ArticleAnalytical chemistry2026
Single-Cell Metabolic Profiling in a Glioblastoma Coculture Model Using AP-MALDI-Based Mass Spectrometry Imaging.
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.
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Who cites it
2 citing papers in PubMed.
- MALDI-TOF Mass Spectrometry for Glioblastoma Secretome Biomarker Screening: A Review of Challenges and Perspectives.Current issues in molecular biology · 2026Review
- Mitochondrial transfer between tumor and immune cells: a nexus of metabolic adaptation and immune dysfunction.Biomarker research · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
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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.
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