We present a spatial omics approach that merges and expands the capabilities of independently performedin situassays on a single tissue section. Our spatial multimodal analysis combines histology, mass spectrometry imaging, and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular weight metabolites across tissue regions. We demonstrate the potential of our method using murine and human brain samples in the context of dopamine and Parkinson’s disease.
from sddb import Catalog
cohort = Catalog().collection("geo-gse232910")
cohort.members # the 18 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-gse232910")
coll.ordered_artifacts.to_dataframe() # the 18 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-gse232910 --dest data/Licenses: 18 NCBI GEO policy
No linked publication found — please cite the data source.