Decoding gene regulation in the fly brain

Janssens, Jasper; Aibar, Sara; Taskiran, Ibrahim Ihsan; Ismail, Joy N.; Gomez, Alicia Estacio; Aughey, Gabriel; Spanier, Katina I.; De Rop, Florian V.; González-Blas, Carmen Bravo; Dionne, Marc; Grimes, Krista; Quan, Xiao Jiang; Papasokrati, Dafni; Hulselmans, Gert; Makhzami, Samira; De Waegeneer, Maxime; Christiaens, Valerie; Southall, Tony; Aerts, Stein

doi:10.1038/s41586-021-04262-z

Cited by 170Genomics and Chromatin DynamicsSingle-cell and spatial transcriptomicsGene Regulatory Network Analysis

Decoding gene regulation in the fly brain

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Janssens, Jasper, Aibar, Sara, Taskiran, Ibrahim Ihsan, Ismail, Joy N., Gomez, Alicia Estacio, Aughey, Gabriel, Spanier, Katina I., De Rop, Florian V. +11 more·2022·Nature

DOI 10.1038/s41586-021-04262-z

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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, approximately 70-90% of accessible regions are active enhancers when their accessibility is cell type specific.
“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]
The main text (p. 4) says that differentially accessible regions (DARs) often function as enhancers, but reports 588 active enhancers among 1,017 Kenyon-cell DARs overlapping tested reporter regions—about 58%. This supports the relationship between cell-type-specific accessibility and enhancer activity, but not the claimed 70–90% rate. (LLM, conf=0.86)
Experimental — accuracy not yet measured
CNN-based enhancer modeling has recently gained traction due to its capacity for interpretation of enhancer grammar.
“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]
In “Combinatorial TF expression is reflected by enhancer architecture” (page not indexed), the authors describe training a convolutional neural network on enhancer-region sequences. Figure 3 (p. 11) shows that interpreting nucleotide contributions reveals transcription-factor motifs, supporting the claimed capacity to interpret enhancer architecture. The paper does not establish that CNN-based modeling has recently gained traction or that this capacity caused its uptake. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Convolutional neural networks can model, predict, and interpret enhancer accessibility or activity from sequence.
“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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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted August 12, 2021. ; https://doi.org/10.1101/2021.08.11.454937doi: bioRxiv preprint https://doi.org/10.1101/2021.08.11.454937 · DOI
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted August 12, 2021. ; https://doi.org/10.1101/2021.08.11.454937doi: bioRxiv preprint https://doi.org/10.1101/2021.08.11.454937 · DOI
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted August 12, 2021. ; https://doi.org/10.1101/2021.08.11.454937doi: bioRxiv preprint https://doi.org/10.1101/2021.08.11.454937 · DOI
Expression Data Using Tree-Based Methods. PLOS ONE 5, e12776 (2010).

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MICA: a multi-omics method to predict gene regulatory networks in early human embryos
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Dissecting the regulatory logic of specification and differentiation during vertebrate embryogenesisPreprint
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Spatiotemporal Control of Neuronal Remodeling by Cell Adhesion Molecules: Insights From Drosophila
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Lola-I is a promoter pioneer factor that establishes de novo Pol II pausing during development
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A chromatin remodelling SWI/SNF subunit, Snr1, regulates neural stem cell determination and differentiation
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Encoding of hunger by the neuronal epigenome slows aging in DrosophilaPreprint
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A high-resolution atlas of the brain predicts lineage and birth order underlying neuronal identity
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Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model
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Differential neuronal survival defines a novel axis of sexual dimorphism in the Drosophila brain
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Self-citation
Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics
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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: Janssens, Jasper
Aibar, Sara
Taskiran, Ibrahim Ihsan
Ismail, Joy N.
Gomez, Alicia Estacio
Aughey, Gabriel
Spanier, Katina I.
Dionne, Marc
Quan, Xiao Jiang
Papasokrati, Dafni
Hulselmans, Gert
Makhzami, Samira
De Waegeneer, Maxime
Christiaens, Valerie
Southall, Tony
Aerts, Stein
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2022-01-05
Volume: 601
Issue: 7894
Pages: 630-636
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-021-04262-z
OpenAlex: W4205345182
Cited by: 170
References: 143
Citations by year: 2026: 22 · 2025: 36 · 2024: 35 · 2023: 40 · 2022: 35 · 2021: 1 · 2013: 1
FWCI: 12.15
Type: journal article
Language: en
Open access: green
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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