<p>Enhancer elements direct cell-type-specific gene expression programs. After injury, cells change their transcriptional state to adapt to stress and initiate repair. Here we investigate how injury-induced transcriptional programs are encoded within enhancers in the mammalian CNS. Leveraging single-nucleus transcriptomics and chromatin accessibility profiling, we identify thousands of injury-induced, cell-type-specific enhancers in the mouse spinal cord after a contusion injury. These are abundant in glial cells and retain cell-type specificity, even when regulating shared wound response genes. By modeling glial injury-responsive enhancers using deep learning, we reveal that their architecture encodes cell-type specificity by integrating generic stimulus response elements with cell identity programs. Finally, through in vivo enhancer screening, we demonstrate that injury-responsive enhancers can selectively target reactive astrocytes across the CNS using therapeutically relevant gene delivery vectors. Our decoding of the principles of injury-responsive enhancers enables the design of sequences that can be programmed to target disease-associated cell states.</p>

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The regulatory code of injury-responsive enhancers enables precision cell-state targeting in the CNS

  • Margherita Zamboni,
  • Adrián Martínez-Martín,
  • Gabriel Rydholm,
  • Timm Häneke,
  • Laura Pintado Almeida,
  • Deniz Seçilmiş,
  • Christoph Ziegenhain,
  • Enric Llorens-Bobadilla

摘要

Enhancer elements direct cell-type-specific gene expression programs. After injury, cells change their transcriptional state to adapt to stress and initiate repair. Here we investigate how injury-induced transcriptional programs are encoded within enhancers in the mammalian CNS. Leveraging single-nucleus transcriptomics and chromatin accessibility profiling, we identify thousands of injury-induced, cell-type-specific enhancers in the mouse spinal cord after a contusion injury. These are abundant in glial cells and retain cell-type specificity, even when regulating shared wound response genes. By modeling glial injury-responsive enhancers using deep learning, we reveal that their architecture encodes cell-type specificity by integrating generic stimulus response elements with cell identity programs. Finally, through in vivo enhancer screening, we demonstrate that injury-responsive enhancers can selectively target reactive astrocytes across the CNS using therapeutically relevant gene delivery vectors. Our decoding of the principles of injury-responsive enhancers enables the design of sequences that can be programmed to target disease-associated cell states.