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Metabolomic Analysis of Glioma Cells Using Nanoflow Liquid Chromatography–Tandem Mass Spectrometry

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Glioblastoma

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1741))

Abstract

Mass spectrometry (MS)-based techniques have been finding utility as sensitive, high throughput metabolite analysis tools for complex biological samples. We describe here a nanoflow liquid chromatography–tandem mass spectrometry (nano-LC-MS/MS) system we developed and applied to metabolic profiling of human cells. Metabolites are extracted from cells using methanol, and filtered through a C18 StageTip to remove large particles. Metabolite samples are separated by HPLC at a flow rate of 400–500 nl/min, then analyzed in both positive and negative ion modes in an LTQ-Orbitrap MS. Metabolite identification and differential analysis are performed using commercial or open source software. Protocols outlined in this chapter describe how nano-LC-MS can be applied to investigate metabolic profiling with limited biomass amount.

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References

  1. Masui K, Tanaka K, Akhavan D, Babic I, Gini B, Matsutani T, Iwanami A, Liu F, Villa GR, Gu Y (2013) mTOR complex 2 controls glycolytic metabolism in glioblastoma through FoxO acetylation and upregulation of c-Myc. Cell Metab 18(5):726–739

    Article  CAS  PubMed  Google Scholar 

  2. Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, Campos C, Fabius AW, Lu C, Ward PS (2012) IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483(7390):479–483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Parsons DW, Jones S, Zhang X, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA Jr, Hartigan J, Smith DR, Strausberg RL, Marie SK, Shinjo SM, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW (2008) An integrated genomic analysis of human glioblastoma multiforme. Science 321(5897):1807–1812. https://doi.org/10.1126/science.1164382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360(8):765–773. https://doi.org/10.1056/NEJMoa0808710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rendina AR, Pietrak B, Smallwood A, Zhao H, Qi H, Quinn C, Adams ND, Concha N, Duraiswami C, Thrall SH (2013) Mutant IDH1 enhances the production of 2-hydroxyglutarate due to its kinetic mechanism. Biochemistry 52(26):4563–4577

    Article  CAS  PubMed  Google Scholar 

  6. Agnihotri S, Zadeh G (2015) Metabolic reprogramming in glioblastoma: the influence of cancer metabolism on epigenetics and unanswered questions. Neuro Oncol 18:160–172

    Article  PubMed  PubMed Central  Google Scholar 

  7. Theodoridis GA, Gika HG, Want EJ, Wilson ID (2012) Liquid chromatography–mass spectrometry based global metabolite profiling: a review. Anal Chim Acta 711:7–16

    Article  CAS  PubMed  Google Scholar 

  8. Kiefer P, Delmotte N, Vorholt JA (2011) Nanoscale ion-pair reversed-phase HPLC−MS for sensitive metabolome analysis. Anal Chem 83(3):850–855

    Google Scholar 

  9. Martano G, Delmotte N, Kiefer P, Christen P, Kentner D, Bumann D, Vorholt JA (2015) Fast sampling method for mammalian cell metabolic analyses using liquid chromatography–mass spectrometry. Nat Protoc 10(1):1–11

    Article  CAS  PubMed  Google Scholar 

  10. Benton HP, Ivanisevic J, Mahieu NG, Kurczy ME, Johnson CH, Franco L, Rinehart D, Valentine E, Gowda H, Ubhi BK, Tautenhahn R, Gieschen A, Fields MW, Patti GJ, Siuzdak G (2015) Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem 87(2):884–891. https://doi.org/10.1021/ac5025649

    Article  CAS  PubMed  Google Scholar 

  11. Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84(11):5035–5039. https://doi.org/10.1021/ac300698c

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807. https://doi.org/10.1093/nar/gks1065

    CAS  PubMed  Google Scholar 

  13. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, Mandal R, Sinelnikov I, Xia J, Jia L, Cruz JA, Lim E, Sobsey CA, Shrivastava S, Huang P, Liu P, Fang L, Peng J, Fradette R, Cheng D, Tzur D, Clements M, Lewis A, De Souza A, Zuniga A, Dawe M, Xiong Y, Clive D, Greiner R, Nazyrova A, Shaykhutdinov R, Li L, Vogel HJ, Forsythe I (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37(Database issue):D603–D610. https://doi.org/10.1093/nar/gkn810

    Article  CAS  PubMed  Google Scholar 

  14. Mahieu NG, Genenbacher JL, Patti GJ (2016) A roadmap for the XCMS family of software solutions in metabolomics. Curr Opin Chem Biol 30:87–93

    Article  CAS  PubMed  Google Scholar 

  15. Vinaixa M, Schymanski EL, Neumann S, Navarro M, Salek RM, Yanes O (2016) Mass spectral databases for LC/MS-and GC/MS-based metabolomics: state of the field and future prospects. TrAC Trends Anal Chem 78:23–35

    Article  CAS  Google Scholar 

  16. Lynn K-S, Cheng M-L, Chen Y-R, Hsu C, Chen A, Lih TM, Chang H-Y, C-j H, Shiao M-S, Pan W-H (2015) Metabolite identification for mass spectrometry-based metabolomics using multiple types of correlated ion information. Anal Chem 87(4):2143–2151

    Article  CAS  PubMed  Google Scholar 

  17. Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2(8):1896–1906. https://doi.org/10.1038/nprot.2007.261

    Article  CAS  PubMed  Google Scholar 

  18. Sapcariu SC, Kanashova T, Weindl D, Ghelfi J, Dittmar G, Hiller K (2014) Simultaneous extraction of proteins and metabolites from cells in culture. MethodsX 1:74–80. https://doi.org/10.1016/j.mex.2014.07.002

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dang L, White DW, Gross S, Bennett BD, Bittinger MA, Driggers EM, Fantin VR, Jang HG, Jin S, Keenan MC, Marks KM, Prins RM, Ward PS, Yen KE, Liau LM, Rabinowitz JD, Cantley LC, Thompson CB, Vander Heiden MG, Su SM (2010) Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 465(7300):966. https://doi.org/10.1038/nature09132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgment

This work was supported by a grant from the 100 Women in Hedge Funds Foundation and NIH NINDS grant P30 NS050276.

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Authors and Affiliations

  1. Department of Cell Biology, Kimmel Center for Biology and Medicine at the Skirball Institute, New York University School of Medicine, New York, NY, USA

    Jingjing Deng & Thomas A. Neubert

  2. Proteomics and Metabolomics Core Facility, Weill Cornell Medicine, New York, NY, USA

    Guoan Zhang

Authors
  1. Jingjing Deng
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  2. Guoan Zhang
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  3. Thomas A. Neubert
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Corresponding author

Correspondence to Thomas A. Neubert .

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Editors and Affiliations

  1. Department of Neurosurgery, NYU School of Medicine, New York, New York, USA

    Dimitris G. Placantonakis

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Deng, J., Zhang, G., Neubert, T.A. (2018). Metabolomic Analysis of Glioma Cells Using Nanoflow Liquid Chromatography–Tandem Mass Spectrometry. In: Placantonakis, D. (eds) Glioblastoma. Methods in Molecular Biology, vol 1741. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7659-1_10

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  • DOI: https://doi.org/10.1007/978-1-4939-7659-1_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7658-4

  • Online ISBN: 978-1-4939-7659-1

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