Visium

Mouse colon

GSM9779866

silver Interactive viewer H&E colitis ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9779866_SITSB12_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM9779866_SITSB12_hires_image': DataArray[cyx] (3, 2000, 2000)
│     └── 'GSM9779866_SITSB12_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM9779866_SITSB12': GeoDataFrame shape: (3356, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3356, 19465)
with coordinate systems:
    ▸ 'GSM9779866_SITSB12', with elements:
        GSM9779866_SITSB12_full_image (Images), GSM9779866_SITSB12_hires_image (Images), GSM9779866_SITSB12_lowres_image (Images), GSM9779866_SITSB12 (Shapes)
    ▸ 'GSM9779866_SITSB12_downscaled_hires', with elements:
        GSM9779866_SITSB12_hires_image (Images), GSM9779866_SITSB12 (Shapes)
    ▸ 'GSM9779866_SITSB12_downscaled_lowres', with elements:
        GSM9779866_SITSB12_lowres_image (Images), GSM9779866_SITSB12 (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("2xA3VkUKX9ACK0uo")   # 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

Load from the CLI

# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/2xA3VkUKX9ACK0uo

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 2xA3VkUKX9ACK0uo

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["2xA3VkUKX9ACK0uo0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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

Part of collection

Paired mutation calling and spatial transcriptomics identify cellular neighborhoods associated with neoplastic outcomes in mouse colitis→

32 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: colon
Condition: colitis
Modality: RNA
Observations: 3,356
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 3,000 × 3,000 px
mpp: 4.298 µm/px
Size: 32 MB
Sample ID: GSM9779866
License: CC BY 4.0
Published: Jun 5, 2026

Cite this dataset

doi.org/10.1038/s41588-026-02673-0 ↗

BibTeX

@article{Moutin_2026,
  title = {Paired mutation calling and spatial transcriptomics identify cellular neighborhoods associated with the neoplastic outcome of mouse colitis},
  volume = {58},
  ISSN = {1546-1718},
  url = {http://dx.doi.org/10.1038/s41588-026-02673-0},
  DOI = {10.1038/s41588-026-02673-0},
  number = {8},
  journal = {Nature Genetics},
  publisher = {Springer Science and Business Media LLC},
  author = {Moutin, Elisa B. and Chang, Linus L. H. and Giavara, Giada and Mehmed, Shenay and Colombé, Mathilde and Boquetale, Carla and Marks, Kate and Lourenço, Filipe C. and Skoufou-Papoutsaki, Nefeli and Kemp, Richard and Gascard, Philippe and Tlsty, Thea D. and Tourigny, David S. and Winton, Douglas J.},
  year = {2026},
  month = July,
  pages = {1918–1927}
}

Source: NCBI GEO ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB