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RADICL-seq RNA-Chromatin Interaction Mapping Service

Uncovering the Regulatory Landscape of Chromatin-Associated RNAs with Superior Specificity.

  • High Specificity: RNase H digestion minimizes false positives.
  • Genome-Wide: Unbiased capture of concurrent chromatin-associated RNAs.
  • Verifiable QC: Transparent reporting of valid pairs and unique mapping rates.
  • Analysis-Ready: Interaction heatmaps, Circos plots, and peak annotation.
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RADICL-seq workflow concept showing RNA-chromatin interaction mapping

Overview: Decoding the Regulatory Role of Chromatin-Associated RNAs

In the field of 3D genomics, understanding how RNA molecules—particularly long non-coding RNAs (lncRNAs)—interact with DNA is crucial for deciphering gene regulation. Whether acting in cis (near their transcription site) or in trans (at distant genomic loci), these RNAs are key drivers of nuclear organization and transcriptional control.

Our RADICL-seq RNA-Chromatin Interaction Mapping Service offers a robust solution for researchers investigating these mechanisms. By capturing the direct physical contacts between RNA and chromatin, this method allows you to identify genomic binding sites of specific lncRNAs, map enhancer-promoter interactions mediated by RNA, and explore the role of RNA in maintaining nuclear compartments.

Unlike methods that rely solely on proximity ligation, RADICL-seq employs a bridge adapter to link the RNA 3' end to the DNA 5' end. This unique approach, combined with enzymatic digestion, significantly reduces background noise, providing a clearer picture of the interactome.

Why RADICL-seq?

  • Specificity: RNase H digestion removes background R-loops.
  • Scope: Genome-wide detection without prior target knowledge.
  • Resolution: High sensitivity for both cis and trans interactions.
  • Evidence: Defensible data for V2G and enhancer assignment.

Why Choose RADICL-seq? (Method Selection Guide)

Choosing the right 3D genomics method is critical to avoiding costly rework. RADICL-seq is particularly suited for researchers who need a balance of high sensitivity and broad genome coverage.

Feature RADICL-seq GRID-seq ChIRP/RAP
Scope Genome-Wide (All RNAs) Genome-Wide (All RNAs) Targeted (Specific RNA)
Ligation Chemistry Bridge Adapter (High Specificity) Bivalent Linker N/A (Hybridization based)
Noise Reduction RNase H Digestion Standard Wash Stringent Wash
Trans-Interaction Sensitivity High Moderate High

Method Selection Note: If you are specifically looking for linker-mediated analysis with a different chemical approach, please explore our GRID-seq Service. For investigations focused specifically on R-loops (RNA-DNA hybrids), consider our R-loop Sequencing Service.

Standardized Workflow: From Sample to Discovery

We operate under a strict "Research Use Only" (RUO) framework, ensuring that every step of the process—from sample handling to data delivery—is optimized for scientific reproducibility.

1. Sample Preparation & Crosslinking

Cells are fixed with formaldehyde to preserve RNA-chromatin interactions in situ. Proper fixation is critical for retaining nuclear architecture.

2. Chromatin Digestion

Genomic DNA is digested using a restriction enzyme (e.g., DpnII) to create accessible DNA ends for subsequent ligation.

3. Bridge Adapter Ligation

A biotinylated bridge adapter is ligated between the RNA and the digested DNA. This is the core technology that distinguishes RADICL-seq.

4. RNase H Digestion & Purification

RNase H is used to reduce background by digesting RNA that is hybridized to DNA (R-loops) or non-specifically bound, ensuring only ligated chimeras remain.

5. Library Construction

The RNA-DNA chimeras are converted into a sequencing library using standard Illumina-compatible protocols.

6. Sequencing

Deep sequencing (PE150) is performed to ensure sufficient coverage of complex interactions across the genome.

7. Bioinformatics Analysis

Raw data is processed to filter artifacts, map reads, and generate interaction matrices and visualizations.

RADICL-seq workflow showing RNase H and bridge adapter steps

Sample Requirements & Submission Guidelines

To ensure the highest probability of success and high-quality data output, please adhere to the following sample preparation guidelines.

Sample Type Input Amount (Recommended) Input Amount (Minimum) Storage/Condition
Cell Lines (Vertebrate) 2 - 5 x 106 cells 1 x 106 cells Fresh or Cryopreserved (Flash frozen)
Primary Cells > 2 x 106 cells Consult Team Freshly isolated preferred
Animal Tissue 20 - 50 mg 10 mg Snap-frozen in liquid nitrogen

⚠️ Risk Alert: For samples with low cell counts or fragile nuclei, please contact our technical team prior to shipment. Cryopreserved samples must be transported on dry ice to prevent degradation of nuclear architecture.

Key Applications: Mechanism to Validation

lncRNA Mechanism Studies

Determine whether a candidate lncRNA functions by recruiting chromatin modifiers to specific loci. Map high-resolution contact domains for specific transcripts.

V2G (Variant-to-Gene) Mapping

Link non-coding GWAS variants to target genes by analyzing the RNA-mediated chromatin folding landscape and potential regulatory loops.

Nuclear Compartmentalization

Investigate how RNA contributes to the formation of A/B compartments and phase separation in the nucleus, providing structural context to gene regulation.

De Novo Discovery

Identify completely new RNA-chromatin interactions in non-model species or specialized cell types without relying on known targets.

Demo Results: What You Get

We bridge the gap between "sequencing data" and "usable biology". Our deliverables are structured to be used immediately by both wet-lab scientists and bioinformaticians.

  • Raw Data: FastQ files (Cleaned and trimmed).
  • Alignment Files: BAM files containing mapped RNA and DNA reads.
  • Interaction Matrices: .cool or .hic files compatible with standard viewers (e.g., Juicebox, HiGlass).
  • Visualization: Circos Plots and Meta-Gene Profiles.
  • Annotation Tables: List of significant RNA-chromatin interaction peaks linked to nearest genes.
Download Sample Report (PDF)

RADICL-seq heatmap of RNA-chromatin interactions

Featured Case Study: Uncovering lncRNA-Chromatin Targets

Scenario: Decoding the lncRNA-Chromatin Interactome in mESCs

Researchers sought to define the specific genomic binding sites of major lncRNAs, such as NEAT1 and Malat1, in mouse embryonic stem cells (mESCs). Understanding these interactions is vital for explaining how these RNAs regulate gene expression and nuclear architecture.

RADICL-seq was employed due to its ability to distinguish chromatin-associated RNAs from soluble RNAs. The protocol utilized RNase H digestion to remove RNA-DNA hybrids (R-loops) that could confound the signal, ensuring that only direct RNA-chromatin contacts were sequenced.

Specific Localization: The data revealed that NEAT1 preferentially occupies genomic regions associated with nuclear speckles, while Malat1 binds to active gene bodies.

Trans-Interactions: The study successfully mapped trans-interactions, showing how these lncRNAs contact multiple chromosomes simultaneously.

Clear Signal: The use of the bridge adapter strategy resulted in high-resolution maps with reduced background compared to traditional proximity ligation methods.

Circos plot showing global RNA-DNA contacts

RADICL-seq provided defensible evidence of the distinct roles these lncRNAs play in organizing the genome, validating the method as a powerful tool for chromatin biology. This case illustrates the power of RADICL-seq for distinguishing specific RNA-chromatin interactions from background noise.

(Reference: Bonetti, A., et al. (2020). RADICL-seq identifies general and cell type–specific principles of genome-wide RNA-chromatin interactions. Nature Communications.)

Frequently Asked Questions

References

  1. Bonetti, A., Agostini, F., Suzuki, A. M., Hashimoto, K., Pascarella, G., Gimenez, J., ... & Carninci, P. (2020). RADICL-seq identifies general and cell type–specific principles of genome-wide RNA-chromatin interactions. Nature Communications, 11(1), 1018.
  2. Li, X., Zhou, B., Chen, L., Gou, L. T., Li, H., & Fu, X. D. (2017). GRID-seq reveals the global RNA–chromatin interactome. Nature Biotechnology, 35(10), 940-950.
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High-confidence 3D genomics services for chromatin interaction analysis and regulatory insight.

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