Visium

Human brain

GSM9497618

silver Interactive viewer H&E glioblastoma No linked publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 1999, 2000)
│     └── 'GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix': GeoDataFrame shape: (1202, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1202, 36601)
with coordinate systems:
    ▸ 'GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix', with elements:
        GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_hires_image (Images), GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_lowres_image (Images), GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix (Shapes)
    ▸ 'GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_downscaled_hires', with elements:
        GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_hires_image (Images), GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix (Shapes)
    ▸ 'GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_downscaled_lowres', with elements:
        GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix_lowres_image (Images), GSM9497618_TWBK-B185-02_filtered_feature_bc_matrix_TWBK-B185-02_filtered_feature_bc_matrix (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("Y9uHynTLefnSLbmM")   # 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/Y9uHynTLefnSLbmM

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Y9uHynTLefnSLbmM

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["Y9uHynTLefnSLbmM0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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

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: 1,202
Tier: silver
Chemistry: Visium v1
Resolution: 2,000 × 1,999 px
mpp: 3.797 µm/px
Size: 16 MB
Sample ID: GSM9497618
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