Visium

Mouse brain

GSM9377369

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9377369_Visium_323-D1_hires_image': DataArray[cyx] (3, 1889, 2000)
│     └── 'GSM9377369_Visium_323-D1_lowres_image': DataArray[cyx] (3, 567, 600)
├── Shapes
│     └── 'GSM9377369_Visium_323-D1': GeoDataFrame shape: (3812, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3812, 33696)
with coordinate systems:
    ▸ 'GSM9377369_Visium_323-D1', with elements:
        GSM9377369_Visium_323-D1_hires_image (Images), GSM9377369_Visium_323-D1_lowres_image (Images), GSM9377369_Visium_323-D1 (Shapes)
    ▸ 'GSM9377369_Visium_323-D1_downscaled_hires', with elements:
        GSM9377369_Visium_323-D1_hires_image (Images), GSM9377369_Visium_323-D1 (Shapes)
    ▸ 'GSM9377369_Visium_323-D1_downscaled_lowres', with elements:
        GSM9377369_Visium_323-D1_lowres_image (Images), GSM9377369_Visium_323-D1 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("vqhArhiXMPNRToUD0000")
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("vqhArhiXMPNRToUD")   # 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 vqhArhiXMPNRToUD0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/vqhArhiXMPNRToUD

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid vqhArhiXMPNRToUD

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

The spatiotemporal dynamics of postnatal vascularization in the mouse brain→

46 datasets · time course

Metadata

Technology: Visium
Organism: mouse
Tissue: brain
Modality: RNA
Observations (spots): 3,812
Tier: bronze
Chemistry: Visium v1
Resolution: 2,000 × 1,889 px
mpp: 4.081 µm/px
Size: 70.1 MB
Sample ID: GSM9377369
License: NCBI GEO policy
Published: Mar 27, 2026

Cite this dataset

doi.org/10.1016/j.cell.2026.03.013 ↗

BibTeX

@article{de_Launoit_2026,
  title = {The spatiotemporal dynamics of postnatal vascularization in the mouse brain},
  volume = {189},
  ISSN = {0092-8674},
  url = {http://dx.doi.org/10.1016/j.cell.2026.03.013},
  DOI = {10.1016/j.cell.2026.03.013},
  number = {10},
  journal = {Cell},
  publisher = {Elsevier BV},
  author = {de Launoit, Elisa and Skriabine, Sophie and Doumazane, Etienne and Bizou, Mathilde and Pommé, Rémi and Lienhard, Gabrièle and Rajot, Domitille and Mikhailenko, Alina and Vieites-Prado, Alba and Lannes, Mathilde and Fabiano, Natalia and Mathé, Louise and Cagnone, Gael and Mouton, Laura and Santin, Mathieu D. and Candelier, Séverine and Gaspar, Patricia and Rousseau, Charly V. and Dubrac, Alexandre and Renier, Nicolas},
  year = {2026},
  month = May,
  pages = {3107–3123.e18}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB