Visium

Human brain white matter

GSM8522426

bronze Interactive viewer H&E multiple sclerosis Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8522426_03_SP1_hires_image': DataArray[cyx] (3, 2000, 1964)
│     └── 'GSM8522426_03_SP1_lowres_image': DataArray[cyx] (3, 600, 589)
├── Shapes
│     └── 'GSM8522426_03_SP1': GeoDataFrame shape: (4163, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (4163, 36601)
with coordinate systems:
    ▸ 'GSM8522426_03_SP1', with elements:
        GSM8522426_03_SP1_hires_image (Images), GSM8522426_03_SP1_lowres_image (Images), GSM8522426_03_SP1 (Shapes)
    ▸ 'GSM8522426_03_SP1_downscaled_hires', with elements:
        GSM8522426_03_SP1_hires_image (Images), GSM8522426_03_SP1 (Shapes)
    ▸ 'GSM8522426_03_SP1_downscaled_lowres', with elements:
        GSM8522426_03_SP1_lowres_image (Images), GSM8522426_03_SP1 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("bQKhE8ShmbVs9xgZ0000")
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("bQKhE8ShmbVs9xgZ")   # 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 bQKhE8ShmbVs9xgZ0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/bQKhE8ShmbVs9xgZ

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid bQKhE8ShmbVs9xgZ

Install (Python 3.12+) Docs →

License: NCBI GEO policy

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

INFLAMMATION-INDUCED SENESCENT-LIKE GLIA REWIRE CELLULAR NETWORKS IN MULTIPLE SCLEROSIS [Spatial transcriptomic]→

12 datasets · replicates

Metadata

Technology: Visium
Organism: human
Tissue: brain white matter
Condition: multiple sclerosis
Modality: RNA
Observations (spots): 4,163
Tier: bronze
Chemistry: Visium v1
Resolution: 1,964 × 2,000 px
mpp: 3.629 µm/px
Size: 11.7 MB
Sample ID: GSM8522426
License: NCBI GEO policy
Published: Jul 31, 2025

Cite this dataset

doi.org/10.1038/s41467-025-63371-9 ↗

BibTeX

@article{Fagiani_2025,
  title = {Spatially-restricted inflammation-induced senescent-like glia in multiple sclerosis and patient-derived organoids},
  volume = {16},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-025-63371-9},
  DOI = {10.1038/s41467-025-63371-9},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Fagiani, Francesca and Pedrini, Edoardo and Martire, Maria Sofia and Gastoldi, Gaia and Vanden Bulcke, Colin and Lin, Jing-Ping and Maric, Dragan and Brambilla, Elena and Ruffini, Francesca and Peri, Carolina and Calabresi, Peter A. and Maggi, Pietro and Panina-Bordignon, Paola and Martino, Gianvito and Reich, Daniel S. and Absinta, Martina},
  year = {2025},
  month = Sept
}

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