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:

Interrogating in vivo T-cell metabolism in mice using stable isotope labeling metabolomics and rapid cell sorting

Nature Protocols volume 16, pages 4494–4521 (2021)Cite this article

Subjects

Abstract

T cells are integral players in the adaptive immune system that readily adapt their metabolism to meet their energetic and biosynthetic needs. A major hurdle to understand physiologic T-cell metabolism has been the differences between in vitro cell culture conditions and the complex in vivo milieu. To address this, we have developed a protocol that merges traditional immunology infection models with whole-body metabolite infusion and mass-spectrometry-based metabolomic profiling to assess T-cell metabolism in vivo. In this protocol, pathogen-infected mice are infused via the tail vein with an isotopically labeled metabolite (2–6 h), followed by rapid magnetic bead isolation to purify T-cell populations (<1 h) and then stable isotope labeling analysis conducted by mass spectrometry (~1–2 d). This procedure enables researchers to evaluate metabolic substrate utilization into central carbon metabolic pathways (i.e., glycolysis and the tricarboxylic acid cycle) by specific T-cell subpopulations in the context of physiological immune responses in vivo.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Overview of the procedure.
Fig. 2: Preparation of infusion lines.
Fig. 3: Bead isolation procedure.
Fig. 4: Blood glucose enrichment of 13C-glucose label in Listeria-infected mice.
Fig. 5: FACS analysis of T cells following magnetic bead isolation procedure.
Fig. 6: 13C-glucose labeling patterns in T cells isolated from Listeria-infected mice.
Fig. 7: Differentially enriched 13C-glucose-derived metabolites.
Fig. 8: Pathway analysis of 13C-glucose-depedent nucleotide metabolism in activated T cells from Listeria-infected mice.

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

References

  1. Cham, C. M., Driessens, G., O’Keefe, J. P. & Gajewski, T. F. Glucose deprivation inhibits multiple key gene expression events and effector functions in CD8+ T cells. Eur. J. Immunol. 38, 2438–2450 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chang, C. H. et al. Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239–1251 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ma, E. H. et al. Metabolic profiling using stable isotope tracing reveals distinct patterns of glucose utilization by physiologically activated CD8+ T cells. Immunity 51, 856–870.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Johnson, M. O. et al. Distinct regulation of Th17 and Th1 cell differentiation by glutaminase-dependent metabolism. Cell (2018). https://doi.org/10.1016/j.cell.2018.10.001

  5. Blagih, J. et al. The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo. Immunity 42, 41–54 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Leone, R. D. et al. Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion. Science. 366, 1013–1021 (2019). https://doi.org/10.1126/science.aav2588

  7. Balmer, M. L. et al. Memory CD8+ T cells require increased concentrations of acetate induced by stress for optimal function. Immunity 44, 1312–1324 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Qiu, J. et al. Acetate promotes T cell effector function during glucose restriction. Cell Rep. 27, 2063–2074.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ma, E. H. et al. Serine is an essential metabolite for effector T cell expansion. Cell Metab. 25, 345–357 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Davidson, S. M. et al. Environment impacts the metabolic dependencies of Ras-driven non-small cell lung cancer. Cell Metab. 23, 517–528 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cantor, J. R. The rise of physiologic media. Trends Cell Biol. 29, 854–861 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ackermann, T. & Tardito, S. Cell culture medium formulation and its implications in cancer metabolism. Trends Cancer 5, 329–332 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Voorde, J. V. et al. Improving the metabolic fidelity of cancer models with a physiological cell culture medium. Sci. Adv. 5, eaau7314 (2019).

    Article  CAS  Google Scholar 

  14. Cantor, J. R. et al. Physiologic medium rewires cellular metabolism and reveals uric acid as an endogenous inhibitor of UMP synthase HHS Public Access. Cell 169, 258–272 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kaymak, I., Williams, K. S., Cantor, J. R. & Jones, R. G. Immunometabolic interplay in the tumor microenvironment. Cancer Cell 39, 28–37 (2020).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  16. Hensley, C. T. et al. Metabolic heterogeneity in human lung tumors. Cell 164, 681–694 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sellers, K. et al. Pyruvate carboxylase is critical for non-small-cell lung cancer proliferation. J. Clin. Invest. 125, 687–698 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Faubert, B. et al. Lactate metabolism in human lung tumors. Cell 171, 358–371.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yuan, M. et al. Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC–MS/MS. Nat. Protoc. 14, 313–330 (2019). https://doi.org/10.1038/s41596-018-0102-x

  20. Roy, D. G., Kaymak, I., Williams, K. S., Ma, E. H. & Jones, R. G. Immunometabolism in the tumor microenvironment. Annu. Rev. Cancer Biol. 5, annurev-cancerbio-030518-055817 (2021).

  21. Geltink, R. I. K., Kyle, R. L. & Pearce, E. L. Unraveling the complex interplay between T cell metabolism and function. Annu. Rev. Immunol. 36, 461–488 (2018). https://doi.org/10.1146/annurev-immunol-042617-053019

  22. Wang, R. & Green, D. R. Metabolic checkpoints in activated T cells. Nat. Immunol. 13, 907–915 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. Pearce, E. L., Poffenberger, M. C., Chang, C. H. & Jones, R. G. Fueling immunity: insights into metabolism and lymphocyte function. Science 342, 1242454 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. O’Sullivan, D. & Pearce, E. L. Targeting T cell metabolism for therapy. Trends Immunol. 36, 71–80 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Llufrio, E. M., Wang, L., Naser, F. J. & Patti, G. J. Sorting cells alters their redox state and cellular metabolome. Redox Biol. 16, 381–387 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Binek, A. et al. Flow cytometry has a significant impact on the cellular metabolome. J. Proteome Res. 18, 169–181 (2019).

    CAS  PubMed  Google Scholar 

  27. Chen, W. W., Freinkman, E. & Sabatini, D. M. Rapid immunopurification of mitochondria for metabolite profiling and absolute quantification of matrix metabolites. Nat. Protoc. 12, 2215–2231 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Szabo, P. A. et al. Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease. Nat. Commun. https://doi.org/10.1038/s41467-019-12464-3 (2019).

  29. Miragaia, R. J. et al. Single-cell transcriptomics of regulatory t cells reveals trajectories of tissue adaptation. Immunity https://doi.org/10.1016/j.immuni.2019.01.001 (2019).

  30. Lau, A. N. et al. Dissecting cell-type-specific metabolism in pancreatic ductal adenocarcinoma. eLife https://doi.org/10.7554/eLife.56782 (2020).

  31. Fernández-García, J., Altea-Manzano, P., Pranzini, E. & Fendt, S. M. Stable isotopes for tracing mammalian-cell metabolism in vivo. Trends Biochem. Sci. https://doi.org/10.1016/j.tibs.2019.12.002 (2020).

  32. Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell https://doi.org/10.1016/j.cell.2018.03.055 (2018).

  33. Andrikopoulos, S., Blair, A. R., Deluca, N., Fam, B. C. & Proietto, J. Evaluating the glucose tolerance test in mice. Am. J. Physiol. Endocrinol. Metab. https://doi.org/10.1152/ajpendo.90617.2008 (2008).

  34. Tompkins, S. C. et al. Disrupting mitochondrial pyruvate uptake directs glutamine into the TCA cycle away from glutathione synthesis and impairs hepatocellular tumorigenesis. Cell Rep. https://doi.org/10.1016/j.celrep.2019.07.098 (2019).

  35. Marin-Valencia, I. et al. Analysis of tumor metabolism reveals mitochondrial glucose oxidation in genetically diverse human glioblastomas in the mouse brain in vivo. Cell Metab. 15, 827–837 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Overmyer, K. A., Thonusin, C., Qi, N. R., Burant, C. F. & Evans, C. R. Impact of anesthesia and euthanasia on metabolomics of mammalian tissues: studies in a C57BL/6J mouse model. PLoS One 10, e0117232 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Guo, N. L., Zhang, J. X., Wu, J. P. & Xu, Y. H. Isoflurane promotes glucose metabolism through up-regulation of miR-21 and suppresses mitochondrial oxidative phosphorylation in ovarian scancer cells. Biosci. Rep. 37, BSR20170818 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rao, L. K., Flaker, A. M., Friedel, C. C. & Kharasch, E. D. Role of cytochrome P4502B6 polymorphisms in ketamine metabolism and clearance. Anesthesiology 125, 1103–1112 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Kharasch, E. D. & Thummel, K. E. Identification of cytochrome P450 2E1 as the predominant enzyme catalyzing human liver microsomal defluorination of sevoflurane, isoflurane, and methoxyflurane. Anesthesiology 79, 795–807 (1993).

    Article  CAS  PubMed  Google Scholar 

  40. Quaegebeur, A. et al. Deletion or inhibition of the oxygen sensor PHD1 protects against ischemic stroke via reprogramming of neuronal metabolism. Cell Metab. https://doi.org/10.1016/j.cmet.2015.12.007 (2016).

  41. Elia, I. et al. Proline metabolism supports metastasis formation and could be inhibited to selectively target metastasizing cancer cells. Nat. Commun. https://doi.org/10.1038/ncomms15267 (2017).

  42. Jang, C. et al. The small intestine converts dietary fructose into glucose and organic acids. Cell Metab. https://doi.org/10.1016/j.cmet.2017.12.016 (2018).

  43. Sun, R. C. et al. Noninvasive liquid diet delivery of stable isotopes into mouse models for deep metabolic network tracing. Nat. Commun. 8, 1646 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Goldberg, E. L. et al. Ketogenic diet activates protective γδ T cell responses against influenza virus infection. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aav2026 (2019).

  45. Dyar, K. A. et al. Atlas of circadian metabolism reveals system-wide coordination and communication between clocks. Cell 174, 1571–1585.e11 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Eckel-Mahan, K. L. et al. Reprogramming of the circadian clock by nutritional challenge. Cell 155, 1464–1478 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kinouchi, K. et al. Fasting imparts a switch to alternative daily pathways in liver and muscle. Cell Rep. 25, 3299–3314.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Shen, H. et al. Recombinant Listeria monocytogenes as a live vaccine vehicle for the induction of protective anti-viral cell-mediated immunity. Proc. Natl Acad. Sci. USA 92, 3987–3991 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Shen, H. et al. Compartmentalization of bacterial antigens: differential effects on priming of CD8 T cells and protective immunity. Cell 92, 535–545 (1998).

    Article  CAS  PubMed  Google Scholar 

  50. Pope, C. et al. Organ-specific regulation of the CD8 T cell response to Listeria monocytogenes infection. J. Immunol. https://doi.org/10.4049/jimmunol.166.5.3402 (2001).

  51. Agrawal, S. et al. El-MAVEN: a fast, robust, and user-friendly mass spectrometry data processing engine for metabolomics. Methods Mol. Biol. 1978, 301–321 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Badovinac, V. P., Haring, J. S. & Harty, J. T. Initial T cell receptor transgenic cell precursor frequency dictates critical aspects of the CD8(+) T cell response to infection. Immunity 26, 827–841 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Best, J. A. et al. Transcriptional insights into the CD8 + T cell response to infection and memory T cell formation. Nat. Immunol. 14, 404–412 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang, Y. et al. Uncoupling hepatic oxidative phosphorylation reduces tumor growth in two murine models of colon cancer. Cell Rep. 24, 47–55 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Tardito, S. et al. Glutamine synthetase activity fuels nucleotide biosynthesis and supports growth of glutamine-restricted glioblastoma. Nat. Cell Biol. 17, 1556–1568 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).

    Article  CAS  PubMed  Google Scholar 

  58. Heinrich, P. et al. Correcting for natural isotope abundance and tracer impurity in MS-, MS/MS- and high-resolution-multiple-tracer-data from stable isotope labeling experiments with IsoCorrectoR. Sci. Rep. 8, 17910 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Trefely, S., Ashwell, P. & Snyder, N. W. FluxFix: automatic isotopologue normalization for metabolic tracer analysis. BMC Bioinformatics 17, 485 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Izreig, S. et al. Repression of LKB1 by miR-17∼92 sensitizes MYC-dependent lymphoma to biguanide treatment. Preprint at bioRxiv https://doi.org/10.1101/2019.12.20.883025 (2019).

  61. Janzer, A. et al. Metformin and phenformin deplete tricarboxylic acid cycle and glycolytic intermediates during cell transformation and NTPs in cancer stem cells. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1409844111 (2014).

  62. Spalding, J. L., Naser, F. J., Mahieu, N. G., Johnson, S. L. & Patti, G. J. Trace phosphate improves ZIC-pHILIC peak shape, sensitivity, and coverage for untargeted metabolomics. J. Proteome Res. 17, 3537–3546 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mahieu, N. G. & Patti, G. J. Systems-level annotation of a metabolomics data set reduces 25 000 features to fewer than 1000 unique metabolites. Anal. Chem. 89, 10397–10406 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Yang, Q. et al. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res. https://doi.org/10.1093/nar/gkaa258 (2020).

Download references

Acknowledgements

We thank E. Levine, R. Sheridan and staff from the Van Andel Institute (VAI) Metabolomics and Bioenergetics and Flow Cytometry core facilities for technical assistance. We thank J. Wedberg and M. Minard for administrative assistance. Funding: The VAI Metabolomics and Bioenergetics Core Facility is supported by the Department of Metabolism and Nutritional Programming at VAI. This research has been supported by grants from the CIHR (MOP-142259 to RGJ) and funding from VAI and Agios Pharmaceuticals.

Author information

Author notes

  1. These authors contributed equally: Ryan D. Sheldon, Eric H. Ma.

Authors and Affiliations

  1. Department of Metabolism and Nutritional Programming, Van Andel Institute, Grand Rapids, MI, USA

    Ryan D. Sheldon, Eric H. Ma, Lisa M. DeCamp, Kelsey S. Williams & Russell G. Jones

  2. Metabolomics and Bioenergetics Core Facility, Van Andel Institute, Grand Rapids, MI, USA

    Ryan D. Sheldon

Authors
  1. Ryan D. Sheldon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric H. Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lisa M. DeCamp
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kelsey S. Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Russell G. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: R.D.S., E.H.M. and R.G.J.; validation: R.D.S. and E.H.M.; investigation: R.D.S., L.M.D. and E.H.M.; data curation: R.D.S. and E.H.M.; writing—original draft: R.D.S. and E.H.M.; writing—review and editing: R.D.S., E.H.M., K.S.W. and R.G.J.; visualization: K.S.W.; supervision: R.G.J.; funding acquisition: R.G.J.

Corresponding author

Correspondence to Russell G. Jones.

Ethics declarations

Competing interests

R.G.J. is a consultant for Agios Pharmaceuticals and serves on the Scientific Advisory Board of ImmunoMet Therapeutics.

Additional information

Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Related links

Key reference using this protocol

Ma, E. H. et al. Immunity 51, 856–870.e5 (2019): https://doi.org/10.1016/j.immuni.2019.09.003

Supplementary information

Reporting Summary

Supplementary Table 1

Compound list using the described HILIC LC/MS method. Important: Retention times will vary from lab to lab and should be confirmed by chemical standards.

Supplementary Table 2

Compound list using the described GC/MS method. The quantifier ion is a molecular ion that can be used for isotope analysis. The qualifier ion should have the same retention and is used to improve confidence in compound identification. Important: Retention times will vary from lab to lab and should be confirmed by chemical standards.

Supplementary Table 3

This Excel document contains three tabs. The “Raw Tracing Data” tab contains raw peak areas and natural abundance corrected values and fractional enrichment for each compound. The “T cells total 13C-enrichment” contains the total fractional enrichment of 13C carbon in each compound. These data have been filtered to include only compounds with at least 5% enrichment on average in at least one group. The “Subset >5% enrichment” contains this filtered data. In each tab, sample identifiers are as follows: Phenformin group: S1-00, S1-01, S1-30; Vehicle group: S2-03, S2-10, S2-30.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheldon, R.D., Ma, E.H., DeCamp, L.M. et al. Interrogating in vivo T-cell metabolism in mice using stable isotope labeling metabolomics and rapid cell sorting. Nat Protoc 16, 4494–4521 (2021). https://doi.org/10.1038/s41596-021-00586-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00586-2

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

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer