Visium
Mouse adrenal gland
Spatial Transcriptomic Profiling of the Mouse Adrenal Gland and Adjacent Adipose Tissue [GSM8659692]
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8659692_hires_image': DataArray[cyx] (3, 1818, 2000)
│ └── 'GSM8659692_lowres_image': DataArray[cyx] (3, 2275, 2500)
├── Shapes
│ └── 'GSM8659692': GeoDataFrame shape: (992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (992, 19465)
with coordinate systems:
▸ 'GSM8659692', with elements:
GSM8659692_hires_image (Images), GSM8659692_lowres_image (Images), GSM8659692 (Shapes)
▸ 'GSM8659692_downscaled_hires', with elements:
GSM8659692_hires_image (Images), GSM8659692 (Shapes)
▸ 'GSM8659692_downscaled_lowres', with elements:
GSM8659692_lowres_image (Images), GSM8659692 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("H4oI8Y0jp3pTKUib0001")
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("H4oI8Y0jp3pTKUib") # 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 H4oI8Y0jp3pTKUib0001 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/H4oI8Y0jp3pTKUib
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid H4oI8Y0jp3pTKUibLicense: NCBI GEO policy
Metadata
- Technology
- Visium
- Vendor
- 10x Genomics
- Organism
- mouse
- Tissue
- adrenal gland
- Modality
- RNA
- Observations (spots)
- 992
- Tier
- bronze
- Chemistry
- Visium CytAssist v2
- Resolution
- 2,500 × 2,275 px
- mpp
- 7.395 µm/px
- Size
- 29.1 MB
- Sample ID
- GSM8659692
- License
- NCBI GEO policy
- Published
- Aug 7, 2025
Cite this dataset
doi.org/10.1007/s00418-025-02446-6 ↗ BibTeX
@article{Blatkiewicz_2025,
title = {Identification of conserved canonical marker genes in human and mouse adrenal glands using Visium spatial transcriptomics},
volume = {164},
ISSN = {1432-119X},
url = {http://dx.doi.org/10.1007/s00418-025-02446-6},
DOI = {10.1007/s00418-025-02446-6},
number = {1},
journal = {Histochemistry and Cell Biology},
publisher = {Springer Science and Business Media LLC},
author = {Blatkiewicz, Małgorzata and Szyszka, Marta and Hryhorowicz, Szymon and Suszyńska-Zajczyk, Joanna and Porzionato, Andrea and Plewiński, Adam and Malendowicz, Ludwik K. and Rucinski, Marcin},
year = {2025},
month = Dec
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB