Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch16:AIN-01-0232:kidney
bronze acute kidney injury ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch16_013_image': DataTree[cyx] (35, 688, 466), (35, 344, 233), (35, 172, 116), (35, 86, 58), (35, 43, 29)
│ ├── 'img_Batch16_014_image': DataTree[cyx] (35, 623, 484), (35, 311, 242), (35, 155, 121), (35, 77, 60), (35, 38, 30)
│ ├── 'img_Batch16_015_image': DataTree[cyx] (35, 447, 449), (35, 223, 224), (35, 111, 112), (35, 55, 56), (35, 27, 28)
│ ├── 'img_Batch16_016_image': DataTree[cyx] (35, 851, 488), (35, 425, 244), (35, 212, 122), (35, 106, 61), (35, 53, 30)
│ ├── 'img_Batch16_017_image': DataTree[cyx] (35, 393, 540), (35, 196, 270), (35, 98, 135), (35, 49, 67), (35, 24, 33)
│ ├── 'img_Batch16_018_image': DataTree[cyx] (35, 422, 584), (35, 211, 292), (35, 105, 146), (35, 52, 73), (35, 26, 36)
│ ├── 'img_Batch16_019_image': DataTree[cyx] (35, 362, 443), (35, 181, 221), (35, 90, 110), (35, 45, 55), (35, 22, 27)
│ └── 'img_Batch16_020_image': DataTree[cyx] (35, 1016, 452), (35, 508, 226), (35, 254, 113), (35, 127, 56), (35, 63, 28)
├── Labels
│ ├── 'img_Batch16_013_cells_labels': DataTree[yx] (688, 466), (344, 233), (172, 116), (86, 58), (43, 29)
│ ├── 'img_Batch16_014_cells_labels': DataTree[yx] (623, 484), (311, 242), (155, 121), (77, 60), (38, 30)
│ ├── 'img_Batch16_015_cells_labels': DataTree[yx] (447, 449), (223, 224), (111, 112), (55, 56), (27, 28)
│ ├── 'img_Batch16_016_cells_labels': DataTree[yx] (851, 488), (425, 244), (212, 122), (106, 61), (53, 30)
│ ├── 'img_Batch16_017_cells_labels': DataTree[yx] (393, 540), (196, 270), (98, 135), (49, 67), (24, 33)
│ ├── 'img_Batch16_018_cells_labels': DataTree[yx] (422, 584), (211, 292), (105, 146), (52, 73), (26, 36)
│ ├── 'img_Batch16_019_cells_labels': DataTree[yx] (362, 443), (181, 221), (90, 110), (45, 55), (22, 27)
│ └── 'img_Batch16_020_cells_labels': DataTree[yx] (1016, 452), (508, 226), (254, 113), (127, 56), (63, 28)
├── Shapes
│ ├── 'img_Batch16_013_cell_boundaries': GeoDataFrame shape: (1200, 2) (2D shapes)
│ ├── 'img_Batch16_014_cell_boundaries': GeoDataFrame shape: (1305, 2) (2D shapes)
│ ├── 'img_Batch16_015_cell_boundaries': GeoDataFrame shape: (773, 2) (2D shapes)
│ ├── 'img_Batch16_016_cell_boundaries': GeoDataFrame shape: (1832, 2) (2D shapes)
│ ├── 'img_Batch16_017_cell_boundaries': GeoDataFrame shape: (835, 2) (2D shapes)
│ ├── 'img_Batch16_018_cell_boundaries': GeoDataFrame shape: (1027, 2) (2D shapes)
│ ├── 'img_Batch16_019_cell_boundaries': GeoDataFrame shape: (972, 2) (2D shapes)
│ └── 'img_Batch16_020_cell_boundaries': GeoDataFrame shape: (2444, 2) (2D shapes)
└── Tables
└── 'table': AnnData (10388, 35)
with coordinate systems:
▸ 'img_Batch16_013', with elements:
img_Batch16_013_image (Images), img_Batch16_013_cells_labels (Labels), img_Batch16_013_cell_boundaries (Shapes)
▸ 'img_Batch16_014', with elements:
img_Batch16_014_image (Images), img_Batch16_014_cells_labels (Labels), img_Batch16_014_cell_boundaries (Shapes)
▸ 'img_Batch16_015', with elements:
img_Batch16_015_image (Images), img_Batch16_015_cells_labels (Labels), img_Batch16_015_cell_boundaries (Shapes)
▸ 'img_Batch16_016', with elements:
img_Batch16_016_image (Images), img_Batch16_016_cells_labels (Labels), img_Batch16_016_cell_boundaries (Shapes)
▸ 'img_Batch16_017', with elements:
img_Batch16_017_image (Images), img_Batch16_017_cells_labels (Labels), img_Batch16_017_cell_boundaries (Shapes)
▸ 'img_Batch16_018', with elements:
img_Batch16_018_image (Images), img_Batch16_018_cells_labels (Labels), img_Batch16_018_cell_boundaries (Shapes)
▸ 'img_Batch16_019', with elements:
img_Batch16_019_image (Images), img_Batch16_019_cells_labels (Labels), img_Batch16_019_cell_boundaries (Shapes)
▸ 'img_Batch16_020', with elements:
img_Batch16_020_image (Images), img_Batch16_020_cells_labels (Labels), img_Batch16_020_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("89xt35DQBVWy9TUk0000")
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("89xt35DQBVWy9TUk") # 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 89xt35DQBVWy9TUk0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/89xt35DQBVWy9TUk
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 89xt35DQBVWy9TUkLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute kidney injury
- Modality
- protein
- Observations (cells)
- 10,388
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 66.6 MB
- Sample ID
- S-BIAD3694:discovery:Batch16:AIN-01-0232: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