Decoding gene regulation in the fly brain

Nature
  • 1.

    Li, H. et al. Classifying Drosophila olfactory projection neuron subtypes by single-cell RNA sequencing. Cell 171, 1206–1220 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 2.

    Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 3.

    Konstantinides, N. et al. Phenotypic convergence: distinct transcription factors regulate common terminal features. Cell 174, 622–635 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 4.

    Croset, V., Treiber, C. D. & Waddell, S. Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics. eLife 7, e34550 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 5.

    Özel, M. N. et al. Neuronal diversity and convergence in a visual system developmental atlas. Nature 589, 88–95 (2020).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 6.

    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).

    CAS 
    PubMed 

    Google Scholar
     

  • 7.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 8.

    Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 9.

    Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 10.

    Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 11.

    Robie, A. A. et al. Mapping the neural substrates of behavior. Cell 170, 393–406 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • 12.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 13.

    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).

  • 14.

    Allen, A. M. et al. A single-cell transcriptomic atlas of the adult Drosophila ventral nerve cord. eLife 9, e54074 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 15.

    Doe, C. Q. Temporal patterning in the Drosophila CNS. Annu. Rev. Cell Dev. Biol. 33, 219–240 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • 16.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 17.

    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).

    CAS 
    PubMed 

    Google Scholar
     

  • 18.

    Kurmangaliyev, Y. Z., Yoo, J., LoCascio, S. A. & Zipursky, S. L. Modular transcriptional programs separately define axon and dendrite connectivity. eLife 8, e50822 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 19.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 20.

    Masserdotti, G., Gascón, S. & Götz, M. Direct neuronal reprogramming: learning from and for development. Development 143, 2494–2510 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • 21.

    Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 22.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 23.

    Bravo González-Blas, C. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods 16, 397–400 (2019).

    PubMed 

    Google Scholar
     

  • 24.

    Kirilly, D. et al. A genetic pathway composed of Sox14 and Mical governs severing of dendrites during pruning. Nat. Neurosci. 12, 1497–1505 (2009).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 25.

    Atak, Z. K. et al. Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning. Genome Res. 31, 1082–1096 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 26.

    Minnoye, L. et al. Cross-species analysis of enhancer logic using deep learning. Genome Res. 30, 1815–1834 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 27.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 28.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 29.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 30.

    Davis, F. P. et al. A genetic, genomic, and computational resource for exploring neural circuit function. eLife 9, e50901 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 31.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 32.

    Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30, 4765–4774 (2017).


    Google Scholar
     

  • 33.

    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).

  • 34.

    Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 35.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 36.

    Mackay, T. F. C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 37.

    Jacobs, J. et al. The transcription factor Grainy head primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes. Nat. Genet. 50, 1011–1020 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 38.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 39.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Trevino, A. E. et al. Chromatin accessibility dynamics in a model of human forebrain development. Science 367, eaay1645 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 41.

    Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 42.

    Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 43.

    Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 44.

    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).

    CAS 
    PubMed 

    Google Scholar
     

  • 45.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 46.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 47.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 48.

    Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021).

    CAS 
    PubMed 

    Google Scholar
     

  • 49.

    Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 50.

    Gramates, L. S. et al. FlyBase at 25: looking to the future. Nucleic Acids Res. 45, D663–D671 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • 51.

    Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 52.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 53.

    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).


    Google Scholar
     

  • 54.

    Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 55.

    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).

  • 56.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 57.

    Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 58.

    Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).

    PubMed 

    Google Scholar
     

  • 59.

    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).

    CAS 

    Google Scholar
     

  • 60.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 61.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 62.

    Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 63.

    Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 64.

    Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 65.

    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).

    CAS 
    PubMed 

    Google Scholar
     

  • 66.

    Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 67.

    Aronesty et al. ea-utils: ‘Command-line tools for processing biological sequencing data’. https://github.com/ExpressionAnalysis/ea-utils (2011).

  • 68.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 69.

    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).

    PubMed 

    Google Scholar
     

  • 70.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 71.

    Marshall, O. J. & Brand, A. H. damidseq_pipeline: an automated pipeline for processing DamID sequencing datasets. Bioinformatics 31, 3371–3373 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 72.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 73.

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet 
    MATH 

    Google Scholar
     

  • 74.

    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).

    CAS 
    PubMed 

    Google Scholar
     

  • 75.

    Davis, C. A. et al. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

    CAS 
    PubMed 

    Google Scholar
     

  • 76.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 77.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 78.

    Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. Preprint at arXiv (2019).

  • 79.

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 80.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 81.

    Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 82.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 83.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 84.

    Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 85.

    Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 86.

    Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python Science Conf. 92–96 (2010).

  • 87.

    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).

  • Products You May Like

    Articles You May Like

    REAL TIME – Corona Virus Statistical Data (Worldwide)
    Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa
    NASA Shares Photo of Two Seemingly Colliding Galaxies Captured by Hubble
    Rivian stock plunges after news of Amazon-Stellantis deal
    Close-up with a parasite that can blind
    Red Planet or Red Velvet Cake? ESA Releases Delightful Image of Mars