Visium
Human brain
GSM9497614
silver Interactive viewer H&E glioblastoma No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 1969, 2000)
│ └── 'GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 591, 600)
├── Shapes
│ └── 'GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix': GeoDataFrame shape: (2138, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2138, 36601)
with coordinate systems:
▸ 'GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix', with elements:
GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_hires_image (Images), GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_lowres_image (Images), GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix (Shapes)
▸ 'GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_downscaled_hires', with elements:
GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_hires_image (Images), GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix (Shapes)
▸ 'GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_downscaled_lowres', with elements:
GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix_lowres_image (Images), GSM9497614_TWBK-B176-02-01_filtered_feature_bc_matrix_TWBK-B176-02-01_filtered_feature_bc_matrix (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("UTlKgw42swaIzNSi") # 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 Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/UTlKgw42swaIzNSi
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid UTlKgw42swaIzNSi Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["UTlKgw42swaIzNSi0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart 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
- 2,138
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,969 px
- mpp
- 4.429 µm/px
- Size
- 35 MB
- Sample ID
- GSM9497614
- License
- unknown
- Published
- Jun 12, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB