Visium

Mouse lung

GSM8059173

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8059173_A_hires_image': DataArray[cyx] (3, 1815, 2000)
│     └── 'GSM8059173_A_lowres_image': DataArray[cyx] (3, 545, 600)
├── Shapes
│     └── 'GSM8059173_A': GeoDataFrame shape: (7088, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (7088, 19465)
with coordinate systems:
    ▸ 'GSM8059173_A', with elements:
        GSM8059173_A_hires_image (Images), GSM8059173_A_lowres_image (Images), GSM8059173_A (Shapes)
    ▸ 'GSM8059173_A_downscaled_hires', with elements:
        GSM8059173_A_hires_image (Images), GSM8059173_A (Shapes)
    ▸ 'GSM8059173_A_downscaled_lowres', with elements:
        GSM8059173_A_lowres_image (Images), GSM8059173_A (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("of5ZuNHVFCJupUNa0000")
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("of5ZuNHVFCJupUNa")   # 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 of5ZuNHVFCJupUNa0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/of5ZuNHVFCJupUNa

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid of5ZuNHVFCJupUNa

Install (Python 3.12+) Docs →

License: NCBI GEO policy

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

Spatial transcriptomics reveals C10 tumor transcriptomic changes generated by Smarta cell transfer→

2 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: lung
Modality: RNA
Observations (spots): 7,088
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 1,815 px
mpp: 6.397 µm/px
Size: 188 MB
Sample ID: GSM8059173
License: NCBI GEO policy
Published: Jun 22, 2026

Cite this dataset

doi.org/10.1126/science.ads7910 ↗

BibTeX

@article{Lian_2026,
  title = {CD4
                    <sup>+</sup>
                    T cells impair tumor growth through IL-3 and TNF-dependent vascular damage},
  volume = {392},
  ISSN = {1095-9203},
  url = {http://dx.doi.org/10.1126/science.ads7910},
  DOI = {10.1126/science.ads7910},
  number = {6804},
  journal = {Science},
  publisher = {American Association for the Advancement of Science (AAAS)},
  author = {Lian, Qiaoshi and Nie, Jia and Singh, Jatinder and Chen, Qiang and Matta, Jennifer and Chan, Waipan and Balmaceno-Criss, Mariah and Vacchio, Melanie S. and Yu, Weiming and Clark, Alexander D. and Edmondson, Elijah and Kelly, Michael C. and Germain, Ronald N. and Bosselut, Rémy},
  year = {2026},
  month = June
}

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