Collections / Ex-ST
Ex-ST
Datasets5
Size~5.8 GB
AssayVisium
Samplemouse · brain
KindPublication
Download the whole collection
Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="doi-10.1101/2022.10.25.513696")
coll.ordered_artifacts.df() # the 5 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()} 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
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
}