Skip to main content

Personalized Metabolomics

  • Protocol
  • First Online:
High-Throughput Metabolomics

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

Abstract

The human metabolome is the cumulative product of ingested metabolites and those produced by the body and its microbiota. Together these metabolites can dynamically report on the health and disease state of an individual, as well as their response to drug treatments and other external perturbations. Profiling metabolites in human body fluids provides an opportunity to identify biomarkers and stratify patients for personalized treatments but requires the development of high-throughput approaches compatible with large cohort and longitudinal studies. Here we review in detail sample preparation and analytical liquid chromatography-mass spectrometry (LC-MS) methods to measure the broad chemical diversity of metabolites found in human plasma and urine.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Snyder M (2016) Genomics and personalized medicine: what everyone needs to know, 1st edn. Oxford University Press, New York, 166 pp

    Google Scholar 

  2. Esplin ED, Oei L, Snyder MP (2014) Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease. Pharmacogenomics 15(14):1771–1790

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Madhukar NS, Elemento O (2018) Bioinformatics approaches to predict drug responses from genomic sequencing. Methods Mol Biol 1711:277–296

    CAS  PubMed  Google Scholar 

  4. Karczewski KJ, Snyder MP (2018) Integrative omics for health and disease. Nat Rev Genet 19(5):299–310

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Chen R et al (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148(6):1293–1307

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Piening BD et al (2018) Integrative personal omics profiles during periods of weight gain and loss. Cell Syst 6(2):157–170.e8

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Lee S et al (2016) Integrated network analysis reveals an association between plasma mannose levels and insulin resistance. Cell Metab 24(1):172–184

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Pedersen HK et al (2016) Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535(7612):376–381

    CAS  PubMed  Google Scholar 

  9. Beger RD et al (2016) Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 12(10):149

    PubMed  PubMed Central  Google Scholar 

  10. Gieger C et al (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4(11):e1000282

    PubMed  PubMed Central  Google Scholar 

  11. Contrepois K, Jiang L, Snyder M (2015) Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry. Mol Cell Proteomics 14(6):1684–1695

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18(1):83

    PubMed  PubMed Central  Google Scholar 

  13. Yin P, Lehmann R, Xu G (2015) Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem 407(17):4879–4892

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Tautenhahn R et al (2012) XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem 84(11):5035–5039

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14.10.1–14.10.91

    Google Scholar 

  16. Cambiaghi A, Ferrario M, Masseroli M (2017) Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Brief Bioinform 18(3):498–510

    Google Scholar 

  17. Veselkov KA et al (2011) Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Anal Chem 83(15):5864–5872

    CAS  PubMed  Google Scholar 

  18. Wu Y, Li L (2016) Sample normalization methods in quantitative metabolomics. J Chromatogr A 1430:80–95

    CAS  PubMed  Google Scholar 

  19. Guijas C et al (2018) METLIN: a technology platform for identifying knowns and unknowns. Anal Chem 90(5):3156–3164

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgment

This work was supported by NIH grant F32 HL 132452 to D.P.M.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael P. Snyder .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Marciano, D.P., Snyder, M.P. (2019). Personalized 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_27

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9236-2_27

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9235-5

  • Online ISBN: 978-1-4939-9236-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics