Visium

Human kidney

GSM9026963

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9026963_hires_image': DataArray[cyx] (3, 1856, 2000)
│     └── 'GSM9026963_lowres_image': DataArray[cyx] (3, 557, 600)
├── Shapes
│     └── 'GSM9026963': GeoDataFrame shape: (1000, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1000, 36601)
with coordinate systems:
    ▸ 'GSM9026963', with elements:
        GSM9026963_hires_image (Images), GSM9026963_lowres_image (Images), GSM9026963 (Shapes)
    ▸ 'GSM9026963_downscaled_hires', with elements:
        GSM9026963_hires_image (Images), GSM9026963 (Shapes)
    ▸ 'GSM9026963_downscaled_lowres', with elements:
        GSM9026963_lowres_image (Images), GSM9026963 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("dNKDxjdgqkz7CUZs0000")
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("dNKDxjdgqkz7CUZs")   # 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 dNKDxjdgqkz7CUZs0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/dNKDxjdgqkz7CUZs

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid dNKDxjdgqkz7CUZs

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Metadata

Technology: Visium
Organism: human
Tissue: kidney
Modality: RNA
Observations (spots): 1,000
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 1,856 px
mpp: 3.618 µm/px
Size: 10.8 MB
Sample ID: GSM9026963
License: NCBI GEO policy
Published: Jul 20, 2025

Cite this dataset

doi.org/10.1126/sciadv.adv8918 ↗

BibTeX

@article{Asghari_2025,
  title = {Integration of spatial protein imaging and transcriptomics in the human kidney tracks the regenerative potential of proximal tubules},
  volume = {11},
  ISSN = {2375-2548},
  url = {http://dx.doi.org/10.1126/sciadv.adv8918},
  DOI = {10.1126/sciadv.adv8918},
  number = {33},
  journal = {Science Advances},
  publisher = {American Association for the Advancement of Science (AAAS)},
  author = {Asghari, Mahla and Sabo, Angela R. and Barwinska, Daria and Melo Ferreira, Ricardo and Ferkowicz, Michael J. and Bowen, William S. and Cheng, Ying-Hua and Gisch, Debora L. and Gulbronson, Connor and Phillips, Carrie L. and Kelly, Katherine J. and Sutton, Timothy A. and Williams, James C. and Vazquez, Miguel and O’Toole, John and Palevsky, Paul and Rosas, Sylvia E. and Waikar, Sushrut S. and Kiryluk, Krzysztof and Parikh, Chirag and Hodgin, Jeffrey B. and Sarder, Pinaki and De Boer, Ian H. and Himmelfarb, Jonathan and Kretzler, Matthias and Jain, Sanjay and Eadon, Michael T. and Winfree, Seth and El-Achkar, Tarek M. and Dagher, Pierre C.},
  year = {2025},
  month = Aug
}

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