Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity [scRNA-Seq]. Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity [scRNA-Seq]

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 predictor, and design a second generation of enhancers with improved specificity. Our design methods embed relevant transcription factor binding site (TFBS) motifs with higher frequencies 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 enhancers as short as 50bp can maintain specificity. Overall design: We transfected a library of synthetic enhancers (R1-MPRA) into two different human cell lines, HepG2 and K562. We quantified enhancer strength at the single cell level and correlate enhancer activity with transcriptome analysis.

合成生物学领域一项重要且尚未被广泛解决的核心难题，是如何将基因表达精准靶向至特定细胞类型。本研究采用迭代深度学习方法，设计在两种人类细胞系间表现出显著差异活性的合成增强子。我们首先基于已发表的增强子活性与染色质可及性数据集训练模型，并以此为指导设计合成增强子，使其预测特异性达到最大化。我们通过实验验证了这些序列，利用实测数据重新优化预测模型，并设计出特异性得到提升的第二代增强子。本研究的设计方法相较于同类内源增强子，以更高的频率嵌入相关转录因子结合位点（Transcription Factor Binding Site，TFBS）基序，同时采用更具选择性的基序词汇库；我们还证实，增强子活性与单细胞水平的转录因子表达量呈显著相关性。最后，我们通过扰动实验解析了优质增强子的因果特征，并证实仅50碱基对（bp）长度的增强子即可维持其特异性。整体实验设计：我们将合成增强子文库（R1-MPRA）转染至两种不同的人类细胞系——HepG2与K562中。我们在单细胞水平量化增强子强度，并将增强子活性与转录组分析结果进行关联。