Comprehensive Review of ChIRP-seq: Mapping RNA-Chromatin Interactions at Genome Scale

Long non-coding RNAs (lncRNAs) and other regulatory RNAs are now recognised as essential players in shaping chromatin structure and regulating gene activity. These molecules interact with DNA and proteins to control transcriptional states across the genome. As research uncovers the complexity of these RNA–chromatin interactions, scientists need experimental methods that can capture them with both high specificity and genome-wide resolution.

Chromatin Isolation by RNA Purification followed by sequencing (ChIRP-seq) was developed in 2011 to address this challenge. The technique isolates chromatin fragments bound to specific RNAs using biotin-labelled antisense probes, followed by next-generation sequencing to map binding regions across the genome.

In this review, we provide an in-depth overview of ChIRP-seq—its core principles, major technical innovations, and representative applications in RNA–chromatin research. We describe the biochemical workflow and data analysis pipeline, and discuss seminal studies that used ChIRP-seq to reveal lncRNA-mediated regulation, including Xist-driven X-chromosome inactivation, Ppp1r1b-associated differentiation control, and HOTAIR-dependent trans-regulatory mechanisms. Positioned alongside complementary techniques such as RAP-seq and CHART-seq, ChIRP-seq continues to be a cornerstone in deciphering the spatial organisation of RNA-regulated chromatin domains.

Introduction

RNA molecules are now recognised as versatile regulators that extend far beyond their traditional role as messengers in protein synthesis. Among these, long noncoding RNAs (lncRNAs)—transcripts longer than 200 nucleotides with minimal or no protein-coding capacity—have gained prominence as master regulators of gene expression, chromatin organisation, and nuclear structure.

Extensive research (Yang et al., 2020; Poltronieri, 2024; Du et al., 2020; Obuse & Hirose, 2023) has revealed that lncRNAs modulate chromatin architecture and transcriptional dynamics through direct interactions with DNA, RNA, and chromatin-associated proteins. Mechanistically, these molecules operate through diverse modes of action. Some act as molecular scaffolds, assembling multi-protein complexes involved in epigenetic regulation. Others serve as decoys, sequestering transcription factors or RNA-binding proteins to fine-tune gene activity. A third group functions as sequence-specific guides, recruiting chromatin modifiers to targeted genomic loci (Boque-Sastre & Guil, 2020; Poltronieri, 2024).

These mechanisms allow lncRNAs to exert both cis-regulatory effects, acting locally near their transcription sites, and trans-regulatory effects, influencing distant genomic regions (Amanda Piveta et al., 2024; Bhavya et al., 2024). Through these layered regulatory pathways, lncRNAs contribute to cell identity, developmental programming, and genome stability (Per et al., 2022).

Representative models of these mechanisms are illustrated in Figures 1 and 2, depicting how lncRNAs integrate into chromatin and nuclear regulatory networks.

Figure 1. Cis-acting chromatin-bound lncRNAs regulate nearby genes by recruiting chromatin modifiers or altering local epigenetic states. (Modified from Kopp & Mendell 2018)

Figure 2. Trans-acting chromatin-bound lncRNAs modulate distant loci by guiding protein complexes or organizing nuclear domains. Modified from Kopp & Mendell (2018)

(Top) The ncRNA associates with chromatin-modifying complexes to alter the epigenetic state of distant genomic loci, thereby modulating target gene transcription. (Bottom) The ncRNA influences gene expression by forming or recruiting nuclear substructures, such as chromatin domains or nuclear bodies, which spatially organize and coordinate transcriptional activity at associated loci. (Modified from Kopp & Mendell, 2018).

Functionally, lncRNAs regulate chromatin through two principal modes of action—cis and trans. In cis regulation, lncRNAs act near their sites of transcription, as exemplified by Xist and Airn, which recruit the Polycomb Repressive Complex 2 (PRC2) to mediate local gene silencing. In contrast, trans-acting lncRNAs such as HOTAIR and Ppp1r1b-lncRNA guide PRC2 to distant genomic loci, orchestrating epigenetic modifications across chromosomes. These models underscore the functional versatility of lncRNAs in establishing context-dependent chromatin states throughout the genome.

To unravel these complex RNA–chromatin regulatory networks, researchers have developed a series of biochemical mapping technologies capable of capturing RNA–DNA interactions at high resolution. Among them, Chromatin Isolation by RNA Purification followed by sequencing (ChIRP-seq), introduced by Chu et al. in 2011, remains one of the most influential. This RNA-centric technique isolates chromatin fragments associated with specific RNAs using biotinylated antisense probes, followed by next-generation sequencing to generate genome-wide interaction maps.

As the first scalable platform for global RNA–chromatin profiling, ChIRP-seq has been pivotal in elucidating lncRNA-mediated regulatory mechanisms, including Xist-driven X-chromosome inactivation, HOTAIR-mediated trans-silencing, and Ppp1r1b-guided chromatin organisation (Tian & Hu, 2020; Wang et al., 2021; Delhaye et al., 2022; Hwang et al., 2023). Its impact has positioned ChIRP-seq as a cornerstone technology in RNA-based epigenomics, bridging transcriptional regulation with three-dimensional chromatin structure.

Principle of ChIRP – Seq

At the core of ChIRP-seq lies the principle of sequence-specific nucleic acid hybridisation. In this method, biotinylated antisense DNA probes are carefully designed to hybridise with complementary regions of a target RNA molecule (Chu et al., 2011). These probes are applied to crosslinked chromatin lysates, typically fixed with formaldehyde, to preserve native RNA–DNA–protein interactions within the nucleus. Crosslinking stabilises these complexes, maintaining their three-dimensional structure and ensuring accurate recovery of biologically relevant chromatin contacts (Chu & Chang, 2016).

After hybridisation, RNA–chromatin complexes bound to the probes are isolated using the strong biotin–streptavidin interaction on magnetic beads (Mimoso & Goldman, 2023). A series of stringent washes follows to eliminate non-specific background, enhancing the signal-to-noise ratio (An et al., 2022). The captured complexes are then reverse crosslinked, usually by heat treatment with proteinase K, to release the associated DNA fragments (Fan et al., 2024). These fragments correspond to genomic regions physically linked to the target RNA under native conditions. The purified DNA is subsequently prepared for high-throughput sequencing.

Sequencing reads are aligned to a reference genome to reveal the precise binding landscape of the RNA species. Through this workflow, ChIRP-seq transforms the molecular specificity of probe-based capture into a genome-wide RNA–chromatin interaction map. Its adaptability has been demonstrated for multiple RNA categories—including long noncoding RNAs (lncRNAs), enhancer RNAs (eRNAs), and telomeric repeat-containing RNAs (TERRAs)—establishing ChIRP-seq as a powerful bridge between biochemical RNA studies and epigenomic profiling (Tian & Hu, 2020; Zapparoli et al., 2020; Chebly et al., 2022).

Figure 3. Workflow of ChIRP-seq

Chromatin and long non-coding RNA (lncRNA): protein complexes are crosslinked in vivo. Biotin-labeled tiling probes hybridize to the target lncRNA, after which magnetic streptavidin beads are used to purify the chromatin complexes followed by stringent washing. A mixture of RNase A and RNase H is then applied to release the DNA or proteins bound to the lncRNA. A potential lncRNA-binding sequence is indicated in orange.

Experimental Workflow of ChIRP-seq

Probe Design and Synthesis

The ChIRP-seq experiment begins with the design of biotin-labelled antisense DNA oligonucleotide probes targeting the RNA of interest. Effective probe design ensures sequence specificity, balanced GC content (40–60%), and the avoidance of highly structured regions that could hinder hybridisation. To achieve full transcript coverage, probes are tiled at ~20-nt intervals and divided into odd and even probe pools, allowing internal validation of specificity and reproducibility (Chu et al., 2011; Tian & Hu, 2020).

Crosslinking and Nuclei Isolation

Cells are crosslinked with 1% glutaraldehyde for strong fixation or 1% formaldehyde for milder crosslinking. This stabilises RNA–chromatin and RNA–protein complexes in their native conformation, preserving genuine molecular interactions for downstream capture (Chu et al., 2011; Chu & Chang, 2016).

Chromatin Extraction and Fragmentation

Crosslinked nuclei are lysed, and chromatin is sonicated to generate fragments averaging 100–500 bp (optimally 200–500 bp). Fragment size is verified by agarose gel electrophoresis or Bioanalyzer to ensure consistency and efficient hybridisation (Chu et al., 2011; Alfeghaly et al., 2021).

Probe Hybridisation

Biotinylated probes are incubated with fragmented chromatin at 37 °C for several hours to overnight under stringent hybridisation conditions. The buffer composition is optimised to promote specific RNA–DNA duplex formation while minimising off-target binding (Chu & Chang, 2016).

Affinity Capture and Washing

Following hybridisation, streptavidin-coated magnetic beads are introduced to isolate the biotin-bound RNA–chromatin complexes. A sequence of high-stringency washes (2× to 0.1× SSC) removes non-specific binders, enriching for target-specific chromatin fragments and maximising signal-to-noise ratio (Chu & Chang, 2016).

DNA Isolation and Library Preparation

Captured complexes are reverse crosslinked by proteinase K digestion at 65 °C for 1 hour to release bound DNA. Purified DNA fragments are processed through standard NGS library preparation steps—end repair, adapter ligation, and PCR amplification—ready for sequencing (Chu et al., 2011).

Sequencing and Mapping

Prepared libraries are sequenced on short-read NGS platforms such as Illumina. Reads are aligned to the reference genome (e.g., hg38, mm10), and enrichment regions relative to input or control probe pools are identified as candidate RNA-interacting loci. Typical sequencing depth ranges from 20–50 million reads per sample (Alfeghaly et al., 2021).

Data Analysis and Interpretation

Once sequencing is complete, the ChIRP-seq dataset undergoes several computational analyses to define RNA-chromatin interaction landscapes and infer functional relevance (Chu et al., 2011; Tian & Hu, 2020; Alfeghaly et al., 2021).

Quality Control (QC)

Raw reads are assessed using FastQC or MultiQC to check base quality, adapter contamination, and duplication levels. Low-quality sequences and adapters are trimmed with Trimmomatic or Cutadapt to ensure reliable downstream mapping.

Read Alignment

High-quality reads are aligned to the genome using Bowtie2 or BWA with high-stringency parameters. Only uniquely mapped reads are retained to reduce false positives and improve data confidence.

Read Filtering

Post-alignment filtering removes PCR duplicates, low-quality reads, and non-nuclear contaminants (e.g., mitochondrial or chloroplast sequences), ensuring the dataset reflects true RNA-chromatin interactions.

Peak Calling and Enrichment Detection

Regions of significant enrichment are identified using MACS2 or similar peak-calling algorithms. Because RNA–chromatin interactions produce broader signals than transcription factor peaks, parameters such as window size and FDR threshold are adjusted accordingly. Input DNA or control probe pools serve as background models for statistical detection.

Normalisation and Replicate Analysis

Signals are normalised across odd/even probe pools and biological replicates. Reproducibility is assessed via Pearson or Spearman correlation coefficients, with visualisation through scatterplots or heatmaps to identify outliers and ensure experimental consistency.

Peak Annotation

Identified peaks are annotated to genomic features using ChIPseeker, HOMER, or annotatePeaks.pl. Peaks can be classified as promoter-proximal, intragenic, or intergenic, offering insights into the regulatory potential of the RNA. For instance, Hwang et al. (2023) found that ~46% of peaks associated with a myogenic lncRNA overlapped annotated gene regions, suggesting strong cis-regulatory activity.

Functional Integration

To enhance biological interpretation, ChIRP-seq results can be integrated with other datasets such as RNA-seq (transcriptional output), ATAC-seq (chromatin accessibility), or Hi-C (3D genome conformation). This multi-omics approach reveals whether RNA occupancy corresponds to gene activation, repression, or chromatin looping.

Visualisation

Visualisation tools like IGV or the UCSC Genome Browser allow direct inspection of enriched loci. Additionally, deepTools can generate heatmaps and metagene plots to summarise RNA-binding trends across genomic features.

Experimental Validation

Computational findings should be experimentally validated to confirm functional relevance. Common approaches include:

• ChIRP-qPCR to test enrichment at selected loci;
• RNA knockdown or knockout to assess RNA dependency;
• Luciferase reporter assays or CRISPR-based perturbations to determine regulatory impact.

Strengths and Limitations of ChIRP-seq

Strengths

ChIRP-seq enables genome-wide and high-resolution mapping of RNA–chromatin interactions, typically achieving spatial precision within a few hundred base pairs (Chu et al., 2011). The method offers high sensitivity—especially valuable for abundant or stably associated RNAs—and is applicable to a wide range of RNA species, including both coding and noncoding transcripts (Alfeghaly et al., 2021).

A key advantage of ChIRP-seq lies in its modular, extensible design. Its hybridisation-based framework can be adapted for related applications:

• ChIRP–MS, for proteomic identification of RNA-binding proteins;
• ChIRP–RNA, for detection of RNA–RNA interactions (Chu & Chang, 2018; Bozděchová et al., 2024).

By mapping endogenous RNA occupancy without the need for genetic tagging or artificial labelling, ChIRP-seq enables the discovery of previously uncharacterised RNA-binding loci across intergenic, enhancer, and repetitive genomic regions. These findings have broadened understanding of epigenomic regulation, revealing novel mechanisms of transcriptional control and chromatin organisation (Muers, 2011; Zapparoli et al., 2020).

Limitations

Despite its strengths, ChIRP-seq presents several technical and interpretative challenges. The method often requires large quantities of input material—typically more than 10⁷ cells—to offset sample loss during crosslinking, hybridisation, and washing (Chu et al., 2011). Probe design is another critical factor: poor tiling strategy, GC-content bias, or inclusion of repetitive elements can reduce capture efficiency and compromise specificity (Alfeghaly et al., 2021).

Additionally, crosslinking and chromatin fragmentation can distort the representation of weak or transient RNA–chromatin contacts (Matthew D, 2013; Chu & Chang, 2016). Non-specific enrichment of abundant RNAs or open chromatin regions may generate false-positive peaks (Tian & Hu, 2020). Importantly, the physical proximity captured by ChIRP-seq does not always imply functional regulation.

To ensure biological relevance, functional validation is essential. This may include ChIRP–qPCR to confirm enrichment at specific loci, RNA interference or CRISPR interference (CRISPRi) to assess gene regulatory effects, and orthogonal approaches to exclude experimental artefacts (John et al., 2020). Robust experimental design—featuring negative controls, odd/even probe replicates, and biological replicates—is crucial for achieving reproducible and interpretable results.

Comparison with Related Methods

Table 1 Comparison of major RNA–chromatin interaction mapping methods

Applications and Case Studies

ChIRP-seq has become an essential method for genome-wide, high-resolution mapping of RNA–chromatin interactions in diverse biological systems. By identifying the genomic loci associated with specific RNAs, it provides direct insights into RNA-mediated epigenetic regulation, chromatin organisation, and transcriptional control.

Among the regulatory RNA classes investigated using ChIRP-seq, long noncoding RNAs (lncRNAs) remain the most comprehensively characterised, offering clear mechanistic models for RNA-guided chromatin modulation. The following representative case studies—XIST, Ppp1r1b-lncRNA, and HOTAIR—demonstrate how ChIRP-seq has revealed both cis- and trans-acting regulatory mechanisms, underscoring its capacity to decipher RNA–chromatin networks at the genomic scale.

XIST RNA and X-Chromosome Inactivation

XIST (X-inactive specific transcript), the master lncRNA encoded within the X-inactivation centre (Xic), orchestrates X-chromosome inactivation (XCI) in female mammals. Upon transcription, XIST RNA spreads along the inactive X chromosome (Xi), forming a chromosome-wide RNA coat that silences transcription.

Mechanistically, the 5′ RepA domain of XIST recruits the Polycomb Repressive Complex 2 (PRC2), leading to deposition of the repressive H3K27me3 histone mark (Zhao et al., 2008). The A-repeat region interacts with SHARP/SPEN, which recruits the SMRT–HDAC3 deacetylase complex to reinforce silencing (McHugh et al., 2015). In parallel, hnRNP U serves as a nuclear scaffold, anchoring XIST RNA to chromatin and maintaining the inactive state (Hasegawa et al., 2010).

Together, these multi-protein interactions position XIST as a prototypical cis-acting lncRNA, coordinating chromosome-wide epigenetic silencing through layered recruitment of repressive complexes.

Ppp1r1b-lncRNA in Myogenic Differentiation

To extend these insights, ChIRP-seq was used to profile Ppp1r1b-lncRNA, a regulatory RNA involved in skeletal muscle differentiation. Previous studies demonstrated that this lncRNA interacts with EZH2, the catalytic component of PRC2, to regulate H3K27me3 at the promoters of MyoD1 and TBX5, thereby influencing both cardiac and skeletal muscle development (Kang et al., 2020).

Genome-wide ChIRP-seq profiling identified ~99,700 high-confidence binding sites (average peak width: ~558 bp), with 46% overlapping annotated gene regions, including over 1,100 promoters. Notably, ~12% of peaks mapped to enhancer regions showing tissue-specific enrichment in fetal heart and muscle (Hwang et al., 2023).

Integration of ChIRP-seq, ChIP-seq, and RNA-seq datasets revealed that Ppp1r1b-lncRNA modulates chromatin accessibility and gene activation by altering PRC2 occupancy at lineage-specific regulatory regions. Collectively, these findings position Ppp1r1b-lncRNA as a key epigenetic regulator orchestrating cell type–specific chromatin remodelling and transcriptional reprogramming.

HOTAIR and Trans-acting Chromatin Silencing

Unlike most lncRNAs that act in cis, HOTAIR (HOX antisense intergenic RNA) is a landmark example of trans-acting chromatin regulation. This 2.2-kb transcript, originating from the HOXC locus, is spliced, polyadenylated, and functions to repress gene expression across distant genomic regions.

Rinn et al. (2007) first demonstrated that HOTAIR binds the HOXD locus, maintaining its repressive chromatin state. Using ChIRP-seq, Chu et al. (2011) generated a genome-wide interaction map of HOTAIR, confirming its enrichment within the HOXD cluster. Knockdown of HOTAIR resulted in reactivation of HOXD genes and a significant loss of H3K27me3, supporting its role in recruiting repressive machinery.

Further RIP and RNA pull-down assays established that HOTAIR interacts with both PRC2 and the LSD1/CoREST/REST complex. Together, these interactions mediate H3K27 trimethylation and H3K4 demethylation, producing a repressive chromatin environment that silences transcription.

Subsequent studies confirmed that depletion of other PRC2-associated lncRNAs across cell types led to similar derepression of target genes (Khalil et al., 2009), demonstrating the broader relevance of lncRNA-guided trans-acting silencing in mammalian gene regulation.

Summary:

These representative studies highlight how ChIRP-seq has transformed understanding of lncRNA-mediated chromatin regulation. From XIST's cis-acting chromosome silencing to HOTAIR's trans-acting repression, ChIRP-seq continues to provide a powerful framework for dissecting RNA-driven epigenetic landscapes across biological systems.

Conclusion

ChIRP-seq has become a cornerstone in the study of RNA–chromatin interactions, offering genome-wide insights into how noncoding RNAs influence transcriptional regulation, chromatin remodelling, and nuclear organisation. By combining high molecular specificity, fine spatial resolution, and broad RNA compatibility, the method provides a robust platform for dissecting the epigenetic functions of RNA molecules at unprecedented depth.

Nevertheless, several technical challenges persist. The method's high input requirement, the need for precise probe optimisation, and the difficulty in distinguishing functional from non-functional binding events can complicate interpretation. Ongoing improvements in probe design, crosslinking chemistry, and bioinformatic normalisation are steadily enhancing ChIRP-seq's sensitivity, reproducibility, and quantitative accuracy.

Looking ahead, emerging innovations—such as single-cell ChIRP-seq, spatially resolved RNA–chromatin mapping, and integrative multi-omics frameworks—promise to extend the method's resolution to the allele-specific and quantitative level. These advances will not only deepen understanding of RNA-guided chromatin architecture, but also accelerate applications in disease epigenomics and RNA-targeted therapeutic discovery.

Collectively, these developments position ChIRP-seq as both a foundational and evolving technology, one that continues to illuminate the complex epigenetic networks underlying genome regulation in the post-genomic era.

FAQ

Q: What exactly does ChIRP-seq reveal about RNA–chromatin interactions?

A: ChIRP-seq maps the genomic locations where a specific RNA (such as a lncRNA or circRNA) binds chromatin, capturing its associated DNA fragments and thereby uncovering potential regulatory sites (both cis and trans) on the genome.

Q: How does ChIRP-seq differ from ChIP-seq or other interaction-mapping methods?

A: Unlike ChIP-seq, which targets protein–DNA interactions via antibodies, ChIRP-seq uses biotin-labeled antisense probes to pull down an RNA of interest and its bound chromatin. This allows direct detection of RNA-guided chromatin binding, rather than strictly protein-centric binding.

Q: What sample types and input amounts are compatible with ChIRP-seq?

A: ChIRP-seq works with cultured cells and tissue samples (fresh or frozen) where sufficient RNA–chromatin complexes can be captured. Because the capture relies on probe hybridisation to the target RNA, sample quality (cross-linking, sonication, RNA integrity) matters.

Q: Can ChIRP-seq detect interactions of low-abundance RNAs?

A: Yes, although sensitivity drops as abundance decreases. Probe tiling across the RNA and stringent pull-down/wash conditions help improve specificity. Some service providers optimise workflows for low-copy RNAs.

Q: What are the common analysis outputs one should expect from a ChIRP-seq experiment?

A: Typical outputs include a list of genomic binding peaks (with annotation to promoters, enhancers, intergenic regions), genome browser tracks (BAM/peak files), GO/pathway enrichment of nearby genes, and integrated views (e.g., in IGV) to visualise binding relative to gene models.

Q: What are the key limitations and pitfalls to be aware of when using ChIRP-seq?

A: Key limitations include the requirement for significant input material, potential non-specific binding from abundant RNAs or open chromatin regions, the need for careful probe design (avoiding repeats, balancing GC content), and the fact that binding alone does not imply functional regulation.

Q: How should one validate ChIRP-seq findings to confirm biological relevance?

A: Validation can include ChIRP-qPCR for selected loci, RNA knockdown/knockout to assess downstream gene expression effects, reporter assays or CRISPR perturbation of binding sites, and integration with orthogonal data such as RNA-seq, ATAC-seq, Hi-C for structural context.

Q: When is ChIRP-seq a good choice versus other RNA–chromatin interaction methods like RAP-DNA or CHART-seq?

A: ChIRP-seq offers a good balance of throughput and specificity for genome-wide mapping of RNA-chromatin interactions. If utmost specificity is needed (e.g., fewer off-target captures), RAP-DNA may be appropriate; for simpler workflows with shorter probe sets, CHART-seq might suffice.

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