Visium HD
Human kidney
GSM9071196
silver Interactive viewer H&E nephrotic syndrome No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9071196_7_A1_hires_image': DataArray[cyx] (3, 6000, 4751)
│ └── 'GSM9071196_7_A1_lowres_image': DataArray[cyx] (3, 600, 475)
├── Shapes
│ ├── 'GSM9071196_7_A1_square_002um': GeoDataFrame shape: (1936880, 1) (2D shapes)
│ ├── 'GSM9071196_7_A1_square_008um': GeoDataFrame shape: (121055, 1) (2D shapes)
│ └── 'GSM9071196_7_A1_square_008um_for_vitessce': GeoDataFrame shape: (121055, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (1936880, 18085)
└── 'square_008um': AnnData (121055, 18085)
with coordinate systems:
▸ 'GSM9071196_7_A1', with elements:
GSM9071196_7_A1_hires_image (Images), GSM9071196_7_A1_lowres_image (Images), GSM9071196_7_A1_square_002um (Shapes), GSM9071196_7_A1_square_008um (Shapes), GSM9071196_7_A1_square_008um_for_vitessce (Shapes)
▸ 'GSM9071196_7_A1_downscaled_hires', with elements:
GSM9071196_7_A1_hires_image (Images), GSM9071196_7_A1_square_002um (Shapes), GSM9071196_7_A1_square_008um (Shapes), GSM9071196_7_A1_square_008um_for_vitessce (Shapes)
▸ 'GSM9071196_7_A1_downscaled_lowres', with elements:
GSM9071196_7_A1_lowres_image (Images), GSM9071196_7_A1_square_002um (Shapes), GSM9071196_7_A1_square_008um (Shapes), GSM9071196_7_A1_square_008um_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("o93Vzi69X8aLFU9m") # 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/o93Vzi69X8aLFU9m
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid o93Vzi69X8aLFU9m Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["o93Vzi69X8aLFU9m0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
High resolution spatial trancrioptomics combined with deep learning-based image segmentation enables single-cell spatial profiling of archival FFPE kidney tissue from patients with idiopathic nephrotic syndrome→ 3 datasets · comparison
Metadata
- Technology
- Visium HD
- Organism
- human
- Tissue
- kidney
- Condition
- nephrotic syndrome
- Modality
- RNA
- Observations
- 121,055
- Tier
- silver
- Resolution
- 4,751 × 6,000 px
- mpp
- 0.793 µm/px
- Size
- 199 MB
- Sample ID
- GSM9071196
- License
- unknown
- Published
- Dec 8, 2025
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB