Small intestinal neuroendocrine tumors (SI-NETs) frequently present as multifocal lesions, but the spatial and molecular mechanisms underlying their development and heterogeneity remain unclear. This study aimed to characterize the phenotypic subtypes of tumor cells across anatomical sites in multifocal SI-NETs and identify local microenvironmental factors influencing tumor development.
from sddb import Catalog
cohort = Catalog().collection("geo-gse301265")
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-gse301265")
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-gse301265 --dest data/Licenses: 8 NCBI GEO policy
@article{Yogo_2026,
title = {Spatial Transcriptomics Reveals Location-Specific Tumor Cell Subtypes and Signaling within Multifocal Small Intestinal Neuroendocrine Tumors},
volume = {32},
ISSN = {1557-3265},
url = {http://dx.doi.org/10.1158/1078-0432.ccr-25-2854},
DOI = {10.1158/1078-0432.ccr-25-2854},
number = {10},
journal = {Clinical Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Yogo, Akitada and Akanuma, Naoki and Kim, Grace E. and Mäkinen, Netta and Thirlwell, Chrissie and Nakakura, Eric K.},
year = {2026},
month = Mar,
pages = {2098–2109}
}