Abstract / Summary
Glioblastoma (GBM) exhibits marked spatial heterogeneity that is lost after tissue dissociation for single–cell RNA sequencing. Here, we developed a spatial–statistical framework to characterize tumor–microenvironment organization and derive molecularly predictable spatial phenotypes from CosMx data. Eight GBM specimens comprising 2,427,362 cells, 15 cell populations, and four malignant states were analyzed, revealing substantial inter–patient differences in cellular composition and density. Fine finite–element meshes best preserved continuous spatial intensity and supported whole–tissue Log–Gaussian Cox Process modelling. Immune populations, including macrophages, microglia, monocytes, cDCs, neutrophils, and T cells, showed spatial attraction toward malignant states. These models generated cell–level measures of conditional intensity and tumor–proximity probability. Transcript abundances and metabolic pathway scores served as the sole predictive features to model these spatial phenotypes. Transcriptomic representations outperformed metabolic pathway scores. Leave–one–patient–out validation reduced predictive performance, and patient–wise harmonization failed to restore cross–patient transportability. Spatial outcome distributions varied between specimens, indicating that molecular–spatial relationships are strongly conditioned by patient-specific tumor architecture. Predicted interaction scores stratified transcriptional subclusters within cell types in external non–spatial single-cell datasets. This framework links spatial point–process phenotypes to molecular states and provides a basis for inferring spatial context from non–spatial single–cell data. Generalizing these models requires larger and more diverse spatial cohorts.