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10x Xenium

Mouse brain

GSM9489188

silver Interactive viewer ✓ Verified publication
Mouse brain preview
print(sdata) 
SpatialData object
├── Images
│     └── 'morphology_focus': DataTree[cyx] (1, 13730, 17127), (1, 6865, 8563), (1, 3432, 4281), (1, 1716, 2140), (1, 858, 1070)
├── Labels
│     ├── 'cell_labels': DataTree[yx] (13730, 17127), (6865, 8563), (3432, 4281), (1716, 2140), (858, 1070)
│     └── 'nucleus_labels': DataTree[yx] (13730, 17127), (6865, 8563), (3432, 4281), (1716, 2140), (858, 1070)
├── Points
│     └── 'transcripts': DataFrame with shape: (10653021, 13) (3D points)
├── Shapes
│     ├── 'cell_boundaries': GeoDataFrame shape: (60038, 1) (2D shapes)
│     ├── 'cell_circles': GeoDataFrame shape: (60038, 2) (2D shapes)
│     └── 'nucleus_boundaries': GeoDataFrame shape: (60038, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (60038, 347)
with coordinate systems:
    ▸ 'global', with elements:
        morphology_focus (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("T9Q8m47Lh79ELPtl")   # 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/T9Q8m47Lh79ELPtl

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid T9Q8m47Lh79ELPtl
Load with the Python client 
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["T9Q8m47Lh79ELPtl0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

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Part of collection
Single-cell multiomic approaches define a gradual, spatially-regulated epigenetic and transcriptional transition from embryonic to adult neural stem cells [Xenium]→
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Metadata

Technology
10x Xenium
Organism
mouse
Tissue
brain
Modality
RNA
Observations
60,038
Transcripts
10,653,021
Tier
silver
Chemistry
Xenium v1
Size
620 MB
Sample ID
GSM9489188
License
NCBI GEO policy
Published
Jun 18, 2026

Cite this dataset

doi.org/10.1016/j.stemcr.2026.102967 ↗
BibTeX 
@article{Wang_2026,
  title = {Single-cell multiomic approaches define a gradual, spatially regulated epigenetic and transcriptional transition from embryonic to adult neural stem cells},
  volume = {21},
  ISSN = {2213-6711},
  url = {http://dx.doi.org/10.1016/j.stemcr.2026.102967},
  DOI = {10.1016/j.stemcr.2026.102967},
  number = {7},
  journal = {Stem Cell Reports},
  publisher = {Elsevier BV},
  author = {Wang, Beatrix S. and Karamboulas, Konstantina and Tahmasian, Nareh and Dennis, Daniel J. and Kaplan, David R. and Miller, Freda D.},
  year = {2026},
  month = July,
  pages = {102967}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB