Visium

Human brain

GSM8506650

silver Interactive viewer H&E glioblastoma ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8506650_25526_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM8506650_25526_hires_image': DataArray[cyx] (3, 2000, 1377)
│     └── 'GSM8506650_25526_lowres_image': DataArray[cyx] (3, 600, 413)
├── Shapes
│     └── 'GSM8506650_25526': GeoDataFrame shape: (3128, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3128, 18085)
with coordinate systems:
    ▸ 'GSM8506650_25526', with elements:
        GSM8506650_25526_full_image (Images), GSM8506650_25526_hires_image (Images), GSM8506650_25526_lowres_image (Images), GSM8506650_25526 (Shapes)
    ▸ 'GSM8506650_25526_downscaled_hires', with elements:
        GSM8506650_25526_hires_image (Images), GSM8506650_25526 (Shapes)
    ▸ 'GSM8506650_25526_downscaled_lowres', with elements:
        GSM8506650_25526_lowres_image (Images), GSM8506650_25526 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("OJxCbnpMyapaOF6S0000")
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("OJxCbnpMyapaOF6S")   # 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 OJxCbnpMyapaOF6S0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/OJxCbnpMyapaOF6S

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid OJxCbnpMyapaOF6S

Install (Python 3.12+) Docs →

License: CC BY 4.0

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Part of collection

Distinct Myeloid Derived Suppressor Cell Populations in Human Glioblastoma→

2 datasets · replicates

Metadata

Technology: Visium
Organism: human
Tissue: brain
Condition: glioblastoma
Modality: RNA
Observations (spots): 3,128
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 1,377 × 2,000 px
mpp: 9.201 µm/px
Size: 83.6 MB
Sample ID: GSM8506650
License: CC BY 4.0
Published: Jan 23, 2025

Cite this dataset

doi.org/10.1126/science.abm5214 ↗

BibTeX

@article{Jackson_2025,
  title = {Distinct myeloid-derived suppressor cell populations in human glioblastoma},
  volume = {387},
  ISSN = {1095-9203},
  url = {http://dx.doi.org/10.1126/science.abm5214},
  DOI = {10.1126/science.abm5214},
  number = {6731},
  journal = {Science},
  publisher = {American Association for the Advancement of Science (AAAS)},
  author = {Jackson, Christina and Cherry, Christopher and Bom, Sadhana and Dykema, Arbor G. and Wang, Rulin and Thompson, Elizabeth and Zhang, Ming and Li, Runzhe and Ji, Zhicheng and Hou, Wenpin and Zhan, Wentao and Zhang, Hao and Choi, John and Vaghasia, Ajay and Hansen, Landon and Wang, William and Bergsneider, Brandon and Jones, Kate M. and Rodriguez, Fausto and Weingart, Jon and Lucas, Calixto-Hope and Powell, Jonathan and Elisseeff, Jennifer and Yegnasubramanian, Srinivasan and Lim, Michael and Bettegowda, Chetan and Ji, Hongkai and Pardoll, Drew},
  year = {2025},
  month = Jan
}

Source: NCBI GEO ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB