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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch15:AIN-01-0224:kidney

bronze acute kidney injury ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch15_002_image': DataTree[cyx] (35, 535, 1585), (35, 267, 792), (35, 133, 396), (35, 66, 198), (35, 33, 99)
│     ├── 'img_Batch15_003_image': DataTree[cyx] (35, 1513, 817), (35, 756, 408), (35, 378, 204), (35, 189, 102), (35, 94, 51)
│     ├── 'img_Batch15_004_image': DataTree[cyx] (35, 1522, 526), (35, 761, 263), (35, 380, 131), (35, 190, 65), (35, 95, 32)
│     ├── 'img_Batch15_005_image': DataTree[cyx] (35, 648, 129), (35, 324, 64), (35, 162, 32), (35, 81, 16), (35, 40, 8)
│     ├── 'img_Batch15_006_image': DataTree[cyx] (35, 709, 537), (35, 354, 268), (35, 177, 134), (35, 88, 67), (35, 44, 33)
│     ├── 'img_Batch15_007_image': DataTree[cyx] (35, 891, 553), (35, 445, 276), (35, 222, 138), (35, 111, 69), (35, 55, 34)
│     ├── 'img_Batch15_008_image': DataTree[cyx] (35, 1020, 589), (35, 510, 294), (35, 255, 147), (35, 127, 73), (35, 63, 36)
│     ├── 'img_Batch15_009_image': DataTree[cyx] (35, 511, 292), (35, 255, 146), (35, 127, 73), (35, 63, 36), (35, 31, 18)
│     ├── 'img_Batch15_010_image': DataTree[cyx] (35, 334, 330), (35, 167, 165), (35, 83, 82), (35, 41, 41), (35, 20, 20)
│     ├── 'img_Batch15_011_image': DataTree[cyx] (35, 202, 570), (35, 101, 285), (35, 50, 142), (35, 25, 71), (35, 12, 35)
│     ├── 'img_Batch15_012_image': DataTree[cyx] (35, 364, 165), (35, 182, 82), (35, 91, 41), (35, 45, 20), (35, 22, 10)
│     ├── 'img_Batch15_013_image': DataTree[cyx] (35, 898, 269), (35, 449, 134), (35, 224, 67), (35, 112, 33), (35, 56, 16)
│     ├── 'img_Batch15_014_image': DataTree[cyx] (35, 690, 218), (35, 345, 109), (35, 172, 54), (35, 86, 27), (35, 43, 13)
│     ├── 'img_Batch15_018_image': DataTree[cyx] (35, 903, 251), (35, 451, 125), (35, 225, 62), (35, 112, 31), (35, 56, 15)
│     └── 'img_Batch15_019_image': DataTree[cyx] (35, 1568, 165), (35, 784, 82), (35, 392, 41), (35, 196, 20), (35, 98, 10)
├── Labels
│     ├── 'img_Batch15_002_cells_labels': DataTree[yx] (535, 1585), (267, 792), (133, 396), (66, 198), (33, 99)
│     ├── 'img_Batch15_003_cells_labels': DataTree[yx] (1513, 817), (756, 408), (378, 204), (189, 102), (94, 51)
│     ├── 'img_Batch15_004_cells_labels': DataTree[yx] (1522, 526), (761, 263), (380, 131), (190, 65), (95, 32)
│     ├── 'img_Batch15_005_cells_labels': DataTree[yx] (648, 129), (324, 64), (162, 32), (81, 16), (40, 8)
│     ├── 'img_Batch15_006_cells_labels': DataTree[yx] (709, 537), (354, 268), (177, 134), (88, 67), (44, 33)
│     ├── 'img_Batch15_007_cells_labels': DataTree[yx] (891, 553), (445, 276), (222, 138), (111, 69), (55, 34)
│     ├── 'img_Batch15_008_cells_labels': DataTree[yx] (1020, 589), (510, 294), (255, 147), (127, 73), (63, 36)
│     ├── 'img_Batch15_009_cells_labels': DataTree[yx] (511, 292), (255, 146), (127, 73), (63, 36), (31, 18)
│     ├── 'img_Batch15_010_cells_labels': DataTree[yx] (334, 330), (167, 165), (83, 82), (41, 41), (20, 20)
│     ├── 'img_Batch15_011_cells_labels': DataTree[yx] (202, 570), (101, 285), (50, 142), (25, 71), (12, 35)
│     ├── 'img_Batch15_012_cells_labels': DataTree[yx] (364, 165), (182, 82), (91, 41), (45, 20), (22, 10)
│     ├── 'img_Batch15_013_cells_labels': DataTree[yx] (898, 269), (449, 134), (224, 67), (112, 33), (56, 16)
│     ├── 'img_Batch15_014_cells_labels': DataTree[yx] (690, 218), (345, 109), (172, 54), (86, 27), (43, 13)
│     ├── 'img_Batch15_018_cells_labels': DataTree[yx] (903, 251), (451, 125), (225, 62), (112, 31), (56, 15)
│     └── 'img_Batch15_019_cells_labels': DataTree[yx] (1568, 165), (784, 82), (392, 41), (196, 20), (98, 10)
├── Shapes
│     ├── 'img_Batch15_002_cell_boundaries': GeoDataFrame shape: (4203, 2) (2D shapes)
│     ├── 'img_Batch15_003_cell_boundaries': GeoDataFrame shape: (2832, 2) (2D shapes)
│     ├── 'img_Batch15_004_cell_boundaries': GeoDataFrame shape: (2551, 2) (2D shapes)
│     ├── 'img_Batch15_005_cell_boundaries': GeoDataFrame shape: (411, 2) (2D shapes)
│     ├── 'img_Batch15_006_cell_boundaries': GeoDataFrame shape: (1392, 2) (2D shapes)
│     ├── 'img_Batch15_007_cell_boundaries': GeoDataFrame shape: (1958, 2) (2D shapes)
│     ├── 'img_Batch15_008_cell_boundaries': GeoDataFrame shape: (2136, 2) (2D shapes)
│     ├── 'img_Batch15_009_cell_boundaries': GeoDataFrame shape: (821, 2) (2D shapes)
│     ├── 'img_Batch15_010_cell_boundaries': GeoDataFrame shape: (653, 2) (2D shapes)
│     ├── 'img_Batch15_011_cell_boundaries': GeoDataFrame shape: (578, 2) (2D shapes)
│     ├── 'img_Batch15_012_cell_boundaries': GeoDataFrame shape: (351, 2) (2D shapes)
│     ├── 'img_Batch15_013_cell_boundaries': GeoDataFrame shape: (1402, 2) (2D shapes)
│     ├── 'img_Batch15_014_cell_boundaries': GeoDataFrame shape: (904, 2) (2D shapes)
│     ├── 'img_Batch15_018_cell_boundaries': GeoDataFrame shape: (950, 2) (2D shapes)
│     └── 'img_Batch15_019_cell_boundaries': GeoDataFrame shape: (1060, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (22202, 35)
with coordinate systems:
    ▸ 'img_Batch15_002', with elements:
        img_Batch15_002_image (Images), img_Batch15_002_cells_labels (Labels), img_Batch15_002_cell_boundaries (Shapes)
    ▸ 'img_Batch15_003', with elements:
        img_Batch15_003_image (Images), img_Batch15_003_cells_labels (Labels), img_Batch15_003_cell_boundaries (Shapes)
    ▸ 'img_Batch15_004', with elements:
        img_Batch15_004_image (Images), img_Batch15_004_cells_labels (Labels), img_Batch15_004_cell_boundaries (Shapes)
    ▸ 'img_Batch15_005', with elements:
        img_Batch15_005_image (Images), img_Batch15_005_cells_labels (Labels), img_Batch15_005_cell_boundaries (Shapes)
    ▸ 'img_Batch15_006', with elements:
        img_Batch15_006_image (Images), img_Batch15_006_cells_labels (Labels), img_Batch15_006_cell_boundaries (Shapes)
    ▸ 'img_Batch15_007', with elements:
        img_Batch15_007_image (Images), img_Batch15_007_cells_labels (Labels), img_Batch15_007_cell_boundaries (Shapes)
    ▸ 'img_Batch15_008', with elements:
        img_Batch15_008_image (Images), img_Batch15_008_cells_labels (Labels), img_Batch15_008_cell_boundaries (Shapes)
    ▸ 'img_Batch15_009', with elements:
        img_Batch15_009_image (Images), img_Batch15_009_cells_labels (Labels), img_Batch15_009_cell_boundaries (Shapes)
    ▸ 'img_Batch15_010', with elements:
        img_Batch15_010_image (Images), img_Batch15_010_cells_labels (Labels), img_Batch15_010_cell_boundaries (Shapes)
    ▸ 'img_Batch15_011', with elements:
        img_Batch15_011_image (Images), img_Batch15_011_cells_labels (Labels), img_Batch15_011_cell_boundaries (Shapes)
    ▸ 'img_Batch15_012', with elements:
        img_Batch15_012_image (Images), img_Batch15_012_cells_labels (Labels), img_Batch15_012_cell_boundaries (Shapes)
    ▸ 'img_Batch15_013', with elements:
        img_Batch15_013_image (Images), img_Batch15_013_cells_labels (Labels), img_Batch15_013_cell_boundaries (Shapes)
    ▸ 'img_Batch15_014', with elements:
        img_Batch15_014_image (Images), img_Batch15_014_cells_labels (Labels), img_Batch15_014_cell_boundaries (Shapes)
    ▸ 'img_Batch15_018', with elements:
        img_Batch15_018_image (Images), img_Batch15_018_cells_labels (Labels), img_Batch15_018_cell_boundaries (Shapes)
    ▸ 'img_Batch15_019', with elements:
        img_Batch15_019_image (Images), img_Batch15_019_cells_labels (Labels), img_Batch15_019_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("qQ6L1CGhkQWDtleN0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
Install (Python 3.12+) Docs →
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("qQ6L1CGhkQWDtleN")   # 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
Install (Python 3.12+) Docs →
Shell 
# sddb client: download the .zarr into data/
sddb-cli download qQ6L1CGhkQWDtleN0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/qQ6L1CGhkQWDtleN

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qQ6L1CGhkQWDtleN
Install (Python 3.12+) Docs →

License: CC0 1.0

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67 datasets · publication

Metadata

Technology
Imaging Mass Cytometry
Organism
human
Tissue
kidney
Condition
acute kidney injury
Modality
protein
Observations (cells)
22,202
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
159 MB
Sample ID
S-BIAD3694:discovery:Batch15:AIN-01-0224: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