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Capture-C Service: High-Resolution Promoter-Enhancer Mapping

Pinpoint the physical connections between promoters and distal regulatory elements. Our Capture-C Service (based on Next-Generation Capture-C) combines 3C library preparation with oligonucleotide capture enrichment to map chromatin interactions at restriction-fragment resolution. Ideal for verifying GWAS variant targets and characterizing complex enhancer hubs without the prohibitive cost of whole-genome sequencing (RUO).

  • Ultra-High Resolution: Map interactions at the level of single restriction fragments (<500bp).
  • Massively Parallel: Target hundreds to thousands of viewpoints (promoters/SNPs) in one experiment.
  • Cost-Effective: Focus sequencing power only on your regions of interest.
Design Your Capture Panel

Capture-C workflow showing oligonucleotide capture enrichment

Overview: Unmatched Resolution for Your Regions of Interest

In the era of post-GWAS functional genomics, the bottleneck has shifted from discovering risk variants to identifying their target genes. For decision-makers in drug discovery and genetics, the challenge is clear: How do you validate physical connections between non-coding variants and gene promoters without the prohibitive cost of deep genome-wide sequencing?

Standard tools often force a compromise. Hi-C offers a global view but requires massive sequencing depth to resolve specific loops, often failing to pinpoint the exact enhancer. 4C-Seq offers high resolution but is limited to single viewpoints, making it inefficient for screening hundreds of candidates.

Our Capture-C Service (based on Next-Generation Capture-C technology) eliminates this compromise. By combining high-resolution 3C library preparation with a highly efficient Oligonucleotide Capture enrichment, we deliver Restriction Fragment Resolution (<500 bp) for hundreds to thousands of loci simultaneously.

This "Massively Parallel" approach allows you to focus your sequencing budget exclusively on your Regions of Interest (ROI). Whether you are validating a panel of GWAS hits or characterizing super-enhancer hubs, Capture-C provides the sharpest, most cost-effective 3D maps available, turning ambiguous genetic signals into actionable biological targets.

(Note: This service is for Research Use Only. It is not intended for use in diagnostic procedures or clinical decision-making.)

Key Advantages

  • Restriction Fragment Resolution: See interactions at ~400-500bp precision, far exceeding standard Hi-C (5kb).
  • Scalable Throughput: Analyze 50 to 50,000 viewpoints in a single assay.
  • Double-Capture Sensitivity: Proprietary double-enrichment protocol reduces off-target noise to <5%.
  • V2G Precision: Directly link non-coding GWAS variants to their effector genes.

Applications: From GWAS Hits to Target Genes

Capture-C is widely regarded as the gold standard for Variant-to-Gene (V2G) mapping. It provides the high-sensitivity data needed to move from association to mechanism.

Variant-to-Gene (V2G) Assignment

Over 90% of disease-associated variants reside in non-coding regions. We design probes targeting the promoters of all genes within a GWAS Risk Locus. Capture-C reveals physical loops connecting the variant to distal promoters. This provides direct, physical evidence to identify the causal gene—which is frequently not the nearest gene—reducing the risk of pursuing false targets.

Characterizing Super-Enhancers & Hubs

Super-enhancers function as complex "hubs" where multiple regulatory elements collaborate to drive high-level expression of identity genes (e.g., MYC or Globin). By tiling probes across an entire super-enhancer region, Capture-C dissects the internal topology of the hub, revealing exactly which constituent enhancers are physically engaged with the promoter in specific cell states.

Comparative Topology (Disease vs. Normal)

Detecting subtle changes in chromatin looping requires high dynamic range. Because Capture-C enriches the signal by 100–1000 fold compared to standard Hi-C, it provides the statistical power to quantify differential looping interactions. You can confidently detect fold-changes in interaction frequency between healthy and diseased samples or before and after drug treatment.

Comparison: Capture-C vs. Hi-C vs. 4C-Seq

Feature Capture-C Standard Hi-C 4C-Seq
Interaction Scope Many-to-All (Hundreds/Thousands of Viewpoints) All-to-All (Whole Genome) One-to-All (Single Viewpoint)
Resolution High (Restriction Fragment, <500bp) Moderate (Bin size, usually 5-10kb) High (Restriction Fragment)
Sequencing Efficiency High (Enriched for ROI) Low (Reads spread across genome) High (PCR enriched)
Throughput Massively Parallel (Panel based) Genome-wide Low (1-2 viewpoints per prep)
Cost per Locus Low (for multi-gene panels) High (requires deep sequencing) Low (for single locus)
Best For V2G Mapping, Promoter Interactomes TADs, Compartments, Scaffolding Validating single loops

Select Hi-C Sequencing if you need a hypothesis-free, genome-wide discovery tool. Select Capture-C if you have a defined list of targets and need the highest possible resolution.

Our Capture-C Workflow: Double-Capture Strategy

To ensure your data is clean and actionable, we utilize an optimized Double-Capture protocol. This rigorous process minimizes background noise, ensuring that the reads you pay for are the reads you need.

Step 1: 3C Library Preparation (High-Res Digestion)
Unlike standard Hi-C which often uses 6-cutter enzymes, we digest chromatin with frequent 4-cutter restriction enzymes (typically DpnII or NlaIII). This fragments the genome into smaller pieces, allowing for much finer mapping resolution (<500 bp). Ligation is performed in large volumes to favor intramolecular joining.

Step 2: Library Indexing & Sonication
The large 3C circles are sonicated into smaller fragments compatible with sequencing. Sequencing adapters are added early in the process. We perform strict QC on sonication fragment size distribution to ensure optimal capture efficiency.

Step 3: Double Hybridization Capture (The Competitive Edge)
We use a custom pool of biotinylated RNA oligonucleotide probes (120-mer) designed to target your specific viewpoints. We perform two consecutive rounds of hybridization and bead pull-down. While a single capture might leave ~50% off-target reads, our double-capture protocol typically reduces noise to <5%.

Step 4: Sequencing & Bioinformatics
Libraries are sequenced on Illumina platforms (PE150). We process the data to generate "Virtual 4C" tracks for every viewpoint. We identify significant interactions using statistical algorithms (e.g., CHiCAGO) and visualize them alongside your ChIP-seq or ATAC-seq data.

Capture-C workflow diagram showing double oligonucleotide capture

Sample Requirements

Capture-C is compatible with a wide range of sample types. We have optimized protocols for both standard and low-input scenarios.

Sample Type Minimum Input Preferred Input Key Requirements
Cell Lines 50,000 cells 1 - 5 million cells Freshly cross-linked (1-2% Formaldehyde). Single-cell suspension required.
Primary Cells (Sorted) 10,000 cells (Low-Input) 500,000+ cells High viability (>90%) pre-fixation is critical for library complexity.
Blood / PBMCs 1 mL whole blood 3 - 5 mL whole blood Isolate PBMCs immediately or cross-link fresh. Do not freeze whole blood.
Tissue (Soft) 10 mg 20 - 50 mg Flash-frozen or fresh. Homogenization into single nuclei is the first step.
Nuclei 50,000 nuclei 1 - 2 million nuclei Ideal for frozen tissues. Integrity of nuclei must be checked post-isolation.

Demo Results: Capture-C vs. Hi-C

The difference in clarity between Capture-C and standard Hi-C is striking when visualizing specific loci.

Figure 1: Genomic Track Comparison

Top Track (Standard Hi-C): The heatmap often appears as a vague "cloud" of interactions. The resolution (typically 5-10kb bins) is insufficient to distinguishing individual enhancer elements or determine exactly where the loop anchors.

Bottom Track (Capture-C): Presented as a "Virtual 4C" profile. The data reveals sharp, distinct peaks corresponding exactly to the known Hypersensitive Sites of the Locus Control Region (LCR). The signal-to-noise ratio is orders of magnitude higher than Hi-C, allowing for the precise definition of the promoter-enhancer loop.

Comparison of chromatin interaction resolution between Hi-C and Capture-CFigure 1: Resolution Advantage

Case Study: 3D Chromatin Maps of Autoimmune Disease

This recent study illustrates the power of Capture-C to resolve complex genetic mechanisms across diverse cell types.

The Challenge

GWAS have identified thousands of variants associated with autoimmune diseases, but identifying the target genes remained a massive challenge. Non-coding variants often lack obvious targets.

The Solution

A large research consortium employed Promoter-focused Capture-C to generate high-resolution V2G maps across 57 different human cell types, interrogating thousands of promoters simultaneously.

The Results

The study successfully linked thousands of autoimmune GWAS variants to their effector genes. Crucially, they found that many variants bypass the nearest gene to regulate distal targets via long-range looping. The Capture-C data revealed highly cell-type-specific regulatory networks.

Capture-C V2G mapping across 57 cell types

The Conclusion

Capture-C proved to be a scalable, high-precision engine for translating genetic associations into biological mechanisms, providing a roadmap for future therapeutic development.

Source: Manduchi, E., et al. "3D chromatin-based variant-to-gene maps across 57 human cell types reveal the cellular and genetic architecture of autoimmune disease susceptibility." Genome Biology (2025).

FAQ: Probes & Throughput

References

  1. Manduchi, E., et al. 3D chromatin-based variant-to-gene maps across 57 human cell types reveal the cellular and genetic architecture of autoimmune disease susceptibility. Genome Biology. 2025;26.
  2. Hughes, J.R., et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nature Genetics. 2014;46(2):205-212.
  3. Davies, J.O., et al. Re-engineering the zinc-finger loop-loop motif between the beta-globin locus control region and the globin promoter. Nature Genetics. 2016;48(1):11-12.
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