GEO series
Cell-intrinsic metabolic phenotypes identified in glioblastoma patients using mass spectrometry imaging of 13C-labeled glucose metabolism
GSE288836
Homo sapiens (human)
RNA-seq (gene expression)
35 samples
Submitted 2025/03/25
Platform GPL24676
At a glance
RNA-seq (gene expression) · human · 35 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Topic
- GlioblastomaThis series is part of our curated briefing on it.
- Samples
- 35 samples: 12 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of (13)C-labelled glucose metabolismNature metabolism · 2025
Summary (written by the submitters)
Transcriptomic studies have attempted to classify glioblastoma (GB) into subtypes that predict survival and that have different therapeutic vulnerabilities. Here we identified three metabolic subtypes: glycolytic, oxidative and a mixed glycolytic/oxidative by mass spectrometry imaging of rapidly excised tumour sections from two GB patients infused with [U-13C]glucose and from spatial transcriptomic analysis of contiguous sections.
Read the full summary ↓
The phenotypes did not correlate with microenvironmental features, including proliferation rate, immune cell infiltration, and vascularisation, were retained when patient-derived cells were grown in vitro, or as orthotopically implanted xenografts, and were robust to changes in oxygen concentration, demonstrating their cell intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes are large enough to be detected using clinically applicable metabolic imaging techniques. A limitation of the study is that it is based on only two patient tumours, albeit on multiple sections, and therefore represents a proof-of-concept study. Glioblastoma is the most common primary adult brain cancer. Transcriptomic analyses have attempted to classify GB into subtypes that could predict treatment response, with a recent study that used a pathway-based classification defining metabolism-associated subtypes with distinct therapeutic vulnerabilities. These included a mitochondrial subtype, which is associated with a more favourable clinical outcome and that is sensitive to inhibitors of oxidative phosphorylation, and a glycolytic/plurimetabolic subtype that is resistant to multiple treatment types. An important question is the extent to which the metabolism displayed by tumour cells in vivo are cell intrinsic and how much they are defined by the tumour microenvironment. We have addressed this question using mass spectrometry imaging of rapidly excised tumour sections from GB patients infused with [U-13C]glucose immediately prior to surgery to image tumour cell metabolic fluxes in vivo and from a spatial transcriptomic analysis of adjacent sections. To our knowledge, this is the first report demonstrating high resolution imaging of metabolic fluxes in a human tumour in vivo.
Published in
Tsyben A, Dannhorn A, Hamm G et al.
· Nature metabolism 2025
· PMID 40389678
· doi:10.1038/s42255-025-01293-y
This dataset
Download · which file do I need?
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
1 · Just have a look
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
2 · Re-analyse it
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE288836_RAW.tar (it can run to tens of gigabytes).
3 · Start from the raw reads
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1219581 and SRA study SRP561771. Searching any of these in the dataset finder brings you back here.
Study design
12 conditions, mostly in triplicate
AT8C1, Hypoxia, ×4
AT8C2, Normoxia, ×3
S2, Normoxia, ×3
A11, Normoxia, ×3
SP20, Normoxia, ×3
AT8C1, Normoxia, ×3
AT5C1, Normoxia, ×3
AT3, Normoxia, ×3
+6 more
Supports a between-group comparison across 33 samples.
How we read this: 12 replicated groups read from 35 sample titles; they account for 33 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
Samples in this study
- GSM8776326 AT2, Normoxia, Rep1 AT2, Normoxia,
- GSM8776327 A11, Hypoxia, Rep4a
- GSM8776328 AT8C2, Normoxia, Rep1 AT8C2, Normoxia,
- GSM8776329 S2, Normoxia, Rep1 S2, Normoxia,
- GSM8776330 AT2, Normoxia, Rep2 AT2, Normoxia,
- GSM8776331 A11, Normoxia, Rep3 A11, Normoxia,
- GSM8776332 AT8C2, Normoxia, Rep3 AT8C2, Normoxia,
- GSM8776333 SP20, Normoxia, Rep4 SP20, Normoxia,
- GSM8776334 AT8C1, Hypoxia, Rep3 AT8C1, Hypoxia,
- GSM8776335 AT2, Hypoxia, Rep1 AT2, Hypoxia,
- GSM8776336 S2, Normoxia, Rep3 S2, Normoxia,
- GSM8776337 S2, Hypoxia, Rep3 S2, Hypoxia,
- GSM8776338 AT8C1, Hypoxia, Rep2 AT8C1, Hypoxia,
- GSM8776339 A11, Hypoxia, Rep3 A11, Hypoxia,
- GSM8776340 AT8C1, Normoxia, Rep2 AT8C1, Normoxia,
- GSM8776341 SP20, Normoxia, Rep3 SP20, Normoxia,
- GSM8776342 AT8C1, Normoxia, Rep4 AT8C1, Normoxia,
- GSM8776343 AT8C1, Hypoxia, Rep1 AT8C1, Hypoxia,
- GSM8776344 AT5C1, Normoxia, Rep3 AT5C1, Normoxia,
- GSM8776345 AT3, Normoxia, Rep3 AT3, Normoxia,
- GSM8776346 AT5C1, Normoxia, Rep1 AT5C1, Normoxia,
- GSM8776347 AT8C2, Normoxia, Rep4 AT8C2, Normoxia,
- GSM8776348 AT5C1, Normoxia, Rep2 AT5C1, Normoxia,
- GSM8776349 SP20, Normoxia, Rep1 SP20, Normoxia,
- GSM8776350 A11, Normoxia, Rep2 A11, Normoxia,
- GSM8776351 AT8C1, Normoxia, Rep3 AT8C1, Normoxia,
- GSM8776352 S2, Normoxia, Rep2 S2, Normoxia,
- GSM8776353 S2, Hypoxia, Rep2 S2, Hypoxia,
- GSM8776354 AT3, Normoxia, Rep2 AT3, Normoxia,
- GSM8776355 AT2, Hypoxia, Rep4 AT2, Hypoxia,
- GSM8776356 A11, Hypoxia, Rep2 A11, Hypoxia,
- GSM8776357 A11, Hypoxia, Rep4b
- GSM8776358 AT8C1, Hypoxia, Rep4 AT8C1, Hypoxia,
- GSM8776359 AT3, Normoxia, Rep1 AT3, Normoxia,
- GSM8776360 A11, Normoxia, Rep1 A11, Normoxia,
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Share this dataset
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.