Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Deuterium metabolic imaging phenotypes mouse glioblastoma heterogeneity through glucose turnover kinetics.

Rui Vasco Simoes | Rafael Neto Henriques | Jonas L Olesen | Beatriz M Cardoso | Francisca F Fernandes | Mariana A V Monteiro | Sune N Jespersen | Tânia Carvalho | Noam Shemesh
eLife | 2025

Glioblastomas are aggressive brain tumors with dismal prognosis. One of the main bottlenecks for developing more effective therapies for glioblastoma stems from their histologic and molecular heterogeneity, leading to distinct tumor microenvironments and disease phenotypes. Effectively characterizing these features would improve the clinical management of glioblastoma. Glucose flux rates through glycolysis and mitochondrial oxidation have been recently shown to quantitatively depict glioblastoma proliferation in mouse models (GL261 and CT2A tumors) using dynamic glucose-enhanced (DGE) deuterium spectroscopy. However, the spatial features of tumor microenvironment phenotypes remain hitherto unresolved. Here, we develop a DGE Deuterium Metabolic Imaging (DMI) approach for profiling tumor microenvironments through glucose conversion kinetics. Using a multimodal combination of tumor mouse models, novel strategies for spectroscopic imaging and noise attenuation, and histopathological correlations, we show that tumor lactate turnover mirrors phenotype differences between GL261 and CT2A mouse glioblastoma, whereas recycling of the peritumoral glutamate-glutamine pool is a potential marker of invasion capacity in pooled cohorts, linked to secondary brain lesions. These findings were validated by histopathological characterization of each tumor, including cell density and proliferation, peritumoral invasion and distant migration, and immune cell infiltration. Our study bodes well for precision neuro-oncology, highlighting the importance of mapping glucose flux rates to better understand the metabolic heterogeneity of glioblastoma and its links to disease phenotypes.

Pubmed ID: 40035743

Research resources used in this publication

None found

Additional research tools detected in this publication

Antibodies used in this publication

None found

Associated grants

  • Agency: H2020 Marie Skłodowska-Curie Actions,
    Id: 10.3030/844776
  • Agency: Fundação para a Ciência e a Tecnologia,
    Id: 10.54499/2021.02777.ceecind/cp1675/ct0003
  • Agency: Fundação Champalimaud,
    Id: internal

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


QuPath (tool)

RRID:SCR_018257

Open Source software package for digital pathology image analysis. Used for whole slide image analysis and digital pathology. Provides researchers with batch processing and scripting functionality, and extensible platform with which to develop and share new algorithms to analyze complex tissue images.

View all literature mentions