Glioblastoma (GBM) is the most common and aggressive malignant tumor in the central nervous system, with a five-year survival rate as low as 6.8 percent.1 GBM tumors are heterogeneous, which poses major challenges for prognosis and treatment. Not only do these tumor cells have different expression profiles, but their cellular subtypes and spatial organization can also vary among patients, according to studies using single-cell RNA sequencing (scRNA-seq).2
While these previous experiments provided researchers with tumor cell transcriptome profiles, the tissue dissociation required for scRNA-seq sample preparations meant that they could not capture the spatial dynamics of cellular interactions. Retaining spatial information while assessing GBM prognosis is important because cellular interactions and tumor architecture play critical roles in driving clonal evolution, tumor progression, and therapeutic resistance.3-5 Therefore, researchers recently developed spatial transcriptomic profiling techniques in intact tissue to explore how cellular composition and spatial architecture contribute to GBM prognosis.6
One problem ...
