Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch4:AIN-01-0052:kidney
bronze acute tubulointerstitial nephritis ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch4_001_image': DataTree[cyx] (35, 986, 495), (35, 493, 247), (35, 246, 123), (35, 123, 61), (35, 61, 30)
│ ├── 'img_Batch4_002_image': DataTree[cyx] (35, 938, 492), (35, 469, 246), (35, 234, 123), (35, 117, 61), (35, 58, 30)
│ ├── 'img_Batch4_003_image': DataTree[cyx] (35, 888, 424), (35, 444, 212), (35, 222, 106), (35, 111, 53), (35, 55, 26)
│ ├── 'img_Batch4_004_image': DataTree[cyx] (35, 674, 750), (35, 337, 375), (35, 168, 187), (35, 84, 93), (35, 42, 46)
│ ├── 'img_Batch4_005_image': DataTree[cyx] (35, 2069, 670), (35, 1034, 335), (35, 517, 167), (35, 258, 83), (35, 129, 41)
│ ├── 'img_Batch4_006_image': DataTree[cyx] (35, 676, 522), (35, 338, 261), (35, 169, 130), (35, 84, 65), (35, 42, 32)
│ ├── 'img_Batch4_007_image': DataTree[cyx] (35, 574, 1059), (35, 287, 529), (35, 143, 264), (35, 71, 132), (35, 35, 66)
│ ├── 'img_Batch4_008_image': DataTree[cyx] (35, 389, 798), (35, 194, 399), (35, 97, 199), (35, 48, 99), (35, 24, 49)
│ └── 'img_Batch4_009_image': DataTree[cyx] (35, 812, 754), (35, 406, 377), (35, 203, 188), (35, 101, 94), (35, 50, 47)
├── Labels
│ ├── 'img_Batch4_001_cells_labels': DataTree[yx] (986, 495), (493, 247), (246, 123), (123, 61), (61, 30)
│ ├── 'img_Batch4_002_cells_labels': DataTree[yx] (938, 492), (469, 246), (234, 123), (117, 61), (58, 30)
│ ├── 'img_Batch4_003_cells_labels': DataTree[yx] (888, 424), (444, 212), (222, 106), (111, 53), (55, 26)
│ ├── 'img_Batch4_004_cells_labels': DataTree[yx] (674, 750), (337, 375), (168, 187), (84, 93), (42, 46)
│ ├── 'img_Batch4_005_cells_labels': DataTree[yx] (2069, 670), (1034, 335), (517, 167), (258, 83), (129, 41)
│ ├── 'img_Batch4_006_cells_labels': DataTree[yx] (676, 522), (338, 261), (169, 130), (84, 65), (42, 32)
│ ├── 'img_Batch4_007_cells_labels': DataTree[yx] (574, 1059), (287, 529), (143, 264), (71, 132), (35, 66)
│ ├── 'img_Batch4_008_cells_labels': DataTree[yx] (389, 798), (194, 399), (97, 199), (48, 99), (24, 49)
│ └── 'img_Batch4_009_cells_labels': DataTree[yx] (812, 754), (406, 377), (203, 188), (101, 94), (50, 47)
├── Shapes
│ ├── 'img_Batch4_001_cell_boundaries': GeoDataFrame shape: (2573, 2) (2D shapes)
│ ├── 'img_Batch4_002_cell_boundaries': GeoDataFrame shape: (1616, 2) (2D shapes)
│ ├── 'img_Batch4_003_cell_boundaries': GeoDataFrame shape: (2183, 2) (2D shapes)
│ ├── 'img_Batch4_004_cell_boundaries': GeoDataFrame shape: (2003, 2) (2D shapes)
│ ├── 'img_Batch4_005_cell_boundaries': GeoDataFrame shape: (8145, 2) (2D shapes)
│ ├── 'img_Batch4_006_cell_boundaries': GeoDataFrame shape: (2402, 2) (2D shapes)
│ ├── 'img_Batch4_007_cell_boundaries': GeoDataFrame shape: (2487, 2) (2D shapes)
│ ├── 'img_Batch4_008_cell_boundaries': GeoDataFrame shape: (1509, 2) (2D shapes)
│ └── 'img_Batch4_009_cell_boundaries': GeoDataFrame shape: (2692, 2) (2D shapes)
└── Tables
└── 'table': AnnData (25610, 35)
with coordinate systems:
▸ 'img_Batch4_001', with elements:
img_Batch4_001_image (Images), img_Batch4_001_cells_labels (Labels), img_Batch4_001_cell_boundaries (Shapes)
▸ 'img_Batch4_002', with elements:
img_Batch4_002_image (Images), img_Batch4_002_cells_labels (Labels), img_Batch4_002_cell_boundaries (Shapes)
▸ 'img_Batch4_003', with elements:
img_Batch4_003_image (Images), img_Batch4_003_cells_labels (Labels), img_Batch4_003_cell_boundaries (Shapes)
▸ 'img_Batch4_004', with elements:
img_Batch4_004_image (Images), img_Batch4_004_cells_labels (Labels), img_Batch4_004_cell_boundaries (Shapes)
▸ 'img_Batch4_005', with elements:
img_Batch4_005_image (Images), img_Batch4_005_cells_labels (Labels), img_Batch4_005_cell_boundaries (Shapes)
▸ 'img_Batch4_006', with elements:
img_Batch4_006_image (Images), img_Batch4_006_cells_labels (Labels), img_Batch4_006_cell_boundaries (Shapes)
▸ 'img_Batch4_007', with elements:
img_Batch4_007_image (Images), img_Batch4_007_cells_labels (Labels), img_Batch4_007_cell_boundaries (Shapes)
▸ 'img_Batch4_008', with elements:
img_Batch4_008_image (Images), img_Batch4_008_cells_labels (Labels), img_Batch4_008_cell_boundaries (Shapes)
▸ 'img_Batch4_009', with elements:
img_Batch4_009_image (Images), img_Batch4_009_cells_labels (Labels), img_Batch4_009_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("41I5eru0wcjlZqjR0000")
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("41I5eru0wcjlZqjR") # 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 41I5eru0wcjlZqjR0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/41I5eru0wcjlZqjR
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 41I5eru0wcjlZqjRLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute tubulointerstitial nephritis
- Modality
- protein
- Observations (cells)
- 25,610
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 191 MB
- Sample ID
- S-BIAD3694:discovery:Batch4:AIN-01-0052: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