Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch22:AIN-02-0059:kidney

bronze acute tubulointerstitial nephritis ✓ Verified publication

Imaging Mass Cytometry

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print(sdata)

SpatialData object
├── Images
│     ├── 'img_Batch22_001_image': DataTree[cyx] (35, 1101, 755), (35, 550, 377), (35, 275, 188), (35, 137, 94), (35, 68, 47)
│     ├── 'img_Batch22_002_image': DataTree[cyx] (35, 1409, 707), (35, 704, 353), (35, 352, 176), (35, 176, 88), (35, 88, 44)
│     ├── 'img_Batch22_003_image': DataTree[cyx] (35, 808, 488), (35, 404, 244), (35, 202, 122), (35, 101, 61), (35, 50, 30)
│     ├── 'img_Batch22_004_image': DataTree[cyx] (35, 3484, 472), (35, 1742, 236), (35, 871, 118), (35, 435, 59), (35, 217, 29)
│     ├── 'img_Batch22_005_image': DataTree[cyx] (35, 714, 1350), (35, 357, 675), (35, 178, 337), (35, 89, 168), (35, 44, 84)
│     ├── 'img_Batch22_006_image': DataTree[cyx] (35, 1475, 361), (35, 737, 180), (35, 368, 90), (35, 184, 45), (35, 92, 22)
│     ├── 'img_Batch22_007_image': DataTree[cyx] (35, 636, 1640), (35, 318, 820), (35, 159, 410), (35, 79, 205), (35, 39, 102)
│     ├── 'img_Batch22_008_image': DataTree[cyx] (35, 525, 321), (35, 262, 160), (35, 131, 80), (35, 65, 40), (35, 32, 20)
│     ├── 'img_Batch22_009_image': DataTree[cyx] (35, 388, 643), (35, 194, 321), (35, 97, 160), (35, 48, 80), (35, 24, 40)
│     ├── 'img_Batch22_010_image': DataTree[cyx] (35, 1310, 323), (35, 655, 161), (35, 327, 80), (35, 163, 40), (35, 81, 20)
│     └── 'img_Batch22_011_image': DataTree[cyx] (35, 765, 738), (35, 382, 369), (35, 191, 184), (35, 95, 92), (35, 47, 46)
├── Labels
│     ├── 'img_Batch22_001_cells_labels': DataTree[yx] (1101, 755), (550, 377), (275, 188), (137, 94), (68, 47)
│     ├── 'img_Batch22_002_cells_labels': DataTree[yx] (1409, 707), (704, 353), (352, 176), (176, 88), (88, 44)
│     ├── 'img_Batch22_003_cells_labels': DataTree[yx] (808, 488), (404, 244), (202, 122), (101, 61), (50, 30)
│     ├── 'img_Batch22_004_cells_labels': DataTree[yx] (3484, 472), (1742, 236), (871, 118), (435, 59), (217, 29)
│     ├── 'img_Batch22_005_cells_labels': DataTree[yx] (714, 1350), (357, 675), (178, 337), (89, 168), (44, 84)
│     ├── 'img_Batch22_006_cells_labels': DataTree[yx] (1475, 361), (737, 180), (368, 90), (184, 45), (92, 22)
│     ├── 'img_Batch22_007_cells_labels': DataTree[yx] (636, 1640), (318, 820), (159, 410), (79, 205), (39, 102)
│     ├── 'img_Batch22_008_cells_labels': DataTree[yx] (525, 321), (262, 160), (131, 80), (65, 40), (32, 20)
│     ├── 'img_Batch22_009_cells_labels': DataTree[yx] (388, 643), (194, 321), (97, 160), (48, 80), (24, 40)
│     ├── 'img_Batch22_010_cells_labels': DataTree[yx] (1310, 323), (655, 161), (327, 80), (163, 40), (81, 20)
│     └── 'img_Batch22_011_cells_labels': DataTree[yx] (765, 738), (382, 369), (191, 184), (95, 92), (47, 46)
├── Shapes
│     ├── 'img_Batch22_001_cell_boundaries': GeoDataFrame shape: (3588, 2) (2D shapes)
│     ├── 'img_Batch22_002_cell_boundaries': GeoDataFrame shape: (2287, 2) (2D shapes)
│     ├── 'img_Batch22_003_cell_boundaries': GeoDataFrame shape: (593, 2) (2D shapes)
│     ├── 'img_Batch22_004_cell_boundaries': GeoDataFrame shape: (3554, 2) (2D shapes)
│     ├── 'img_Batch22_005_cell_boundaries': GeoDataFrame shape: (2248, 2) (2D shapes)
│     ├── 'img_Batch22_006_cell_boundaries': GeoDataFrame shape: (1343, 2) (2D shapes)
│     ├── 'img_Batch22_007_cell_boundaries': GeoDataFrame shape: (3647, 2) (2D shapes)
│     ├── 'img_Batch22_008_cell_boundaries': GeoDataFrame shape: (597, 2) (2D shapes)
│     ├── 'img_Batch22_009_cell_boundaries': GeoDataFrame shape: (613, 2) (2D shapes)
│     ├── 'img_Batch22_010_cell_boundaries': GeoDataFrame shape: (2156, 2) (2D shapes)
│     └── 'img_Batch22_011_cell_boundaries': GeoDataFrame shape: (925, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (21551, 35)
with coordinate systems:
    ▸ 'img_Batch22_001', with elements:
        img_Batch22_001_image (Images), img_Batch22_001_cells_labels (Labels), img_Batch22_001_cell_boundaries (Shapes)
    ▸ 'img_Batch22_002', with elements:
        img_Batch22_002_image (Images), img_Batch22_002_cells_labels (Labels), img_Batch22_002_cell_boundaries (Shapes)
    ▸ 'img_Batch22_003', with elements:
        img_Batch22_003_image (Images), img_Batch22_003_cells_labels (Labels), img_Batch22_003_cell_boundaries (Shapes)
    ▸ 'img_Batch22_004', with elements:
        img_Batch22_004_image (Images), img_Batch22_004_cells_labels (Labels), img_Batch22_004_cell_boundaries (Shapes)
    ▸ 'img_Batch22_005', with elements:
        img_Batch22_005_image (Images), img_Batch22_005_cells_labels (Labels), img_Batch22_005_cell_boundaries (Shapes)
    ▸ 'img_Batch22_006', with elements:
        img_Batch22_006_image (Images), img_Batch22_006_cells_labels (Labels), img_Batch22_006_cell_boundaries (Shapes)
    ▸ 'img_Batch22_007', with elements:
        img_Batch22_007_image (Images), img_Batch22_007_cells_labels (Labels), img_Batch22_007_cell_boundaries (Shapes)
    ▸ 'img_Batch22_008', with elements:
        img_Batch22_008_image (Images), img_Batch22_008_cells_labels (Labels), img_Batch22_008_cell_boundaries (Shapes)
    ▸ 'img_Batch22_009', with elements:
        img_Batch22_009_image (Images), img_Batch22_009_cells_labels (Labels), img_Batch22_009_cell_boundaries (Shapes)
    ▸ 'img_Batch22_010', with elements:
        img_Batch22_010_image (Images), img_Batch22_010_cells_labels (Labels), img_Batch22_010_cell_boundaries (Shapes)
    ▸ 'img_Batch22_011', with elements:
        img_Batch22_011_image (Images), img_Batch22_011_cells_labels (Labels), img_Batch22_011_cell_boundaries (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("mDSVpr4IWOQ76icf0000")
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("mDSVpr4IWOQ76icf")   # 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 mDSVpr4IWOQ76icf0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/mDSVpr4IWOQ76icf

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid mDSVpr4IWOQ76icf

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 tubulointerstitial nephritis
Modality: protein
Observations (cells): 21,551
Tier: bronze
Chemistry: IMC
Staining: Antibodies
Size: 167 MB
Sample ID: S-BIAD3694:discovery:Batch22:AIN-02-0059: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