Skip to main content
SpringerLink
Log in
Book cover

Metabolomics: From Fundamentals to Clinical Applications pp 45–76Cite as

Nuclear Magnetic Resonance Strategies for Metabolic Analysis

  • Chapter
  • First Online:

Part of the book series: Advances in Experimental Medicine and Biology ((PMISB,volume 965))

Abstract

NMR spectroscopy is a powerful tool for metabolomic studies, offering highly reproducible and quantitative analyses. This burgeoning field of NMR metabolomics has been greatly aided by the development of modern spectrometers and software, allowing high-throughput analysis with near real-time feedback. Whilst one-dimensional proton (1D-1H) NMR analysis is best described and remains most widely used, a plethora of alternative NMR techniques are now available that offer additional chemical and structural information and resolve many of the limitations of conventional 1D-1H NMR such as spectral overlay. In this book chapter, we review the principal concepts of practical NMR spectroscopy, from common sample preparation protocols to the benefits and theoretical concepts underpinning the commonly used pulse sequences. Finally, as a case study to highlight the utility of NMR as a method for metabolomic investigation, we have detailed how NMR has been used to gain valuable insight into the metabolism occurring in kidneys prior to transplantation and the potential implications of this.

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

Chapter
USD   29.95
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

Learn about institutional subscriptions

Abbreviations

AsLS:

Asymmetric least square smoothing

AST:

Aspartate transaminase

BML-NMR:

Birmingham Metabolite Library Nuclear Magnetic Resonance

BMRB:

BioMagResBank

CluPA:

Cluster-based peak alignment

COW:

Correlation optimized warping

CPMG:

Carr-Purcell-Meiboom-Gill

DGF:

Delayed graft function

DTW:

Dynamic time warping

EDTA:

Ethylene diamine tetra acetate

FFT:

Fast Fourier transform

FID:

Free induction decay

FW:

Iterative fuzzy warping

GST:

Glutathione-S-transferase

HR-MAS:

High-resolution magic angle spinning

HMBC:

Heteronuclear multiple-bond correlation

HMDB:

Human Metabolome Database

HMP:

Hypothermic machine perfusion

HSQC:

Heteronuclear single quantum coherence

ICA:

Independent component analysis

Icoshift:

Interval correlated shifting

IGF:

Immediate graft function

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KOH:

Potassium hydroxide

LC-MS:

Liquid chromatography-mass spectrometry

LDH:

Lactate dehydrogenase

LOWESS:

Locally weighted scatterplot smoothing

MMCD:

Madison Metabolomics Consortium Database

MTBE:

Methyl tert-butyl ether

NMR:

Nuclear magnetic resonance

NUS:

Non-uniform sampling

OPLS-DA:

Orthogonal partial least square – discriminant analysis

PARS:

Peak alignment using reduced set mapping

PCA:

Principal component analysis

PLS:

Partial least square

PLS-DA:

Partial least square – discriminant analysis

PQN:

Probabilistic quotient normalization

SCS:

Static cold storage

SPE:

Solid phase extraction

TMAO:

Trimethylamine-N-oxide

TMSP:

Trimethylsilylpropanoate

TOCSY:

Total correlation spectroscopy

TSA:

Total spectral area

Vast:

Variable stability

References

  1. Bory C, Boulieu R, Chantin C, Mathieu M. Diagnosis of alcaptonuria: rapid analysis of homogentisic acid by HPLC. Clin Chim Acta. 1990;189(1):7–11.

    Article  CAS  PubMed  Google Scholar 

  2. Wishart DS. Metabolomics: the principles and potential applications to transplantation. Am J Transplant. 2005;5(12):2814–20.

    Article  CAS  PubMed  Google Scholar 

  3. Bales JR, Bell JD, Nicholson JK, Sadler PJ. 1H NMR studies of urine during fasting: excretion of ketone bodies and acetylcarnitine. Magn Reson Med. 1986;3(6):849–56.

    Article  CAS  PubMed  Google Scholar 

  4. Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR, et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem. 2014;86(19):9887–94.

    Article  CAS  PubMed  Google Scholar 

  5. Griffiths WJ, Wang Y. Mass spectrometry: from proteomics to metabolomics and lipidomics. Chem Soc Rev. 2009;38(7):1882–96.

    Article  CAS  PubMed  Google Scholar 

  6. Ludwig C, Ward DG, Martin A, Viant MR, Ismail T, Johnson PJ, et al. Fast targeted multidimensional NMR metabolomics of colorectal cancer. Magn Reson Chem MRC. 2009;47 Suppl 1:S68–73.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Sadagopan NP, Li W, Cook JA, Galvan B, Weller DL, Fountain ST, Cohen LH. Investigation of EDTA anticoagulant in plasma to improve the throughput of liquid chromatography/tandem mass spectrometric assays. Rapid Commun Mass Spectrom. 2003;17(10):1065–70.

    Article  CAS  PubMed  Google Scholar 

  9. Faijes M, Mars AE, Smid EJ. Comparison of quenching and extraction methodologies for metabolome analysis of Lactobacillus plantarum. Microb Cell Factories. 2007;6(1):1.

    Article  CAS  Google Scholar 

  10. Warne MA, Lenz EM, Osborn D, Weeks JM, Nicholson JK. Comparative biochemistry and short-term starvation effects on the earthworms Eisenia veneta and Lumbricus terrestris studied by 1 H NMR spectroscopy and pattern recognition. Soil Biol Biochem. 2001;33(9):1171–80.

    Article  CAS  Google Scholar 

  11. Rosenblum ES, Viant MR, Braid BM, Moore JD, Friedman CS, Tjeerdema RS. Characterizing the metabolic actions of natural stresses in the California red abalone, Haliotis rufescens using 1H NMR metabolomics. Metabolomics. 2005;1(2):199–209.

    Article  CAS  Google Scholar 

  12. Viant MR, Rosenblum ES, Tjeerdema RS. NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health. Environ Sci Technol. 2003;37(21):4982–9.

    Article  CAS  PubMed  Google Scholar 

  13. Bouchereau A, Guénot P, Larher F. Analysis of amines in plant materials. J Chromatogr B Biomed Sci App. 2000;747(1):49–67.

    Article  CAS  Google Scholar 

  14. Anastassiades M, Lehotay SJ, Štajnbaher D, Schenck FJ. Fast and easy multiresidue method employing acetonitrile extraction/partitioning and “dispersive solid-phase extraction” for the determination of pesticide residues in produce. J AOAC Int. 2003;86(2):412–31.

    CAS  PubMed  Google Scholar 

  15. Mackay GM, Zheng L, van den Broek NJ, Gottlieb E. Chapter five: analysis of cell metabolism using LC-MS and isotope tracers. Methods Enzymol. 2015;561:171–96.

    Article  PubMed  Google Scholar 

  16. Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37(8):911–7.

    Article  CAS  PubMed  Google Scholar 

  17. Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res. 2008;49(5):1137–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lin CY, Wu H, Tjeerdema RS, Viant MR. Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics. Metabolomics. 2007;3(1):55–67.

    Article  CAS  Google Scholar 

  19. David A, Abdul-Sada A, Lange A, Tyler CR, Hill EM. A new approach for plasma (xeno) metabolomics based on solid-phase extraction and nanoflow liquid chromatography-nanoelectrospray ionisation mass spectrometry. J Chromatogr A. 2014;1365:72–85.

    Article  CAS  PubMed  Google Scholar 

  20. Chetwynd AJ, Abdul-Sada A, Hill EM. Solid-phase extraction and nanoflow liquid chromatography-nanoelectrospray ionization mass spectrometry for improved global urine metabolomics. Anal Chem. 2015;87:1158–65.

    Article  CAS  PubMed  Google Scholar 

  21. Martínez‐Bisbal MC, Monleon D, Assemat O, Piotto M, Piquer J, Llacer JL, Celda B. Determination of metabolite concentrations in human brain tumour biopsy samples using HR‐MAS and ERETIC measurements. NMR Biomed. 2009;22(2):199–206.

    Article  PubMed  CAS  Google Scholar 

  22. Bharti SK, Sinha N, Joshi BS, Mandal SK, Roy R, Khetrapal CL. Improved quantification from 1H-NMR spectra using reduced repetition times. Metabolomics. 2008;4(4):367–76.

    Article  CAS  Google Scholar 

  23. Rabeson H, Fauvelle F, Testylier G, Foquin A, Carpentier P, Dorandeu F, Graveron‐Demilly D. Quantitation with QUEST of brain HRMAS‐NMR signals: application to metabolic disorders in experimental epileptic seizures. Magn Reson Med. 2008;59(6):1266–73.

    Article  CAS  PubMed  Google Scholar 

  24. Elbayed K, Berl V, Debeuckelaere C, Moussallieh FM, Piotto M, Namer IJ, Lepoittevin JP. HR-MAS NMR spectroscopy of reconstructed human epidermis: potential for the in situ investigation of the chemical interactions between skin allergens and nucleophilic amino acids. Chem Res Toxicol. 2013;26(1):136–45.

    Article  CAS  PubMed  Google Scholar 

  25. Heude C, Lemasson E, Elbayed K, Piotto M. Rapid assessment of fish freshness and quality by 1H HR-MAS NMR spectroscopy. Food Anal Methods. 2015;8(4):907–15.

    Article  Google Scholar 

  26. Lippens G, Bourdonneau M, Dhalluin C, Warras R, Richert T, Seetharaman C, Boutillon C, Piotto M. Study of compounds attached to solid supports using high resolution magic angle spinning NMR. Curr Org Chem. 1999;3:147–69.

    CAS  Google Scholar 

  27. Martínez-Bisbal MC, Martí-Bonmatí L, Piquer J, Revert A, Ferrer P, Llácer JL, et al. 1H and 13C HR-MAS spectroscopy of intact biopsy samples ex vivo and in vivo 1H MRS study of human high grade gliomas. NMR Biomed. 2004;17(4):191–205.

    Article  PubMed  CAS  Google Scholar 

  28. Guéron M, Plateau P, Decorps M. Solvent signal suppression in NMR. Prog Nucl Magn Reson Spectrosc. 1991;23(2):135–209.

    Article  Google Scholar 

  29. Nguyen BD, Meng X, Donovan KJ, Shaka AJ. SOGGY: solvent-optimized double gradient spectroscopy for water suppression. A comparison with some existing techniques. J Magn Reson. 2007;184(2):263–74.

    Article  CAS  PubMed  Google Scholar 

  30. Ludwig C, Günther UL. MetaboLab – advanced NMR data processing and analysis for metabolomics. BMC Bioinformatics. 2011;12:366.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Aue WP, Karhan J, Ernst RR. Homonuclear broad band decoupling and two‐dimensional J‐resolved NMR spectroscopy. J Chem Phys. 1976;64(10):4226–7.

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. Peti W, Griesinger C, Bermel W. Adiabatic TOCSY for C, C and H, H J-transfer. J Biomol NMR. 2000;18(3):199–205.

    Article  CAS  PubMed  Google Scholar 

  34. Piotto M, Bourdonneau M, Furrer J, Bianco A, Raya J, Elbayed K. Destruction of magnetization during TOCSY experiments performed under magic angle spinning: effect of radial B1 inhomogeneities. J Magn Reson. 2001;149(1):114–8.

    Article  CAS  Google Scholar 

  35. Lane AN, Fan TW-M, Bousamra M, Higashi RM, Yan J, Miller DM. Stable isotope-resolved metabolomics (SIRM) in cancer research with clinical application to nonsmall cell lung cancer. Omics J Integr Biol. 2011;15(3):173–82.

    Article  CAS  Google Scholar 

  36. Hollinshead KER, Williams DS, Tennant DA, Ludwig C. Probing cancer cell metabolism using NMR spectroscopy. Adv Exp Med Biol. 2016;899:89–111.

    Article  PubMed  Google Scholar 

  37. Lussey-Lepoutre C, Hollinshead KER, Ludwig C, Menara M, Morin A, Castro-Vega L-J, et al. Loss of succinate dehydrogenase activity results in dependency on pyruvate carboxylation for cellular anabolism. Nat Commun. 2015;6:8784.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tiziani S, Lodi A, Ludwig C, Parsons HM, Viant MR. Effects of the application of different window functions and projection methods on processing of 1 H J-resolved nuclear magnetic resonance spectra for metabolomics. Anal Chim Acta. 2008;610(1):80–8.

    Article  CAS  PubMed  Google Scholar 

  39. Wider G. Elimination of baseline artifacts in NMR spectra by oversampling. J Magn Reson. 1990;89(2):406–9.

    CAS  Google Scholar 

  40. Marion D, Bax A. Baseline correction of 2D FT NMR spectra using a simple linear prediction extrapolation of the time-domain data. J Magn Reson. 1989;83(1):205–11.

    CAS  Google Scholar 

  41. Gan F, Ruan G, Mo J. Baseline correction by improved iterative polynomial fitting with automatic threshold. Chemom Intell Lab Syst. 2006;82(1):59–65.

    Article  CAS  Google Scholar 

  42. Eilers PH, Boelens HF. Baseline correction with asymmetric least squares smoothing. Leiden Univ Med Cent Rep. 2005;1:1.

    Google Scholar 

  43. Xi Y, Rocke DM. Baseline correction for NMR spectroscopic metabolomics data analysis. BMC Bioinformatics. 2008;9(1):1.

    Article  CAS  Google Scholar 

  44. Dietrich W, Rüdel CH, Neumann M. Fast and precise automatic baseline correction of one-and two-dimensional NMR spectra. J Magn Reson. 1991;91(1):1–11.

    CAS  Google Scholar 

  45. Chang D, Banack CD, Shah SL. Robust baseline correction algorithm for signal dense NMR spectra. J Magn Reson. 2007;187(2):288–92.

    Article  CAS  PubMed  Google Scholar 

  46. Tomasi G, Van Den Berg F, Andersson C. Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. J Chemom. 2004;18(5):231–41.

    Article  CAS  Google Scholar 

  47. Savorani F, Tomasi G, Engelsen SB. Icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson. 2010;202(2):190–202.

    Article  CAS  PubMed  Google Scholar 

  48. Vu TN, Valkenborg D, Smets K, Verwaest KA, Dommisse R, Lemière F, Laukens K. An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC Bioinformatics. 2011;12(1):405.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Torgrip RJO, Aberg M, Karlberg B, Jacobsson SP. Peak alignment using reduced set mapping. J Chemom. 2003;17(11):573–82.

    Article  CAS  Google Scholar 

  50. Wu W, Daszykowski M, Walczak B, Sweatman BC, Connor SC, Haselden JN, Lutz M. Peak alignment of urine NMR spectra using fuzzy warping. J Chem Inf Model. 2006;46(2):863–75.

    Article  CAS  PubMed  Google Scholar 

  51. Davis RA, Charlton AJ, Godward J, Jones SA, Harrison M, Wilson JC. Adaptive binning: an improved binning method for metabolomics data using the undecimated wavelet transform. Chemom Intell Lab Syst. 2007;85(1):144–54.

    Article  CAS  Google Scholar 

  52. Anderson PE, Reo NV, DelRaso NJ, Doom TE, Raymer ML. Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics. Metabolomics. 2008;4(3):261–72.

    Article  CAS  Google Scholar 

  53. De Meyer T, Sinnaeve D, Van Gasse B, Tsiporkova E, Rietzschel ER, De Buyzere ML, et al. NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem. 2008;80(10):3783–90.

    Article  PubMed  CAS  Google Scholar 

  54. Anderson PE, Mahle DA, Doom TE, Reo NV, DelRaso NJ, Raymer ML. Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data. Metabolomics. 2011;7(2):179–90.

    Article  CAS  Google Scholar 

  55. Sousa SAA, Magalhães A, Ferreira MMC. Optimized bucketing for NMR spectra: three case studies. Chemom Intell Lab Syst. 2013;122:93–102.

    Google Scholar 

  56. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006;78(13):4281–90.

    Article  CAS  PubMed  Google Scholar 

  57. Dong J, Cheng KK, Xu J, Chen Z, Griffin JL. Group aggregating normalization method for the preprocessing of NMR-based metabolomic data. Chemom Intell Lab Syst. 2011;108(2):123–32.

    Article  CAS  Google Scholar 

  58. van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006;7(1):1.

    Article  CAS  Google Scholar 

  59. Parsons HM, Ludwig C, Günther UL, Viant MR. Improved classification accuracy in 1-and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation. BMC Bioinformatics. 2007;8(1):234.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987;2(1–3):37–52.

    Article  CAS  Google Scholar 

  61. Lee TW. Independent component analysis. Springer US, Boston MA, USA; 1998. p. 27–66.

    Google Scholar 

  62. Bylesjö M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J. OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification. J Chemom. 2006;20(8–10):341–51.

    Article  CAS  Google Scholar 

  63. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007;35(Database issue):D521–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0 – the Human Metabolome Database in 2013. Nucleic Acids Res. 2013;41(Database issue):D801–7.

    Article  CAS  PubMed  Google Scholar 

  65. Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF, et al. Metabolite identification via the Madison metabolomics consortium database. Nat Biotechnol. 2008;26(2):162–4.

    Article  CAS  PubMed  Google Scholar 

  66. Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, et al. BioMagResBank. Nucleic Acids Res. 2008;36(Database issue):D402–8.

    CAS  PubMed  Google Scholar 

  67. Ludwig C, Easton JM, Lodi A, Tiziani S, Manzoor SE, Southam AD, et al. Birmingham Metabolite Library: a publicly accessible database of 1-D 1H and 2-D 1H J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics. 2011;8(1):8–18.

    Article  CAS  Google Scholar 

  68. Bingol K, Li D-W, Bruschweiler-Li L, Cabrera OA, Megraw T, Zhang F, et al. Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra. ACS Chem Biol. 2015;10(2):452–9.

    Article  CAS  PubMed  Google Scholar 

  69. Bingol K, Zhang F, Bruschweiler-Li L, Brüschweiler R. TOCCATA: a customized carbon total correlation spectroscopy NMR metabolomics database. Anal Chem. 2012;84(21):9395–401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. NHS Blood and Transplant. Organ donation and transplantation activity report 2014/15 [Internet]. 2015. Available from: http://nhsbtmediaservices.blob.core.windows.net/organ-donation-assets/pdfs/activity_report_2014_15.pdf.

  71. Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med. 1999;341(23):1725–30.

    Article  CAS  PubMed  Google Scholar 

  72. Jofré R, López-Gómez JM, Moreno F, Sanz-Guajardo D, Valderrábano F. Changes in quality of life after renal transplantation. Am J Kidney Dis Off J Natl Kidney Found. 1998;32(1):93–100.

    Article  Google Scholar 

  73. Fiebiger W, Mitterbauer C, Oberbauer R. Health-related quality of life outcomes after kidney transplantation. Health Qual Life Outcomes. 2004;2:2.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Lee CY, Mangino MJ. Preservation methods for kidney and liver. Organogenesis. 2009;5(3):105–12.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Opelz G, Döhler B. Multicenter analysis of kidney preservation. Transplantation. 2007;83(3):247–53.

    Article  PubMed  Google Scholar 

  76. Moers C, Smits JM, Maathuis M-HJ, Treckmann J, van Gelder F, Napieralski BP, et al. Machine perfusion or cold storage in deceased-donor kidney transplantation. N Engl J Med. 2009;360(1):7–19.

    Article  CAS  PubMed  Google Scholar 

  77. Moers C, Pirenne J, Paul A, Ploeg RJ, Machine Preservation Trial Study Group. Machine perfusion or cold storage in deceased-donor kidney transplantation. N Engl J Med. 2012;366(8):770–1.

    Article  CAS  PubMed  Google Scholar 

  78. O’Callaghan JM, Morgan RD, Knight SR, Morris PJ. Systematic review and meta-analysis of hypothermic machine perfusion versus static cold storage of kidney allografts on transplant outcomes. Br J Surg. 2013;100(8):991–1001.

    Article  PubMed  Google Scholar 

  79. Taylor MJ, Baicu SC. Current state of hypothermic machine perfusion preservation of organs: the clinical perspective. Cryobiology. 2010;60(3 Suppl):S20–35.

    Article  PubMed  Google Scholar 

  80. Fuller BJ, Lee CY. Hypothermic perfusion preservation: the future of organ preservation revisited? Cryobiology. 2007;54(2):129–45.

    Article  CAS  PubMed  Google Scholar 

  81. Serkova N, Fuller TF, Klawitter J, Freise CE, Niemann CU. H-NMR-based metabolic signatures of mild and severe ischemia/reperfusion injury in rat kidney transplants. Kidney Int. 2005;67(3):1142–51.

    Article  CAS  PubMed  Google Scholar 

  82. Hauet T, Baumert H, Gibelin H, Hameury F, Goujon JM, Carretier M, et al. Noninvasive monitoring of citrate, acetate, lactate, and renal medullary osmolyte excretion in urine as biomarkers of exposure to ischemic reperfusion injury. Cryobiology. 2000;41(4):280–91.

    Article  CAS  PubMed  Google Scholar 

  83. Bon D, Billault C, Claire B, Thuillier R, Hebrard W, Boildieu N, et al. Analysis of perfusates during hypothermic machine perfusion by NMR spectroscopy: a potential tool for predicting kidney graft outcome. Transplantation. 2014;97(8):810–6.

    Article  CAS  PubMed  Google Scholar 

  84. Kienana M, Lydie N-D, Jean-Michel H, Binta D, Matthias B, Patrick E, et al. Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined (1)H NMR and GC-MS approach. Mol Biosyst. 2015;11(9):2493–510.

    Article  CAS  PubMed  Google Scholar 

  85. Guy AJ, Nath J, Cobbold M, Ludwig C, Tennant DA, Inston NG, et al. Metabolomic analysis of perfusate during hypothermic machine perfusion of human cadaveric kidneys. Transplantation. 2015;99(4):754–9.

    Article  CAS  PubMed  Google Scholar 

  86. Nath J, Guy A, Smith TB, Cobbold M, Inston NG, Hodson J, et al. Metabolomic perfusate analysis during kidney machine perfusion: the pig provides an appropriate model for human studies. Bruns H, editor. PLoS ONE. 2014;9(12):e114818.

    Google Scholar 

  87. Faure JP. Polyethylene glycol reduces early and long-term cold ischemia-reperfusion and renal medulla injury. J Pharmacol Exp Ther. 2002;302(3):861–70.

    Article  CAS  PubMed  Google Scholar 

  88. Foxall PJ, Mellotte GJ, Bending MR, Lindon JC, Nicholson JK. NMR spectroscopy as a novel approach to the monitoring of renal transplant function. Kidney Int. 1993;43(1):234–45.

    Article  CAS  PubMed  Google Scholar 

  89. Port FK, Bragg-Gresham JL, Metzger RA, Dykstra DM, Gillespie BW, Young EW, et al. Donor characteristics associated with reduced graft survival: an approach to expanding the pool of kidney donors. Transplantation. 2002;74(9):1281–6.

    Article  PubMed  Google Scholar 

  90. Southard JH, Belzer FO. Organ preservation. Annu Rev Med. 1995;46:235–47.

    Article  CAS  PubMed  Google Scholar 

  91. Boutilier RG. Mechanisms of cell survival in hypoxia and hypothermia. J Exp Biol. 2001;204(Pt 18):3171–81.

    CAS  PubMed  Google Scholar 

  92. Hauet T, Gibelin H, Godart C, Eugene M, Carretier M. Kidney retrieval conditions influence damage to renal medulla: evaluation by proton nuclear magnetic resonance (NMR) spectroscopy. Clin Chem Lab Med. 2000;38(11):1085–92.

    CAS  PubMed  Google Scholar 

  93. Li L, Sui W, Che W, Li W, Chen J, Li H, et al. 1H NMR-based metabolic profiling of human serum before and after renal transplantation. ASAIO J. 2013;59(3):268–93.

    Article  CAS  Google Scholar 

  94. Buchs J-B, Lazeyras F, Ruttimann R, Nastasi A, Morel P. Oxygenated hypothermic pulsatile perfusion versus cold static storage for kidneys from non heart-beating donors tested by in-line ATP resynthesis to establish a strategy of preservation. Perfusion. 2011;26(2):159–65.

    Article  PubMed  Google Scholar 

  95. von Elverfeldt D, Niekisch M, Quaschning T, El Saman A, Kirste G, Krämer-Guth A, et al. Kinetics of PME/Pi in pig kidneys during cold ischemia. NMR Biomed. 2007;20(7):652–7.

    Article  CAS  Google Scholar 

  96. Bretan PN, Baldwin N, Novick AC, Majors A, Easley K, Ng T, et al. Pretransplant assessment of renal viability by phosphorus-31 magnetic resonance spectroscopy. Clinical experience in 40 recipient patients. Transplantation. 1989;48(1):48–53.

    Article  PubMed  Google Scholar 

  97. Möller HE, Gaupp A, Vestring T, Dietl KH, Vermathen P, Buchholz B, et al. Kinetics of metabolism in human kidney transplants measured by dynamic 31P NMR spectroscopy. Z Für Naturforschung C J Biosci. 1995;50(5–6):439–50.

    Google Scholar 

  98. Kurková D, Herynek V, Gintelová J, Táborský P, Hájek M. Potential of 31P magnetic resonance spectroscopy in monitoring the viability of human renal grafts stored in Euro-Collins perfusion solution. Physiol Res Acad Sci Bohemoslov. 1995;44(5):327–32.

    Google Scholar 

  99. Hené RJ, van der Grond J, Boer WH, Mali WP, Koomans HA. Pre-transplantation assessment of renal viability with 31P magnetic resonance spectroscopy. Kidney Int. 1994;46(6):1694–9.

    Article  PubMed  Google Scholar 

  100. Pomer S, Hull WE, Röhl L. Assessment of renal viability for transplantation by high field 31P-NMR. Transplant Proc. 1988;20(5):899–901.

    CAS  PubMed  Google Scholar 

  101. Lazeyras F, Buhler L, Vallee J-P, Hergt M, Nastasi A, Ruttimann R, et al. Detection of ATP by “in line” 31P magnetic resonance spectroscopy during oxygenated hypothermic pulsatile perfusion of pigs’ kidneys. Magma N Y N. 2012;25(5):391–9.

    Article  CAS  Google Scholar 

  102. Seto K, Ikehira H, Obata T, Sakamoto K, Yamada K, Kashiwabara H, et al. Long-term assessment of posttransplant renal prognosis with 31 P magnetic resonance spectroscopy. Transplantation. 2001;72(4):627–30.

    Article  CAS  PubMed  Google Scholar 

  103. Giraud S, Favreau F, Chatauret N, Thuillier R, Maiga S, Hauet T. Contribution of large pig for renal ischemia-reperfusion and transplantation studies: the preclinical model. J Biomed Biotechnol. 2011;2011:1–14.

    Article  Google Scholar 

  104. Sachs DH. The pig as a potential xenograft donor. Vet Immunol Immunopathol. 1994;43(1–3):185–91.

    Article  CAS  PubMed  Google Scholar 

  105. Hannon JP, Bossone CA, Wade CE. Normal physiological values for conscious pigs used in biomedical research. Lab Anim Sci. 1990;40(3):293–8.

    CAS  PubMed  Google Scholar 

  106. Swindle MM. The development of swine models in drug discovery and development. Future Med Chem. 2012;4(14):1771–2.

    Article  CAS  PubMed  Google Scholar 

  107. Cockfield SM, Moore RB, Todd G, Solez K, Gourishankar S. The prognostic utility of deceased donor implantation biopsy in determining function and graft survival after kidney transplantation. Transplantation. 2010;89(5):559–66.

    Article  PubMed  Google Scholar 

  108. Jochmans I, Pirenne J. Graft quality assessment in kidney transplantation: not an exact science yet! Curr Opin Organ Transplant. 2011;16(2):174–9.

    Article  CAS  PubMed  Google Scholar 

  109. Louvar DW, Li N, Snyder J, Peng Y, Kasiske BL, Israni AK. “Nature versus nurture” study of deceased-donor pairs in kidney transplantation. J Am Soc Nephrol. 2009;20(6):1351–8.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Bohra R, Klepacki J, Klawitter J, Klawitter J, Thurman JM, Christians U. Proteomics and metabolomics in renal transplantation-quo vadis? Transpl Int. 2013;26(3):225–41.

    Article  CAS  PubMed  Google Scholar 

  111. Jochmans I, Moers C, Smits JM, Leuvenink HGD, Treckmann J, Paul A, et al. The prognostic value of renal resistance during hypothermic machine perfusion of deceased donor kidneys. Am J Transplant. 2011;11(10):2214–20.

    Article  CAS  PubMed  Google Scholar 

  112. Sonnenday CJ, Cooper M, Kraus E, Gage F, Handley C, Montgomery RA. The hazards of basing acceptance of cadaveric renal allografts on pulsatile perfusion parameters alone. Transplantation. 2003;75(12):2029–33.

    Article  PubMed  Google Scholar 

  113. Mozes MF, Skolek RB, Korf BC. Use of perfusion parameters in predicting outcomes of machine-preserved kidneys. Transplant Proc. 2005;37(1):350–1.

    Article  CAS  PubMed  Google Scholar 

  114. Guarrera JV, Goldstein MJ, Samstein B, Henry S, Reverte C, Arrington B, et al. “When good kidneys pump badly”: outcomes of deceased donor renal allografts with poor pulsatile perfusion characteristics. Transpl Int. 2010;23(4):444–6.

    Article  PubMed  Google Scholar 

  115. Bhangoo RS, Hall IE, Reese PP, Parikh CR. Deceased-donor kidney perfusate and urine biomarkers for kidney allograft outcomes: a systematic review. Nephrol Dial Transplant. 2012;27(8):3305–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Dorman D, Roberts J. Nuclear magnetic resonance spectroscopy: 13C spectra of some common nucleotides. Proc Natl Acad Sci U S A. 1970;65:19–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Peltz M, He T-T, Adams GA, Chao RY, Meyer DM, Jessen ME. Characterizing lung metabolism with carbon-13 magnetic resonance spectroscopy in a small-animal model: evidence of gluconeogenesis during hypothermic storage. Transplantation. 2005;80(3):417–20.

    Article  CAS  PubMed  Google Scholar 

  118. Gilbert NF, Meyer PE, Tauriainen MP, Chao RY, Patel JB, Malloy CR, et al. Effects of hypothermia on myocardial substrate selection. Ann Thorac Surg. 2002;74(4):1208–12.

    Article  PubMed  Google Scholar 

  119. Peltz M, He T-T, Adams GA, Koshy S, Burgess SC, Chao RY, et al. Perfusion preservation maintains myocardial ATP levels and reduces apoptosis in an ex vivo rat heart transplantation model. Surgery. 2005;138(4):795–805.

    Article  PubMed  Google Scholar 

  120. Rosenbaum DH, Peltz M, DiMaio JM, Meyer DM, Wait MA, Merritt ME, et al. Perfusion preservation versus static preservation for cardiac transplantation: effects on myocardial function and metabolism. J Heart Lung Transplant. 2008;27(1):93–9.

    Article  PubMed  Google Scholar 

  121. Fan TWM, Lane AN, Higashi RM, Farag MA, Gao H, Bousamra M, et al. Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer. 2009;8:41.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Nath J, Smith T, Hollis A, Ebbs S, Canbilen S, Tennant D, et al. 13C glucose labelling studies using 2D NMR is a useful tool for determining ex vivo whole organ metabolism during hypothermic machine perfusion of kidneys. Transpl Res. 2016;5(7).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NERC Metabolomics Facility, School of Biosciences, University of Birmingham, Birmingham, UK

    Clement Heude

  2. Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, UK

    Clement Heude

  3. Institute of Metabolism and Systems Research (IMSR), College of Medical and Dental Sciences, University of Birmingham, Birmingham, B152TT, UK

    Jay Nath, John Bosco Carrigan & Christian Ludwig

Authors
  1. Clement Heude
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jay Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Bosco Carrigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Ludwig
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Campinas (UNICAMP) Institute of Chemistry Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Campinas, Brazil

    Alessandra Sussulini

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Heude, C., Nath, J., Carrigan, J.B., Ludwig, C. (2017). Nuclear Magnetic Resonance Strategies for Metabolic Analysis. In: Sussulini, A. (eds) Metabolomics: From Fundamentals to Clinical Applications. Advances in Experimental Medicine and Biology(), vol 965. Springer, Cham. https://doi.org/10.1007/978-3-319-47656-8_3

Download citation

Publish with us

Policies and ethics

Search

Navigation