Collections / Spatial transcriptomic analysis of mouse liver in a high-fat diet model with kimchi supplementation

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.

Datasets4
Size~196 MB
AssayVisium
Samplemouse · liver
KindComparison
Flag a data issue
www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE326636 ↗
Python · sddb client
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/")
Python · lamindb

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()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse326636 --dest data/

Licenses: 4 NCBI GEO policy

Open in Vitessce
Visium
Mouse liver
GSM9635996
mouse liver 45.9 MB 2,967 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueliver
Size45.9 MB
Spots2,967
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse liver
GSM9635997
mouse liver 46.2 MB 3,062 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueliver
Size46.2 MB
Spots3,062
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse liver
GSM9635995
mouse liver 61.3 MB 3,712 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueliver
Size61.3 MB
Spots3,712
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse liver
GSM9635998
mouse liver 42.6 MB 2,760 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueliver
Size42.6 MB
Spots2,760
silver H&E NCBI GEO policy Open in Vitessce

Cite this collection

No linked publication found — please cite the data source.