Visium HD
Mouse brain
GSM9817887
bronze Interactive viewer H&E cerebral arteriopathy, autosomal dominant, with subcortical infarcts and leukoencephalopathy, type 1 Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9817887_Tg88_148_A1_008um_hires_image': DataArray[cyx] (3, 4400, 6000)
│ └── 'GSM9817887_Tg88_148_A1_008um_lowres_image': DataArray[cyx] (3, 440, 600)
├── Shapes
│ ├── 'GSM9817887_Tg88_148_A1_008um_square_008um': GeoDataFrame shape: (346469, 1) (2D shapes)
│ └── 'GSM9817887_Tg88_148_A1_008um_square_008um_for_vitessce': GeoDataFrame shape: (346469, 2) (2D shapes)
└── Tables
└── 'square_008um': AnnData (346469, 19059)
with coordinate systems:
▸ 'GSM9817887_Tg88_148_A1_008um', with elements:
GSM9817887_Tg88_148_A1_008um_hires_image (Images), GSM9817887_Tg88_148_A1_008um_lowres_image (Images), GSM9817887_Tg88_148_A1_008um_square_008um (Shapes), GSM9817887_Tg88_148_A1_008um_square_008um_for_vitessce (Shapes)
▸ 'GSM9817887_Tg88_148_A1_008um_downscaled_hires', with elements:
GSM9817887_Tg88_148_A1_008um_hires_image (Images), GSM9817887_Tg88_148_A1_008um_square_008um (Shapes), GSM9817887_Tg88_148_A1_008um_square_008um_for_vitessce (Shapes)
▸ 'GSM9817887_Tg88_148_A1_008um_downscaled_lowres', with elements:
GSM9817887_Tg88_148_A1_008um_lowres_image (Images), GSM9817887_Tg88_148_A1_008um_square_008um (Shapes), GSM9817887_Tg88_148_A1_008um_square_008um_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("msqLVC4LXFZ5oufN") # 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/msqLVC4LXFZ5oufN
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid msqLVC4LXFZ5oufN Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["msqLVC4LXFZ5oufN0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Pericyte KATP Channel Hyperactivity Redistributes Cortical Blood Flow in a CADASIL Mouse Model→ 8 datasets · comparison
Metadata
- Technology
- Visium HD
- Organism
- mouse
- Tissue
- brain
- Condition
- cerebral arteriopathy, autosomal dominant, with subcortical infarcts and leukoencephalopathy, type 1
- Modality
- RNA
- Observations
- 346,469
- Tier
- bronze
- Resolution
- 6,000 × 4,400 px
- mpp
- 1.191 µm/px
- Size
- 1.0 GB
- Sample ID
- GSM9817887
- License
- unknown
- Published
- Jun 22, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB