Collections / Multimodal spatial transcriptomic characterization of mouse kidney injury and repair [Visium]
Multimodal spatial transcriptomic characterization of mouse kidney injury and repair [Visium]
Spatial transcriptomics (ST) technologies enable the mapping of gene expression to specific regions within tissues. However, current ST platforms present inherent trade-offs between resolution and throughput, which cannot be entirely addressed by a single technology. To unravel the spatiotemporal landscape of gene expression and cellular interactions during kidney injury and repair, we employed an integrated approach combining Xenium's high-resolution in situ sequencing with Visium's whole-transcriptome spatial profiling.
Datasets6
Size~534 MB
AssayVisium
Samplemouse · kidney
KindComparison
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Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse269622")
coll.ordered_artifacts.df() # the 6 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()} Member datasets · 6
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