2023/12/12 by Bernardo P. de Almeida, Christoph Schaub, Michaela Pagani +3 · 1 voice · 5 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Genomics and Chromatin Dynamics #Genomics and Phylogenetic Studies #Plant Molecular Biology Research
paper · pdf · doi:10.1038/s41586-023-06905-9
openalex publication_date 2023/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.