Visium HD
Human kidney
GSM9467249
bronze Interactive viewer H&E type 2 diabetes mellitus Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9467249_patient1_hires_image': DataArray[cyx] (3, 6000, 5266)
│ └── 'GSM9467249_patient1_lowres_image': DataArray[cyx] (3, 600, 527)
├── Shapes
│ ├── 'GSM9467249_patient1_square_008um': GeoDataFrame shape: (154937, 1) (2D shapes)
│ └── 'GSM9467249_patient1_square_008um_for_vitessce': GeoDataFrame shape: (154937, 2) (2D shapes)
└── Tables
└── 'square_008um': AnnData (154937, 37082)
with coordinate systems:
▸ 'GSM9467249_patient1', with elements:
GSM9467249_patient1_hires_image (Images), GSM9467249_patient1_lowres_image (Images), GSM9467249_patient1_square_008um (Shapes), GSM9467249_patient1_square_008um_for_vitessce (Shapes)
▸ 'GSM9467249_patient1_downscaled_hires', with elements:
GSM9467249_patient1_hires_image (Images), GSM9467249_patient1_square_008um (Shapes), GSM9467249_patient1_square_008um_for_vitessce (Shapes)
▸ 'GSM9467249_patient1_downscaled_lowres', with elements:
GSM9467249_patient1_lowres_image (Images), GSM9467249_patient1_square_008um (Shapes), GSM9467249_patient1_square_008um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("bv10bJQZ5klI5nMu0000")
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("bv10bJQZ5klI5nMu") # 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 bv10bJQZ5klI5nMu0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/bv10bJQZ5klI5nMu
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid bv10bJQZ5klI5nMuLicense: NCBI GEO policy
Part of collection
HIF regulatory network reflects kidney disease progression in diabetes and reversal with SGLT2 inhibition→ 4 datasets · publication
Metadata
- Technology
- Visium HD
- Organism
- human
- Tissue
- kidney
- Condition
- type 2 diabetes mellitus
- Modality
- RNA
- Observations (bins)
- 154,937
- Tier
- bronze
- Magnification
- 20x
- Resolution
- 5,266 × 6,000 px
- mpp
- 0.549 µm/px
- Size
- 67.2 MB
- Sample ID
- GSM9467249
- License
- NCBI GEO policy
- Published
- Feb 5, 2026
Cite this dataset
doi.org/10.1038/s41392-026-02653-0 ↗ BibTeX
@article{Nair_2026,
title = {Hypoxia inducible factor network reflects kidney disease progression in diabetes and sodium-glucose co-transporters inhibition},
volume = {11},
ISSN = {2059-3635},
url = {http://dx.doi.org/10.1038/s41392-026-02653-0},
DOI = {10.1038/s41392-026-02653-0},
number = {1},
journal = {Signal Transduction and Targeted Therapy},
publisher = {Springer Science and Business Media LLC},
author = {Nair, Viji and Minakawa, Akihiro and Smith, Cathy and Berthier, Celine C. and Hartman, John and Menon, Rajasree and Fermin, Damian and Otto, Edgar A. and Gisch, Debora L. and Eadon, Michael T. and Jain, Sanjay and Choi, Ye Ji and Pyle, Laura and Eichinger, Felix and Alakwaa, Fadhl M. and Rose, Michael P. and Subramanian, Lalita and El Saghir, Jamal and Beamish, Jeffrey A. and Hodgin, Jeffrey B. and Brosius, Frank C. and Bjornstad, Petter and Nelson, Peter J. and Harder, Jennifer L. and Werner, Thomas and Kretzler, Matthias and Schaub, Jennifer A.},
year = {2026},
month = Apr
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB