Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch2:AIN-01-0017:kidney
bronze acute kidney injury ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch2_007_image': DataTree[cyx] (35, 1045, 941), (35, 522, 470), (35, 261, 235), (35, 130, 117), (35, 65, 58)
│ ├── 'img_Batch2_008_image': DataTree[cyx] (35, 1664, 710), (35, 832, 355), (35, 416, 177), (35, 208, 88), (35, 104, 44)
│ ├── 'img_Batch2_009_image': DataTree[cyx] (35, 2506, 787), (35, 1253, 393), (35, 626, 196), (35, 313, 98), (35, 156, 49)
│ └── 'img_Batch2_010_image': DataTree[cyx] (35, 3786, 683), (35, 1893, 341), (35, 946, 170), (35, 473, 85), (35, 236, 42)
├── Labels
│ ├── 'img_Batch2_007_cells_labels': DataTree[yx] (1045, 941), (522, 470), (261, 235), (130, 117), (65, 58)
│ ├── 'img_Batch2_008_cells_labels': DataTree[yx] (1664, 710), (832, 355), (416, 177), (208, 88), (104, 44)
│ ├── 'img_Batch2_009_cells_labels': DataTree[yx] (2506, 787), (1253, 393), (626, 196), (313, 98), (156, 49)
│ └── 'img_Batch2_010_cells_labels': DataTree[yx] (3786, 683), (1893, 341), (946, 170), (473, 85), (236, 42)
├── Shapes
│ ├── 'img_Batch2_007_cell_boundaries': GeoDataFrame shape: (3894, 2) (2D shapes)
│ ├── 'img_Batch2_008_cell_boundaries': GeoDataFrame shape: (5404, 2) (2D shapes)
│ ├── 'img_Batch2_009_cell_boundaries': GeoDataFrame shape: (9014, 2) (2D shapes)
│ └── 'img_Batch2_010_cell_boundaries': GeoDataFrame shape: (12771, 2) (2D shapes)
└── Tables
└── 'table': AnnData (31083, 35)
with coordinate systems:
▸ 'img_Batch2_007', with elements:
img_Batch2_007_image (Images), img_Batch2_007_cells_labels (Labels), img_Batch2_007_cell_boundaries (Shapes)
▸ 'img_Batch2_008', with elements:
img_Batch2_008_image (Images), img_Batch2_008_cells_labels (Labels), img_Batch2_008_cell_boundaries (Shapes)
▸ 'img_Batch2_009', with elements:
img_Batch2_009_image (Images), img_Batch2_009_cells_labels (Labels), img_Batch2_009_cell_boundaries (Shapes)
▸ 'img_Batch2_010', with elements:
img_Batch2_010_image (Images), img_Batch2_010_cells_labels (Labels), img_Batch2_010_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("1yNoHpZAb0xnna510000")
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("1yNoHpZAb0xnna51") # 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 1yNoHpZAb0xnna510000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/1yNoHpZAb0xnna51
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 1yNoHpZAb0xnna51License: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute kidney injury
- Modality
- protein
- Observations (cells)
- 31,083
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 237 MB
- Sample ID
- S-BIAD3694:discovery:Batch2:AIN-01-0017: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