Advertisement

Journal of Biomolecular NMR

, Volume 49, Issue 3–4, pp 231–243 | Cite as

Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks

  • Patrizia Bernini
  • Ivano Bertini
  • Claudio Luchinat
  • Paola Nincheri
  • Samuele Staderini
  • Paola Turano
Article

Abstract

1H NMR metabolic profiling of urine, serum and plasma has been used to monitor the impact of the pre-analytical steps on the sample quality and stability in order to propose standard operating procedures (SOPs) for deposition in biobanks. We analyzed the quality of serum and plasma samples as a function of the elapsed time (t = 0−4 h) between blood collection and processing and of the time from processing to freezing (up to 24 h). The stability of the urine metabolic profile over time (up to 24 h) at various storage temperatures was monitored as a function of the different pre-analytical treatments like pre-storage centrifugation, filtration, and addition of the bacteriostatic preservative sodium azide. Appreciable changes in the profiles, reflecting changes in the concentration of a number of metabolites, were detected and discussed in terms of chemical and enzymatic reactions for both blood and urine samples. Appropriate procedures for blood derivatives collection and urine preservation/storage that allow maintaining as much as possible the original metabolic profile of the fresh samples emerge, and are proposed as SOPs for biobanking.

Keywords

Metabolomics NMR spectroscopy Serum Plasma Urine Biobanks 

Notes

Acknowledgments

Research funded by the European Union Seventh Framework Programme [FP7/2007-2013] under grant agreement no 222916. Manfred Spraul and Hartmut Schäfer (Bruker BioSpin) are acknowledged for many discussions over the years. We thank Monica Biondi for her assistance in blood collection.

Supplementary material

10858_2011_9489_MOESM1_ESM.pdf (1017 kb)
Supplementary material 1 (PDF 1016 kb)

References

  1. Akira K, Masu S, Imachi M, Mitome H, Hashimoto M, Hashimoto T (2008) 1H NMR-based metabonomic analysis of urine from young spontaneously hypertensive rats. J Pharm Biomed Anal 46:550–556CrossRefGoogle Scholar
  2. Assfalg M, Bertini I, Colangiuli D, Luchinat C, Schäfer H, Schütz B, Spraul M (2008) Evidence of different metabolic phenotypes in humans. Proc Natl Acad Sci USA 105:1420–1424CrossRefADSGoogle Scholar
  3. Barton RH, Nicholson JK, Elliott P, Holmes E (2008) High-throughput 1H NMR-based metabolomic analysis of human serum and urine for large-scale epidemiological studies: validation study. Int J Epidemiol 37:i31–i40CrossRefGoogle Scholar
  4. Bartsch T, Alfke K, Wolff S, Rohr A, Jansen O, Deuschl G (2008) Focal MR spectroscopy of hippocampal CA-1 lesions in transient global amnesia. Neurology 70:1030–1035CrossRefGoogle Scholar
  5. Baynes JW, Dominiczak MH (2010) Medical biochemistry. Elsevier, AmsterdamGoogle Scholar
  6. Beckonert O, Keun HC, Ebbels TMD, 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. Nature 11:2692–2703Google Scholar
  7. Bernini P, Bertini I, Luchinat C, Nepi S, Saccenti E, Schäfer H, Schütz B, Spraul M, Tenori L (2009) Individual human phenotypes in metabolic space and time. J Proteome Res 8:4264–4271CrossRefGoogle Scholar
  8. Bertini I, Calabrò A, De Carli V, Luchinat C, Nepi S, Porfirio B, Renzi D, Saccenti E, Tenori L (2009) Metabonomic signature of celiac disease. J Proteome Res 8:170–177CrossRefGoogle Scholar
  9. Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethell HW, Clarke S, Schofield PM, McKilligin E, Mosedale DE, Grainger DJ (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 8:1439–1444CrossRefGoogle Scholar
  10. Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A (2007) Metabolomics: available results, current research projects in breast cancer, and future applications. J Clin Onc 25:2840–2846CrossRefGoogle Scholar
  11. Coen M, O’Sullivan M, Bubb WA, Kuchel PW, Sorrell T (2005) Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin Infect Dis 41:1582–1590CrossRefGoogle Scholar
  12. Constantinou MA, Theocharis SE, Mikros E (2007) Application of metabonomics on an experimental model of fibrosis and cirrhosis induced by thioacetamide in rats. Toxicol Appl Pharmacol 218:11–19CrossRefGoogle Scholar
  13. Coolen SA, Daykin CA, van Duynhoven JP, van Dorsten FA, Wulfert F, Mathot J, Scheltinga MR, Stroosma O, Vader H, Wijnen MH (2008) Measurement of ischaemia-reperfusion in patients with intermittent claudication using NMR-based metabonomics. NMR Biomed 21:686–695CrossRefGoogle Scholar
  14. Dunn WB, Broadhurst D, Ellis DI, Brown M, Halsall A, O’Hagan S, Spasic I, Tseng A, Douglas BK (2008) A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int J Epidemiol 37:i23–i30CrossRefGoogle Scholar
  15. Fearnside JF, Dumas ME, Rothwell AR, Wilder SP, Cloarec O, Toye A, Blancher C, Holmes E, Tatoud R, Barton RH, Scott J, Nicholson JK, Gauguier D (2008) Phylometabonomic patterns of adaptation to high fat diet feeding in inbred mice. PLoS ONE 3:e1668CrossRefADSGoogle Scholar
  16. Fiehn O (2002) Metabolomics–the link between genotypes and phenotypes. Plant Mol Biol 48:155–171CrossRefGoogle Scholar
  17. Gao XX, Ge HM, Zheng WF, Tan RX (2008) NMR-based metabonomics for detection of Helicobacter pylori infection in gerbils: which is more descriptive. Helicobacter 13:103–111CrossRefGoogle Scholar
  18. Garber K (2004) Energy boost: the Warburg effect returns in a new theory of cancer. J Natl Cancer Inst 96:1805–1806CrossRefGoogle Scholar
  19. German JB, Hammock BD, Watkins SM (2005) Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 1:3–9CrossRefGoogle Scholar
  20. Griffin JL, Scott J, Nicholson JK (2007) The influence of pharmacogenetics on fatty liver disease in the wistar and kyoto rats: a combined transcriptomic and metabonomic study. J Proteome Res 6:54–61CrossRefGoogle Scholar
  21. Holmes E, Loo RL, Stamler J, Bictash M, Yap IKS, Chan Q, Ebbels T, Delorio M, Brown IJ, Veselkov KA, Daviglus ML, Kesteloot H, Ueshima H, Zhao L, Nicholson JK, Elliott P (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453:396–401CrossRefADSGoogle Scholar
  22. Jackson C, Best N, Elliott P (2008) The UK Biobank sample handling and storage validation studies. Int J Epidemiol 37:i2–i6CrossRefGoogle Scholar
  23. Jung M, Pergande M (1983) Particulate and free enzyme activity in urine as a result of the shedding of brush-border membranes from kidney. Clin Chem 29(2):392–393Google Scholar
  24. Koukoulaki M, Donovan MO, Pursglove S, Alexopoulou D, Hadjiconstantinou V, Drakopoulos S (2008) Prospective study of urine cytology screening for BK polyomavirus replication in renal transplant recipients. Cytopathology 19:385–388CrossRefGoogle Scholar
  25. Lindon JC, Holmes E, Nicholson JK (2004) Toxicological applications of magnetic resonance. Prog NMR Spectrosc 45:109–143CrossRefGoogle Scholar
  26. Madsen R, Lundstedt T, Trygg J (2010) Chemometrics in metabolomics—a review in human disease diagnosis. Anal Chim Acta 659(1–2):23–33CrossRefGoogle Scholar
  27. Makinen VP, Soininen P, Forsblom C, Parkkonen M, Ingman P, Kaski K, Groop PH, Ala-Korpela M (2008) 1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death. Mol Syst Biol 4:167CrossRefGoogle Scholar
  28. Marchesi JR, Holmes E, Khan F, Kochhar S, Scanlan P, Shanahan F, Wilson ID, Wang Y (2007) Rapid and noninvasive metabonomic characterization of inflammatory bowel disease. J Proteome Res 6:546–551CrossRefGoogle Scholar
  29. Nicholson JK, Lindon JC (2008) Metabonomics. Nature 455:1054–1056CrossRefADSGoogle Scholar
  30. Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189CrossRefGoogle Scholar
  31. Oakman C, Tenori L, Biganzoli L, Santarpia L, Cappadodona S, Luchinat C, Di Leo A (2010) Uncovering the metabolomic fingerprint of breast cancer. Int J Biochem Cell Biol (in press)Google Scholar
  32. Peakman TC, Elliott P (2008) UK Biobank Pilot Study: stability of haematological and clinical chemistry analytes. Int J Epidemiol 37:i16–i22CrossRefGoogle Scholar
  33. Qiu YP, Cai GX, Su MM, Chen T, Zheng X, Xu Y, Ni Y, Zhao A, Xu LX, Cai S, Jia W (2009) Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS. J Proteome Res 8:4844–4850CrossRefGoogle Scholar
  34. Saude EJ, Sykes BD (2007) Urine stability for metabolomic studies: effects of preparation and storage. Metabolomics 3:19–27CrossRefGoogle Scholar
  35. Schnackenberg LK, Sun J, Espandiari P, Holland RD, Hanig J, Beger RD (2007) Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis. BMC Bioinf 8(Suppl 7):S3CrossRefGoogle Scholar
  36. Shaw RJ (2006) Glucose metabolism and cancer. Curr Opin Cell Biol 18:598–608CrossRefGoogle Scholar
  37. Silwood CJL, Grootveld M, Lynch E (2002) 1H NMR investigations of the molecular nature of low-molecular-mass calcium ions in biofluids. J Biol Inorg Chem 7:46–57CrossRefGoogle Scholar
  38. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457:910–914CrossRefADSGoogle Scholar
  39. Suna T, Salminen A, Soininen P, Laatikainen R, Ingman P, Makela S, Savolainen MJ, Hannuksela ML, Jauhiainen M, Taskinen M, Kaski K, Ala-Korpela M (2006) 1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organising-map. NMR Biomed 20:658–672CrossRefGoogle Scholar
  40. Teichert F, Verschoyle RD, Greaves P, Edwards RE, Teahan O, Jones DJ, Wilson ID, Farmer PB, Steward WP, Gant TW, Gescher AJ, Keun HC (2008) Metabolic profiling of transgenic adenocarcinoma of mouse prostate (TRAMP) Tissue by (1)H-NMR analysis: evidence for unusual phospholipid metabolism. Prostate 68:1035–1047CrossRefGoogle Scholar
  41. Warburg O (1956) On the origin of cancer cells. Science 123:309–314CrossRefADSGoogle Scholar
  42. Weckwerth W (2007) Metabolomics methods and protocols. Humana Press Inc., New YorkGoogle Scholar
  43. Yuille M, Illig T, Hveem K, Schmitz G, Hansen J, Neumaier M, Tybring G, Wichmann E, Ollier B (2010) Laboratory management of samples in biobanks: European consensus expert group report. Biopreserv Biobank 8(1):65–69CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Patrizia Bernini
    • 1
    • 3
  • Ivano Bertini
    • 1
    • 2
  • Claudio Luchinat
    • 1
    • 2
  • Paola Nincheri
    • 3
  • Samuele Staderini
    • 3
  • Paola Turano
    • 1
    • 2
  1. 1.Magnetic Resonance Center (CERM)University of FlorenceSesto FiorentinoItaly
  2. 2.Department of ChemistryUniversity of FlorenceSesto FiorentinoItaly
  3. 3.FiorGen FoundationSesto FiorentinoItaly

Personalised recommendations