Visium HD
Mouse small intestine
GSM9210422
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Shapes
│ ├── 'GSM9210422_Control_HD_square_008um': GeoDataFrame shape: (500025, 1) (2D shapes)
│ └── 'GSM9210422_Control_HD_square_008um_for_vitessce': GeoDataFrame shape: (500025, 2) (2D shapes)
└── Tables
└── 'square_008um': AnnData (500025, 19059)
with coordinate systems:
▸ 'GSM9210422_Control_HD', with elements:
GSM9210422_Control_HD_square_008um (Shapes), GSM9210422_Control_HD_square_008um_for_vitessce (Shapes)
▸ 'GSM9210422_Control_HD_downscaled_hires', with elements:
GSM9210422_Control_HD_square_008um (Shapes), GSM9210422_Control_HD_square_008um_for_vitessce (Shapes)
▸ 'GSM9210422_Control_HD_downscaled_lowres', with elements:
GSM9210422_Control_HD_square_008um (Shapes), GSM9210422_Control_HD_square_008um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("qSjJC5EuFHGEk2EW0000")
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("qSjJC5EuFHGEk2EW") # 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 qSjJC5EuFHGEk2EW0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qSjJC5EuFHGEk2EW
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qSjJC5EuFHGEk2EWLicense: NCBI GEO policy
Metadata
- Technology
- Visium HD
- Vendor
- 10x Genomics
- Organism
- mouse
- Tissue
- small intestine
- Modality
- RNA
- Observations (bins)
- 500,025
- Tier
- bronze
- Size
- 1.2 GB
- Sample ID
- GSM9210422
- License
- NCBI GEO policy
- Published
- Mar 22, 2026
Cite this dataset
doi.org/10.1016/j.jcmgh.2026.101740 ↗ BibTeX
@article{Nakata_2026,
title = {Dietary Stearic Acid Accelerates Intestinal Tumorigenesis via Fatty Acid-binding Protein 5 Without Promoting Obesity},
volume = {20},
ISSN = {2352-345X},
url = {http://dx.doi.org/10.1016/j.jcmgh.2026.101740},
DOI = {10.1016/j.jcmgh.2026.101740},
number = {5},
journal = {Cellular and Molecular Gastroenterology and Hepatology},
publisher = {Elsevier BV},
author = {Nakata, Kazuaki and Komiyama, Seiga and Sekine, Keisuke and Nagai, Motoyoshi and Okawa, Takuma and Ohashi, Wakana and Nakano, Kenta and Okamura, Tadashi and Kozono, Takuma and Takemura, Nobuyuki and Yamada, Kazuhiko and Kokudo, Norihiro and Dohi, Taeko and Hase, Koji and Kawamura, Yuki I.},
year = {2026},
pages = {101740}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB