Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch6:L102:kidney
bronze ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch6_005_image': DataTree[cyx] (35, 4444, 668), (35, 2222, 334), (35, 1111, 167), (35, 555, 83), (35, 277, 41)
│ ├── 'img_Batch6_006_image': DataTree[cyx] (35, 1563, 699), (35, 781, 349), (35, 390, 174), (35, 195, 87), (35, 97, 43)
│ ├── 'img_Batch6_007_image': DataTree[cyx] (35, 1330, 657), (35, 665, 328), (35, 332, 164), (35, 166, 82), (35, 83, 41)
│ └── 'img_Batch6_008_image': DataTree[cyx] (35, 935, 691), (35, 467, 345), (35, 233, 172), (35, 116, 86), (35, 58, 43)
├── Labels
│ ├── 'img_Batch6_005_cells_labels': DataTree[yx] (4444, 668), (2222, 334), (1111, 167), (555, 83), (277, 41)
│ ├── 'img_Batch6_006_cells_labels': DataTree[yx] (1563, 699), (781, 349), (390, 174), (195, 87), (97, 43)
│ ├── 'img_Batch6_007_cells_labels': DataTree[yx] (1330, 657), (665, 328), (332, 164), (166, 82), (83, 41)
│ └── 'img_Batch6_008_cells_labels': DataTree[yx] (935, 691), (467, 345), (233, 172), (116, 86), (58, 43)
├── Shapes
│ ├── 'img_Batch6_005_cell_boundaries': GeoDataFrame shape: (14491, 2) (2D shapes)
│ ├── 'img_Batch6_006_cell_boundaries': GeoDataFrame shape: (4695, 2) (2D shapes)
│ ├── 'img_Batch6_007_cell_boundaries': GeoDataFrame shape: (3668, 2) (2D shapes)
│ └── 'img_Batch6_008_cell_boundaries': GeoDataFrame shape: (2537, 2) (2D shapes)
└── Tables
└── 'table': AnnData (25391, 35)
with coordinate systems:
▸ 'img_Batch6_005', with elements:
img_Batch6_005_image (Images), img_Batch6_005_cells_labels (Labels), img_Batch6_005_cell_boundaries (Shapes)
▸ 'img_Batch6_006', with elements:
img_Batch6_006_image (Images), img_Batch6_006_cells_labels (Labels), img_Batch6_006_cell_boundaries (Shapes)
▸ 'img_Batch6_007', with elements:
img_Batch6_007_image (Images), img_Batch6_007_cells_labels (Labels), img_Batch6_007_cell_boundaries (Shapes)
▸ 'img_Batch6_008', with elements:
img_Batch6_008_image (Images), img_Batch6_008_cells_labels (Labels), img_Batch6_008_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("a2XFQxUKg8hPTC5d0000")
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("a2XFQxUKg8hPTC5d") # 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 a2XFQxUKg8hPTC5d0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/a2XFQxUKg8hPTC5d
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid a2XFQxUKg8hPTC5dLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Modality
- protein
- Observations (cells)
- 25,391
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 222 MB
- Sample ID
- S-BIAD3694:discovery:Batch6:L102: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