Collections / Comparing 10x Visium spatial transcriptomic technologies

Comparing 10x Visium spatial transcriptomic technologies

Spatial transcriptomics facilitates the understanding of gene expression within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics’ Visium which provides whole tissue section profiling, enabling whole transcriptome spatial analysis. Our dataset comprises spleen tissue from mice infected with malaria, spanning multiple experiments and sample preparation protocols for tissue preservation, either as fresh frozen at optimal cutting temperature (OCT) or formalin-fixed paraffin-embedded (FFPE). Tissue placement was also considered, comparing direct tissue placement on the slide with the use of CytAssist (CA), which expands the Visium platform’s capabilities by allowing for the pre-selection of tissue sections and genes through a set of probes. We also include a matching scRNA-seq dataset that can be integrated with the spatial data.

Datasets4
Size~268 MB
AssayVisium
Samplemouse · spleen
KindOther
Flag a data issue
PMID:40156041 ↗
Download the whole collection
Python · sddb client 
from sddb import Catalog

cohort = Catalog().collection("geo-gse254652")
cohort.members        # the 4 datasets as a table

print(cohort.licenses())              # check the terms before you download

sdatas = cohort.to_dict()             # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")
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")
coll = db.Collection.get(key="geo-gse254652")
coll.ordered_artifacts.to_dataframe()   # the 4 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
Install (Python 3.12+) Docs →
Shell 
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse254652 --dest data/
Install (Python 3.12+) Docs →

Licenses: 4 CC BY 4.0

Member datasets · 4
Open in Vitessce
Visium
Mouse spleen
GSM8047883
mouse spleen malaria 75.0 MB 2,553 spots silver H&E
TechnologyVisium
Organismmouse
Tissuespleen
Conditionmalaria
Size75.0 MB
Spots2,553
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse spleen
GSM8047884
mouse spleen malaria 69.0 MB 2,498 spots silver H&E
TechnologyVisium
Organismmouse
Tissuespleen
Conditionmalaria
Size69.0 MB
Spots2,498
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse spleen
GSM8047885
mouse spleen malaria 70.0 MB 2,002 spots silver H&E
TechnologyVisium
Organismmouse
Tissuespleen
Conditionmalaria
Size70.0 MB
Spots2,002
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse spleen
GSM8047886
mouse spleen malaria 54.0 MB 1,711 spots silver H&E
TechnologyVisium
Organismmouse
Tissuespleen
Conditionmalaria
Size54.0 MB
Spots1,711
silver H&E Open in Vitessce

Cite this collection

doi.org/10.1186/s13059-025-03543-4 ↗
BibTeX 
@article{Du_2025,
  title = {Benchmarking spatial transcriptomics technologies with the multi-sample SpatialBenchVisium dataset},
  volume = {26},
  ISSN = {1474-760X},
  url = {http://dx.doi.org/10.1186/s13059-025-03543-4},
  DOI = {10.1186/s13059-025-03543-4},
  number = {1},
  journal = {Genome Biology},
  publisher = {Springer Science and Business Media LLC},
  author = {Du, Mei R. M. and Wang, Changqing and Law, Charity W. and Amann-Zalcenstein, Daniela and Anttila, Casey J. A. and Ling, Ling and Hickey, Peter F. and Sargeant, Callum J. and Chen, Yunshun and Ioannidis, Lisa J. and Rajasekhar, Pradeep and Yip, Raymond K. H. and Rogers, Kelly L. and Hansen, Diana S. and Bowden, Rory and Ritchie, Matthew E.},
  year = {2025},
  month = Mar
}