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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch9:596-1:kidney

bronze ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch9_001_image': DataTree[cyx] (35, 700, 1500), (35, 350, 750), (35, 175, 375), (35, 87, 187), (35, 43, 93)
│     └── 'img_Batch9_002_image': DataTree[cyx] (35, 500, 500), (35, 250, 250), (35, 125, 125), (35, 62, 62), (35, 31, 31)
├── Labels
│     ├── 'img_Batch9_001_cells_labels': DataTree[yx] (700, 1500), (350, 750), (175, 375), (87, 187), (43, 93)
│     └── 'img_Batch9_002_cells_labels': DataTree[yx] (500, 500), (250, 250), (125, 125), (62, 62), (31, 31)
├── Shapes
│     ├── 'img_Batch9_001_cell_boundaries': GeoDataFrame shape: (6160, 2) (2D shapes)
│     └── 'img_Batch9_002_cell_boundaries': GeoDataFrame shape: (1076, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (7236, 35)
with coordinate systems:
    ▸ 'img_Batch9_001', with elements:
        img_Batch9_001_image (Images), img_Batch9_001_cells_labels (Labels), img_Batch9_001_cell_boundaries (Shapes)
    ▸ 'img_Batch9_002', with elements:
        img_Batch9_002_image (Images), img_Batch9_002_cells_labels (Labels), img_Batch9_002_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("VDVDY6YapFYtYXhd0000")
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("VDVDY6YapFYtYXhd")   # 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 VDVDY6YapFYtYXhd0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/VDVDY6YapFYtYXhd

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid VDVDY6YapFYtYXhd
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)
7,236
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
46.1 MB
Sample ID
S-BIAD3694:discovery:Batch9:596-1: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