10x Xenium

Mouse brain

GSM8647387

Interactive viewer demyelinating disease of central nervous system ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'morphology_focus': DataTree[cyx] (1, 30681, 42680), (1, 15340, 21340), (1, 7670, 10670), (1, 3835, 5335), (1, 1917, 2667)
│     └── 'morphology_mip': DataTree[cyx] (1, 30681, 42680), (1, 15340, 21340), (1, 7670, 10670), (1, 3835, 5335), (1, 1917, 2667)
├── Labels
│     ├── 'cell_labels': DataTree[yx] (30681, 42680), (15340, 21340), (7670, 10670), (3835, 5335), (1917, 2667)
│     └── 'nucleus_labels': DataTree[yx] (30681, 42680), (15340, 21340), (7670, 10670), (3835, 5335), (1917, 2667)
├── Points
│     └── 'transcripts': DataFrame with shape: (53350086, 10) (3D points)
├── Shapes
│     ├── 'cell_boundaries': GeoDataFrame shape: (135899, 1) (2D shapes)
│     ├── 'cell_circles': GeoDataFrame shape: (135899, 2) (2D shapes)
│     └── 'nucleus_boundaries': GeoDataFrame shape: (135899, 1) (2D shapes)
└── Tables
      └── 'table': AnnData (135899, 347)
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("w79NAhvVNd6ZkJK4")   # 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/w79NAhvVNd6ZkJK4

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid w79NAhvVNd6ZkJK4

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["w79NAhvVNd6ZkJK40001"])[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 approached deine forebrain neural stem cell niches and identify microglial ligands that enhance precursor-mediated remyelination (Xenium)→

19 datasets · other

Metadata

Technology: 10x Xenium
Organism: mouse
Tissue: brain
Condition: demyelinating disease of central nervous system
Modality: RNA
Transcripts: 53,350,086
Chemistry: Xenium v1
Size: 5.4 GB
Sample ID: GSM8647387
License: NCBI GEO policy
Published: Dec 20, 2024

Cite this dataset

doi.org/10.1016/j.celrep.2024.115194 ↗

BibTeX

@article{Willis_2025,
  title = {Single cell approaches define neural stem cell niches and identify microglial ligands that can enhance precursor-mediated oligodendrogenesis},
  volume = {44},
  ISSN = {2211-1247},
  url = {http://dx.doi.org/10.1016/j.celrep.2024.115194},
  DOI = {10.1016/j.celrep.2024.115194},
  number = {1},
  journal = {Cell Reports},
  publisher = {Elsevier BV},
  author = {Willis, Ashleigh and Jeong, Danielle and Liu, Yunlong and Lithopoulos, Marissa A. and Yuzwa, Scott A. and Frankland, Paul W. and Kaplan, David R. and Miller, Freda D.},
  year = {2025},
  month = Jan,
  pages = {115194}
}

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