All steps are conducted under strict quality control to ensure optimal specificity and recovery of true RNA–chromatin contacts.
Identify the precise genomic targets of coding and non-coding RNAs, including cell type–specific RNA-binding landscapes.
Experimental Design
Researchers applied RADICL-seq to two mouse cell types:
- mESCs (embryonic stem cells)
- mOPCs (oligodendrocyte progenitor cells)
They used 1% formaldehyde to crosslink RNA–protein–DNA complexes in intact nuclei, followed by DNase I digestion, RNase H treatment, ligation via a biotinylated adapter, EcoP15I digestion, and NGS mapping to capture genome-wide RNA–chromatin contacts.
Key Insights
Transcript Class Distribution
- ~89% of significant RNA–chromatin interactions derive from protein-coding transcripts; ~10% from lncRNAs.
- Demonstrates RADICL-seq's quantitative detection of diverse RNA classes.
Distinct Cell-Type Interaction Patterns
- Panel b (in the figure above): volcano-like histogram shows mESC- and mOPC-enriched RNA–DNA interactions.
Global Interaction Architecture
- Circos plots e and f illustrate distinct RNA–chromatin landscapes in mESCs vs. mOPCs.In mESCs, RNA contacts are more focused and cis-dominant.
- In mOPCs, there's notable expansion into trans interactions (long-range, across chromosomes).
Implications for 3D Genome Regulation
- Differential RNA occupancy implies dynamic regulation of chromatin structure during cell differentiation.
- Supports transcription's role in shaping nuclear architecture.
RADICL-seq method for the identification of RNA–chromatin interactions.
Conclusion
This study compellingly demonstrates RADICL-seq's ability to:
- Quantitatively map diverse RNA classes interacting with chromatin
- Reveal distinct RNA–chromatin architectures between cell types
- Enable deeper understanding of how RNA modulates 3D genome organization during development
This case robustly supports using RADICL-seq as a powerful platform for uncovering regulatory RNA dynamics in functional genomics and drug development studies
