Visium

Mouse skin of body

GSM8541073

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8541073_D21_2_hires_image': DataArray[cyx] (3, 2000, 1817)
│     └── 'GSM8541073_D21_2_lowres_image': DataArray[cyx] (3, 600, 545)
├── Shapes
│     └── 'GSM8541073_D21_2': GeoDataFrame shape: (389, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (389, 32285)
with coordinate systems:
    ▸ 'GSM8541073_D21_2', with elements:
        GSM8541073_D21_2_hires_image (Images), GSM8541073_D21_2_lowres_image (Images), GSM8541073_D21_2 (Shapes)
    ▸ 'GSM8541073_D21_2_downscaled_hires', with elements:
        GSM8541073_D21_2_hires_image (Images), GSM8541073_D21_2 (Shapes)
    ▸ 'GSM8541073_D21_2_downscaled_lowres', with elements:
        GSM8541073_D21_2_lowres_image (Images), GSM8541073_D21_2 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("mdjuFEGFFvNlnXRH0000")
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("mdjuFEGFFvNlnXRH")   # 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 mdjuFEGFFvNlnXRH0000 --dest data/

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

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

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Multi-omic analysis reveals retinoic acid molecular drivers for fibrosis and regenerative repair in skin fibrosis [Spatial transcriptomics]→

5 datasets · other

Metadata

Technology: Visium
Organism: mouse
Tissue: skin of body
Modality: RNA
Observations (spots): 389
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,817 × 2,000 px
mpp: 4.052 µm/px
Size: 12.0 MB
Sample ID: GSM8541073
License: NCBI GEO policy
Published: Aug 13, 2025

Cite this dataset

doi.org/10.1016/j.stem.2025.07.010 ↗

BibTeX

@article{Griffin_2025,
  title = {Multi-omic analysis reveals retinoic acid molecular drivers for dermal fibrosis and regenerative repair in the skin},
  volume = {32},
  ISSN = {1934-5909},
  url = {http://dx.doi.org/10.1016/j.stem.2025.07.010},
  DOI = {10.1016/j.stem.2025.07.010},
  number = {9},
  journal = {Cell Stem Cell},
  publisher = {Elsevier BV},
  author = {Griffin, Michelle and Guo, Jason L. and Parker, Jennifer B.L. and Kuhnert, Maxwell and Li, Dayan J. and Valencia, Caleb and Morgan, Annah and Downer, Mauricio and Cotterell, Asha C. and Lu, John M. and Dilorio, Sarah and Bauer-Rowe Ramos, Khristian Eric and Januszyk, Michael and Chang, Howard Y. and Wan, Derrick C. and Longaker, Michael T.},
  year = {2025},
  month = Sept,
  pages = {1421–1437.e6}
}

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