Collections / Spatial transcriptomics map of the embryonic mouse brain: a tool to explore neurogenesis

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.

Datasets4
Size~71.2 MB
AssayVisium
Samplemouse · brain
KindOther
Flag a data issue
PMID:37855382 ↗
Python · sddb client
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/")
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-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()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse240715 --dest data/

Licenses: 4 NCBI GEO policy

Open in Vitessce
Visium
Mouse brain
GSM7708220
mouse brain 15.9 MB 984 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuebrain
Size15.9 MB
Spots984
Magnificationother · 3.361 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM7708218
mouse brain 18.3 MB 1,196 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuebrain
Size18.3 MB
Spots1,196
Magnificationother · 3.577 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM7708221
mouse brain 18.4 MB 1,266 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuebrain
Size18.4 MB
Spots1,266
Magnificationother · 3.322 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM7708219
mouse brain 18.6 MB 1,152 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuebrain
Size18.6 MB
Spots1,152
Magnificationother · 3.517 µm/px
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1242/bio.060151 ↗
BibTeX
@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
}