Evidence map›Paper›PMID 41943439›Full record

ArticleAnalytical chemistry2026

Single-Cell Metabolic Profiling in a Glioblastoma Coculture Model Using AP-MALDI-Based Mass Spectrometry Imaging.

Une Kontrimaite, Kei F Carver Wong, Sandra Martínez-Jarquín, Phoebe McCrorie, Ruman Rahman, Dong-Hyun Kim

Abstract read
In one paragraph

Article in Analytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

6 authors.

Une KontrimaiteBiodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, NG7 2RD U.K.ORCID 0000-0003-3750-2605
Kei F Carver WongCentre for Analytical Bioscience, Advanced Materials & Health Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD U.K.
Sandra Martínez-JarquínCentre for Analytical Bioscience, Advanced Materials & Health Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD U.K.ORCID 0000-0002-4908-8934
Phoebe McCrorieBiodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, NG7 2RD U.K.
Ruman RahmanBiodiscovery Institute, School of Medicine, University of Nottingham, Nottingham, NG7 2RD U.K.ORCID 0000-0002-6541-9983
Dong-Hyun KimCentre for Analytical Bioscience, Advanced Materials & Health Technologies Division, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD U.K.ORCID 0000-0002-3689-2130

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry imaging enables spatially resolved, label-free detection of metabolites in tissue and culture systems, providing insight into their metabolic landscapes and spatial distribution. However, conventional approaches often lack the spatial resolution and specificity needed to investigate metabolic heterogeneity at the single-cell level, particularly in physiologically relevant models. Here, we present a single-cell ambient mass spectrometry imaging platform, enabling direct chemical mapping of metabolites at a 10 μm resolution. This method integrates cell labeling, high-resolution microscopy, and AP-MALDI Orbitrap mass spectrometry imaging to achieve cell-type-specific metabolite profiling. To demonstrate its application, we applied this approach to glioblastoma (GBM), an aggressive adult brain tumor characterized by cellular heterogeneity, metabolic adaptation, and infiltrative growth within the tumor microenvironment. A coculture model combining patient-derived glioblastoma invasive margin cells with human cortical astrocytes was used to recapitulate the invasive niche. Distinct metabolic signatures emerged upon glioblastoma-astrocyte interaction, involving pathways related to nucleotide metabolism, phospholipid turnover, and tyrosine metabolism. These findings suggest cell-type-specific metabolic activity and a potential intercellular metabolic interplay. Overall, this workflow offers a broadly accessible and robust approach for investigating metabolic heterogeneity at cellular resolution, enabling insights into metabolic interactions of heterogeneous cell types in both disease and nondisease settings.

Indexed as

GlioblastomaSingle-Cell AnalysisTumor MicroenvironmentBrain NeoplasmsCoculture TechniquesMetabolomicsPhospholipidsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationPhospholipids

Identifiers

PMID41943439
PMCPMC13103930

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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.