Collections / Spatial Multi-omics Reveals the Heterogeneity of Tumor Microenvironment in the OSF-Associated OSCC
Spatial Multi-omics Reveals the Heterogeneity of Tumor Microenvironment in the OSF-Associated OSCC
Oral submucous fibrosis (OSF) is one of the most common precancerous malignant orders (PMO) with high rates exacerbating into oral squamous cell carcinoma (OSCC), mostly occurred in patients with chewing betel-nut habits in Southeast Asia and Pacific region. However, the spatial characteristics and heteogeneities of tumor microenvironment (TME) in OSF-associated OSCC still remains unclear. Here, we characterized the spatiotemporal changes of OSF-associated OSCC at different malignant states by performing 10x Visium Spatial Transcriptomics (ST) sequencing and airflow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADESI-MSI) analysis.
Datasets4
Size~215 MB
AssayVisium
Samplehuman · mouth
KindReplicates
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Python · lamindb
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse220978")
coll.ordered_artifacts.df() # the 4 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 · 4
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