Visium

Mouse small intestine

GSM8514376

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8514376_CD_hires_image': DataArray[cyx] (3, 800, 2000)
│     └── 'GSM8514376_CD_lowres_image': DataArray[cyx] (3, 240, 600)
├── Shapes
│     └── 'GSM8514376_CD': GeoDataFrame shape: (3894, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3894, 19465)
with coordinate systems:
    ▸ 'GSM8514376_CD', with elements:
        GSM8514376_CD_hires_image (Images), GSM8514376_CD_lowres_image (Images), GSM8514376_CD (Shapes)
    ▸ 'GSM8514376_CD_downscaled_hires', with elements:
        GSM8514376_CD_hires_image (Images), GSM8514376_CD (Shapes)
    ▸ 'GSM8514376_CD_downscaled_lowres', with elements:
        GSM8514376_CD_lowres_image (Images), GSM8514376_CD (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("7nANAXEEhj6WHiCU0000")
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("7nANAXEEhj6WHiCU")   # 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 7nANAXEEhj6WHiCU0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/7nANAXEEhj6WHiCU

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 7nANAXEEhj6WHiCU

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

10X Visium Spatial transcriptomics of small intestine from mice fed stearic acid-rich high fat diet (HFD)→

2 datasets · comparison

Metadata

Technology: Visium
Organism: mouse
Tissue: small intestine
Modality: RNA
Observations (spots): 3,894
Tier: bronze
Chemistry: Visium v1
Resolution: 2,000 × 800 px
mpp: 8.540 µm/px
Size: 58.1 MB
Sample ID: GSM8514376
License: NCBI GEO policy
Published: Mar 22, 2026

Cite this dataset

doi.org/10.1016/j.jcmgh.2026.101740 ↗

BibTeX

@article{Nakata_2026,
  title = {Dietary Stearic Acid Accelerates Intestinal Tumorigenesis via Fatty Acid-binding Protein 5 Without Promoting Obesity},
  volume = {20},
  ISSN = {2352-345X},
  url = {http://dx.doi.org/10.1016/j.jcmgh.2026.101740},
  DOI = {10.1016/j.jcmgh.2026.101740},
  number = {5},
  journal = {Cellular and Molecular Gastroenterology and Hepatology},
  publisher = {Elsevier BV},
  author = {Nakata, Kazuaki and Komiyama, Seiga and Sekine, Keisuke and Nagai, Motoyoshi and Okawa, Takuma and Ohashi, Wakana and Nakano, Kenta and Okamura, Tadashi and Kozono, Takuma and Takemura, Nobuyuki and Yamada, Kazuhiko and Kokudo, Norihiro and Dohi, Taeko and Hase, Koji and Kawamura, Yuki I.},
  year = {2026},
  pages = {101740}
}

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