Visium

Human skin of body

GSM9060733

bronze Interactive viewer H&E cutaneous melanoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9060733_AdjIII-0022_processed_hires_image': DataArray[cyx] (3, 1775, 2000)
│     └── 'GSM9060733_AdjIII-0022_processed_lowres_image': DataArray[cyx] (3, 533, 600)
├── Shapes
│     └── 'GSM9060733_AdjIII-0022_processed': GeoDataFrame shape: (2783, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2783, 18085)
with coordinate systems:
    ▸ 'GSM9060733_AdjIII-0022_processed', with elements:
        GSM9060733_AdjIII-0022_processed_hires_image (Images), GSM9060733_AdjIII-0022_processed_lowres_image (Images), GSM9060733_AdjIII-0022_processed (Shapes)
    ▸ 'GSM9060733_AdjIII-0022_processed_downscaled_hires', with elements:
        GSM9060733_AdjIII-0022_processed_hires_image (Images), GSM9060733_AdjIII-0022_processed (Shapes)
    ▸ 'GSM9060733_AdjIII-0022_processed_downscaled_lowres', with elements:
        GSM9060733_AdjIII-0022_processed_lowres_image (Images), GSM9060733_AdjIII-0022_processed (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("IFQycmvT75TLBoN80000")
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("IFQycmvT75TLBoN8")   # 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 IFQycmvT75TLBoN80000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/IFQycmvT75TLBoN8

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid IFQycmvT75TLBoN8

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Spatial tumour-immune ecosystems shape the efficacy of anti-PD1 immunotherapy in primary cutaneous melanoma [Spatial Transcriptomics]→

4 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: skin of body
Condition: cutaneous melanoma
Modality: RNA
Observations (spots): 2,783
Tier: bronze
Chemistry: Visium v1
Resolution: 2,000 × 1,775 px
mpp: 4.226 µm/px
Size: 66.9 MB
Sample ID: GSM9060733
License: NCBI GEO policy
Published: Jun 23, 2026

Cite this dataset

doi.org/10.1136/jitc-2025-014030 ↗

BibTeX

@article{Pham_2026,
  title = {Melanoma cell states shape spatial tumor-immune ecosystems to dictate the efficacy of anti-PD1 immunotherapy},
  volume = {14},
  ISSN = {2051-1426},
  url = {http://dx.doi.org/10.1136/jitc-2025-014030},
  DOI = {10.1136/jitc-2025-014030},
  number = {6},
  journal = {Journal for ImmunoTherapy of Cancer},
  publisher = {BMJ},
  author = {Pham, Félix K and Dufeu, Marion and Benboubker, Valentin and Grimont, Maxime and Lhorisson, Amélie and Berthet, Justine and Donzel, Marie and Schneider, Raphaël and Tonon, Laurie and Doffin, Anne-Claire and Boivin, Félix and Durand, Simon and Dubois, Bertrand and Lopez, Jonathan and Caux, Christophe and Valladeau-Guilemond, Jenny and Eberhardt, Anaïs and Dalle, Stéphane and Caramel, Julie},
  year = {2026},
  month = June,
  pages = {e014030}
}

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