Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch12:AIN-01-0142:kidney
bronze acute kidney injury ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch12_014_image': DataTree[cyx] (35, 286, 206), (35, 143, 103), (35, 71, 51), (35, 35, 25), (35, 17, 12)
│ ├── 'img_Batch12_015_image': DataTree[cyx] (35, 229, 222), (35, 114, 111), (35, 57, 55), (35, 28, 27), (35, 14, 13)
│ ├── 'img_Batch12_016_image': DataTree[cyx] (35, 364, 173), (35, 182, 86), (35, 91, 43), (35, 45, 21), (35, 22, 10)
│ ├── 'img_Batch12_017_image': DataTree[cyx] (35, 308, 522), (35, 154, 261), (35, 77, 130), (35, 38, 65), (35, 19, 32)
│ ├── 'img_Batch12_018_image': DataTree[cyx] (35, 397, 487), (35, 198, 243), (35, 99, 121), (35, 49, 60), (35, 24, 30)
│ ├── 'img_Batch12_019_image': DataTree[cyx] (35, 468, 474), (35, 234, 237), (35, 117, 118), (35, 58, 59), (35, 29, 29)
│ ├── 'img_Batch12_020_image': DataTree[cyx] (35, 209, 222), (35, 104, 111), (35, 52, 55), (35, 26, 27), (35, 13, 13)
│ └── 'img_Batch12_023_image': DataTree[cyx] (35, 415, 409), (35, 207, 204), (35, 103, 102), (35, 51, 51), (35, 25, 25)
├── Labels
│ ├── 'img_Batch12_014_cells_labels': DataTree[yx] (286, 206), (143, 103), (71, 51), (35, 25), (17, 12)
│ ├── 'img_Batch12_015_cells_labels': DataTree[yx] (229, 222), (114, 111), (57, 55), (28, 27), (14, 13)
│ ├── 'img_Batch12_016_cells_labels': DataTree[yx] (364, 173), (182, 86), (91, 43), (45, 21), (22, 10)
│ ├── 'img_Batch12_017_cells_labels': DataTree[yx] (308, 522), (154, 261), (77, 130), (38, 65), (19, 32)
│ ├── 'img_Batch12_018_cells_labels': DataTree[yx] (397, 487), (198, 243), (99, 121), (49, 60), (24, 30)
│ ├── 'img_Batch12_019_cells_labels': DataTree[yx] (468, 474), (234, 237), (117, 118), (58, 59), (29, 29)
│ ├── 'img_Batch12_020_cells_labels': DataTree[yx] (209, 222), (104, 111), (52, 55), (26, 27), (13, 13)
│ └── 'img_Batch12_023_cells_labels': DataTree[yx] (415, 409), (207, 204), (103, 102), (51, 51), (25, 25)
├── Shapes
│ ├── 'img_Batch12_014_cell_boundaries': GeoDataFrame shape: (393, 2) (2D shapes)
│ ├── 'img_Batch12_015_cell_boundaries': GeoDataFrame shape: (318, 2) (2D shapes)
│ ├── 'img_Batch12_016_cell_boundaries': GeoDataFrame shape: (193, 2) (2D shapes)
│ ├── 'img_Batch12_017_cell_boundaries': GeoDataFrame shape: (1221, 2) (2D shapes)
│ ├── 'img_Batch12_018_cell_boundaries': GeoDataFrame shape: (1314, 2) (2D shapes)
│ ├── 'img_Batch12_019_cell_boundaries': GeoDataFrame shape: (1394, 2) (2D shapes)
│ ├── 'img_Batch12_020_cell_boundaries': GeoDataFrame shape: (259, 2) (2D shapes)
│ └── 'img_Batch12_023_cell_boundaries': GeoDataFrame shape: (1244, 2) (2D shapes)
└── Tables
└── 'table': AnnData (6336, 35)
with coordinate systems:
▸ 'img_Batch12_014', with elements:
img_Batch12_014_image (Images), img_Batch12_014_cells_labels (Labels), img_Batch12_014_cell_boundaries (Shapes)
▸ 'img_Batch12_015', with elements:
img_Batch12_015_image (Images), img_Batch12_015_cells_labels (Labels), img_Batch12_015_cell_boundaries (Shapes)
▸ 'img_Batch12_016', with elements:
img_Batch12_016_image (Images), img_Batch12_016_cells_labels (Labels), img_Batch12_016_cell_boundaries (Shapes)
▸ 'img_Batch12_017', with elements:
img_Batch12_017_image (Images), img_Batch12_017_cells_labels (Labels), img_Batch12_017_cell_boundaries (Shapes)
▸ 'img_Batch12_018', with elements:
img_Batch12_018_image (Images), img_Batch12_018_cells_labels (Labels), img_Batch12_018_cell_boundaries (Shapes)
▸ 'img_Batch12_019', with elements:
img_Batch12_019_image (Images), img_Batch12_019_cells_labels (Labels), img_Batch12_019_cell_boundaries (Shapes)
▸ 'img_Batch12_020', with elements:
img_Batch12_020_image (Images), img_Batch12_020_cells_labels (Labels), img_Batch12_020_cell_boundaries (Shapes)
▸ 'img_Batch12_023', with elements:
img_Batch12_023_image (Images), img_Batch12_023_cells_labels (Labels), img_Batch12_023_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("1Yhb28Jh1oXMuGbe0000")
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("1Yhb28Jh1oXMuGbe") # 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 1Yhb28Jh1oXMuGbe0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/1Yhb28Jh1oXMuGbe
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 1Yhb28Jh1oXMuGbeLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute kidney injury
- Modality
- protein
- Observations (cells)
- 6,336
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 37.3 MB
- Sample ID
- S-BIAD3694:discovery:Batch12:AIN-01-0142: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