Spatial transcriptomics map of the embryonic mouse brain: a tool to explore neurogenesis
The developing brain has a complex and well-organized anatomical structure comprising different types of neural and non-neural cells. Stem cells, progenitors, and newborn neurons tightly interact with their neighbouring cells and tissue microenvironment, and this intricate interplay ultimately shapes the output of neurogenesis. Given the relevance of spatial cues during brain development, we acknowledge the necessity for a transcriptomics atlas within the tissue context accessible to the neurodevelopmental community. To fulfil this need, we offer an open-access spatial gene expression browser of the embryonic mouse brain at the peak of neurogenesis. Using 10x Visium technology, we generated spatially-resolved RNAseq data from E13.5 embryonic brain sections. Unsupervised clustering reliably defined specific cell type populations of diverse lineages and maturational states. Differential expression analysis revealed unique transcriptional signatures across specific embryonic brain areas, uncovering novel features inherent to particular anatomical domains. Furthermore, we integrated single-cell RNAseq data from E13.5 mouse brains into our Spatial Transcriptomics data, adding tissue context to single-cell resolution. In summary, we provide a valuable tool that enables the exploration and discovery of unforeseen molecular players involved in neurogenesis, particularly in the crosstalk between different cell types.
from sddb import Catalog
cohort = Catalog().collection("geo-gse240715")
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/") 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-gse240715")
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()] # sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse240715 --dest data/ Licenses: 4 NCBI GEO policy
Cite this collection
doi.org/10.1242/bio.060151 ↗@article{Di_Marco_2023,
title = {Spatial transcriptomics map of the embryonic mouse brain – a tool to explore neurogenesis},
volume = {12},
ISSN = {2046-6390},
url = {http://dx.doi.org/10.1242/bio.060151},
DOI = {10.1242/bio.060151},
number = {10},
journal = {Biology Open},
publisher = {The Company of Biologists},
author = {Di Marco, Barbara and Vázquez-Marín, Javier and Monyer, Hannah and Centanin, Lázaro and Alfonso, Julieta},
year = {2023},
month = Oct
}