Visium

Human kidney

GSM9034734

silver Interactive viewer H&E tuberous sclerosis ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9034734_TSC_AML_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM9034734_TSC_AML_hires_image': DataArray[cyx] (3, 2000, 1903)
│     └── 'GSM9034734_TSC_AML_lowres_image': DataArray[cyx] (3, 600, 571)
├── Shapes
│     └── 'GSM9034734_TSC_AML': GeoDataFrame shape: (3428, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3428, 18085)
with coordinate systems:
    ▸ 'GSM9034734_TSC_AML', with elements:
        GSM9034734_TSC_AML_full_image (Images), GSM9034734_TSC_AML_hires_image (Images), GSM9034734_TSC_AML_lowres_image (Images), GSM9034734_TSC_AML (Shapes)
    ▸ 'GSM9034734_TSC_AML_downscaled_hires', with elements:
        GSM9034734_TSC_AML_hires_image (Images), GSM9034734_TSC_AML (Shapes)
    ▸ 'GSM9034734_TSC_AML_downscaled_lowres', with elements:
        GSM9034734_TSC_AML_lowres_image (Images), GSM9034734_TSC_AML (Shapes)

Load in Python

import lamindb as ln

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

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

Load with the Python client

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

ds = Catalog().cohort(uids=["JTuCMWBShYwbqHSG0000"])[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

Profiling the Microenvironment in Tuberous Sclerosis Complex-Associated Angiomyolipoma: Insights from Spatial Gene Expression Analysis→

3 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: kidney
Condition: tuberous sclerosis
Modality: RNA
Observations: 3,428
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 1,903 × 2,000 px
mpp: 5.227 µm/px
Size: 84 MB
Sample ID: GSM9034734
License: NCBI GEO policy
Published: Dec 9, 2025

Cite this dataset

doi.org/10.1038/s41598-025-26541-9 ↗

BibTeX

@article{Watanabe_2025,
  title = {Spatial gene expression profiling reveals the distinct microenvironment of tuberous sclerosis complex-associated angiomyolipoma},
  volume = {15},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-025-26541-9},
  DOI = {10.1038/s41598-025-26541-9},
  number = {1},
  journal = {Scientific Reports},
  publisher = {Springer Science and Business Media LLC},
  author = {Watanabe, Ryuta and Fukumoto, Tetsuya and Miura, Noriyoshi and Kurata, Mie and Kitazawa, Riko and Kikugawa, Tadahiko and Saika, Takashi},
  year = {2025},
  month = Nov
}

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