Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch20:AIN-01-0252:kidney

bronze acute kidney injury ✓ Verified publication

Imaging Mass Cytometry

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print(sdata)

SpatialData object
├── Images
│     ├── 'img_Batch20_003_image': DataTree[cyx] (35, 1712, 725), (35, 856, 362), (35, 428, 181), (35, 214, 90), (35, 107, 45)
│     ├── 'img_Batch20_004_image': DataTree[cyx] (35, 1227, 833), (35, 613, 416), (35, 306, 208), (35, 153, 104), (35, 76, 52)
│     ├── 'img_Batch20_005_image': DataTree[cyx] (35, 794, 806), (35, 397, 403), (35, 198, 201), (35, 99, 100), (35, 49, 50)
│     ├── 'img_Batch20_006_image': DataTree[cyx] (35, 782, 562), (35, 391, 281), (35, 195, 140), (35, 97, 70), (35, 48, 35)
│     ├── 'img_Batch20_007_image': DataTree[cyx] (35, 2531, 666), (35, 1265, 333), (35, 632, 166), (35, 316, 83), (35, 158, 41)
│     ├── 'img_Batch20_008_image': DataTree[cyx] (35, 971, 636), (35, 485, 318), (35, 242, 159), (35, 121, 79), (35, 60, 39)
│     └── 'img_Batch20_009_image': DataTree[cyx] (35, 4373, 867), (35, 2186, 433), (35, 1093, 216), (35, 546, 108), (35, 273, 54)
├── Labels
│     ├── 'img_Batch20_003_cells_labels': DataTree[yx] (1712, 725), (856, 362), (428, 181), (214, 90), (107, 45)
│     ├── 'img_Batch20_004_cells_labels': DataTree[yx] (1227, 833), (613, 416), (306, 208), (153, 104), (76, 52)
│     ├── 'img_Batch20_005_cells_labels': DataTree[yx] (794, 806), (397, 403), (198, 201), (99, 100), (49, 50)
│     ├── 'img_Batch20_006_cells_labels': DataTree[yx] (782, 562), (391, 281), (195, 140), (97, 70), (48, 35)
│     ├── 'img_Batch20_007_cells_labels': DataTree[yx] (2531, 666), (1265, 333), (632, 166), (316, 83), (158, 41)
│     ├── 'img_Batch20_008_cells_labels': DataTree[yx] (971, 636), (485, 318), (242, 159), (121, 79), (60, 39)
│     └── 'img_Batch20_009_cells_labels': DataTree[yx] (4373, 867), (2186, 433), (1093, 216), (546, 108), (273, 54)
├── Shapes
│     ├── 'img_Batch20_003_cell_boundaries': GeoDataFrame shape: (3855, 2) (2D shapes)
│     ├── 'img_Batch20_004_cell_boundaries': GeoDataFrame shape: (2667, 2) (2D shapes)
│     ├── 'img_Batch20_005_cell_boundaries': GeoDataFrame shape: (1863, 2) (2D shapes)
│     ├── 'img_Batch20_006_cell_boundaries': GeoDataFrame shape: (1561, 2) (2D shapes)
│     ├── 'img_Batch20_007_cell_boundaries': GeoDataFrame shape: (6164, 2) (2D shapes)
│     ├── 'img_Batch20_008_cell_boundaries': GeoDataFrame shape: (2305, 2) (2D shapes)
│     └── 'img_Batch20_009_cell_boundaries': GeoDataFrame shape: (9696, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (28111, 35)
with coordinate systems:
    ▸ 'img_Batch20_003', with elements:
        img_Batch20_003_image (Images), img_Batch20_003_cells_labels (Labels), img_Batch20_003_cell_boundaries (Shapes)
    ▸ 'img_Batch20_004', with elements:
        img_Batch20_004_image (Images), img_Batch20_004_cells_labels (Labels), img_Batch20_004_cell_boundaries (Shapes)
    ▸ 'img_Batch20_005', with elements:
        img_Batch20_005_image (Images), img_Batch20_005_cells_labels (Labels), img_Batch20_005_cell_boundaries (Shapes)
    ▸ 'img_Batch20_006', with elements:
        img_Batch20_006_image (Images), img_Batch20_006_cells_labels (Labels), img_Batch20_006_cell_boundaries (Shapes)
    ▸ 'img_Batch20_007', with elements:
        img_Batch20_007_image (Images), img_Batch20_007_cells_labels (Labels), img_Batch20_007_cell_boundaries (Shapes)
    ▸ 'img_Batch20_008', with elements:
        img_Batch20_008_image (Images), img_Batch20_008_cells_labels (Labels), img_Batch20_008_cell_boundaries (Shapes)
    ▸ 'img_Batch20_009', with elements:
        img_Batch20_009_image (Images), img_Batch20_009_cells_labels (Labels), img_Batch20_009_cell_boundaries (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("jeJdqlB08eresqxh0000")
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("jeJdqlB08eresqxh")   # 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 jeJdqlB08eresqxh0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/jeJdqlB08eresqxh

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jeJdqlB08eresqxh

Install (Python 3.12+) Docs →

License: CC0 1.0

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Part of collection

bia-s-biad3694→

67 datasets · publication

Metadata

Technology: Imaging Mass Cytometry
Organism: human
Tissue: kidney
Condition: acute kidney injury
Modality: protein
Observations (cells): 28,111
Tier: bronze
Chemistry: IMC
Staining: Antibodies
Size: 240 MB
Sample ID: S-BIAD3694:discovery:Batch20:AIN-01-0252: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