Collections / Spatial transcriptomic analysis of developing hearts at embryonic and neonatal stages
Spatial transcriptomic analysis of developing hearts at embryonic and neonatal stages
Cd1 wildetype mice, Wt1-CreER;Rosa-mTmG mice, and Wt1-CreER;Rosa-DTA mice derived hearts were isolated and directly embeded. The hearts were then sectioned for spatial transcriptomic analysis.
Datasets8
Size~117 MB
AssayVisium
Samplemouse · heart
KindOther
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse282547")
cohort.members # the 8 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/") 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-gse282547")
coll.ordered_artifacts.to_dataframe() # the 8 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()] Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse282547 --dest data/ Licenses: 8 NCBI GEO policy
Member datasets · 8
Cite this collection
doi.org/10.1038/s42003-026-10259-z ↗ BibTeX
@article{Hu_2026,
title = {Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment},
volume = {9},
ISSN = {2399-3642},
url = {http://dx.doi.org/10.1038/s42003-026-10259-z},
DOI = {10.1038/s42003-026-10259-z},
number = {1},
journal = {Communications Biology},
publisher = {Springer Science and Business Media LLC},
author = {Hu, Junqi and He, Haoting and Xu, Juan and MacDonald, William A. and He, Yuanhang and Liu, Tianhao and Chen, Wei and Li, Guang},
year = {2026},
month = May
}