GEO series

SAGA/ATAC complexes sustain aberrant chromatin regulation and metabolic programs in diffuse midline glioma

GSE317235 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/01/26 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 18 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 18 samples: 6 conditions, mostly in triplicateGroups 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.

Summary (written by the submitters)

Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors characterized by chromatin and transcriptional dysregulation induced by H3K27M mutations. Strategies for overcoming epigenetic dysfunction to reduce DMG tumorigenesis remain limited. We identified multiple components of the SAGA and ATAC chromatin regulatory complexes as DMG genetic dependencies and found that genetic or pharmacological inhibition of the SAGA/ATAC-associated chromatin reader, SGF29, reduces DMG proliferation.

Read the full summary ↓

Small molecules targeting SAGA/ATAC-associated histone acetylation, ubiquitination, and methylation similarly suppressed DMG growth. Further chromatin profiling and RNAseq analyses reveal that SGF29 controls H3K9ac and H3K4me3 dynamics at both H3K27M-bound and H3K27M-independent target genes linked to proliferation, differentiation, and metabolism. Finally, we find that SAGA/ATAC inhibition may reduce DMG viability by repressing cholesterol metabolism gene expression and show that combinations of cholesterol- and SAGA/ATAC-targeting drugs synergistically reduce DMG growth. These findings reveal a functional link between SAGA/ATAC-dependent chromatin regulation and both transcriptional and metabolic dysregulation underlying DMG malignancy.

This dataset

Open GSE317235 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 GSE317235_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1406324 and SRA study SRP665414. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

DIPGXIII_RNAseq_CTL ×3 DIPGXIII_RNAseq_SGF29sgA ×3 DIPGXIII_RNAseq_SGF29sgC ×3 DIPGXIII_RNAseq_DMSO ×3 DIPGXIII_RNAseq_CPTH2 ×3 DIPGXIII_RNAseq_GSK4027 ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM9467631 DIPGXIII_RNAseq_CTL.1 DIPGXIII_RNAseq_CTL
GSM9467632 DIPGXIII_RNAseq_CTL.2 DIPGXIII_RNAseq_CTL
GSM9467633 DIPGXIII_RNAseq_CTL.3 DIPGXIII_RNAseq_CTL
GSM9467634 DIPGXIII_RNAseq_SGF29sgA.1 DIPGXIII_RNAseq_SGF29sgA
GSM9467635 DIPGXIII_RNAseq_SGF29sgA.2 DIPGXIII_RNAseq_SGF29sgA
GSM9467636 DIPGXIII_RNAseq_SGF29sgA.3 DIPGXIII_RNAseq_SGF29sgA
GSM9467637 DIPGXIII_RNAseq_SGF29sgC.1 DIPGXIII_RNAseq_SGF29sgC
GSM9467638 DIPGXIII_RNAseq_SGF29sgC.2 DIPGXIII_RNAseq_SGF29sgC
GSM9467639 DIPGXIII_RNAseq_SGF29sgC.3 DIPGXIII_RNAseq_SGF29sgC
GSM9467640 DIPGXIII_RNAseq_DMSO.1 DIPGXIII_RNAseq_DMSO
GSM9467641 DIPGXIII_RNAseq_DMSO.2 DIPGXIII_RNAseq_DMSO
GSM9467642 DIPGXIII_RNAseq_DMSO.3 DIPGXIII_RNAseq_DMSO
GSM9467643 DIPGXIII_RNAseq_CPTH2.1 DIPGXIII_RNAseq_CPTH2
GSM9467644 DIPGXIII_RNAseq_CPTH2.2 DIPGXIII_RNAseq_CPTH2
GSM9467645 DIPGXIII_RNAseq_CPTH2.3 DIPGXIII_RNAseq_CPTH2
GSM9467646 DIPGXIII_RNAseq_GSK4027.1 DIPGXIII_RNAseq_GSK4027
GSM9467647 DIPGXIII_RNAseq_GSK4027.2 DIPGXIII_RNAseq_GSK4027
GSM9467648 DIPGXIII_RNAseq_GSK4027.3 DIPGXIII_RNAseq_GSK4027

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GSE146585 Functional genomics assays, including ChIP-seq, ATAC-seq, RNA-seq, GRO-seq, and in situ Hi-C, in the context of replicative senescence 223 samples shares atac
GSE281526 Single-nuclei RNA-seq and ATAC-seq for Human Retina from Diverse Ancestry Backgrounds 185 samples shares atac
GSE330130 A multi-omics characterization of the human lumbar spinal cord and motor cortex reveals non-overlapping molecular signatures in ALS [snRNA-Seq; snATAC-Seq] 87 samples shares atac
GSE327580 Acquired genetic and cell state changes in IDH-mutant glioma progression [snATAC] 48 samples shares atac

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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.