Journal Article

Decoding gene regulation in the fly brain

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

2022

Overview

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. A chromatin accessibility atlas of 240,919 cells in the adult and developing Drosophila brain reveals 95,000 enhancers, which are integrated in cell-type specific enhancer gene regulatory networks and decoded into combinations of functional transcription factor binding sites using deep learning.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 13/62 / 14/19 / 38/15 / 38/23 / 38/35 / 38/39 / 42/89 / 45/43 / 631/114/2114 / 631/136/368/2430 / 631/208/200 / 631/378/87 / 64/24 / Accessibility / Animals / Binding sites / Brain / Brain - metabolism / Chromatin / Deep learning / Drosophila - genetics / Electron microscopy / Enhancers / Gene expression / Gene Expression Regulation / Gene Expression Regulation, Developmental / Gene mapping / Gene regulation / Gene Regulatory Networks - genetics / Genes / Genetic engineering / Glial cells / Humanities and Social Sciences / Insects / Microscopy / multidisciplinary / Nervous system / Neurogenesis / Neuronal-glial interactions / Regulatory sequences / Science / Science (multidisciplinary) / Three dimensional analysis / Transcription factors / Transcription Factors - metabolism / Transcriptomes