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Cited by 87Genomics and Chromatin DynamicsCRISPR and Genetic EngineeringGene Regulatory Network Analysis

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo

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de Almeida, Bernardo P., Schaub, Christoph, Pagani, Michaela, Secchia, Stefano, Furlong, Eileen E. M., Stark, Alexander·2024·Nature
DOI 10.1038/s41586-023-06905-9PMC PMC10830412
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Abstract

. 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.

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  • The design rules identified here are transparent insights into pairwise interactions between TF motifs.

    “Unlike recent studies that used genomic data and deep learning for designing cell-type-specific enhancers in the fruit fly61,62 or in cancer cell lines,63 the design rules identified here are transparent insights into pairwise interactions between TF motifs.”

    From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [61]

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Cited by

Sorted by citations

Authors
de Almeida, B. P., Schaub, C., Pagani, M., Secchia, S., Furlong, E. E. M., & Stark, A.
Affiliations
  • European Molecular Biology Laboratory (DE)
  • Research Institute of Molecular Pathology (AT)
Corresponding author
Stark, Alexander
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-12-12
Volume
626
Issue
7997
Pages
207-211
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-06905-9
PMC
PMC10830412
OpenAlex
W4389613185
Cited by
87
References
11
Citations by year
2026: 29 · 2025: 33 · 2024: 23 · 2023: 2
FWCI
7.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Österreichische Forschungsförderungsgesellschaft
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
synthetic enhancers, tissue-specific enhancers, Drosophila melanogaster embryo, transfer learning, convolutional neural networks, ATAC-seq, deep learning, central nervous system, epidermis, intestine, muscle, brain
MeSH terms
Deep Learning, Animals, Chromatin, Drosophila, Drosophila melanogaster, Embryo, Nonmammalian, Enhancer Elements, Genetic, Organ Specificity, Reproducibility of Results, Neural Networks, Computer, Transposases, Synthetic Biology, Single-Cell Analysis, Datasets as Topic

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