Visium

Human zone of skin

GSM9338174

Interactive viewer H&E systemic sclerosis ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9338174_SSC4_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM9338174_SSC4_hires_image': DataArray[cyx] (3, 2000, 1730)
│     └── 'GSM9338174_SSC4_lowres_image': DataArray[cyx] (3, 600, 519)
├── Shapes
│     └── 'GSM9338174_SSC4': GeoDataFrame shape: (4954, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4954, 37082)
with coordinate systems:
    ▸ 'GSM9338174_SSC4', with elements:
        GSM9338174_SSC4_full_image (Images), GSM9338174_SSC4_hires_image (Images), GSM9338174_SSC4_lowres_image (Images), GSM9338174_SSC4 (Shapes)
    ▸ 'GSM9338174_SSC4_downscaled_hires', with elements:
        GSM9338174_SSC4_hires_image (Images), GSM9338174_SSC4 (Shapes)
    ▸ 'GSM9338174_SSC4_downscaled_lowres', with elements:
        GSM9338174_SSC4_lowres_image (Images), GSM9338174_SSC4 (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("8iF49EkrWlLIa4Kc")   # 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

Load from the CLI

# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/8iF49EkrWlLIa4Kc

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 8iF49EkrWlLIa4Kc

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["8iF49EkrWlLIa4Kc0001"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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

Part of collection

Single-nuclei multiome and spatial analyses of early, untreated SSc skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling→

10 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: zone of skin
Condition: systemic sclerosis
Modality: RNA
Chemistry: Visium CytAssist v2
Resolution: 1,730 × 2,000 px
mpp: 1.525 µm/px
Size: 35 MB
Sample ID: GSM9338174
License: CC BY 4.0
Published: Dec 12, 2025

Cite this dataset

doi.org/10.1172/jci.insight.198954 ↗

BibTeX

@article{Jarnagin_2025,
  title = {Multimodal analyses of early, untreated systemic sclerosis skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling},
  volume = {11},
  ISSN = {2379-3708},
  url = {http://dx.doi.org/10.1172/jci.insight.198954},
  DOI = {10.1172/jci.insight.198954},
  number = {3},
  journal = {JCI Insight},
  publisher = {American Society for Clinical Investigation},
  author = {Jarnagin, Helen C. and Parvizi, Rezvan and Gong, Zhiyun and Gedert, Rosemary and Xing, Xianying and Tsoi, Lam (Alex) C. and Bogle, Rachael and Morrisson, Madeline J. and Perreard, Laurent and Pioli, Patricia A. and Kolling, Fred and Gudjonsson, Johann E. and Khanna, Dinesh and Whitfield, Michael L.},
  year = {2025},
  month = Dec
}

Source: NCBI GEO ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB