Visium

Human bone element

GSM8875102

bronze Interactive viewer H&E osteosarcoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8875102_PT6_hires_image': DataArray[cyx] (3, 2000, 1982)
│     └── 'GSM8875102_PT6_lowres_image': DataArray[cyx] (3, 600, 595)
├── Shapes
│     └── 'GSM8875102_PT6': GeoDataFrame shape: (461, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (461, 17943)
with coordinate systems:
    ▸ 'GSM8875102_PT6', with elements:
        GSM8875102_PT6_hires_image (Images), GSM8875102_PT6_lowres_image (Images), GSM8875102_PT6 (Shapes)
    ▸ 'GSM8875102_PT6_downscaled_hires', with elements:
        GSM8875102_PT6_hires_image (Images), GSM8875102_PT6 (Shapes)
    ▸ 'GSM8875102_PT6_downscaled_lowres', with elements:
        GSM8875102_PT6_lowres_image (Images), GSM8875102_PT6 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("IQG0WXGoATTQOqFU0000")
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("IQG0WXGoATTQOqFU")   # 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 IQG0WXGoATTQOqFU0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/IQG0WXGoATTQOqFU

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid IQG0WXGoATTQOqFU

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial transcriptomic atlas of aggressive osteosarcomas reveals shared gene surface expression and immune landscape.→

26 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: bone element
Condition: osteosarcoma
Modality: RNA
Observations (spots): 461
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,982 × 2,000 px
mpp: 3.363 µm/px
Size: 12.6 MB
Sample ID: GSM8875102
License: NCBI GEO policy
Published: Apr 10, 2026

Cite this dataset

doi.org/10.1038/s41467-026-74603-x ↗

BibTeX

@article{Moquin_Beaudry_2026,
  title = {Spatial transcriptomic atlas of aggressive osteosarcomas reveals shared immune landscape and targetable surface markers},
  volume = {17},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-026-74603-x},
  DOI = {10.1038/s41467-026-74603-x},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Moquin-Beaudry, Gaël and Marques Da Costa, Maria Eugénia and Khneisser, Pierre and Thuilliez, Corentin and Audinot, Baptiste and Zair, Hanane and Signolle, Nicolas and McDermott, Michael and Tabone, Marie-Dominique and Scoazec, Jean-Yves and Badoual, Cécile and Geoerger, Birgit and Drubay, Damien and Gaspar, Nathalie and Marchais, Antonin},
  year = {2026},
  month = July
}

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