Visium HD
Mouse vestibular ganglion
GSM9269270
silver Interactive viewer H&E ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9269270_square_002um_hires_image': DataArray[cyx] (3, 6000, 4958)
│ └── 'GSM9269270_square_002um_lowres_image': DataArray[cyx] (3, 600, 496)
├── Shapes
│ ├── 'GSM9269270_square_002um_square_002um': GeoDataFrame shape: (1173664, 1) (2D shapes)
│ ├── 'GSM9269270_square_002um_square_008um': GeoDataFrame shape: (73354, 1) (2D shapes)
│ ├── 'GSM9269270_square_002um_square_016um': GeoDataFrame shape: (18880, 1) (2D shapes)
│ └── 'GSM9269270_square_002um_square_016um_for_vitessce': GeoDataFrame shape: (18880, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (1173664, 19059)
├── 'square_008um': AnnData (73354, 19059)
└── 'square_016um': AnnData (18880, 19059)
with coordinate systems:
▸ 'GSM9269270_square_002um', with elements:
GSM9269270_square_002um_hires_image (Images), GSM9269270_square_002um_lowres_image (Images), GSM9269270_square_002um_square_002um (Shapes), GSM9269270_square_002um_square_008um (Shapes), GSM9269270_square_002um_square_016um (Shapes), GSM9269270_square_002um_square_016um_for_vitessce (Shapes)
▸ 'GSM9269270_square_002um_downscaled_hires', with elements:
GSM9269270_square_002um_hires_image (Images), GSM9269270_square_002um_square_002um (Shapes), GSM9269270_square_002um_square_008um (Shapes), GSM9269270_square_002um_square_016um (Shapes), GSM9269270_square_002um_square_016um_for_vitessce (Shapes)
▸ 'GSM9269270_square_002um_downscaled_lowres', with elements:
GSM9269270_square_002um_lowres_image (Images), GSM9269270_square_002um_square_002um (Shapes), GSM9269270_square_002um_square_008um (Shapes), GSM9269270_square_002um_square_016um (Shapes), GSM9269270_square_002um_square_016um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("23MS517DzrV5V6nc0000")
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("23MS517DzrV5V6nc") # 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 23MS517DzrV5V6nc0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/23MS517DzrV5V6nc
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 23MS517DzrV5V6ncLicense: NCBI GEO policy
Part of collection
Morphological and Functional Diversity of Spatially Resolved Vestibular Ganglion Neuron Cell Types→ 2 datasets · comparison
Metadata
- Technology
- Visium HD
- Organism
- mouse
- Tissue
- vestibular ganglion
- Modality
- RNA
- Observations (bins)
- 73,354
- Tier
- silver
- Resolution
- 4,958 × 6,000 px
- mpp
- 0.917 µm/px
- Size
- 725 MB
- Sample ID
- GSM9269270
- License
- NCBI GEO policy
- Published
- Jul 7, 2026
Cite this dataset
doi.org/10.1073/pnas.2530677123 ↗ BibTeX
@article{Liu_2026,
title = {Morphological and functional diversity of spatially resolved vestibular ganglion neuron cell types},
volume = {123},
ISSN = {1091-6490},
url = {http://dx.doi.org/10.1073/pnas.2530677123},
DOI = {10.1073/pnas.2530677123},
number = {28},
journal = {Proceedings of the National Academy of Sciences},
publisher = {National Academy of Sciences},
author = {Liu, Ruiqi and Liu, Jingyue and Chen, Zhiyu and Li, Jingying and Liu, Zhiyong and Sun, Shuohao},
year = {2026},
month = July
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB