Assessing the Impact of the Nutrient Microenvironment on the Metabolism of Effector CD8+ T Cells

  • Juan Fernández-García
  • Sarah-Maria FendtEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1862)


Immune cell function is tightly regulated by cellular metabolism, which in turn is strongly linked to the nutrient availability in the microenvironment surrounding the cells. This link is critical for effector CD8+ T cells which, after activation, must migrate from nutrient-rich environments into nutrient-scarce regions such as the tumor microenvironment. Assessing how nutrient availability modulates the metabolism of effector CD8+ T cells is thus key for understanding how harsh environments may impair their proliferation and effector function. Here, we describe an approach to systematically study the impact of the nutrient microenvironment on the metabolism of effector CD8+ T cells, based on performing stable 13C isotope labeling measurements on in vitro-differentiated murine effector CD8+ T cells.

Key words

CD8+ T cells Immunometabolism 13C tracer analysis Nutrient microenvironment Custom media formulations 



JFG is supported by an FWO (Fonds voor Wetenschappelijk Onderzoek–Vlaanderen, Research Foundation–Flanders) Postdoctoral Fellowship. S-MF acknowledges funding from the European Research Council under the ERC Consolidator Grant Agreement no. 771486 – MetaRegulation, Marie Curie – CIG, FWO – Odysseus II, FWO – Research Grants/Projects, Eugène Yourassowsky Schenking, and KU Leuven – Methusalem Co-Funding. We would like to acknowledge for image elements used in Fig. 1 (Creative Commons license CC BY-NC-SA 4.0).


  1. 1.
    Parham P (2014) The immune system. Garland Science, New York, NYCrossRefGoogle Scholar
  2. 2.
    Pearce EJ, Pearce EL (2017) Driving immunity: all roads lead to metabolism. Nat Rev Immunol 18(2):81–82CrossRefGoogle Scholar
  3. 3.
    Loftus RM, Finlay DK (2016) Immunometabolism: cellular metabolism turns immune regulator. J Biol Chem 291(1):1–10CrossRefGoogle Scholar
  4. 4.
    O’Neill LAJ, Kishton RJ, Rathmell J (2016) A guide to immunometabolism for immunologists. Nat Rev Immunol 16(9):553–565CrossRefGoogle Scholar
  5. 5.
    Buck MD, Sowell RT, Kaech SM et al (2017) Metabolic instruction of immunity. Cell 169(4):570–586CrossRefGoogle Scholar
  6. 6.
    Wang R, Green DR (2012) Metabolic reprogramming and metabolic dependency in T cells. Immunol Rev 249(1):14–26CrossRefGoogle Scholar
  7. 7.
    Pearce EL, Poffenberger MC, Chang CH et al (2013) Fueling immunity: insights into metabolism and lymphocyte function. Science 342(6155):1242454CrossRefGoogle Scholar
  8. 8.
    Buck MD, O’Sullivan D, Pearce EL (2015) T cell metabolism drives immunity. J Exp Med 212(9):1345–1360CrossRefGoogle Scholar
  9. 9.
    Chang CH, Curtis JD, Maggi LB et al (2013) Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153(6):1239–1251CrossRefGoogle Scholar
  10. 10.
    Renner K, Singer K, Koehl GE et al (2017) Metabolic hallmarks of tumor and immune cells in the tumor microenvironment. Front Immunol 8(8):1–11Google Scholar
  11. 11.
    Lunt SY, Fendt S-M (2018) Metabolism – a cornerstone of cancer initiation, progression, immune evasion and treatment response. Curr Opin Syst Biol 8:67–72CrossRefGoogle Scholar
  12. 12.
    Cham CM, Driessens G, O’Keefe JP et al (2008) Glucose deprivation inhibits multiple key gene expression events and effector functions in CD8+ T cells. Eur J Immunol 38(9):2438–2450CrossRefGoogle Scholar
  13. 13.
    Chang CH, Qiu J, O’Sullivan D et al (2015) Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162(6):1229–1241CrossRefGoogle Scholar
  14. 14.
    Geiger R, Rieckmann JC, Wolf T et al (2016) L-arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167(3):829–842.e13CrossRefGoogle Scholar
  15. 15.
    Ma EH, Bantug G, Griss T et al (2017) Serine is an essential metabolite for effector T cell expansion. Cell Metab 25:1–13CrossRefGoogle Scholar
  16. 16.
    Fischer K, Hoffmann P, Voelkl S et al (2007) Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood 109(9):3812–3819CrossRefGoogle Scholar
  17. 17.
    Pilon-Thomas S, Kodumudi KN, El-Kenawi AE et al (2016) Neutralization of tumor acidity improves antitumor responses to immunotherapy. Cancer Res 76(6):1381–1390CrossRefGoogle Scholar
  18. 18.
    Kalderon B, Gopher A, Lapidot A (1987) A quantitative analysis of the metabolic pathways of hepatic glucose synthesis in vivo with 13C-labeled substrates. FEBS Lett 213(1):209–214CrossRefGoogle Scholar
  19. 19.
    Katz J, Lee WN, Wals PA et al (1989) Studies of glycogen synthesis and the krebs cycle by mass isotopomer analysis with [U-13C]glucose in rats. J Biol Chem 264(22):12994–13004PubMedGoogle Scholar
  20. 20.
    Mueller D, Heinzle E (2013) Stable isotope-assisted metabolomics to detect metabolic flux changes in mammalian cell cultures. Curr Opin Biotechnol 24(1):54–59CrossRefGoogle Scholar
  21. 21.
    Buescher JM, Antoniewicz MR, Boros LG et al (2015) A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr Opin Biotechnol 34:189–201CrossRefGoogle Scholar
  22. 22.
    Wittmann C, Heinzle E (1999) Mass spectrometry for metabolic flux analysis. Biotechnol Bioeng 62(6):739–750CrossRefGoogle Scholar
  23. 23.
    Sauer U (2006) Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2:62CrossRefGoogle Scholar
  24. 24.
    Niedenführ S, Wiechert W, Nöh K (2015) How to measure metabolic fluxes: a taxonomic guide for 13C fluxomics. Curr Opin Biotechnol 34:82–90CrossRefGoogle Scholar
  25. 25.
    Tardito S, Oudin A, Ahmed SU et al (2015) Glutamine synthetase activity fuels nucleotide biosynthesis and supports growth of glutamine-restricted glioblastoma. Nat Cell Biol 17(12):1556–1568CrossRefGoogle Scholar
  26. 26.
    Elia I, Broekaert D, Christen S et al (2017) Proline metabolism supports metastasis formation and could be inhibited to selectively target metastasizing cancer cells. Nat Commun 8:15267CrossRefGoogle Scholar
  27. 27.
    Christen S, Lorendeau D, Schmieder R et al (2016) Breast cancer-derived lung metastases show increased pyruvate carboxylase-dependent anaplerosis. Cell Rep 17(3):837–848CrossRefGoogle Scholar
  28. 28.
    Lorendeau D, Rinaldi G, Boon R et al (2017) Dual loss of succinate dehydrogenase (SDH) and complex I activity is necessary to recapitulate the metabolic phenotype of SDH mutant tumors. Metab Eng 43(B):187–197CrossRefGoogle Scholar
  29. 29.
    Berthois Y, Katzenellenbogen JA, Katzenellenbogen BS (1986) Phenol red in tissue culture media is a weak estrogen: implications concerning the study of estrogen-responsive cells in culture. Proc Natl Acad Sci U S A 83(8):2496–2500CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Cellular Metabolism and Metabolic Regulation, VIB Center for Cancer BiologyVIBLeuvenBelgium
  2. 2.Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, Leuven Cancer Institute (LKI)KU LeuvenLeuvenBelgium
  3. 3.Laboratory of Cellular Metabolism and Metabolic Regulation, Department of OncologyVIB-KU Leuven Center for Cancer BiologyLeuvenBelgium

Personalised recommendations