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Multimodal single cell profiling of bone fracture microenvironment

GSE273792 Mus musculus (mouse) Single-cell RNA-seq 24 samples Submitted 2025/02/25 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 24 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
24 samples: 24 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.
Summary (written by the submitters)
Age-specific alterations of the adaptive immune system include low-grade chronic inflammation, which contributes to the unfavorable outcome of the bone healing process in the elderly. In this work, we aimed to characterize the effect of immune experience and age on cellular phenotype during the early inflammatory phase after bone fracture by using a mouse model which included young, aged, and immune-aged mice 2- and 5-days post osteotomy.
Read the full summary ↓
Single-cell proteo-genomics quantified thousands of transcriptomes of cells isolated from three different bone regions (fracture hematoma, proximal and distal bone marrow). The fracture hematoma is a very distinct tissue consisting of coagulated blood caused by rupture of adjacent blood vessels, bone marrow influx and cells attracted from the surrounding tissue. The hematoma matures with the healing process and the adjacent bone marrow (proximal and distal to the fracture gap) supports the healing process.
This dataset
Open GSE273792 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 GSE273792_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 PRJNA1143063 and SRA study SRP523762. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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.