Spatial transcriptomic analysis of mouse liver in a high-fat diet model with kimchi supplementation
This study generated 10x Genomics Visium spatial transcriptomic profiles from mouse liver sections to investigate the effects of high-fat diet and kimchi supplementation on hepatic gene expression organization. Samples were obtained from male C57BL/6J mice assigned to normal diet (ND), high-fat diet (HFD), high-fat diet supplemented with spontaneously fermented kimchi (S-K), and high-fat diet supplemented with starter kimchi fermented with Leuconostoc mesenteroides KCKM0828 (LMS-K). The dataset was designed to assess diet-associated transcriptional changes across the liver lobule and to evaluate how kimchi intervention modulates zonation-related hepatic programs along the portal-to-central axis. Raw FASTQ files were prepared for SRA submission through GEO, and processed outputs include filtered gene-barcode matrices, HDF5 matrices, spatial coordinate files, scale factor files, tissue images, and summary reports.
from sddb import Catalog
cohort = Catalog().collection("geo-gse326636")
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-gse326636")
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-gse326636 --dest data/ Licenses: 4 NCBI GEO policy
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