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PLAC-seq Service for Promoter-Anchored Chromatin Interactions

CD Genomics' PLAC-seq Service (Proximity Ligation-Assisted ChIP-seq) offers a highly efficient way to map chromatin loops anchored by specific proteins, such as H3K4me3 at promoters. Designed for Research Use Only (RUO), this in situ method captures long-range enhancer-promoter interactions with 10x lower input than traditional ChIA-PET. We provide verified .hic interaction maps and rigorous QC based on FRiP scores, enabling precise Variant-to-Gene (V2G) mapping for disease research.

  • Target: Promoter-centric (H3K4me3) or Enhancer-centric (H3K27ac) loops.
  • Input: Optimized for 1–5 million cells.
  • Efficiency: In situ ligation maximizes valid interaction pairs.
  • Resolution: <5kb resolution at protein anchors.
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PLAC-seq Service figure

PLAC-seq Service for Promoter-Anchored Chromatin Interactions

PLAC-seq is a targeted 3D genomics method that enriches long-range contacts anchored at specific chromatin features (most commonly H3K4me3 promoters or H3K27ac enhancers).

By combining in situ proximity ligation with immunoprecipitation, it reduces background from random ligation and produces focused, promoter-anchored loop maps for regulatory interpretation.

When to Choose PLAC-seq

  • You need promoter-anchored chromatin loops for enhancer–promoter interaction mapping.
  • You are working with limited material such as biopsy tissue or sorted immune cell populations.
  • You want a targeted approach for Variant-to-Gene (V2G) assignment after GWAS.
  • You prefer focused loop maps over genome-wide exploratory interaction datasets.

Why Choose PLAC-seq for Regulatory Mapping?

In the past, researchers relying on ChIA-PET (Chromatin Interaction Analysis by Paired-End Tag Sequencing) faced significant challenges. The old method required huge amounts of starting material (often over 100 million cells) and produced data with a lot of "noise" (false signals). PLAC-seq was developed to solve these specific problems.

1. Superior Efficiency via In Situ Ligation

The biggest technical advantage of PLAC-seq is when the ligation happens.

Old Way (ChIA-PET): Cells are lysed (broken open), and chromatin is diluted in a large volume of liquid before ligation. This dilution can cause DNA strands that are not actually close to each other to accidentally stick together, creating false data.

PLAC-seq Way: We perform the proximity ligation step inside the intact nucleus (in situ) before breaking the cell open. The nuclear envelope keeps the DNA in its natural tight bundle. This ensures that when we connect two pieces of DNA, they were truly close to each other in the cell.

Result: You get a much cleaner signal. We typically see a significant increase in "cis-interactions" (real local loops) compared to "trans-interactions" (noise between chromosomes).

2. Low Input Requirements (1–5 Million Cells)

Because the chemistry is more efficient, we do not need nearly as many cells.

Standard Input: We recommend 1 to 5 million cells per experiment.

Benefit: This opens the door for analyzing difficult samples, such as patient biopsy tissues, specific immune cell subsets sorted by flow cytometry (FACS), or differentiating stem cells. You no longer need to grow massive flasks of cells just to get one data point.

3. High Resolution at Lower Cost

In 3D genomics, "resolution" means how small a window you can zoom into. To see a loop between a specific enhancer and a promoter, you typically need "kilobase-scale" resolution (<5kb).

Cost Savings: Because PLAC-seq enriches only for the DNA connected to your target protein (like H3K4me3), we don't waste sequencing reads on the empty deserts of the genome.

Efficiency: You can achieve high-resolution loop maps with 300–400 million reads, whereas achieving the same resolution with whole-genome In situ Hi-C might require billions of reads.

PLAC-seq Service figure

PLAC-seq reveals physical connections (arcs) between promoters and distal regulatory elements that standard ChIP-seq peaks cannot detect.

Key Applications: From V2G to Transcriptional Networks

When applied to promoters and enhancers, PLAC-seq supports regulatory interpretation and post-GWAS target discovery workflows.

Promoter-Centric Interactome (H3K4me3)

Map promoter-anchored chromatin loops across active genes to build a promoter interactome for your specific cell type.

Active Enhancer Loops (H3K27ac)

Reveal enhancer connectivity and regulatory hubs, including super-enhancer-associated interactions.

Variant-to-Gene (V2G) Assignment

Link non-coding GWAS loci to target promoters using physical enhancer–promoter contacts rather than nearest-gene assumptions.

Regulatory Network Interpretation

Integrate loop maps with epigenomic profiling and transcriptional networks to prioritize functional regulatory elements.

Our PLAC-seq service is a specialized tool. It is not for looking at the whole genome structure (like TADs); it is for answering specific questions about gene regulation.

Promoter-Centric Interactome (H3K4me3)

This is the most common application. H3K4me3 is a chemical mark found on active promoters (the start sites of genes). By performing PLAC-seq with an antibody against H3K4me3, we can map every loop connected to an active gene.

Goal: Identify all the "distal" (far away) regulatory elements that control your specific genes.

Outcome: Build a "Promoter Interactome" map that shows the wiring diagram of gene control in your specific cell type.

Active Enhancer Loops (H3K27ac)

For researchers studying "Super-Enhancers," H3K27ac is the target of choice. Similar to our HiChIP Service, PLAC-seq with H3K27ac can reveal the internal structure of super-enhancers.

Goal: See how multiple enhancers cluster together.

Outcome: Understand complex regulatory hubs that drive cell identity.

Variant-to-Gene (V2G) Assignment for GWAS Studies

This is one of the most powerful applications in human genetics. Many disease-associated genetic variants found by GWAS are located in non-coding regions (enhancers). The challenge is figuring out which gene that enhancer is regulating.

The Problem: Which gene does the variant affect? The nearest gene on the linear strand is often not the target.

The PLAC-seq Solution: PLAC-seq provides physical evidence. If we see a loop connecting the variant location to a gene 50,000 bases away, that gene is the likely target.

Benefit: This helps prioritize drug targets by identifying the actual genes driving disease risk.

Validated PLAC-seq Workflow

We follow a rigorous, step-by-step protocol to ensuring consistency and high data quality. Our workflow combines wet-lab precision with advanced bioinformatics.

PLAC-seq Service figure

The PLAC-seq workflow combines in situ proximity ligation with specific protein enrichment to capture long-range loops efficiently.

Step 1: Crosslinking & In Situ Ligation

The process begins with living cells. We treat them with formaldehyde to "freeze" (crosslink) the protein and DNA interactions in place.

Cell Lysis: We gently open the cell membrane but keep the nucleus intact.

Digestion: We use a restriction enzyme (typically MboI or CviQI) to cut the DNA into small pieces inside the nucleus.

Proximity Ligation: We add a "biotin" marker and an enzyme (ligase) to glue the cut DNA ends back together. Because this happens inside the nucleus, DNA ends that are physically close (looped) are glued together, even if they are far apart on the chromosome.

Step 2: Chromatin Shearing & Immunoprecipitation (ChIP)

Now we break open the nuclei and shear the chromatin into fragments (~200-500 bp).

Immunoprecipitation: We use a high-quality antibody against your target (e.g., H3K4me3) to pull down only the chromatin fragments that contain that protein mark. This is the key enrichment step.

Step 3: Biotin Pull-Down & Library Preparation

We use streptavidin beads to capture the biotin-labeled ligation junctions. These junctions represent true 3D interactions.

Library Prep: We add sequencing adapters and perform PCR amplification to create the final PLAC-seq library.

Step 4: High-Throughput Sequencing

The final library is sequenced on an Illumina platform. We recommend paired-end 150 bp reads for optimal mapping of ligation junctions.

Bioinformatic Analysis Pipeline

Step 1: Read Alignment: Raw reads are aligned to the reference genome.

Step 2: Filtering & Valid Pair Calling: We identify valid interaction pairs and remove PCR duplicates.

Step 3: Loop Calling: We use MAPS (Model-based Analysis of PLAC-seq) to statistically identify significant chromatin loops, correcting for biases like genomic distance and ChIP enrichment.

Step 4: Visualization: We generate .hic contact maps for visualization in Juicebox and other 3D genome browsers.

Data Quality & QC Standards

Our QC standards focus on the three key performance indicators for PLAC-seq data: ChIP enrichment, library complexity, and interaction signal.

Enrichment Efficiency (FRiP)

FRiP stands for "Fraction of Reads in Peaks." It measures how well the ChIP step worked.

FRiP expectations vary by target type, antibody performance, and sample quality. We report FRiP together with enrichment diagnostics so you can judge signal concentration for downstream loop calling.

Meaning: If the FRiP is low, it means the antibody did not grab the protein well, and the data might look like random noise.

Library Complexity (NRF)

NRF stands for "Non-Redundant Fraction." It measures diversity.

We summarize duplication and non-redundancy metrics across read depths, helping determine whether additional sequencing is likely to add unique information.

Meaning: If NRF is low, it means we are sequencing the same exact DNA molecule over and over (PCR duplicates). High complexity ensures you are getting unique data for your money.

Interaction Signal (cis/trans Ratio)

This measures the quality of the 3D capture.

We report cis/trans balance and distance-dependent contact profiles to evaluate in-nucleus ligation quality and provide actionable recommendations when trans contacts are elevated.

Meaning: A high cis/trans ratio proves that the in situ ligation worked correctly and preserved the local 3D structure. If the trans signal is too high, it indicates the nuclei broke open during the experiment, causing random "soup" ligation.

Metric What We Report
FRiP FRiP expectations vary by target type, antibody performance, and sample quality. We report FRiP together with enrichment diagnostics so you can judge signal concentration for downstream loop calling.
NRF We summarize duplication and non-redundancy metrics across read depths, helping determine whether additional sequencing is likely to add unique information.
cis/trans We report cis/trans balance and distance-dependent contact profiles to evaluate in-nucleus ligation quality and provide actionable recommendations when trans contacts are elevated.

Case Study: Brain cell type–specific enhancer–promoter interactome maps and disease-risk association (Science, 2019)

Understanding how non-coding disease-risk variants act in the brain requires cell type–specific enhancer–promoter connectivity maps. Bulk tissue profiles can mask regulatory wiring that is specific to neurons, microglia, or oligodendrocytes.

Nott, A., et al. generated brain cell type–resolved regulatory atlases and promoter-centered 3D connectivity maps using PLAC-seq. The work integrated PLAC-seq with complementary epigenomic profiling to support regulatory interpretation.

The study showed that enhancer–promoter connections and disease-variant enrichment patterns differ by brain cell type. Connectivity maps supported Variant-to-Gene (V2G) assignment by linking non-coding regions to promoters beyond nearest-gene assumptions.

This case illustrates how promoter-anchored interaction mapping can refine post-GWAS target discovery in a cell type–specific manner for complex neurological traits and diseases.

Ready to Map Your Promoters?

Don't let the complexity of 3D genomics slow down your research. Whether you need to validate a specific enhancer-promoter loop or map the entire regulatory network of your cell type, CD Genomics has the expertise to guide you.

[Request a Project Consultation] Discuss your protein target and sample type with our specialists.

Explore 3D Multi-omics Integration Combine PLAC-seq with RNA-seq for a complete functional picture.

Compliance & Trust (RUO Statement)

For Research Use Only (RUO). Not for use in diagnostic procedures. The content provided on this page, including application examples and workflow descriptions, is for educational and research planning purposes only. CD Genomics does not provide clinical diagnostic services. All technical specifications (e.g., input requirements, resolution) are based on standard protocols and historical data; actual results may vary depending on sample quality and experimental design.

Frequently Asked Questions

References

  1. Fang, R., et al. (2016). Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Research. https://doi.org/10.1038/cr.2016.137
  2. Reiff, S.B., et al. (2022). The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data. Nat Commun 13, 2365 (2022). https://doi.org/10.1038/s41467-022-29697-4
  3. Nott, A., et al. (2019). Brain cell type–specific enhancer–promoter interactome maps and disease-risk association. Science (2019). https://doi.org/10.1126/science.aay0793
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