High-resolution, strand-specific R-loop mapping via S9.6-based DNA–RNA immunoprecipitation and high-throughput sequencing

Lionel A. Sanz, Frederic Chedin

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4 Scopus citations

Abstract

R-loops are prevalent three-stranded non-B DNA structures composed of an RNA–DNA hybrid and a single strand of DNA. R-loops are implicated in various basic nuclear processes, such as class-switch recombination, transcription termination and chromatin patterning. Perturbations in R-loop metabolism have been linked to genomic instability and have been implicated in human disorders, including cancer. As a consequence, the accurate mapping of these structures has been of increasing interest in recent years. Here, we describe two related immunoprecipitation-based methods for mapping R-loop structures: basic DRIP-seq (DNA–RNA immunoprecipitation followed by high-throughput DNA sequencing), an easy, robust, but resolution-limited technique; and DRIPc-seq (DNA–RNA immunoprecipitation followed by cDNA conversion coupled to high-throughput sequencing), a high-resolution and strand-specific iteration of the method that permits accurate R-loop mapping genome wide. Briefly, after gentle DNA extraction and restriction digestion with a cocktail of enzymes, R-loop structures are immunoprecipitated with the anti-RNA–DNA hybrid S9.6 antibody. Compared with DRIP-seq, in which the immunoprecipitated DNA is directly sequenced, DRIPc-seq permits the recovery of the RNA moiety of R-loops, and these RNA strands are subjected to strand-specific RNA sequencing (RNA-seq) analysis. DRIPc-seq can be performed in 5 d and can be applied to any cell type, provided sufficient starting material can be collected. Accurately mapping R-loop distribution in various cell lines and under varied conditions is essential to understanding the formation, roles and dynamic resolution of these important structures.

Original languageEnglish (US)
JournalNature Protocols
DOIs
StatePublished - Jan 1 2019

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ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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