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Tytuł pozycji:

Decoding gene regulation in the fly brain.

Tytuł:
Decoding gene regulation in the fly brain.
Autorzy:
Janssens J; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Aibar S; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Taskiran II; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Ismail JN; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Gomez AE; Department of Life Sciences, Imperial College London, London, UK.
Aughey G; Department of Life Sciences, Imperial College London, London, UK.
Spanier KI; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
De Rop FV; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
González-Blas CB; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Dionne M; Department of Life Sciences, Imperial College London, London, UK.
Grimes K; Department of Life Sciences, Imperial College London, London, UK.
Quan XJ; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Papasokrati D; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Hulselmans G; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Makhzami S; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
De Waegeneer M; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Christiaens V; VIB Center for Brain & Disease Research, Leuven, Belgium.; Department of Human Genetics, KU Leuven, Leuven, Belgium.
Southall T; Department of Life Sciences, Imperial College London, London, UK.
Aerts S; VIB Center for Brain & Disease Research, Leuven, Belgium. .; Department of Human Genetics, KU Leuven, Leuven, Belgium. .
Źródło:
Nature [Nature] 2022 Jan; Vol. 601 (7894), pp. 630-636. Date of Electronic Publication: 2022 Jan 05.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
Język:
English
Imprint Name(s):
Publication: Basingstoke : Nature Publishing Group
Original Publication: London, Macmillan Journals ltd.
MeSH Terms:
Drosophila*/genetics
Gene Expression Regulation*
Animals ; Brain/metabolism ; Gene Expression Regulation, Developmental ; Gene Regulatory Networks/genetics ; Transcription Factors/metabolism
References:
Li, H. et al. Classifying Drosophila olfactory projection neuron subtypes by single-cell RNA sequencing. Cell 171, 1206–1220 (2017). (PMID: 29149607609547910.1016/j.cell.2017.10.019)
Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998 (2018). (PMID: 29909982608693510.1016/j.cell.2018.05.057)
Konstantinides, N. et al. Phenotypic convergence: distinct transcription factors regulate common terminal features. Cell 174, 622–635 (2018). (PMID: 29909983608216810.1016/j.cell.2018.05.021)
Croset, V., Treiber, C. D. & Waddell, S. Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics. eLife 7, e34550 (2018). (PMID: 29671739592776710.7554/eLife.34550)
Özel, M. N. et al. Neuronal diversity and convergence in a visual system developmental atlas. Nature 589, 88–95 (2020). (PMID: 33149298779085710.1038/s41586-020-2879-3)
Kurmangaliyev, Y. Z., Yoo, J., Valdes-Aleman, J., Sanfilippo, P. & Zipursky, S. L. Transcriptional programs of circuit assembly in the Drosophila visual system. Neuron 108, 1045–1057 (2020). (PMID: 3312587210.1016/j.neuron.2020.10.006)
Costa, M., Manton, J. D., Ostrovsky, A. D., Prohaska, S. & Jefferis, G. S. X. E. NBLAST: rapid, sensitive comparison of neuronal structure and construction of neuron family databases. Neuron 91, 293–311 (2016). (PMID: 27373836496124510.1016/j.neuron.2016.06.012)
Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743 (2018). (PMID: 30033368606399510.1016/j.cell.2018.06.019)
Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020). (PMID: 32880371754673810.7554/eLife.57443)
Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001 (2012). (PMID: 23063364351502110.1016/j.celrep.2012.09.011)
Robie, A. A. et al. Mapping the neural substrates of behavior. Cell 170, 393–406 (2017). (PMID: 2870900410.1016/j.cell.2017.06.032)
Ravenscroft, T. A. et al. Drosophila voltage-gated sodium channels are only expressed in active neurons and are localized to distal axonal initial segment-like domains. J. Neurosci. 40, 7999–8024 (2020). (PMID: 32928889757464710.1523/JNEUROSCI.0142-20.2020)
Konstantinides, N. et al. A comprehensive series of temporal transcription factors in the fly visual system. Preprint at https://doi.org/10.1101/2021.06.13.448242 (2021).
Allen, A. M. et al. A single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord. eLife 9, e54074 (2020). (PMID: 32314735717397410.7554/eLife.54074)
Doe, C. Q. Temporal patterning in the Drosophila CNS. Annu. Rev. Cell Dev. Biol. 33, 219–240 (2017). (PMID: 2899243910.1146/annurev-cellbio-111315-125210)
Estacio-Gómez, A., Hassan, A., Walmsley, E., Le, L. W. & Southall, T. D. Dynamic neurotransmitter specific transcription factor expression profiles during Drosophila development. Biol. Open 9, bio052928 (2020). (PMID: 32493733728629410.1242/bio.052928)
Komiyama, T., Johnson, W. A., Luo, L. & Jefferis, G. S. X. E. From lineage to wiring specificity. POU domain transcription factors control precise connections of Drosophila olfactory projection neurons. Cell 112, 157–167 (2003). (PMID: 1255390510.1016/S0092-8674(03)00030-8)
Kurmangaliyev, Y. Z., Yoo, J., LoCascio, S. A. & Zipursky, S. L. Modular transcriptional programs separately define axon and dendrite connectivity. eLife 8, e50822 (2019). (PMID: 31687928685580410.7554/eLife.50822)
Schilling, T., Ali, A. H., Leonhardt, A., Borst, A. & Pujol-Martí, J. Transcriptional control of morphological properties of direction-selective T4/T5 neurons in Drosophila. Development 146, dev169763 (2019). (PMID: 30642835636113010.1242/dev.169763)
Masserdotti, G., Gascón, S. & Götz, M. Direct neuronal reprogramming: learning from and for development. Development 143, 2494–2510 (2016). (PMID: 2743603910.1242/dev.092163)
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017). (PMID: 28991892593767610.1038/nmeth.4463)
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). (PMID: 26083756468594810.1038/nature14590)
Bravo González-Blas, C. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods 16, 397–400 (2019). (PMID: 3096262310.1038/s41592-019-0367-1)
Kirilly, D. et al. A genetic pathway composed of Sox14 and Mical governs severing of dendrites during pruning. Nat. Neurosci. 12, 1497–1505 (2009). (PMID: 19881505310187610.1038/nn.2415)
Atak, Z. K. et al. Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning. Genome Res. 31, 1082–1096 (2021). (PMID: 33832990816858410.1101/gr.260851.120)
Minnoye, L. et al. Cross-species analysis of enhancer logic using deep learning. Genome Res. 30, 1815–1834 (2020). (PMID: 32732264770673110.1101/gr.260844.120)
Avet-Rochex, A., Maierbrugger, K. T. & Bateman, J. M. Glial enriched gene expression profiling identifies novel factors regulating the proliferation of specific glial subtypes in the Drosophila brain. Gene Expr. Patterns 16, 61–68 (2014). (PMID: 25217886422272510.1016/j.gep.2014.09.001)
Crittenden, J. R., Skoulakis, E. M. C., Goldstein, E. S. & Davis, R. L. Drosophila mef2 is essential for normal mushroom body and wing development. Biol. Open 7, bio035618 (2018). (PMID: 30115617617693710.1242/bio.035618)
Minocha, S., Boll, W. & Noll, M. Crucial roles of Pox neuro in the developing ellipsoid body and antennal lobes of the Drosophila brain. PLoS ONE 12, e0176002 (2017). (PMID: 28441464540478210.1371/journal.pone.0176002)
Davis, F. P. et al. A genetic, genomic, and computational resource for exploring neural circuit function. eLife 9, e50901 (2020). (PMID: 31939737703497910.7554/eLife.50901)
Naidu, V. G. et al. Temporal progression of Drosophila medulla neuroblasts generates the transcription factor combination to control T1 neuron morphogenesis. Dev. Biol. 464, 35–44 (2020). (PMID: 32442418737727910.1016/j.ydbio.2020.05.005)
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30, 4765–4774 (2017).
Shrikumar, A. et al. Technical note on transcription factor motif discovery from importance scores (TF-MoDISco) version 0.5.6.5. Preprint at https://arxiv.org/abs/1811.00416 (2020).
Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019). (PMID: 31036827648867210.1038/s41467-019-09982-5)
Southall, T. D. et al. Cell-type-specific profiling of gene expression and chromatin binding without cell isolation: assaying RNA Pol II occupancy in neural stem cells. Dev. Cell 26, 101–112 (2013). (PMID: 23792147371459010.1016/j.devcel.2013.05.020)
Mackay, T. F. C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012). (PMID: 22318601368399010.1038/nature10811)
Jacobs, J. et al. The transcription factor Grainy head primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes. Nat. Genet. 50, 1011–1020 (2018). (PMID: 29867222603130710.1038/s41588-018-0140-x)
Southall, T. D., Davidson, C. M., Miller, C., Carr, A. & Brand, A. H. Dedifferentiation of neurons precedes tumor formation in lola mutants. Dev. Cell 28, 685–696 (2014). (PMID: 24631403397865510.1016/j.devcel.2014.01.030)
Yang, J., Ramos, E. & Corces, V. G. The BEAF-32 insulator coordinates genome organization and function during the evolution of Drosophila species. Genome Res. 22, 2199–2207 (2012). (PMID: 22895281348354910.1101/gr.142125.112)
Trevino, A. E. et al. Chromatin accessibility dynamics in a model of human forebrain development. Science 367, eaay1645 (2020). (PMID: 31974223731375710.1126/science.aay1645)
Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020). (PMID: 33098772766973510.1016/j.cell.2020.09.056)
Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018). (PMID: 30078704615830010.1016/j.cell.2018.06.052)
Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020). (PMID: 33184180778529810.1126/science.aba7612)
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018). (PMID: 2922746910.1038/nbt.4038)
Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018). (PMID: 29434377586207310.1038/s41593-018-0079-3)
Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019). (PMID: 31611697689313810.1038/s41587-019-0290-0)
Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019). (PMID: 31695190723156010.1038/s41594-019-0323-x)
Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021). (PMID: 33603233881299610.1038/s41588-021-00782-6)
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). (PMID: 32015543705664410.1038/s41592-019-0686-2)
Gramates, L. S. et al. FlyBase at 25: looking to the future. Nucleic Acids Res. 45, D663–D671 (2017). (PMID: 2779947010.1093/nar/gkw1016)
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018). (PMID: 2922747010.1038/nbt.4042)
Herrmann, C., Van de Sande, B., Potier, D. & Aerts, S. i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules. Nucleic Acids Res. 40, e114 (2012). (PMID: 22718975342458310.1093/nar/gks543)
Chen, J., Li, K., Zhu, J. & Chen, W. WarpLDA: a cache efficient O(1) algorithm for latent dirichlet allocation. Proc. VLDB Endow. 9, 744–755 (2016). (PMID: 10.14778/2977797.2977801)
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021). (PMID: 33633365801221010.1038/s41588-021-00790-6)
De Waegeneer, M., Flerin, C. C., Davie, K. & Hulselmans, G. vib-singlecell-nf/vsn-pipelines: v0.26.1. Zenodo https://doi.org/10.5281/ZENODO.3703108 (2021).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018). (PMID: 29409532580205410.1186/s13059-017-1382-0)
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). (PMID: 31740819688469310.1038/s41592-019-0619-0)
Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020). (PMID: 3256188810.1038/s41596-020-0336-2)
Stanescu, D. E., Yu, R., Won, K.-J. & Stoffers, D. A. Single cell transcriptomic profiling of mouse pancreatic progenitors. Physiol. Genom. 49, 105–114 (2017). (PMID: 10.1152/physiolgenomics.00114.2016)
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019). (PMID: 31178118668739810.1016/j.cell.2019.05.031)
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). (PMID: 19505943272300210.1093/bioinformatics/btp352)
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019). (PMID: 31249361659758210.1038/s41598-019-45839-z)
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016). (PMID: 27079975498787610.1093/nar/gkw257)
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008). (PMID: 18798982259271510.1186/gb-2008-9-9-r137)
Shih, M.-F. M., Davis, F. P., Henry, G. L. & Dubnau, J. Nuclear transcriptomes of the seven neuronal cell types that constitute the Drosophila mushroom bodies. G3 9, 81–94 (2019). (PMID: 3039701710.1534/g3.118.200726)
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017). (PMID: 28846090562310610.1038/nmeth.4396)
Aronesty et al. ea-utils: ‘Command-line tools for processing biological sequencing data’. https://github.com/ExpressionAnalysis/ea-utils (2011).
Imrichová, H., Hulselmans, G., Kalender Atak, Z., Potier, D. & Aerts, S. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. 43, W57–W64 (2015). (PMID: 25925574448928210.1093/nar/gkv395)
Aughey, G. N., Delandre, C., McMullen, J. P. D., Southall, T. D. & Marshall, O. J. FlyORF-TaDa allows rapid generation of new lines for in vivo cell-type-specific profiling of protein-DNA interactions in Drosophila melanogaster. G3 11, jkaa005 (2021). (PMID: 3356123910.1093/g3journal/jkaa005)
Marshall, O. J., Southall, T. D., Cheetham, S. W. & Brand, A. H. Cell-type-specific profiling of protein-DNA interactions without cell isolation using targeted DamID with next-generation sequencing. Nat. Protoc. 11, 1586–1598 (2016). (PMID: 27490632703295510.1038/nprot.2016.084)
Marshall, O. J. & Brand, A. H. damidseq_pipeline: an automated pipeline for processing DamID sequencing datasets. Bioinformatics 31, 3371–3373 (2015). (PMID: 26112292459590510.1093/bioinformatics/btv386)
Aerts, S. et al. Robust target gene discovery through transcriptome perturbations and genome-wide enhancer predictions in Drosophila uncovers a regulatory basis for sensory specification. PLoS Biol. 8, e1000435 (2010). (PMID: 20668662291065110.1371/journal.pbio.1000435)
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Kudron, M. M. et al. The ModERN resource: genome-wide binding profiles for hundreds of Drosophila and Caenorhabditis elegans transcription factors. Genetics 208, 937–949 (2018). (PMID: 2928466010.1534/genetics.117.300657)
Davis, C. A. et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018). (PMID: 2912624910.1093/nar/gkx1081)
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). (PMID: 1459765840376910.1101/gr.1239303)
Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107 (2016). (PMID: 27084946491410410.1093/nar/gkw226)
Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. Preprint at arXiv (2019).
Bravo González-Blas, C. et al. Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics. Mol. Syst. Biol. 16, e9438 (2020). (PMID: 32431014723781810.15252/msb.20209438)
Frith, M. C., Li, M. C. & Weng, Z. Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 31, 3666–3668 (2003). (PMID: 1282438916894710.1093/nar/gkg540)
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). (PMID: 30787437643495210.1038/s41586-019-0969-x)
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017). (PMID: 28825705576454710.1038/nmeth.4402)
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014). (PMID: 24658644412233310.1038/nbt.2859)
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020). (PMID: 32939066775946110.1038/s41586-020-2649-2)
Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018). (PMID: 29539636586672010.1038/nature25981)
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python Science Conf. 92–96 (2010).
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).
Grant Information:
207467/Z/17/Z United Kingdom WT_ Wellcome Trust; MR/L018802/1 United Kingdom MRC_ Medical Research Council; MR/L018802/2 United Kingdom MRC_ Medical Research Council
Substance Nomenclature:
0 (Transcription Factors)
Entry Date(s):
Date Created: 20220106 Date Completed: 20220422 Latest Revision: 20230222
Update Code:
20240104
DOI:
10.1038/s41586-021-04262-z
PMID:
34987221
Czasopismo naukowe
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.
(© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

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