This article helps you choose among Hi-C, Micro-C, Capture Hi-C and HiChIP for 3D genome studies. It explains how each chromatin interaction method works, what kind of 3D genome features it captures, and how to match methods to real project questions.

Use Hi-C for global 3D genome architecture, Micro-C for high-resolution loops, Capture Hi-C for focused loci such as promoters or GWAS blocks, and HiChIP for histone-mark–anchored contacts linked to specific proteins or chromatin states.

Quick decision list

• Hi-C – Best for genome-wide compartments, TADs, and broad structural comparisons.
• Micro-C – Best for fine promoter–enhancer loops and nucleosome-scale interactions across the genome.
• Capture Hi-C – Best for dense contact maps around predefined promoters, enhancers, or GWAS regions.
• HiChIP – Best for 3D contacts anchored at regions marked by a given histone modification or protein.

3D Genome Mapping in Practice: Why Method Choice Matters

Chromatin interaction sequencing is now a core part of epigenomic sequencing and bioinformatics analysis. Yet many teams still ask the same question at the start of a 3D genomics project:

"For this system, should we run Hi-C, Micro-C, Capture Hi-C, or even HiChIP?"

The answer matters for several reasons:

• It defines which 3D features you can confidently interpret: compartments, TADs, loops, or histone-mark–anchored contacts.
• It affects sequencing depth and budget for each biological replicate.
• It shapes the complexity of 3D genome mapping and downstream data analysis.

A mismatch between method and question can produce technically sound data that still fails to resolve key regulatory mechanisms. This guide looks at Hi-C vs Micro-C vs Capture Hi-C, with HiChIP as a complementary option, from a decision-making perspective rather than as a purely technical protocol manual.

Quick Answer: When to Use Hi-C, Micro-C, Capture Hi-C, or HiChIP

Hi-C, Micro-C, Capture Hi-C, and HiChIP all measure chromatin contacts, but they do so in different ways and with different biological emphasis.

• Hi-C is a genome-wide chromatin interaction sequencing method that converts physical DNA–DNA contacts into a contact matrix across the whole genome.
• Micro-C is a Hi-C–like 3D genome mapping method that uses micrococcal nuclease to achieve nucleosome-level resolution.
• Capture Hi-C enriches a Hi-C library for predefined genomic regions, such as promoter panels or GWAS loci, using hybridisation probes.
• HiChIP combines chromatin immunoprecipitation with proximity ligation to capture 3D interactions anchored by a specific histone mark or protein.

In practice:

• Start with Hi-C if you want to survey global 3D genome architecture and compartment changes.
• Choose Micro-C when the main aim is high-resolution loop detection and fine-scale regulatory structure.
• Use Capture Hi-C if you have a clear list of regions and need dense contact information around them.
• Consider HiChIP when you are specifically interested in H3K27ac-marked enhancers, TF-bound regions, or other protein-defined anchors.

The rest of this article explains why these rules of thumb work, and how to translate them into solid study designs and appropriate epigenomic sequencing services.

How Hi-C, Micro-C, Capture Hi-C and HiChIP Work at the Technical Level

Hi-C: Genome-Wide Chromatin Contacts and 3D Architecture

Hi-C is a chromosome conformation capture method that turns spatial DNA contacts into paired reads. A typical in situ Hi-C workflow includes:

1. Cross-linking cells to stabilise DNA–protein–DNA contacts.
2. Restriction digestion of chromatin into fragments.
3. End labelling and ligation under dilute conditions so fragments that were close in the nucleus are joined.
4. Library preparation and sequencing to read ligation junctions.

After alignment and binning, the project team obtains genome-wide contact maps.

Overview of a Hi-C experiment and data analysis workflow, from cross-linking and restriction digestion to read mapping, contact matrix generation and downstream identification of compartments, TADs and loops. (Kolovos P. et al. (2014) Epigenetics & Chromatin)

These support analysis of:

• A/B compartments and their shifts between conditions.
• Topologically associating domains (TADs) and boundary changes.
• Larger chromatin loops and broad interaction patterns.

From a practical standpoint, Hi-C is the most established option, with many published protocols and analysis workflows. When we support Hi-C sequencing projects, most optimisation time goes into fixation conditions and restriction digestion, because they strongly affect library complexity and contact patterns.

Micro-C: Nucleosome-Resolution Chromatin Interaction Sequencing

Micro-C was developed to extend 3D genome mapping down to the nucleosome scale. Instead of restriction enzymes, it uses micrococcal nuclease (MNase) to digest chromatin into mono- or oligo-nucleosomes. The later steps—ligation, library construction, sequencing—are conceptually similar to Hi-C.

As a result, Micro-C:

• Produces more uniform fragmentation with fewer restriction-site gaps.
• Supports smaller bin sizes for contact maps, often in the hundreds of base pairs.
• Captures fine-scale loops and local chromatin fibre organisation.

However, MNase digestion is sensitive to chromatin state. In our experience, successful Micro-C projects always begin with careful MNase titration on a small batch of the same sample type. Over-digestion can remove informative fragments; under-digestion lowers effective resolution. Micro-C also typically requires higher sequencing depth than a basic Hi-C experiment to achieve usable signal at very fine bins.

Conceptual comparison of Micro-C and Hi-C, highlighting nucleosome-level fragmentation, reduced restriction site gaps and the resulting higher resolution chromatin contact maps across genomic regions. (Hsieh T.-H.S. et al. (2015) Genome Biology)

Capture Hi-C: Targeted 3D Contacts at Promoters, Enhancers, and GWAS Loci

Capture Hi-C sits between global and highly targeted approaches. It:

• Starts from a standard Hi-C library.
• Uses biotinylated probes to hybridise to chosen genomic regions.
• Enriches for ligation products involving those regions.

This makes Capture Hi-C ideal when:

• You already have promoter panels, enhancer lists, or non-coding GWAS blocks to investigate.
• You want dense interaction data in these regions without sequencing the whole genome at extreme depth.
• You are planning variant–gene mapping or focused promoter–enhancer contact analysis.

Overview of a Capture Hi-C experiment and CHiCAGO analysis pipeline, illustrating probe-based enrichment of promoter-anchored contacts and statistical modelling of significant DNA looping interactions. (Cairns J. et al. (2016) Genome Biology)

The design of capture probes is critical. We usually combine previous ATAC-seq, ChIP-seq, or DNA methylation data with gene annotations when helping clients build capture panels, then validate enrichment profiles on pilot libraries before committing to full-scale Capture Hi-C sequencing.

HiChIP: Histone-Mark–Anchored and Protein-Centred 3D Contacts

HiChIP is an anchor-based chromatin interaction assay. It effectively asks:

"What 3D contacts involve genomic regions bound by this protein or carrying this histone mark?"

Overview of the HiChIPdb resource, summarizing large-scale HiChIP data collection, unified processing, loop calling and functional annotation of protein-centric chromatin interactions across diverse cell types. (Zeng W. et al. (2023) Nucleic Acids Research)

In HiChIP:

1. Cells are cross-linked and chromatin is processed similarly to Hi-C.
2. Chromatin fragments are immunoprecipitated using an antibody against a histone mark or protein (for example, H3K27ac).
3. Proximity ligation and library preparation capture interactions anchored at immunoprecipitated regions.

HiChIP is not a direct alternative to Hi-C or Micro-C, but a complement. It does not provide a uniform view of all contacts, but rather of contacts associated with particular regulatory features. For instance, H3K27ac HiChIP can highlight active enhancer–promoter loops more efficiently than a promoter-based Capture Hi-C design in some contexts.

Side-by-Side Comparison: Resolution, Input, Depth, and Budget Considerations

The table below summarises Hi-C vs Micro-C vs Capture Hi-C vs Hi-Chip in key practical dimensions. These are typical patterns rather than strict rules; specific protocols may differ.

For epigenomic sequencing services, this comparison often drives the first discussion:

• Hi-C for global mapping and discovery.
• Micro-C for fine-scale genome-wide loops.
• Capture Hi-C for dense regional mapping of known loci.
• HiChIP for mark-specific regulatory network mapping.

Later sections show how these differences translate into concrete 3D genome study designs.

Designing a 3D Genomics Study: Aligning Biological Questions with Methods

Genome-Wide 3D Architecture and Compartment Changes

If your central question is "Does this perturbation change genome architecture at a global scale?", a Hi-C–centred design usually makes the most sense.

Typical goals in this category include:

• Identifying A/B compartment switches between cell types or treatments.
• Comparing TAD structures and their boundaries across conditions.
• Observing large loops and domain-level reorganisation.

A practical Hi-C study plan often includes:

• At least two biological replicates per condition, more in heterogeneous systems.
• Sequencing depth chosen to support 50–100 kb bins for reliable compartment and TAD analysis.
• Parallel RNA-seq or ATAC-seq to interpret changes in the context of gene expression and chromatin accessibility.

At this stage, links to a Hi-C technology introduction, Hi-C data analysis: from basics to advanced techniques, and a 3D genome bioinformatics analysis service page can guide readers towards concrete workflows, reports, and example deliverables.

High-Resolution Loop and Promoter–Enhancer Contact Mapping

When the aim is to study promoter–enhancer loops and local rewiring, the trade-off becomes Global vs Focused: Micro-C vs Capture Hi-C vs HiChIP.

A useful rule of thumb is:

• Use Micro-C when:
  • You want a genome-wide view of loops and local structures.
  • You expect important loops outside any pre-defined panel.
  • You can allocate enough depth to maintain signal at fine bin sizes.
• Use Capture Hi-C when:
  • You have a clear set of promoters, enhancers, or candidate regions.
  • You want maximum detail within that set without exploring the entire genome at high depth.
• Use HiChIP when:
  • You care about contacts associated with a specific histone mark or protein, such as H3K27ac or a key TF.
  • You want to overlay protein occupancy and 3D structure in a single assay.

A sophisticated project may combine these approaches over time—first Micro-C or Hi-C for discovery, then Capture Hi-C or HiChIP for focused validation of selected regulatory hubs.

Fine-Mapping Non-Coding GWAS Variants and Candidate Loci

For GWAS-driven projects, the candidate non-coding regions are usually known, but the target genes and contact partners are not. In this scenario, Capture Hi-C is often the most efficient first choice.

A typical workflow is:

1. Define GWAS blocks and candidate regulatory regions using LD patterns and epigenomic data.
2. Design a Capture Hi-C panel covering these blocks and relevant promoters.
3. Generate capture libraries on a Hi-C background and sequence to obtain dense interaction maps.
4. Integrate contacts with expression data, eQTL results, or CRISPR functional screens.

HiChIP can then complement this by focusing on interactions that specifically involve active or repressed states, depending on the histone mark or factor chosen. Integration of Capture Hi-C and HiChIP with regulatory network analysis services helps prioritise variant–gene pairs with consistent evidence across layers.

Working with Limited or Heterogeneous Samples

Many 3D genomics studies now use primary tissues, small patient-derived cohorts, or complex mixtures of cell types. These introduce constraints that affect method selection.

Practical points to consider:

• If input is sufficient for bulk assays, optimised in situ Hi-C often provides the most robust starting point.
• With very limited material, it may be safer to invest in pilot optimisation than attempt the highest-resolution method immediately.
• For strongly heterogeneous systems, bulk Hi-C or Micro-C can be combined with 3D genomics at the level of individual cells or ATAC-seq at the level of individual cells to deconvolve cell-type-specific structure.

Practical Lessons from Real Projects: Sample Prep, QC and Interpretation

Sample Integrity and Fixation

From real project experience, sample quality and fixation are the most common early failure points.

Key lessons include:

• Avoid over-confluent or stressed cultures; basic cellular health metrics before fixation help catch problems early.
• Adjust fixation time and cross-linker concentration per cell type; over-fixation can reduce ligation, while under-fixation leads to noisy contact maps.
• For Micro-C and HiChIP, perform a small optimisation panel of fixation and digestion on pilot samples instead of jumping directly to full-scale experiments.

These steps may feel slow, but they can often prevent entire batches of Hi-C, Micro-C, or HiChIP libraries from failing QC.

Library QC and Early-Stage Metrics

Well-designed 3D genome bioinformatics pipelines include early QC metrics that capture library quality before deep interpretation. Common ones are:

• Valid interaction pairs as a fraction of total reads.
• Duplicate rate and library complexity estimates.
• Distance distribution of contacts, revealing the balance of short- and long-range interactions.

Projects that pay attention to these metrics usually avoid investing heavily in downstream analyses on suboptimal data. When we run Hi-C and Micro-C data processing, the QC report is often the first document a PI or project manager reviews before deciding on additional sequencing or replicate runs.

Interpreting Results Across Methods

The meaning of contacts differs slightly by method:

• Hi-C emphasises large-scale organisation and is ideal for comparing compartments, TAD structures, and broad architectural changes.
• Micro-C gives sharper views of loop patterns and local chromatin fibre behaviour, making it useful for studying promoter–enhancer rewiring and nucleosome-scale structure.
• Capture Hi-C zooms into specific regions, such as disease loci, and provides dense contacts centered on these areas.
• HiChIP highlights contacts associated with specific marks or proteins, providing an efficient way to map active enhancer–promoter networks.

Combining these outputs with other epigenomic sequencing services—ATAC-seq, ChIP-seq, DNA methylation mapping, RNA-seq—allows you to build multi-layer regulatory models that explain both 3D structure and functional readouts.

FAQs About Hi-C, Micro-C, Capture Hi-C and HiChIP

Is Micro-C always better than Hi-C for detecting chromatin loops?

Micro-C often detects more short-range loops because of its higher resolution and more uniform fragmentation. However, it is not automatically the best option in every project. It requires careful MNase optimisation and typically higher sequencing depth. If your main questions concern compartments, TADs, or broad rearrangements, a well-designed Hi-C experiment can deliver the necessary insight in a more cost-effective way.

How many biological replicates are recommended for Hi-C, Micro-C or Capture Hi-C?

Most studies use at least two biological replicates per condition, and sometimes three or more for noisy systems or primary tissues. Two replicates provide a basic check on reproducibility of contact maps and loop calls. For subtle effects or complex models, additional replicates improve statistical power and help separate real rewiring from technical variability.

What sequencing depth do I need for a standard Hi-C or Micro-C project?

For mammalian genomes, many Hi-C projects use hundreds of millions of read pairs per sample, chosen to support the desired resolution (for example 50–100 kb bins). Micro-C often needs deeper sequencing to maintain signal at finer bins that resolve nucleosome-scale loops. Instead of focusing on a fixed number, it is better to define target bin sizes and features, then estimate depth together with a Hi-C or Micro-C data analysis service provider.

When should I choose Capture Hi-C instead of running a deeper Hi-C or Micro-C experiment?

Capture Hi-C makes the most sense when you already have a defined set of regions—such as GWAS blocks, promoter lists, or enhancer clusters—and you want dense contacts around them. In that situation, increasing depth on whole-genome Hi-C may still leave those regions under-sampled, while Capture Hi-C concentrates reads where they matter most. If your question is exploratory and you do not yet know which regions to focus on, starting with Hi-C or Micro-C is usually more flexible.

How should I decide between Capture Hi-C and HiChIP for promoter–enhancer mapping?

Capture Hi-C enriches contacts involving specific genomic coordinates, regardless of which proteins bind there. HiChIP enriches contacts anchored by a particular histone mark or protein. If your question is primarily structural—"which genes contact these non-coding loci?"—Capture Hi-C is often the more straightforward choice. If you want to know "which 3D contacts are associated with active enhancers or a given transcription factor?", a mark- or protein-based HiChIP design is more appropriate. In practice, some teams run both, using HiChIP to highlight functionally active interactions within the structural network revealed by Capture Hi-C or Hi-C.

From Concept to 3D Genomics Study Plan: Turning Method Choice into Action

Choosing between Hi-C, Micro-C, Capture Hi-C and HiChIP is less about which protocol is trendiest and more about aligning each technique with your biological questions, sample constraints, and analysis capacity.

A focused planning process usually includes:

• Clarifying whether your main interest is global architecture, local loops, specific loci, or mark-anchored contacts.
• Deciding on the most suitable combination of Hi-C, Micro-C, Capture Hi-C, and HiChIP to address those aims.
• Designing sample numbers, control conditions, and replicates that match your statistical objectives.
• Selecting appropriate epigenomic sequencing services and 3D genome bioinformatics analysis pipelines to handle data at scale.

If you are shaping a new 3D genome project and want to move from high-level ideas to a concrete plan, you can:

• Review a Hi-C technology introduction, Hi-C data analysis guide, and future of 3D genomics article to refine your approach.
• Share your study outline, sample types, and constraints with our team to obtain a tailored proposal that combines Hi-C, Micro-C, Capture Hi-C and HiChIP sequencing and analysis, strictly for research purposes.

With a clear view of Hi-C vs Micro-C vs Capture Hi-C vs Hi-Chip, you can make method choices that fit both your scientific goals and practical limits—and turn 3D genome mapping into actionable insight rather than an expensive experiment in the dark.

References

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2. Mozziconacci, J., Koszul, R. Filling the gap: Micro-C accesses the nucleosomal fiber at 100–1000 bp resolution. Genome Biology 16, 169 (2015).
3. Cairns, J., Freire-Pritchett, P., Wingett, S.W. et al. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biology 17, 127 (2016).
4. Zeng, W., Liu, Q., Yin, Q., Jiang, R., Wong, W.H. HiChIPdb: a comprehensive database of HiChIP regulatory interactions. Nucleic Acids Research 51(D1), D159–D166 (2023).
5. Lieberman-Aiden, E., van Berkum, N.L., Williams, L. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
6. Rao, S.S.P., Huntley, M.H., Durand, N.C. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
7. Mumbach, J.A., Satpathy, A.T., Boyle, E.A. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nature Methods 13, 919–922 (2016).