Visium
Human kidney
GSM8896252
bronze Interactive viewer H&E clear cell renal carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8896252_sc06T_ST_hires_image': DataArray[cyx] (3, 1838, 2000)
│ └── 'GSM8896252_sc06T_ST_lowres_image': DataArray[cyx] (3, 552, 600)
├── Shapes
│ └── 'GSM8896252_sc06T_ST': GeoDataFrame shape: (4749, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4749, 17943)
with coordinate systems:
▸ 'GSM8896252_sc06T_ST', with elements:
GSM8896252_sc06T_ST_hires_image (Images), GSM8896252_sc06T_ST_lowres_image (Images), GSM8896252_sc06T_ST (Shapes)
▸ 'GSM8896252_sc06T_ST_downscaled_hires', with elements:
GSM8896252_sc06T_ST_hires_image (Images), GSM8896252_sc06T_ST (Shapes)
▸ 'GSM8896252_sc06T_ST_downscaled_lowres', with elements:
GSM8896252_sc06T_ST_lowres_image (Images), GSM8896252_sc06T_ST (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("bhnum8QTLYDUhm1E0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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("bhnum8QTLYDUhm1E") # 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download bhnum8QTLYDUhm1E0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/bhnum8QTLYDUhm1E
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid bhnum8QTLYDUhm1ELicense: NCBI GEO policy
Part of collection
Hypoxia shapes both therapeutic response and resistance in metastatic clear cell renal cell carcinoma [Spatial Transcriptomics]→ 3 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- kidney
- Condition
- clear cell renal carcinoma
- Modality
- RNA
- Observations (spots)
- 4,749
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,838 px
- mpp
- 3.816 µm/px
- Size
- 27.5 MB
- Sample ID
- GSM8896252
- License
- NCBI GEO policy
- Published
- Apr 23, 2026
Cite this dataset
doi.org/10.1016/j.ccell.2026.05.007 ↗ BibTeX
@article{Vuong_2026,
title = {Hypoxia shapes both therapeutic response and resistance in metastatic clear cell renal cell carcinoma},
volume = {44},
ISSN = {1535-6108},
url = {http://dx.doi.org/10.1016/j.ccell.2026.05.007},
DOI = {10.1016/j.ccell.2026.05.007},
number = {7},
journal = {Cancer Cell},
publisher = {Elsevier BV},
author = {Vuong, Lynda and Cornish, Andrew E. and Pfeil, Jennifer and Khandwala, Yash and Jiang, Hui and Zheng, Doris X.T. and Mascareno, Eduardo A. and Rittenhouse, Nicole and Rappold, Phillip M. and Leibold, Josef and Sabio, Erich A. and Weiss, Kate and Gonzalez, Carlene and Ji, Liangliang and Zhang, Jing and Adlesic, Mojca and Akin, Oguz and Flynn, Jessica and Krishna, Chirag and Lopez Sanmiguel, Andrea and Sager, Rebecca A. and Tan, Tiak J. and Sanchez, Alejandro and Di Natale, Renzo G. and Blum, Kyle A. and Russo, Paul and Coleman, Jonathan A. and Frew, Ian J. and Chowell, Diego and Chen, Ying-Bei and Solit, David B. and Ostrovnaya, Irina and Motzer, Robert J. and Voss, Martin H. and Kotecha, Ritesh R. and Chan, Timothy A. and Jackson, Hartland W. and Kuo, Fengshen and Li, Ming O. and Hakimi, A. Ari},
year = {2026},
month = July,
pages = {1477–1495.e15}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB