ArticleActa neuropathologica communications2019
Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells.
Article in Acta neuropathologica communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- ARNT2 repression disrupts neuronal identity and promotes glioblastoma growth.Cell communication and signaling : CCS · 2026Article
- A unique malignant cell type per patient tumor encoded in each cancer cell transcriptome.iScience · 2026Article
- Nicotinamide metabolism reprogramming drives reversible senescence of glioblastoma cells.Cellular and molecular life sciences : CMLS · 2025Article
- Current Applications of Single-Cell RNA Sequencing in Glioblastoma: A Scoping Review.Brain sciences · 2025Review
- Bulk and single-cell transcriptome revealed the metabolic heterogeneity in human glioma.Heliyon · 2025Article
- Mitochondria Transfer from Mesenchymal Stem Cells Confers Chemoresistance to Glioblastoma Stem Cells through Metabolic Rewiring.Cancer research communications · 2023Article
- Review
- Glioma Cells Expressing High Levels of ALDH5A1 Exhibit Enhanced Migration Transcriptional Signature in Patient Tumors.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Article
- Glioblastoma cell motility depends on enhanced oxidative stress coupled with mobilization of a sulfurtransferase.Cell death & disease · 2022Article
- Glioblastoma Multiforme Tumors in Women Have a Lower Expression of Fatty Acid ElongasesBrain sciences · 2022Article
- DNA methylation-based age acceleration observed in IDH wild-type glioblastoma is associated with better outcome-including in elderly patients.Acta neuropathologica communications · 2022Article
- A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data.Genome research · 2021Article
- Gene expression profiling of 19q-loss astrocytomas suggest a specific pattern associated with the better prognosis.Journal of neuro-oncology · 2021Article
- Case of multifocal glioblastoma with four fusion transcripts ofCold Spring Harbor molecular case studies · 2021Article
- FABP7 Facilitates Uptake of Docosahexaenoic Acid in Glioblastoma Neural Stem-like Cells.Nutrients · 2021Article
- The Glioblastoma Microenvironment: Morphology, Metabolism, and Molecular Signature of Glial Dynamics to Discover Metabolic Rewiring Sequence.International journal of molecular sciences · 2021Review
- Recent advances in understanding the role of metabolic heterogeneities in cell migration.Faculty reviews · 2021Review
- Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome.Metabolites · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma cell ability to adapt their functioning to microenvironment changes is a source of the extensive intra-tumor heterogeneity characteristic of this devastating malignant brain tumor. A systemic view of the metabolic pathways underlying glioblastoma cell functioning states is lacking. We analyzed public single cell RNA-sequencing data from glioblastoma surgical resections, which offer the closest available view of tumor cell heterogeneity as encountered at the time of patients' diagnosis. Unsupervised analyses revealed that information dispersed throughout the cell transcript repertoires encoded the identity of each tumor and masked information related to cell functioning states. Data reduction based on an experimentally-defined signature of transcription factors overcame this hurdle. It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using independent datasets of glioblastoma cell and tissue transcriptomes, patient-derived cell lines and orthotopic xenografts. Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in anti-oxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors.
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