Large-Scale Profiling of Cellular Metabolic Activities Using Deep 13C Labeling Medium

  • Nina Grankvist
  • Jeramie D. Watrous
  • Mohit Jain
  • Roland Nilsson
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)


The recently developed deep labeling method allows for large-scale profiling of metabolic activities in human cells or tissues using isotope tracing with a highly 13C enriched culture medium in combination with liquid chromatography–high resolution mass spectrometry. This method generates mass spectrometry data sets where endogenous cellular products can be identified, and active pathways can be determined from observed 13C mass isotopomers of the various metabolites measured. Here we describe in detail the experimental procedures for deep labeling experiments in cultured mammalian cells, including synthesis of the deep labeling medium, experimental considerations for cell culture, metabolite extractions and sample preparation, and liquid chromatography–mass spectrometry. We also outline a workflow for the downstream data analysis using publicly available software.

Key words

Cell culture Stable isotope tracing experiments Metabolism Metabolomics LC-HRMS 



This work was supported by grants from the Swedish Foundation for Strategic Research (FFL12-0220.006) and Karolinska Institutet to R.N., the National Institutes of Health (NIH) 1R01ES027595 and 1S10OD020025 to M.J., and NIH K01DK116917 to J.D.W.


  1. 1.
    Vander Heiden MG (2011) Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov 10:671–684CrossRefGoogle Scholar
  2. 2.
    Freitag J, Berod L, Kamradt T, Sparwasser T (2016) Immunometabolism and autoimmunity. Immunol Cell Biol 94:925–934CrossRefGoogle Scholar
  3. 3.
    Viant MR, Kurland IJ, Jones MR, Dunn WB (2017) How close are we to complete annotation of metabolomes? Curr Opin Chem Biol 36:64–69CrossRefGoogle Scholar
  4. 4.
    Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL et al (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336:1040–1044CrossRefGoogle Scholar
  5. 5.
    Shlomi T, Cabili MN, Herrgard MJ, Palsson BØ, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26:1003–1010CrossRefGoogle Scholar
  6. 6.
    Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8:e1002518CrossRefGoogle Scholar
  7. 7.
    Grankvist N, Watrous JD, Lehmann KA, Lyutvinskiy Y, Jain M, Nilsson R et al (2018) Profiling the metabolism of human cells by deep 13C labeling. Cell Chem Biol 25:1419–1427CrossRefGoogle Scholar
  8. 8.
    Moore GE, Gerner RE, Franklin H (1967) Culture of normal human leukocytes. JAMA 199:519–524CrossRefGoogle Scholar
  9. 9.
    Lyutvinskiy Y, Watrous JD, Jain M, Nilsson R (2017) A web service framework for interactive analysis of metabolomics data. Anal Chem 89:5713–5718CrossRefGoogle Scholar
  10. 10.
    Wein J, Goetz IE (1973) Asparaginase and glutaminase activities in culture media containing dialyzed fetal calf serum. In Vitro 9:186–193CrossRefGoogle Scholar
  11. 11.
    Katunuma N, Huzino A, Tomino I (1967) Organ specific control of glutamine metabolism. Adv Enzym Regul 5:55–58CrossRefGoogle Scholar
  12. 12.
    Kihara H, de la Flor SD (1968) Arginase in fetal calf serum. Exp Biol Med 129:303–304CrossRefGoogle Scholar
  13. 13.
    Jain M, Ngoy S, SA S, RA S, Rhee EP, Liao R et al (2014) A systematic survey of lipids across mouse tissues. Am J Physiol Endocrinol Metab 306:E854–E868CrossRefGoogle Scholar
  14. 14.
    C a S, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787CrossRefGoogle Scholar
  15. 15.
    Pluskal T, Castillo S, Villar-briones A, Oresic M (2010) MZmine 2 : modular framework for processing , visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11:395CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Nina Grankvist
    • 1
    • 2
    • 3
    • 4
    • 5
  • Jeramie D. Watrous
    • 4
    • 5
  • Mohit Jain
    • 4
    • 5
  • Roland Nilsson
    • 1
    • 2
    • 3
  1. 1.Cardiovascular Medicine Unit, Department of Medicine, SolnaKarolinska InstitutetStockholmSweden
  2. 2.Karolinska University HospitalStockholmSweden
  3. 3.Center for Molecular MedicineKarolinska InstitutetStockholmSweden
  4. 4.Department of MedicineUniversity of California San DiegoLa JollaUSA
  5. 5.Department of PharmacologyUniversity of California San DiegoLa JollaUSA

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