Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cis-regulatory chromatin loops arise before TADs and gene activation, and are independent of cell fate during early Drosophila development

Abstract

Acquisition of cell fate is thought to rely on the specific interaction of remote cis-regulatory modules (CRMs), for example, enhancers and target promoters. However, the precise interplay between chromatin structure and gene expression is still unclear, particularly within multicellular developing organisms. In the present study, we employ Hi-M, a single-cell spatial genomics approach, to detect CRM–promoter looping interactions within topologically associating domains (TADs) during early Drosophila development. By comparing cis-regulatory loops in alternate cell types, we show that physical proximity does not necessarily instruct transcriptional states. Moreover, multi-way analyses reveal that multiple CRMs spatially coalesce to form hubs. Loops and CRM hubs are established early during development, before the emergence of TADs. Moreover, CRM hubs are formed, in part, via the action of the pioneer transcription factor Zelda and precede transcriptional activation. Our approach provides insight into the role of CRM–promoter interactions in defining transcriptional states, as well as distinct cell types.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Hi-M reveals widespread cis-regulatory chromatin loops and hubs within TADs.
Fig. 2: CRM–CRM and CRM–P loop frequencies are similar between cell types.
Fig. 3: CRM loops and hubs precede TAD formation and gene expression.
Fig. 4: Formation of CRM loops and hubs in the doc-TAD requires the pioneer factor Zld.

Similar content being viewed by others

Data availability

The Oligopaint public database (http://genetics.med.harvard.edu) was used to select Oligopaints. Publicly available datasets used in the present study (accession nos. GSE86966, GSE25180, E-MTAB-4918, GSM763062, GSE58935, GSE16245, GSE68983, GSE68654, E-MTAB-1673, GSE62904 and GSE65441) are detailed in Supplementary Table 9. Data for matrices in Figs. 14 and in Extended Data Figs. are publicly available at https://github.com/NollmannLab/Espinola-Goetz-2021. Source data are provided with this paper.

Code availability

Code used in this manuscript is available at https://github.com/NollmannLab/Espinola-Goetz-2021.

References

  1. Bickmore, W. A. The spatial organization of the human genome. Annu. Rev. Genomics Hum. Genet. 14, 67–84 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Cavalli, G. & Misteli, T. Functional implications of genome topology. Nat. Struct. Mol. Biol. 20, 290–299 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bulger, M. & Groudine, M. Functional and mechanistic diversity of distal transcription enhancers. Cell 144, 327–339 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Alberts, B. et al. Molecular Biology of the Cell 6th edn (W. W. Norton & Company, 2017).

  5. de Laat, W. & Duboule, D. Topology of mammalian developmental enhancers and their regulatory landscapes. Nature 502, 499–506 (2013).

    Article  PubMed  CAS  Google Scholar 

  6. Fukaya, T., Lim, B. & Levine, M. Enhancer control of transcriptional bursting. Cell 166, 358–368 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bartman, C. R., Hsu, S. C., Hsiung, C. C.-S., Raj, A. & Blobel, G. A. Enhancer regulation of transcriptional bursting parameters revealed by forced chromatin looping. Mol. Cell 62, 237–247 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Schwarzer, W. & Spitz, F. The architecture of gene expression: integrating dispersed cis-regulatory modules into coherent regulatory domains. Curr. Opin. Genet. Dev. 27, 74–82 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Symmons, O. et al. Functional and topological characteristics of mammalian regulatory domains. Genome Res. 24, 390–400 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lupiáñez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene–enhancer interactions. Cell 161, 1012–1025 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Ji, X. et al. 3D chromosome regulatory landscape of human pluripotent cells. Cell Stem Cell 18, 262–275 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Dowen, J. M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ron, G., Globerson, Y., Moran, D. & Kaplan, T. Promoter–enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains. Nat. Commun. 8, 2237 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Furlong, E. E. M. & Levine, M. Developmental enhancers and chromosome topology. Science 361, 1341–1345 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 162, 687–688 (2015).

    Article  CAS  Google Scholar 

  16. Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ghavi-Helm, Y. et al. Enhancer loops appear stable during development and are associated with paused polymerase. Nature 512, 96–100 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Montavon, T. et al. A regulatory archipelago controls Hox genes transcription in digits. Cell 147, 1132–1145 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Chen, H. et al. Dynamic interplay between enhancer–promoter topology and gene activity. Nat. Genet. 50, 1296–1303 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bartman, C. R. et al. Transcriptional burst initiation and polymerase pause release are key control points of transcriptional regulation. Mol. Cell 73, 519–532.e4 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Morgan, S. L. et al. Manipulation of nuclear architecture through CRISPR-mediated chromosomal looping. Nat. Commun. 8, 15993 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Deng, W. et al. Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor. Cell 149, 1233–1244 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Larsson, A. J. M. et al. Genomic encoding of transcriptional burst kinetics. Nature 565, 251–254 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Alexander, J. M. et al. Live-cell imaging reveals enhancer-dependent Sox2 transcription in the absence of enhancer proximity. eLife 8, e41769 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Benabdallah, N. S. et al. Decreased enhancer–promoter proximity accompanying enhancer activation. Mol. Cell 76, 473–484.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. McCord, R. P., Kaplan, N. & Giorgetti, L. Chromosome conformation capture and beyond: toward an integrative view of chromosome structure and function. Mol. Cell 77, 688–708 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. van Steensel, B. & Furlong, E. E. M. The role of transcription in shaping the spatial organization of the genome. Nat. Rev. Mol. Cell Biol. 20, 327–337 (2019).

    PubMed  PubMed Central  Google Scholar 

  28. Lim, B. & Levine, M. S. Enhancer–promoter communication: hubs or loops? Curr. Opin. Genet. Dev. 67, 5–9 (2021).

  29. Allahyar, A. et al. Enhancer hubs and loop collisions identified from single-allele topologies. Nat. Genet. 50, 1151–1160 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Oudelaar, A. M. et al. Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains. Nat. Genet. 50, 1744–1751 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Oudelaar, A. M. et al. A revised model for promoter competition based on multi-way chromatin interactions at the α-globin locus. Nat. Commun. 10, 5412 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Hsieh, T.-H. S. et al. Resolving the 3D landscape of transcription-linked mammalian chromatin folding. Mol. Cell 78, 539–553.e8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tsai, A., Alves, M. R. & Crocker, J. Multi-enhancer transcriptional hubs confer phenotypic robustness. eLife 8, e45325 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Baudement, M.-O. et al. High-salt-recovered sequences are associated with the active chromosomal compartment and with large ribonucleoprotein complexes including nuclear bodies. Genome Res. 28, 1733–1746 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mir, M. et al. Dynamic multifactor hubs interact transiently with sites of active transcription in embryos. eLife 7, e40497 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Dufourt, J. et al. Temporal control of gene expression by the pioneer factor Zelda through transient interactions in hubs. Nat. Commun. 9, 5194 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, eaar3958 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Boija, A. et al. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell 175, 1842–1855.e16 (2018).

    Article  CAS  PubMed  Google Scholar 

  39. Tsai, A. et al. Nuclear microenvironments modulate transcription from low-affinity enhancers. eLife 6, e28975 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hug, C. B., Grimaldi, A. G., Kruse, K. & Vaquerizas, J. M. Chromatin architecture emerges during zygotic genome activation independent of transcription. Cell 169, 216–228.e19 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Vallot, A. & Tachibana, K. The emergence of genome architecture and zygotic genome activation. Curr. Opin. Cell Biol. 64, 50–57 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Porcher, A. & Dostatni, N. The bicoid morphogen system. Curr. Biol. 20, R249–R254 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Bonn, S. & Furlong, E. E. M. cis-Regulatory networks during development: a view of Drosophila. Curr. Opin. Genet. Dev. 18, 513–520 (2008).

    Article  CAS  PubMed  Google Scholar 

  44. Stathopoulos, A. & Levine, M. Localized repressors delineate the neurogenic ectoderm in the early Drosophila embryo. Dev. Biol. 280, 482–493 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Schulz, K. N. & Harrison, M. M. Mechanisms regulating zygotic genome activation. Nat. Rev. Genet. 20, 221–234 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ogiyama, Y., Schuettengruber, B., Papadopoulos, G. L., Chang, J.-M. & Cavalli, G. Polycomb-dependent chromatin looping contributes to gene silencing during drosophila development. Mol. Cell 71, 73–88.e5 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Cardozo Gizzi, A. M. et al. Microscopy-based chromosome conformation capture enables simultaneous visualization of genome organization and transcription in intact organisms. Mol. Cell https://doi.org/10.1016/j.molcel.2019.01.011 (2019).

  48. Cardozo Gizzi, A. M. et al. Direct and simultaneous observation of transcription and chromosome architecture in single cells with Hi-M. Nat. Protoc. 15, 840–876 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Mateo, L. J. et al. Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature 568, 49–54 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Beliveau, B. J. et al. Versatile design and synthesis platform for visualizing genomes with Oligopaint FISH probes. Proc. Natl Acad. Sci. USA 109, 21301–21306 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Reim, I. & Frasch, M. The Dorsocross T-box genes are key components of the regulatory network controlling early cardiogenesis in Drosophila. Development 132, 4911–4925 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. Hamm, D. C. et al. A conserved maternal-specific repressive domain in Zelda revealed by Cas9-mediated mutagenesis in Drosophila melanogaster. PLoS Genet. 13, e1007120 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Koenecke, N., Johnston, J., Gaertner, B., Natarajan, M. & Zeitlinger, J. Genome-wide identification of Drosophila dorso-ventral enhancers by differential histone acetylation analysis. Genome Biol. 17, 196 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Harrison, M. M., Li, X.-Y., Kaplan, T., Botchan, M. R. & Eisen, M. B. Zelda binding in the early Drosophila melanogaster embryo marks regions subsequently activated at the maternal-to-zygotic transition. PLoS Genet. 7, e1002266 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Nien, C.-Y. et al. Temporal coordination of gene networks by Zelda in the early Drosophila embryo. PLoS Genet. 7, e1002339 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hannon, C. E., Blythe, S. A. & Wieschaus, E. F. Concentration dependent chromatin states induced by the bicoid morphogen gradient. eLife 6, e28275 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Li, X.-Y., Harrison, M. M., Villalta, J. E., Kaplan, T. & Eisen, M. B. Establishment of regions of genomic activity during the Drosophila maternal to zygotic transition. eLife 3, 127–144.e23 (2014).

    Google Scholar 

  58. Rivera, J., Keränen, S. V. E., Gallo, S. M. & Halfon, M. S. REDfly: the transcriptional regulatory element database for Drosophila. Nucleic Acids Res. 47, D828–D834 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Blythe, S. A. & Wieschaus, E. F. Establishment and maintenance of heritable chromatin structure during early embryogenesis. eLife 5, e20148 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Senecal, A. et al. Transcription factors modulate c-Fos transcriptional bursts. Cell Rep. 8, 75–83 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Hao, N., Shearwin, K. E. & Dodd, I. B. Positive and negative control of enhancer-promoter interactions by other DNA loops generates specificity and tunability. Cell Rep. 26, 2419–2433.e3 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Lesne, A., Riposo, J., Roger, P., Cournac, A. & Mozziconacci, J. 3D genome reconstruction from chromosomal contacts. Nat. Methods 11, 1141–1143 (2014).

    Article  CAS  PubMed  Google Scholar 

  63. Stein, D. S. & Stevens, L. M. Maternal control of the Drosophila dorsal–ventral body axis. Wiley Interdiscip. Rev. Dev. Biol. 3, 301–330 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Deignan, L. et al. Regulation of the BMP signaling-responsive transcriptional network in the Drosophila embryo. PLoS Genet. 12, e1006164 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Van Bortle, K., Peterson, A. J., Takenaka, N., O’Connor, M. B. & Corces, V. G. CTCF-dependent co-localization of canonical Smad signaling factors at architectural protein binding sites in D. melanogaster. Cell Cycle 14, 2677–2687 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Kvon, E. Z. et al. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature 512, 91–95 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. Weiss, A. et al. A conserved activation element in BMP signaling during Drosophila development. Nat. Struct. Mol. Biol. 17, 69–76 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. Sun, Y. et al. Zelda overcomes the high intrinsic nucleosome barrier at enhancers during Drosophila zygotic genome activation. Genome Res. 25, 1703–1714 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Schulz, K. N. et al. Zelda is differentially required for chromatin accessibility, transcription factor binding, and gene expression in the early Drosophila embryo. Genome Res. 25, 1715–1726 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Liang, H.-L. et al. The zinc-finger protein Zelda is a key activator of the early zygotic genome in Drosophila. Nature 456, 400–403 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Loubiere, V., Papadopoulos, G. L., Szabo, Q., Martinez, A.-M. & Cavalli, G. Widespread activation of developmental gene expression characterized by PRC1-dependent chromatin looping. Sci. Adv. 6, eaax4001 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ing-Simmons, E. et al. Independence of chromatin conformation and gene regulation during Drosophila dorsoventral patterning. Nat. Genet. https://doi.org/10.1038/s41588-021-00799-x (2021).

  73. Gisselbrecht, S. S. et al. Transcriptional silencers in Drosophila serve a dual role as transcriptional enhancers in alternate cellular contexts. Mol. Cell 77, 324–337.e8 (2020).

    Article  CAS  PubMed  Google Scholar 

  74. Chopra, V. S., Kong, N. & Levine, M. Transcriptional repression via antilooping in the Drosophila embryo. Proc. Natl Acad. Sci. USA 109, 9460–9464 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Rubin, A. J. et al. Lineage-specific dynamic and pre-established enhancer-promoter contacts cooperate in terminal differentiation. Nat. Genet. 49, 1522–1528 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Paliou, C. et al. Preformed chromatin topology assists transcriptional robustness of during limb development. Proc. Natl Acad. Sci. USA 116, 12390–12399 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Cattoni, D. I. et al. Single-cell absolute contact probability detection reveals chromosomes are organized by multiple low-frequency yet specific interactions. Nat. Commun. 8, 1753 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Finn, E. H. et al. Extensive heterogeneity and intrinsic variation in spatial genome organization. Cell 176, 1502–1515.e10 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Gu, B. et al. Transcription-coupled changes in nuclear mobility of mammalian cis-regulatory elements. Science 359, 1050–1055 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Jackson, D. A., Iborra, F. J., Manders, E. M. & Cook, P. R. Numbers and organization of RNA polymerases, nascent transcripts, and transcription units in HeLa nuclei. Mol. Biol. Cell 9, 1523–1536 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Cho, W.-K. et al. Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361, 412–415 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 361, eaar2555 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Lesne, A., Baudement, M.-O., Rebouissou, C. & Forné, T. Exploring mammalian genome within phase-separated nuclear bodies: experimental methods and implications for gene expression. Genes 10, 1049 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  84. Lim, B., Heist, T., Levine, M. & Fukaya, T. Visualization of transvection in living Drosophila embryos. Mol. Cell 70, 287–296.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Eagen, K. P., Aiden, E. L. & Kornberg, R. D. Polycomb-mediated chromatin loops revealed by a subkilobase-resolution chromatin interaction map. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1701291114 (2017).

  86. Zhang, H. et al. Chromatin structure dynamics during the mitosis-to-G1 phase transition. Nature 576, 158–162 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Ghavi-Helm, Y. et al. Highly rearranged chromosomes reveal uncoupling between genome topology and gene expression. Nat. Genet. 51, 1272–1282 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Lagha, M. et al. Paused Pol II coordinates tissue morphogenesis in the Drosophila embryo. Cell 153, 976–987 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Saunders, A., Core, L. J., Sutcliffe, C., Lis, J. T. & Ashe, H. L. Extensive polymerase pausing during Drosophila axis patterning enables high-level and pliable transcription. Genes Dev. 27, 1146–1158 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Negre, N. et al. A cis-regulatory map of the Drosophila genome. Nature 471, 527–531 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

    Article  CAS  PubMed  Google Scholar 

  92. Kahn, T. G. et al. Interdependence of PRC1 and PRC2 for recruitment to polycomb response elements. Nucleic Acids Res. 44, 10132–10149 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. The Astropy Collaboration et al. The Astropy Project: building an open-science project and status of the v2.0 core package. Astronom. J. 156, 123–1412 (2018).

    Article  Google Scholar 

  94. Morlot, J.-B., Mozziconacci, J. & Lesne, A. Network concepts for analyzing 3D genome structure from chromosomal contact maps. EPJ Nonlinear Biomed. Physics 4, 2 (2016).

    Article  Google Scholar 

  95. Chen, K. et al. A global change in RNA polymerase II pausing during the Drosophila midblastula transition. eLife 2, e00861 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Boettiger, A. N. et al. Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature 529, 418–422 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank N. Benabdallah, G. Cavalli, T. Forne, T. Robert, J. Bonnet and members of the Lagha and Nollmann laboratories for their critical reading of the manuscript. We thank A. Makrini and D. Cattoni for help with bioinformatic analysis. This project was funded by an ERC Consolidator Grant from the European Union’s Horizon 2020 Research and Innovation Program (grant no. 724429 to M.N.). We thank the Bettencourt–Schueller Foundation for their prize Coup d’élan pour la recherche Française, the France-BioImaging infrastructure supported by the French National Research Agency (grant no. ANR-10-INBS-04, Investments for the Future), the Labex EpiGenMed (ANR-10-LABX-12-01) and the Drosophila facility (BioCampus Montpellier, CNRS, INSERM, Université de Montpellier, France). M.G. was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation; project no. 431471305). M.L.’s laboratory is supported by an ERC Starting Grant (SyncDev, grant no. 679792) and CNRS. M.B. and O.M. are supported by an FRM PhD fellowship.

Author information

Authors and Affiliations

Authors

Contributions

A.M.C.G., M.L. and M.N. conceived the study and the design. S.M.E., C.H., M.B. and I.R. acquired the data. M.G., S.M.E., M.B., O.M., I.R. and M.N. analyzed the data. M.G., M.N., J.B.F. and O.M. provided the software. S.M.E., M.G., O.M., I.R., M.B., M.N. and M.L. interpreted the data. M.L. and M.N. wrote the manuscript. J.-B.F., C.H. and I.R. provided the reagents. S.M.E., M.G. and M.N. did the visualization of the study. M.N. and M.L. supervised the study. M.N. and M.L. acquired funds.

Corresponding authors

Correspondence to Mounia Lagha or Marcelo Nollmann.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Justin Crocker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Hi-M allows high-resolution chromatin tracing in the doc-TAD.

a, Schematic representation of the genomic positions of barcodes for the low (green) and high (red) resolution doc Hi-M libraries. Triangles demarcate the two TADs registered in this genomic region90. b. Chip-seq profiles for architectural proteins in the doc-TAD54,90. Cis-regulatory modules (CRMa-d) from Fig. 1c are highlighted by blue bars. c. Typical maximum intensity projection displaying the fluorescence emission signal from a single barcode in a section of an embryo (outline in red). Emissions from individual barcodes appear as diffraction-limited spots. d. Map of pairwise distance distributions for all barcode combinations. The order of the distributions follow that in the Hi-M matrix (Fig. 1f). Blue shade represents a kernel density estimation with a bandwidth of 0.2 μm, red line represents the maximum of the distribution, and black vertical lines on the x-axis represent individual data points. e. Efficiency of barcode detection and distribution of number of barcodes detected per cell. f. To verify that uneven barcode efficiencies did not affect our results, we plotted the pairwise distance distributions for the full dataset (right) and half the data (left, here cells were randomly chosen). Map of pairwise distributions is centered at the barcode bin (4,13). g. Hi-M contact probability map (left) and inverse pairwise distance map (right) for the same experiment (doc-TAD, all cells). N = 37129, n = 29. h, Pearson correlation coefficient of the contact probability of the full doc-TAD Hi-M dataset (nc14, dorsal cells displaying doc1 expression) against subsets with a fraction of cells. One hundred random subsets were generated for each tested subset size. The central bar indicates the mean and the error bars indicate the extreme values of the distribution. i. Distribution of radii of gyration for the doc-TAD calculated from single cells. Blue shade represents a kernel density estimation with a bandwidth of 0.1 μm, black vertical lines on the x-axis represent individual data points. Dashed line represents the maximum of the distribution. The size of the doc-TAD, as estimated from its radius of gyration (0.27 ± 0.1 μm), was comparable with that of TADs of similar genomic sizes96.

Extended Data Fig. 2 doc genes are highly co-expressed and doc CRMs spatially cluster, as do CRMs in the sna locus.

a, RNA-FISH staining for doc1, doc2 and doc3 in late nc14 embryos. Scale bars: 50 𝜇m/ 5 𝜇m (inset). b, Percentage of one or two active transcription sites/nuclei for doc1 and doc2. c. Percentage of cells displaying active transcription spots from 2-color RNA-FISH imaging of doc1-doc2 and doc2-doc3. Most nuclei (>70%) displayed co-activation of doc1 + doc2 and of doc2 + doc3. For this latter, a larger percentage of nuclei expressed only doc2 (~40%), because of the low efficiency of labeling of doc3 nascent mRNA (small intronic size). d, Comparison of contact maps from nuclei displaying at least one active doc1 RNA-FISH spot (top right matrix) and from a subset (33%) of nuclei displaying the strongest doc1 RNA-FISH signals. e, 4 M profiles derived from Hi-M maps of dorsal ectoderm cells in nc14. f, 4 M virtual profile for nuclei displaying at least one active doc1 RNA-FISH spot (solid pink line) and from a subset of nuclei (33%) with the highest doc1 signals (dashed dark pink). g, Epigenetic profile of selected regions around the esg and sna genes within the sna-TAD. Accessibility (ATAC-seq), pioneer factor binding (Zelda), transcriptional activity (RNA-seq), chromatin marks for active enhancers marks (H3K4me1), and for the transcriptional activators Dorsal, Zen and Mad are shown. A subset of barcodes were annotated as cis-regulatory modules (shown in cyan): CRM169 harbors the canonical H3K4me1 active enhancer mark; CRM160, and shadow sna enhancer were described in the RedFly database. Magenta barcode harbours the esg promoter and the blue barcode contains the sna promoter and its primary enhancer. See Supplementary Table 1 for more details. h, Hi-M contact probability map of the sna locus. Yellow arrow shows interactions between CRMs, red arrow between CRMs and promoters, and green arrow between promoters. i, Multi-way interactions between promoters (panels (i-iii) and CRMs (panels iv-vi). Number of nuclei and embryos examined as in panel r. j, 3D topological representation of the sna-TAD. Bead colors are as in panel d. Barcode 44 contains the wor promoter.

Extended Data Fig. 3 doc CRM loops are similar between three presumptive tissues.

a, Comparison of 4 M profiles between DE (magenta) and NE (orange) for different anchors within the doc-TAD (Panels i-iii: promoters. Panel iv: control. Panels v-viii: CRMs). Anchors are indicated by vertical purple lines. Peaks in the profiles are annotated with the corresponding CRMs (a-d) b, Comparison of 4 M profiles between DE (magenta) and M (green) for different anchors within the doc-TAD (Panels i-iii: promoters. Panel iv: control. Panels v-viii: CRMs). Anchors are indicated by vertical purple lines. Peaks in the profiles are annotated with the corresponding CRMs (a-d). Right panel: scheme indicating the three presumptive tissues.

Extended Data Fig. 4 sna CRM loops are similar between three presumptive tissues.

a, Panel i: Hi-M contact probability map of the sna locus for M (upper-right map) versus DE (lower-left map). Inset show a magnification of the region around sna. Panel ii: Same but for the difference between M and DE Hi-M maps. Blue indicates larger contact probabilities in M whereas red indicates larger contact probabilities in DE. Panel iii: Similar to panel i, but for M (upper-right map) versus NE (lower-left map). Panel iv: Similar to panel ii, but for M versus NE. N: number of nuclei. n: number of embryos. b, Comparison of 4 M profiles between M (green) and DE/NE(orange). Anchors within the sna-TAD are indicated in each panel by vertical purple lines. A subset of peaks is annotated using the nomenclature from Fig. 1d. c, Comparisons of multi-way maps for M (upper-right map) versus DE (lower-left map) in the sna locus using the anchors indicated in each panel by pictograms and dark blue crosses. Maps are color-coded according to the scale bar on the right. Number of embryos and nuclei as in panel c. d, Similar to panel e, but for M (upper-right map) versus NE (lower-left map). Number of embryos and nuclei as in panel c.

Extended Data Fig. 5 doc CRM loops are established early in development.

a, Comparison of 4 M profiles between embryos in nc14 (blue lines) and nc11 (orange) for different anchors within the doc-TAD (Panels i-iii: promoters. Panel iv: control. Panels v-viii: CRMs). The position of the anchor is indicated by a vertical purple line. Peaks in the profiles are annotated with the corresponding CRMs (a-d). b, Similar to panel A, but comparing 4 M profiles between embryos in nc14 (blue lines) and nc12 (orange). c, Similar to panel A, but comparing 4 M profiles between embryos in nc14 (blue lines) and nc13 (orange). d, Comparison of multi-way interaction matrices of nc14 (upper-right map) and nc11 (lower-left map). Anchors (dark blue crosses) are as follows: doc3, doc3, doc1 promoters (panels i-iii), control region (panel iv), CRMa-d (panels v to viii). Representative image of DAPI-stained nuclei for nc11 is shown on top. Barcodes are shown on the left and bottom of multi-way maps. Number of nuclei (nc11): N = 1320, number of embryos (nc11); n = 4. Number of nuclei (nc14): N = 37129, number of embryos (nc14); n = 29. e, Similar to panel d, but for nc14 (upper-right map) and nc12 (lower-left map). Representative image of DAPI-stained nuclei for nc12 is shown on top. Number of nuclei (nc12): N = 2154, number of embryos (nc12); n = 4. f, Similar to panel d, but for nc14 (upper-right map) and nc13 (lower-left map). Representative image of DAPI-stained nuclei for nc13 is shown on top. Number of nuclei (nc13): N = 7597, number of embryos (nc13); n = 8.

Extended Data Fig. 6 sna CRM loops are established early in development.

a, Comparison of Hi-M contact probability maps in the sna locus for nc14 (upper-right map) and nc11 (panel i), nc12 (panel ii), nc13 (panel iii) and 14 (panel iv) (lower-left maps). Maps are color-coded according to the scale bar on the right. Inset on the bottom of each map shows a magnification of the region around esg and sna CRMs (see Extended Data Fig. 2 and Supplementary Table 1). b, Comparison of multi-way contact maps between nc14 (upper-right maps) and nc11 (lower-left maps). Maps are color-coded according to the scale bar on the right. The position of anchors are indicated by dark blue crosses. White boxes indicate contacts already present at nc11 that persist through nc14. Number of nuclei and embryos examined as indicated in panel a. c, Similar to panel b, but comparing nc14 to nc12. Green boxes indicate contacts that emerge at nc12 and persist at nc14. Number of nuclei and embryos examined as indicated in panel a. d, Similar to panel b, but comparing nc14 to nc13. Yellow boxes indicate interactions that appear at nc13 (at the TAD border). Number of nuclei and embryos examined as indicated in panel a.

Extended Data Fig. 7 Perturbation of gene expression and CRM loops by enhancer deletion and Zld depletion.

a. Scheme of the wild type doc locus (+/+) and the doc locus after CRISPR/Cas9 genome editing (ΔCRMc/ΔCRMc). doc enhancer/CRMc, FRT sequence and primers used for genotyping are in teal, yellow and blue, respectively. Genotyping PCR products on agarose gel electrophoresis are shown in the lower panel. Orange and green stars correspond to the bands of the expected sizes after amplification using primers flanking the doc enhancer/CRMc sequence. See Methods for further details. b, RNA-FISH imaging of doc1 and doc2 in the CRMc-deletion mutant. Scale bars: 50 𝜇m/ 5 𝜇m (inset). c, RNA-FISH imaging of doc1, doc2 and sna in control (RNAi white) and RNAi Zld embryos. Black arrows show the doc1 and doc2 expression patterns in the anterior part of the embryo. Grey arrows indicate the absence (doc1, doc2) or perturbation (sna) of gene expression patterns in RNAi-Zld embryos. Scale bar: 50 𝜇m. d, Tracks for pioneer factor binding (Zelda) and RNA Pol2 binding in the doc-TAD. See Supplementary Table 1 for assignment of CRMb-d. e, Transcription levels (RNAseq) of doc1, doc2 and doc3 in wild-type versus zld- embryos69. f, Hi-C matrix for a genomic region containing doc-TAD in wild-type and Zld-depleted embryos. Data from Hug et al. (2017)40. g, Distribution of radius of gyration for the doc-TAD in Zld-depleted embryos (see Extended Fig. 1i for wild-type). h-j, Log2(observed/expected) average contact frequencies between Zelda bound regions at long-range distances (> 250 kb) ranked by increasing Zelda enrichment in nc14 (panel g), nc14 zld-RNAi (panel h) and at short-range ( 250 kb) in nc14 triptolide-treated embryos (panel i). k, Violin plot of intragroup Log2(observed/expected) distribution between 62 selected pre-MBT enhancers and neighbouring sequences (± 5 kb) in nc14 (upper panel) and nc14 zld-RNAi (lower panel). The central white marker indicates the median and the vertical black lines indicate the extreme values of the distribution. The coordinates of enhancers and closest pre-MBT genes are listed in Supplementary Table 8.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–11

Reporting Summary

Supplementary Table

Supplementary Tables 1–9

Source data

Source Data Extended Data Fig. 7

Unprocessed agarose gel.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Espinola, S.M., Götz, M., Bellec, M. et al. Cis-regulatory chromatin loops arise before TADs and gene activation, and are independent of cell fate during early Drosophila development. Nat Genet 53, 477–486 (2021). https://doi.org/10.1038/s41588-021-00816-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-021-00816-z

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing