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Hypoxic stress dysregulates functions of glioma-associated myeloid cells through epigenomic and transcriptional programs [CUT&RUN]

GSE300328 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/08/28 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 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
Mus musculus (mouse)
Samples
12 samples: 12 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
Hypoxic stress dysregulates functions of glioma-associated myeloid cells through epigenomic and transcriptional programsCell reports · 2025
Summary (written by the submitters)
Hypoxia rapidly alters gene expression to allow cellular adaptation to challenging conditions and support tumour growth. Hypoxia also affects the chromatin structure by modifications of histones and DNA methylation. Glioblastoma (GBM) is an aggressive, deadly primary brain tumour for which there is no effective treatment. The tumour microenvironment of GBM is highly heterogeneous, with infiltration of glioma-associated microglia and macrophages (GAMs) and the presence of necrotic, hypoxic regions.
Read the full summary ↓
The mechanisms through which hypoxia alters the tumour microenvironment and regulates functions of infiltrating immune cells remain poorly understood. Here, we show that hypoxia modulates the expression of myeloid markers in distinct ways: upregulates the monocytic marker Lgals3 expression and downregulates the microglial markers P2ry12 and Tmem119 in microglial and monocytic GAMs in vitro and in vivo, as shown using human and mouse GBM single-cell transcriptomics datasets. The genome-wide hypoxia-dependent transcriptomic changes in microglial cells were determined in microglia-glioma co-cultures. Numerous GAM subtype markers were dysregulated in response to hypoxic stress due to associated changes in chromatin accessibility, as determined using ATACseq. While hypoxia alone drives a decrease of the overall chromatin accessibility at gene promoters, the exposure to glioma cells under hypoxic conditions leads to both increases and decreases of chromatin accessibility at promoter regions in microglial cells. Hypoxia downregulates the chromatin accessibility at the regions enriched in motifs of transcription factors regarded as master regulators of microglial cell identity and function, including SPI1 or IRF8. Moreover, changes in histone acetylation are responsible for Lgals3 upregulation and lipid accumulation in GAMs in hypoxic conditions. Overall, our data highlights the importance of hypoxic stress as a strong intratumoral regulator of myeloid cell functions, which adds complexity to the characterisation of particular GAMs subpopulations.
Published in
Hypoxic stress dysregulates functions of glioma-associated myeloid cells through epigenomic and transcriptional programs
Dzwigonska M, Rosa P, Lipiec S et al. · Cell reports 2025 · PMID 40911414 · doi:10.1016/j.celrep.2025.116222
This dataset
Open GSE300328 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE300328_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1280101 and SRA study SRP593380. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40911414) ↗
Study design
12 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 12 sample titles: 12 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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