10x Xenium
Mouse spleen
GSM8382628
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 17106, 14295), (1, 8553, 7147), (1, 4276, 3573), (1, 2138, 1786), (1, 1069, 893)
│ └── 'morphology_mip': DataTree[cyx] (1, 17106, 14295), (1, 8553, 7147), (1, 4276, 3573), (1, 2138, 1786), (1, 1069, 893)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (17106, 14295), (8553, 7147), (4276, 3573), (2138, 1786), (1069, 893)
│ └── 'nucleus_labels': DataTree[yx] (17106, 14295), (8553, 7147), (4276, 3573), (2138, 1786), (1069, 893)
├── Points
│ └── 'transcripts': DataFrame with shape: (11507913, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (108338, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (108338, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (108338, 1) (2D shapes)
└── Tables
└── 'table': AnnData (108338, 477)
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) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("gIG3DisRJ9uZAdC50000")
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("gIG3DisRJ9uZAdC5") # 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 gIG3DisRJ9uZAdC50000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/gIG3DisRJ9uZAdC5
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid gIG3DisRJ9uZAdC5License: NCBI GEO policy
Part of collection
High resolution mapping of erythroid niches in mice using subcellular spatial transcriptomics→ 6 datasets · publication
Metadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- spleen
- Modality
- RNA
- Observations (cells)
- 108,338
- Transcripts
- 11,507,913
- Tier
- bronze
- Size
- 1.0 GB
- Sample ID
- GSM8382628
- License
- NCBI GEO policy
- Published
- May 18, 2026
Cite this dataset
doi.org/10.1038/s41588-026-02671-2 ↗ BibTeX
@article{Han_2026,
title = {Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humans},
volume = {58},
ISSN = {1546-1718},
url = {http://dx.doi.org/10.1038/s41588-026-02671-2},
DOI = {10.1038/s41588-026-02671-2},
number = {7},
journal = {Nature Genetics},
publisher = {Springer Science and Business Media LLC},
author = {Han, Xu and Ren, Kehan and Wang, Pan and Bi, Honghao and Li, Ermin and Aydemir, Inci and Ji, Amy and Cai, Wenjie and Soleimanisardoo, Laya and Wai, Ching Man and Schipma, Matthew J. and Liu, Yijie and Goldstein, Jeffery and Sukhanova, Madina and Yang, Jing and Ji, Peng},
year = {2026},
month = July,
pages = {1620–1631}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB