Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch17:AIN-02-0018:kidney
bronze acute tubulointerstitial nephritis ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch17_001_image': DataTree[cyx] (35, 1636, 389), (35, 818, 194), (35, 409, 97), (35, 204, 48), (35, 102, 24)
│ ├── 'img_Batch17_002_image': DataTree[cyx] (35, 728, 307), (35, 364, 153), (35, 182, 76), (35, 91, 38), (35, 45, 19)
│ ├── 'img_Batch17_003_image': DataTree[cyx] (35, 358, 401), (35, 179, 200), (35, 89, 100), (35, 44, 50), (35, 22, 25)
│ ├── 'img_Batch17_004_image': DataTree[cyx] (35, 210, 554), (35, 105, 277), (35, 52, 138), (35, 26, 69), (35, 13, 34)
│ ├── 'img_Batch17_005_image': DataTree[cyx] (35, 300, 498), (35, 150, 249), (35, 75, 124), (35, 37, 62), (35, 18, 31)
│ ├── 'img_Batch17_007_image': DataTree[cyx] (35, 335, 180), (35, 167, 90), (35, 83, 45), (35, 41, 22), (35, 20, 11)
│ ├── 'img_Batch17_008_image': DataTree[cyx] (35, 355, 256), (35, 177, 128), (35, 88, 64), (35, 44, 32), (35, 22, 16)
│ └── 'img_Batch17_009_image': DataTree[cyx] (35, 166, 413), (35, 83, 206), (35, 41, 103), (35, 20, 51), (35, 10, 25)
├── Labels
│ ├── 'img_Batch17_001_cells_labels': DataTree[yx] (1636, 389), (818, 194), (409, 97), (204, 48), (102, 24)
│ ├── 'img_Batch17_002_cells_labels': DataTree[yx] (728, 307), (364, 153), (182, 76), (91, 38), (45, 19)
│ ├── 'img_Batch17_003_cells_labels': DataTree[yx] (358, 401), (179, 200), (89, 100), (44, 50), (22, 25)
│ ├── 'img_Batch17_004_cells_labels': DataTree[yx] (210, 554), (105, 277), (52, 138), (26, 69), (13, 34)
│ ├── 'img_Batch17_005_cells_labels': DataTree[yx] (300, 498), (150, 249), (75, 124), (37, 62), (18, 31)
│ ├── 'img_Batch17_007_cells_labels': DataTree[yx] (335, 180), (167, 90), (83, 45), (41, 22), (20, 11)
│ ├── 'img_Batch17_008_cells_labels': DataTree[yx] (355, 256), (177, 128), (88, 64), (44, 32), (22, 16)
│ └── 'img_Batch17_009_cells_labels': DataTree[yx] (166, 413), (83, 206), (41, 103), (20, 51), (10, 25)
├── Shapes
│ ├── 'img_Batch17_001_cell_boundaries': GeoDataFrame shape: (3641, 2) (2D shapes)
│ ├── 'img_Batch17_002_cell_boundaries': GeoDataFrame shape: (1100, 2) (2D shapes)
│ ├── 'img_Batch17_003_cell_boundaries': GeoDataFrame shape: (728, 2) (2D shapes)
│ ├── 'img_Batch17_004_cell_boundaries': GeoDataFrame shape: (639, 2) (2D shapes)
│ ├── 'img_Batch17_005_cell_boundaries': GeoDataFrame shape: (809, 2) (2D shapes)
│ ├── 'img_Batch17_007_cell_boundaries': GeoDataFrame shape: (442, 2) (2D shapes)
│ ├── 'img_Batch17_008_cell_boundaries': GeoDataFrame shape: (513, 2) (2D shapes)
│ └── 'img_Batch17_009_cell_boundaries': GeoDataFrame shape: (358, 2) (2D shapes)
└── Tables
└── 'table': AnnData (8230, 35)
with coordinate systems:
▸ 'img_Batch17_001', with elements:
img_Batch17_001_image (Images), img_Batch17_001_cells_labels (Labels), img_Batch17_001_cell_boundaries (Shapes)
▸ 'img_Batch17_002', with elements:
img_Batch17_002_image (Images), img_Batch17_002_cells_labels (Labels), img_Batch17_002_cell_boundaries (Shapes)
▸ 'img_Batch17_003', with elements:
img_Batch17_003_image (Images), img_Batch17_003_cells_labels (Labels), img_Batch17_003_cell_boundaries (Shapes)
▸ 'img_Batch17_004', with elements:
img_Batch17_004_image (Images), img_Batch17_004_cells_labels (Labels), img_Batch17_004_cell_boundaries (Shapes)
▸ 'img_Batch17_005', with elements:
img_Batch17_005_image (Images), img_Batch17_005_cells_labels (Labels), img_Batch17_005_cell_boundaries (Shapes)
▸ 'img_Batch17_007', with elements:
img_Batch17_007_image (Images), img_Batch17_007_cells_labels (Labels), img_Batch17_007_cell_boundaries (Shapes)
▸ 'img_Batch17_008', with elements:
img_Batch17_008_image (Images), img_Batch17_008_cells_labels (Labels), img_Batch17_008_cell_boundaries (Shapes)
▸ 'img_Batch17_009', with elements:
img_Batch17_009_image (Images), img_Batch17_009_cells_labels (Labels), img_Batch17_009_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("eQoPNLBrzwB48cF80000")
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("eQoPNLBrzwB48cF8") # 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 eQoPNLBrzwB48cF80000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/eQoPNLBrzwB48cF8
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid eQoPNLBrzwB48cF8License: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute tubulointerstitial nephritis
- Modality
- protein
- Observations (cells)
- 8,230
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 67.9 MB
- Sample ID
- S-BIAD3694:discovery:Batch17:AIN-02-0018: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