Science, vol. 9, no. 3, pp. 129–140, 1971. View at Publisher · View at Google Scholar
  25. L. Pauling, A. B. Robinson, R. Teranishi, and P. Cary, “Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography,” Proceedings of the National Academy of Sciences of the United States of America, vol. 68, no. 10, pp. 2374–2376, 1971. View at Publisher · View at Google Scholar · View at Scopus
  26. J. K. Nicholson, J. C. Lindon, and E. Holmes, “‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data,” Xenobiotica, vol. 29, no. 11, pp. 1181–1189, 1999. View at Publisher · View at Google Scholar · View at Scopus
  27. O. Fiehn, “Metabolomics—the link between genotypes and phenotypes,” Plant Molecular Biology, vol. 48, no. 1-2, pp. 155–171, 2002. View at Publisher · View at Google Scholar · View at Scopus
  28. D. S. Wishart, “Emerging applications of metabolomics in drug discovery and precision medicine,” Nature Reviews Drug Discovery, 2016. View at Publisher · View at Google Scholar
  29. K. M. Sas, A. Karnovsky, G. Michailidis, and S. Pennathur, “Metabolomics and diabetes: analytical and computational approaches,” Diabetes, vol. 64, no. 3, pp. 718–732, 2015. View at Publisher · View at Google Scholar
  30. M. Oldiges, S. Lütz, S. Pflug, K. Schroer, N. Stein, and C. Wiendahl, “Metabolomics: current state and evolving methodologies and tools,” Applied Microbiology and Biotechnology, vol. 76, no. 3, pp. 495–511, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. N. Rifai, M. A. Gillette, and S. A. Carr, “Protein biomarker discovery and validation: the long and uncertain path to clinical utility,” Nature Biotechnology, vol. 24, no. 8, pp. 971–983, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. H. G. Gika, G. A. Theodoridis, and I. D. Wilson, “Liquid chromatography and ultra-performance liquid chromatography–mass spectrometry fingerprinting of human urine: sample stability under different handling and storage conditions for metabonomics studies,” Journal of Chromatography A, vol. 1189, no. 1-2, pp. 314–322, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. S. T. Kong, H.-S. Lin, J. Ching, and P. C. Ho, “Evaluation of dried blood spots as sample matrix for gas chromatography/mass spectrometry based metabolomic profiling,” Analytical Chemistry, vol. 83, no. 11, pp. 4314–4318, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Michopoulos, G. Theodoridis, C. J. Smith, and I. D. Wilson, “Metabolite profiles from dried blood spots for metabonomic studies using UPLC combined with orthogonal acceleration ToF-MS: effects of different papers and sample storage stability,” Bioanalysis, vol. 3, no. 24, pp. 2757–2767, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. I. Wilson, “Global metabolic profiling (metabonomics/metabolomics) using dried blood spots: advantages and pitfalls,” Bioanalysis, vol. 3, no. 20, pp. 2255–2257, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. Z. Pan and D. Raftery, “Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics,” Analytical and Bioanalytical Chemistry, vol. 387, no. 2, pp. 525–527, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Ivanisevic, Z.-J. Zhu, L. Plate et al., “Toward ‘Omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism,” Analytical Chemistry, vol. 85, no. 14, pp. 6876–6884, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. P. A. Vorkas, G. Isaac, M. A. Anwar et al., “Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease,” Analytical Chemistry, vol. 87, no. 8, pp. 4184–4193, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. L. Blanchet, A. Smolinska, A. Attali et al., “Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis,” BMC Bioinformatics, vol. 12, article 254, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. R. A. van den Berg, H. C. J. Hoefsloot, J. A. Westerhuis, A. K. Smilde, and M. J. van der Werf, “Centering, scaling, and transformations: improving the biological information content of metabolomics data,” BMC Genomics, vol. 7, article 142, 2006. View at Publisher · View at Google Scholar · View at Scopus
  41. Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society, Series B: Methodological, vol. 57, no. 1, pp. 289–300, 1995. View at Google Scholar · View at MathSciNet
  42. C. Fernández-Lainez, J. J. Aguilar-Lemus, M. Vela-Amieva, and I. Ibarra-González, “Tandem mass spectrometry newborn screening for inborn errors of intermediary metabolism: abnormal profile interpretation,” Current Medicinal Chemistry, vol. 19, no. 26, pp. 4511–4522, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. G. la Marca, “Mass spectrometry in clinical chemistry: the case of newborn screening,” Journal of Pharmaceutical and Biomedical Analysis, vol. 101, pp. 174–182, 2014. View at Publisher · View at Google Scholar · View at Scopus
  44. G. La Marca, S. Malvagia, E. Pasquini, M. Innocenti, M. A. Donati, and E. Zammarchi, “Rapid 2nd-tier test for measurement of 3-OH-propionic and methylmalonic acids on dried blood spots: reducing the false-positive rate for propionylcarnitine during expanded newborn screening by liquid chromatography-tandem mass spectrometry,” Clinical Chemistry, vol. 53, no. 7, pp. 1364–1369, 2007. View at Publisher · View at Google Scholar · View at Scopus
  45. E. Scolamiero, C. Cozzolino, L. Albano et al., “Targeted metabolomics in the expanded newborn screening for inborn errors of metabolism,” Molecular BioSystems, vol. 11, no. 6, pp. 1525–1535, 2015. View at Publisher · View at Google Scholar · View at Scopus
  46. D. Ombrone, F. Salvatore, and M. Ruoppolo, “Quantitative liquid chromatography coupled with tandem mass spectrometry analysis of urinary acylglycines: Application to the diagnosis of inborn errors of metabolism,” Analytical Biochemistry, vol. 417, no. 1, pp. 122–128, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Dénes, E. Szabó, S. L. Robinette et al., “Metabonomics of newborn screening dried blood spot samples: a novel approach in the screening and diagnostics of inborn errors of metabolism,” Analytical Chemistry, vol. 84, no. 22, pp. 10113–10120, 2012. View at Publisher · View at Google Scholar · View at Scopus
  48. P. Wojtowicz, J. Zrostlíková, T. Kovalczuk, J. Schůrek, and T. Adam, “Evaluation of comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry for the diagnosis of inherited metabolic disorders using an automated data processing strategy,” Journal of Chromatography A, vol. 1217, no. 51, pp. 8054–8061, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. W. R. Wikoff, J. A. Gangoiti, B. A. Barshop, and G. Siuzdak, “Metabolomics identifies perturbations in human disorders of propionate metabolism,” Clinical Chemistry, vol. 53, no. 12, pp. 2169–2176, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. C. J. Rebouche and A. G. Engel, “Tissue distribution of carnitine biosynthetic enzymes in man,” Biochim Biophys Acta, vol. 630, no. 1, pp. 22–29, 1980. View at Publisher · View at Google Scholar · View at Scopus
  51. S. Berardi, B. Stieger, S. Wachter, B. O'Neill, and S. Krahenbühl, “Characterization of a sodium-dependent transport system for butyrobetaine into rat liver plasma membrane vesicles,” Hepatology, vol. 28, no. 2, pp. 521–525, 1998. View at Publisher · View at Google Scholar · View at Scopus
  52. M. J. Miller, A. D. Kennedy, A. D. Eckhart et al., “Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism,” Journal of Inherited Metabolic Disease, vol. 38, no. 6, pp. 1029–1039, 2015. View at Publisher · View at Google Scholar · View at Scopus
  53. C. D. Dehaven, A. M. Evans, H. Dai, and K. A. Lawton, “Organization of GC/MS and LC/MS metabolomics data into chemical libraries,” Journal of Cheminformatics, vol. 2, no. 1, article 9, 2010. View at Publisher · View at Google Scholar · View at Scopus
  54. M. R. Wilkins, E. Gasteiger, J.-C. Sanchez, R. D. Appel, and D. F. Hochstrasser, “Protein identification with sequence tags,” Current Biology, vol. 6, no. 12, pp. 1543–1544, 1996. View at Publisher · View at Google Scholar · View at Scopus
  55. T. C. Walther and M. Mann, “Mass spectrometry-based proteomics in cell biology,” Journal of Cell Biology, vol. 190, no. 4, pp. 491–500, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. N. L. Anderson, A. D. Matheson, and S. Steiner, “Proteomics: applications in basic and applied biology,” Current Opinion in Biotechnology, vol. 11, no. 4, pp. 408–412, 2000. View at Publisher · View at Google Scholar · View at Scopus
  57. M. Bantscheff, S. Lemeer, M. M. Savitski, and B. Kuster, “Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present,” Analytical and Bioanalytical Chemistry, vol. 404, no. 4, pp. 939–965, 2012. View at Publisher · View at Google Scholar · View at Scopus
  58. C. E. Parker, T. W. Pearson, N. L. Anderson, and C. H. Borchers, “Mass-spectrometry-based clinical proteomics—a review and prospective,” Analyst, vol. 135, no. 8, pp. 1830–1838, 2010. View at Publisher · View at Google Scholar · View at Scopus
  59. Y.-K. Paik, H. Kim, E.-Y. Lee, M.-S. Kwon, and S. Y. Cho, “Overview and introduction to clinical proteomics,” Methods in Molecular Biology, vol. 428, pp. 1–31, 2007. View at Google Scholar · View at Scopus
  60. T. E. Fehniger and G. Marko-Varga, “Proteomics and disease revisited: the challenge of providing proteomic tools into clinical practice,” Journal of Proteome Research, vol. 9, no. 3, pp. 1191–1192, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. C. A. Crutchfield, S. N. Thomas, L. J. Sokoll, and D. W. Chan, “Advances in mass spectrometry-based clinical biomarker discovery,” Clinical Proteomics, vol. 13, no. 1, 2016. View at Publisher · View at Google Scholar
  62. S. Camerini and P. Mauri, “The role of protein and peptide separation before mass spectrometry analysis in clinical proteomics,” Journal of Chromatography A, vol. 1381, pp. 1–12, 2015. View at Publisher · View at Google Scholar · View at Scopus
  63. M. Larance and A. I. Lamond, “Multidimensional proteomics for cell biology,” Nature Reviews Molecular Cell Biology, vol. 16, no. 5, pp. 269–280, 2015. View at Publisher · View at Google Scholar · View at Scopus
  64. M. Sandin, A. Chawade, and F. Levander, “Is label-free LC-MS/MS ready for biomarker discovery?” Proteomics—Clinical Applications, vol. 9, no. 3-4, pp. 289–294, 2015. View at Publisher · View at Google Scholar · View at Scopus
  65. X. Chen, S. Wei, Y. Ji, X. Guo, and F. Yang, “Quantitative proteomics using SILAC: principles, applications, and developments,” Proteomics, vol. 15, no. 18, pp. 3175–3192, 2015. View at Publisher · View at Google Scholar · View at Scopus
  66. G. Arentz, F. Weiland, M. K. Oehler, and P. Hoffmann, “State of the art of 2D DIGE,” Proteomics—Clinical Applications, vol. 9, no. 3-4, pp. 277–288, 2015. View at Publisher · View at Google Scholar · View at Scopus
  67. H.-C. Liang, E. Lahert, I. Pike, and M. Ward, “Quantitation of protein post-translational modifications using isobaric tandem mass tags,” Bioanalysis, vol. 7, no. 3, pp. 383–400, 2015. View at Publisher · View at Google Scholar · View at Scopus
  68. N. Rauniyar and J. R. Yates III, “Isobaric labeling-based relative quantification in shotgun proteomics,” Journal of Proteome Research, vol. 13, no. 12, pp. 5293–5309, 2014. View at Publisher · View at Google Scholar · View at Scopus
  69. E. Hoedt, G. Zhang, and T. A. Neubert, “Stable isotope labeling by amino acids in cell culture (SILAC) for quantitative proteomics,” Advances in Experimental Medicine and Biology, vol. 806, pp. 93–106, 2014. View at Publisher · View at Google Scholar · View at Scopus
  70. D. A. Megger, T. Bracht, H. E. Meyer, and B. Sitek, “Label-free quantification in clinical proteomics,” Biochimica et Biophysica Acta—Proteins and Proteomics, vol. 1834, no. 8, pp. 1581–1590, 2013. View at Publisher · View at Google Scholar · View at Scopus
  71. M. Sandin, J. Teleman, J. Malmström, and F. Levander, “Data processing methods and quality control strategies for label-free LC-MS protein quantification,” Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics, vol. 1844, no. 1, pp. 29–41, 2014. View at Publisher · View at Google Scholar · View at Scopus
  72. K. A. Neilson, N. A. Ali, S. Muralidharan et al., “Less label, more free: approaches in label-free quantitative mass spectrometry,” Proteomics, vol. 11, no. 4, pp. 535–553, 2011. View at Publisher · View at Google Scholar · View at Scopus
  73. W. Zhu, J. W. Smith, and C. Huang, “Mass spectrometry-based label-free quantitative proteomics,” Journal of Biomedicine and Biotechnology, vol. 2010, Article ID 840518, 6 pages, 2010. View at Publisher · View at Google Scholar
  74. E. Imperlini, S. Orrù, C. Corbo, A. Daniele, and F. Salvatore, “Altered brain protein expression profiles are associated with molecular neurological dysfunction in the PKU mouse model,” Journal of Neurochemistry, vol. 129, no. 6, pp. 1002–1012, 2014. View at Publisher · View at Google Scholar · View at Scopus
  75. M. Caterino, C. Corbo, E. Imperlini et al., “Differential proteomic analysis in human cells subjected to ribosomal stress,” Proteomics, vol. 13, no. 7, pp. 1220–1227, 2013. View at Publisher · View at Google Scholar · View at Scopus
  76. M. Caterino, A. Aspesi, E. Pavesi et al., “Analysis of the interactome of ribosomal protein S19 mutants,” Proteomics, vol. 14, no. 20, pp. 2286–2296, 2014. View at Publisher · View at Google Scholar · View at Scopus
  77. E. Nigro, E. Imperlini, O. Scudiero et al., “Differentially expressed and activated proteins associated with non small cell lung cancer tissues,” Respiratory Research, vol. 16, no. 1, article 74, 2015. View at Publisher · View at Google Scholar · View at Scopus
  78. D. M. Freund and J. E. Prenni, “Improved detection of quantitative differences using a combination of spectral counting and MS/MS total ion current,” Journal of Proteome Research, vol. 12, no. 4, pp. 1996–2004, 2013. View at Publisher · View at Google Scholar · View at Scopus
  79. T. Rabilloud, “Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains,” Proteomics, vol. 2, no. 1, pp. 3–10, 2002. View at Publisher · View at Google Scholar · View at Scopus
  80. P. Feist and A. B. Hummon, “Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples,” International Journal of Molecular Sciences, vol. 16, no. 2, pp. 3537–3563, 2015. View at Publisher · View at Google Scholar · View at Scopus
  81. J. Dittrich, S. Becker, M. Hecht, and U. Ceglarek, “Sample preparation strategies for targeted proteomics via proteotypic peptides in human blood using liquid chromatography tandem mass spectrometry,” Proteomics—Clinical Applications, vol. 9, no. 1-2, pp. 5–16, 2015. View at Publisher · View at Google Scholar · View at Scopus
  82. A. Posch, “Sample preparation guidelines for two-dimensional electrophoresis,” Archives of Physiology and Biochemistry, vol. 120, no. 5, pp. 192–197, 2014. View at Publisher · View at Google Scholar · View at Scopus
  83. X. Li and T. Franz, “Up to date sample preparation of proteins for mass spectrometric analysis,” Archives of Physiology and Biochemistry, vol. 120, no. 5, pp. 188–191, 2014. View at Publisher · View at Google Scholar · View at Scopus
  84. P. Iadarola, M. Fumagalli, A. M. Bardoni, R. Salvini, and S. Viglio, “Recent applications of CE- and HPLC-MS in the analysis of human fluids,” Electrophoresis, vol. 37, no. 1, pp. 212–230, 2016. View at Publisher · View at Google Scholar
  85. J. D. Egertson, A. Kuehn, G. E. Merrihew et al., “Multiplexed MS/MS for improved data-independent acquisition,” Nature Methods, vol. 10, no. 8, pp. 744–746, 2013. View at Publisher · View at Google Scholar · View at Scopus
  86. L. C. Gillet, P. Navarro, S. Tate et al., “Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis,” Molecular & Cellular Proteomics, vol. 11, no. 6, Article ID O111.016717, 2012. View at Publisher · View at Google Scholar · View at Scopus
  87. H. A. Ebhardt, A. Root, C. Sander, and R. Aebersold, “Applications of targeted proteomics in systems biology and translational medicine,” Proteomics, vol. 15, no. 18, pp. 3193–3208, 2015. View at Publisher · View at Google Scholar · View at Scopus
  88. A. Karimpour-Fard, L. E. Epperson, and L. E. Hunter, “A survey of computational tools for downstream analysis of proteomic and other omic datasets,” Human Genomics, vol. 9, article 28, 2015. View at Publisher · View at Google Scholar · View at Scopus
  89. E. Richard, L. Monteoliva, S. Juarez et al., “Quantitative analysis of mitochondrial protein expression in methylmalonic acidemia by two-dimensional difference gel electrophoresis,” Journal of Proteome Research, vol. 5, no. 7, pp. 1602–1610, 2006. View at Publisher · View at Google Scholar · View at Scopus
  90. L. Hannibal, P. M. DiBello, M. Yu et al., “The MMACHC proteome: hallmarks of functional cobalamin deficiency in humans,” Molecular Genetics and Metabolism, vol. 103, no. 3, pp. 226–239, 2011. View at Publisher · View at Google Scholar · View at Scopus
  91. M. Caterino, A. Pastore, M. G. Strozziero et al., “The proteome of cblC defect: in vivo elucidation of altered cellular pathways in humans,” Journal of Inherited Metabolic Disease, vol. 38, no. 5, pp. 969–979, 2015. View at Publisher · View at Google Scholar · View at Scopus
  92. M. Caterino, R. J. Chandler, J. L. Sloan et al., “The proteome of methylmalonic acidemia (MMA): the elucidation of altered pathways in patient livers,” Molecular BioSystems, vol. 12, no. 2, pp. 566–574, 2016. View at Publisher · View at Google Scholar
  93. A. Pastore, D. Martinelli, F. Piemonte et al., “Glutathione metabolism in cobalamin deficiency type C (cblC),” Journal of Inherited Metabolic Disease, vol. 37, no. 1, pp. 125–129, 2014. View at Publisher · View at Google Scholar · View at Scopus
  94. D. McHugh, C. A. Cameron, J. E. Abdenur et al., “Clinical validation of cutoff target ranges in newborn screening of metabolic disorders by tandem mass spectrometry: a worldwide collaborative project,” Genetics in Medicine, vol. 13, no. 3, pp. 230–254, 2011. View at Publisher · View at Google Scholar
  95. G. Marquardt, R. Currier, D. M. McHugh et al., “Enhanced interpretation of newborn screening results without analyte cutoff values,” Genetics in Medicine, vol. 14, no. 7, pp. 648–655, 2012. View at Publisher · View at Google Scholar