Abstract
Long-noncoding RNAs (lncRNAs) are emerging as regulators of development and disease. lncRNAs are expressed in exquisitely precise expression patterns in vivo and many interact with chromatin to regulate gene expression. However, the limited sensitivity of RNA-purification techniques has precluded the identification of genomic targets of cell-type specific lncRNAs. RNA-DamID is a powerful new approach to understand the mechanisms by which lncRNAs act in vivo. RNA-DamID is highly sensitive and accurate, and can resolve cell-type-specific chromatin binding patterns without cell isolation. The determinants of RNA-chromatin interactions can be identified with RNA-DamID by analyzing RNA and protein cofactor mutants. Here we describe how to implement RNA-DamID and the design considerations to take into account to accurately identify lncRNA-chromatin interactions in vivo.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Warner JR, Soeiro R, Birnboim HC et al (1966) Rapidly labeled HeLa cell nuclear RNA. I. Identification by zone sedimentation of a heterogeneous fraction separate from ribosomal precursor RNA. J Mol Biol 19:349–361
Paul J, Duerksen JD (1975) Chromatin-associated RNA content of heterochromatin and euchromatin. Mol Cell Biochem 9:9–16
Mayfield JE, Bonner J (1971) Tissue differences in rat chromosomal RNA. Proc Natl Acad Sci U S A 68:2652–2655
Britten RJ, Davidson EH (1969) Gene regulation for higher cells: a theory. Science 165:349–357
Davidson EH, Klein WH, Britten RJ (1977) Sequence organization in animal DNA and a speculation on hnRNA as a coordinate regulatory transcript. Dev Biol 55:69–84
Mattick JS (1994) Introns: evolution and function. Curr Opin Genet Dev 4(6):823–831
Brown CJ, Hendrich BD, Rupert JL et al (1992) The human XIST gene: analysis of a 17 kb inactive X-specific RNA that contains conserved repeats and is highly localized within the nucleus. Cell 71:527–542
Brockdorff N, Ashworth A, Kay GF et al (1992) The product of the mouse Xist gene is a 15 kb inactive X-specific transcript containing no conserved ORF and located in the nucleus. Cell 71:515–526
Meller VH, Wu KH, Roman G et al (1997) roX1 RNA paints the X chromosome of male drosophila and is regulated by the dosage compensation system. Cell 88:445–457
Chu C, Qu K, Zhong FL et al (2011) Genomic maps of long noncoding RNA occupancy reveal principles of RNA-chromatin interactions. Mol Cell 44:667–678. https://doi.org/10.1016/j.molcel.2011.08.027
Simon MD, Wang CI, Kharchenko PV et al (2011) The genomic binding sites of a noncoding RNA. Proc Natl Acad Sci U S A 108:20497–20502. https://doi.org/10.1073/pnas.1113536108
Engreitz JM, Pandya-Jones A, McDonel P et al (2013) The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science 341:1237973. https://doi.org/10.1126/science.1237973
Li X, Zhou B, Chen L et al (2017) GRID-seq reveals the global RNA–chromatin interactome. Nat Biotechnol 35:940–950. https://doi.org/10.1038/nbt.3968
Bell JC, Jukam D, Teran NA et al (2018) Chromatin-associated RNA sequencing (ChAR-seq) maps genome-wide RNA-to-DNA contacts. Elife 7:e27024. https://doi.org/10.7554/eLife.27024
Sridhar B, Rivas-Astroza M, Nguyen TC et al (2017) Systematic mapping of RNA-chromatin interactions in vivo. Curr Biol 27:602–609. https://doi.org/10.7554/eLife.27024
Gloss BS, Dinger ME (2015) The specificity of long noncoding RNA expression. Biochim Biophys Acta 1859:16–22. https://doi.org/10.1016/j.bbagrm.2015.08.005
Bell CC, Amaral PP, Kalsbeek A et al (2016) The Evx1/Evx1as gene locus regulates anterior-posterior patterning during gastrulation. Sci Rep 6:26657. https://doi.org/10.1038/srep26657
Cabili MN, Trapnell C, Goff L et al (2011) Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 25:1915–1927. https://doi.org/10.1101/gad.17446611
Mercer TR, Dinger ME, Sunkin SM et al (2008) Specific expression of long noncoding RNAs in the mouse brain. Proc Natl Acad Sci U S A 105:716–721. https://doi.org/10.1073/pnas.0706729105
Gloss BS, Signal B, Cheetham SW et al (2017) High resolution temporal transcriptomics of mouse embryoid body development reveals complex expression dynamics of coding and noncoding loci. Sci Rep 7:6731. https://doi.org/10.1038/s41598-017-06110-5
Chu C, Spitale RC, Chang HY (2015) Technologies to probe functions and mechanisms of long noncoding RNAs. Nat Struct Mol Biol 22:29–35. https://doi.org/10.1038/nsmb.2921
Cheetham SW, Brand AH (2018) RNA-DamID reveals cell-type-specific binding of roX RNAs at chromatin-entry sites. Nat Struct Mol Biol 25:109–114. https://doi.org/10.1038/s41594-017-0006-4
Kind J, Pagie L, de Vries SS et al (2015) Genome-wide maps of nuclear lamina interactions in single human cells. Cell 163:134–147. https://doi.org/10.1016/j.cell.2015.08.040
Tosti L, Ashmore J, Tan BSN et al (2018) Mapping transcription factor occupancy using minimal numbers of cells in vitro and in vivo. Genome Res 28:592–605. https://doi.org/10.1101/gr.227124.117
Cheetham SW, Gruhn WH, van den Ameele J et al (2018) Targeted DamID reveals differential binding of mammalian pluripotency factors. Development 145:dev170209. https://doi.org/10.1242/dev.170209
Aughey GN, Cheetham SW, Southall TD (2019) DamID as a versatile tool for understanding gene regulation. Development 146:dev173666. https://doi.org/10.1242/dev.173666
van den Ameele J, Krautz R, Brand AH (2019) TaDa! Analysing cell type-specific chromatin in vivo with targeted DamID. Curr Opin Neurobiol 56:160–166. https://doi.org/10.1016/j.conb.2019.01.021
Vogel MJ, Peric-Hupkes D, van Steensel B (2007) Detection of in vivo protein–DNA interactions using DamID in mammalian cells. Nat Protoc 2:1467–1478. https://doi.org/10.1038/nprot.2007.148
Aughey GN, Estacio Gomez A, Thomson J et al (2018) CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiation in vivo. Elife 7:e32341. https://doi.org/10.7554/eLife.32341
Marshall OJ, Brand AH (2015) Damidseq-pipeline: an automated pipeline for processing DamID sequencing datasets. Bioinformatics 31:3371–3373. https://doi.org/10.1093/bioinformatics/btv386
Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137. https://doi.org/10.1186/gb-2008-9-9-r137
Acknowledgments
A.H.B is funded by a Royal Society Darwin Trust Research Professorship and Wellcome Trust Senior Investigator Award 103792. A.H.B acknowledges core funding to The Gurdon Institute from the Wellcome Trust (092096) and CRUK (C6946/A14492). S.W.C. acknowledges support from a National Health and Medical Research Council (NHMRC) Early Career Fellowship (GNT1161832) and Mater Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Cheetham, S.W., Brand, A.H. (2020). Mapping RNA–Chromatin Interactions In Vivo with RNA-DamID. In: Ørom, U. (eds) RNA-Chromatin Interactions. Methods in Molecular Biology, vol 2161. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0680-3_18
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0680-3_18
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0679-7
Online ISBN: 978-1-0716-0680-3
eBook Packages: Springer Protocols