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

Ryan D. Sheldon, Russell G. Jones, Ralph J. DeBerardinis, Marisa K. Kilgour, Sarah MacPherson, Lauren G. Zacharias, Jodi LeBlanc, Sindy Babinszky, Gabrielle Kowalchuk, Scott Parks, Phineas T. Hamilton, Peter H. Watson, Julian J. Lum

Published: 2022-08-18 DOI: 10.1038/s41596-022-00729-z

mass spectrometry

ovarian cancer

metabolite profiling

immune cells

cellular enrichment

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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 flu
Introduction
Materials for Procedure 1
Materials for Procedure 2
Materials for Procedure 3
Procedure 1: establishing a team and workflow for patient enrollment, biospecimen/data collection and storage of biospecimens
Procedure 2: cellular enrichment and metabolite profiling by MS
Procedure 3: high-dimensional flow cytometry for metabolic profiling
Timing
Anticipated results
References
ACKNOWLEDGMENTS

Balkwill, F. R., Capasso, M. & Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 125, 5591–5596 (2012).
Anderson, N. M. & Simon, M. C. The tumor microenvironment. Curr. Biol. 30, R921–R925 (2020).
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).

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

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