Visium

Human brain

GSM9497623

silver H&E glioblastoma No linked publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 1950, 2000)
│     └── 'GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 585, 600)
├── Shapes
│     └── 'GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix': GeoDataFrame shape: (2461, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2461, 36601)
with coordinate systems:
    ▸ 'GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix', with elements:
        GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_hires_image (Images), GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_lowres_image (Images), GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix (Shapes)
    ▸ 'GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_downscaled_hires', with elements:
        GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_hires_image (Images), GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix (Shapes)
    ▸ 'GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_downscaled_lowres', with elements:
        GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix_lowres_image (Images), GSM9497623_TWBK-B186-04_filtered_feature_bc_matrix_TWBK-B186-04_filtered_feature_bc_matrix (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("jkeyxJ1u2kAJY5Yg0000")
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("jkeyxJ1u2kAJY5Yg")   # 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 jkeyxJ1u2kAJY5Yg0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/jkeyxJ1u2kAJY5Yg

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jkeyxJ1u2kAJY5Yg

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions [Spatial Transcriptomics]→

22 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: brain
Condition: glioblastoma
Modality: RNA
Observations (spots): 2,461
Tier: silver
Chemistry: Visium v1
Resolution: 2,000 × 1,950 px
mpp: 4.454 µm/px
Size: 29.1 MB
Sample ID: GSM9497623
License: NCBI GEO policy
Published: Jun 12, 2026

Cite this dataset

No linked publication found — please cite the data source: NCBI GEO ↗.

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