GEO series

PGC-1alpha pathway dysregulation disrupts myofiber specification in a mouse model of SBMA [AR113Q RNA-Seq]

GSE305395 Mus musculus (mouse) Single-nucleus RNA-seq 27 samples Submitted 2026/04/15 Platform GPL34328Platform GPL24247

At a glance

Single-nucleus RNA-seq · mouse · 27 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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: 27 samples: 7 conditions, mostly with about 4 replicates eachGroups 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: PGC-1α pathway dysregulation disrupts myofiber specification in a mouse model of SBMAJCI insight · 2026

Summary (written by the submitters)

Skeletal muscle pathology is a critical but poorly understood contributor to neuromuscular degeneration in spinal and bulbar muscular atrophy (SBMA), a CAG/polyglutamine (polyQ) expansion disorder caused by mutation in the androgen receptor (AR). Using a gene-targeted SBMA mouse model, we applied single-nucleus RNA sequencing to identify a previously unrecognized, disease-specific population of skeletal muscle myonuclei that replace normal myonuclear subtypes.

Read the full summary ↓

This transition was driven by dysregulation of the PGC-1α pathway, a central regulator of myofiber specification and metabolic identity. PGC-1α dysfunction in SBMA muscle was age-, hormone-, and polyQ length–dependent and was partially rescued by subcutaneous delivery of AR-targeted antisense oligonucleotides. Integrated ChIP-seq and RNA-seq analyses revealed that aberrant PGC-1α activity promoted the expression of a distinct set of myofiber specification genes while downregulating those defining healthy Type IIb and Type IIx myonuclei. We propose a model in which this dysfunction arose downstream of polyQ-mediated sequestration of PGC-1α cofactors MEF2, CREB, and CBP, leading to transcriptional reprogramming and cellular dysfunction. These findings implicate PGC-1α dysregulation as a key event linking AR polyQ expansion to skeletal muscle degeneration and suggest a shared mechanism for polyQ-mediated muscle pathology across related neurodegenerative diseases.

Published in

PGC-1α pathway dysregulation disrupts myofiber specification in a mouse model of SBMA

Kuo CJ, Chopp LB, Yu Z et al. · JCI insight 2026 · PMID 42096288 · doi:10.1172/jci.insight.203215

This dataset

Open GSE305395 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 GSE305395_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: 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 PRJNA1305455 and SRA study SRP608729. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42096288) ↗

Study design

7 conditions, mostly with about 4 replicates each

52w-WT ×4 52w-AR113Q ×4 52w-ASO ×4 26w-WT ×4 26w-AR113Q ×4 5w-AR113Q ×4 5w-WT ×3

Supports a between-group comparison across 27 samples.

How we read this: 7 replicated groups read from 27 sample titles; they account for 27 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

GSM9170951 52w-WT1 52w-WT
GSM9170952 52w-WT2 52w-WT
GSM9170953 52w-WT3 52w-WT
GSM9170954 52w-WT4 52w-WT
GSM9170955 52w-AR113Q1 52w-AR113Q
GSM9170956 52w-AR113Q2 52w-AR113Q
GSM9170957 52w-AR113Q3 52w-AR113Q
GSM9170958 52w-AR113Q4 52w-AR113Q
GSM9170959 52w-ASO1 52w-ASO
GSM9170960 52w-ASO2 52w-ASO
GSM9170961 52w-ASO3 52w-ASO
GSM9170962 52w-ASO4 52w-ASO
GSM9170963 26w-WT1 26w-WT
GSM9170964 26w-WT2 26w-WT
GSM9170965 26w-WT3 26w-WT
GSM9170966 26w-WT4 26w-WT
GSM9170967 26w-AR113Q1 26w-AR113Q
GSM9170968 26w-AR113Q2 26w-AR113Q
GSM9170969 26w-AR113Q3 26w-AR113Q
GSM9170970 26w-AR113Q4 26w-AR113Q
GSM9170971 5w-WT1 5w-WT
GSM9170972 5w-AR113Q1 5w-AR113Q
GSM9170973 5w-WT2 5w-WT
GSM9170974 5w-AR113Q2 5w-AR113Q
GSM9170975 5w-WT3 5w-WT
GSM9170976 5w-AR113Q3 5w-AR113Q
GSM9170977 5w-AR113Q4 5w-AR113Q

Datasets on a similar topic

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GSE260638 A MOZ-TIF2 leukemic mouse model displays KAT6 inhibitor-sensitive H3K23 propionylation and dysregulation of a set of highly active developmental genes [RNA-seq] 40 samples shares dysregulation, rna-seq
GSE273270 Cell type-specific dysregulation of gene expression due to Chd8 loss of function during mouse cortical development [bulk RNA-seq] 36 samples shares dysregulation, rna-seq
GSE273271 Cell type-specific dysregulation of gene expression due to Chd8 loss of function during mouse cortical development [scRNA-seq] 32 samples shares dysregulation, rna-seq
GSE295923 Maternal Vitamin C Deficiency and Genetic Risk Factors Contribute to Congenital Malformations through Dysregulation of DNA Methylation. [RNA-Seq E8.5] 29 samples shares dysregulation, rna-seq

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