Spatially-resolved molecular sex differences at single cell resolution in the adult human hypothalamus
The hypothalamus (HYP) underlies basal endocrine, physiological, and behavioral drives which inherently differ across species and sexes—ranging from growth and puberty to appetite and metabolism to social behavior. Especially in the rodent brain, these sex-differentiated functions correspond to differences in the volume, cell types, and gene expression of distinct HYP subareas. Recent studies have elucidated the transcriptional profile of single cells in the rodent ventromedial hypothalamus (VMH) and arcuate (ARC), which influence appetitive/social behaviors and growth/metabolism, respectively. However, there is a paucity of studies examining the molecular architecture of the adult human HYP, and transcriptional sex differences in the human VMH and ARC have not been characterized in single-cell or spatial contexts. Using 10x Genomics Visium and Xenium platforms, we generated a multimodal, spatially-resolved molecular atlas of human postmortem VMH and ARC from male and female adult donors. Sex-differential gene expression analysis within these regions revealed correlated autosomal expression differences, with stronger overall sex effects in the ARC. We then leveraged the single-cell-resolution of Xenium to localize sex-differential expression to cell types, finding most occur in ESR1- and KISS1-expressing neurons of the ARC. Finally, we investigated VMH and ARC gene expression in relation to genes associated with neuropsychiatric diseases, identifying a striking enrichment of genes upregulated in the male VMH for known and candidate risk genes for autism spectrum disorders. This work provides a multisample view of the human VMH and ARC in young/middle adulthood across both sexes, including data at both transcriptome-wide and single-cell resolutions, the first single-cell transcriptional atlas of molecular sex differences in the adult human VMH and ARC, and highlights potential sex-differentiated roles for VMH and ARC in neurobehavioral disorders.
from sddb import Catalog
cohort = Catalog().collection("geo-gse280316")
cohort.members # the 10 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-gse280316")
coll.ordered_artifacts.to_dataframe() # the 10 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-gse280316 --dest data/ Licenses: 10 NCBI GEO policy
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No linked publication found — please cite the data source.