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Physiomimetic 3D skeletal muscle reveals senescence-driven matreotype remodeling driving paracrine senescence and regenerative failure

GSE318627 Mus musculus (mouse) RNA-seq (gene expression) 9 samples Submitted 2026/08/03 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 9 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
Mus musculus (mouse)
Samples
9 samples: 3 × 3D muscle construct, irradiated, biol vs 3 × 3D muscle construct, SnC cocultured, biol vs 2 × 3D muscle construct, normal, biolGroups 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)
We developed a physiomimetic, 3D engineered skeletal muscle platform by embedding C2C12 myoblasts within muscle tissue–derived extracellular matrix (mdECM) to model how cellular senescence remodels the muscle niche and propagates dysfunction. Senescence was induced in 3D constructs by a single 20 Gy X-ray irradiation, resulting in impaired myogenic differentiation and structural maturation.
Read the full summary ↓
To define senescence-associated transcriptional programs and paracrine senescence, we performed bulk RNA-sequencing on (i) irradiated senescent (SnC) versus (ii) non-irradiated normal 3D muscle constructs, and (iii) normal constructs exposed in a co-culture to senescent construct–derived secretome (SASP). These datasets enable quantitative characterization of senescence-driven gene expression changes linked to ECM remodeling, inflammation, and loss of myogenic/contractile programs in a physiologically relevant 3D muscle model.
This dataset
Open GSE318627 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 GSE318627_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 PRJNA1419609 and SRA study SRP675133. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × 3D muscle construct, irradiated, biol vs 3 × 3D muscle construct, SnC cocultured, biol vs 2 × 3D muscle construct, normal, biol

Supports a between-group comparison across 8 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 8 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
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.