GEO series
Acquired genetic and cell state changes in IDH-mutant glioma progression [snATAC]
GSE327580
Homo sapiens (human)
ChIP-seq / ATAC-seq (genome binding and open chromatin)
48 samples
Submitted 2026/04/23
Platform GPL24676
At a glance
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 48 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Samples
- 48 samples: 22 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 GSE327580_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1451270. Searching any of these in the dataset finder brings you back here.
Study design
22 conditions, mostly in duplicate
Patient 59, 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
Patient 80, Timepoint ×2
+14 more
Supports a between-group comparison across 48 samples.
How we read this: 22 replicated groups read from 48 sample titles; they account for 48 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
- GSM9661823 Patient 57, Timepoint 1 Patient 57, Timepoint
- GSM9661824 Patient 57, Timepoint 2 Patient 57, Timepoint
- GSM9661825 Patient 59, Timepoint 1 Patient 59, Timepoint
- GSM9661826 Patient 59, Timepoint 2 Patient 59, Timepoint
- GSM9661827 Patient 59, Timepoint 3 Patient 59, Timepoint
- GSM9661828 Patient 60, Timepoint 1 Patient 60, Timepoint
- GSM9661829 Patient 60, Timepoint 2 Patient 60, Timepoint
- GSM9661830 Patient 61, Timepoint 1 Patient 61, Timepoint
- GSM9661831 Patient 61, Timepoint 2 Patient 61, Timepoint
- GSM9661832 Patient 80, Timepoint 1 Patient 80, Timepoint
- GSM9661833 Patient 80, Timepoint 2 Patient 80, Timepoint
- GSM9661834 Patient 81, Timepoint 1 Patient 81, Timepoint
- GSM9661835 Patient 81, Timepoint 2 Patient 81, Timepoint
- GSM9661836 Patient 85, Timepoint 1 Patient 85, Timepoint
- GSM9661837 Patient 85, Timepoint 2 Patient 85, Timepoint
- GSM9661838 Patient 91, Timepoint 1 Patient 91, Timepoint
- GSM9661839 Patient 91, Timepoint 2 Patient 91, Timepoint
- GSM9661840 Patient 100, Timepoint 1 Patient 100, Timepoint
- GSM9661841 Patient 100, Timepoint 2 Patient 100, Timepoint
- GSM9661842 Patient 100, Timepoint 3 Patient 100, Timepoint
- GSM9661843 Patient 101, Timepoint 1 Patient 101, Timepoint
- GSM9661844 Patient 101, Timepoint 2 Patient 101, Timepoint
- GSM9661845 Patient 103, Timepoint 1 Patient 103, Timepoint
- GSM9661846 Patient 103, Timepoint 2 Patient 103, Timepoint
- GSM9661847 Patient 104, Timepoint 1 Patient 104, Timepoint
- GSM9661848 Patient 104, Timepoint 2 Patient 104, Timepoint
- GSM9661849 Patient 105, Timepoint 1 Patient 105, Timepoint
- GSM9661850 Patient 105, Timepoint 2 Patient 105, Timepoint
- GSM9661851 Patient 105, Timepoint 3 Patient 105, Timepoint
- GSM9661852 Patient 106, Timepoint 1 Patient 106, Timepoint
- GSM9661853 Patient 106, Timepoint 2 Patient 106, Timepoint
- GSM9661854 Patient 107, Timepoint 1 Patient 107, Timepoint
- GSM9661855 Patient 107, Timepoint 2 Patient 107, Timepoint
- GSM9661856 Patient 108, Timepoint 1 Patient 108, Timepoint
- GSM9661857 Patient 108, Timepoint 2 Patient 108, Timepoint
- GSM9661858 Patient 109, Timepoint 1 Patient 109, Timepoint
- GSM9661859 Patient 109, Timepoint 2 Patient 109, Timepoint
- GSM9661860 Patient 110, Timepoint 1 Patient 110, Timepoint
- GSM9661861 Patient 110, Timepoint 2 Patient 110, Timepoint
- GSM9661862 Patient 111, Timepoint 1 Patient 111, Timepoint
+ 8 more — browse all 48 samples with per-sample file links →
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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.