Defining Cellular Diversity at the Swine Maternal-Fetal Interface Using Spatial Transcriptomics and Organoids [MFI017_Visium]
In this study we had two primary aims: 1.) spatially define the transcriptional signatures of porcine maternal-fetal interface and 2.) develop and validate an organoid model which better recapitulated the porcine placenta. Using mid-gestation maternal-fetal interfaces of commercial landrace/large white composite gilts we performed spatial transcriptomics (n=4 interfaces) using Visium v1 spatial transcriptomics. We then went on to isolate trophoblast organoids from fresh-term placentas from crossbred sows consisting</p><p>655 of Yorkshire, Large White, and Landrace breeds. We then characterized the transcriptional profile of these organoids using bulk RNA-seq from 3 seperate lines using a standard Illumina library preparation. To characterize these organoids we performed single cell RNA-sequencing on 3 separate lines of swine trophoblast organoids using a standard 10x Genomics Single Cell 3' Gene Expression platform. All reads/samples were mapped to Sus scrofa v11.1.
from sddb import Catalog
cohort = Catalog().collection("geo-gse296975")
cohort.members # the 4 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-gse296975")
coll.ordered_artifacts.to_dataframe() # the 4 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-gse296975 --dest data/ Licenses: 4 NCBI GEO policy
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