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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch18:AIN-02-0011:kidney

bronze acute tubulointerstitial nephritis ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch18_001_image': DataTree[cyx] (35, 1807, 571), (35, 903, 285), (35, 451, 142), (35, 225, 71), (35, 112, 35)
│     ├── 'img_Batch18_002_image': DataTree[cyx] (35, 988, 706), (35, 494, 353), (35, 247, 176), (35, 123, 88), (35, 61, 44)
│     └── 'img_Batch18_003_image': DataTree[cyx] (35, 1322, 306), (35, 661, 153), (35, 330, 76), (35, 165, 38), (35, 82, 19)
├── Labels
│     ├── 'img_Batch18_001_cells_labels': DataTree[yx] (1807, 571), (903, 285), (451, 142), (225, 71), (112, 35)
│     ├── 'img_Batch18_002_cells_labels': DataTree[yx] (988, 706), (494, 353), (247, 176), (123, 88), (61, 44)
│     └── 'img_Batch18_003_cells_labels': DataTree[yx] (1322, 306), (661, 153), (330, 76), (165, 38), (82, 19)
├── Shapes
│     ├── 'img_Batch18_001_cell_boundaries': GeoDataFrame shape: (2551, 2) (2D shapes)
│     ├── 'img_Batch18_002_cell_boundaries': GeoDataFrame shape: (1983, 2) (2D shapes)
│     └── 'img_Batch18_003_cell_boundaries': GeoDataFrame shape: (1847, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (6381, 35)
with coordinate systems:
    ▸ 'img_Batch18_001', with elements:
        img_Batch18_001_image (Images), img_Batch18_001_cells_labels (Labels), img_Batch18_001_cell_boundaries (Shapes)
    ▸ 'img_Batch18_002', with elements:
        img_Batch18_002_image (Images), img_Batch18_002_cells_labels (Labels), img_Batch18_002_cell_boundaries (Shapes)
    ▸ 'img_Batch18_003', with elements:
        img_Batch18_003_image (Images), img_Batch18_003_cells_labels (Labels), img_Batch18_003_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("6vug6UBwl44QFx6N0000")
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("6vug6UBwl44QFx6N")   # 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 6vug6UBwl44QFx6N0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/6vug6UBwl44QFx6N

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 6vug6UBwl44QFx6N
Install (Python 3.12+) Docs →

License: CC0 1.0

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Metadata

Technology
Imaging Mass Cytometry
Organism
human
Tissue
kidney
Condition
acute tubulointerstitial nephritis
Modality
protein
Observations (cells)
6,381
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
53.8 MB
Sample ID
S-BIAD3694:discovery:Batch18:AIN-02-0011: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