Predictive design of tissue-specific mammalian enhancers that function in vivo in the mouse embryo
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Abstract
Enhancers control tissue-specific gene expression across metazoans. Although deep learning has enabled enhancer prediction and design in mammalian cell lines and invertebrate systems, it remains unclear whether such approaches can operate within the regulatory complexity of mammalian tissues in vivo. Here, we present a general strategy for designing tissue-specific enhancers that function reliably in mice. We use deep learning to train compact convolutional neural networks (CNNs) on genome-wide chromatin accessibility and fine-tune them via transfer learning on validated human and mouse enhancers. Guided by these models, we design fifteen synthetic enhancers for the heart, limb, and central nervous system (CNS) in mouse embryos, all of which are active in their intended target tissue. Our work establishes a generalizable framework for programmable control of mammalian gene expression in vivo, opening new avenues in functional genomics, synthetic biology, and gene therapy.
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Right panel: representative LacZ-stained transgenic E11.5 mouse embryos carrying five distinctsynthetic enhancer sequences.
These numbers count embryos showing activity in the target tissue. Have you systematically quantified whether these enhancers also drive expression elsewhere (off-target) across all samples?
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Scoring of tissue positivity was461performed for each embryo by two independent scorers in a non-blinded fashion
Appreciate the transparency here. Given that the scorers know which tissue each enhancer was designed to target, blinded scoring would strengthen confidence in the specificity claims. Was there a reason blinding wasn't feasible?
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fifteen were selected for in vivo validation
How were the five candidates per tissue selected? Were there score thresholds, or specific motif criteria?
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Figures S1H–S1J)
Pretty cool how well this worked. These data are convincing that the TL model genuinely helps and the success you found isn't just due to there being accessible peaks.
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