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Principles of reproducible metabolite profiling of enriched lymphocytes in tumors and ascites from human ovarian cancer

Abstract

Identifying metabolites and delineating their immune-regulatory contribution in the tumor microenvironment is an area of intense study. Interrogating metabolites and metabolic networks among immune cell subsets and host cells from resected tissues and fluids of human patients presents a major challenge, owing to the specialized handling of samples for downstream metabolomics. To address this, we first outline the importance of collaborating with a biobank for coordinating and streamlining workflow for point of care, sample collection, processing and cryopreservation. After specimen collection, we describe our 60-min rapid bead-based cellular enrichment method that supports metabolite analysis between T cells and tumor cells by mass spectrometry. We also describe how the metabolic data can be complemented with metabolic profiling by flow cytometry. This protocol can serve as a foundation for interrogating the metabolism of cell subsets from primary human ovarian cancer.

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Fig. 1: Overall workflow.
Fig. 2: Schematic of cell enrichment and metabolite profiling (Step 31).
Fig. 3: Metabolomics processing workflow.
Fig. 4: Flow cytometry plate layout.
Fig. 5: Successful enrichment of tumor and T-cell populations from matched ascites and tumor before analysis by MS reveals key differences between cell types and compartments.
Fig. 6: Pathway analysis of measured versus detected metabolites from patient specimens.
Fig. 7: Metabolic profiling by flow cytometry leads to identification of general metabolic characteristics for specific cell populations.

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Data availability

Data generated and analyzed in this study are included in ref. 7. Flow cytometry data are deposited at Flow Repository (FR-FCM-Z2NH) (https://flowrepository.org/id/FR-FCM-Z2NH). Processed data files and scripts to reproduce metabolomics and scRNA-seq analyses are available at https://github.com/vicDRC/BCCJJL01_ovarian. Additional data are available from the corresponding author upon reasonable request.

References

  1. Anderson, N. M. & Simon, M. C. The tumor microenvironment. Curr. Biol. 30, R921–R925 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).

    Article  CAS  PubMed  Google Scholar 

  3. García-Cañaveras, J. C. & Lahoz, A. Tumor microenvironment-derived metabolites: a guide to find new metabolic therapeutic targets and biomarkers. Cancers 13, 3230 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bunse, L. et al. Suppression of antitumor T cell immunity by the oncometabolite (R)-2-hydroxyglutarate. Nat. Med. 24, 1192–1203 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Allard, B., Beavis, P. A., Darcy, P. K. & Stagg, J. Immunosuppressive activities of adenosine in cancer. Curr. Opin. Pharmacol. 29, 7–16 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Labadie, B. W., Bao, R. & Luke, J. J. Reimagining IDO pathway inhibition in cancer immunotherapy via downstream focus on the tryptophan-kynurenine-aryl hydrocarbon axis. Clin. Cancer Res. J. Am. Assoc. Cancer Res. 25, 1462–1471 (2019).

    Article  CAS  Google Scholar 

  7. Kilgour, M. K. et al. 1-Methylnicotinamide is an immune regulatory metabolite in human ovarian cancer. Sci. Adv. 7, eabe1174 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rush, A. et al. Research perspective on utilizing and valuing tumor biobanks. Biopreserv. Biobank. 17, 219–229 (2019).

    Article  PubMed  Google Scholar 

  9. Agrawal, L., Engel, K. B., Greytak, S. R. & Moore, H. M. Understanding preanalytical variables and their effects on clinical biomarkers of oncology and immunotherapy. Semin. Cancer Biol. 52, 26–38 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. 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 

  11. 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 

  12. 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 

  13. DeVilbiss, A. W. et al. Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues. eLife 10, e61980 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hirayama, A. et al. Effects of processing and storage conditions on charged metabolomic profiles in blood. Electrophoresis 36, 2148–2155 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Siska, P. J. et al. Mitochondrial dysregulation and glycolytic insufficiency functionally impair CD8 T cells infiltrating human renal cell carcinoma. JCI Insight 2, 93411 (2017).

    Article  PubMed  Google Scholar 

  16. Ho, P.-C. et al. Phosphoenolpyruvate Is a Metabolic Checkpoint of Anti-tumor T. Cell Responses Cell 162, 1217–1228 (2015).

    CAS  PubMed  Google Scholar 

  17. Scharping, N. E. et al. The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral t cell metabolic insufficiency and dysfunction. Immunity 45, 374–388 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Baumann, T. et al. Regulatory myeloid cells paralyze T cells through cell–cell transfer of the metabolite methylglyoxal. Nat. Immunol. 21, 555–566 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Reinfeld, B. I. et al. Cell-programmed nutrient partitioning in the tumour microenvironment. Nature 593, 282–288 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sinclair, L. V., Barthelemy, C. & Cantrell, D. A. Single cell glucose uptake assays: a cautionary tale. Immunometabolism 2, e200029 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Xu, H. et al. Cyanine-based 1-amino-1-deoxyglucose as fluorescent probes for glucose transporter mediated bioimaging. Biochem. Biophys. Res. Commun. 474, 240–246 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang, J. et al. Chapter Nineteen - 13C isotope-assisted methods for quantifying glutamine metabolism in cancer cells. in Methods in Enzymology (eds Galluzzi, L. & Kroemer, G.) 542, 369–389 (Academic Press, 2014).

  24. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Smyth, G. K. limma: Linear Models for Microarray Data. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds Gentleman, R. et al.) 397–420 (Springer, 2005).

  28. Sheldon, R. D., Ma, E. H., DeCamp, L. M., Williams, K. S. & Jones, R. G. Interrogating in vivo T-cell metabolism in mice using stable isotope labeling metabolomics and rapid cell sorting. Nat. Protoc. 16, 4494–4521 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Mullen, A. R. et al. Oxidation of alpha-ketoglutarate is required for reductive carboxylation in cancer cells with mitochondrial defects. Cell Rep. 7, 1679–1690 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Haukaas, T. H. et al. Impact of freezing delay time on tissue samples for metabolomic studies. Front. Oncol. 6, 17 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Xiao, B., Deng, X., Zhou, W. & Tan, E.-K. Flow cytometry-based assessment of mitophagy using MitoTracker. Front. Cell. Neurosci. 10, 76 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Park, L. M., Lannigan, J. & Jaimes, M. C. OMIP-069: forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytom. A 97, 1044–1051 (2020).

    Article  CAS  Google Scholar 

  33. Brummelman, J. et al. Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry. Nat. Protoc. 14, 1946–1969 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Darzi, Y., Letunic, I., Bork, P. & Yamada, T. iPath3.0: interactive pathways explorer v3. Nucleic Acids Res. 46, W510–W513 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by research grants to J.J.L. from the US Department of Defense Ovarian Cancer Research Program Pilot Award (W81XWH-18-1-0264) and Canadian Institutes of Health Research (MOP-142351 and PJT-162279). R.G.J. is supported by grants from the Canadian Institutes of Health Research (MOP-142259) and funds from the Van Andel Institute. M.K.K. is supported by a University of Victoria Graduate Award. P.T.H. is supported by a Canadian Institutes for Health Research Postdoctoral Fellowship and by research grants from the Carraresi Family Foundation Award. Carraresi Foundation OVCARE Research Grants are supported by the VGH & UBC Hospital Foundation. R.J.D. is supported by grants from the National Cancer Institute (R35CA22044901) and the Cancer Prevention and Research Institute of Texas (RP180778, Project 3 and Metabolism Core). We also gratefully acknowledge support for this work by the Biobanking and Biospecimen Research Services Program at BC Cancer (supported by the Provincial Health Services Authority) and the Canadian Tissue Repository Network (funded by grants from the Institute of Cancer Research, Canadian Institutes of Health Research and the Terry Fox Research Institute, and from the Canadian Cancer Research Alliance). We also acknowledge support for this work by the Immune Response to Ovarian and other Gynecological Cancers (IROC) team for development of the standard operating procedures, and processing and storage of biospecimens in collaboration with the Tumor Tissue Repository team (K. Lawrence, S. Dee, S. O’Donoghue and T. Tarling) who also provided input to this manuscript or were involved in collecting biospecimens. Figures were generated using BioRender.com.

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M.K.K., S.M., P.T.H., L.G.Z., J.L., S.B., S.P., R.D.S., R.G.J., R.J.D., P.H.W. and J.J.L. wrote and edited the manuscript. M.K.K., S.M. and G.K. designed figures.

Corresponding author

Correspondence to Julian J. Lum.

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R.J.D. is a member of the scientific advisory boards of Vida Ventures and Agios Pharmaceuticals and is a founder of Atavistik Biosciences.

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Nature Protocols thanks Massimiliano Mazzone, Mahima Swamy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key reference using this protocol

Kilgour, M. K. et al. Sci. Adv. 7, eabe1174 (2021): https://doi.org/10.1126/sciadv.abe1174

Key data used in this protocol

Kilgour, M. K. et al. Sci. Adv. 7, eabe1174 (2021): https://doi.org/10.1126/sciadv.abe1174

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Supplementary Information

Supplementary Notes 1–5 and Supplementary Method 1.

Reporting Summary

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Supplementary Tables 1–5

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Kilgour, M.K., MacPherson, S., Zacharias, L.G. et al. Principles of reproducible metabolite profiling of enriched lymphocytes in tumors and ascites from human ovarian cancer. Nat Protoc 17, 2668–2698 (2022). https://doi.org/10.1038/s41596-022-00729-z

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