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Mass Spectrometry-Based Metabolomic Multiplatform for Alzheimer’s Disease Research

  • Raúl González-Domínguez
  • Álvaro González-Domínguez
  • Ana Sayago
  • Ángeles Fernández-Recamales
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1750)

Abstract

The integration of complementary analytical platforms has emerged as a suitable strategy to perform a comprehensive metabolomic characterization of complex biological systems. In this work, we describe the most important issues to be considered for the application of a mass spectrometry multiplatform in Alzheimer’s disease research, which combines direct analysis with electrospray and atmospheric pressure photoionization sources, as well as orthogonal hyphenated approaches based on reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus). It is noteworthy that the metabolomic pipeline here detailed has demonstrated a great potential for the investigation of metabolic perturbations underlying Alzheimer’s disease pathogenesis.

Key words

Metabolomics Mass spectrometry Multiplatform Alzheimer’s disease Direct MS analysis Ultrahigh-performance liquid chromatography Gas chromatography 

References

  1. 1.
    Reitz C, Brayne C, Mayeux R (2011) Epidemiology of Alzheimer disease. Nat Rev Neurol 7:137–152CrossRefGoogle Scholar
  2. 2.
    Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer’s disease. Lancet 368:387–403CrossRefGoogle Scholar
  3. 3.
    Maccioni RB, Muñoz JP, Barbeito L (2001) The molecular bases of Alzheimer’s disease and other neurodegenerative disorders. Arch Med Res 32:367–381CrossRefGoogle Scholar
  4. 4.
    González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2014) Characterization of metal profiles in serum during the progression of Alzheimer’s disease. Metallomics 9:292–300CrossRefGoogle Scholar
  5. 5.
    Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, Delacourte A, Galasko D, Gauthier S, Jicha G, Meguro K, O’Brien J, Pasquier F, Robert P, Rossor M, Salloway S, Stern Y, Visser PJ, Scheltens P (2007) Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS–ADRDA criteria. Lancet Neurol 6:734–746CrossRefGoogle Scholar
  6. 6.
    González-Domínguez R, Sayago A, Fernández-Recamales A (2017) Metabolomics in Alzheimer’s disease: the need of complementary analytical platforms for the identification of biomarkers to unravel the underlying pathology. J Chromatogr B Analyt Technol Biomed Life Sci 1071:75–92CrossRefGoogle Scholar
  7. 7.
    Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40:387–426CrossRefGoogle Scholar
  8. 8.
    Emwas AHM, Salek RM, Griffin JL, Merzaban J (2013) NMR-based metabolomics in human disease diagnosis: applications, limitations, and recommendations. Metabolomics 9:1048–1072CrossRefGoogle Scholar
  9. 9.
    Theodoridis G, Gika HG, Wilson ID (2011) Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies. Mass Spectrom Rev 30:884–906PubMedGoogle Scholar
  10. 10.
    González-Domínguez R, Sayago A, Fernández-Recamales A (2017) Direct infusion mass spectrometry for metabolomic phenotyping of diseases. Bioanalysis 9:131–148CrossRefGoogle Scholar
  11. 11.
    Kuehnbaum NL, Britz-McKibbin P (2013) New advances in separation science for metabolomics: resolving chemical diversity in a post-genomic era. Chem Rev 113:2437–2468CrossRefGoogle Scholar
  12. 12.
    Pasikanti KK, Ho PC, Chan EC (2008) Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. J Chromatogr B Analyt Technol Biomed Life Sci 871:202–211CrossRefGoogle Scholar
  13. 13.
    Jankowsky JL, Fadale DJ, Anderson J, GM X, Gonzales V, Jenkins NA, Copeland NG, Lee MK, Younkin LH, Wagner SL, Younkin SG, Borchelt DR (2004) Mutant presenilins specifically elevate the levels of the 42 residue beta-amyloid peptide in vivo: evidence for augmentation of a 42-specific g secretase. Hum Mol Genet 13:159–170CrossRefGoogle Scholar
  14. 14.
    González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2014) Using direct infusion mass spectrometry for serum metabolomics in Alzheimer’s disease. Anal Bioanal Chem 406:7137–7148CrossRefGoogle Scholar
  15. 15.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2014) Region-specific metabolic alterations in the brain of the APP/PS1 transgenic mice of Alzheimer’s disease. Biochim Biophys Acta 1842:2395–2402CrossRefGoogle Scholar
  16. 16.
    Gago-Tinoco A, González-Domínguez R, García-Barrera T, Blasco-Moreno J, Bebianno MJ, Gómez-Ariza JL (2014) Metabolic signatures associated with environmental pollution by metals in Doñana National Park using P. clarkii as bioindicator. Environ Sci Pollut Res Int 21:13315–13323CrossRefGoogle Scholar
  17. 17.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2015) High throughput multi-organ metabolomics in the APP/PS1 mouse model of Alzheimer’s disease. Electrophoresis 36:2237–2249CrossRefGoogle Scholar
  18. 18.
    Sangster T, Major H, Plumb R, Wilson AJ, Wilson ID (2006) A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131:1075–1078CrossRefGoogle Scholar
  19. 19.
    González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2015) Metabolite profiling for the identification of altered metabolic pathways in Alzheimer’s disease. J Pharm Biomed Anal 107:75–81CrossRefGoogle Scholar
  20. 20.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2015) Metabolomic screening of regional brain alterations in the APP/PS1 transgenic model of Alzheimer’s disease by direct infusion mass spectrometry. J Pharm Biomed Anal 102:425–435CrossRefGoogle Scholar
  21. 21.
    González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2015) Application of a novel metabolomic approach based on atmospheric pressure photoionization mass spectrometry using flow injection analysis for the study of Alzheimer’s disease. Talanta 131:480–489CrossRefGoogle Scholar
  22. 22.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2015) Application of metabolomics based on direct mass spectrometry analysis for the elucidation of altered metabolic pathways in serum from the APP/PS1 transgenic model of Alzheimer’s disease. J Pharm Biomed Anal 107:378–385CrossRefGoogle Scholar
  23. 23.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2015) Deciphering metabolic abnormalities associated with Alzheimer’s disease in the APP/PS1 mouse model using integrated metabolomic approaches. Biochimie 110:119–128CrossRefGoogle Scholar
  24. 24.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2015) Metabolomic investigation of systemic manifestations associated with Alzheimer’s disease in the APP/PS1 transgenic mouse model. Mol BioSyst 11:2429–2440CrossRefGoogle Scholar
  25. 25.
    González-Domínguez R, García-Barrera T, Vitorica J, Gómez-Ariza JL (2015) Metabolomics reveals significant impairments in the immune system of the APP/PS1 transgenic mice of Alzheimer’s disease. Electrophoresis 36:577–587CrossRefGoogle Scholar
  26. 26.
    Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787CrossRefGoogle Scholar
  27. 27.
    Veselkov KA, Vingara LK, Masson P, Robinette SL, Want E, Li JV, Barton RH, Boursier-Neyret C, Walther B, Ebbels TM, Pelczer I, Holmes E, Lindon JC, Nicholson JK (2011) Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Anal Chem 83:5864–5872CrossRefGoogle Scholar
  28. 28.
    van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142CrossRefGoogle Scholar
  29. 29.
    González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2012) Iberian ham typification by direct infusion electrospray and photospray ionization mass spectrometry fingerprinting. Rapid Commun Mass Spectrom 26:835–844CrossRefGoogle Scholar
  30. 30.
    Pulfer M, Murphy RC (2003) Electrospray mass spectrometry of phospholipids. Mass Spectrom Rev 22:332–364CrossRefGoogle Scholar
  31. 31.
    Wang C, Xie S, Yang J, Yang Q, Xu G (2004) Structural identification of human blood phospholipids using liquid chromatography/quadrupole-linear ion trap mass spectrometry. Anal Chim Acta 525:1–10CrossRefGoogle Scholar
  32. 32.
    González-Domínguez R, García-Barrera T, Gómez-Ariza JL (2014) Combination of metabolomic and phospholipid-profiling approaches for the study of Alzheimer’s disease. J Proteome 104:37–47CrossRefGoogle Scholar
  33. 33.
    Haynes CA, Allegood JC, Park H, Sullards MC (2009) Sphingolipidomics: methods for the comprehensive analysis of sphingolipids. J Chromatogr B 877:2696–2708CrossRefGoogle Scholar
  34. 34.
    Liebisch G, Binder M, Schifferer R, Langmann T, Schulz B, Schmitz G (2006) High throughput quantification of cholesterol and cholesteryl ester by electrospray ionization tandem mass spectrometry (ESI-MS/MS). Biochim Biophys Acta 1761:121–128CrossRefGoogle Scholar
  35. 35.
    Vernez L, Hopfgartner G, Wenk M, Krahenbuhl S (2003) Determination of carnitine and acylcarnitines in urine by high-performance liquid chromatography-electrospray ionization ion trap tandem mass spectrometry. J Chromatogr A 984:203–213CrossRefGoogle Scholar
  36. 36.
    González-Domínguez R, García A, García-Barrera T, Barbas C, Gómez-Ariza JL (2014) Metabolomic profiling of serum in the progression of Alzheimer’s disease by capillary electrophoresis-mass spectrometry. Electrophoresis 35:3321–3330CrossRefGoogle Scholar
  37. 37.
    González-Domínguez R, Rupérez FJ, García-Barrera T, Barbas C, Gómez-Ariza JL (2016) Metabolomic-driven elucidation of pathological mechanisms associated with Alzheimer’s disease and mild cognitive impairment. Curr Alzheimer Res 13:641–653CrossRefGoogle Scholar
  38. 38.
    González-Domínguez R, Castilla-Quintero R, García-Barrera T, Gómez-Ariza JL (2014) Development of a metabolomic approach based on urine samples and direct infusion mass spectrometry. Anal Biochem 465:20–27CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Raúl González-Domínguez
    • 1
    • 2
  • Álvaro González-Domínguez
    • 1
    • 2
  • Ana Sayago
    • 1
    • 2
  • Ángeles Fernández-Recamales
    • 1
    • 2
  1. 1.Department of Chemistry, Faculty of Experimental SciencesUniversity of HuelvaHuelvaSpain
  2. 2.International Campus of Excellence CeiA3University of HuelvaHuelvaSpain

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