← All datasets
Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch6:AIN-01-0081:kidney

bronze acute tubulointerstitial nephritis ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch6_001_image': DataTree[cyx] (35, 1285, 1135), (35, 642, 567), (35, 321, 283), (35, 160, 141), (35, 80, 70)
│     ├── 'img_Batch6_002_image': DataTree[cyx] (35, 6470, 711), (35, 3235, 355), (35, 1617, 177), (35, 808, 88), (35, 404, 44)
│     └── 'img_Batch6_003_image': DataTree[cyx] (35, 1666, 606), (35, 833, 303), (35, 416, 151), (35, 208, 75), (35, 104, 37)
├── Labels
│     ├── 'img_Batch6_001_cells_labels': DataTree[yx] (1285, 1135), (642, 567), (321, 283), (160, 141), (80, 70)
│     ├── 'img_Batch6_002_cells_labels': DataTree[yx] (6470, 711), (3235, 355), (1617, 177), (808, 88), (404, 44)
│     └── 'img_Batch6_003_cells_labels': DataTree[yx] (1666, 606), (833, 303), (416, 151), (208, 75), (104, 37)
├── Shapes
│     ├── 'img_Batch6_001_cell_boundaries': GeoDataFrame shape: (6802, 2) (2D shapes)
│     ├── 'img_Batch6_002_cell_boundaries': GeoDataFrame shape: (24852, 2) (2D shapes)
│     └── 'img_Batch6_003_cell_boundaries': GeoDataFrame shape: (6685, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (38339, 35)
with coordinate systems:
    ▸ 'img_Batch6_001', with elements:
        img_Batch6_001_image (Images), img_Batch6_001_cells_labels (Labels), img_Batch6_001_cell_boundaries (Shapes)
    ▸ 'img_Batch6_002', with elements:
        img_Batch6_002_image (Images), img_Batch6_002_cells_labels (Labels), img_Batch6_002_cell_boundaries (Shapes)
    ▸ 'img_Batch6_003', with elements:
        img_Batch6_003_image (Images), img_Batch6_003_cells_labels (Labels), img_Batch6_003_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("CxRQY2CljkCEem7Z0000")
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("CxRQY2CljkCEem7Z")   # 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 CxRQY2CljkCEem7Z0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/CxRQY2CljkCEem7Z

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid CxRQY2CljkCEem7Z
Install (Python 3.12+) Docs →

License: CC0 1.0

View on Lamin ↗ Flag a data issue
Part of collection
bia-s-biad3694→
67 datasets · publication

Metadata

Technology
Imaging Mass Cytometry
Organism
human
Tissue
kidney
Condition
acute tubulointerstitial nephritis
Modality
protein
Observations (cells)
38,339
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
274 MB
Sample ID
S-BIAD3694:discovery:Batch6:AIN-01-0081: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