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.

  • Protocol
  • Published:

Kinetic flux profiling for quantitation of cellular metabolic fluxes

Abstract

This protocol enables quantitation of metabolic fluxes in cultured cells. Measurements are based on the kinetics of cellular incorporation of stable isotope from nutrient into downstream metabolites. At multiple time points, after cells are rapidly switched from unlabeled to isotope-labeled nutrient, metabolism is quenched, metabolites are extracted and the extract is analyzed by chromatography–mass spectrometry. Resulting plots of unlabeled compound versus time follow variants of exponential decay, with the flux equal to the decay rate multiplied by the intracellular metabolite concentration. Because labeling is typically fast (t1/2≤5 min for central metabolites in Escherichia coli), variations on this approach can effectively probe dynamically changing metabolic fluxes. This protocol is exemplified using E. coli and nitrogen labeling, for which quantitative flux data for 15 metabolites can be obtained over 3 d of work. Applications to adherent mammalian cells are also discussed.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Illustration of the basic concept of KFP.
Figure 2
Figure 3
Figure 4: Sample chromatograms and results for KFP.
Figure 5: Example pathway for derivation of equations and quantitative treatment of KFP data.
Figure 6: Central nitrogen-assimilation pathways and selected nitrogen-consuming pathways in E. coli.
Figure 7: Selected nitrogen KFP results for E. coli.
Figure 8: Sample result for differential KFP.

Similar content being viewed by others

References

  1. Yuan, J., Fowler, W.U., Kimball, E., Lu, W. & Rabinowitz, J.D. Kinetic flux profiling of nitrogen assimilation in Escherichia coli. Nat. Chem. Biol. 2, 529–530 (2006).

    Article  CAS  Google Scholar 

  2. Yuan, J. & Rabinowitz, J.D. Differentiating metabolites formed from de novo synthesis versus macromolecule decomposition. J. Am. Chem. Soc. 129, 9294–9295 (2007).

    Article  CAS  Google Scholar 

  3. Brauer, M.J. et al. Conservation of the metabolomic response to starvation across two divergent microbes. Proc. Natl. Acad. Sci. USA 103, 19302–19307 (2006).

    Article  CAS  Google Scholar 

  4. Kemp, G.J., Meyerspeer, M. & Moser, E. Absolute quantification of phosphorus metabolite concentrations in human muscle in vivo by 31P MRS: a quantitative review. NMR Biomed. 20, 555–565 (2007).

    Article  CAS  Google Scholar 

  5. Cudalbu, C., Cavassila, S., Rabeson, H., van Ormondt, D. & Graveron-Demilly, D. Influence of measured and simulated basis sets on metabolite concentration estimates. NMR Biomed. 21, 627–636 (2008).

    Article  CAS  Google Scholar 

  6. Wu, L. et al. Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal. Biochem. 336, 164–171 (2005).

    Article  CAS  Google Scholar 

  7. Bennett, B.D., Yuan, J., Kimball, E.H. & Rabinowitz, J.D. Absolute quantitation of intracellular metabolite concentrations by an isotope ratio-based approach. Nat. Protoc. 3, 1299–1311 (2008).

    Article  CAS  Google Scholar 

  8. Ikeda, T.P., Shauger, A.E. & Kustu, S. Salmonella typhimurium apparently perceives external nitrogen limitation as internal glutamine limitation. J. Mol. Biol. 259, 589–607 (1996).

    Article  CAS  Google Scholar 

  9. Schaub, J., Schiesling, C., Reuss, M. & Dauner, M. Integrated sampling procedure for metabolome analysis. Biotechnol. Prog. 22, 1434–1442 (2006).

    Article  CAS  Google Scholar 

  10. Villas-Boas, S.G., Hojer-Pedersen, J., Akesson, M., Smedsgaard, J. & Nielsen, J. Global metabolite analysis of yeast: evaluation of sample preparation methods. Yeast 22, 1155–1169 (2005).

    Article  CAS  Google Scholar 

  11. Visser, D. et al. Rapid sampling for analysis of in vivo kinetics using the BioScope: a system for continuous-pulse experiments. Biotechnol. Bioeng. 79, 674–681 (2002).

    Article  CAS  Google Scholar 

  12. Rabinowitz, J.D. Cellular metabolomics of Escherichia coli. Expert Rev. Proteomics 4, 187–198 (2007).

    Article  CAS  Google Scholar 

  13. Rabinowitz, J.D. & Kimball, E. Acidic acetonitrile for cellular metabolome extraction from Escherichia coli. Anal. Chem. 79, 6167–6173 (2007).

    Article  CAS  Google Scholar 

  14. Shalwitz, R.A., Beth, T.J., MacLeod, A.M., Tucker, S.J. & Rolison, G.G. Use of 2H2O to study labeling in plasma glucose and hepatic glycogen during a hyperglycemic clamp. Am. J. Physiol. 266, E433–E437 (1994).

    PubMed  Google Scholar 

  15. Baranyai, J.M. & Blum, J.J. Quantitative-analysis of intermediary metabolism in rat hepatocytes incubated in the presence and absence of ethanol with a substrate mixture including ketoleucine. Biochem. J. 258, 121–140 (1989).

    Article  CAS  Google Scholar 

  16. Wright, B.E. & Reimers, J.M. Steady-state models of glucose-perturbed Dictyostelium discoideum. J. Biol. Chem. 263, 14906–14912 (1988).

    CAS  PubMed  Google Scholar 

  17. Rabkin, M. & Blum, J.J. Quantitative analysis of intermediary metabolism in hepatocytes incubated in the presence and absence of glucagon with a substrate mixture containing glucose, ribose, fructose, alanine and acetate. Biochem. J. 225, 761–786 (1985).

    Article  CAS  Google Scholar 

  18. Crawford, J.M. & Blum, J.J. Quantitative-analysis of flux along the gluconeogenic, glycolytic and pentose-phosphate pathways under reducing conditions in hepatocytes isolated from fed rats. Biochem. J. 212, 595–598 (1983).

    Article  Google Scholar 

  19. Kelly, P.J., Kelleher, J.K. & Wright, B.E. Tricarboxylic-acid cycle in dictyostelium-discoideum—metabolite concentrations, oxygen-uptake and C-14-labeled amino-acid labeling patterns. Biochem. J. 184, 581–588 (1979).

    Article  CAS  Google Scholar 

  20. Katz, J., Wals, P.A. & Rognstad, R. Glucose phosphorylation, glucose-6-phosphatase, and recycling in rat hepatocytes. J. Biol. Chem. 253, 4530–4536 (1978).

    CAS  PubMed  Google Scholar 

  21. Edwards, J.S., Covert, M. & Palsson, B. Metabolic modelling of microbes: the flux-balance approach. Environ. Microbiol. 4, 133–140 (2002).

    Article  Google Scholar 

  22. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).

    Article  Google Scholar 

  23. Edwards, J.S., Ibarra, R.U. & Palsson, B.O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19, 125–130 (2001).

    Article  CAS  Google Scholar 

  24. Ibarra, R.U., Edwards, J.S. & Palsson, B.O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002).

    Article  CAS  Google Scholar 

  25. Fong, S.S., Marciniak, J.Y. & Palsson, B.O. Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model. J. Bacteriol. 185, 6400–6408 (2003).

    Article  CAS  Google Scholar 

  26. Segre, D., Vitkup, D. & Church, G.M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 99, 15112–15117 (2002).

    Article  CAS  Google Scholar 

  27. Duarte, N.C., Herrgard, M.J. & Palsson, B.O. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14, 1298–1309 (2004).

    Article  CAS  Google Scholar 

  28. Duarte, N.C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA 104, 1777–1782 (2007).

    Article  CAS  Google Scholar 

  29. Fischer, E. & Sauer, U. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat. Genet. 37, 636–640 (2005).

    Article  CAS  Google Scholar 

  30. Fischer, E. & Sauer, U. Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS. Eur. J. Biochem. 270, 880–891 (2003).

    Article  CAS  Google Scholar 

  31. van Winden, W.A. et al. Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of (13)C-labeled primary metabolites. FEMS Yeast Res. 5, 559–568 (2005).

    Article  CAS  Google Scholar 

  32. Schmidt, K., Carlsen, M., Nielsen, J. & Villadsen, J. Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices. Biotechnol. Bioeng. 55, 831–840 (1997).

    Article  CAS  Google Scholar 

  33. Schmidt, K. et al. Quantification of intracellular metabolic fluxes from fractional enrichment and 13C-13C coupling constraints on the isotopomer distribution in labeled biomass components. Metab. Eng. 1, 166–179 (1999).

    Article  CAS  Google Scholar 

  34. Kimball, E. & Rabinowitz, J.D. Identifying decomposition products in extracts of cellular metabolites. Anal. Biochem. 358, 273–280 (2006).

    Article  CAS  Google Scholar 

  35. Bajad, S.U. et al. Separation and quantitation of water soluble cellular metabolites by hydrophilic interaction chromatography-tandem mass spectrometry. J. Chromatogr. A 1125, 76–88 (2006).

    Article  CAS  Google Scholar 

  36. Luo, B., Groenke, K., Takors, R., Wandrey, C. & Oldiges, M. Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography-mass spectrometry. J. Chromatogr. A 1147, 153–164 (2007).

    Article  CAS  Google Scholar 

  37. Werf, M.J., Overkamp, K.M., Muilwijk, B., Coulier, L. & Hankemeier, T. Microbial metabolomics: toward a platform with full metabolome coverage. Anal. Biochem. 370, 17–25 (2007).

    Article  Google Scholar 

  38. Lu, W. & Bennett, B.D. Analytical strategies for LC-MS-based targeted metabolomics. J. Chromatogr. B doi:10.1016/j.jchromb2008.04.031.

  39. Mashego, M.R. et al. MIRACLE: mass isotopomer ratio analysis of U-13C-labeled extracts. A new method for accurate quantification of changes in concentrations of intracellular metabolites. Biotechnol. Bioeng. 85, 620–628 (2004).

    Article  CAS  Google Scholar 

  40. Tempest, D.W., Meers, J.L. & Brown, C.M. Synthesis of glutamate in Aerobacter aerogenes by a hitherto unknown route. Biochem. J. 117, 405–407 (1970).

    Article  CAS  Google Scholar 

  41. Reitzer, L. Nitrogen assimilation and global regulation in Escherichia coli. Annu. Rev. Microbiol. 57, 155–176 (2003).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by the Beckman Foundation, NSF DDDAS grant CNS-0540181, American Heart Association grant 0635188N, NSF CAREER Award MCB-0643859, NIH grant AI078063, and NIH grant GM071508 for Center of Quantitative Biology at Princeton University. We thank Wenyun Lu, Elizabeth Kimball, Sunil Bajad and Joshua Munger for their contributions to the development of the protocols presented here, and David Botstein for suggesting the filter culture approach which played a pivotal role in the development of KFP.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joshua D Rabinowitz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yuan, J., Bennett, B. & Rabinowitz, J. Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nat Protoc 3, 1328–1340 (2008). https://doi.org/10.1038/nprot.2008.131

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2008.131

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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