Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch16:AIN-01-0188:kidney
bronze acute tubulointerstitial nephritis ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch16_001_image': DataTree[cyx] (35, 684, 360), (35, 342, 180), (35, 171, 90), (35, 85, 45), (35, 42, 22)
│ ├── 'img_Batch16_002_image': DataTree[cyx] (35, 410, 624), (35, 205, 312), (35, 102, 156), (35, 51, 78), (35, 25, 39)
│ ├── 'img_Batch16_003_image': DataTree[cyx] (35, 378, 602), (35, 189, 301), (35, 94, 150), (35, 47, 75), (35, 23, 37)
│ ├── 'img_Batch16_004_image': DataTree[cyx] (35, 464, 562), (35, 232, 281), (35, 116, 140), (35, 58, 70), (35, 29, 35)
│ ├── 'img_Batch16_005_image': DataTree[cyx] (35, 808, 526), (35, 404, 263), (35, 202, 131), (35, 101, 65), (35, 50, 32)
│ ├── 'img_Batch16_006_image': DataTree[cyx] (35, 1090, 438), (35, 545, 219), (35, 272, 109), (35, 136, 54), (35, 68, 27)
│ ├── 'img_Batch16_007_image': DataTree[cyx] (35, 718, 465), (35, 359, 232), (35, 179, 116), (35, 89, 58), (35, 44, 29)
│ ├── 'img_Batch16_008_image': DataTree[cyx] (35, 774, 521), (35, 387, 260), (35, 193, 130), (35, 96, 65), (35, 48, 32)
│ ├── 'img_Batch16_009_image': DataTree[cyx] (35, 899, 752), (35, 449, 376), (35, 224, 188), (35, 112, 94), (35, 56, 47)
│ ├── 'img_Batch16_010_image': DataTree[cyx] (35, 997, 813), (35, 498, 406), (35, 249, 203), (35, 124, 101), (35, 62, 50)
│ ├── 'img_Batch16_011_image': DataTree[cyx] (35, 1722, 825), (35, 861, 412), (35, 430, 206), (35, 215, 103), (35, 107, 51)
│ ├── 'img_Batch16_012_image': DataTree[cyx] (35, 1137, 647), (35, 568, 323), (35, 284, 161), (35, 142, 80), (35, 71, 40)
│ └── 'img_Batch16_033_image': DataTree[cyx] (35, 787, 273), (35, 393, 136), (35, 196, 68), (35, 98, 34), (35, 49, 17)
├── Labels
│ ├── 'img_Batch16_001_cells_labels': DataTree[yx] (684, 360), (342, 180), (171, 90), (85, 45), (42, 22)
│ ├── 'img_Batch16_002_cells_labels': DataTree[yx] (410, 624), (205, 312), (102, 156), (51, 78), (25, 39)
│ ├── 'img_Batch16_003_cells_labels': DataTree[yx] (378, 602), (189, 301), (94, 150), (47, 75), (23, 37)
│ ├── 'img_Batch16_004_cells_labels': DataTree[yx] (464, 562), (232, 281), (116, 140), (58, 70), (29, 35)
│ ├── 'img_Batch16_005_cells_labels': DataTree[yx] (808, 526), (404, 263), (202, 131), (101, 65), (50, 32)
│ ├── 'img_Batch16_006_cells_labels': DataTree[yx] (1090, 438), (545, 219), (272, 109), (136, 54), (68, 27)
│ ├── 'img_Batch16_007_cells_labels': DataTree[yx] (718, 465), (359, 232), (179, 116), (89, 58), (44, 29)
│ ├── 'img_Batch16_008_cells_labels': DataTree[yx] (774, 521), (387, 260), (193, 130), (96, 65), (48, 32)
│ ├── 'img_Batch16_009_cells_labels': DataTree[yx] (899, 752), (449, 376), (224, 188), (112, 94), (56, 47)
│ ├── 'img_Batch16_010_cells_labels': DataTree[yx] (997, 813), (498, 406), (249, 203), (124, 101), (62, 50)
│ ├── 'img_Batch16_011_cells_labels': DataTree[yx] (1722, 825), (861, 412), (430, 206), (215, 103), (107, 51)
│ ├── 'img_Batch16_012_cells_labels': DataTree[yx] (1137, 647), (568, 323), (284, 161), (142, 80), (71, 40)
│ └── 'img_Batch16_033_cells_labels': DataTree[yx] (787, 273), (393, 136), (196, 68), (98, 34), (49, 17)
├── Shapes
│ ├── 'img_Batch16_001_cell_boundaries': GeoDataFrame shape: (1697, 2) (2D shapes)
│ ├── 'img_Batch16_002_cell_boundaries': GeoDataFrame shape: (1281, 2) (2D shapes)
│ ├── 'img_Batch16_003_cell_boundaries': GeoDataFrame shape: (1074, 2) (2D shapes)
│ ├── 'img_Batch16_004_cell_boundaries': GeoDataFrame shape: (1868, 2) (2D shapes)
│ ├── 'img_Batch16_005_cell_boundaries': GeoDataFrame shape: (3008, 2) (2D shapes)
│ ├── 'img_Batch16_006_cell_boundaries': GeoDataFrame shape: (3025, 2) (2D shapes)
│ ├── 'img_Batch16_007_cell_boundaries': GeoDataFrame shape: (1558, 2) (2D shapes)
│ ├── 'img_Batch16_008_cell_boundaries': GeoDataFrame shape: (1861, 2) (2D shapes)
│ ├── 'img_Batch16_009_cell_boundaries': GeoDataFrame shape: (3713, 2) (2D shapes)
│ ├── 'img_Batch16_010_cell_boundaries': GeoDataFrame shape: (3950, 2) (2D shapes)
│ ├── 'img_Batch16_011_cell_boundaries': GeoDataFrame shape: (7268, 2) (2D shapes)
│ ├── 'img_Batch16_012_cell_boundaries': GeoDataFrame shape: (4122, 2) (2D shapes)
│ └── 'img_Batch16_033_cell_boundaries': GeoDataFrame shape: (1268, 2) (2D shapes)
└── Tables
└── 'table': AnnData (35693, 35)
with coordinate systems:
▸ 'img_Batch16_001', with elements:
img_Batch16_001_image (Images), img_Batch16_001_cells_labels (Labels), img_Batch16_001_cell_boundaries (Shapes)
▸ 'img_Batch16_002', with elements:
img_Batch16_002_image (Images), img_Batch16_002_cells_labels (Labels), img_Batch16_002_cell_boundaries (Shapes)
▸ 'img_Batch16_003', with elements:
img_Batch16_003_image (Images), img_Batch16_003_cells_labels (Labels), img_Batch16_003_cell_boundaries (Shapes)
▸ 'img_Batch16_004', with elements:
img_Batch16_004_image (Images), img_Batch16_004_cells_labels (Labels), img_Batch16_004_cell_boundaries (Shapes)
▸ 'img_Batch16_005', with elements:
img_Batch16_005_image (Images), img_Batch16_005_cells_labels (Labels), img_Batch16_005_cell_boundaries (Shapes)
▸ 'img_Batch16_006', with elements:
img_Batch16_006_image (Images), img_Batch16_006_cells_labels (Labels), img_Batch16_006_cell_boundaries (Shapes)
▸ 'img_Batch16_007', with elements:
img_Batch16_007_image (Images), img_Batch16_007_cells_labels (Labels), img_Batch16_007_cell_boundaries (Shapes)
▸ 'img_Batch16_008', with elements:
img_Batch16_008_image (Images), img_Batch16_008_cells_labels (Labels), img_Batch16_008_cell_boundaries (Shapes)
▸ 'img_Batch16_009', with elements:
img_Batch16_009_image (Images), img_Batch16_009_cells_labels (Labels), img_Batch16_009_cell_boundaries (Shapes)
▸ 'img_Batch16_010', with elements:
img_Batch16_010_image (Images), img_Batch16_010_cells_labels (Labels), img_Batch16_010_cell_boundaries (Shapes)
▸ 'img_Batch16_011', with elements:
img_Batch16_011_image (Images), img_Batch16_011_cells_labels (Labels), img_Batch16_011_cell_boundaries (Shapes)
▸ 'img_Batch16_012', with elements:
img_Batch16_012_image (Images), img_Batch16_012_cells_labels (Labels), img_Batch16_012_cell_boundaries (Shapes)
▸ 'img_Batch16_033', with elements:
img_Batch16_033_image (Images), img_Batch16_033_cells_labels (Labels), img_Batch16_033_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("RcmAZlhsP6IhdxFR0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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("RcmAZlhsP6IhdxFR") # 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download RcmAZlhsP6IhdxFR0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/RcmAZlhsP6IhdxFR
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid RcmAZlhsP6IhdxFRLicense: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Condition
- acute tubulointerstitial nephritis
- Modality
- protein
- Observations (cells)
- 35,693
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 211 MB
- Sample ID
- S-BIAD3694:discovery:Batch16:AIN-01-0188: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