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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch4:AIN-01-0022:kidney

bronze acute kidney injury ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch4_010_image': DataTree[cyx] (35, 1210, 987), (35, 605, 493), (35, 302, 246), (35, 151, 123), (35, 75, 61)
│     ├── 'img_Batch4_011_image': DataTree[cyx] (35, 872, 976), (35, 436, 488), (35, 218, 244), (35, 109, 122), (35, 54, 61)
│     ├── 'img_Batch4_012_image': DataTree[cyx] (35, 1520, 547), (35, 760, 273), (35, 380, 136), (35, 190, 68), (35, 95, 34)
│     ├── 'img_Batch4_013_image': DataTree[cyx] (35, 1232, 547), (35, 616, 273), (35, 308, 136), (35, 154, 68), (35, 77, 34)
│     ├── 'img_Batch4_014_image': DataTree[cyx] (35, 2223, 865), (35, 1111, 432), (35, 555, 216), (35, 277, 108), (35, 138, 54)
│     ├── 'img_Batch4_015_image': DataTree[cyx] (35, 1259, 882), (35, 629, 441), (35, 314, 220), (35, 157, 110), (35, 78, 55)
│     └── 'img_Batch4_016_image': DataTree[cyx] (35, 1468, 806), (35, 734, 403), (35, 367, 201), (35, 183, 100), (35, 91, 50)
├── Labels
│     ├── 'img_Batch4_010_cells_labels': DataTree[yx] (1210, 987), (605, 493), (302, 246), (151, 123), (75, 61)
│     ├── 'img_Batch4_011_cells_labels': DataTree[yx] (872, 976), (436, 488), (218, 244), (109, 122), (54, 61)
│     ├── 'img_Batch4_012_cells_labels': DataTree[yx] (1520, 547), (760, 273), (380, 136), (190, 68), (95, 34)
│     ├── 'img_Batch4_013_cells_labels': DataTree[yx] (1232, 547), (616, 273), (308, 136), (154, 68), (77, 34)
│     ├── 'img_Batch4_014_cells_labels': DataTree[yx] (2223, 865), (1111, 432), (555, 216), (277, 108), (138, 54)
│     ├── 'img_Batch4_015_cells_labels': DataTree[yx] (1259, 882), (629, 441), (314, 220), (157, 110), (78, 55)
│     └── 'img_Batch4_016_cells_labels': DataTree[yx] (1468, 806), (734, 403), (367, 201), (183, 100), (91, 50)
├── Shapes
│     ├── 'img_Batch4_010_cell_boundaries': GeoDataFrame shape: (2933, 2) (2D shapes)
│     ├── 'img_Batch4_011_cell_boundaries': GeoDataFrame shape: (1781, 2) (2D shapes)
│     ├── 'img_Batch4_012_cell_boundaries': GeoDataFrame shape: (3109, 2) (2D shapes)
│     ├── 'img_Batch4_013_cell_boundaries': GeoDataFrame shape: (2639, 2) (2D shapes)
│     ├── 'img_Batch4_014_cell_boundaries': GeoDataFrame shape: (8004, 2) (2D shapes)
│     ├── 'img_Batch4_015_cell_boundaries': GeoDataFrame shape: (3919, 2) (2D shapes)
│     └── 'img_Batch4_016_cell_boundaries': GeoDataFrame shape: (2896, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (25281, 35)
with coordinate systems:
    ▸ 'img_Batch4_010', with elements:
        img_Batch4_010_image (Images), img_Batch4_010_cells_labels (Labels), img_Batch4_010_cell_boundaries (Shapes)
    ▸ 'img_Batch4_011', with elements:
        img_Batch4_011_image (Images), img_Batch4_011_cells_labels (Labels), img_Batch4_011_cell_boundaries (Shapes)
    ▸ 'img_Batch4_012', with elements:
        img_Batch4_012_image (Images), img_Batch4_012_cells_labels (Labels), img_Batch4_012_cell_boundaries (Shapes)
    ▸ 'img_Batch4_013', with elements:
        img_Batch4_013_image (Images), img_Batch4_013_cells_labels (Labels), img_Batch4_013_cell_boundaries (Shapes)
    ▸ 'img_Batch4_014', with elements:
        img_Batch4_014_image (Images), img_Batch4_014_cells_labels (Labels), img_Batch4_014_cell_boundaries (Shapes)
    ▸ 'img_Batch4_015', with elements:
        img_Batch4_015_image (Images), img_Batch4_015_cells_labels (Labels), img_Batch4_015_cell_boundaries (Shapes)
    ▸ 'img_Batch4_016', with elements:
        img_Batch4_016_image (Images), img_Batch4_016_cells_labels (Labels), img_Batch4_016_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("gleJcE21Kd21pBdM0000")
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("gleJcE21Kd21pBdM")   # 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 gleJcE21Kd21pBdM0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/gleJcE21Kd21pBdM

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid gleJcE21Kd21pBdM
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)
25,281
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
224 MB
Sample ID
S-BIAD3694:discovery:Batch4:AIN-01-0022: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