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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
Published on: May 24, 2017
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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen
Jong Ghut Ashley Aw1, Yang Shen2, Niranjan Nagarajan2
1Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR.
Journal of Visualized Experiments : Jove
|June 2, 2017
Summary
This study introduces Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH), a new method to map all RNA-RNA interactions genome-wide in vivo. SPLASH provides an unbiased view of RNA interactions within cells.
Area of Science:
- Molecular Biology
- Genomics
- Biochemistry
Background:
- Understanding RNA-RNA interactions is crucial for deciphering RNA-based gene regulation.
- Existing methods often focus on specific RNA or protein interactions, limiting a comprehensive view.
- The full scope of RNA interactions within cells remains largely unexplored.
Purpose of the Study:
- To present a novel, unbiased, genome-wide method for capturing RNA-RNA interactions in vivo.
- To enable the study of both intramolecular and intermolecular RNA base-pairing globally.
- To facilitate research into the dynamics of RNA organization across various cellular contexts and organisms.
Main Methods:
- Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH) utilizes in vivo crosslinking to capture interacting RNAs.
- Proximity ligation is employed to join crosslinked RNA fragments.
- High-throughput sequencing is used to identify and map these RNA interactions genome-wide.
Main Results:
- SPLASH allows for the unbiased, genome-wide identification of intramolecular and intermolecular RNA interactions in vivo.
- The method has been successfully applied to bacteria, yeast, and human cells.
- It captures a global view of RNA base-pairing partners.
Conclusions:
- SPLASH is a powerful tool for comprehensively studying RNA-RNA interactions across diverse biological systems.
- This method advances our understanding of RNA-based gene regulation and cellular organization.
- The protocol is efficient, with experimental and computational workflows completed in approximately 12 days.

