Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch14:AIN-01-0158:kidney
bronze acute tubulointerstitial nephritis ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch14_001_image': DataTree[cyx] (35, 920, 549), (35, 460, 274), (35, 230, 137), (35, 115, 68), (35, 57, 34)
│ ├── 'img_Batch14_002_image': DataTree[cyx] (35, 759, 574), (35, 379, 287), (35, 189, 143), (35, 94, 71), (35, 47, 35)
│ ├── 'img_Batch14_003_image': DataTree[cyx] (35, 1296, 631), (35, 648, 315), (35, 324, 157), (35, 162, 78), (35, 81, 39)
│ ├── 'img_Batch14_004_image': DataTree[cyx] (35, 1157, 656), (35, 578, 328), (35, 289, 164), (35, 144, 82), (35, 72, 41)
│ ├── 'img_Batch14_005_image': DataTree[cyx] (35, 739, 542), (35, 369, 271), (35, 184, 135), (35, 92, 67), (35, 46, 33)
│ ├── 'img_Batch14_006_image': DataTree[cyx] (35, 728, 715), (35, 364, 357), (35, 182, 178), (35, 91, 89), (35, 45, 44)
│ ├── 'img_Batch14_007_image': DataTree[cyx] (35, 744, 662), (35, 372, 331), (35, 186, 165), (35, 93, 82), (35, 46, 41)
│ ├── 'img_Batch14_008_image': DataTree[cyx] (35, 1052, 593), (35, 526, 296), (35, 263, 148), (35, 131, 74), (35, 65, 37)
│ ├── 'img_Batch14_009_image': DataTree[cyx] (35, 791, 549), (35, 395, 274), (35, 197, 137), (35, 98, 68), (35, 49, 34)
│ └── 'img_Batch14_010_image': DataTree[cyx] (35, 787, 511), (35, 393, 255), (35, 196, 127), (35, 98, 63), (35, 49, 31)
├── Labels
│ ├── 'img_Batch14_001_cells_labels': DataTree[yx] (920, 549), (460, 274), (230, 137), (115, 68), (57, 34)
│ ├── 'img_Batch14_002_cells_labels': DataTree[yx] (759, 574), (379, 287), (189, 143), (94, 71), (47, 35)
│ ├── 'img_Batch14_003_cells_labels': DataTree[yx] (1296, 631), (648, 315), (324, 157), (162, 78), (81, 39)
│ ├── 'img_Batch14_004_cells_labels': DataTree[yx] (1157, 656), (578, 328), (289, 164), (144, 82), (72, 41)
│ ├── 'img_Batch14_005_cells_labels': DataTree[yx] (739, 542), (369, 271), (184, 135), (92, 67), (46, 33)
│ ├── 'img_Batch14_006_cells_labels': DataTree[yx] (728, 715), (364, 357), (182, 178), (91, 89), (45, 44)
│ ├── 'img_Batch14_007_cells_labels': DataTree[yx] (744, 662), (372, 331), (186, 165), (93, 82), (46, 41)
│ ├── 'img_Batch14_008_cells_labels': DataTree[yx] (1052, 593), (526, 296), (263, 148), (131, 74), (65, 37)
│ ├── 'img_Batch14_009_cells_labels': DataTree[yx] (791, 549), (395, 274), (197, 137), (98, 68), (49, 34)
│ └── 'img_Batch14_010_cells_labels': DataTree[yx] (787, 511), (393, 255), (196, 127), (98, 63), (49, 31)
├── Shapes
│ ├── 'img_Batch14_001_cell_boundaries': GeoDataFrame shape: (2127, 2) (2D shapes)
│ ├── 'img_Batch14_002_cell_boundaries': GeoDataFrame shape: (2222, 2) (2D shapes)
│ ├── 'img_Batch14_003_cell_boundaries': GeoDataFrame shape: (4216, 2) (2D shapes)
│ ├── 'img_Batch14_004_cell_boundaries': GeoDataFrame shape: (3762, 2) (2D shapes)
│ ├── 'img_Batch14_005_cell_boundaries': GeoDataFrame shape: (2020, 2) (2D shapes)
│ ├── 'img_Batch14_006_cell_boundaries': GeoDataFrame shape: (2359, 2) (2D shapes)
│ ├── 'img_Batch14_007_cell_boundaries': GeoDataFrame shape: (2295, 2) (2D shapes)
│ ├── 'img_Batch14_008_cell_boundaries': GeoDataFrame shape: (2906, 2) (2D shapes)
│ ├── 'img_Batch14_009_cell_boundaries': GeoDataFrame shape: (2058, 2) (2D shapes)
│ └── 'img_Batch14_010_cell_boundaries': GeoDataFrame shape: (1831, 2) (2D shapes)
└── Tables
└── 'table': AnnData (25796, 35)
with coordinate systems:
▸ 'img_Batch14_001', with elements:
img_Batch14_001_image (Images), img_Batch14_001_cells_labels (Labels), img_Batch14_001_cell_boundaries (Shapes)
▸ 'img_Batch14_002', with elements:
img_Batch14_002_image (Images), img_Batch14_002_cells_labels (Labels), img_Batch14_002_cell_boundaries (Shapes)
▸ 'img_Batch14_003', with elements:
img_Batch14_003_image (Images), img_Batch14_003_cells_labels (Labels), img_Batch14_003_cell_boundaries (Shapes)
▸ 'img_Batch14_004', with elements:
img_Batch14_004_image (Images), img_Batch14_004_cells_labels (Labels), img_Batch14_004_cell_boundaries (Shapes)
▸ 'img_Batch14_005', with elements:
img_Batch14_005_image (Images), img_Batch14_005_cells_labels (Labels), img_Batch14_005_cell_boundaries (Shapes)
▸ 'img_Batch14_006', with elements:
img_Batch14_006_image (Images), img_Batch14_006_cells_labels (Labels), img_Batch14_006_cell_boundaries (Shapes)
▸ 'img_Batch14_007', with elements:
img_Batch14_007_image (Images), img_Batch14_007_cells_labels (Labels), img_Batch14_007_cell_boundaries (Shapes)
▸ 'img_Batch14_008', with elements:
img_Batch14_008_image (Images), img_Batch14_008_cells_labels (Labels), img_Batch14_008_cell_boundaries (Shapes)
▸ 'img_Batch14_009', with elements:
img_Batch14_009_image (Images), img_Batch14_009_cells_labels (Labels), img_Batch14_009_cell_boundaries (Shapes)
▸ 'img_Batch14_010', with elements:
img_Batch14_010_image (Images), img_Batch14_010_cells_labels (Labels), img_Batch14_010_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("LuhmWQJaTO3sK3yM0000")
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("LuhmWQJaTO3sK3yM") # 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 LuhmWQJaTO3sK3yM0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/LuhmWQJaTO3sK3yM
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid LuhmWQJaTO3sK3yMLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute tubulointerstitial nephritis
- Modality
- protein
- Observations (cells)
- 25,796
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 273 MB
- Sample ID
- S-BIAD3694:discovery:Batch14:AIN-01-0158: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