from sddb import Catalog
cohort = Catalog().collection("geo-gse327284")
cohort.members # the 2 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-gse327284")
coll.ordered_artifacts.to_dataframe() # the 2 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-gse327284 --dest data/Licenses: 2 NCBI GEO policy
The extent to which the cerebrovasculature is affected in various brain disorders is still not well understood. To address this, we established a transcriptomic repository of major vascular cell types and microglia to compare the global transcriptomic response in mouse models of three human brain disorders linked to neuroinflammation and associated vascular reactivity: Alzheimer's disease (AD), traumatic brain injury (TBI), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Single-cell analysis of >250,000 cells at different disease stages led to identification of two previously unknown vascular cell subtypes, expanded the endothelial zonation spectrum and allowed for a detailed analysis of the cellular and molecular responses. Surprisingly, most vascular cell types lacked major transcriptomic changes across the three conditions, while microglia exhibited significant, disease-specific transcriptional changes. Notably, microglial responses converged between late-stage TBI and AD, offering insights into the predisposition for neurodegeneration following TBI.
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