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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch23:RCC_501:kidney

bronze ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch23_023_image': DataTree[cyx] (35, 957, 1279), (35, 478, 639), (35, 239, 319), (35, 119, 159), (35, 59, 79)
│     └── 'img_Batch23_024_image': DataTree[cyx] (35, 1147, 1672), (35, 573, 836), (35, 286, 418), (35, 143, 209), (35, 71, 104)
├── Labels
│     ├── 'img_Batch23_023_cells_labels': DataTree[yx] (957, 1279), (478, 639), (239, 319), (119, 159), (59, 79)
│     └── 'img_Batch23_024_cells_labels': DataTree[yx] (1147, 1672), (573, 836), (286, 418), (143, 209), (71, 104)
├── Shapes
│     ├── 'img_Batch23_023_cell_boundaries': GeoDataFrame shape: (6060, 2) (2D shapes)
│     └── 'img_Batch23_024_cell_boundaries': GeoDataFrame shape: (10500, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (16560, 35)
with coordinate systems:
    ▸ 'img_Batch23_023', with elements:
        img_Batch23_023_image (Images), img_Batch23_023_cells_labels (Labels), img_Batch23_023_cell_boundaries (Shapes)
    ▸ 'img_Batch23_024', with elements:
        img_Batch23_024_image (Images), img_Batch23_024_cells_labels (Labels), img_Batch23_024_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("hjMTFEyANpWqmdmn0000")
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("hjMTFEyANpWqmdmn")   # 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 hjMTFEyANpWqmdmn0000 --dest data/

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

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

License: CC0 1.0

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Metadata

Technology
Imaging Mass Cytometry
Organism
human
Tissue
kidney
Modality
protein
Observations (cells)
16,560
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
142 MB
Sample ID
S-BIAD3694:discovery:Batch23:RCC_501: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