Evidence map›Paper›PMID 40035743›Full record

ArticleeLife2025

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

Abstract read
In one paragraph

Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
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  8. In vivoMagnetic resonance in medicine · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Rui Vasco SimoesPreclinical MRI, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.ORCID https://orcid.org/0000-0001-7574-4723
Rafael Neto HenriquesPreclinical MRI, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Jonas L OlesenCenter of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
Beatriz M CardosoPreclinical MRI, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Francisca F FernandesPreclinical MRI, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Mariana A V MonteiroHistopathology Platform, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Sune N JespersenCenter of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
Tânia CarvalhoHistopathology Platform, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Noam ShemeshPreclinical MRI, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.ORCID https://orcid.org/0000-0001-6681-5876

Funding

Fundação Champalimaud internalFundação para a Ciência e a Tecnologia 10.54499/2021.02777.ceecind/cp1675/ct0003H2020 Marie Skłodowska-Curie Actions 10.3030/844776
6 · The paper itself

Abstract

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.

Indexed as

Brain NeoplasmsDeuteriumGlioblastomaGlucoseAnimalsDisease Models, AnimalGlycolysisKineticsMicePhenotypeTumor MicroenvironmentDeuteriumGlucosecancer biologydeuterium metabolic imagingglioblastomaglycolysiskinetic modelingmitochondrial metabolismmouse

Identifiers

PMID40035743
PMCPMC11879113

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.