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Acquired genetic and cell state changes in IDH-mutant glioma progression [coculture experiments]

GSE324714 Mus musculus (mouse) Single-nucleus RNA-seq 16 samples Submitted 2026/04/20 Platform GPL34284Platform GPL35012
At a glance

Single-nucleus RNA-seq · mouse · 16 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
16 samples: 4 × MGG152_monoculture vs 4 × MGG152_monoculture_irradiation vs 4 × MGG152_coculture vs 4 × MGG152_coculture_irradiationGroups 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
Acquired genetic and cell-state changes in IDH-mutant glioma progressionNature · 2026
Summary (written by the submitters)
Isocitrate dehydrogenase (IDH)-mutant gliomas are malignant brain tumors that typically arise in early- to mid-adulthood and nearly always recur following treatment. However, the genetic and cellular state changes that drive IDH-mutant glioma progression under treatment remain incompletely understood. Here, we integrated single-nucleus transcriptomic profiles, chromatin accessibility profiles and bulk DNA/RNA sequencing from 75 temporally separated gliomas across 35 patients comprising both the oligodendroglioma and astrocytoma IDH-mutant glioma tumor types.
Read the full summary ↓
We show that malignant cell states transcriptionally resemble stages of normal glial-neuronal lineage development or a reactive mesenchymal-like state, mirroring states previously described in IDH-wildtype glioblastoma. Malignant cell states displayed distinct chromatin accessibility profiles that were comparable between both IDH-mutant glioma types. The abundance of less differentiated malignant cells increased with grade and with genetic alterations such as PDGFRA amplification. Longitudinal analysis highlighted two major malignant cell state transition patterns. First, reduced lineage differentiation and increased proliferative malignant cells at recurrence were pronounced in gliomas that acquired recurrence-associated genetic events, including treatment-associated hypermutation, increased copy number changes, and cell cycle alterations. Second, increased mesenchymal-like state abundance occurred independently of acquired genetic alterations and instead coincided with heightened macrophage expression. Overall, our findings provide an integrative model that traces the cell-intrinsic and extrinsic factors that shape cellular states during IDH-mutant glioma disease progression.
Published in
Acquired genetic and cell-state changes in IDH-mutant glioma progression
Johnson KC, Spitzer A, Varn FS et al. · Nature 2026 · PMID 42236943 · doi:10.1038/s41586-026-10612-6
This dataset
Open GSE324714 in finder → Find more RNA-seq datasets →
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.

Series matrix ↓
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 GSE324714_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1436624. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42236943) ↗
Study design
4 × MGG152_monoculture vs 4 × MGG152_monoculture_irradiation vs 4 × MGG152_coculture vs 4 × MGG152_coculture_irradiation

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
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