Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch1:AIN-01-0014:kidney
bronze acute kidney injury ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch1_004_image': DataTree[cyx] (35, 1390, 579), (35, 695, 289), (35, 347, 144), (35, 173, 72), (35, 86, 36)
│ ├── 'img_Batch1_005_image': DataTree[cyx] (35, 2709, 613), (35, 1354, 306), (35, 677, 153), (35, 338, 76), (35, 169, 38)
│ ├── 'img_Batch1_006_image': DataTree[cyx] (35, 2076, 481), (35, 1038, 240), (35, 519, 120), (35, 259, 60), (35, 129, 30)
│ └── 'img_Batch1_007_image': DataTree[cyx] (35, 926, 407), (35, 463, 203), (35, 231, 101), (35, 115, 50), (35, 57, 25)
├── Labels
│ ├── 'img_Batch1_004_cells_labels': DataTree[yx] (1390, 579), (695, 289), (347, 144), (173, 72), (86, 36)
│ ├── 'img_Batch1_005_cells_labels': DataTree[yx] (2709, 613), (1354, 306), (677, 153), (338, 76), (169, 38)
│ ├── 'img_Batch1_006_cells_labels': DataTree[yx] (2076, 481), (1038, 240), (519, 120), (259, 60), (129, 30)
│ └── 'img_Batch1_007_cells_labels': DataTree[yx] (926, 407), (463, 203), (231, 101), (115, 50), (57, 25)
├── Shapes
│ ├── 'img_Batch1_004_cell_boundaries': GeoDataFrame shape: (2916, 2) (2D shapes)
│ ├── 'img_Batch1_005_cell_boundaries': GeoDataFrame shape: (3693, 2) (2D shapes)
│ ├── 'img_Batch1_006_cell_boundaries': GeoDataFrame shape: (2092, 2) (2D shapes)
│ └── 'img_Batch1_007_cell_boundaries': GeoDataFrame shape: (880, 2) (2D shapes)
└── Tables
└── 'table': AnnData (9581, 35)
with coordinate systems:
▸ 'img_Batch1_004', with elements:
img_Batch1_004_image (Images), img_Batch1_004_cells_labels (Labels), img_Batch1_004_cell_boundaries (Shapes)
▸ 'img_Batch1_005', with elements:
img_Batch1_005_image (Images), img_Batch1_005_cells_labels (Labels), img_Batch1_005_cell_boundaries (Shapes)
▸ 'img_Batch1_006', with elements:
img_Batch1_006_image (Images), img_Batch1_006_cells_labels (Labels), img_Batch1_006_cell_boundaries (Shapes)
▸ 'img_Batch1_007', with elements:
img_Batch1_007_image (Images), img_Batch1_007_cells_labels (Labels), img_Batch1_007_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("qQVNxxPPGGlEES8f0000")
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("qQVNxxPPGGlEES8f") # 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 qQVNxxPPGGlEES8f0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qQVNxxPPGGlEES8f
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qQVNxxPPGGlEES8fLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute kidney injury
- Modality
- protein
- Observations (cells)
- 9,581
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 94.3 MB
- Sample ID
- S-BIAD3694:discovery:Batch1:AIN-01-0014:kidney
- License
- CC0 1.0
- Published
- Aug 18, 2026
Cite this dataset
doi.org/10.1038/s41467-026-77210-y ↗ BibTeX
@article{bia:S-BIAD3694,
doi = {10.1038/s41467-026-77210-y},
title = {S-BIAD3694:discovery:Batch2:L108:kidney}
}Source: BioImage Archive ↗ · License: CC0 1.0 ↗ · Reprocessed to SpatialData by SpatialData-DB