Visium

Human bone marrow

GSM8329297

bronze Interactive viewer H&E plasma cell myeloma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8329297_hMM5_hires_image': DataArray[cyx] (3, 2000, 2000)
│     └── 'GSM8329297_hMM5_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM8329297_hMM5': GeoDataFrame shape: (2993, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2993, 18085)
with coordinate systems:
    ▸ 'GSM8329297_hMM5', with elements:
        GSM8329297_hMM5_hires_image (Images), GSM8329297_hMM5_lowres_image (Images), GSM8329297_hMM5 (Shapes)
    ▸ 'GSM8329297_hMM5_downscaled_hires', with elements:
        GSM8329297_hMM5_hires_image (Images), GSM8329297_hMM5 (Shapes)
    ▸ 'GSM8329297_hMM5_downscaled_lowres', with elements:
        GSM8329297_hMM5_lowres_image (Images), GSM8329297_hMM5 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("RsOUlldRLqesWV5B0000")
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("RsOUlldRLqesWV5B")   # 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 RsOUlldRLqesWV5B0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/RsOUlldRLqesWV5B

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid RsOUlldRLqesWV5B

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial transcriptomics unveils novel potential disease mechanisms associated with the microenvironment in Multiple Myeloma→

14 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: bone marrow
Condition: plasma cell myeloma
Modality: RNA
Observations (spots): 2,993
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 2,000 px
mpp: 5.466 µm/px
Size: 10.1 MB
Sample ID: GSM8329297
License: NCBI GEO policy
Published: Jun 9, 2025

Cite this dataset

doi.org/10.1111/acel.70475 ↗

BibTeX

@article{Cenzano_2026,
  title = {Single‐Cell Profiling Reveals
                    <scp>RAB13</scp>
                    <sup>+</sup>
                    Endothelial Cells and Profibrotic Mesenchymal Cells in Aged Human Bone Marrow},
  volume = {25},
  ISSN = {1474-9726},
  url = {http://dx.doi.org/10.1111/acel.70475},
  DOI = {10.1111/acel.70475},
  number = {4},
  journal = {Aging Cell},
  publisher = {Wiley},
  author = {Cenzano, Itziar and Cócera, Miguel and Lopez‐Perez, Ana Rosa and Campos‐Dopazo, Lorea and Ruiz, Javier and Sancho, Ignacio and Martin‐Uriz, Patxi San and Aguirre‐Ruiz, Paula and Sarvide, Sarai and Vilas‐Zornoza, Amaia and Ripalda‐Cemborain, Purificacion and Alignani, Diego and Lopez, Aitziber and Barrios, Marta Miñana and Agreda, Delia Quilez and Ye, Jin and Lehmann, Robert and Sudupe, Laura and Abengozar‐Muela, Marta and Lopez‐Janeiro, Álvaro and Tamariz‐Amador, Luis‐Esteban and Muiños‐López, Emma and Saez, Borja and Tegner, Jesper and Calvo, Isabel A. and Gomez‐Cabrero, David and Prosper, Felipe},
  year = {2026},
  month = Apr
}

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