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Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch13:AIN-01-0153:kidney

bronze acute tubulointerstitial nephritis ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch13_001_image': DataTree[cyx] (35, 1684, 347), (35, 842, 173), (35, 421, 86), (35, 210, 43), (35, 105, 21)
│     ├── 'img_Batch13_002_image': DataTree[cyx] (35, 624, 268), (35, 312, 134), (35, 156, 67), (35, 78, 33), (35, 39, 16)
│     ├── 'img_Batch13_003_image': DataTree[cyx] (35, 929, 296), (35, 464, 148), (35, 232, 74), (35, 116, 37), (35, 58, 18)
│     ├── 'img_Batch13_004_image': DataTree[cyx] (35, 423, 318), (35, 211, 159), (35, 105, 79), (35, 52, 39), (35, 26, 19)
│     ├── 'img_Batch13_005_image': DataTree[cyx] (35, 518, 399), (35, 259, 199), (35, 129, 99), (35, 64, 49), (35, 32, 24)
│     ├── 'img_Batch13_006_image': DataTree[cyx] (35, 410, 472), (35, 205, 236), (35, 102, 118), (35, 51, 59), (35, 25, 29)
│     ├── 'img_Batch13_007_image': DataTree[cyx] (35, 455, 450), (35, 227, 225), (35, 113, 112), (35, 56, 56), (35, 28, 28)
│     ├── 'img_Batch13_008_image': DataTree[cyx] (35, 365, 497), (35, 182, 248), (35, 91, 124), (35, 45, 62), (35, 22, 31)
│     ├── 'img_Batch13_009_image': DataTree[cyx] (35, 378, 509), (35, 189, 254), (35, 94, 127), (35, 47, 63), (35, 23, 31)
│     ├── 'img_Batch13_010_image': DataTree[cyx] (35, 297, 467), (35, 148, 233), (35, 74, 116), (35, 37, 58), (35, 18, 29)
│     ├── 'img_Batch13_011_image': DataTree[cyx] (35, 480, 685), (35, 240, 342), (35, 120, 171), (35, 60, 85), (35, 30, 42)
│     ├── 'img_Batch13_012_image': DataTree[cyx] (35, 462, 751), (35, 231, 375), (35, 115, 187), (35, 57, 93), (35, 28, 46)
│     ├── 'img_Batch13_013_image': DataTree[cyx] (35, 495, 624), (35, 247, 312), (35, 123, 156), (35, 61, 78), (35, 30, 39)
│     └── 'img_Batch13_014_image': DataTree[cyx] (35, 512, 491), (35, 256, 245), (35, 128, 122), (35, 64, 61), (35, 32, 30)
├── Labels
│     ├── 'img_Batch13_001_cells_labels': DataTree[yx] (1684, 347), (842, 173), (421, 86), (210, 43), (105, 21)
│     ├── 'img_Batch13_002_cells_labels': DataTree[yx] (624, 268), (312, 134), (156, 67), (78, 33), (39, 16)
│     ├── 'img_Batch13_003_cells_labels': DataTree[yx] (929, 296), (464, 148), (232, 74), (116, 37), (58, 18)
│     ├── 'img_Batch13_004_cells_labels': DataTree[yx] (423, 318), (211, 159), (105, 79), (52, 39), (26, 19)
│     ├── 'img_Batch13_005_cells_labels': DataTree[yx] (518, 399), (259, 199), (129, 99), (64, 49), (32, 24)
│     ├── 'img_Batch13_006_cells_labels': DataTree[yx] (410, 472), (205, 236), (102, 118), (51, 59), (25, 29)
│     ├── 'img_Batch13_007_cells_labels': DataTree[yx] (455, 450), (227, 225), (113, 112), (56, 56), (28, 28)
│     ├── 'img_Batch13_008_cells_labels': DataTree[yx] (365, 497), (182, 248), (91, 124), (45, 62), (22, 31)
│     ├── 'img_Batch13_009_cells_labels': DataTree[yx] (378, 509), (189, 254), (94, 127), (47, 63), (23, 31)
│     ├── 'img_Batch13_010_cells_labels': DataTree[yx] (297, 467), (148, 233), (74, 116), (37, 58), (18, 29)
│     ├── 'img_Batch13_011_cells_labels': DataTree[yx] (480, 685), (240, 342), (120, 171), (60, 85), (30, 42)
│     ├── 'img_Batch13_012_cells_labels': DataTree[yx] (462, 751), (231, 375), (115, 187), (57, 93), (28, 46)
│     ├── 'img_Batch13_013_cells_labels': DataTree[yx] (495, 624), (247, 312), (123, 156), (61, 78), (30, 39)
│     └── 'img_Batch13_014_cells_labels': DataTree[yx] (512, 491), (256, 245), (128, 122), (64, 61), (32, 30)
├── Shapes
│     ├── 'img_Batch13_001_cell_boundaries': GeoDataFrame shape: (2687, 2) (2D shapes)
│     ├── 'img_Batch13_002_cell_boundaries': GeoDataFrame shape: (857, 2) (2D shapes)
│     ├── 'img_Batch13_003_cell_boundaries': GeoDataFrame shape: (1150, 2) (2D shapes)
│     ├── 'img_Batch13_004_cell_boundaries': GeoDataFrame shape: (614, 2) (2D shapes)
│     ├── 'img_Batch13_005_cell_boundaries': GeoDataFrame shape: (822, 2) (2D shapes)
│     ├── 'img_Batch13_006_cell_boundaries': GeoDataFrame shape: (978, 2) (2D shapes)
│     ├── 'img_Batch13_007_cell_boundaries': GeoDataFrame shape: (1022, 2) (2D shapes)
│     ├── 'img_Batch13_008_cell_boundaries': GeoDataFrame shape: (940, 2) (2D shapes)
│     ├── 'img_Batch13_009_cell_boundaries': GeoDataFrame shape: (922, 2) (2D shapes)
│     ├── 'img_Batch13_010_cell_boundaries': GeoDataFrame shape: (742, 2) (2D shapes)
│     ├── 'img_Batch13_011_cell_boundaries': GeoDataFrame shape: (1810, 2) (2D shapes)
│     ├── 'img_Batch13_012_cell_boundaries': GeoDataFrame shape: (1791, 2) (2D shapes)
│     ├── 'img_Batch13_013_cell_boundaries': GeoDataFrame shape: (1523, 2) (2D shapes)
│     └── 'img_Batch13_014_cell_boundaries': GeoDataFrame shape: (1213, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (17071, 35)
with coordinate systems:
    ▸ 'img_Batch13_001', with elements:
        img_Batch13_001_image (Images), img_Batch13_001_cells_labels (Labels), img_Batch13_001_cell_boundaries (Shapes)
    ▸ 'img_Batch13_002', with elements:
        img_Batch13_002_image (Images), img_Batch13_002_cells_labels (Labels), img_Batch13_002_cell_boundaries (Shapes)
    ▸ 'img_Batch13_003', with elements:
        img_Batch13_003_image (Images), img_Batch13_003_cells_labels (Labels), img_Batch13_003_cell_boundaries (Shapes)
    ▸ 'img_Batch13_004', with elements:
        img_Batch13_004_image (Images), img_Batch13_004_cells_labels (Labels), img_Batch13_004_cell_boundaries (Shapes)
    ▸ 'img_Batch13_005', with elements:
        img_Batch13_005_image (Images), img_Batch13_005_cells_labels (Labels), img_Batch13_005_cell_boundaries (Shapes)
    ▸ 'img_Batch13_006', with elements:
        img_Batch13_006_image (Images), img_Batch13_006_cells_labels (Labels), img_Batch13_006_cell_boundaries (Shapes)
    ▸ 'img_Batch13_007', with elements:
        img_Batch13_007_image (Images), img_Batch13_007_cells_labels (Labels), img_Batch13_007_cell_boundaries (Shapes)
    ▸ 'img_Batch13_008', with elements:
        img_Batch13_008_image (Images), img_Batch13_008_cells_labels (Labels), img_Batch13_008_cell_boundaries (Shapes)
    ▸ 'img_Batch13_009', with elements:
        img_Batch13_009_image (Images), img_Batch13_009_cells_labels (Labels), img_Batch13_009_cell_boundaries (Shapes)
    ▸ 'img_Batch13_010', with elements:
        img_Batch13_010_image (Images), img_Batch13_010_cells_labels (Labels), img_Batch13_010_cell_boundaries (Shapes)
    ▸ 'img_Batch13_011', with elements:
        img_Batch13_011_image (Images), img_Batch13_011_cells_labels (Labels), img_Batch13_011_cell_boundaries (Shapes)
    ▸ 'img_Batch13_012', with elements:
        img_Batch13_012_image (Images), img_Batch13_012_cells_labels (Labels), img_Batch13_012_cell_boundaries (Shapes)
    ▸ 'img_Batch13_013', with elements:
        img_Batch13_013_image (Images), img_Batch13_013_cells_labels (Labels), img_Batch13_013_cell_boundaries (Shapes)
    ▸ 'img_Batch13_014', with elements:
        img_Batch13_014_image (Images), img_Batch13_014_cells_labels (Labels), img_Batch13_014_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("WebwP1OCWbFRFi0Y0000")
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("WebwP1OCWbFRFi0Y")   # 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 WebwP1OCWbFRFi0Y0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/WebwP1OCWbFRFi0Y

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid WebwP1OCWbFRFi0Y
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)
17,071
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
90.3 MB
Sample ID
S-BIAD3694:discovery:Batch13:AIN-01-0153: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