Imaging Mass Cytometry
Human kidney
S-BIAD3694:discovery:Batch20:L116:kidney
bronze ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'img_Batch20_010_image': DataTree[cyx] (35, 2121, 709), (35, 1060, 354), (35, 530, 177), (35, 265, 88), (35, 132, 44)
│ ├── 'img_Batch20_011_image': DataTree[cyx] (35, 2991, 878), (35, 1495, 439), (35, 747, 219), (35, 373, 109), (35, 186, 54)
│ ├── 'img_Batch20_012_image': DataTree[cyx] (35, 875, 664), (35, 437, 332), (35, 218, 166), (35, 109, 83), (35, 54, 41)
│ └── 'img_Batch20_013_image': DataTree[cyx] (35, 1147, 536), (35, 573, 268), (35, 286, 134), (35, 143, 67), (35, 71, 33)
├── Labels
│ ├── 'img_Batch20_010_cells_labels': DataTree[yx] (2121, 709), (1060, 354), (530, 177), (265, 88), (132, 44)
│ ├── 'img_Batch20_011_cells_labels': DataTree[yx] (2991, 878), (1495, 439), (747, 219), (373, 109), (186, 54)
│ ├── 'img_Batch20_012_cells_labels': DataTree[yx] (875, 664), (437, 332), (218, 166), (109, 83), (54, 41)
│ └── 'img_Batch20_013_cells_labels': DataTree[yx] (1147, 536), (573, 268), (286, 134), (143, 67), (71, 33)
├── Shapes
│ ├── 'img_Batch20_010_cell_boundaries': GeoDataFrame shape: (6162, 2) (2D shapes)
│ ├── 'img_Batch20_011_cell_boundaries': GeoDataFrame shape: (9431, 2) (2D shapes)
│ ├── 'img_Batch20_012_cell_boundaries': GeoDataFrame shape: (1874, 2) (2D shapes)
│ └── 'img_Batch20_013_cell_boundaries': GeoDataFrame shape: (2462, 2) (2D shapes)
└── Tables
└── 'table': AnnData (19929, 35)
with coordinate systems:
▸ 'img_Batch20_010', with elements:
img_Batch20_010_image (Images), img_Batch20_010_cells_labels (Labels), img_Batch20_010_cell_boundaries (Shapes)
▸ 'img_Batch20_011', with elements:
img_Batch20_011_image (Images), img_Batch20_011_cells_labels (Labels), img_Batch20_011_cell_boundaries (Shapes)
▸ 'img_Batch20_012', with elements:
img_Batch20_012_image (Images), img_Batch20_012_cells_labels (Labels), img_Batch20_012_cell_boundaries (Shapes)
▸ 'img_Batch20_013', with elements:
img_Batch20_013_image (Images), img_Batch20_013_cells_labels (Labels), img_Batch20_013_cell_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("2V7FJUTh2Qja38g10000")
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("2V7FJUTh2Qja38g1") # 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 2V7FJUTh2Qja38g10000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/2V7FJUTh2Qja38g1
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 2V7FJUTh2Qja38g1License: CC0 1.0
Metadata
- Technology
- Imaging Mass Cytometry
- Organism
- human
- Tissue
- kidney
- Modality
- protein
- Observations (cells)
- 19,929
- Tier
- bronze
- Chemistry
- IMC
- Staining
- Antibodies
- Size
- 196 MB
- Sample ID
- S-BIAD3694:discovery:Batch20:L116: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