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Decoding gene regulation in the fly brain
Open accessPreprint — published as 10.1038/s41586-021-04262-z · Paper page
Abstract
Summary: The Drosophila brain is a work horse in neuroscience. Single-cell transcriptome analysis 1–5, 3D morphological classification 6 , and detailed EM mapping of the connectome 7–10 have revealed an immense diversity of neuronal and glial cell types that underlie the wide array of functional and behavioral traits in the fruit fly. The identities of these cell types are controlled by – still unknown – gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. To characterize the GRN for each cell type in the Drosophila brain, we profiled chromatin accessibility of 240,919 single cells spanning nine developmental timepoints, and integrated this data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which around 70,000 are linked to specific developmental trajectories, involving neurogenesis, reprogramming and maturation. For 40 cell types, their uniquely accessible regions could be associated with their expressed transcription factors and downstream target genes, through a combination of motif discovery, network inference techniques, and deep learning. We illustrate how these “enhancer-GRNs” can be used to reveal enhancer architectures leading to a better understanding of neuronal regulatory diversity. Finally, our atlas of regulatory elements can be used to design genetic driver lines for specific cell types at specific timepoints, facilitating the characterization of brain cell types and the manipulation of brain function.
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“methods for inferring enhancer-driven GRNs (eGRNs) that integrate enhancer sequence, chromatin accessibility and gene expression22–24 found that the accuracy to predict active enhancers increases by ~10% compared to using accessibility alone25.”
From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [25]
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
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
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
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
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Flagged first, then by citations
3 of the 143 references couldn’t be matched to a known paper.
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- 10.1093/bioinformatics/btx194. · DOI · Paper page
- 10.1101/2020.04.28.066498 · DOI
- Shrikumar, A. , Greenside, P. & Kundaje, A . Learning Important Features Through Propagating Activation Differences. arXiv:1704.02685 [cs] (2019). · arXiv
Cited by
Sorted by citations
Items 101–173 of 173
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Godoy, P., & Hao, N. (2025) · Trends in Cell Biology · doi:10.1016/j.tcb.2025.02.006 · Cited by 5
Dissecting the enhancer gene regulatory network in early Drosophila spermatogenesis
y Sanchez, P. V. N., Sekhar, P. S., Yildirim, K., Lange, T., Kreplin, L. Z., Boopathy, V. M., de Souza, S. R., Dammer, K., Ibberson, D., Wang, Q., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62046-9 · Cited by 5
MICA: A multi-omics method to predict gene regulatory networks in early human embryosPreprint
Alanis‐Lobato, G., Bartlett, T. E., Huang, Q., Simon, C. S., McCarthy, A., Elder, K. T., Snell, P., Christie, L., & Niakan, K. K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.03.527081 · Cited by 5
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Nayyar, N., Arya, S. K., Harrison, D. A., & Palli, S. R. (2025) · Current Opinion in Insect Science · doi:10.1016/j.cois.2025.101455 · Cited by 5
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Xia, Y., & Huo, Y. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108802 · Cited by 4
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(2023) · Nature Biotechnology · doi:10.1038/s41587-023-01897-3 · Cited by 4
Butterfly eyespots exhibit unique patterns of open chromatin
Murugesan, S. N., & Monteiro, A. (2023) · F1000Research · doi:10.12688/f1000research.133789.1 · Cited by 4
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Gaudier‐Diaz, M. M., Parekh, S. V., Penton, R. E., Robertson, S. D., & Thomas, A. (2023) · Journal of Undergraduate Neuroscience Education · doi:10.59390/rhsn3470 · Cited by 4
Merrill, C. B., Titos, I., Pabon, M. A., Montgomery, A., Rodan, A. R., & Rothenfluh, A. (2024) · Science Advances · doi:10.1126/sciadv.adi4393 · Cited by 4
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Metadata
- Authors
- Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., Waegeneer, M. D., Christiaens, V., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2021-08-12
- Volume
- 601
- Issue
- 7894
- Pages
- 630-636
- ISSN
- 0028-0836 (ISSN-L)
- OpenAlex
- W4205345182
- Cited by
- 170
- References
- 143
- Citations by year
- 2026: 22 · 2025: 36 · 2024: 35 · 2023: 40 · 2022: 35 · 2021: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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