Visium
Mouse lung
GSM9191676
bronze Interactive viewer H&E lung adenocarcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9191676_KPU-_hires_image': DataArray[cyx] (3, 1189, 2000)
│ └── 'GSM9191676_KPU-_lowres_image': DataArray[cyx] (3, 357, 600)
├── Shapes
│ └── 'GSM9191676_KPU-': GeoDataFrame shape: (3511, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3511, 19465)
with coordinate systems:
▸ 'GSM9191676_KPU-', with elements:
GSM9191676_KPU-_hires_image (Images), GSM9191676_KPU-_lowres_image (Images), GSM9191676_KPU- (Shapes)
▸ 'GSM9191676_KPU-_downscaled_hires', with elements:
GSM9191676_KPU-_hires_image (Images), GSM9191676_KPU- (Shapes)
▸ 'GSM9191676_KPU-_downscaled_lowres', with elements:
GSM9191676_KPU-_lowres_image (Images), GSM9191676_KPU- (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("U6HZp3LaGMZ4We7Z0000")
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("U6HZp3LaGMZ4We7Z") # 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 U6HZp3LaGMZ4We7Z0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/U6HZp3LaGMZ4We7Z
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid U6HZp3LaGMZ4We7ZLicense: NCBI GEO policy
Part of collection
Genetic deletion of Usp22 in KRASG12D-p53-null driven mouse lung cancer [Visium]→ 2 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- lung
- Condition
- lung adenocarcinoma
- Modality
- RNA
- Observations (spots)
- 3,511
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,189 px
- mpp
- 5.974 µm/px
- Size
- 70.2 MB
- Sample ID
- GSM9191676
- License
- NCBI GEO policy
- Published
- Aug 20, 2026
Cite this dataset
doi.org/10.7150/thno.125898 ↗ BibTeX
@article{Zhang_2026,
title = {Targeting USP22 reprograms the tumor microenvironment and sensitizes KRAS/p53-driven lung cancer to anti-PD-1 immunotherapy},
volume = {16},
ISSN = {1838-7640},
url = {http://dx.doi.org/10.7150/thno.125898},
DOI = {10.7150/thno.125898},
number = {15},
journal = {Theranostics},
publisher = {Ivyspring International Publisher},
author = {Zhang, Keqiang and Ouyang, Ching and Castillo, Jonathan and Wang, Jinhui and Tsark, Walter and Gao, Yuanyuan and Li, Wendong and Yang, Minxiao and Li, Aimin and Oeung, Britney and Egelston, Colt and Wu, Jun and Raul, Mahima and Arvanitis, Leonidas and Marconett, Crystal and Raz, Dan J},
year = {2026},
month = Aug,
pages = {8708–8732}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB