Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch13:AIN-01-0217:kidney
bronze acute kidney injury ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch13_015_image': DataTree[cyx] (35, 998, 944), (35, 499, 472), (35, 249, 236), (35, 124, 118), (35, 62, 59)
│ ├── 'img_Batch13_016_image': DataTree[cyx] (35, 1621, 783), (35, 810, 391), (35, 405, 195), (35, 202, 97), (35, 101, 48)
│ ├── 'img_Batch13_017_image': DataTree[cyx] (35, 1742, 739), (35, 871, 369), (35, 435, 184), (35, 217, 92), (35, 108, 46)
│ ├── 'img_Batch13_018_image': DataTree[cyx] (35, 1964, 792), (35, 982, 396), (35, 491, 198), (35, 245, 99), (35, 122, 49)
│ ├── 'img_Batch13_019_image': DataTree[cyx] (35, 2919, 838), (35, 1459, 419), (35, 729, 209), (35, 364, 104), (35, 182, 52)
│ ├── 'img_Batch13_020_image': DataTree[cyx] (35, 1552, 902), (35, 776, 451), (35, 388, 225), (35, 194, 112), (35, 97, 56)
│ ├── 'img_Batch13_021_image': DataTree[cyx] (35, 1294, 882), (35, 647, 441), (35, 323, 220), (35, 161, 110), (35, 80, 55)
│ └── 'img_Batch13_022_image': DataTree[cyx] (35, 1148, 686), (35, 574, 343), (35, 287, 171), (35, 143, 85), (35, 71, 42)
├── Labels
│ ├── 'img_Batch13_015_cells_labels': DataTree[yx] (998, 944), (499, 472), (249, 236), (124, 118), (62, 59)
│ ├── 'img_Batch13_016_cells_labels': DataTree[yx] (1621, 783), (810, 391), (405, 195), (202, 97), (101, 48)
│ ├── 'img_Batch13_017_cells_labels': DataTree[yx] (1742, 739), (871, 369), (435, 184), (217, 92), (108, 46)
│ ├── 'img_Batch13_018_cells_labels': DataTree[yx] (1964, 792), (982, 396), (491, 198), (245, 99), (122, 49)
│ ├── 'img_Batch13_019_cells_labels': DataTree[yx] (2919, 838), (1459, 419), (729, 209), (364, 104), (182, 52)
│ ├── 'img_Batch13_020_cells_labels': DataTree[yx] (1552, 902), (776, 451), (388, 225), (194, 112), (97, 56)
│ ├── 'img_Batch13_021_cells_labels': DataTree[yx] (1294, 882), (647, 441), (323, 220), (161, 110), (80, 55)
│ └── 'img_Batch13_022_cells_labels': DataTree[yx] (1148, 686), (574, 343), (287, 171), (143, 85), (71, 42)
├── Shapes
│ ├── 'img_Batch13_015_cell_boundaries': GeoDataFrame shape: (4256, 2) (2D shapes)
│ ├── 'img_Batch13_016_cell_boundaries': GeoDataFrame shape: (5644, 2) (2D shapes)
│ ├── 'img_Batch13_017_cell_boundaries': GeoDataFrame shape: (6400, 2) (2D shapes)
│ ├── 'img_Batch13_018_cell_boundaries': GeoDataFrame shape: (6899, 2) (2D shapes)
│ ├── 'img_Batch13_019_cell_boundaries': GeoDataFrame shape: (10865, 2) (2D shapes)
│ ├── 'img_Batch13_020_cell_boundaries': GeoDataFrame shape: (3841, 2) (2D shapes)
│ ├── 'img_Batch13_021_cell_boundaries': GeoDataFrame shape: (3137, 2) (2D shapes)
│ └── 'img_Batch13_022_cell_boundaries': GeoDataFrame shape: (2552, 2) (2D shapes)
└── Tables
└── 'table': AnnData (43594, 35)
with coordinate systems:
▸ 'img_Batch13_015', with elements:
img_Batch13_015_image (Images), img_Batch13_015_cells_labels (Labels), img_Batch13_015_cell_boundaries (Shapes)
▸ 'img_Batch13_016', with elements:
img_Batch13_016_image (Images), img_Batch13_016_cells_labels (Labels), img_Batch13_016_cell_boundaries (Shapes)
▸ 'img_Batch13_017', with elements:
img_Batch13_017_image (Images), img_Batch13_017_cells_labels (Labels), img_Batch13_017_cell_boundaries (Shapes)
▸ 'img_Batch13_018', with elements:
img_Batch13_018_image (Images), img_Batch13_018_cells_labels (Labels), img_Batch13_018_cell_boundaries (Shapes)
▸ 'img_Batch13_019', with elements:
img_Batch13_019_image (Images), img_Batch13_019_cells_labels (Labels), img_Batch13_019_cell_boundaries (Shapes)
▸ 'img_Batch13_020', with elements:
img_Batch13_020_image (Images), img_Batch13_020_cells_labels (Labels), img_Batch13_020_cell_boundaries (Shapes)
▸ 'img_Batch13_021', with elements:
img_Batch13_021_image (Images), img_Batch13_021_cells_labels (Labels), img_Batch13_021_cell_boundaries (Shapes)
▸ 'img_Batch13_022', with elements:
img_Batch13_022_image (Images), img_Batch13_022_cells_labels (Labels), img_Batch13_022_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("zodBYz4tm9ZDujdj0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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("zodBYz4tm9ZDujdj") # 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download zodBYz4tm9ZDujdj0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/zodBYz4tm9ZDujdj
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid zodBYz4tm9ZDujdjLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute kidney injury
- Modality
- protein
- Observations (cells)
- 43,594
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 277 MB
- Sample ID
- S-BIAD3694:discovery:Batch13:AIN-01-0217: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