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Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem

Author

Listed:
  • Michaela Schwaiger-Haber

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Ethan Stancliffe

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Dhanalakshmi S. Anbukumar

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Blake Sells

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Jia Yi

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Kevin Cho

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Kayla Adkins-Travis

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Milan G. Chheda

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Leah P. Shriver

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

  • Gary J. Patti

    (Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis
    Washington University in St. Louis)

Abstract

Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma.

Suggested Citation

  • Michaela Schwaiger-Haber & Ethan Stancliffe & Dhanalakshmi S. Anbukumar & Blake Sells & Jia Yi & Kevin Cho & Kayla Adkins-Travis & Milan G. Chheda & Leah P. Shriver & Gary J. Patti, 2023. "Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38403-x
    DOI: 10.1038/s41467-023-38403-x
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    References listed on IDEAS

    as
    1. Ramon C. Sun & Teresa W.-M. Fan & Pan Deng & Richard M. Higashi & Andrew N. Lane & Anh-Thu Le & Timothy L. Scott & Qiushi Sun & Marc O. Warmoes & Ye Yang, 2017. "Noninvasive liquid diet delivery of stable isotopes into mouse models for deep metabolic network tracing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
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