Kinetic flux profiling for quantitation of cellular metabolic fluxes

241 Carl Icahn Laboratory, Department of Chemistry and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
Nature Protocol (Impact Factor: 9.67). 02/2008; 3(8):1328-40. DOI: 10.1038/nprot.2008.131
Source: PubMed


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 (t(1/2)<or=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 approximately 15 metabolites can be obtained over 3 d of work. Applications to adherent mammalian cells are also discussed.

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    • "Relative amounts of metabolites were calculated by summing up all isotopomers of a given metabolite and normalized to the internal standard and cell number. Natural occurring 13 C was accounted for as described by Yuan et al. (2008). "
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    • "The resulting liquids were lyophilized and then resuspended in 300 ml 40:40:20 acetonitrile/methanol/water with 0.1 M formic acid for MS analysis. Two LC-MS/MS analyses, one in each of positive and negative ion modes, were performed for each sample, and relative metabolite levels and fluxes were analyzed as previously described (Rabinowitz and Kimball, 2007; Bennett et al., 2008; Yuan et al., 2008). "
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    • "Methods available to model fluxes from labeling data are outlined in Supplemental Methods 2 online. To model fluxes from our data set, we chose to use kinetic flux profiling (KFP) (Yuan et al., 2006, 2008). While some of the existing approaches allow usage of different fragments and, thus, potentially more informative data sets, KFP facilitates the computation of individual reaction fluxes in complex networks from incomplete data sets. "
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