Molecular mechanisms underlying dorsolateral prefrontal cortex (dlPFC) dysfunction in schizophrenia (SCZ) are poorly understood. dlPFC cell types are spatially organized across the six layers into functional microcircuits, which regulate cognitive and emotional processes that are implicated in SCZ. While regional specificity across cortical layers and cell types has been demonstrated for some SCZ-linked genes, spatially-resolved transcriptomics (SRT) can more definitively map molecular associations of disease. We investigated spatial gene expression changes in the human dlPFC from neurotypical control (n=31) and SCZ (n=32) brain donors using the Visium platform with incorporation of immunostaining to label perineuronal nets, neurons and vasculature. SCZ-associated DEGs were then mapped across these labeled cellular microenvironments and cortical layers. Major transcriptional alterations were identified in synaptic and neuroimmune pathways, which we localized to neuropil and glia-enriched domains. Integrative analysis with bulk and single-cell RNA studies highlighted distinct roles for spatially-localized glial cell populations, and identified enrichment of novel DEGs in endothelial cells and microglia. These findings were supported by enrichment of SCZ genetic risk across similar domains, and association of laminae-specific transcription factors to SCZ risk variants. Cellular resolution SRT using Xenium extended laminar disease associations to spatially-organized cell types. The findings highlight unique advantages of SRT to identify novel SCZ-related biology, particularly for cellular populations and compartments that may be missed with alternative technologies. We provide data resources to enable sample-level visualization of the spatial transcriptomics data (Samui) and to explore SCZ-associated DEGs across spatial domains (iSEE-layer adjusted) and cellular microenvironments (iSEE-Neuropil, iSEE-Neuron, iSEE-vasculature, iSEE-PNNs).
from sddb import Catalog
cohort = Catalog().collection("geo-gse307403")
cohort.members # the 62 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-gse307403")
coll.ordered_artifacts.to_dataframe() # the 62 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-gse307403 --dest data/Licenses: 62 NCBI GEO policy
No linked publication found — please cite the data source.