Hi-C Service for Chromatin Interaction and 3D Genome Analysis

DNA in the nucleus is folded into a complex three-dimensional architecture that brings distant regulatory elements — enhancers, promoters, insulators — into physical proximity. These long-range chromatin interactions govern gene expression, DNA replication, and genome stability. Disruption of normal 3D chromatin organization is increasingly linked to developmental disorders, cancer, and other diseases.

Hi-C (high-throughput chromosome conformation capture) is the most widely used method for genome-wide mapping of chromatin interactions. It combines formaldehyde crosslinking, restriction enzyme digestion, biotin fill-in, intramolecular ligation, and high-throughput sequencing to generate pairwise contact frequency matrices that represent the three-dimensional folding of chromosomes. From these matrices, researchers can map A/B compartments, topologically associating domains (TADs), and chromatin loops — the fundamental structural layers of the 3D genome.

At CD Genomics, we provide a complete Hi-C service — from sample crosslinking and library preparation through next-generation sequencing and advanced bioinformatics analysis. Whether you are investigating cancer genome reorganization, developmental gene regulation, or disease-associated noncoding variants, our Hi-C service delivers publication-ready 3D genome maps with comprehensive quality control at every step.

Key Highlights:

End-to-End Hi-C Workflow — from cell crosslinking and restriction enzyme digestion through intramolecular ligation, library preparation, sequencing, and multi-resolution contact matrix generation
Multi-Resolution Analysis — A/B compartment calling, TAD boundary identification, and statistically significant chromatin loop detection at resolutions tailored to your biological question
Rigorous Quality Control — integrated QC checkpoints at every stage: crosslinking efficiency, restriction digestion, ligation, library quality, and sequencing metrics (cis/trans ratio, valid pair percentage)
Comprehensive Bioinformatics — standardized analysis pipeline delivering contact matrices (.hic/.cool), compartment tracks, TAD annotations, loop calls, and interactive visualizations

Discuss Your Hi-C Project

Hi-C service illustration showing 3D genome folding from DNA loops to chromosome territories with chromatin interaction heatmap

Technology

What Is Hi-C and How Does It Work

Hi-C (high-throughput chromosome conformation capture) is a genome-wide method that captures pairwise chromatin interactions by physically linking DNA fragments that are spatially adjacent in the nucleus. The technology builds on the original chromosome conformation capture (3C) concept but scales from one-to-one locus detection to all-to-all, genome-wide interaction mapping.

The Hi-C Workflow Principle

The Hi-C protocol proceeds through the following steps:

Crosslinking — Living cells are treated with formaldehyde to covalently crosslink proteins to DNA and proteins to proteins, fixing chromatin contacts in their native three-dimensional state.
Restriction Digestion and Biotin Fill-in — Crosslinked chromatin is digested with a restriction enzyme (typically HindIII, MboI, or DpnII), generating staggered cuts across the genome. The resulting 5' overhangs are filled in with biotin-14-dCTP, marking the restriction sites.
Intramolecular Ligation — Under dilute conditions, DNA ends that are physically close in 3D space — because they were crosslinked within the same chromatin complex — are ligated together. This step creates chimeric DNA molecules where each end originates from a different genomic locus that was in spatial proximity.
Crosslink Reversal and DNA Purification — Crosslinks are reversed by heat and proteinase K treatment. DNA is purified and sheared to the desired fragment size.
Biotin Enrichment and Library Preparation — Biotin-tagged ligation junctions are enriched by streptavidin bead pulldown, selecting for the chimeric ligation products. Sequencing adapters are ligated and the library is amplified.
Paired-End Sequencing and Data Analysis — Paired-end sequencing reads are mapped to the reference genome. Each read pair that maps to two different restriction fragments represents a detected chromatin contact. Millions to billions of such contacts are aggregated into a contact matrix.

What Hi-C Reveals: The Hierarchical Layers of 3D Genome Organization

From the Hi-C contact matrix, three fundamental layers of genome architecture can be resolved:

A/B Compartments (Megabase Scale): At the coarsest level, the genome partitions into two spatial compartments. The A compartment contains gene-rich, transcriptionally active, early-replicating chromatin with open (DNase I-hypersensitive) configuration. The B compartment contains gene-poor, transcriptionally silent, late-replicating chromatin in a more compact state. Compartment identity is not fixed — genomic regions can switch compartments during differentiation, development, and disease.

Topologically Associating Domains (TADs, Submegabase Scale): Within compartments, the genome is organized into TADs — contiguous regions (typically 100 kb–1 Mb in humans) within which chromatin interacts preferentially. TAD boundaries are enriched for CTCF binding sites and cohesin, and they function as insulators that restrict enhancer-promoter communication to within-domain interactions. TAD disruption — by structural variants, boundary deletions, or CTCF site mutations — can cause enhancer adoption (ectopic oncogene activation) or enhancer disconnection (loss of normal gene expression).

Chromatin Loops (Kilobase Scale): At the finest level, specific pairs of loci interact at frequencies significantly above the genomic background. These loops typically represent enhancer-promoter contacts, CTCF-CTCF homodimerization at domain boundaries, or cohesin-mediated interactions. Loop calling identifies statistically significant point-to-point interactions genome-wide.

The 4D Nucleome (4DN) consortium and ENCODE project have generated extensive reference Hi-C datasets across diverse human and mouse cell types, establishing Hi-C as a foundational tool for understanding 3D genome biology and its role in health and disease.

Workflow

Hi-C Service Workflow and Quality Control

Our Hi-C service follows a rigorously optimized protocol with integrated quality control at every stage, ensuring reproducible, publication-ready 3D genome maps.

Sample Receipt and QC — QC Checkpoint: Cell viability assessed by trypan blue staining or flow cytometry; cell count verified; for pre-crosslinked samples, crosslinking efficiency validated. Samples failing QC thresholds are flagged, and we consult with you before proceeding.
Expert Tip: Adherent cells should be harvested at 70-80% confluency — overconfluent cultures accumulate apoptotic cells that inflate trans-interaction background. For suspension lines such as K562 and GM12878, we routinely achieve >95% viability. For new sample types, we run a small-scale crosslinking and digestion pilot before committing the full material.
Crosslinking and Cell Lysis — QC Checkpoint: Formaldehyde crosslinking performed under standardized conditions; reaction quenched with glycine; cell lysis and nuclei isolation verified by microscopy.
Expert Tip: Crosslinking is the most operator-sensitive step and the most common source of failed libraries from client-submitted samples. We optimize formaldehyde concentration per sample type (1% for most cell lines, 2% for primary cells and tissues) and verify crosslinking adequacy at the digestion QC gate rather than relying on fixed time points. Over-crosslinking — which restricts enzyme access and produces sparse contact matrices — is more frequent than under-crosslinking.
Restriction Digestion and Biotin Fill-in — QC Checkpoint: Restriction enzyme digestion efficiency assessed by gel electrophoresis or qPCR across control loci; biotin incorporation quantified. Incomplete digestion reduces ligation efficiency and contact coverage.
Expert Tip: We select restriction enzymes based on genome composition: MboI (GATC, ~256 bp resolution) for human and mouse — its dam-methylation sensitivity enriches cuts at CpG-island-associated open chromatin; DpnII or HindIII for AT-rich and highly methylated genomes. Digestion efficiency below 70% triggers a protocol review — the most common culprit is over-crosslinking.
Intramolecular Ligation and DNA Purification — QC Checkpoint: Ligation efficiency measured by comparing ligated vs unligated product on agarose gel; crosslink reversal and protein removal efficiency verified by OD260/280 ratio; DNA yield quantified.
Expert Tip: Ligation is performed under dilute conditions (<2 ng/μL DNA) to favor intramolecular events. We titrate ligation volume per sample based on post-digestion DNA yield — samples with compact chromatin (quiescent lymphocytes, senescent cells) require adjusted ratios. An unusually high trans-interaction fraction almost always traces back to viability or crosslinking issues at earlier steps, not to the ligation itself.
Biotin Enrichment and Library Preparation — QC Checkpoint: DNA shearing size distribution (Bioanalyzer/TapeStation); streptavidin pulldown enrichment efficiency by qPCR; library yield and fragment size distribution; adapter dimer content.
Expert Tip: Fragment size selection is critical: 300-500 bp provides the best balance between amplification efficiency and short-range interaction resolution; for loop-focused projects we narrow to 200-350 bp. We use calibrated wash stringency with a spike-in internal control to monitor enrichment specificity. Adapter dimer >5% triggers an additional AMPure cleanup before sequencing.
Sequencing and Data QC — QC Checkpoint: Q30 scores, duplication rate, and total read yield; Hi-C-specific metrics including cis/trans interaction ratio, percentage of valid interaction pairs, restriction fragment coverage uniformity, and library complexity.
Expert Tip: The valid interaction pair percentage is the single most informative Hi-C library QC metric: >60% is achievable for high-quality mammalian cell samples; <45% warrants investigation before investing in deep sequencing. We recommend a shallow pilot run (10-20 million read pairs) to assess cis/trans ratio, valid-pair fraction, and fragment coverage distribution before committing to full-depth sequencing.

All QC metrics are documented in the project QC report, which is delivered alongside your data package. A project scientist reviews the results with you and is available to answer questions about data interpretation and downstream analysis.

Request Your Hi-C Project Workflow

Sample

Sample Requirements and Preparation Guidelines

Hi-C requires freshly crosslinked, viable cells. The crosslinking step must be performed on living cells to capture native chromatin contacts in situ. Frozen cell pellets or tissue samples cannot be used for standard Hi-C unless they were crosslinked before freezing.

Sample Type	Input Requirement	Processing Requirement	Storage and Shipping	Key Considerations
Mammalian cultured cells	Standard: ≥1×10⁷ cells Low-input: ≥5×10⁵ cells	Fresh crosslinking with formaldehyde before harvest; cell viability ≥90% (log-phase recommended)	Crosslinked cell pellets on dry ice; or viable cells in culture medium (express, 4°C)	Optimal viability critical; overgrowth or contamination will reduce library quality. Adherent cells: harvest at 70-80% confluency.
Mammalian fresh tissue	Standard: 50-500 mg fresh tissue	Fresh tissue dissociation to single cells or nuclei, then crosslink immediately	Fresh tissue in cold preservation medium (express, 4°C); snap-freeze only after crosslinking	Heart, lung, muscle generally yield better results than liver or adipose tissue. Flash-frozen uncrosslinked tissue NOT suitable.
Mammalian whole blood	≥1-2 mL (EDTA/Heparin tube)	PBMC isolation or direct crosslinking; minimize time from collection to processing	Ambient temperature (do not freeze); process within 24-48 hours	EDTA preferred over heparin for downstream enzymatic steps. Nucleated blood from birds, reptiles, fish: ≥100 μL.
Primary cells / clinical samples	Standard: ≥1×10⁷ viable cells	Immediate crosslinking upon collection; viability assessment before processing	Crosslinked pellets on dry ice	Minimize time between collection and crosslinking. Clinical samples require IRB approval documentation.
Plant tissue (young leaf)	100-500 mg young leaf tissue	Species-specific crosslinking; formaldehyde concentration adjusted for cell wall penetration	Fresh tissue express-shipped; contact us for species-specific guidance	Restriction enzyme selection depends on genome composition and methylation status. Young tissue preferred for lower secondary metabolite interference.
Insects / other invertebrates	Whole intact specimens (multiple for small organisms) or ≥50 mg tissue	Species-specific crosslinking conditions	Contact us for species-specific guidance	AT-rich genomes may require alternative restriction enzymes (DpnII instead of MboI).

Sample Preparation Recommendations:

Viability: Cell viability ≥90% is strongly recommended. Dead cells produce random ligation products that degrade Hi-C data quality by increasing the trans-interaction background.
Cell counting: Accurate cell counting before crosslinking is essential for determining appropriate reagent volumes. Use an automated cell counter or hemocytometer with trypan blue exclusion.
Crosslinking timing: Over-crosslinking reduces restriction enzyme accessibility and generates false-negative contacts. Under-crosslinking fails to capture transient interactions. Follow the protocol exactly.
Storage: Once crosslinked and quenched, cell pellets can be snap-frozen in liquid nitrogen and stored at -80°C or shipped on dry ice. Do not freeze viable cells before crosslinking.
Buffer preparation: Use freshly prepared formaldehyde solution. Methanol-free formaldehyde (ampule format) is recommended for reproducible crosslinking.

We provide a detailed, sample-type-specific crosslinking protocol to all customers before sample collection. For laboratories without cell culture facilities or access to fresh tissue, contact us to discuss alternative approaches.

Get Your Sample Preparation Protocol

Bioinformatics

Hi-C Bioinformatics Analysis and Data Outputs

Hi-C data analysis requires specialized computational tools and substantial computing resources. At CD Genomics, we process your Hi-C data through a standardized, validated bioinformatics pipeline that transforms raw sequencing reads into interpretable 3D genome maps at multiple resolutions.

Analysis Pipeline

Analysis Step	Description	Key Tools
Read alignment and filtering	Paired-end reads aligned to reference genome; non-unique, duplicate, and dangling-end reads filtered out	BWA-MEM, HiC-Pro, pairtools
Contact matrix generation	Valid interaction pairs binned into contact matrices at multiple resolutions (1 Mb, 500 kb, 100 kb, 50 kb, 25 kb, 10 kb, 5 kb as appropriate)	HiC-Pro, cooler
Matrix normalization	Iterative correction and eigenvector decomposition (ICE) to remove systematic biases including GC content, mappability, and restriction fragment length	HiC-Pro, HiCExplorer, cooltools
A/B compartment calling	Eigenvector decomposition of the Pearson correlation matrix; sign correlated with gene density to assign A (positive, active) and B (negative, inactive) compartments	HiCExplorer, cooltools
TAD calling	Topologically associating domain identification using insulation score or directionality index algorithms	HiCExplorer, TopDom, HiCDB
Loop calling	Detection of statistically significant point-to-point interactions using convolutional or statistical models	HiCCUPS (Juicer), FitHiC, Mustache
Visualization	Interactive contact map generation, virtual 4C track extraction, compartment profile plotting	Juicebox, Higlass, HiCExplorer

Standard Data Deliverables

Deliverable	Description
Raw sequencing data	Demultiplexed paired-end reads with quality scores
Aligned and filtered reads	Quality-filtered, deduplicated alignments of valid Hi-C read pairs
Contact matrices	Multi-resolution contact matrices at resolutions appropriate for your sequencing depth
A/B compartment tracks	Genome-wide compartment eigenvectors for each sample
TAD annotations	TAD boundary coordinates with confidence scores
Loop calls	Significant chromatin loops with statistical metrics (p-value, FDR)
Interactive visualization	Juicebox-compatible contact maps for interactive exploration
QC report	Comprehensive QC metrics and assessment summary
Methods summary	Detailed protocol and analysis method description for publication methods sections

Optional Advanced Analyses

Differential interaction analysis — identification of genomic regions with significantly altered contact frequencies between two or more experimental conditions
Multi-omics integration — combined analysis of Hi-C data with RNA-seq, ChIP-seq, or ATAC-seq to annotate enhancer-promoter interactions with expression and chromatin state data
Capture Hi-C probe design — custom oligonucleotide probe panels for targeted enrichment and deep interaction mapping at user-specified genomic regions (e.g., GWAS loci, candidate gene promoters)
Virtual 4C analysis — extraction of the genome-wide interaction profile for any locus of interest from Hi-C data, presented as a viewpoint-centric track
Non-mammalian genome 3D analysis — adapted pipelines for plants, insects, fungi, and other non-mammalian species
Custom publication-ready figures — contact maps, virtual 4C tracks, differential interaction heatmaps, and multi-omics integration visualizations
Epigenomic data analysis — see our dedicated Epigenomic Data Analysis service

Demo

Representative Data and Demo Results

The composite image below illustrates the standard Hi-C data types delivered with each project. All panels represent outputs from our bioinformatics pipeline and reflect the analysis depth provided as standard.

Genome-Wide Contact Matrix and Compartment Analysis:

Contact matrix heatmap (Panel A) — genome-wide Hi-C contact frequency map at chromosome-scale resolution showing the classic plaid pattern of alternating A (active) and B (inactive) compartments. The checkerboard pattern reflects the preferential interaction of A regions with other A regions and B regions with other B regions.
A/B compartment eigenvector track (Panel B) — first eigenvector values plotted along a representative chromosome. Positive values (red) indicate the A compartment, negative values (blue) indicate the B compartment. Gene density and GC content tracks are aligned for reference.
TAD triangle map (Panel C) — high-resolution view of a representative genomic locus showing TADs as triangles of enriched contact frequency along the diagonal. TAD boundaries are marked as vertical/horizontal lines where inter-TAD contact frequency drops sharply.

Loop Detection and Differential Analysis:

Chromatin loop arc diagram (Panel D) — significant chromatin loops displayed as arcs connecting interacting loci overlaid on the Hi-C contact map. Loop calls are filtered by statistical significance (FDR) and annotated to the nearest genes.
Differential interaction heatmap (Panel E) — comparison of Hi-C contact frequencies between two conditions. Regions with significantly increased interaction in condition A versus condition B are shown in red; decreased interactions in blue. Highlights condition-specific 3D genome reorganization.
Virtual 4C interaction profile (Panel F) — interaction frequency plotted as a function of genomic distance from a user-specified viewpoint locus. Peaks indicate regions that physically contact the viewpoint at frequencies above the genomic background, identifying candidate regulatory interactions.

All demo results are generated from representative datasets. Actual deliverables are customized to your experimental design, species, and research question. A project scientist will review the final data package with you and assist with interpretation.

Applications

Applications of Hi-C in Biomedical Research

Hi-C has become an indispensable tool across a broad range of biomedical and biological research areas. Below are representative applications supported by recent peer-reviewed studies.

Cancer Genome Organization

Cancer genomes harbor widespread structural variations (SVs) that rewire the 3D genome. Hi-C maps these SVs in their native spatial context, revealing how translocations, inversions, and deletions alter TAD boundaries, create aberrant enhancer-promoter contacts, and activate oncogenes. A study of 28 glioblastoma patient-derived stem cell lines used ultra-deep Hi-C at 5 kb resolution to identify patient-idiosyncratic "neoloops" that sustain tumor-specific transcriptional programs (Xie et al., Nature Communications, 2024). In colorectal cancer, Hi-C with machine learning identified clonal enhancer hijacking events as prognostic markers with a Hazard Ratio of 3.6, outperforming KRAS and MSI status (Kim et al., Cell Reports, 2023).

Developmental Biology and Cell Differentiation

During development, the 3D genome undergoes dynamic reorganization that establishes cell-type-specific gene expression programs. Hi-C captures these changes over time and across lineages, revealing how chromatin topology shapes cell fate. Deep Hi-C sequencing of human retinal organoids across five developmental stages mapped stage-specific chromatin loops and TAD restructuring that shape transcriptional neighborhoods for photoreceptor and retinal cell fate specification (Qu et al., Cell Reports, 2023).

Drug Resistance Mechanisms

Acquired drug resistance is a major clinical challenge, and growing evidence implicates 3D genome reorganization in resistance development. A study of triple-negative breast cancer PDX models used Hi-C integrated with RNA-seq and whole-genome sequencing to demonstrate that carboplatin resistance is accompanied by increased short-range chromatin interactions, new TAD formation, and chromatin state switching toward a more active configuration. Transcription factors TP53, TP63, BATF, and FOS-JUN were identified as drivers of the rewired 3D genome (Dozmorov et al., Scientific Reports, 2023).

Non-Coding Variant Interpretation

Many disease-associated variants identified by GWAS fall within non-coding regions, making their functional interpretation challenging. Hi-C links these variants to their target genes by mapping the 3D chromatin contacts that connect regulatory elements to promoters. A comparative Hi-C study of human neural retina and retinal pigment epithelium (RPE) found that approximately 60% of inherited retinal disease genes are marked by tissue-differential 3D genome topology, including tissue-specific TADs and chromatin loops that explain disease gene vulnerability (D'haene et al., Genome Biology, 2024).

Neurological and Psychiatric Disorders

The brain expresses a disproportionately large number of genes relative to other tissues, and many are regulated by long-range enhancer-promoter interactions that are specific to neuronal cell types. Hi-C has been used to map neuron-specific chromatin loops at loci associated with autism spectrum disorder, schizophrenia, and Alzheimer's disease, linking non-coding GWAS variants to their target genes through cell-type-specific 3D chromatin contacts. 4DN and PsychENCODE consortia have generated reference Hi-C datasets from human brain tissue and neuronal cell types to support these analyses.

Non-Mammalian and Agricultural Genomics

Hi-C is widely applied in non-mammalian species including plants, insects, and fungi. Plant 3D genomics studies have used Hi-C to characterize TAD-like domain formation and chromatin organization in Arabidopsis, rice, maize, wheat, and soybean — linking 3D chromatin structure to agronomic trait regulation. Hi-C has also been applied to Plasmodium (malaria parasite) for understanding virulence gene regulation and to Drosophila for developmental chromatin architecture studies.

Case

Case Study: Hi-C Reveals Enhancer Hijacking as Prognostic Marker in Colorectal Cancer

In Situ Hi-C of 40 Colorectal Cancer Tumors Identifies Clonal Enhancer-Hijacking Structural Variants

Source: Kim K, et al., Cell Reports, 2023. https://doi.org/10.1016/j.celrep.2023.112778

Background. Colorectal cancer (CRC) is characterized by widespread genomic structural variations (SVs), but how these SVs rewire the 3D cancer genome and drive oncogene activation has been difficult to study systematically. Most standard sequencing approaches — including whole-genome and whole-exome sequencing — cannot resolve the clonal architecture of tumors or link SVs to specific 3D chromatin contact changes. As a result, the functional impact of most cancer SVs on 3D genome organization remains unknown.

Methods. The study performed in situ Hi-C sequencing on tumor samples from 40 CRC patients, generating 3D genome interaction maps for each tumor. Using a machine-learning approach called CaTCH, the authors systematically identified clonal SVs from Hi-C data and quantified their effects on 3D genome organization — including TAD boundary disruption, de novo chromatin contacts spanning multiple TADs, and enhancer hijacking events. Hi-C data was integrated with whole-genome sequencing and RNA-seq for comprehensive characterization of the 3D cancer genome.

Hi-C contact matrices from Kim et al., Cell Reports, 2023 — clonal enhancer-hijacking structural variants identified in colorectal cancer patient tumors through 3D genome profiling.

Results. Hi-C analysis revealed hundreds of clonal SVs that produced de novo chromatin contacts spanning multiple TAD boundaries. Super-enhancer hijacking events — where potent enhancers are repositioned near oncogenes through SVs — were identified as recurrent events in CRC. Clonal enhancer hijacking targeting genes such as TOP2B served as a powerful independent prognostic marker (Hazard Ratio = 3.6), outperforming established clinical markers including KRAS mutation status and microsatellite instability (MSI). CRISPR-Cas9 reconstruction of patient-specific SVs validated that these 3D genome alterations directly drive oncogene activation and drug resistance phenotypes.

Conclusion. This study demonstrates that Hi-C-based 3D genome profiling can uncover clinically relevant structural variants that are invisible to standard DNA sequencing. The discovery that clonal enhancer hijacking independently predicts patient survival highlights the translational value of chromatin conformation capture in precision oncology. For researchers studying cancer genomics, this work illustrates how Hi-C bridges the gap between structural variant detection and functional characterization of their 3D genome consequences.

Method Comparison

Comparison of 3C-Based Chromosome Conformation Capture Methods

The chromosome conformation capture (3C) family includes several methods that differ in throughput, resolution, and the types of interactions they detect. Selecting the right method depends on your biological question and experimental scale.

Method	Interaction Detection	Resolution	Throughput	Protein Enrichment	Best For
3C	One-to-one: single locus pair	High (at the primer/probe level)	Low	No	Validating a predicted interaction between two known loci
4C	One-to-all: all interactions with one viewpoint	~1–5 kb (depth-dependent)	Medium	No	Identifying all interacting partners of a specific locus (enhancer, GWAS SNP, gene promoter)
5C	Many-to-many: interactions within a defined region	~1–5 kb	Medium	No	Detailed interaction mapping across a targeted genomic region (up to several Mb)
Hi-C	All-to-all: genome-wide	1 kb–1 Mb (depth-dependent)	High	No	Unbiased, genome-wide 3D genome architecture mapping
Capture Hi-C	All-to-all within captured regions	<1–5 kb at targeted regions	High (targeted)	No	Deep interaction mapping at hundreds of specific loci (GWAS regions, promoter interactome)
HiChIP	All-to-all, enriched for protein-mediated contacts	1–5 kb	High	Yes	Protein-centric 3D interaction mapping (H3K27ac for enhancer interactome, CTCF for insulator loops)

Choosing the Right 3D Genomics Method

For genome-wide, unbiased 3D genome mapping: Standard Hi-C is the method of choice for comprehensive chromatin architecture analysis, compartment and TAD calling, and genome-wide loop detection.
For targeted high-resolution interaction mapping: Capture Hi-C uses oligonucleotide probes to enrich interactions involving regions of interest — ideal for fine-mapping GWAS loci or mapping the promoter-enhancer interactome of specific genes.
For protein-centric chromatin interaction mapping: HiChIP combines Hi-C with chromatin immunoprecipitation to enrich for contacts mediated by a specific protein. H3K27ac HiChIP maps the active enhancer interactome; CTCF HiChIP maps insulator-mediated loops.
For interaction validation: 3C-qPCR remains the simplest and most cost-effective approach for confirming a predicted interaction between two specific loci.
For locus-centric discovery: 4C-seq identifies all genomic regions that interact with a single locus of interest — suitable for characterizing the regulatory neighborhood of a candidate enhancer, GWAS SNP, or disease-associated gene.

CD Genomics offers multiple 3C-based methods. Our Chromatin Analysis Services team can help you select the optimal approach based on your research question, sample type, and resolution requirements.

Get Method Selection Guidance

FAQ

Frequently Asked Questions

Q: What is the difference between Hi-C, Capture Hi-C, HiChIP, and Micro-C?

A: Hi-C maps all genome-wide chromatin contacts without target enrichment, making it the method of choice for unbiased 3D genome architecture analysis. Capture Hi-C uses oligonucleotide probes to enrich interactions involving specific genomic regions of interest, providing higher resolution at those loci at lower total sequencing cost for targeted studies. HiChIP combines Hi-C with chromatin immunoprecipitation (ChIP) to enrich for contacts mediated by a specific protein — H3K27ac HiChIP captures the active enhancer interactome, CTCF HiChIP captures insulator-mediated loops. Micro-C uses micrococcal nuclease instead of restriction enzymes, achieving nucleosome-resolution (~200 bp) contact mapping but requiring substantially deeper sequencing. Contact us to discuss which method fits your biological question.

Q: How many cells do I need for a Hi-C experiment?

A: Input requirements vary by sample type and experimental goals. Mammalian cell culture typically requires millions of viable cells per sample, but the exact number depends on your genome size, desired resolution, and sample characteristics. Contact us with your sample type and research question for project-specific guidance. Our scientists will work with you to determine the optimal cell number and provide a detailed sample preparation protocol before you start your experiment.

Q: Can Hi-C be performed on frozen or FFPE samples?

A: Standard Hi-C requires crosslinking of live cells to capture native chromatin contacts in situ. Frozen tissue or cell pellets cannot be used unless they were crosslinked with formaldehyde before freezing. FFPE (formalin-fixed paraffin-embedded) samples are generally not compatible with Hi-C because the formaldehyde crosslinks from the fixation process are irreversible under standard Hi-C conditions and interfere with restriction enzyme digestion and the intramolecular ligation step. Contact us to discuss whether alternative 3C-based methods or specialized protocols may be suitable for your sample type.

Q: What resolution can Hi-C achieve, and how does sequencing depth affect resolution?

A: Hi-C resolution is primarily determined by sequencing depth and the restriction enzyme cutting frequency in your genome. At typical sequencing depths: ~100 million read pairs provides ~1 Mb resolution suitable for compartment-level analysis; ~300-400 million read pairs enables TAD-level analysis at 40-100 kb resolution; ~800 million to 1 billion read pairs enables loop calling and finer-scale analysis at 5-25 kb resolution. The relationship between sequencing depth and resolution is not linear — each doubling of depth improves resolution by roughly √2. Our team will recommend the optimal sequencing depth for your specific research question and budget.

Q: Can Hi-C detect interactions between specific genes and their regulatory elements?

A: Yes. Hi-C data can be queried to identify all chromatin interactions involving any genomic locus of interest through virtual 4C analysis — extracting interaction frequency profiles for a specific viewpoint. For studies primarily focused on a defined set of genes or regulatory regions, Capture Hi-C provides deeper interaction coverage at those specific loci, enabling more sensitive detection of gene-enhancer contacts. HiChIP with H3K27ac specifically enriches for active enhancer-promoter interactions mediated by the active chromatin landscape. Contact us to determine which approach best matches your study goals.

Q: What species are supported for Hi-C analysis?

A: We support Hi-C analysis for a wide range of species including human, mouse, rat, zebrafish, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, rice, maize, soybean, wheat, and many others. The key requirement is an assembled reference genome for read alignment and contact matrix construction. Restriction enzyme selection (typically MboI for mammalian genomes; may differ for species with unusual GC content or repeat structure) will be optimized based on your species' genome composition. Contact us to confirm support for your species of interest.

Q: What is the difference between A and B compartments in Hi-C data?

A: At the megabase scale, Hi-C contact matrices reveal that the genome spatially partitions into two compartments. The A compartment corresponds to gene-rich, transcriptionally active, DNase I-hypersensitive, early-replicating chromatin that tends to localize toward the nuclear interior. The B compartment corresponds to gene-poor, transcriptionally silent, late-replicating chromatin that tends to localize toward the nuclear periphery and nucleoli. Compartment identity can switch during differentiation, development, and disease — for example, genes in loci that switch from B to A during neural differentiation are frequently upregulated, and vice versa. Compartment switching analysis is a standard output of our Hi-C bioinformatics pipeline.

Q: How do TADs relate to gene regulation and disease?

A: Topologically Associating Domains (TADs) are submegabase regions (typically 100 kb–1 Mb in humans) within which chromatin interacts preferentially. TAD boundaries are enriched for CTCF binding and function as insulators — they restrict enhancer-promoter communication such that enhancers within a TAD predominantly regulate genes within the same TAD. Disruption of TAD boundaries — by structural variants (deletions, inversions, duplications), CTCF binding site mutations, or altered DNA methylation at CTCF motifs — can cause ectopic enhancer-promoter contacts across domain boundaries. This "enhancer adoption" can activate proto-oncogenes in cancer or misexpress developmental genes in congenital disorders. Hi-C is the primary method for detecting TAD disruption in patient samples and disease models.

Q: Do you provide bioinformatics support for custom Hi-C analysis questions?

A: Yes. In addition to our standardized analysis pipeline, we offer custom bioinformatics services for specialized analyses including: differential interaction detection between experimental conditions or time points; multi-omics data integration (Hi-C + RNA-seq + ChIP-seq/ATAC-seq); publicly available Hi-C data re-analysis and integration with your data; identification of structural variants from Hi-C data; allele-specific and haplotype-resolved Hi-C analysis; custom publication-quality figure generation; and custom analysis scripts or pipelines tailored to your specific research questions. Contact us with your analysis requirements.

References

Kim K, et al. Spatial and clonality-resolved 3D cancer genome alterations reveal enhancer-hijacking as a potential prognostic marker for colorectal cancer. Cell Reports, 2023, 42(7):112778. https://doi.org/10.1016/j.celrep.2023.112778
Xie T, et al. Pervasive structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs. Nature Communications, 2024, 15:3905. https://doi.org/10.1038/s41467-024-48053-2
Dozmorov MG, et al. Rewiring of the 3D genome during acquisition of carboplatin resistance in a triple-negative breast cancer patient-derived xenograft. Scientific Reports, 2023, 13:5420. https://doi.org/10.1038/s41598-023-32568-7
D'haene E, et al. Comparative 3D genome analysis between neural retina and RPE reveals differential cis-regulatory interactions at retinal disease loci. Genome Biology, 2024, 25:123. https://doi.org/10.1186/s13059-024-03250-6
Qu Z, et al. Stage-specific dynamic reorganization of genome topology shapes transcriptional neighborhoods in developing human retinal organoids. Cell Reports, 2023, 42(12):113543. https://doi.org/10.1016/j.celrep.2023.113543

! For research use only. Not intended for clinical diagnosis, treatment, or individual health assessments. The case study presented is from published, peer-reviewed literature and is provided for informational purposes to illustrate the application of Hi-C technology. It does not represent work performed by CD Genomics unless explicitly stated. All service specifications, turnaround times, and deliverables are project-dependent and will be confirmed in writing before project initiation.

! For research purposes only. Not intended for clinical diagnosis, treatment, or individual health assessments.