Visium HD

Human knee

GSM8993724

silver Interactive viewer H&E ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8993724_hires_image': DataArray[cyx] (3, 6000, 5009)
│     └── 'GSM8993724_lowres_image': DataArray[cyx] (3, 600, 501)
├── Shapes
│     ├── 'GSM8993724_square_016um': GeoDataFrame shape: (88814, 1) (2D shapes)
│     └── 'GSM8993724_square_016um_for_vitessce': GeoDataFrame shape: (88814, 2) (2D shapes)
└── Tables
      └── 'square_016um': AnnData (88814, 18085)
with coordinate systems:
    ▸ 'GSM8993724', with elements:
        GSM8993724_hires_image (Images), GSM8993724_lowres_image (Images), GSM8993724_square_016um (Shapes), GSM8993724_square_016um_for_vitessce (Shapes)
    ▸ 'GSM8993724_downscaled_hires', with elements:
        GSM8993724_hires_image (Images), GSM8993724_square_016um (Shapes), GSM8993724_square_016um_for_vitessce (Shapes)
    ▸ 'GSM8993724_downscaled_lowres', with elements:
        GSM8993724_lowres_image (Images), GSM8993724_square_016um (Shapes), GSM8993724_square_016um_for_vitessce (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("lSQ8Kzne2c0iOktx0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)

Install (Python 3.12+) Docs →

Python · lamindb

Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("lSQ8Kzne2c0iOktx")   # stem uid: Lamin resolves it to the latest version

sdata = artifact.load()   # cache, then load into memory
group = artifact.open()   # stream without downloading
path  = artifact.cache()  # sync into the local cache, returns the path

Install (Python 3.12+) Docs →

Shell

# sddb client: download the .zarr into data/
sddb-cli download lSQ8Kzne2c0iOktx0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/lSQ8Kzne2c0iOktx

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid lSQ8Kzne2c0iOktx

Install (Python 3.12+) Docs →

License: NCBI GEO policy

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

geo-gse260926→

1 datasets · publication

Metadata

Technology: Visium HD
Organism: human
Tissue: knee
Modality: RNA
Observations (bins): 88,814
Tier: silver
Resolution: 5,009 × 6,000 px
mpp: 1.560 µm/px
Size: 326 MB
Sample ID: GSM8993724
License: NCBI GEO policy
Published: Nov 26, 2025

Cite this dataset

doi.org/10.1016/j.joca.2025.12.025 ↗

BibTeX

@article{Jachim_2026,
  title = {Leveraging single cell multiomic analyses to identify gene regulatory networks that drive human articular cartilage cell fate},
  volume = {34},
  ISSN = {1063-4584},
  url = {http://dx.doi.org/10.1016/j.joca.2025.12.025},
  DOI = {10.1016/j.joca.2025.12.025},
  number = {3},
  journal = {Osteoarthritis and Cartilage},
  publisher = {Elsevier BV},
  author = {Jachim, Sarah K. and Venkatasubramanian, Divya and Senevirathne, Gayani and Zhu, Andrew S. and Vitale, Concetta S. and Melendez, Delmy L. and Zhang, Cheng-Hai and Lassar, Andrew B. and Capellini, Terence D. and Craft, April M.},
  year = {2026},
  month = Mar,
  pages = {358–371}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB