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Imaging Mass Cytometry

Mouse lung

S-BIAD3184:slide3:mouse35

bronze Under review Provisional
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_m16263_slide3_007_image': DataTree[cyx] (76, 690, 660), (76, 345, 330), (76, 172, 165), (76, 86, 82), (76, 43, 41)
│     ├── 'img_m16263_slide3_008_image': DataTree[cyx] (76, 690, 660), (76, 345, 330), (76, 172, 165), (76, 86, 82), (76, 43, 41)
│     └── 'img_m16263_slide3_009_image': DataTree[cyx] (76, 690, 660), (76, 345, 330), (76, 172, 165), (76, 86, 82), (76, 43, 41)
├── Labels
│     ├── 'img_m16263_slide3_007_cells_labels': DataTree[yx] (690, 660), (345, 330), (172, 165), (86, 82), (43, 41)
│     ├── 'img_m16263_slide3_008_cells_labels': DataTree[yx] (690, 660), (345, 330), (172, 165), (86, 82), (43, 41)
│     └── 'img_m16263_slide3_009_cells_labels': DataTree[yx] (690, 660), (345, 330), (172, 165), (86, 82), (43, 41)
├── Shapes
│     ├── 'img_m16263_slide3_007_cell_boundaries': GeoDataFrame shape: (1814, 2) (2D shapes)
│     ├── 'img_m16263_slide3_008_cell_boundaries': GeoDataFrame shape: (1992, 2) (2D shapes)
│     └── 'img_m16263_slide3_009_cell_boundaries': GeoDataFrame shape: (1950, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (5756, 76)
with coordinate systems:
    ▸ 'img_m16263_slide3_007', with elements:
        img_m16263_slide3_007_image (Images), img_m16263_slide3_007_cells_labels (Labels), img_m16263_slide3_007_cell_boundaries (Shapes)
    ▸ 'img_m16263_slide3_008', with elements:
        img_m16263_slide3_008_image (Images), img_m16263_slide3_008_cells_labels (Labels), img_m16263_slide3_008_cell_boundaries (Shapes)
    ▸ 'img_m16263_slide3_009', with elements:
        img_m16263_slide3_009_image (Images), img_m16263_slide3_009_cells_labels (Labels), img_m16263_slide3_009_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("nXVVjKAwHkjpFJej0000")
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("nXVVjKAwHkjpFJej")   # 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 nXVVjKAwHkjpFJej0000 --dest data/

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

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid nXVVjKAwHkjpFJej
Install (Python 3.12+) Docs →

License: CC BY 4.0

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bia-s-biad3184→
12 datasets · publication

Metadata

Technology
Imaging Mass Cytometry
Organism
mouse
Tissue
lung
Modality
protein
Observations (cells)
5,756
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
316 MB
Sample ID
S-BIAD3184:slide3:mouse35
License
CC BY 4.0
Published
Jun 23, 2026
Review
treatment disagrees within the deposit: processed_data/metadata.csv=PBS, raw_data/metadata.csv=DRA, study JSON sample table=PBS (ROIs m16263_slide3_007, m16263_slide3_008, m16263_slide3_009)

Cite this dataset

doi.org/10.1101/2024.11.15.623782 ↗
BibTeX 
@article{bia:S-BIAD3184,
  doi = {10.1101/2024.11.15.623782},
  title = {S-BIAD3184:slide3:mouse35},
  year = {2024}
}

Source: BioImage Archive ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB