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GRID-seq Analysis and Sequencing Service

Global RNA Interaction with DNA sequencing (GRID-seq) is a high-throughput technology designed to map exactly where RNA molecules land on DNA across the entire genome. Unlike targeted methods that require specific probes, our GRID-seq Analysis and Sequencing Service comprehensively captures the global network of RNA-chromatin interactions in a single experiment.

  • Global View: Map all RNA-DNA interactions at once.
  • High Specificity: Uses a special bivalent linker to lock RNA and DNA together.
  • Analysis-Ready: Receive clean interaction matrices, normalized heatmaps, and visualization files.
  • Verified Quality: Transparent QC reports with valid pair counts and signal-to-noise ratios.
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3D visualization of RNA-chromatin interactions mapped by GRID-seq

Overview: Uncovering the Global RNA-Chromatin Interactome

To understand how genes are controlled, we need to look at the three-dimensional (3D) structure of the nucleus. Inside the cell nucleus, DNA is not just a straight line; it is folded into complex shapes. RNA molecules, which are made from DNA, often stick around and bind back to the DNA. These interactions are critical. They act like "address labels," guiding regulatory proteins to turn genes on or off.

For a long time, studying these interactions was difficult. Researchers had to guess which RNA was important and design specific probes to find it (methods like ChIRP or RAP). This was like trying to find a specific book in a library by checking one shelf at a time.

GRID-seq changes the game. It is an unbiased, "all-to-all" method. It works like taking a snapshot of the entire library at once. It captures every instance where an RNA molecule is physically touching a DNA segment. By doing this, we can see the full picture of the RNA-Chromatin Interactome. This allows scientists to discover new regulatory mechanisms without needing to know the target ahead of time.

How It Works (Simplified)

The core magic of GRID-seq lies in its chemistry. We use a bivalent linker. Think of this linker as a bridge with two different hands:

  1. One hand grabs the RNA.
  2. The other hand grabs the DNA.
  3. Because the linker is very short, it only connects RNA and DNA that are physically close to each other inside the nucleus.
  4. We then sequence these connections to create a map.

Why Choose GRID-seq?

  • True Global Detection: "Many-to-many" capture of thousands of RNAs and their DNA targets in a single run, unlike "one-to-many" methods like ChIRP.
  • Bivalent Linker Advantage: In situ ligation ensures only true nuclear interactions are captured, significantly reducing background noise compared to proximity ligation.
  • Broad Compatibility: Optimized for mammalian (Human, Mouse) and plant (Arabidopsis, Rice) genomes with standard input requirements.

Applications: From Super-Enhancers to V2G Assignment

Enhancer-Promoter Loop Discovery

Genes have "switches" called promoters. But the instructions often come from distant "super-enhancers." GRID-seq maps where eRNAs travel. If an eRNA lands on a gene promoter, it is strong evidence of regulation. This is crucial for Variant-to-Gene (V2G) assignment, explaining how non-coding mutations disrupt connections.

3D Genome Architecture

GRID-seq reveals how Chromatin-associated RNAs (caRNAs) act as the "architectural glue" of the nucleus. It detects RNA enrichment at TAD boundaries and within nuclear compartments, helping researchers model the 3D genome with unprecedented accuracy by integrating RNA data with Hi-C maps.

lncRNA Mechanism Studies

We classify lncRNA interactions as Cis-acting (staying near their transcription site) or Trans-acting (traveling to distant chromosomes). Our analysis pipeline automatically categorizes these, helping you determine whether to focus on local chromatin remodeling or distal gene regulation.

Validated Workflow: Sample to Analysis-Ready Report

1. Nuclei Preparation and QC

We carefully extract nuclei from your cells, ensuring the nuclear membrane stays intact so DNA and RNA don't mix randomly. We inspect nuclei under a microscope to guarantee they are clean and intact.

2. In Situ Ligation (The Critical Step)

We add the special bivalent linker to the nuclei. This linker has a specific tag that grabs RNA on one end and DNA on the other. This step happens in situ to preserve the native 3D structure and lock interactions in place.

3. Library Construction

We purify DNA-RNA-Linker chimera molecules and digest them (e.g., MmeI) to produce uniform fragments. We perform rigorous size selection (200–500 bp) to remove "empty" linkers, ensuring reads represent true RNA-DNA contacts. QC is performed via Qubit and Agilent 2100 Bioanalyzer.

4. Deep Sequencing

We sequence the library using the Illumina platform (PE150 mode). This reads 150 letters from both ends, capturing the sequence of both the RNA and DNA parts to detect rare regulatory interactions.

5. Bioinformatics Analysis (GridTools)

Raw data is processed using the GridTools pipeline: trimming linkers, mapping RNA/DNA separately, removing PCR duplicates, and calling significant interaction peaks. We generate normalized matrices and multi-resolution heatmaps.

GRID-seq library preparation and sequencing workflow steps

Quality Control & Deliverables

Sample Requirements & Specs

Specification Requirement
Sample Type Cell lines (preferred) or Tissues
Fixation 1% Formaldehyde crosslinking (Required)
Cell Count > 1 × 10^7 cells recommended (min 1M)
Species Human, Mouse, Rat, Fruit fly, Arabidopsis, etc.
Key QC Metrics Valid Pairs, Cis/Trans Ratio, Linker Efficiency, Library Complexity

Standard Report
A PDF summary with methods, QC charts (Total Reads, Mapping Rate, Unique Valid Pairs), and key findings.

Processed Matrices
.hic or .cool files compatible with standard viewers like Juicebox for exploring the 3D map.

Visualization Tracks
.bw (BigWig) files to load into the UCSC Genome Browser to see peaks of RNA landing on DNA.

Interaction List
Excel tables listing the specific genomic coordinates of statistically significant RNA-DNA contacts.

Case Study: Deciphering Super-Enhancer Regulation

A research team was studying a specific cancer cell line. They identified "Super-Enhancers"—large regions of DNA that drive powerful cancer genes. However, they did not know exactly which genes these enhancers were activating. The linear distance on the chromosome was too far to guess. They needed to see the physical connections.

The team utilized our GRID-seq Analysis and Sequencing Service.

  • Input: 5 million fixed cancer cells.
  • Library Prep: Standard in situ GRID-seq protocol with bivalent linker ligation.
  • Sequencing: Deep sequencing (PE150) to capture rare interactions.

The analysis revealed a clear network of interactions.

  • Observation: The GRID-seq map showed that RNA produced at the Super-Enhancers (eRNA) was physically looping over to contact specific gene promoters.
  • Discovery: They found that one Super-Enhancer was skipping over its nearest neighbor gene and activating a different oncogene far away.
  • Visualization: The provided Heatmap clearly displayed a bright "dot" at the intersection of the enhancer and the promoter, representing the strong RNA-mediated contact.

GRID-seq interaction matrix showing RNA-DNA contacts

By using GRID-seq, the researchers moved from guessing to knowing. They successfully assigned the Super-Enhancer to its correct target gene (V2G assignment), providing the evidence needed for their publication and potential therapeutic targeting.

Frequently Asked Questions

References

  1. Li, X., Zhou, B., Chen, L. et al. GRID-seq reveals the global RNA-chromatin interactome. Nature Biotechnology 35, 940–950 (2017).
  2. Li, X. & Fu, X. GRID-seq for comprehensive analysis of global RNA–chromatin interactions. Nature Protocols 14, 2037–2061 (2019).

Compliance & Trust
Research Use Only (RUO): This service is designed for academic and preclinical research purposes only. The results are not intended for use in clinical diagnosis or therapeutic decision-making.
Data Privacy: We adhere to strict confidentiality agreements. Your genomic data is processed on secure servers and is never shared with third parties.

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High-confidence 3D genomics services for chromatin interaction analysis and regulatory insight.

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