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Longitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiation [scRNA-seq]

GSE292130 Homo sapiens (human) Single-cell RNA-seq 31 samples Submitted 2026/04/27 Platform GPL24676Platform GPL30173
At a glance

Single-cell RNA-seq · human · 31 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
31 samples: 31 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
Longitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiationNature genetics · 2026
Summary (written by the submitters)
IDH-mutant gliomas are initially slow-growing but invariably progress to fatal disease. Major gaps remain in our understanding of the molecular underpinnings and cellular dynamics that drive IDH-mutant evolution. To address this, we integrated joint-capture multi-omic single-cell profiling of DNA methylation, transcriptome and genotype in a longitudinal cohort of 35 IDH-mutant gliomas.
Read the full summary ↓
Transcriptional and epigenetic comparisons of cell states during glioma progression revealed increased stem-like states, decreased differentiation and identified potential cell states regulators. Single-cell DNA methylation analysis demonstrated methylation loss in progressed tumors, however global DNA methylation was similar between different malignant cells states within individual tumors. This suggests that lower DNA methylation is not due to a change in cell state composition, but rather that it may alter cell state landscapes. To address whether decreased methylation in progressed tumors affects cellular dynamics, we applied a quantitative framework to directly measure cell state heritability and plasticity based on transcriptional annotation of high-resolution phylogenetic trees in clinical samples. Our analysis suggested that decreased methylation reshapes cellular architecture to increased heritability of stem-like states and decreased differentiation. Our study provides insight into the functional impact of DNA methylation on glioma evolution, integrating transcriptional cancer cell states with epigenetic encoding and cell state transition dynamics during cancer progression.
Published in
Longitudinal changes in DNA methylation in IDH-mutant glioma fuel disease progression through altered cell state differentiation
Nomura M, Raviram R, Schiffman JS et al. · Nature genetics 2026 · PMID 42332268 · doi:10.1038/s41588-026-02642-7
This dataset
Open GSE292130 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 GSE292130_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 PRJNA1237244. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42332268) ↗
Study design
31 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 31 sample titles: 31 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
Datasets on a similar topic

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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.