10x Xenium

Mouse brain

GSM9489188

silver Interactive viewer ✓ Verified publication

Preview Client Lamin CLI

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)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("T9Q8m47Lh79ELPtl0000")
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("T9Q8m47Lh79ELPtl")   # 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 T9Q8m47Lh79ELPtl0000 --dest data/

# or lamin: 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

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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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]→

10 datasets · comparison

Metadata

Technology: 10x Xenium
Organism: mouse
Tissue: brain
Modality: RNA
Observations (cells): 60,038
Transcripts: 10,653,021
Tier: silver
Chemistry: Xenium v1
Size: 650 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