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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch10:AIN-01-0108:kidney

bronze acute kidney injury ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch10_004_image': DataTree[cyx] (35, 1833, 470), (35, 916, 235), (35, 458, 117), (35, 229, 58), (35, 114, 29)
│     ├── 'img_Batch10_005_image': DataTree[cyx] (35, 3175, 460), (35, 1587, 230), (35, 793, 115), (35, 396, 57), (35, 198, 28)
│     └── 'img_Batch10_011_image': DataTree[cyx] (35, 736, 312), (35, 368, 156), (35, 184, 78), (35, 92, 39), (35, 46, 19)
├── Labels
│     ├── 'img_Batch10_004_cells_labels': DataTree[yx] (1833, 470), (916, 235), (458, 117), (229, 58), (114, 29)
│     ├── 'img_Batch10_005_cells_labels': DataTree[yx] (3175, 460), (1587, 230), (793, 115), (396, 57), (198, 28)
│     └── 'img_Batch10_011_cells_labels': DataTree[yx] (736, 312), (368, 156), (184, 78), (92, 39), (46, 19)
├── Shapes
│     ├── 'img_Batch10_004_cell_boundaries': GeoDataFrame shape: (3411, 2) (2D shapes)
│     ├── 'img_Batch10_005_cell_boundaries': GeoDataFrame shape: (6420, 2) (2D shapes)
│     └── 'img_Batch10_011_cell_boundaries': GeoDataFrame shape: (918, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (10749, 35)
with coordinate systems:
    ▸ 'img_Batch10_004', with elements:
        img_Batch10_004_image (Images), img_Batch10_004_cells_labels (Labels), img_Batch10_004_cell_boundaries (Shapes)
    ▸ 'img_Batch10_005', with elements:
        img_Batch10_005_image (Images), img_Batch10_005_cells_labels (Labels), img_Batch10_005_cell_boundaries (Shapes)
    ▸ 'img_Batch10_011', with elements:
        img_Batch10_011_image (Images), img_Batch10_011_cells_labels (Labels), img_Batch10_011_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("20evfUlFzx9KfabT0000")
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("20evfUlFzx9KfabT")   # 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 20evfUlFzx9KfabT0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/20evfUlFzx9KfabT

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 20evfUlFzx9KfabT
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)
10,749
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
116 MB
Sample ID
S-BIAD3694:discovery:Batch10:AIN-01-0108: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