Visium HD
Mouse brain
GSM9817886
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9817886_Tg129_213_D1_008um_hires_image': DataArray[cyx] (3, 5370, 6000)
│ └── 'GSM9817886_Tg129_213_D1_008um_lowres_image': DataArray[cyx] (3, 537, 600)
├── Shapes
│ ├── 'GSM9817886_Tg129_213_D1_008um_square_008um': GeoDataFrame shape: (217363, 1) (2D shapes)
│ └── 'GSM9817886_Tg129_213_D1_008um_square_008um_for_vitessce': GeoDataFrame shape: (217363, 2) (2D shapes)
└── Tables
└── 'square_008um': AnnData (217363, 19059)
with coordinate systems:
▸ 'GSM9817886_Tg129_213_D1_008um', with elements:
GSM9817886_Tg129_213_D1_008um_hires_image (Images), GSM9817886_Tg129_213_D1_008um_lowres_image (Images), GSM9817886_Tg129_213_D1_008um_square_008um (Shapes), GSM9817886_Tg129_213_D1_008um_square_008um_for_vitessce (Shapes)
▸ 'GSM9817886_Tg129_213_D1_008um_downscaled_hires', with elements:
GSM9817886_Tg129_213_D1_008um_hires_image (Images), GSM9817886_Tg129_213_D1_008um_square_008um (Shapes), GSM9817886_Tg129_213_D1_008um_square_008um_for_vitessce (Shapes)
▸ 'GSM9817886_Tg129_213_D1_008um_downscaled_lowres', with elements:
GSM9817886_Tg129_213_D1_008um_lowres_image (Images), GSM9817886_Tg129_213_D1_008um_square_008um (Shapes), GSM9817886_Tg129_213_D1_008um_square_008um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("zmFhjkR2UErAjwVb0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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("zmFhjkR2UErAjwVb") # 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download zmFhjkR2UErAjwVb0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/zmFhjkR2UErAjwVb
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid zmFhjkR2UErAjwVbLicense: NCBI GEO policy
Part 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
- Modality
- RNA
- Observations (bins)
- 217,363
- Tier
- bronze
- Resolution
- 6,000 × 5,370 px
- mpp
- 0.896 µm/px
- Size
- 674 MB
- Sample ID
- GSM9817886
- License
- NCBI GEO policy
- Published
- Jun 22, 2026
Cite this dataset
doi.org/10.1038/s44161-026-00844-0 ↗ BibTeX
@article{Jeffrey_2026,
title = {Pericyte KATP channel hyperactivity redistributes cortical blood flow in a CADASIL mouse model},
volume = {5},
ISSN = {2731-0590},
url = {http://dx.doi.org/10.1038/s44161-026-00844-0},
DOI = {10.1038/s44161-026-00844-0},
number = {8},
journal = {Nature Cardiovascular Research},
publisher = {Springer Science and Business Media LLC},
author = {Jeffrey, Danielle A. and Prince, Eric W. and Khakpour, Niloufar and Ferris, Hannah R. and Peters, Colin H. and Seedorf, Gregory and Romero, Phinea Z. and Bueno Guerrero, Mayra and Russell, Abigail N. and Glodoski, Katherine and Futia, Gregory L. and Bonney, Stephanie K. and Thornton, Michael A. and Gibson, Emily A. and Proenza, Catherine and Garcia, Anastacia M. and Tsoukias, Nikolaos M. and Dabertrand, Fabrice},
year = {2026},
month = Aug,
pages = {725–743}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB