ArticleCell systems2025
Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity.
Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.Nature genetics · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion.bioRxiv : the preprint server for biology · 2026Article
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- From Natural Discovery to AI-Guided Design: A Curated Collection of Compact Enhancers for Crop Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Scaling perturbations: beyond genome-scale CRISPR screens.bioRxiv : the preprint server for biology · 2026Article
- Deep learning-guided design of cell type-specific AAV promoters.bioRxiv : the preprint server for biology · 2026Article
- BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026Article
- Uncertainty-aware genomic deep learning with knowledge distillation.NPJ artificial intelligence · 2026Article
- Predictive design of tissue-specific mammalian enhancers that functionbioRxiv : the preprint server for biology · 2025Article
- Motif-based models accurately predict cell type-specific distal regulatory elements.Nature communications · 2025Article
- Widespread low-affinity motifs enhance chromatin accessibility and regulatory potential in mESCs.bioRxiv : the preprint server for biology · 2025Article
- Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.bioRxiv : the preprint server for biology · 2025Article
- In Vivo Targeted Reprogramming of Cardiac Fibroblasts for Heart Regeneration: Advances and Therapeutic Potential.Bioengineering (Basel, Switzerland) · 2025Review
- Lineage-specific regulatory evolution: insights from massively parallel reporter assays.Current opinion in genetics & development · 2025Review
- Active learning of enhancers and silencers in the developing neural retina.Cell systems · 2025Article
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- A generative framework for enhanced cell-type specificity in rationally designed mRNAs.bioRxiv : the preprint server for biology · 2024Article
- Uncertainty-aware genomic deep learning with knowledge distillation.bioRxiv : the preprint server for biology · 2024Article
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Abstract
An important and largely unsolved problem in synthetic biology is how to target gene expression to specific cell types. Here, we apply iterative deep learning to design synthetic enhancers with strong differential activity between two human cell lines. We initially train models on published datasets of enhancer activity and chromatin accessibility and use them to guide the design of synthetic enhancers that maximize predicted specificity. We experimentally validate these sequences, use the measurements to re-optimize the model, and design a second generation of enhancers with improved specificity. Our design methods embed relevant transcription factor binding site (TFBS) motifs with higher frequency than comparable endogenous enhancers while using a more selective motif vocabulary, and we show that enhancer activity is correlated with transcription factor expression at the single-cell level. Finally, we characterize causal features of top enhancers via perturbation experiments and show that enhancers as short as 50 bp can maintain specificity. A record of this paper's transparent peer review process is included in the supplemental information.
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