Thyroid cancer ranks as the ninth most common cancer type in terms of incidence worldwide. Furthermore, the global incidence of thyroid cancer has been on the rise over the past decades.Spatial transcriptomics technology systematically profiles gene expression across tissue space by combing high-throughput RNA sequencing and imaging techniques. The applications of ST have revealed high-resolution spatial architecture and cellular crosstalk in many tumor types, advancing the discovery of new targets for diagnosis and therapy. However, the spatial architecture in thyroid cancer and the structural differences in the TME among papillary thyroid cancer (PTC), locally advanced thyroid cancer (LPTC), and Anaplastic thyroid carcinoma (ATC) have been little investigated. Herein , we applied ST and scRNA-seq data to reveal the spatial difference of TME in PTC, LPTC, and ATC samples.
from sddb import Catalog
cohort = Catalog().collection("geo-gse250521")
cohort.members # the 16 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-gse250521")
coll.ordered_artifacts.to_dataframe() # the 16 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-gse250521 --dest data/Licenses: 16 NCBI GEO policy
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