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Enhancer rewiring orchestrates inflammation and loss of cell identity during muscle stem cell aging [CUT&RUN]

GSE293129 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 36 samples Submitted 2025/11/13 Platform GPL30172
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 36 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
36 samples: 36 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
Enhancer Rewiring Orchestrates Inflammation and Loss of Cell Identity During Muscle Stem Cell AgingAging cell · 2026
Summary (written by the submitters)
Loss of regeneration is a key feature of aging organs, often linked to stem cell exhaustion. Skeletal muscle stem cells (MuSCs) undergo age-related numerical and functional decline, contributing to reduced regenerative potential. Using low-input multi-omics, we systematically profiled the epigenome, transcriptome, and 3D genome of MuSCs from individual mice across 3 age groups (young, old, and geriatric) and both sexes.
Read the full summary ↓
At baseline, young male MuSCs showed reduced expression of cell cycle-related mRNAs. In aged mice, particularly males, MuSCs exhibited early alterations (emerging during the transition from young to old age), including enhanced proinflammatory signaling, and loss of cell identity. Late alterations (emerging during the transition from old to geriatric age) included heightened inflammation, widespread enhancer activation, and extensive 3D genome rewiring. Proinflammatory pathways were enriched for interferon signaling and correlated with endogenous retroviral expression and NFκB activity. Late-stage epigenome and 3D genome rewiring reflected downstream degenerative changes in muscle organization, response to cytokines, and loss of myogenic identity. Thus, progressive molecular shifts may explain the aggravated proliferative deficit and functional impairment observed in MuSCs during aging.
Published in
Enhancer Rewiring Orchestrates Inflammation and Loss of Cell Identity During Muscle Stem Cell Aging
Shi C, Yang N, Pizano E et al. · Aging cell 2026 · PMID 41239549 · doi:10.1111/acel.70289
This dataset
Open GSE293129 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 GSE293129_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 PRJNA1242858 and SRA study SRP573663. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41239549) ↗
Study design
36 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 36 sample titles: 36 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.