Decoding Plasticity Regulators and Transition Trajectories in Glioblastoma with Single-cell Multiomics [scRNA-Seq]

Whether such plasticity reflects stochastic state switching or is governed by predictable gene regulation has remained unresolved. We applied scDORI, a deep-learning framework that infers enhancer-driven gene regulatory networks at single cell resolution, to single-nucleus multi-ome profiles of over one million cells from primary GBs.

Integrating computational inference with systematic gain-of-function screens across patient-derived models, we show that GB cellular plasticity is governed by a structured regulatory program with an asymmetric interplay of activators and repressors. The resulting transition landscape is hierarchical and predictable, yet permits cross-lineage trajectories absent from normal neurodevelopment. Within this landscape, low-plasticity states such as the Neuronal-like state are not stable differentiation endpoints but actively maintained configurations, stabilized by a network of safeguard repressors that silence alternate fates.