10x Xenium
Mouse forebrain
GSM8965607
silver Interactive viewer ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 37568, 39896), (1, 18784, 19948), (1, 9392, 9974), (1, 4696, 4987), (1, 2348, 2493)
│ └── 'morphology_mip': DataTree[cyx] (1, 37568, 39896), (1, 18784, 19948), (1, 9392, 9974), (1, 4696, 4987), (1, 2348, 2493)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (37568, 39896), (18784, 19948), (9392, 9974), (4696, 4987), (2348, 2493)
│ └── 'nucleus_labels': DataTree[yx] (37568, 39896), (18784, 19948), (9392, 9974), (4696, 4987), (2348, 2493)
├── Points
│ └── 'transcripts': DataFrame with shape: (60971125, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (99608, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (99608, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (99608, 1) (2D shapes)
└── Tables
└── 'table': AnnData (99608, 480)
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("aHKR5QROlCrl8CO2") # 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/aHKR5QROlCrl8CO2
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid aHKR5QROlCrl8CO2 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["aHKR5QROlCrl8CO20000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Single cell approaches define the murine leptomeninges:cortical brain interface as a distinct cellular neighborhood comprised of neural and nonneural cell types→ 7 datasets · other
Metadata
- Technology
- 10x Xenium
- Organism
- mouse
- Tissue
- forebrain
- Modality
- RNA
- Observations
- 99,608
- Transcripts
- 60,971,125
- Tier
- silver
- Chemistry
- Xenium v1
- Size
- 5.6 GB
- Sample ID
- GSM8965607
- License
- NCBI GEO policy
- Published
- Aug 1, 2025
Cite this dataset
doi.org/10.1523/eneuro.0046-25.2025 ↗ BibTeX
@article{Ebert_2025,
title = {Single-Cell Approaches Define the Murine Leptomeninges: Cortical Brain Interface as a Distinct Cellular Neighborhood Composed of Neural and Non-neural Cell Types},
volume = {12},
ISSN = {2373-2822},
url = {http://dx.doi.org/10.1523/eneuro.0046-25.2025},
DOI = {10.1523/eneuro.0046-25.2025},
number = {8},
journal = {eneuro},
publisher = {Society for Neuroscience},
author = {Ebert, Sarah N. and Eisner, Christine and Karamboulas, Konstantina and Bernier, Louis-Philippe and Kaplan, David R. and MacVicar, Brian A. and Miller, Freda D.},
year = {2025},
month = Aug,
pages = {ENEURO.0046–25.2025}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB