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Mapping Somatosensory Afferent Circuitry to Bone Identifies Neurotrophic Signals Required for Fracture Healing

GSE265817 Mus musculus (mouse) Single-cell RNA-seq 13 samples Submitted 2025/10/22 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 13 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
13 samples: 13 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
Mapping somatosensory afferent circuitry to bone identifies neurotrophic signals required for fracture healingScience (New York, N.Y.) · 2026
Summary (written by the submitters)
A central feature of bone injury is the sensation of pain, transmitted by nociceptive sensory nerves that innervate the skeleton. Indeed skeletal-innervating peripheral neurons are known to regulate skeletal repair and pathophysiology. Here, using retrograde peripheral nerve labeling and single cell RNA sequencing (scRNA-seq), we precisely identify the unique molecular signature of skeletal-innervating sensory neurons and the shifting neuronal transcriptomic landscape after bone fracture.
Read the full summary ↓
Two methods to surgically or genetically denervate fractured bones were used in combination with scRNA-seq to implicate defective mesenchymal cell proliferation and osteodifferentiation as underlying the poor bone repair capacity in the presence of attenuated skeletal innervation. Multi-tissue scRNA-seq and interactome analyses implicated neuron-derived FGF9 as a potent regulator of fracture repair, a finding confirmed by in vitro assessments of neuron-to-skeletal mesenchyme interactions. Identification of FGF9 as a novel, neuron-derived skeletal growth factor uncovers a potential target for improving tissue repair.
Published in
Mapping somatosensory afferent circuitry to bone identifies neurotrophic signals required for fracture healing
Xu M, Li Z, Thottappillil N et al. · Science (New York, N.Y.) 2026 · PMID 41505527 · doi:10.1126/science.adr9608
This dataset
Open GSE265817 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 GSE265817_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 PRJNA1104258 and SRA study SRP503866. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41505527) ↗
Study design
13 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 13 sample titles: 13 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.