Visium

Mouse lung

GSM9119670

bronze Interactive viewer H&E lung carcinoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9119670_AG-F-6091_hires_image': DataArray[cyx] (3, 2000, 1914)
│     └── 'GSM9119670_AG-F-6091_lowres_image': DataArray[cyx] (3, 600, 574)
├── Shapes
│     └── 'GSM9119670_AG-F-6091': GeoDataFrame shape: (2697, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2697, 32285)
with coordinate systems:
    ▸ 'GSM9119670_AG-F-6091', with elements:
        GSM9119670_AG-F-6091_hires_image (Images), GSM9119670_AG-F-6091_lowres_image (Images), GSM9119670_AG-F-6091 (Shapes)
    ▸ 'GSM9119670_AG-F-6091_downscaled_hires', with elements:
        GSM9119670_AG-F-6091_hires_image (Images), GSM9119670_AG-F-6091 (Shapes)
    ▸ 'GSM9119670_AG-F-6091_downscaled_lowres', with elements:
        GSM9119670_AG-F-6091_lowres_image (Images), GSM9119670_AG-F-6091 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("K5L1PhWVJxnkBfUN0000")
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("K5L1PhWVJxnkBfUN")   # 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 K5L1PhWVJxnkBfUN0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/K5L1PhWVJxnkBfUN

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid K5L1PhWVJxnkBfUN

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

A novel CAF population coordinates hyper-suppressive regulatory T cell recruitment and localization in lung cancer [Spatial Transcriptomics]→

2 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: lung
Condition: lung carcinoma
Modality: RNA
Observations (spots): 2,697
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,914 × 2,000 px
mpp: 3.707 µm/px
Size: 30.6 MB
Sample ID: GSM9119670
License: NCBI GEO policy
Published: Jun 29, 2026

Cite this dataset

doi.org/10.1038/s41590-026-02607-2 ↗

BibTeX

@article{Ringham_2026,
  title = {A novel CAF population coordinates hyper-suppressive regulatory T cell recruitment and localization in lung cancer},
  volume = {27},
  ISSN = {1529-2916},
  url = {http://dx.doi.org/10.1038/s41590-026-02607-2},
  DOI = {10.1038/s41590-026-02607-2},
  number = {9},
  journal = {Nature Immunology},
  publisher = {Springer Science and Business Media LLC},
  author = {Ringham, Olivia R. and Rivera, Monica and Loffredo, Lucas F. and Ozsoy, Melih Arda and Healy, Christina M. and Cheng, Maye F. and Jin, Yinuo and Chen, Noah and de los Santos-Alexis, Kenia and Azizi, Elham and Saqi, Anjali and Buechler, Matthew B. and Concepcion-Crisol, Carla P. and Arpaia, Nicholas},
  year = {2026},
  month = Aug,
  pages = {1899–1912}
}

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