Reconstructing the single-cell spatiotemporal dynamics of glioblastoma invasion

ABSTRACT: Glioblastoma invasion into healthy brain tissue remains a major barrier to effective treatment, yet its spatiotemporal dynamics are difficult to quantify at single-cell resolution in a scalable, patient-specific manner. Here, we introduce GlioTrace, an ex vivo imaging and computational framework for real-time tracking of glioblastoma invasion in patient-derived xenograft brain slices. Combining whole-specimen confocal microscopy with machine learning-based morphology classification and probabilistic modeling of state transitions, GlioTrace reconstructs how tumor cells switch between invasion phenotypes and how these dynamics are shaped by local tissue context and therapy. Across six patient-derived models, GlioTrace reveals that invasion plasticity is patient-specific, spatially organized, and modulated by vascular proximity and microglial interaction. Targeted therapies selectively alter phenotype composition and transition probabilities, whereas standard-of-care treatment enriches a highly motile, transition-connected branching state consistent with a treatment-tolerant invasive phenotype. GlioTrace provides a scalable framework for quantifying glioblastoma invasion dynamics and comparing anti-invasive responses across patient-derived models.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE344470 | GEO | 2026/08/21

REPOSITORIES: GEO