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HDAC4 myonuclear reprogramming underlies denervation and weakness following musculoskeletal trauma

GSE314430 Homo sapiens (human) Single-nucleus RNA-seq 43 samples Submitted 2026/05/15 Platform GPL24676Platform GPL20301
At a glance

Single-nucleus RNA-seq · human · 43 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
Homo sapiens (human)
Samples
43 samples: 34 distinct titles, 1 of them repeatedGroups 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
Multi-omics analysis reveals sex-specific etiology of human muscle weakness following musculoskeletal injuryBMC medicine · 2026
Summary (written by the submitters)
Muscle weakness following joint injury contributes to substantial functional deficits and disability. The molecular etiology of prolonged muscle atrophy and weakness following knee ligament injury remains undefined, limiting our understanding of the inceptive events underlying poor functional recovery. We studied the muscle transcriptome in men and women in response to ligament injury and surgical reconstruction, revealing substantial injury- and reconstructive surgery-dependent changes, with sustained increases in genes associated with neuromuscular junction remodeling.
Read the full summary ↓
We identified histone deacetylase 4 (HDAC4) as one of the most upregulated targets in muscle following both injury and reconstruction, and multi-omics integration of HDAC4 ChIP-sequencing and RNA-sequencing revealed a coordinated repression of genes associated with metabolism and contractile function following injury. We further applied single nucleus RNA-sequencing and uncovered aberrant neuromuscular signaling and myonuclear reprogramming. Our multi-omics and single-nucleus transcriptome analyses demonstrate unique myocellular and neuromuscular adaptations that could explain persistent functional deficits following acute musculoskeletal injury.
Published in
Multi-omics analysis reveals sex-specific etiology of human muscle weakness following musculoskeletal injury
Keeble AR, Owen AM, Thomas NT et al. · BMC medicine 2026 · PMID 41882639 · doi:10.1186/s12916-026-04818-8
This dataset
Open GSE314430 in finder → Find more human 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 GSE314430_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 PRJNA1391070 and SRA study SRP656666. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41882639) ↗
Study design
34 distinct titles, 1 of them repeated
7d post-Sx, ×10

How we read this: Read from 43 sample titles: 34 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

+ 3 more — browse all 43 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.