GEO series

Acquired genetic and cell state changes in IDH-mutant glioma progression

GSE324694 Homo sapiens (human) Single-nucleus RNA-seq 10 samples Submitted 2026/04/20 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 10 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: Homo sapiens (human)
Samples: 10 samples: 10 samples, each with a distinct title — no replicated groupsGroups 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 GSE324694 in finder → Find more human 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 GSE324694_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 PRJNA1436575. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42236943) ↗

Study design

10 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 10 sample titles: 10 distinct titles with little repetition. 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

GSM9583081 T394NS_DMSO_CTR
GSM9583082 T407NS_DMSO_CTR
GSM9583083 T394NS_Dasatinib_1uM
GSM9583084 T394NS_Dasatinib_5uM
GSM9583085 T394NS_CP-673451_1uM
GSM9583086 T394NS_CP-673451_5uM
GSM9583087 T407NS_Dasatinib_1uM
GSM9583088 T407NS_Dasatinib_5uM
GSM9583089 T407NS_CP-673451_1uM
GSM9583090 T407NS_CP-673451_5uM

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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.