Visium

Human skin of body

GSM9368378

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9368378_SpHyp_264_hires_image': DataArray[cyx] (3, 2000, 1859)
│     └── 'GSM9368378_SpHyp_264_lowres_image': DataArray[cyx] (3, 600, 558)
├── Shapes
│     └── 'GSM9368378_SpHyp_264': GeoDataFrame shape: (1941, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1941, 18085)
with coordinate systems:
    ▸ 'GSM9368378_SpHyp_264', with elements:
        GSM9368378_SpHyp_264_hires_image (Images), GSM9368378_SpHyp_264_lowres_image (Images), GSM9368378_SpHyp_264 (Shapes)
    ▸ 'GSM9368378_SpHyp_264_downscaled_hires', with elements:
        GSM9368378_SpHyp_264_hires_image (Images), GSM9368378_SpHyp_264 (Shapes)
    ▸ 'GSM9368378_SpHyp_264_downscaled_lowres', with elements:
        GSM9368378_SpHyp_264_lowres_image (Images), GSM9368378_SpHyp_264 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("NvG6IYtZq7nyhk2x0000")
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("NvG6IYtZq7nyhk2x")   # 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 NvG6IYtZq7nyhk2x0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/NvG6IYtZq7nyhk2x

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid NvG6IYtZq7nyhk2x

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Identification of a long-term surviving human mesenchymal stromal cell subpopulation upon intradermal injection in immunodeficient mice→

7 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: skin of body
Modality: RNA
Observations (spots): 1,941
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,859 × 2,000 px
mpp: 3.380 µm/px
Size: 13.5 MB
Sample ID: GSM9368378
License: NCBI GEO policy
Published: Sep 23, 2026

Cite this dataset

doi.org/10.1186/s13287-026-05101-6 ↗

BibTeX

@article{Bonnet_des_Claustres_2026,
  title = {Identification of a long-term surviving human mesenchymal stromal cell subpopulation and implications for recessive dystrophic epidermolysis bullosa treatment},
  volume = {17},
  ISSN = {1757-6512},
  url = {http://dx.doi.org/10.1186/s13287-026-05101-6},
  DOI = {10.1186/s13287-026-05101-6},
  number = {1},
  journal = {Stem Cell Research &amp; Therapy},
  publisher = {Springer Science and Business Media LLC},
  author = {Bonnet des Claustres, Mathilde and Gaucher, Sonia and Carbone, Francesco and Peltzer, Juliette and Luka, Marine and Masson, Cécile and Nitschké, Patrick and Hovnanian, Alain and Titeux, Matthias},
  year = {2026},
  month = June
}

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