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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch23:AIN-02-0071:kidney

bronze acute tubulointerstitial nephritis ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch23_001_image': DataTree[cyx] (35, 762, 617), (35, 381, 308), (35, 190, 154), (35, 95, 77), (35, 47, 38)
│     ├── 'img_Batch23_002_image': DataTree[cyx] (35, 636, 714), (35, 318, 357), (35, 159, 178), (35, 79, 89), (35, 39, 44)
│     ├── 'img_Batch23_003_image': DataTree[cyx] (35, 692, 695), (35, 346, 347), (35, 173, 173), (35, 86, 86), (35, 43, 43)
│     ├── 'img_Batch23_004_image': DataTree[cyx] (35, 948, 558), (35, 474, 279), (35, 237, 139), (35, 118, 69), (35, 59, 34)
│     ├── 'img_Batch23_005_image': DataTree[cyx] (35, 1811, 411), (35, 905, 205), (35, 452, 102), (35, 226, 51), (35, 113, 25)
│     ├── 'img_Batch23_006_image': DataTree[cyx] (35, 1138, 408), (35, 569, 204), (35, 284, 102), (35, 142, 51), (35, 71, 25)
│     ├── 'img_Batch23_007_image': DataTree[cyx] (35, 923, 543), (35, 461, 271), (35, 230, 135), (35, 115, 67), (35, 57, 33)
│     ├── 'img_Batch23_008_image': DataTree[cyx] (35, 656, 493), (35, 328, 246), (35, 164, 123), (35, 82, 61), (35, 41, 30)
│     ├── 'img_Batch23_009_image': DataTree[cyx] (35, 756, 498), (35, 378, 249), (35, 189, 124), (35, 94, 62), (35, 47, 31)
│     ├── 'img_Batch23_010_image': DataTree[cyx] (35, 460, 1139), (35, 230, 569), (35, 115, 284), (35, 57, 142), (35, 28, 71)
│     ├── 'img_Batch23_011_image': DataTree[cyx] (35, 1705, 404), (35, 852, 202), (35, 426, 101), (35, 213, 50), (35, 106, 25)
│     ├── 'img_Batch23_012_image': DataTree[cyx] (35, 828, 698), (35, 414, 349), (35, 207, 174), (35, 103, 87), (35, 51, 43)
│     ├── 'img_Batch23_013_image': DataTree[cyx] (35, 914, 786), (35, 457, 393), (35, 228, 196), (35, 114, 98), (35, 57, 49)
│     ├── 'img_Batch23_014_image': DataTree[cyx] (35, 780, 882), (35, 390, 441), (35, 195, 220), (35, 97, 110), (35, 48, 55)
│     └── 'img_Batch23_015_image': DataTree[cyx] (35, 646, 806), (35, 323, 403), (35, 161, 201), (35, 80, 100), (35, 40, 50)
├── Labels
│     ├── 'img_Batch23_001_cells_labels': DataTree[yx] (762, 617), (381, 308), (190, 154), (95, 77), (47, 38)
│     ├── 'img_Batch23_002_cells_labels': DataTree[yx] (636, 714), (318, 357), (159, 178), (79, 89), (39, 44)
│     ├── 'img_Batch23_003_cells_labels': DataTree[yx] (692, 695), (346, 347), (173, 173), (86, 86), (43, 43)
│     ├── 'img_Batch23_004_cells_labels': DataTree[yx] (948, 558), (474, 279), (237, 139), (118, 69), (59, 34)
│     ├── 'img_Batch23_005_cells_labels': DataTree[yx] (1811, 411), (905, 205), (452, 102), (226, 51), (113, 25)
│     ├── 'img_Batch23_006_cells_labels': DataTree[yx] (1138, 408), (569, 204), (284, 102), (142, 51), (71, 25)
│     ├── 'img_Batch23_007_cells_labels': DataTree[yx] (923, 543), (461, 271), (230, 135), (115, 67), (57, 33)
│     ├── 'img_Batch23_008_cells_labels': DataTree[yx] (656, 493), (328, 246), (164, 123), (82, 61), (41, 30)
│     ├── 'img_Batch23_009_cells_labels': DataTree[yx] (756, 498), (378, 249), (189, 124), (94, 62), (47, 31)
│     ├── 'img_Batch23_010_cells_labels': DataTree[yx] (460, 1139), (230, 569), (115, 284), (57, 142), (28, 71)
│     ├── 'img_Batch23_011_cells_labels': DataTree[yx] (1705, 404), (852, 202), (426, 101), (213, 50), (106, 25)
│     ├── 'img_Batch23_012_cells_labels': DataTree[yx] (828, 698), (414, 349), (207, 174), (103, 87), (51, 43)
│     ├── 'img_Batch23_013_cells_labels': DataTree[yx] (914, 786), (457, 393), (228, 196), (114, 98), (57, 49)
│     ├── 'img_Batch23_014_cells_labels': DataTree[yx] (780, 882), (390, 441), (195, 220), (97, 110), (48, 55)
│     └── 'img_Batch23_015_cells_labels': DataTree[yx] (646, 806), (323, 403), (161, 201), (80, 100), (40, 50)
├── Shapes
│     ├── 'img_Batch23_001_cell_boundaries': GeoDataFrame shape: (2252, 2) (2D shapes)
│     ├── 'img_Batch23_002_cell_boundaries': GeoDataFrame shape: (1897, 2) (2D shapes)
│     ├── 'img_Batch23_003_cell_boundaries': GeoDataFrame shape: (2074, 2) (2D shapes)
│     ├── 'img_Batch23_004_cell_boundaries': GeoDataFrame shape: (2228, 2) (2D shapes)
│     ├── 'img_Batch23_005_cell_boundaries': GeoDataFrame shape: (3291, 2) (2D shapes)
│     ├── 'img_Batch23_006_cell_boundaries': GeoDataFrame shape: (1386, 2) (2D shapes)
│     ├── 'img_Batch23_007_cell_boundaries': GeoDataFrame shape: (1754, 2) (2D shapes)
│     ├── 'img_Batch23_008_cell_boundaries': GeoDataFrame shape: (951, 2) (2D shapes)
│     ├── 'img_Batch23_009_cell_boundaries': GeoDataFrame shape: (941, 2) (2D shapes)
│     ├── 'img_Batch23_010_cell_boundaries': GeoDataFrame shape: (2265, 2) (2D shapes)
│     ├── 'img_Batch23_011_cell_boundaries': GeoDataFrame shape: (3407, 2) (2D shapes)
│     ├── 'img_Batch23_012_cell_boundaries': GeoDataFrame shape: (2594, 2) (2D shapes)
│     ├── 'img_Batch23_013_cell_boundaries': GeoDataFrame shape: (2949, 2) (2D shapes)
│     ├── 'img_Batch23_014_cell_boundaries': GeoDataFrame shape: (2535, 2) (2D shapes)
│     └── 'img_Batch23_015_cell_boundaries': GeoDataFrame shape: (2229, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (32753, 35)
with coordinate systems:
    ▸ 'img_Batch23_001', with elements:
        img_Batch23_001_image (Images), img_Batch23_001_cells_labels (Labels), img_Batch23_001_cell_boundaries (Shapes)
    ▸ 'img_Batch23_002', with elements:
        img_Batch23_002_image (Images), img_Batch23_002_cells_labels (Labels), img_Batch23_002_cell_boundaries (Shapes)
    ▸ 'img_Batch23_003', with elements:
        img_Batch23_003_image (Images), img_Batch23_003_cells_labels (Labels), img_Batch23_003_cell_boundaries (Shapes)
    ▸ 'img_Batch23_004', with elements:
        img_Batch23_004_image (Images), img_Batch23_004_cells_labels (Labels), img_Batch23_004_cell_boundaries (Shapes)
    ▸ 'img_Batch23_005', with elements:
        img_Batch23_005_image (Images), img_Batch23_005_cells_labels (Labels), img_Batch23_005_cell_boundaries (Shapes)
    ▸ 'img_Batch23_006', with elements:
        img_Batch23_006_image (Images), img_Batch23_006_cells_labels (Labels), img_Batch23_006_cell_boundaries (Shapes)
    ▸ 'img_Batch23_007', with elements:
        img_Batch23_007_image (Images), img_Batch23_007_cells_labels (Labels), img_Batch23_007_cell_boundaries (Shapes)
    ▸ 'img_Batch23_008', with elements:
        img_Batch23_008_image (Images), img_Batch23_008_cells_labels (Labels), img_Batch23_008_cell_boundaries (Shapes)
    ▸ 'img_Batch23_009', with elements:
        img_Batch23_009_image (Images), img_Batch23_009_cells_labels (Labels), img_Batch23_009_cell_boundaries (Shapes)
    ▸ 'img_Batch23_010', with elements:
        img_Batch23_010_image (Images), img_Batch23_010_cells_labels (Labels), img_Batch23_010_cell_boundaries (Shapes)
    ▸ 'img_Batch23_011', with elements:
        img_Batch23_011_image (Images), img_Batch23_011_cells_labels (Labels), img_Batch23_011_cell_boundaries (Shapes)
    ▸ 'img_Batch23_012', with elements:
        img_Batch23_012_image (Images), img_Batch23_012_cells_labels (Labels), img_Batch23_012_cell_boundaries (Shapes)
    ▸ 'img_Batch23_013', with elements:
        img_Batch23_013_image (Images), img_Batch23_013_cells_labels (Labels), img_Batch23_013_cell_boundaries (Shapes)
    ▸ 'img_Batch23_014', with elements:
        img_Batch23_014_image (Images), img_Batch23_014_cells_labels (Labels), img_Batch23_014_cell_boundaries (Shapes)
    ▸ 'img_Batch23_015', with elements:
        img_Batch23_015_image (Images), img_Batch23_015_cells_labels (Labels), img_Batch23_015_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("kblf1vBCIPN8JD1o0000")
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("kblf1vBCIPN8JD1o")   # 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 kblf1vBCIPN8JD1o0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/kblf1vBCIPN8JD1o

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid kblf1vBCIPN8JD1o
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 tubulointerstitial nephritis
Modality
protein
Observations (cells)
32,753
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
335 MB
Sample ID
S-BIAD3694:discovery:Batch23:AIN-02-0071: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