Multiscale spatial transcriptomics resolves the cellular and molecular architecture of the human amygdala [Visium]
The amygdala is a central hub for emotional learning that is dysregulated across numerous psychiatric disorders, yet its molecular architecture in humans remains poorly defined. We here present a spatial transcriptomic atlas of the human amygdala from nine neurotypical donors. Integrating Visium, Xenium, and VisiumHD technologies, we profiled over 1.1 million cells across 13 spatial domains spanning the basolateral complex, central, medial, and cortical nuclei, as well as the intercalated islands, each defined by distinct marker genes and gene co-expression networks. By integrating snRNA-seq reference atlases, we found that each subdivision of the basolateral subnucleus contains distinct excitatory neuron classes. We additionally found three transcriptionally distinct populations of intercalated neuron types, two of which form spatially distinct islands neighboring the basolateral complex, and were able to refine cell type diversity across the central nucleus and related amygdalostriatal transition areas. Finally, we leveraged this atlas to spatially resolve the transcriptomic signatures of alcohol use disorder across cell types and subnuclei. Together, this atlas provides a foundational resource and framework for accelerating cross-species comparisons and human disease-focused investigations.
from sddb import Catalog
cohort = Catalog().collection("geo-gse342709")
cohort.members # the 48 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-gse342709")
coll.ordered_artifacts.to_dataframe() # the 48 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-gse342709 --dest data/ Licenses: 48 NCBI GEO policy
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