Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch17:AIN-01-0236:kidney
bronze acute kidney injury ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch17_010_image': DataTree[cyx] (35, 1105, 352), (35, 552, 176), (35, 276, 88), (35, 138, 44), (35, 69, 22)
│ ├── 'img_Batch17_011_image': DataTree[cyx] (35, 1229, 601), (35, 614, 300), (35, 307, 150), (35, 153, 75), (35, 76, 37)
│ ├── 'img_Batch17_012_image': DataTree[cyx] (35, 914, 655), (35, 457, 327), (35, 228, 163), (35, 114, 81), (35, 57, 40)
│ ├── 'img_Batch17_013_image': DataTree[cyx] (35, 942, 698), (35, 471, 349), (35, 235, 174), (35, 117, 87), (35, 58, 43)
│ ├── 'img_Batch17_014_image': DataTree[cyx] (35, 677, 690), (35, 338, 345), (35, 169, 172), (35, 84, 86), (35, 42, 43)
│ ├── 'img_Batch17_015_image': DataTree[cyx] (35, 982, 836), (35, 491, 418), (35, 245, 209), (35, 122, 104), (35, 61, 52)
│ ├── 'img_Batch17_016_image': DataTree[cyx] (35, 551, 684), (35, 275, 342), (35, 137, 171), (35, 68, 85), (35, 34, 42)
│ ├── 'img_Batch17_017_image': DataTree[cyx] (35, 882, 655), (35, 441, 327), (35, 220, 163), (35, 110, 81), (35, 55, 40)
│ └── 'img_Batch17_018_image': DataTree[cyx] (35, 843, 496), (35, 421, 248), (35, 210, 124), (35, 105, 62), (35, 52, 31)
├── Labels
│ ├── 'img_Batch17_010_cells_labels': DataTree[yx] (1105, 352), (552, 176), (276, 88), (138, 44), (69, 22)
│ ├── 'img_Batch17_011_cells_labels': DataTree[yx] (1229, 601), (614, 300), (307, 150), (153, 75), (76, 37)
│ ├── 'img_Batch17_012_cells_labels': DataTree[yx] (914, 655), (457, 327), (228, 163), (114, 81), (57, 40)
│ ├── 'img_Batch17_013_cells_labels': DataTree[yx] (942, 698), (471, 349), (235, 174), (117, 87), (58, 43)
│ ├── 'img_Batch17_014_cells_labels': DataTree[yx] (677, 690), (338, 345), (169, 172), (84, 86), (42, 43)
│ ├── 'img_Batch17_015_cells_labels': DataTree[yx] (982, 836), (491, 418), (245, 209), (122, 104), (61, 52)
│ ├── 'img_Batch17_016_cells_labels': DataTree[yx] (551, 684), (275, 342), (137, 171), (68, 85), (34, 42)
│ ├── 'img_Batch17_017_cells_labels': DataTree[yx] (882, 655), (441, 327), (220, 163), (110, 81), (55, 40)
│ └── 'img_Batch17_018_cells_labels': DataTree[yx] (843, 496), (421, 248), (210, 124), (105, 62), (52, 31)
├── Shapes
│ ├── 'img_Batch17_010_cell_boundaries': GeoDataFrame shape: (1948, 2) (2D shapes)
│ ├── 'img_Batch17_011_cell_boundaries': GeoDataFrame shape: (2654, 2) (2D shapes)
│ ├── 'img_Batch17_012_cell_boundaries': GeoDataFrame shape: (2106, 2) (2D shapes)
│ ├── 'img_Batch17_013_cell_boundaries': GeoDataFrame shape: (2494, 2) (2D shapes)
│ ├── 'img_Batch17_014_cell_boundaries': GeoDataFrame shape: (1671, 2) (2D shapes)
│ ├── 'img_Batch17_015_cell_boundaries': GeoDataFrame shape: (2662, 2) (2D shapes)
│ ├── 'img_Batch17_016_cell_boundaries': GeoDataFrame shape: (1335, 2) (2D shapes)
│ ├── 'img_Batch17_017_cell_boundaries': GeoDataFrame shape: (2117, 2) (2D shapes)
│ └── 'img_Batch17_018_cell_boundaries': GeoDataFrame shape: (1315, 2) (2D shapes)
└── Tables
└── 'table': AnnData (18302, 35)
with coordinate systems:
▸ 'img_Batch17_010', with elements:
img_Batch17_010_image (Images), img_Batch17_010_cells_labels (Labels), img_Batch17_010_cell_boundaries (Shapes)
▸ 'img_Batch17_011', with elements:
img_Batch17_011_image (Images), img_Batch17_011_cells_labels (Labels), img_Batch17_011_cell_boundaries (Shapes)
▸ 'img_Batch17_012', with elements:
img_Batch17_012_image (Images), img_Batch17_012_cells_labels (Labels), img_Batch17_012_cell_boundaries (Shapes)
▸ 'img_Batch17_013', with elements:
img_Batch17_013_image (Images), img_Batch17_013_cells_labels (Labels), img_Batch17_013_cell_boundaries (Shapes)
▸ 'img_Batch17_014', with elements:
img_Batch17_014_image (Images), img_Batch17_014_cells_labels (Labels), img_Batch17_014_cell_boundaries (Shapes)
▸ 'img_Batch17_015', with elements:
img_Batch17_015_image (Images), img_Batch17_015_cells_labels (Labels), img_Batch17_015_cell_boundaries (Shapes)
▸ 'img_Batch17_016', with elements:
img_Batch17_016_image (Images), img_Batch17_016_cells_labels (Labels), img_Batch17_016_cell_boundaries (Shapes)
▸ 'img_Batch17_017', with elements:
img_Batch17_017_image (Images), img_Batch17_017_cells_labels (Labels), img_Batch17_017_cell_boundaries (Shapes)
▸ 'img_Batch17_018', with elements:
img_Batch17_018_image (Images), img_Batch17_018_cells_labels (Labels), img_Batch17_018_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("tjyY7DQu6pqRtxD80000")
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("tjyY7DQu6pqRtxD8") # 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 tjyY7DQu6pqRtxD80000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/tjyY7DQu6pqRtxD8
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid tjyY7DQu6pqRtxD8License: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute kidney injury
- Modality
- protein
- Observations (cells)
- 18,302
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 166 MB
- Sample ID
- S-BIAD3694:discovery:Batch17:AIN-01-0236: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