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Visium

Human cortex of kidney

GSM9108683

bronze Interactive viewer H&E Provisional
Human cortex of kidney preview
print(sdata)
SpatialData object
├── Images
│     ├── 'GSM9108683_ctrl_hires_image': DataArray[cyx] (3, 1864, 2000)
│     └── 'GSM9108683_ctrl_lowres_image': DataArray[cyx] (3, 559, 600)
├── Shapes
│     └── 'GSM9108683_ctrl': GeoDataFrame shape: (1413, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1413, 36601)
with coordinate systems:
    ▸ 'GSM9108683_ctrl', with elements:
        GSM9108683_ctrl_hires_image (Images), GSM9108683_ctrl_lowres_image (Images), GSM9108683_ctrl (Shapes)
    ▸ 'GSM9108683_ctrl_downscaled_hires', with elements:
        GSM9108683_ctrl_hires_image (Images), GSM9108683_ctrl (Shapes)
    ▸ 'GSM9108683_ctrl_downscaled_lowres', with elements:
        GSM9108683_ctrl_lowres_image (Images), GSM9108683_ctrl (Shapes)
Python · sddb client
from sddb import Catalog

ds = Catalog().dataset("QfhSjeeOYYPe6YuP0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
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("QfhSjeeOYYPe6YuP")   # 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
Shell
# sddb client: download the .zarr into data/
sddb-cli download QfhSjeeOYYPe6YuP0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/QfhSjeeOYYPe6YuP

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid QfhSjeeOYYPe6YuP

License: NCBI GEO policy

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Metadata

Technology
Visium
Organism
human
Tissue
cortex of kidney
Modality
RNA
Observations (spots)
1,413
Tier
bronze
Chemistry
Visium v1
Magnification
other
Resolution
2,000 × 1,864 px
mpp
3.738 µm/px
Size
14.7 MB
Sample ID
GSM9108683
License
NCBI GEO policy
Published
Jul 31, 2026

Cite this dataset

doi.org/10.3389/fimmu.2026.1884100 ↗
BibTeX
@article{Tang_2026,
  title = {Spatial transcriptomics reveals injury-responsive compartments and coordinated immune–fibrotic signaling in ANCA-associated renal vasculitis},
  volume = {17},
  ISSN = {1664-3224},
  url = {http://dx.doi.org/10.3389/fimmu.2026.1884100},
  DOI = {10.3389/fimmu.2026.1884100},
  journal = {Frontiers in Immunology},
  publisher = {Frontiers Media SA},
  author = {Tang, Yucheng and Zhu, Chunhua and Chen, Shuang and Chen, Huimei and Qian, Hai and Zhang, Aihua and Petretto, Enrico},
  year = {2026},
  month = July
}

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