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Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics

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High-Throughput Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1978))

Abstract

Liquid chromatography coupled to mass spectrometry (LC-MS)-based metabolomics and lipidomics offers invaluable tools to qualitatively and quantitatively study biological systems. Historically, unbiased (or discovery) analysis has been performed independently of targeted, quantitative analysis such as multiple reaction monitoring (MRM). These practices have been aptly carried out based on technical limitations of each assay. The wide mass scanning ranges typical of discovery approaches limit assay sensitivity, while targeted methods that improve analyte detection do not acquire data on ions not included in the targeted assay design. Recent improvements to quadrupole-Orbitrap technology have improved both scan speed as well as sensitivity, thus making these instruments more robust. By combining the improved robustness and coverage with stable isotope dilution (SID) techniques, advantages of the separate assays can now be realized in a single run, thereby improving the throughput of this type of analysis.

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References

  1. Dettmer K, Aronov PA, Hammock BD (2007) Mass spectrometry-based metabolomics. Mass Spectrom Rev 26:51–78

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Lu W, Bennett BD, Rabinowitz JD (2008) Analytical strategies for LC-MS-based targeted metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 871:236–242

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Gowda GAN, Djukovic D (2014) Overview of mass spectrometry-based metabolomics: opportunities and challenges. Methods Mol Biol 1198:3–12

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Scheltema RA, Hauschild J-P, Lange O et al (2014) The Q Exactive HF, a Benchtop mass spectrometer with a pre-filter, high-performance quadrupole and an ultra-high-field Orbitrap analyzer. Mol Cell Proteomics 13:3698–3708

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Fiehn O (2002) Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol 48:155–171

    CAS  PubMed  Google Scholar 

  6. Nemkov T, Hansen KC, D’Alessandro A (2017) A three-minute method for high-throughput quantitative metabolomics and quantitative tracing experiments of central carbon and nitrogen pathways. Rapid Commun Mass Spectrom 31:663–673

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Dietmair S, Timmins NE, Gray PP et al (2010) Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem 404:155–164

    CAS  PubMed  Google Scholar 

  8. Kapoore RV, Vaidyanathan S (2016) Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems. Philos Transact A Math Phys Eng Sci 374

    Google Scholar 

  9. Jia W, Xie G, Jia W (2018) Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat Rev Gastroenterol Hepatol 15:111–128

    CAS  PubMed  Google Scholar 

  10. Schaap FG, Trauner M, Jansen PLM (2014) Bile acid receptors as targets for drug development. Nat Rev Gastroenterol Hepatol 11:55–67

    CAS  PubMed  Google Scholar 

  11. Dennis EA, Norris PC (2015) Oxylipid storm in infection and inflammation. Nat Rev Immunol 15:511–523

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Fu X, Felcyn JR, Odem-Davis K et al (2016) Bioactive lipids accumulate in stored red blood cells despite leukoreduction: a targeted metabolomics study. Transfusion (Paris) 56:2560–2570

    CAS  Google Scholar 

  13. de Wolski K, Fu X, Dumont LJ et al (2016) Metabolic pathways that correlate with post-transfusion circulation of stored murine red blood cells. Haematologica 101:578–586

    PubMed  PubMed Central  Google Scholar 

  14. Smith CA, Want EJ, O’Maille G et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787

    CAS  PubMed  Google Scholar 

  15. Tautenhahn R, Patti GJ, Rinehart D et al (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84:5035–5039

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Gowda H, Ivanisevic J, Johnson CH et al (2014) Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 86:6931–6939

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Xia J, Wishart DS (2011) Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc 6:743–760

    CAS  PubMed  Google Scholar 

  18. Chong J, Soufan O, Li C et al (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46:W486–W494

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors are grateful to Sarah Gehrke, B.S. (University of Colorado Denver), for contributions to Table 1.

Conflicts of Interest

The authors disclose that A.D. and T.N. are part of Omix Technologies, Inc.

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, CO, USA

    Julie A. Reisz, Connie Zheng, Angelo D’Alessandro & Travis Nemkov

Authors
  1. Julie A. Reisz
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  2. Connie Zheng
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  3. Angelo D’Alessandro
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  4. Travis Nemkov
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Corresponding author

Correspondence to Travis Nemkov .

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Editors and Affiliations

  1. Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, CO, USA

    Angelo D'Alessandro

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Reisz, J.A., Zheng, C., D’Alessandro, A., Nemkov, T. (2019). Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics. In: D'Alessandro, A. (eds) High-Throughput Metabolomics. Methods in Molecular Biology, vol 1978. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9236-2_8

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  • DOI: https://doi.org/10.1007/978-1-4939-9236-2_8

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9235-5

  • Online ISBN: 978-1-4939-9236-2

  • eBook Packages: Springer Protocols

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