The tumor immune microenvironment (TIME) and tumor heterogeneity shape prognosis and therapeutic response in diffuse large B-cell lymphoma (DLBCL), but HIV-associated DLBCL remains poorly understood. We performed spatial transcriptomics on pre-treatment DLBCL biopsies to characterize the TIME and tumor-intrinsic features across a spectrum of HIV-associated immune dysregulation. Our analysis revealed distinct immune architectures shaped by HIV status and antiretroviral therapy (ART) exposure, with implications for immunotherapeutic strategies in a population historically excluded from such trials.
from sddb import Catalog
cohort = Catalog().collection("geo-gse344506")
cohort.members # the 32 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-gse344506")
coll.ordered_artifacts.to_dataframe() # the 32 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-gse344506 --dest data/Licenses: 32 NCBI GEO policy
No linked publication found — please cite the data source.