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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch19:AIN-01-0247:kidney

bronze acute kidney injury ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch19_004_image': DataTree[cyx] (35, 7089, 934), (35, 3544, 467), (35, 1772, 233), (35, 886, 116), (35, 443, 58)
│     ├── 'img_Batch19_005_image': DataTree[cyx] (35, 1442, 743), (35, 721, 371), (35, 360, 185), (35, 180, 92), (35, 90, 46)
│     └── 'img_Batch19_006_image': DataTree[cyx] (35, 1624, 606), (35, 812, 303), (35, 406, 151), (35, 203, 75), (35, 101, 37)
├── Labels
│     ├── 'img_Batch19_004_cells_labels': DataTree[yx] (7089, 934), (3544, 467), (1772, 233), (886, 116), (443, 58)
│     ├── 'img_Batch19_005_cells_labels': DataTree[yx] (1442, 743), (721, 371), (360, 185), (180, 92), (90, 46)
│     └── 'img_Batch19_006_cells_labels': DataTree[yx] (1624, 606), (812, 303), (406, 151), (203, 75), (101, 37)
├── Shapes
│     ├── 'img_Batch19_004_cell_boundaries': GeoDataFrame shape: (31976, 2) (2D shapes)
│     ├── 'img_Batch19_005_cell_boundaries': GeoDataFrame shape: (5178, 2) (2D shapes)
│     └── 'img_Batch19_006_cell_boundaries': GeoDataFrame shape: (5177, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (42331, 35)
with coordinate systems:
    ▸ 'img_Batch19_004', with elements:
        img_Batch19_004_image (Images), img_Batch19_004_cells_labels (Labels), img_Batch19_004_cell_boundaries (Shapes)
    ▸ 'img_Batch19_005', with elements:
        img_Batch19_005_image (Images), img_Batch19_005_cells_labels (Labels), img_Batch19_005_cell_boundaries (Shapes)
    ▸ 'img_Batch19_006', with elements:
        img_Batch19_006_image (Images), img_Batch19_006_cells_labels (Labels), img_Batch19_006_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("qNnyiar5ozGZ7uSS0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
Install (Python 3.12+) Docs →
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("qNnyiar5ozGZ7uSS")   # 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
Install (Python 3.12+) Docs →
Shell 
# sddb client: download the .zarr into data/
sddb-cli download qNnyiar5ozGZ7uSS0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qNnyiar5ozGZ7uSS

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qNnyiar5ozGZ7uSS
Install (Python 3.12+) Docs →

License: CC0 1.0

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Metadata

Technology
Imaging Mass Cytometry
Organism
human
Tissue
kidney
Condition
acute kidney injury
Modality
protein
Observations (cells)
42,331
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
274 MB
Sample ID
S-BIAD3694:discovery:Batch19:AIN-01-0247: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