10x Xenium
Human bone marrow
GSM8386296
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 34085, 34140), (1, 17042, 17070), (1, 8521, 8535), (1, 4260, 4267), (1, 2130, 2133)
│ └── 'morphology_mip': DataTree[cyx] (1, 34085, 34140), (1, 17042, 17070), (1, 8521, 8535), (1, 4260, 4267), (1, 2130, 2133)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (32837, 34140), (16418, 17070), (8209, 8535), (4104, 4267), (2052, 2133)
│ └── 'nucleus_labels': DataTree[yx] (32697, 34063), (16348, 17031), (8174, 8515), (4087, 4257), (2043, 2128)
├── Points
│ └── 'transcripts': DataFrame with shape: (1684465, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (40939, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (40939, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (40939, 2) (2D shapes)
└── Tables
└── 'table': AnnData (40939, 471)
with coordinate systems:
▸ 'global', with elements:
morphology_focus (Images), morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("0W9hDKMT5xVkIi9m") # 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 Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/0W9hDKMT5xVkIi9m
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 0W9hDKMT5xVkIi9m Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["0W9hDKMT5xVkIi9m0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
High resolution mapping of erythroid niches in human using subcellular spatial transcriptomics→ 3 datasets · publication
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- bone marrow
- Modality
- RNA
- Observations
- 40,939
- Transcripts
- 1,684,465
- Tier
- bronze
- Size
- 1.7 GB
- Sample ID
- GSM8386296
- License
- NCBI GEO policy
- Published
- May 18, 2026
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB