Collections / Ex-ST

Ex-ST

Datasets5
Size~6.2 GB
AssayVisium
Samplemouse · brain
KindPublication
Flag a data issue
10.1101/2022.10.25.513696 ↗
Download the whole collection
Python · sddb client 
from sddb import Catalog

cohort = Catalog().collection("doi-10-1101-2022-10-25-513696")
cohort.members        # the 5 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="doi-10.1101/2022.10.25.513696")
coll.ordered_artifacts.to_dataframe()   # the 5 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 doi-10-1101-2022-10-25-513696 --dest data/
Install (Python 3.12+) Docs →

Licenses: 5 CC BY-NC-SA 4.0

About the study

Expansion Spatial Transcriptomics

Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in diverse tissue contexts, however, their spatial resolution is limited by array density. We present Expansion Spatial Transcriptomics (Ex-ST) to overcome this limitation by clearing and expanding tissue prior to capturing the entire transcriptome. This approach allows us to achieve near cellular resolution and higher capture efficiency of lowly-expressed genes, which we demonstrate using mouse brain samples.

Member datasets · 5
Open in Vitessce
Visium
Mouse brain
MEND130
mouse brain 2.1 GB 4,557 spots silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size2.1 GB
Spots4,557
Magnification40x · 0.172 µm/px
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
MEND129
mouse brain 1.9 GB 4,575 spots silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size1.9 GB
Spots4,575
Magnification40x · 0.174 µm/px
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
MEND123
mouse brain 824 MB 1,490 spots silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size824 MB
Spots1,490
Magnification40x · 0.172 µm/px
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
MEND124
mouse brain 927 MB 1,448 spots silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size927 MB
Spots1,448
Magnification40x · 0.172 µm/px
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
MEND131
mouse brain 528 MB 1,301 spots silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size528 MB
Spots1,301
Magnification20x · 0.574 µm/px
silver H&E Open in Vitessce

Cite this collection

doi.org/10.1101/2022.10.25.513696 ↗
BibTeX 
@article{Fan_2022,
  title = {Expansion Spatial Transcriptomics},
  url = {http://dx.doi.org/10.1101/2022.10.25.513696},
  DOI = {10.1101/2022.10.25.513696},
  publisher = {openRxiv},
  author = {Fan, Yuhang and Andrusivová, Žaneta and Wu, Yunming and Chai, Chew and Larsson, Ludvig and He, Mengxiao and Luo, Liqun and Lundeberg, Joakim and Wang, Bo},
  year = {2022},
  month = Oct
}