Skip to main content

Metabolite Profiling of Clinical Cancer Biofluid Samples by NMR Spectroscopy

  • Protocol
  • First Online:
Book cover Cancer Metabolism

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1928))

Abstract

Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (1H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that 1H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, 1H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for 1H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for 1H NMR experiments, spectral acquisition and processing, and quantitative profiling of 1H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
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. Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314

    Article  CAS  PubMed  Google Scholar 

  2. Otto AM (2016) Warburg effect(s)-a biographical sketch of Otto Warburg and his impacts on tumor metabolism. Cancer Metab 4:5

    Article  PubMed Central  PubMed  Google Scholar 

  3. Wolpaw AJ, Dang CV (2018) MYC-induced metabolic stress and tumorigenesis. Biochim Biophys Acta Rev Cancer 1870(1):43–50

    Article  CAS  PubMed  Google Scholar 

  4. Dowell AC, Cobby E, Wen K, Devall AJ, During V, Anderson J, James ND, Cheng KK, Zeegers MP, Bryan RT, Taylor GS (2017) Interleukin-17-positive mast cells influence outcomes from BCG for patients with CIS: data from a comprehensive characterisation of the immune microenvironment of urothelial bladder cancer. PLoS One 12(9):e0184841

    Article  PubMed Central  PubMed  Google Scholar 

  5. Srinivasan R, Ricketts CJ, Sourbier C, Linehan WM (2015) New strategies in renal cell carcinoma: targeting the genetic and metabolic basis of disease. Clin Cancer Res 21(1):10–17

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Yang G, Shen W, Zhang Y, Liu M, Zhang L, Liu Q, Lu HH, Bo J (2017) Accumulation of myeloid-derived suppressor cells (MDSCs) induced by low levels of IL-6 correlates with poor prognosis in bladder cancer. Oncotarget 8(24):38378–38388

    PubMed Central  PubMed  Google Scholar 

  7. Townsend MK, Bao Y, Poole EM, Bertrand KA, Kraft P, Wolpin BM, Clish CB, Tworoger SS (2016) Impact of pre-analytic blood sample collection factors on metabolomics. Cancer Epidemiol Biomarkers Prev 25(5):823–829

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Xiao Q, Moore SC, Boca SM, Matthews CE, Rothman N, Stolzenberg-Solomon RZ, Sinha R, Cross AJ, Sampson JN (2014) Sources of variability in metabolite measurements from urinary samples. PLoS One 9(5):e95749

    Article  PubMed Central  PubMed  Google Scholar 

  9. Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW, Tan YT, Ji BT, Chow WH, Cai Q, Liu DK, Yang G, Xiang YB, Zheng W, Sinha R, Cross AJ, Moore SC (2013) Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev 22(4):631–640

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. The 5 Core Elements of a Successful Metabolomics Study. Metabolon Inc. http://metabolomics.metabolon.com/acton/media/17033/metabolomics-study-success. Accessed 30 May 2018

  11. 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(1):387–426

    Article  CAS  PubMed  Google Scholar 

  12. Bathe OF, Shaykhutdinov R, Kopciuk K, Weljie AM, McKay A, Sutherland FR, Dixon E, Dunse N, Sotiropoulos D, Vogel HJ (2011) Feasibility of identifying pancreatic cancer based on serum metabolomics. Cancer Epidemiol Biomarkers Prev 20(1):140–147

    Article  CAS  PubMed  Google Scholar 

  13. Falegan OS, Ball MW, Shaykhutdinov RA, Pieroraio PM, Farshidfar F, Vogel HJ, Allaf ME, Hyndman ME (2017) Urine and serum metabolomics analyses may distinguish between stages of renal cell carcinoma. Metabolites 7(1):6

    Article  PubMed Central  Google Scholar 

  14. Farshidfar F, Weljie AM, Kopciuk K, Buie WD, Maclean A, Dixon E, Sutherland FR, Molckovsky A, Vogel HJ, Bathe OF (2012) Serum metabolomic profile as a means to distinguish stage of colorectal cancer. Genome Med 4(5):42

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. McConnell YJ, Farshidfar F, Weljie AM, Kopciuk KA, Dixon E, Ball CG, Sutherland FR, Vogel HJ, Bathe OF (2017) Distinguishing benign from malignant pancreatic and periampullary lesions using combined use of (1)H-NMR spectroscopy and gas chromatography-mass spectrometry. Metabolites 7(1):3

    Article  PubMed Central  Google Scholar 

  16. Lindon JC, Nicholson JK, Holmes E, Everett JR (2000) Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson 12(5):289–320

    Article  CAS  Google Scholar 

  17. Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, Nicholson JK (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2(11):2692–2703

    Article  CAS  PubMed  Google Scholar 

  18. An YJ, Cho HR, Kim TM, Keam B, Kim JW, Wen H, Park CK, Lee SH, Im SA, Kim JE, Choi SH, Park S (2015) An NMR metabolomics approach for the diagnosis of leptomeningeal carcinomatosis in lung adenocarcinoma cancer patients. Int J Cancer 136(1):162–171

    Article  CAS  PubMed  Google Scholar 

  19. Roberts MJ, Richards RS, Chow CWK, Buck M, Yaxley J, Lavin MF, Schirra HJ, Gardiner RA (2017) Seminal plasma enables selection and monitoring of active surveillance candidates using nuclear magnetic resonance-based metabolomics: a preliminary investigation. Prostate Int 5(4):149–157

    Article  PubMed Central  PubMed  Google Scholar 

  20. Wang J, Ma C, Liao Z, Tian B, Lu JP (2011) Study on chronic pancreatitis and pancreatic cancer using MRS and pancreatic juice samples. World J Gastroenterol 17(16):2126–2130

    Article  PubMed Central  PubMed  Google Scholar 

  21. Mickiewicz B, Kelly JJ, Ludwig TE, Weljie AM, Wiley JP, Schmidt TA, Vogel HJ (2015) Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis. J Orthop Res 33(11):1631–1638

    Article  PubMed  Google Scholar 

  22. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM (2006) Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 78(13):4430–4442

    Article  CAS  PubMed  Google Scholar 

  23. Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC (1995) 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal Chem 67(5):793–811

    Article  CAS  PubMed  Google Scholar 

  24. Van QN, Chmurny GN, Veenstra TD (2003) The depletion of protein signals in metabonomics analysis with the WET-CPMG pulse sequence. Biochem Biophys Res Commun 301(4):952–959

    Article  CAS  PubMed  Google Scholar 

  25. Issaq HJ, Van QN, Waybright TJ, Muschik GM, Veenstra TD (2009) Analytical and statistical approaches to metabolomics research. J Sep Sci 32(13):2183–2199

    Article  CAS  PubMed  Google Scholar 

  26. Tiziani S, Emwas AH, Lodi A, Ludwig C, Bunce CM, Viant MR, Gunther UL (2008) Optimized metabolite extraction from blood serum for 1H nuclear magnetic resonance spectroscopy. Anal Biochem 377(1):16–23

    Article  CAS  PubMed  Google Scholar 

  27. Daykin CA, Foxall PJ, Connor SC, Lindon JC, Nicholson JK (2002) The comparison of plasma deproteinization methods for the detection of low-molecular-weight metabolites by (1)H nuclear magnetic resonance spectroscopy. Anal Biochem 304(2):220–230

    Article  CAS  PubMed  Google Scholar 

  28. Zhang B, Xie M, Bruschweiler-Li L, Bruschweiler R (2016) Nanoparticle-assisted removal of protein in human serum for metabolomics studies. Anal Chem 88(1):1003–1007

    Article  CAS  PubMed  Google Scholar 

  29. Ludwig C, Viant MR (2010) Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem Anal 21(1):22–32

    Article  CAS  PubMed  Google Scholar 

  30. Kruk J, Doskocz M, Jodlowska E, Zacharzewska A, Lakomiec J, Czaja K, Kujawski J (2017) NMR techniques in metabolomic studies: a quick overview on examples of utilization. Appl Magn Reson 48(1):1–21

    Article  CAS  PubMed  Google Scholar 

  31. Lewis IA, Schommer SC, Hodis B, Robb KA, Tonelli M, Westler WM, Sussman MR, Markley JL (2007) Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H-13C NMR spectra. Anal Chem 79(24):9385–9390

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Fan TW-M (1996) Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Prog Nucl Magn Reson Spectrosc 28(2):161–219

    Article  CAS  Google Scholar 

  33. Trygg J, Holmes E, Lundstedt T (2007) Chemometrics in metabonomics. J Proteome Res 6(2):469–479

    Article  CAS  PubMed  Google Scholar 

  34. Euceda LR, Giskeodegard GF, Bathen TF (2015) Preprocessing of NMR metabolomics data. Scand J Clin Lab Invest 75(3):193–203

    Article  CAS  PubMed  Google Scholar 

  35. van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142

    Article  PubMed Central  PubMed  Google Scholar 

  36. Nagana Gowda GA, Raftery D (2017) Recent advances in NMR-based metabolomics. Anal Chem 89(1):490–510

    Article  CAS  PubMed  Google Scholar 

  37. Wurtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M (2017) Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology: a primer on -omic technologies. Am J Epidemiol 186(9):1084–1096

    Article  PubMed Central  PubMed  Google Scholar 

  38. Fischer K, Kettunen J, Wurtz P, Haller T, Havulinna AS, Kangas AJ, Soininen P, Esko T, Tammesoo ML, Magi R, Smit S, Palotie A, Ripatti S, Salomaa V, Ala-Korpela M, Perola M, Metspalu A (2014) Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons. PLoS Med 11(2):e1001606

    Article  PubMed Central  PubMed  Google Scholar 

  39. Brockton NT, Gill SJ, Laborge SL, Paterson AH, Cook LS, Vogel HJ, Shemanko CS, Hanley DA, Magliocco AM, Friedenreich CM (2015) The breast cancer to bone (B2B) metastases research program: a multi-disciplinary investigation of bone metastases from breast cancer. BMC Cancer 15:512

    Article  PubMed Central  PubMed  Google Scholar 

  40. Mickiewicz B, Arnold Egloff S, Eskaros AH, Clark PE, Zijlstra A, Vogel HJ, Hyndman ME (2018) Metabolomics of bladder cancer: from metabolic data to clinical diagnosis and prognosis

    Google Scholar 

  41. Lin G, Keshari KR, Park JM (2017) Cancer metabolism and tumor heterogeneity: imaging perspectives using MR imaging and spectroscopy. Contrast Media Mol Imaging 2017:6053879

    Article  PubMed Central  PubMed  Google Scholar 

  42. Bernini P, Bertini I, Luchinat C, Nincheri P, Staderini S, Turano P (2011) Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J Biomol NMR 49(3-4):231–243

    Article  CAS  PubMed  Google Scholar 

  43. Human Metabolome Database (HMDB). www.hmdb.ca. Accessed 20 May 2018

  44. Dieterle F, Ross A, Schlotterbeck G, Senn H (2006) Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem 78(13):4281–4290

    Article  CAS  PubMed  Google Scholar 

  45. Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikstrom C, Wold S (2006) Multi- and megavariate data analysis part I: basic principles and applications. Umetrics AB, Umeå

    Google Scholar 

  46. Teahan O, Gamble S, Holmes E, Waxman J, Nicholson JK, Bevan C, Keun HC (2006) Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal Chem 78(13):4307–4318

    Article  CAS  PubMed  Google Scholar 

  47. Chmurny GN, Hoult DI (1990) The ancient and honourable art of shimming. Concepts Magn Reson 2(3):131–149

    Article  Google Scholar 

  48. Takis PG, Schafer H, Spraul M, Luchinat C (2017) Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool. Nat Commun 8(1):1662

    Article  PubMed Central  PubMed  Google Scholar 

  49. Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML, Goodacre R (2015) A tutorial review: metabolomics and partial least squares-discriminant analysis—a marriage of convenience or a shotgun wedding. Anal Chim Acta 879:10–23

    Article  CAS  PubMed  Google Scholar 

  50. Cuperlovic-Culf M (2018) Machine learning methods for analysis of metabolic data and metabolic pathway modeling. Metabolites 8(1):E4

    Article  PubMed  Google Scholar 

  51. Smolinska A, Blanchet L, Buydens LM, Wijmenga SS (2012) NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review. Anal Chim Acta 750:82–97

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was funded by a CRIO-Cancer Grant from Alberta Innovates Health Solutions. We are indebted to all our colleagues that provided clinical samples, and especially to Drs. Aalim Weljie, Farshad Farshidfar, and Karen Kopciuk for discussions about methodology.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hans J. Vogel .

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

Mickiewicz, B., Hyndman, M.E., Vogel, H.J. (2019). Metabolite Profiling of Clinical Cancer Biofluid Samples by NMR Spectroscopy. In: Haznadar, M. (eds) Cancer Metabolism. Methods in Molecular Biology, vol 1928. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9027-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9027-6_14

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9026-9

  • Online ISBN: 978-1-4939-9027-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics