Researchers decode regulatory programs governing glioblastoma plasticity using deep learning
Scientists have introduced a new deep-learning framework to analyze gene regulation in over one million cells from primary glioblastoma tumors.
New computational tool reveals hidden networks
The study presents scDORI, a system that infers enhancer-driven gene-regulatory networks at single-cell resolution. This approach applies to single-nucleus multiome profiles derived from more than one million cells collected from primary glioblastomas.
Structured programs drive tumor progression
Integration of computational inference with systematic perturbation screens shows that a structured regulatory program governs cellular plasticity. This landscape is hierarchical and predictable yet allows cross-lineage trajectories absent from normal neurodevelopment.
MYT1L maintains neuronal-like identity states
Low-plasticity states such as the neuronal-like state are actively maintained configurations rather than stable differentiation end points. The protein MYT1L acts as the dominant member of a network that silences alternative fates by repressing master regulators.
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