Visium

Human lung

GSM8699171

bronze Interactive viewer H&E obsolete idiopathic pulmonary fibrosis Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8699171_JKPF3_2_hires_image': DataArray[cyx] (3, 2000, 1818)
│     └── 'GSM8699171_JKPF3_2_lowres_image': DataArray[cyx] (4, 600, 545)
├── Shapes
│     └── 'GSM8699171_JKPF3_2': GeoDataFrame shape: (2386, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2386, 33538)
with coordinate systems:
    ▸ 'GSM8699171_JKPF3_2', with elements:
        GSM8699171_JKPF3_2_hires_image (Images), GSM8699171_JKPF3_2_lowres_image (Images), GSM8699171_JKPF3_2 (Shapes)
    ▸ 'GSM8699171_JKPF3_2_downscaled_hires', with elements:
        GSM8699171_JKPF3_2_hires_image (Images), GSM8699171_JKPF3_2 (Shapes)
    ▸ 'GSM8699171_JKPF3_2_downscaled_lowres', with elements:
        GSM8699171_JKPF3_2_lowres_image (Images), GSM8699171_JKPF3_2 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("memJY1w2JJ9a8sl30000")
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("memJY1w2JJ9a8sl3")   # 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 memJY1w2JJ9a8sl30000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/memJY1w2JJ9a8sl3

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid memJY1w2JJ9a8sl3

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Spatially resolved atlas of IPF fibroblastic foci identifies that PAK2 kinase activation is involved in fibrogenesis→

9 datasets · replicates

Metadata

Technology: Visium
Organism: human
Tissue: lung
Condition: obsolete idiopathic pulmonary fibrosis
Modality: RNA
Observations (spots): 2,386
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,818 × 2,000 px
mpp: 4.050 µm/px
Size: 24.3 MB
Sample ID: GSM8699171
License: NCBI GEO policy
Published: Aug 11, 2025

Cite this dataset

doi.org/10.1183/13993003.00022-2025 ↗

BibTeX

@article{Watanabe_2025,
  title = {Integrated spatial and single-cell transcriptomics reveal PAK kinase as a therapeutic target in fibroblastic foci and dense fibrosis of idiopathic pulmonary fibrosis},
  volume = {66},
  ISSN = {1399-3003},
  url = {http://dx.doi.org/10.1183/13993003.00022-2025},
  DOI = {10.1183/13993003.00022-2025},
  number = {4},
  journal = {European Respiratory Journal},
  publisher = {European Respiratory Society (ERS)},
  author = {Watanabe, Naoaki and Yoshida, Masahiro and Hirano, Yuta and Fujimoto, Shota and Matsubayashi, Sachi and Ishiguro, Takashi and Takahashi, Nobumasa and Shimizu, Yoshihiko and Takayanagi, Noboru and Kawabata, Yoshinori and Mori, Yutaro and Okamoto, Koji and Minagawa, Shunsuke and Kuwano, Kazuyoshi and Araya, Jun and Yamamoto, Yusuke and Fujita, Yu},
  year = {2025},
  month = July,
  pages = {2500022}
}

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