GEO series
Multiscale analysis and functional validation of the cellular and genetic determinants of skeletal disease [Human scRNA-seq]
GSE317068
Homo sapiens (human)
Single-cell RNA-seq
32 samples
Submitted 2026/02/04
Platform GPL24676
At a glance
Single-cell RNA-seq · human · 32 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 32 samples: 32 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)
Musculoskeletal diseases are a major health burden. Development of bone-active therapies has been hindered by limited understanding of the cells and genes that regulate the skeleton. We exploited the value of cross-species analysis and developed single-cell methodologies in skeletal tissues to define the critical endosteal compartment that regulates bone turnover.
Read the full summary ↓
Thirty-four distinct cell types were identified, and disease-relevant cells prioritised by enrichment for rare skeletal disorder genes and bone mineral density-associated genes in an extended UK Biobank GWAS. Functional validation was undertaken in over one thousand genetically modified mouse models. Endothelial and vascular smooth muscle cells were identified as novel skeletal disease-relevant cells alongside osteoblast, chondrocyte and osteoclast cell lineages. Hundreds of cell-specific genes with unappreciated roles in skeletal pathophysiology were identified. This comprehensive cellular and molecular framework underpins skeletal physiology and disease, and will help prioritise new therapeutic targets to accelerate development of novel therapies to treat musculoskeletal disease.
This dataset
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.
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 GSE317068_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1405736 and SRA study SRP665012. Searching any of these in the dataset finder brings you back here.
Study design
32 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 32 sample titles: 32 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
- GSM9464638 Patient RRx_011, cells from human femoral head isolated using collagenase A + dispase
- GSM9464639 Patient RRx_011, cells from human femoral head isolated using collagenase IV + dispase
- GSM9464640 Patient RRx_011, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464641 Patient RRx_011, cells from human femoral neck isolated using collagenase IV + dispase
- GSM9464642 Patient RRx_012, cells from human femoral head isolated using collagenase A + dispase
- GSM9464643 Patient RRx_012, cells from human femoral head isolated using collagenase IV + dispase
- GSM9464644 Patient RRx_012, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464645 Patient RRx_012, cells from human femoral neck isolated using collagenase IV + dispase
- GSM9464646 Patient RRx_014, cells from human femoral head isolated using collagenase A + dispase
- GSM9464647 Patient RRx_014, cells from human femoral head isolated using collagenase IV + dispase
- GSM9464648 Patient RRx_014, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464649 Patient RRx_014, cells from human femoral neck isolated using collagenase IV + dispase
- GSM9464650 Patient RRx_020, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464651 Patient RRx_020, cells from human femoral neck isolated using collagenase IV + dispase
- GSM9464652 Patient RRx_022, cells from human femoral head isolated using collagenase A + dispase, from region affected by OA
- GSM9464653 Patient RRx_022, cells from human femoral head isolated using collagenase A + dispase, from region unaffected by OA
- GSM9464654 Patient RRx_022, ALPL-high cells from human femoral neck isolated using collagenase A + dispase
- GSM9464655 Patient RRx_022, ALPL-low cells from human femoral neck isolated using collagenase A + dispase
- GSM9464656 Patient RRx_022, ALPL-negative cells from human femoral neck isolated using collagenase A + dispase
- GSM9464657 Patient RRx_024, cells from human femoral head isolated using collagenase A + dispase, from region affected by OA
- GSM9464658 Patient RRx_024, cells from human femoral head isolated using collagenase A + dispase, from region unaffected by OA
- GSM9464659 Patient RRx_024, ALPL-high cells from human femoral neck isolated using collagenase A + dispase
- GSM9464660 Patient RRx_024, ALPL-low cells from human femoral neck isolated using collagenase A + dispase
- GSM9464661 Patient RRx_024, ALPL-negative cells from human femoral neck isolated using collagenase A + dispase
- GSM9464662 Patient RRx_026, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464663 Patient RRx_026, cells from human femoral neck cortical region isolated using collagenase A + dispase
- GSM9464664 Patient RRx_030, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464665 Patient RRx_030, cells from human femoral neck cortical region isolated using collagenase A + dispase
- GSM9464666 Patient RRx_032, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464667 Patient RRx_032, cells from human femoral neck cortical region isolated using collagenase A + dispase
- GSM9464668 Patient RRx_034, cells from human femoral neck isolated using collagenase A + dispase
- GSM9464669 Patient RRx_034, cells from human femoral neck cortical region isolated using collagenase A + dispase
Datasets on a similar topic
- GSE234181 Long-term multi-modal recording reveals epigenetic adaptation routes in dormant breast cancer cells [scRNA-Seq] 184 samples shares scrna-seq, genetic
- GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares scrna-seq, cellular
- GSE309392 Cancer-associated fibroblasts promote esophageal carcinogenesis and lung metastasis by CDH11-mediated extracellular matrix environment remodeling [scRNA-Seq] 23 samples shares scrna-seq, cellular
- GSE247690 Cellular adaptation to cancer therapy along a resistance continuum [scRNA-seq: PDX] 17 samples shares scrna-seq, cellular
- GSE318418 Immunosuppressive monocytes are enriched in hepatocellular carcinoma patients with liver dysfunction in a phase II trial of combination sorafenib and nivolumab [scRNA-Seq] 16 samples shares scrna-seq, cellular
- GSE289435 Single-cell transcriptional atlas of hematopoiesis reveals genetic and hierarchy-based determinants of aberrant differentiation in AML 13 samples shares determinants, genetic
- GSE272347 Late-stage tertiary lymphoid structures in hepatocellular carcinoma treated with neoadjuvant immune checkpoint blockade [scRNA-seq] 17 samples shares scrna-seq, cellular
- GSE256490 Single-cell atlas of the human brain vasculature across development, adulthood and disease (scRNA-Seq) 71 samples shares scrna-seq
Share this dataset
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.