Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch22:L117:kidney
bronze ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch22_012_image': DataTree[cyx] (35, 1206, 1208), (35, 603, 604), (35, 301, 302), (35, 150, 151), (35, 75, 75)
│ ├── 'img_Batch22_013_image': DataTree[cyx] (35, 1380, 1169), (35, 690, 584), (35, 345, 292), (35, 172, 146), (35, 86, 73)
│ ├── 'img_Batch22_014_image': DataTree[cyx] (35, 3038, 747), (35, 1519, 373), (35, 759, 186), (35, 379, 93), (35, 189, 46)
│ └── 'img_Batch22_015_image': DataTree[cyx] (35, 2334, 1146), (35, 1167, 573), (35, 583, 286), (35, 291, 143), (35, 145, 71)
├── Labels
│ ├── 'img_Batch22_012_cells_labels': DataTree[yx] (1206, 1208), (603, 604), (301, 302), (150, 151), (75, 75)
│ ├── 'img_Batch22_013_cells_labels': DataTree[yx] (1380, 1169), (690, 584), (345, 292), (172, 146), (86, 73)
│ ├── 'img_Batch22_014_cells_labels': DataTree[yx] (3038, 747), (1519, 373), (759, 186), (379, 93), (189, 46)
│ └── 'img_Batch22_015_cells_labels': DataTree[yx] (2334, 1146), (1167, 573), (583, 286), (291, 143), (145, 71)
├── Shapes
│ ├── 'img_Batch22_012_cell_boundaries': GeoDataFrame shape: (2885, 2) (2D shapes)
│ ├── 'img_Batch22_013_cell_boundaries': GeoDataFrame shape: (3680, 2) (2D shapes)
│ ├── 'img_Batch22_014_cell_boundaries': GeoDataFrame shape: (8215, 2) (2D shapes)
│ └── 'img_Batch22_015_cell_boundaries': GeoDataFrame shape: (6730, 2) (2D shapes)
└── Tables
└── 'table': AnnData (21510, 35)
with coordinate systems:
▸ 'img_Batch22_012', with elements:
img_Batch22_012_image (Images), img_Batch22_012_cells_labels (Labels), img_Batch22_012_cell_boundaries (Shapes)
▸ 'img_Batch22_013', with elements:
img_Batch22_013_image (Images), img_Batch22_013_cells_labels (Labels), img_Batch22_013_cell_boundaries (Shapes)
▸ 'img_Batch22_014', with elements:
img_Batch22_014_image (Images), img_Batch22_014_cells_labels (Labels), img_Batch22_014_cell_boundaries (Shapes)
▸ 'img_Batch22_015', with elements:
img_Batch22_015_image (Images), img_Batch22_015_cells_labels (Labels), img_Batch22_015_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("AmbqGlPeetQSUYbP0000")
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("AmbqGlPeetQSUYbP") # 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 AmbqGlPeetQSUYbP0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/AmbqGlPeetQSUYbP
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid AmbqGlPeetQSUYbPLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Modality
- protein
- Observations (cells)
- 21,510
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 206 MB
- Sample ID
- S-BIAD3694:discovery:Batch22:L117: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