Cell type directed design of synthetic enhancers.

Transcriptional enhancers act as docking stations for combinations of transcription factors (TFs) and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here, we show that deep learning models can be used to efficiently design synthetic, cell type specific enhancers, starting from random sequences, and that this optimization process allows for a detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create "dual-code" enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterise enhancer codes through the strength, combination, and arrangement of TF activator and TF repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to similar enhancer rules as Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.