Visium

Mouse liver

GSM9111714

bronze Interactive viewer H&E hepatocellular carcinoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9111714_DEN30w_hires_image': DataArray[cyx] (3, 1855, 2000)
│     └── 'GSM9111714_DEN30w_lowres_image': DataArray[cyx] (3, 557, 600)
├── Shapes
│     └── 'GSM9111714_DEN30w': GeoDataFrame shape: (3073, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3073, 32285)
with coordinate systems:
    ▸ 'GSM9111714_DEN30w', with elements:
        GSM9111714_DEN30w_hires_image (Images), GSM9111714_DEN30w_lowres_image (Images), GSM9111714_DEN30w (Shapes)
    ▸ 'GSM9111714_DEN30w_downscaled_hires', with elements:
        GSM9111714_DEN30w_hires_image (Images), GSM9111714_DEN30w (Shapes)
    ▸ 'GSM9111714_DEN30w_downscaled_lowres', with elements:
        GSM9111714_DEN30w_lowres_image (Images), GSM9111714_DEN30w (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("lz8nNqZDRkLpbPO90000")
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("lz8nNqZDRkLpbPO9")   # 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 lz8nNqZDRkLpbPO90000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/lz8nNqZDRkLpbPO9

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid lz8nNqZDRkLpbPO9

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial transcriptional features of MYCN niche in liver tumorigenesis.→

4 datasets · time course

Metadata

Technology: Visium
Organism: mouse
Tissue: liver
Condition: hepatocellular carcinoma
Modality: RNA
Observations (spots): 3,073
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 1,855 px
mpp: 3.620 µm/px
Size: 41.3 MB
Sample ID: GSM9111714
License: NCBI GEO policy
Published: Jul 30, 2025

Cite this dataset

doi.org/10.1073/pnas.2521923123 ↗

BibTeX

@article{Qin_2026,
  title = {Oncogenic function and transcriptional dynamics of MYCN in liver tumorigenesis},
  volume = {123},
  ISSN = {1091-6490},
  url = {http://dx.doi.org/10.1073/pnas.2521923123},
  DOI = {10.1073/pnas.2521923123},
  number = {8},
  journal = {Proceedings of the National Academy of Sciences},
  publisher = {National Academy of Sciences},
  author = {Qin, Xian-Yang and Xu, Yali and Mishra, Hricha and Shirakami, Yohei and Yeh, Shiou-Hwei and Li, Chiao-Ling and Numata, Kazushi and Suenaga, Yusuke and Wei, Feifei and Ando, Reiko and Nishimura, Hajime and Furuhata, Erina and Maeda, Shiori and Furutani, Yutaka and Yanaka, Kaori and Yamamoto, Masahiro and Goto, Masanori and Takasawa, Akira and Nishikawa, Yuji and Tomita, Hiroyuki and Gailhouste, Luc and Matsuura, Tomokazu and Chen, Pei-Jer and Shimizu, Masahito and Hippo, Yoshitaka and Suzuki, Harukazu},
  year = {2026},
  month = Feb
}

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