We have perfomed Visium HD spatial transcriptomics on archival FFPE kidney biopsies from patients with different manifistations of nephrotic syndrome, i.e., steriod sensitive, frequent relapsing and/or steroid dependent, and focal segmental glomerulosclerosis. Using a pre-trained deep learning model from StarDist to segment nuclei, we reconstructed single-nucleus level spatial trancriptomic datasets through custom binning of subcellular capture areas. We integrated the datasets from the different patients and indentified conserved cell types. Moreover, we identified patient specific transcriptomic differences in the podocyte cell population. This article demonstrates the usefullness of this approach to future studies of pathophysilogical mechanisms in idiopathic nephrotic syndrome.
from sddb import Catalog
cohort = Catalog().collection("geo-gse300895")
cohort.members # the 3 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-gse300895")
coll.ordered_artifacts.to_dataframe() # the 3 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-gse300895 --dest data/Licenses: 3 NCBI GEO policy
