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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch11:AIN-01-0112:kidney

bronze acute kidney injury ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch11_006_image': DataTree[cyx] (35, 4639, 734), (35, 2319, 367), (35, 1159, 183), (35, 579, 91), (35, 289, 45)
│     ├── 'img_Batch11_007_image': DataTree[cyx] (35, 1039, 758), (35, 519, 379), (35, 259, 189), (35, 129, 94), (35, 64, 47)
│     └── 'img_Batch11_008_image': DataTree[cyx] (35, 3806, 884), (35, 1903, 442), (35, 951, 221), (35, 475, 110), (35, 237, 55)
├── Labels
│     ├── 'img_Batch11_006_cells_labels': DataTree[yx] (4639, 734), (2319, 367), (1159, 183), (579, 91), (289, 45)
│     ├── 'img_Batch11_007_cells_labels': DataTree[yx] (1039, 758), (519, 379), (259, 189), (129, 94), (64, 47)
│     └── 'img_Batch11_008_cells_labels': DataTree[yx] (3806, 884), (1903, 442), (951, 221), (475, 110), (237, 55)
├── Shapes
│     ├── 'img_Batch11_006_cell_boundaries': GeoDataFrame shape: (14957, 2) (2D shapes)
│     ├── 'img_Batch11_007_cell_boundaries': GeoDataFrame shape: (3790, 2) (2D shapes)
│     └── 'img_Batch11_008_cell_boundaries': GeoDataFrame shape: (10053, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (28800, 35)
with coordinate systems:
    ▸ 'img_Batch11_006', with elements:
        img_Batch11_006_image (Images), img_Batch11_006_cells_labels (Labels), img_Batch11_006_cell_boundaries (Shapes)
    ▸ 'img_Batch11_007', with elements:
        img_Batch11_007_image (Images), img_Batch11_007_cells_labels (Labels), img_Batch11_007_cell_boundaries (Shapes)
    ▸ 'img_Batch11_008', with elements:
        img_Batch11_008_image (Images), img_Batch11_008_cells_labels (Labels), img_Batch11_008_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("IuVseOsuZcz6uNak0000")
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("IuVseOsuZcz6uNak")   # 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 IuVseOsuZcz6uNak0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/IuVseOsuZcz6uNak

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid IuVseOsuZcz6uNak
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)
28,800
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
197 MB
Sample ID
S-BIAD3694:discovery:Batch11:AIN-01-0112: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