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The regulatory code of injury responsive enhancers enables precision cell state targeting in the CNS [multiome]

GSE304399 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 42 samples Submitted 2025/10/15 Platform GPL30215Platform GPL30172
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 42 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
42 samples: 42 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
The regulatory code of injury-responsive enhancers enables precision cell-state targeting in the CNSNature neuroscience · 2026
Summary (written by the submitters)
Enhancer elements in the genome direct cell type-specific gene expression programs. In response to injury, cells undergo dynamic changes in gene expression that drive adaptation and initiate tissue repair. Here, we investigate how injury-induced gene expression programs are encoded within enhancer elements in the mammalian CNS. Leveraging single-nucleus transcriptomics and chromatin accessibility profiling, we identify thousands of injury-induced, cell type-specific enhancers in the mouse spinal cord.
Read the full summary ↓
These enhancers are more abundant in glial cells and retain cell type specificity, even when regulating shared wound response genes. By modeling glial injury-responsive enhancers using deep learning, we reveal that their architecture encodes cell type specificity by integrating generic stimulus response elements with cell identity programs. Finally, through in vivo enhancer screening, we demonstrate that injury-responsive enhancers can selectively target reactive astrocytes bordering lesion sites across the CNS using therapeutically relevant gene delivery vectors. Our decoding of the principles of injury-responsive enhancers enables the design of sequences that can be programmed to therapeutically target disease related cell states.
Published in
The regulatory code of injury-responsive enhancers enables precision cell-state targeting in the CNS
Zamboni M, Martínez-Martín A, Rydholm G et al. · Nature neuroscience 2026 · PMID 41331142 · doi:10.1038/s41593-025-02131-w
This dataset
Open GSE304399 in finder → Find more mouse 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 GSE304399_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 PRJNA1301245 and SRA study SRP606663. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41331142) ↗
Study design
42 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 42 sample titles: 42 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.