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PAX3-SIX2 muscle stem cell heterogeneity drives muscle-specific repair dynamics diversification

GSE325703 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/03/24 Platform GPL28330
At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
12 samples: 3 × GFP_neg_CTR_plasmid vs 3 × GFP_neg_SIX2_plasmid vs 3 × GFP_pos_CTR_plasmid vs 3 × GFP_pos_SIX2_plasmidGroups 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)
Skeletal muscle regeneration relies on muscle-specific stem cells (MuSCs) expressing PAX7. Its paralog, PAX3, a key regulator of embryonic myogenesis, is selectively expressed in a subset of adult MuSCs, with variable proportions across muscles. Here, using lineage tracing and muscle injury models, we show that PAX3-driven MuSC diversity underlies muscle-specific regeneration rates.
Read the full summary ↓
PAX3⁺ MuSCs exhibit enhanced proliferation and differentiation, supporting rapid repair, while PAX3 loss leads to proliferation arrest and cell death. Single-cell RNA-sequencing revealed that PAX3 regulates SIX2 expression, and that SIX2 modulates MuSC proliferation, with its loss impairing, and ectopic expression promoting, repair dynamics. PAX3/SIX2 expression correlates with distinct proliferative and differentiation behaviors, influencing regeneration in a muscle-type-dependent manner. Our findings uncover an unrecognized layer of muscle-specific regulation in MuSCs-mediated repair and suggest that PAX3-SIX2 heterogeneity could be leveraged for targeted therapies in muscle-wasting diseases.
This dataset
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.

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 GSE325703_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1441483 and SRA study SRP685944. Searching any of these in the dataset finder brings you back here.

Study design
3 × GFP_neg_CTR_plasmid vs 3 × GFP_neg_SIX2_plasmid vs 3 × GFP_pos_CTR_plasmid vs 3 × GFP_pos_SIX2_plasmid

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
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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.