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Visium

Mouse adrenal gland

GSM8659692

bronze Interactive viewer H&E Provisional
Mouse adrenal gland preview
print(sdata) 
SpatialData object
├── Images
│     ├── 'GSM8659692_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── '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_full_image (Images), 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)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("H4oI8Y0jp3pTKUib0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
Install (Python 3.12+) Docs →
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
Install (Python 3.12+) Docs →
Shell 
# sddb client: download the .zarr into data/
sddb-cli download H4oI8Y0jp3pTKUib0000 --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 H4oI8Y0jp3pTKUib
Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Metadata

Technology
Visium
Organism
mouse
Tissue
adrenal gland
Modality
RNA
Observations (spots)
992
Tier
bronze
Chemistry
Visium CytAssist v2
Magnification
40x
Resolution
3,000 × 3,000 px
mpp
0.197 µm/px
Size
50.4 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