Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

1H magnetic resonance spectroscopy of 2H-to-1H exchange quantifies the dynamics of cellular metabolism in vivo

Abstract

Quantitative mapping of the in vivo dynamics of cellular metabolism via non-invasive imaging contributes to our understanding of the initiation and progression of diseases associated with dysregulated metabolic processes. Current methods for imaging cellular metabolism are limited by low sensitivities, costs or the use of specialized hardware. Here, we introduce a method that captures the turnover of cellular metabolites by quantifying signal reductions in proton magnetic resonance spectroscopy (MRS) resulting from the replacement of 1H with 2H. The method, which we termed quantitative exchanged-label turnover MRS, only requires deuterium-labelled glucose and standard magnetic resonance imaging scanners, and with a single acquisition provides steady-state information and metabolic rates for several metabolites. We used the method to monitor glutamate, glutamine, γ-aminobutyric acid and lactate in the brains of unaffected and glioma-bearing rats following the administration of 2H2-labelled glucose and 2H3-labelled acetate. Quantitative exchanged-label turnover MRS should broaden the applications of routine 1H MRS.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Fundamentals of qMRS.
Fig. 2: Simultaneous qMRS and DMRS acquisition in normal rat brain.
Fig. 3: Comparison of qMRS and DMRS metabolite quantification.
Fig. 4: Kinetics of deuterium labelling of neural metabolites.
Fig. 5: Detection of glycolysis in rat glioblastoma.
Fig. 6: Metabolic imaging of neural metabolism.

Data availability

The main data supporting the results of this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too big to be shared publicly, but they are available for research purposes from the corresponding author upon reasonable request.

References

  1. 1.

    DeBerardinis, R. J. & Thompson, C. B. Cellular metabolism and disease: what do metabolic outliers teach us? Cell 148, 1132–1144 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Fuss, T. L. & Cheng, L. L. Metabolic imaging in humans. Top. Magn. Reson. Imaging 25, 223–235 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Kelloff, G. J. et al. Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clin. Cancer Res. 11, 2785–2808 (2005).

    Article  CAS  Google Scholar 

  5. 5.

    Duara, R. et al. Positron emission tomography in Alzheimer’s disease. Neurology 36, 879–887 (1986).

    Article  CAS  Google Scholar 

  6. 6.

    Bergquist, P. J. et al. Cardiac applications of PET-MR. Curr. Cardiol. Rep. 19, 42 (2017).

    Article  Google Scholar 

  7. 7.

    La Fougere, C., Suchorska, B., Bartenstein, P., Kreth, F. W. & Tonn, J. C. Molecular imaging of gliomas with PET: opportunities and limitations. Neuro. Oncol. 13, 806–819 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Morris, P. G. Nuclear Magnetic Resonance Imaging in Medicine and Biology (Clarendon Press, 1986).

  9. 9.

    Van Zijl, P. C. & Yadav, N. N. Chemical exchange saturation transfer (CEST): what is in a name and what isn’t? Magn. Reson. Med. 65, 927–948 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cai, K. et al. Magnetic resonance imaging of glutamate. Nat. Med. 18, 302–306 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Gujar, S. K., Maheshwari, S., Björkman-Burtscher, I. & Sundgren, P. C. Magnetic resonance spectroscopy. J. Neuroophthalmol. 25, 217–226 (2005).

    Article  Google Scholar 

  12. 12.

    Beckmann, N., Turkalj, I., Seelig, J. & Keller, U. Carbon-13 NMR for the assessment of human brain glucose metabolism in vivo. Biochemistry 30, 6362–6366 (1991).

    Article  CAS  Google Scholar 

  13. 13.

    Shulman, R. G. & Rothman, D. L. 13C NMR of intermediary metabolism: implications for systemic physiology. Annu. Rev. Physiol. 63, 15–48 (2001).

    Article  CAS  Google Scholar 

  14. 14.

    De Graaf, R. A., Rothman, D. L. & Behar, K. L. State of the art direct 13C and indirect 1H‐[13C] NMR spectroscopy in vivo. A practical guide. NMR Biomed. 24, 958–972 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Merritt, M. E. et al. Hyperpolarized 13C allows a direct measure of flux through a single enzyme-catalyzed step by NMR. Proc. Natl Acad. Sci. USA 104, 19773–19777 (2007).

    Article  Google Scholar 

  16. 16.

    Ross, B. D., Bhattacharya, P., Wagner, S., Tran, T. & Sailasuta, N. Hyperpolarized MR imaging: neurologic applications of hyperpolarized metabolism. Am. J. Neuroradiol. 31, 24–33 (2010).

    Article  CAS  Google Scholar 

  17. 17.

    Lu, M., Zhu, X. H., Zhang, Y., Mateescu, G. & Chen, W. Quantitative assessment of brain glucose metabolic rates using in vivo deuterium magnetic resonance spectroscopy. J. Cereb. Blood Flow. Metab. 37, 3518–3530 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    De Feyter, H. M. et al. Deuterium metabolic imaging (DMI) for MRI-based 3D mapping of metabolism in vivo. Sci. Adv. 4, eaat7314 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Zhang, L. et al. Spectral tracing of deuterium for imaging glucose metabolism. Nat. Biomed. Eng. 3, 402–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Brindle, K. M. Imaging metabolism with hyperpolarized 13C-labelled cell substrates. J. Am. Chem. Soc. 20, 6418–6427 (2015).

    Article  CAS  Google Scholar 

  21. 21.

    De Graaf, R. A., Mason, G. F., Patel, A. B., Behar, K. L. & Rothman, D. L. In vivo 1H-[13C]-NMR spectroscopy of cerebral metabolism. NMR Biomed. 16, 339–357 (2003).

    Article  CAS  Google Scholar 

  22. 22.

    Provencher, S. W. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 30, 672–679 (1993).

    Article  CAS  Google Scholar 

  23. 23.

    Naressi, A., Couturier, C., Castang, I., de Beer, R. & Graveron-Demilly, D. Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals. Comput. Biol. Med. 31, 269–286 (2001).

    Article  CAS  Google Scholar 

  24. 24.

    Van Eijsden, P., Behar, K. L., Mason, G. F., Braun, K. P. & De Graaf, R. A. In vivo neurochemical profiling of rat brain by 1H‐[13C] NMR spectroscopy: cerebral energetics and glutamatergic/GABAergic neurotransmission. J. Neurochem. 112, 24–33 (2010).

    Article  CAS  Google Scholar 

  25. 25.

    Baslow, M. H. N-acetylaspartate in the vertebrate brain: metabolism and function. Neurochem. Res. 28, 941–953 (2003).

    Article  CAS  Google Scholar 

  26. 26.

    Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Wilson, M. et al. Methodological consensus on clinical proton MRS of the brain: review and recommendations. Magn. Reson. Med. 82, 527–550 (2019).

    Article  Google Scholar 

  28. 28.

    Donoho, D. L. De-noising by soft-thresholding. IEEE T. Inform. Theory 41, 613–627 (1995).

    Article  Google Scholar 

  29. 29.

    Johnstone, I. M. & Silverman, B. W. Needles and straw in haystacks: empirical Bayes estimates of possibly sparse sequences. Ann. Stat. 32, 1594–1649 (2004).

    Article  Google Scholar 

  30. 30.

    Brender, J. R. et al. Dynamic imaging of glucose and lactate metabolism by 13C-MRS without hyperpolarization. Sci. Rep. 9, 3410 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Mason, G. F. et al. Simultaneous determination of the rates of the TCA cycle, glucose utilization, α-ketoglutarate/glutamate exchange, and glutamine synthesis in human brain by NMR. J. Cereb. Blood Flow. Metab. 15, 12–25 (1995).

    Article  CAS  Google Scholar 

  32. 32.

    Bak, L. K., Schousboe, A. & Waagepetersen, H. S. The glutamate/GABA-glutamine cycle: aspects of transport, neurotransmitter homeostasis and ammonia transfer. J. Neurochem. 98, 641–653 (2006).

    Article  CAS  Google Scholar 

  33. 33.

    Wong, C. G. T., Bottiglieri, T. & Snead, O. C. GABA, γ-hydroxybutyric acid, and neurological disease. Ann. Neurol. 54, S3–S12 (2003).

    Article  CAS  Google Scholar 

  34. 34.

    Rothman, D. et al. 13C and 1H-[13C] MRS studies of neuroenergetics and neurotransmitter cycling, applications to neurological and psychiatric disease and brain cancer. NMR Biomed. 32, e4172 (2019).

    Article  Google Scholar 

  35. 35.

    Henry, P. G. et al. In vivo 13C NMR spectroscopy and metabolic modeling in the brain: a practical perspective. Magn. Reson. Imaging 24, 527–539 (2006).

    Article  CAS  Google Scholar 

  36. 36.

    Tiwari, V., Ambadipudi, S. & Patel, A. B. Glutamatergic and GABAergic TCA cycle and neurotransmitter cycling fluxes in different regions of mouse brain. J. Cereb. Blood Flow. Metab. 33, 1523–1531 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Duarte, J. M. & Gruetter, R. Glutamatergic and GABAergic energy metabolism measured in the rat brain by 13C NMR spectroscopy at 14.1 T. J. Neurochem. 126, 579–590 (2013).

    Article  CAS  Google Scholar 

  38. 38.

    Kreis, R. Issues of spectral quality in clinical 1H‐magnetic resonance spectroscopy and a gallery of artifacts. NMR Biomed. 17, 361–381 (2004).

    Article  CAS  Google Scholar 

  39. 39.

    Mullins, P. G., Chen, H., Xu, J., Caprihan, A. & Gasparovic, C. Comparative reliability of proton spectroscopy techniques designed to improve detection of J‐coupled metabolites. Magn. Reson. Med. 60, 964–969 (2008).

    Article  CAS  Google Scholar 

  40. 40.

    Trabesinger, A. H., Weber, O. M., Duc, C. O. & Boesiger, P. Detection of glutathione in the human brain in vivo by means of double quantum coherence filtering. Magn. Reson. Med. 42, 283–289 (1999).

    Article  CAS  Google Scholar 

  41. 41.

    Ko, L., Koestner, A. & Wechsler, W. Morphological characterization of nitrosourea-induced glioma cell lines and clones. Acta Neuropathol. 51, 23–31 (1980).

    Article  CAS  Google Scholar 

  42. 42.

    Kim, S., Pickup, S., Hsu, O. & Poptani, H. Diffusion tensor MRI in rat models of invasive and well-demarcated brain tumors. NMR Biomed. 21, 208–216 (2008).

    Article  Google Scholar 

  43. 43.

    Fitzpatrick, S. M., Hetherington, H. P., Behar, K. L. & Shulman, R. G. The flux from glucose to glutamate in the rat brain in vivo as determined by 1H-observed, 13C-edited NMR spectroscopy. J. Cereb. Blood Flow. Metab. 10, 170–179 (1990).

    Article  CAS  Google Scholar 

  44. 44.

    Patlak, C. S. & Pettigrew, K. D. A method to obtain infusion schedules for prescribed blood concentration time courses. J. Appl. Physiol. 40, 458–463 (1976).

    Article  CAS  Google Scholar 

  45. 45.

    Bottomley, P. A. Spatial localization in NMR spectroscopy in vivo. Ann. NY Acad. Sci. 508, 333–348 (1987).

    Article  CAS  Google Scholar 

  46. 46.

    Tkáč, I., Starčuk, Z., Choi, I. Y. & Gruetter, R. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time. Magn. Reson. Med. 41, 649–656 (1999).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank D. Reddy for help with the animal model preparation, M. Elliot for expertise in MRS acquisition and processing and S. Pickup and W. Liu for technical assistance with using the 9.4 T horizontal bore animal magnetic resonance scanner. The authors also thank G. Mizsei for constructing the deuterium coil, H. Poptani for providing insight into CSI acquisition and processing on the 9.4 T preclinical scanner and R. de Graaf for providing guidance during interfacing of the deuterium coil to the scanner console. This work was carried out at a US National Institutes of Health-supported resource, with funding from the NIBIB (under grant no. P41 EB015893), the National Institute of Neurological Disorders and Stroke (award no. R01NS087516) and training grant no. T32EB020087-02.

Author information

Affiliations

Authors

Contributions

R.R. conceived of and designed the study and contributed to data analyses, manuscript writing and editing. L.J.R. and P.B. contributed to the conception and design of the study, performed the experiments, analysed the data and wrote the manuscript. N.E.W. provided insights on the data analyses and contributed to spectral denoising of the datasets. M.D.S., J.A.D. and M.H. contributed to manuscript writing and editing.

Corresponding author

Correspondence to Ravinder Reddy.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and references.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rich, L.J., Bagga, P., Wilson, N.E. et al. 1H magnetic resonance spectroscopy of 2H-to-1H exchange quantifies the dynamics of cellular metabolism in vivo. Nat Biomed Eng 4, 335–342 (2020). https://doi.org/10.1038/s41551-019-0499-8

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing