The DOI matches its Crossref record; 140 of the 143 references it cites were found in a registry or an index.
Decoding gene regulation in the fly brain
Open accessPublished version of a preprint
Abstract
The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis 1-6, three-dimensional morphological classification 7 and electron microscopy mapping of the connectome 8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation.
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“In Drosophila, different studies have reported that ~70-90% of accessible regions are active enhancers, when their accessibility is cell type specific10–12”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [12]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“CNN-based enhancer modeling has recently gained traction, due to their capacity for the interpretation of enhancer grammar 12,19,20,74”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [12]
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“we and others have recently shown the power of convolutional neural networks (CNN) to model, predict, and interpret an enhancer accessibility or activity from its sequence12,19–21.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [12]
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Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826
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Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N., et al. (2020) · RePEc: Research Papers in Economics · Cited by 17,844
Comprehensive Integration of Single-Cell Data
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Research works printed but not linked: 2
- Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. Preprint at arXiv (2019).
- De Rop, F. V. et al. HyDrop: droplet-based scATAC-seq and scRNA-seq using dissolvable hydrogel beads. Preprint at https://doi.org/10.1101/2021.06.04.447104 (2021). · DOI
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Items 101–103 of 103
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5
Self-citationRegulatory logic of neuronal differentiation in the Drosophila visual system
Treese, M., Chen, Y., Tyree, A., Coyne, R., Lake, C. M., Tabi, O. B., Rajesh, R., Chen, Y.-C. D., Hassan, H., Li, H., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2614314123 · Cited by 6
Design principles of gene circuits for longevity
Godoy, P., & Hao, N. (2025) · Trends in Cell Biology · doi:10.1016/j.tcb.2025.02.006 · Cited by 5
Metadata
- Authors
- Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., Grimes, K., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., De Waegeneer, M., Christiaens, V., Southall, T., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Imperial College London (GB)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Nature
- Published
- 2022-01-05
- Volume
- 601
- Issue
- 7894
- Pages
- 630-636
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4205345182
- Cited by
- 170
- References
- 143
- Citations by year
- 2026: 22 · 2025: 36 · 2024: 35 · 2023: 40 · 2022: 35
- FWCI
- 12.15
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Wellcome Trust (207467/Z/17/Z)
- Medical Research Council (MR/L018802/1, MR/L018802/2)
- Biotechnology and Biological Sciences Research Council (BB/W001004/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Drosophila brain, gene regulatory networks, enhancers, chromatin accessibility, single-cell transcriptomics, transcription factors, transcription factor binding sites, neuronal cell types, neurogenesis, developmental trajectories
- MeSH terms
- Animals, Brain, Drosophila, Gene Expression Regulation, Transcription Factors, Gene Expression Regulation, Developmental, Gene Regulatory Networks
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