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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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
Anderson, N. M. & Simon, M. C. The tumor microenvironment. Curr. Biol. 30, R921–R925 (2020).
Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).
García-Cañaveras, J. C. & Lahoz, A. Tumor microenvironment-derived metabolites: a guide to find new metabolic therapeutic targets and biomarkers. Cancers 13, 3230 (2021).
Bunse, L. et al. Suppression of antitumor T cell immunity by the oncometabolite (R)-2-hydroxyglutarate. Nat. Med. 24, 1192–1203 (2018).
Allard, B., Beavis, P. A., Darcy, P. K. & Stagg, J. Immunosuppressive activities of adenosine in cancer. Curr. Opin. Pharmacol. 29, 7–16 (2016).
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).
Kilgour, M. K. et al. 1-Methylnicotinamide is an immune regulatory metabolite in human ovarian cancer. Sci. Adv. 7, eabe1174 (2021).
Rush, A. et al. Research perspective on utilizing and valuing tumor biobanks. Biopreserv. Biobank. 17, 219–229 (2019).
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).
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).
Binek, A. et al. Flow cytometry has a significant impact on the cellular metabolome. J. Proteome Res. 18, 169–181 (2019).
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).
DeVilbiss, A. W. et al. Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues. eLife 10, e61980 (2021).
Hirayama, A. et al. Effects of processing and storage conditions on charged metabolomic profiles in blood. Electrophoresis 36, 2148–2155 (2015).
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).
Ho, P.-C. et al. Phosphoenolpyruvate Is a Metabolic Checkpoint of Anti-tumor T. Cell Responses Cell 162, 1217–1228 (2015).
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).
Baumann, T. et al. Regulatory myeloid cells paralyze T cells through cell–cell transfer of the metabolite methylglyoxal. Nat. Immunol. 21, 555–566 (2020).
Reinfeld, B. I. et al. Cell-programmed nutrient partitioning in the tumour microenvironment. Nature 593, 282–288 (2021).
Sinclair, L. V., Barthelemy, C. & Cantrell, D. A. Single cell glucose uptake assays: a cautionary tale. Immunometabolism 2, e200029 (2020).
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).
Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).
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).
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).
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
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).
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).
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).
Haukaas, T. H. et al. Impact of freezing delay time on tissue samples for metabolomic studies. Front. Oncol. 6, 17 (2016).
Xiao, B., Deng, X., Zhou, W. & Tan, E.-K. Flow cytometry-based assessment of mitophagy using MitoTracker. Front. Cell. Neurosci. 10, 76 (2016).
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).
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).
Darzi, Y., Letunic, I., Bork, P. & Yamada, T. iPath3.0: interactive pathways explorer v3. Nucleic Acids Res. 46, W510–W513 (2018).
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.
Author information
Authors and Affiliations
Contributions
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
Ethics declarations
Competing interests
R.J.D. is a member of the scientific advisory boards of Vida Ventures and Agios Pharmaceuticals and is a founder of Atavistik Biosciences.
Peer review
Peer review information
Nature Protocols thanks Massimiliano Mazzone, Mahima Swamy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
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
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
Supplementary information
Supplementary Information
Supplementary Notes 1–5 and Supplementary Method 1.
Supplementary Table
Supplementary Tables 1–5
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41596-022-00729-z
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.