Skip to main content

The Advanced Technology and Clinical Application in Metabolomics

  • Chapter
  • First Online:
Clinical Metabolomics Applications in Genetic Diseases
  • 333 Accesses

Abstract

Metabolomics identifies and quantifies small molecules (metabolites) using high-throughput techniques. The biological system metabolome integrates metabolomics data in conjugation with metabolic pathways, including other omics datasets, to produce a network of endogenous metabolites (metabotype) associated with the phenotypes. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry are the main analytical techniques in combination with some separation techniques such as capillary electrophoresis, ultra-high-pressure liquid chromatography, and gas chromatography. The drastic improvement in the detection sensitivity and accuracy of the analytical techniques has widened the covered metabolomics. The comprehensive coverage of metabolomics becomes more integrated with other omics datasets to understand the system-level phenotypic changes and provide insight into the mechanisms that underlie various physiological conditions and diseases. This chapter highlights analytical methods for clinical metabolomics research and personalized medicine. Several innovative clinical metabolomics projects have reached up to patient services are discussed in this chapter.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
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

Similar content being viewed by others

Abbreviations

4MOP:

4-Methyl-2-oxopentanoic acid

BHBA:

β-hydroxybutyric acid

BSTFA:

N,O-bis(trimethylsilyl)trifluoroacetamide

CE:

Capillary electrophoresis

CI:

Chemical ionization

EHMN:

Edinburgh human metabolic network

ESI:

Electrospray ionization

FFPE:

Formalin-fixed paraffin-embedded

GSEA:

Gene set enrichment analysis

HIES:

Hyper-IgE syndromes

HILIC:

Hydrophilic interaction liquid chromatography

HRM:

High-resolution metabolomics

ICR:

Ion cyclotron resonance

LC-MS:

Liquid chromatography-mass spectrometry

LGPC:

Linoleoylglycerophosphocholine

LIT:

Linear quadrupole ion trap

NAFLD:

Nonalcoholic fatty liver disease

NMR:

Nuclear magnetic resonance

QIT:

Quadrupole ion trap

ROC:

Receiver operating characteristic

SMPDB:

Small Molecule Pathway Database

TOF:

Time of flight

References

  1. Holmes E, Antti H. Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst. 2002;127(12):1549–57.

    Article  CAS  PubMed  Google Scholar 

  2. Cravatt BF, Prospero-Garcia O, Siuzdak G, Gilula NB, Henriksen SJ, Boger DL, et al. Chemical characterization of a family of brain lipids that induce sleep. Science (New York, NY). 1995;268(5216):1506–9.

    Article  CAS  Google Scholar 

  3. Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 2022;50(D1):D622–31.

    Article  CAS  PubMed  Google Scholar 

  4. Kennedy AD, Wittmann BM, Evans AM, Miller LAD, Toal DR, Lonergan S, et al. Metabolomics in the clinic: a review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing. J Mass Spectrom. 2018;53(11):1143–54.

    Article  CAS  PubMed  Google Scholar 

  5. Abdel Rahman AM, Pawling J, Ryczko M, Caudy AA, Dennis JW. Targeted metabolomics in cultured cells and tissues by mass spectrometry: method development and validation. Anal Chim Acta. 2014;845:53–61.

    Article  CAS  PubMed  Google Scholar 

  6. Jacob M, Nimer RM, Alabdaljabar MS, Sabi EM, Al-Ansari MM, Housien M, et al. Metabolomics profiling of nephrotic syndrome towards biomarker discovery. Int J Mol Sci. 2022;23(20):12614.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Aleidi SM, Dahabiyeh LA, Gu X, Al Dubayee M, Alshahrani A, Benabdelkamel H, et al. Obesity connected metabolic changes in type 2 diabetic patients treated with metformin. Front Pharmacol. 2020;11:616157.

    Article  CAS  PubMed  Google Scholar 

  8. Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol. 2017;13(5):269–84.

    Article  CAS  PubMed  Google Scholar 

  9. Jacob M, Gu X, Luo X, Al-Mousa H, Arnaout R, Al-Saud B, et al. Metabolomics distinguishes DOCK8 deficiency from atopic dermatitis: towards a biomarker discovery. Metabolites. 2019;9(11):274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jans JJM, Broeks MH, Verhoeven-Duif NM. Metabolomics in diagnostics of inborn metabolic disorders. Curr Opin Syst Biol. 2022;29:100409.

    Article  CAS  Google Scholar 

  11. Heinemann J. Machine learning in untargeted metabolomics experiments. Methods Mol Biol. 2019;1859:287–99.

    Article  CAS  PubMed  Google Scholar 

  12. Beale DJ, Pinu FR, Kouremenos KA, Poojary MM, Narayana VK, Boughton BA, et al. Review of recent developments in GC-MS approaches to metabolomics-based research. Metabolomics. 2018;14(11):152.

    Article  PubMed  Google Scholar 

  13. Ramautar R. Capillary electrophoresis-mass spectrometry for clinical metabolomics. Adv Clin Chem. 2016;74:1–34.

    Article  CAS  PubMed  Google Scholar 

  14. Zhou J, Yin Y. Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry. Analyst. 2016;141(23):6362–73.

    Article  CAS  PubMed  Google Scholar 

  15. Gordillo R. Supercritical fluid chromatography hyphenated to mass spectrometry for metabolomics applications. J Sep Sci. 2021;44(1):448–63.

    Article  CAS  PubMed  Google Scholar 

  16. Spagou K, Tsoukali H, Raikos N, Gika H, Wilson ID, Theodoridis G. Hydrophilic interaction chromatography coupled to MS for metabonomic/metabolomic studies. J Sep Sci. 2010;33(6–7):716–27.

    Article  CAS  PubMed  Google Scholar 

  17. Virgiliou C, Gika HG, Theodoridis GA. HILIC-MS/MS multi-targeted method for metabolomics applications. Methods Mol Biol. 2018;1738:65–81.

    Article  CAS  PubMed  Google Scholar 

  18. Tang DQ, Zou L, Yin XX, Ong CN. HILIC-MS for metabolomics: an attractive and complementary approach to RPLC-MS. Mass Spectrom Rev. 2016;35(5):574–600.

    Article  CAS  PubMed  Google Scholar 

  19. Zhang X, Quinn K, Cruickshank-Quinn C, Reisdorph R, Reisdorph N. The application of ion mobility mass spectrometry to metabolomics. Curr Opin Chem Biol. 2018;42:60–6.

    Article  CAS  PubMed  Google Scholar 

  20. Mairinger T, Causon TJ, Hann S. The potential of ion mobility-mass spectrometry for non-targeted metabolomics. Curr Opin Chem Biol. 2018;42:9–15.

    Article  CAS  PubMed  Google Scholar 

  21. Zandkarimi F, Brown LM. Application of ion mobility mass spectrometry in lipidomics. Adv Exp Med Biol. 2019;1140:317–26.

    Article  CAS  PubMed  Google Scholar 

  22. Uppal K. Models of metabolomic networks. In: Wolkenhauer O, editor. Systems medicine. Oxford: Academic Press; 2021. p. 134–42.

    Chapter  Google Scholar 

  23. Hu Y, Cheng K, He L, Zhang X, Jiang B, Jiang L, et al. NMR-based methods for protein analysis. Anal Chem. 2021;93(4):1866–79.

    Article  CAS  PubMed  Google Scholar 

  24. Marchand J, Martineau E, Guitton Y, Le Bizec B, Dervilly-Pinel G, Giraudeau P. A multidimensional (1)H NMR lipidomics workflow to address chemical food safety issues. Metabolomics. 2018;14(5):60.

    Article  PubMed  Google Scholar 

  25. Wishart DS. NMR metabolomics: a look ahead. J Magn Reson. 1997;2019(306):155–61.

    Google Scholar 

  26. Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, et al. The future of NMR-based metabolomics. Curr Opin Biotechnol. 2017;43:34–40.

    Article  CAS  PubMed  Google Scholar 

  27. Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013;9(2):280–99.

    Article  CAS  PubMed  Google Scholar 

  28. Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A. 2009;106(10):3698–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.

    Article  CAS  PubMed  Google Scholar 

  31. Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49(D1):D545–51.

    Article  CAS  PubMed  Google Scholar 

  32. Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, et al. SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 2014;42(Database issue):D478–84.

    Article  CAS  PubMed  Google Scholar 

  33. Hao T, Ma HW, Zhao XM, Goryanin I. Compartmentalization of the Edinburgh Human Metabolic Network. BMC Bioinform. 2010;11:393.

    Article  Google Scholar 

  34. Kelder T, van Iersel MP, Hanspers K, Kutmon M, Conklin BR, Evelo CT, et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 2012;40(Database issue):D1301–7.

    Article  CAS  PubMed  Google Scholar 

  35. Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2008;36(Database issue):D623–31.

    CAS  PubMed  Google Scholar 

  36. Tilford CA, Siemers NO. Gene set enrichment analysis. Methods Mol Biol. 2009;563:99–121.

    Article  CAS  PubMed  Google Scholar 

  37. Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J. Sparse PLS-based method for overlapping metabolite set enrichment analysis. J Proteome Res. 2021;20(6):3204–13.

    Article  CAS  PubMed  Google Scholar 

  38. Beger RD, Dunn WB, Bandukwala A, Bethan B, Broadhurst D, Clish CB, et al. Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics. 2019;15(1):4.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Zhao X, Modur V, Carayannopoulos LN, Laterza OF. Biomarkers in pharmaceutical research. Clin Chem. 2015;61(11):1343–53.

    Article  CAS  PubMed  Google Scholar 

  40. Ni Y, Xie G, Jia W. Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery. J Proteome Res. 2014;13(9):3857–70.

    Article  CAS  PubMed  Google Scholar 

  41. Liesenfeld DB, Habermann N, Owen RW, Scalbert A, Ulrich CM. Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiol Biomarkers Prev. 2013;22(12):2182–201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Armitage EG, Barbas C. Metabolomics in cancer biomarker discovery: current trends and future perspectives. J Pharm Biomed Anal. 2014;87:1–11.

    Article  CAS  PubMed  Google Scholar 

  43. Monni G, Atzori L, Corda V, Dessolis F, Iuculano A, Hurt KJ, et al. Metabolomics in prenatal medicine: a review. Front Med. 2021;8:645118.

    Article  Google Scholar 

  44. McKeating DR, Fisher JJ, Perkins AV. Elemental metabolomics and pregnancy outcomes. Nutrients. 2019;11(1):73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Carter RA, Pan K, Harville EW, McRitchie S, Sumner S. Metabolomics to reveal biomarkers and pathways of preterm birth: a systematic review and epidemiologic perspective. Metabolomics. 2019;15(9):124.

    Article  CAS  PubMed Central  Google Scholar 

  46. Warburg O. On respiratory impairment in cancer cells. Science. 1956;124(3215):269–70.

    Article  CAS  PubMed  Google Scholar 

  47. Cacciatore S, Loda M. Innovation in metabolomics to improve personalized healthcare. Ann N Y Acad Sci. 2015;1346(1):57–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kelly AD, Breitkopf SB, Yuan M, Goldsmith J, Spentzos D, Asara JM. Metabolomic profiling from formalin-fixed, paraffin-embedded tumor tissue using targeted LC/MS/MS: application in sarcoma. PLoS One. 2011;6(10):e25357.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bathen TF, Geurts B, Sitter B, Fjøsne HE, Lundgren S, Buydens LM, et al. Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery. PLoS One. 2013;8(4):e61578.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Turkoglu O, Zeb A, Graham S, Szyperski T, Szender JB, Odunsi K, et al. Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature. Metabolomics. 2016;12(4):60.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Yu L, Li K, Zhang X. Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review. Oncotarget. 2017;8(70):115774–86.

    Article  PubMed Central  Google Scholar 

  52. Troisi J, Sarno L, Landolfi A, Scala G, Martinelli P, Venturella R, et al. Metabolomic signature of endometrial cancer. J Proteome Res. 2018;17(2):804–12.

    Article  CAS  PubMed  Google Scholar 

  53. Njoku K, Sutton CJ, Whetton AD, Crosbie EJ. Metabolomic biomarkers for detection, prognosis and identifying recurrence in endometrial cancer. Metabolites. 2020;10(8):314.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang F, Zhang Y, Zhao W, Deng K, Wang Z, Yang C, et al. Metabolomics for biomarker discovery in the diagnosis, prognosis, survival and recurrence of colorectal cancer: a systematic review. Oncotarget. 2017;8(21):35460–72.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wishart D. Metabolomics and the multi-omics view of cancer. Metabolites. 2022;12(2):154.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Eberlin LS, Norton I, Orringer D, Dunn IF, Liu X, Ide JL, et al. Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors. Proc Natl Acad Sci U S A. 2013;110(5):1611–6.

    Article  PubMed  PubMed Central  Google Scholar 

  57. St John ER, Balog J, McKenzie JS, Rossi M, Covington A, Muirhead L, et al. Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery. Breast Cancer Res. 2017;19(1):59.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kuc S, Koster MP, Pennings JL, Hankemeier T, Berger R, Harms AC, et al. Metabolomics profiling for identification of novel potential markers in early prediction of preeclampsia. PLoS One. 2014;9(5):e98540.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Duncan KD, Fyrestam J, Lanekoff I. Advances in mass spectrometry based single-cell metabolomics. Analyst. 2019;144(3):782–93.

    Article  CAS  PubMed  Google Scholar 

  60. Ozcelikay G, Kaya SI, Ozkan E, Cetinkaya A, Nemutlu E, Kır S, et al. Sensor-based MIP technologies for targeted metabolomics analysis. TrAC Trends Anal Chem. 2022;146:116487.

    Article  CAS  Google Scholar 

  61. Miller IJ, Peters SR, Overmyer KA, Paulson BR, Westphall MS, Coon JJ. Real-time health monitoring through urine metabolomics. NPJ Digit Med. 2019;2:109.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Cobb J, Gall W, Adam KP, Nakhle P, Button E, Hathorn J, et al. A novel fasting blood test for insulin resistance and prediabetes. J Diabetes Sci Technol. 2013;7(1):100–10.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Gall WE, Beebe K, Lawton KA, Adam KP, Mitchell MW, Nakhle PJ, et al. alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One. 2010;5(5):e10883.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anas M. Abdel Rahman .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Abdel Rahman, A.M. (2023). The Advanced Technology and Clinical Application in Metabolomics. In: Abdel Rahman, A.M. (eds) Clinical Metabolomics Applications in Genetic Diseases. Springer, Singapore. https://doi.org/10.1007/978-981-99-5162-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5162-8_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5161-1

  • Online ISBN: 978-981-99-5162-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics