Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch14:AIN-01-0168:kidney

bronze acute kidney injury ✓ Verified publication

Imaging Mass Cytometry

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'img_Batch14_011_image': DataTree[cyx] (35, 598, 489), (35, 299, 244), (35, 149, 122), (35, 74, 61), (35, 37, 30)
│     ├── 'img_Batch14_012_image': DataTree[cyx] (35, 579, 455), (35, 289, 227), (35, 144, 113), (35, 72, 56), (35, 36, 28)
│     └── 'img_Batch14_013_image': DataTree[cyx] (35, 2216, 408), (35, 1108, 204), (35, 554, 102), (35, 277, 51), (35, 138, 25)
├── Labels
│     ├── 'img_Batch14_011_cells_labels': DataTree[yx] (598, 489), (299, 244), (149, 122), (74, 61), (37, 30)
│     ├── 'img_Batch14_012_cells_labels': DataTree[yx] (579, 455), (289, 227), (144, 113), (72, 56), (36, 28)
│     └── 'img_Batch14_013_cells_labels': DataTree[yx] (2216, 408), (1108, 204), (554, 102), (277, 51), (138, 25)
├── Shapes
│     ├── 'img_Batch14_011_cell_boundaries': GeoDataFrame shape: (1139, 2) (2D shapes)
│     ├── 'img_Batch14_012_cell_boundaries': GeoDataFrame shape: (1233, 2) (2D shapes)
│     └── 'img_Batch14_013_cell_boundaries': GeoDataFrame shape: (5254, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (7626, 35)
with coordinate systems:
    ▸ 'img_Batch14_011', with elements:
        img_Batch14_011_image (Images), img_Batch14_011_cells_labels (Labels), img_Batch14_011_cell_boundaries (Shapes)
    ▸ 'img_Batch14_012', with elements:
        img_Batch14_012_image (Images), img_Batch14_012_cells_labels (Labels), img_Batch14_012_cell_boundaries (Shapes)
    ▸ 'img_Batch14_013', with elements:
        img_Batch14_013_image (Images), img_Batch14_013_cells_labels (Labels), img_Batch14_013_cell_boundaries (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("K51DDvcZCdSJM6pj0000")
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("K51DDvcZCdSJM6pj")   # 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 K51DDvcZCdSJM6pj0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/K51DDvcZCdSJM6pj

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid K51DDvcZCdSJM6pj

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): 7,626
Tier: bronze
Chemistry: IMC
Staining: Antibodies
Size: 62.6 MB
Sample ID: S-BIAD3694:discovery:Batch14:AIN-01-0168: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