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Cited by 175Genomics and Chromatin DynamicsGene Regulatory Network AnalysisCRISPR and Genetic Engineering

Cell-type-directed design of synthetic enhancers

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Taskiran, Ibrahim I., Spanier, Katina I., Dickmänken, Hannah, Kempynck, Niklas, Pančíková, Alexandra, Ekşi, Eren Can, Hulselmans, Gert, Ismail, Joy N. +5 more·2024·Nature
DOI 10.1038/s41586-023-06936-2PMC PMC10830415

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Abstract

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes 1 . 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 2–6 , can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows 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 characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of 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.

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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 [62]

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Cited by

Sorted by citations

Authors
Taskiran, I. I., Spanier, K. I., Dickmänken, H., Kempynck, N., Pančíková, A., Ekşi, E. C., Hulselmans, G., Ismail, J. N., Theunis, K., Vandepoel, R., Christiaens, V., Mauduit, D., & Aerts, S.
Affiliations
  • VIB-KU Leuven Center for Brain & Disease Research (BE)
  • VIB-KU Leuven Center for Cancer Biology (BE)
  • UK Dementia Research Institute (GB)
Corresponding author
Aerts, Stein
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2023-12-12
Volume
626
Issue
7997
Pages
212-220
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-023-06936-2
PMC
PMC10830415
OpenAlex
W4389613138
Cited by
175
References
20
Citations by year
2026: 52 · 2025: 55 · 2024: 49 · 2023: 14 · 2022: 4 · 2013: 1
FWCI
15.24
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, other-oa)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Lirias (KU Leuven) (submittedVersion, cc-by)
Funders
  • Fonds Wetenschappelijk Onderzoek (G094121N)
  • KU Leuven (40007513, F/2020/1396, C14/22/125)
  • Fondation contre le Cancer
  • Bijzonder Onderzoeksfonds UGent
  • H2020 European Research Council (963884)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
Keywords
synthetic enhancers, cell type-specific gene regulation, transcription factor binding motifs, deep learning optimization, glial cells
MeSH terms
Deep Learning, Animals, Brain, Cells, Drosophila, Drosophila melanogaster, Enhancer Elements, Genetic, Gene Expression Regulation, Humans, Neuroglia, Repressor Proteins, Transcription Factors, Animals, Genetically Modified, Synthetic Biology

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