Ovarian cancer is one of the leading causes of death among females worldwide. Recently, PARP inhibitors have become widely used for maintenance therapy in ovarian cancer, leading to improvements in patient prognosis. However, resistance to PARP inhibitors is observed in some cases, presenting a clinical challenge. In this study, we investigated the mechanisms of Olaparib resistance using spatial transcriptomics.
from sddb import Catalog
cohort = Catalog().collection("geo-gse288483")
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-gse288483")
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-gse288483 --dest data/Licenses: 8 NCBI GEO policy
@article{Suzuki_2025,
title = {Exploring drug resistance via intercellular crosstalk using spatial transcriptomics in high-grade serous ovarian carcinoma},
volume = {9},
ISSN = {2397-768X},
url = {http://dx.doi.org/10.1038/s41698-025-01122-1},
DOI = {10.1038/s41698-025-01122-1},
number = {1},
journal = {npj Precision Oncology},
publisher = {Springer Science and Business Media LLC},
author = {Suzuki, Hironori and Yoshida, Kosuke and Yokoi, Akira and Suzuki, Kazuhiro and Hirano, Yuta and Kitagawa, Masami and Asano-Inami, Eri and Yoshihara, Masato and Nagao, Yukari and Tamauchi, Satoshi and Yoshikawa, Nobuhisa and Kajiyama, Hiroaki and Yamamoto, Yusuke},
year = {2025},
month = Nov
}