GEO series
Acquired genetic and cell state changes in IDH-mutant glioma progression [snRNA-seq]
GSE326221
Homo sapiens (human)
Single-nucleus RNA-seq
75 samples
Submitted 2026/04/23
Platform GPL24676
At a glance
Single-nucleus RNA-seq · human · 75 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).
- Organism
- Homo sapiens (human)
- Topic
- Lower-grade gliomaThis series is part of our curated briefing on it.
- Samples
- 75 samples: 35 conditions, mostly in duplicateGroups 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
Johnson KC, Spitzer A, Varn FS et al.
· Nature 2026
· PMID 42236943
· doi:10.1038/s41586-026-10612-6
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 GSE326221_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: 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 PRJNA1444157. Searching any of these in the dataset finder brings you back here.
Study design
35 conditions, mostly in duplicate
Patient 59, Timepoint ×3
Patient 99, Timepoint ×3
Patient 100, Timepoint ×3
Patient 105, Timepoint ×3
Patient 111, Timepoint ×3
Patient 57, Timepoint ×2
Patient 60, Timepoint ×2
Patient 61, Timepoint ×2
+27 more
Supports a between-group comparison across 75 samples.
How we read this: 35 replicated groups read from 75 sample titles; they account for 75 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
- GSM9625434 Patient 57, Timepoint 1 Patient 57, Timepoint
- GSM9625435 Patient 57, Timepoint 2 Patient 57, Timepoint
- GSM9625436 Patient 59, Timepoint 1 Patient 59, Timepoint
- GSM9625437 Patient 59, Timepoint 2 Patient 59, Timepoint
- GSM9625438 Patient 59, Timepoint 3 Patient 59, Timepoint
- GSM9625439 Patient 60, Timepoint 1 Patient 60, Timepoint
- GSM9625440 Patient 60, Timepoint 2 Patient 60, Timepoint
- GSM9625441 Patient 61, Timepoint 1 Patient 61, Timepoint
- GSM9625442 Patient 61, Timepoint 2 Patient 61, Timepoint
- GSM9625443 Patient 71, Timepoint 1 Patient 71, Timepoint
- GSM9625444 Patient 71, Timepoint 2 Patient 71, Timepoint
- GSM9625445 Patient 73, Timepoint 1 Patient 73, Timepoint
- GSM9625446 Patient 73, Timepoint 2 Patient 73, Timepoint
- GSM9625447 Patient 79, Timepoint 1 Patient 79, Timepoint
- GSM9625448 Patient 79, Timepoint 2 Patient 79, Timepoint
- GSM9625449 Patient 80, Timepoint 1 Patient 80, Timepoint
- GSM9625450 Patient 80, Timepoint 2 Patient 80, Timepoint
- GSM9625451 Patient 81, Timepoint 1 Patient 81, Timepoint
- GSM9625452 Patient 81, Timepoint 2 Patient 81, Timepoint
- GSM9625453 Patient 85, Timepoint 1 Patient 85, Timepoint
- GSM9625454 Patient 85, Timepoint 2 Patient 85, Timepoint
- GSM9625455 Patient 91, Timepoint 1 Patient 91, Timepoint
- GSM9625456 Patient 91, Timepoint 2 Patient 91, Timepoint
- GSM9625457 Patient 99, Timepoint 1 Patient 99, Timepoint
- GSM9625458 Patient 99, Timepoint 2 Patient 99, Timepoint
- GSM9625459 Patient 99, Timepoint 3 Patient 99, Timepoint
- GSM9625460 Patient 100, Timepoint 1 Patient 100, Timepoint
- GSM9625461 Patient 100, Timepoint 2 Patient 100, Timepoint
- GSM9625462 Patient 100, Timepoint 3 Patient 100, Timepoint
- GSM9625463 Patient 101, Timepoint 1 Patient 101, Timepoint
- GSM9625464 Patient 101, Timepoint 2 Patient 101, Timepoint
- GSM9625465 Patient 103, Timepoint 1 Patient 103, Timepoint
- GSM9625466 Patient 103, Timepoint 2 Patient 103, Timepoint
- GSM9625467 Patient 104, Timepoint 1 Patient 104, Timepoint
- GSM9625468 Patient 104, Timepoint 2 Patient 104, Timepoint
- GSM9625469 Patient 105, Timepoint 1 Patient 105, Timepoint
- GSM9625470 Patient 105, Timepoint 2 Patient 105, Timepoint
- GSM9625471 Patient 105, Timepoint 3 Patient 105, Timepoint
- GSM9625472 Patient 106, Timepoint 1 Patient 106, Timepoint
- GSM9625473 Patient 106, Timepoint 2 Patient 106, Timepoint
+ 35 more — browse all 75 samples with per-sample file links →
More Lower-grade glioma datasets
- GSE308634 Transcriptional changes mediated by the HDACi panobinostat in IDH-wildtype and IDH-mutant gliomas 54 samples same disease briefing · shares idh-mutant, changes, glioma
- GSE308636 Transcriptional changes mediated by the HDACi belinostat in IDH-mutant glioma cultures with YAP and TAZ overexpression 36 samples same disease briefing · shares idh-mutant, changes, glioma
- GSE308631 Changes in Histone H3 acetylation in panobinostat-treated IDH-wildtype and IDH-mutant glioma cultures 28 samples same disease briefing · shares idh-mutant, changes, glioma
- GSE260997 Mutant IDH inhibitors induce lineage differentiation in IDH-mutant Oligodendroglioma III 2458 samples same disease briefing · shares idh-mutant, glioma
- GSE226321 Multimodal integration of transcriptomics, proteomics and radiomics improves prediction of recurrence in patients with IDH-mutant Glioma 59 samples same disease briefing · shares idh-mutant, glioma
- GSE252245 Exploiting the therapeutic vulnerability of IDH-mutant gliomas with zotiraciclib 24 samples same disease briefing · shares idh-mutant, glioma
- GSE134120 Combination of epigenetic enzyme inhibitors, GSKJ4 and Belinostat, reveals efficacy in IDH1 mutant gliomas 16 samples same disease briefing · shares genetic, glioma
- GSE292732 A pilot trial of perioperative IDH Inhibition in treatment-naïve IDH-mutant glioma 11 samples same disease briefing · shares idh-mutant, glioma
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.