Spatial transcriptomics can capture RNA from even a small number of cells in tissue sections. Using this technology, we analyzed the transcriptome of parathyroid gland cells in FFPE tracheal tissue sections of mice with a Gcm2 variant p.Y392S (Gcm2+/Y392S) compared to WT mice. The mouse Gcm2 variant p.Y392S corresponds to the human GCM2 variant p.Y394S that is seen in a subset of patients with primary hyperparathyroidism. Spatially resolved transcriptome analysis detected increased Lgals3 (Galectin-3) in Gcm2+/Y392S parathyroid gland cells, a marker of parathyroid carcinoma, indicating the growth and malignant potential of parathyroid glands expressing an activating Gcm2 variant.
from sddb import Catalog
cohort = Catalog().collection("geo-gse298115")
cohort.members # the 8 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-gse298115")
coll.ordered_artifacts.to_dataframe() # the 8 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-gse298115 --dest data/Licenses: 8 NCBI GEO policy
No linked publication found — please cite the data source.