Vizgen MERSCOPE

Mouse brain

GSM8253803

bronze Interactive viewer Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'merscope_region_0_z3': DataTree[cyx] (5, 23157, 80764), (5, 11578, 40382), (5, 5789, 20191), (5, 2894, 10095), (5, 1447, 5047)
├── Points
│     └── 'merscope_region_0_transcripts': DataFrame with shape: (10205395, 9) (3D points)
├── Shapes
│     └── 'cell_circles': GeoDataFrame shape: (95624, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (95624, 297)
with coordinate systems:
    ▸ 'global', with elements:
        merscope_region_0_z3 (Images), merscope_region_0_transcripts (Points), cell_circles (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("UQT4FiK6dNTxMarm0000")
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("UQT4FiK6dNTxMarm")   # 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 UQT4FiK6dNTxMarm0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/UQT4FiK6dNTxMarm

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid UQT4FiK6dNTxMarm

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Metadata

Technology: Vizgen MERSCOPE
Organism: mouse
Tissue: brain
Modality: RNA
Observations (cells): 95,624
Transcripts: 10,205,395
Tier: bronze
Size: 7.9 GB
Sample ID: GSM8253803
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