Visium
Human kidney
GSM9155022
silver Interactive viewer H&E ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9155022_control_58055_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM9155022_control_58055_hires_image': DataArray[cyx] (3, 1084, 2000)
│ └── 'GSM9155022_control_58055_lowres_image': DataArray[cyx] (3, 325, 600)
├── Shapes
│ └── 'GSM9155022_control_58055': GeoDataFrame shape: (1052, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1052, 18061)
with coordinate systems:
▸ 'GSM9155022_control_58055', with elements:
GSM9155022_control_58055_full_image (Images), GSM9155022_control_58055_hires_image (Images), GSM9155022_control_58055_lowres_image (Images), GSM9155022_control_58055 (Shapes)
▸ 'GSM9155022_control_58055_downscaled_hires', with elements:
GSM9155022_control_58055_hires_image (Images), GSM9155022_control_58055 (Shapes)
▸ 'GSM9155022_control_58055_downscaled_lowres', with elements:
GSM9155022_control_58055_lowres_image (Images), GSM9155022_control_58055 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("O23PhzYlPAwRAG6u") # 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/O23PhzYlPAwRAG6u
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid O23PhzYlPAwRAG6u Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["O23PhzYlPAwRAG6u0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- human
- Tissue
- kidney
- Modality
- RNA
- Observations
- 1,052
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 2,000 × 1,084 px
- mpp
- 5.090 µm/px
- Size
- 35 MB
- Sample ID
- GSM9155022
- License
- NCBI GEO policy
- Published
- Sep 9, 2025
Cite this dataset
doi.org/10.3389/fimmu.2025.1654741 ↗ BibTeX
@article{Chen_Wongworawat_2025,
title = {Spatial transcriptomics reveals distinct role of monocytes/macrophages with high FCGR3A expression in kidney transplant rejections},
volume = {16},
ISSN = {1664-3224},
url = {http://dx.doi.org/10.3389/fimmu.2025.1654741},
DOI = {10.3389/fimmu.2025.1654741},
journal = {Frontiers in Immunology},
publisher = {Frontiers Media SA},
author = {Chen Wongworawat, Yan and Nepal, Chirag and Duhon, Mark and Chen, Wanqiu and Nguyen, Minh-Tri and Godzik, Adam and Qiu, Xinru and Li, Wei Vivian and Yu, Gary and Villicana, Rafael and Zuppan, Craig and De Vera, Michael and Eadon, Michael T. and Haas, Mark and Wang, Charles},
year = {2025},
month = Sept
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB