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Human Skeletal Development and Regenerative Capacity is Shaped by Skeletal Stem Cell Diversity

GSE234040 Homo sapiens (human) Single-cell RNA-seq 39 samples Submitted 2025/02/25 Platform GPL15520Platform GPL24676
At a glance

Single-cell RNA-seq · human · 39 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
Homo sapiens (human)
Samples
39 samples: 39 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
Human skeletal development and regeneration are shaped by functional diversity of stem cells across skeletal sitesCell stem cell · 2025
SuperSeries — this record groups several sub-series.
Summary (written by the submitters)
This SuperSeries is composed of the SubSeries listed below. The skeleton is one of the most structurally and compositionally diverse organ systems in the human body, depending on unique cellular dynamisms. Here, we integrate prospective isolation of human skeletal stem cells (hSSCs; CD45−CD235a−TIE2−CD31−CD146−PDPN+CD73+CD164+) from ten skeletal sites with functional assays and single-cell RNA sequencing (scRNA-seq) analysis to identify chondrogenic, osteogenic, stromal, and fibrogenic subtypes of hSSCs during development and their linkage to skeletal phenotypes.
Read the full summary ↓
We map the distinct composition of hSSC subtypes across multiple skeletal sites and demonstrate their unique in vivo clonal dynamics. We find that age-related changes in bone formation and regeneration disorders stem from a pathological fibroblastic shift in the hSSC pool. Utilizing a Boolean algorithm, we uncover gene regulatory networks that dictate differences in the ability of hSSCs to generate specific skeletal tissues. Importantly, hSSC lineage dynamics are pharmacologically malleable, providing a new strategy to treat aberrant hSSC diversity central to aging and skeletal maladies.
Published in
Human skeletal development and regeneration are shaped by functional diversity of stem cells across skeletal sites
Ambrosi TH, Taheri S, Chen K et al. · Cell stem cell 2025 · PMID 40118065 · doi:10.1016/j.stem.2025.02.013
This dataset
Open GSE234040 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 GSE234040_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 PRJNA979226 and SRA study SRP440923. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40118065) ↗
Study design
39 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 39 sample titles: 39 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.