Visium

Human mesenteric artery

GSM8881933

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8881933_ND_7_C_hires_image': DataArray[cyx] (3, 2000, 2000)
│     └── 'GSM8881933_ND_7_C_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM8881933_ND_7_C': GeoDataFrame shape: (701, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (701, 36601)
with coordinate systems:
    ▸ 'GSM8881933_ND_7_C', with elements:
        GSM8881933_ND_7_C_hires_image (Images), GSM8881933_ND_7_C_lowres_image (Images), GSM8881933_ND_7_C (Shapes)
    ▸ 'GSM8881933_ND_7_C_downscaled_hires', with elements:
        GSM8881933_ND_7_C_hires_image (Images), GSM8881933_ND_7_C (Shapes)
    ▸ 'GSM8881933_ND_7_C_downscaled_lowres', with elements:
        GSM8881933_ND_7_C_lowres_image (Images), GSM8881933_ND_7_C (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("Pm5TAsJkI4CQZiIc0000")
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("Pm5TAsJkI4CQZiIc")   # 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 Pm5TAsJkI4CQZiIc0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/Pm5TAsJkI4CQZiIc

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid Pm5TAsJkI4CQZiIc

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Mapping Endothelial-Macrophage Interactions in Diabetic Vasculature: Role of TREM2 in Vascular Inflammation and Ischemic Response [Spatial Transcriptomics]→

10 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: mesenteric artery
Modality: RNA
Observations (spots): 701
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 2,000 px
mpp: 3.879 µm/px
Size: 9.0 MB
Sample ID: GSM8881933
License: NCBI GEO policy
Published: Mar 20, 2026

Cite this dataset

doi.org/10.1126/scitranslmed.adu3761 ↗

BibTeX

@article{Malhi_2026,
  title = {Diabetes-induced TREM2–endothelial cell signaling impairs ischemic vascular repair},
  volume = {18},
  ISSN = {1946-6242},
  url = {http://dx.doi.org/10.1126/scitranslmed.adu3761},
  DOI = {10.1126/scitranslmed.adu3761},
  number = {846},
  journal = {Science Translational Medicine},
  publisher = {American Association for the Advancement of Science (AAAS)},
  author = {Malhi, Naseeb Kaur and Luo, Yingjun and Chen, Muxi and Tang, Xiaofang and Yuan, Dongqiang and Chadha, Rahuljeet Singh and Tapia, Alonso and Yin, Shufan and Liu, Xuejing and Qi, Meirigeng and Reddy, Marpadga A. and Sun, Jiawei and Otto, James and Wei, Lu and Cooke, John P. and Lee, Esak and Natarajan, Rama and Southerland, Kevin W. and Chen, Zhen Bouman},
  year = {2026},
  month = Apr
}

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