Visium

Human saphenous vein

GSM9294398

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9294398_3D7_hires_image': DataArray[cyx] (3, 1382, 2000)
│     └── 'GSM9294398_3D7_lowres_image': DataArray[cyx] (3, 415, 600)
├── Shapes
│     └── 'GSM9294398_3D7': GeoDataFrame shape: (4941, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4941, 37082)
with coordinate systems:
    ▸ 'GSM9294398_3D7', with elements:
        GSM9294398_3D7_hires_image (Images), GSM9294398_3D7_lowres_image (Images), GSM9294398_3D7 (Shapes)
    ▸ 'GSM9294398_3D7_downscaled_hires', with elements:
        GSM9294398_3D7_hires_image (Images), GSM9294398_3D7 (Shapes)
    ▸ 'GSM9294398_3D7_downscaled_lowres', with elements:
        GSM9294398_3D7_lowres_image (Images), GSM9294398_3D7 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("KIxx3UYAnVAfXBn60000")
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("KIxx3UYAnVAfXBn6")   # 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 KIxx3UYAnVAfXBn60000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/KIxx3UYAnVAfXBn6

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid KIxx3UYAnVAfXBn6

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Spatial transcriptomic profiling of human saphenous vein grafts reveals molecular remodeling and smooth muscle cell phenotypic transition during early neointima proliferation→

6 datasets · time course

Metadata

Technology: Visium
Organism: human
Tissue: saphenous vein
Modality: RNA
Observations (spots): 4,941
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 1,382 px
mpp: 3.916 µm/px
Size: 6.1 MB
Sample ID: GSM9294398
License: NCBI GEO policy
Published: Mar 11, 2026

Cite this dataset

doi.org/10.1152/ajpheart.00653.2025 ↗

BibTeX

@article{Kim_2026,
  title = {Comprehensive transcriptomics and proteomics analysis of neointima formation in human saphenous vein: implications for bypass graft disease},
  volume = {330},
  ISSN = {1522-1539},
  url = {http://dx.doi.org/10.1152/ajpheart.00653.2025},
  DOI = {10.1152/ajpheart.00653.2025},
  number = {2},
  journal = {American Journal of Physiology-Heart and Circulatory Physiology},
  publisher = {American Physiological Society},
  author = {Kim, David S. and Goo, Brandee and Shi, Hong and Dong, Shuilin and Weintraub, Daniel S. and Coffey, Philip and Veerapaneni, Praneet and Chouhaita, Ronnie and Guduru, Medha and Cyriac, Nicole and Aboud, Ghaith and Ong, Vy and Cave, Stephen and Greenway, Jacob and Mundkur, Rohan and Ahmadieh, Samah and Harb, Ragheb and Ogbi, Mourad and Fulton, David J. and Huo, Yuqing and Zhang, Wei and Long, Xiaochun and Guha, Avirup and Kim, Ha Won and Shi, Yang and Rice, Robert D. and Zhou, Jiang and Chiang, Austin W. T. and Gallo, Dominic R. and Patel, Vijay S. and Lee, Richard and Weintraub, Neal L.},
  year = {2026},
  month = Feb,
  pages = {H620–H641}
}

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