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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch18:AIN-01-0240:kidney

bronze acute kidney injury ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch18_004_image': DataTree[cyx] (35, 2117, 936), (35, 1058, 468), (35, 529, 234), (35, 264, 117), (35, 132, 58)
│     ├── 'img_Batch18_005_image': DataTree[cyx] (35, 1004, 989), (35, 502, 494), (35, 251, 247), (35, 125, 123), (35, 62, 61)
│     └── 'img_Batch18_006_image': DataTree[cyx] (35, 1336, 787), (35, 668, 393), (35, 334, 196), (35, 167, 98), (35, 83, 49)
├── Labels
│     ├── 'img_Batch18_004_cells_labels': DataTree[yx] (2117, 936), (1058, 468), (529, 234), (264, 117), (132, 58)
│     ├── 'img_Batch18_005_cells_labels': DataTree[yx] (1004, 989), (502, 494), (251, 247), (125, 123), (62, 61)
│     └── 'img_Batch18_006_cells_labels': DataTree[yx] (1336, 787), (668, 393), (334, 196), (167, 98), (83, 49)
├── Shapes
│     ├── 'img_Batch18_004_cell_boundaries': GeoDataFrame shape: (8606, 2) (2D shapes)
│     ├── 'img_Batch18_005_cell_boundaries': GeoDataFrame shape: (3716, 2) (2D shapes)
│     └── 'img_Batch18_006_cell_boundaries': GeoDataFrame shape: (3859, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (16181, 35)
with coordinate systems:
    ▸ 'img_Batch18_004', with elements:
        img_Batch18_004_image (Images), img_Batch18_004_cells_labels (Labels), img_Batch18_004_cell_boundaries (Shapes)
    ▸ 'img_Batch18_005', with elements:
        img_Batch18_005_image (Images), img_Batch18_005_cells_labels (Labels), img_Batch18_005_cell_boundaries (Shapes)
    ▸ 'img_Batch18_006', with elements:
        img_Batch18_006_image (Images), img_Batch18_006_cells_labels (Labels), img_Batch18_006_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("LxrBLEeOzuE0mrgw0000")
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("LxrBLEeOzuE0mrgw")   # 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 LxrBLEeOzuE0mrgw0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/LxrBLEeOzuE0mrgw

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid LxrBLEeOzuE0mrgw
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)
16,181
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
95.0 MB
Sample ID
S-BIAD3694:discovery:Batch18:AIN-01-0240: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