Download our epigenetics application guide

Chromatin accessibility and architecture

Chromatin is comprised of histones and DNA: 147 base pairs of DNA wraps around the 8 core histones to form the basic chromatin unit, the nucleosome.

The function of chromatin is to efficiently package DNA into a small volume to fit into the nucleus of a cell and protect the DNA structure and sequence. Packaging DNA into chromatin allows for mitosis and meiosis, prevents chromosome breakage and controls gene expression and DNA replication. Find out more about the structure of chromatin and the methods used to investigate chromatin accessibility and interactions.​

The genome is efficiently packaged into the nucleus. DNA is wrapped around histones to form a

nucleosome, comprised of 147 base pairs of DNA and eight core histone proteins. Nucleosomes are strung together like beads on a string and packaged into higher order chromatin architecture (Figure 1).

Figure 1. Chromatin structure. DNA winds around nucleosomes to form chromatin fiber and then chromosomes.

DNA that is tightly bound in nucleosomes or compacted into higher-order heterochromatin is inaccessible, preventing the binding of transcription factors, transcriptional machinery, and other DNA-binding proteins, resulting in gene silencing. Meanwhile “linker” DNA and open euchromatin architecture are accessible to binding, allowing for active gene transcription.

Chromatin is actively and dynamically remodeled to alter gene expression and cellular programming, for example, during different developmental stages or in response to particular stimuli. Large genomic regions may be silenced or activated, or nucleosomes may be unraveled to access specific genes and DNA sequences.

Examining the chromatin structure and nucleosome positioning reveals epigenetic programs and mechanisms involved in specific cellular processes and disease states.

Methods to study DNA accessibility and nucleosome positioning

Surveying the genome for exposed regions accessible for active transcription vs those bound tightly into heterochromatin can be an essential first step to understanding the relationship between chromatin structure and function in different contexts. To take a snapshot of genomic architecture, researchers may use one of three methods: DNase-seq, MNase-seq, or ATAC-seq.

DNase-seq and ATAC-seq map exposed regions of DNA, whereas MNase-seq maps regions protected by nucleosomes. It is important to keep in mind that these methods provide snapshots of a dynamic process, often averaged across thousands of cells. If a particular region is dynamically changing, or different between cells within the population, the data may seem conflicting between methods. Some single cell analysis methods are evolving to resolve these challenges.

DNase-seq

DNase-seq uses DNase to digest exposed regions of the genome, whereas nucleosome-bound DNA is protected from DNase digestion. The small fragments generated by DNase digestion are then sequenced and mapped to the genome to identify regions of active transcription.

Advantages:

• Most established and practiced method

• DNase cutting bias is well-understood

• Can be adapted to inversely examine protected genomic regions, called DNase footprinting, to identify transcription factor and nucleosome binding sites. However, it is important to use naked DNA as a control for such experiments as DNase I cutting bias can lead to false conclusions

• Possible to adapt for single-cell analysis

Disadvantages

• Technically difficult to master, especially in optimizing digestion conditions for a given cell type/number

• Requires millions of cells, and may be challenging for the analysis of rare patient samples

MNase-seq

In contrast to DNase-seq, MNase-seq uses micrococcal nuclease (MNase), from Staphylococcus aureus, to digest exposed genomic regions. Protected DNA bound to nucleosomes is then recovered and sequenced.

Advantages:

• Common and well established in many cell types of many species, from yeast to humans, with some standardization of digestion and data analysis

• Can be used in combinations with chromatin immunoprecipitation (ChIP-seq), to study regulatory factors that bind to nucleosomes

• Can be adapted to generate base-pair resolution mapping

• Can be adapted to examine nucleosome positioning and DNA methylation state in nucleosome occupancy and methylome sequencing (NOMe-seq)

Disadvantages:

• Requires large numbers of cells (10–20 million)

• Sequence-specific bias in the digestion of AT-rich regions (although most enzymes used in chromatin accessibility assays exhibit similar biases), but also unknown biases that may skew results

• Single-cell analysis not possible yet

ATAC-seq

Established in 2013, the assay for transposase-accessible chromatin (ATAC)-seq inserts sequencing adapters directly into accessible DNA using the enzyme Tn5 transposase. The DNA between the adapters is then amplified with qPCR and sequenced.

ATAC-seq uses a mutant hyperactive Tn5 transposase that is preloaded with DNA adaptors to simultaneously fragment and tag the genome with sequence adaptors (a process called tagmentation). PCR amplification and NGS follow this fragmentation and tagging. The frequency of sequences in a region correlates with open chromatin conformation.

Advantages:

• Easiest method: no sonication, phenol-chloroform extraction, antibodies (ChIP-seq), or enzymatic digestion (DNase-seq, MNase-seq) are required

• Fastest method: <3 hours compared to up to a 4-day protocol

• Best signal-to-noise ratio

• Only 50,000 or fewer cells required (500–50,000 recommended)

• Single-nucleotide resolution possible

• Single-cell analysis is possible with adapted protocols utilizing flow cytometry/microfluidics

Disadvantages

• More expensive, requiring a kit from Illumina (Nextera DNA Library Preparation Kit)

• Least established method requires optimization of cell number and lysis conditions for specific cell types, tissues, and organisms to achieve ideal fragment distributions.

• Cell number defines the quality of the data, with too few cells or too many cells resulting in over or under-transposition that can skew results.

Figure 2. ATAC-seq protocol. The steps displayed in this figure are: Harvest 50,000 cells. An accurate cell number is key to the success of the experiment. Lyse cells to generate a crude nuclei preparation. Tn5 tagmentation simultaneously fragments the genome and tags the resulting DNA with sequencing adapters. Purify the fragmented and tagged DNA. PCR amplify and purify the amplified DNA. Sequence the library and correlate reads with open and closed chromatin.

Chromosome conformation techniques

We can assess the three-dimensional chromatin architecture with chromatin contact mapping to reveal physical interactions between distant genomic regions. This type of mapping is made possible by the advent of chromatin conformation capture (3C) and subsequent methods developed based on this approach. Each of these approaches has particular strengths for particular applications, but selecting a method for a specific purpose can be challenging due to the sheer variety of methodologies.

Figure 3. Chromosome conformation techniques. Various steps of 3C, 4C, 5C, ChIA-PET, and Hi-C.

Chromatin conformation capture (3C)

3C uses formaldehyde cross-linking to lock the three-dimensional chromatin structure in place, followed by restriction enzyme digestion. Excised DNA fragments are then analyzed by qPCR and sequencing to identify where distant DNA regions are connected. This approach for analyzing 3D chromatin structure and interactions in vivo was first developed in 2002 (Dekker et al., 2002), and has since become the foundation for a host of related techniques that have been developed to achieve greater scale, throughput, or specificity.

Circularized chromosome conformation capture (4C)

​4C enables the identification of previously unknown DNA regions that interact with a locus of interest, which makes 4C ideal for discovering novel interactions within a specific region (Dekker et al., 2006).​​

4C helpful hints:

• Choose the right restriction enzymes. More frequent cutters (ie four bp recognition sites) are better for local interactions between the region of interest and nearby sequences on the same chromosome (van der Werken et al., 2012).

• Optimize cross-linking. Lower formaldehyde concentrations promote undesirable region-of-interest self-ligations, but also prevent DNA "hairballs" that hinder restriction enzyme cutting. High formaldehyde concentrations lower self-ligation events but increase hairballs. An optimal formaldehyde concentration should be chosen for the specific experimental situation to balance these considerations. 1% formaldehyde treatment for 10 min is a good starting point for most experiments (van der Werken et al., 2012).

Carbon copy chromosome conformation capture (5C)

5C generates a library of any ligation products from DNA regions that associate with the target loci, which are then analyzed by NGS. 5C is ideal when great detail about all the interactions in a given region is needed, for example when diagramming a detailed interaction matrix of a particular chromosome. However, 5C is not truly genome-wide, since each 5C primer must be designed individually, so it is best suited to a specific regions (Dotsie and Dekker, 2007).

5C helpful hints:

• Select the right restriction enzyme. Choosing an enzyme that functions efficiently under your specific experimental conditions is essential. For example, BamHI is not recommended for most experiments due to inefficiency under 3C conditions (Dotsie et al., 2007).

• Optimize primer design. 5C uses two primers: a forward 5C primer that binds upstream of the ligation site, and a reverse primer that binds immediately downstream. Primer length should be adjusted so that the annealing temperature is about 65°C to allow primers to anneal exactly with their restriction fragments. Ensure that 5C primers are synthesized with a phosphate at the 5' end for ligation.

• Use a control template. This will control for differences in primer efficiency. A control library constructed from the entire genomic region under study is recommended. If this library is not constructed, then researchers should be aware that interaction frequencies would be less precise.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET)

ChIA-PET takes aspects of ChIP and 3C to analyze the interplay of distant DNA regions through a particular protein.

ChIA-PET is best used for discovery experiments involving a protein of interest and unknown DNA binding targets. Transcription factor binding sites, for example, are best studied with ChIA-PET since this technique requires the DNA to be bound by the transcription factor in vivo for the interaction to be called (Fullwood et al., 2009).

ChIA-PET helpful hints:

• Overlap PET tags to reduce background. Like most 3C technologies, background noise is a technical challenge. In ChIA-PET particularly, noise can make it difficult to find long-range interactions with the locus of interest. A useful tip to overcome this is to require PETs to overlap at both ends of the region to be a long-range interaction.

ChIP-loop

ChIP-loop is a mix of ChIP and 3C that employs antibodies targeted to proteins suspected to bind a DNA region of interest. ChIP-loop is ideal to find out if two known DNA regions interact via a protein of interest. ChIP-loop is also well suited to confirmation of suspected interactions, but not the discovery of novel ones (Horike et al., 2005).

ChIP-loop helpful hints:

• Avoid non-native loops. The biggest issue encountered with ChIP-loop is the formation of non-native loops forming during DNA concentration before ligation occurs. A simple way to avoid this is to choose a protocol that performs the precipitation after the ligation step (Simons et al., 2007).

• Validate ChIP-loop interactions. Another challenge in ChIP-loop can be accurate quantitation of ligation products. 3C technologies, especially ChIP-loop, often capture random interactions. To combat this, consider performing a ChIP experiment in parallel and using it to validate the ChIP-loop interactions. If a DNA-protein-DNA interaction identified by ChIP-loop is indeed real, then both DNA-protein interactions should also appear in the ChIP data (Simons et al., 2007).

Hi-C

Hi-C amplifies ligation products from the entire genome and assesses their frequencies by high-throughput sequencing. Hi-C is a great choice when broad coverage of the entire genome is required, and the resolution is not of great concern, mapping the genome-wide changes in chromosome structure in tumor cells (Lieberman-Aiden et al., 2009), for example.

Hi-C helpful hints:

• Optimize library amplification. Hi-C library amplification must generate enough product for analysis, while avoiding PCR artefacts. To do this, the PCR cycle number should be optimized (in the range of 9–15 cycles). If enough product cannot be produced (50 ng of DNA), multiple PCR reactions should be pooled rather than the cycle number increased, five reactions are usually sufficient (Belton et al., 2012).

• Balance read lengths. As with any sequence experiment, high-quality reads are paramount. The read length must be optimal to balance the need for long reads to map interactions, but not too long as to pass through the ligation junction into the partner fragment. Therefore, 50 bp reads are optimal in most cases (Belton et al., 2012).

• Choose an appropriate bin size. This is critical for data analysis. Bin size should be inversely proportional to the number of expected interactions in a region. Use smaller bins for more frequent intra-chromosomal interactions and larger bins for less frequent inter-chromosomal interactions (Belton et al., 2012).

Capture-C

Capture-C uses a combination of 3C and oligonucleotide capture technology (OCT), together with high-throughput sequencing to study hundreds of loci at once. Capture-C is ideal when both high resolution and genomic-wide scale are required. For example, analyzing the functional effect of every disease-associated SNP in the genome on local chromatin structure (Hughes et al., 2014).

Capture-C helpful hints:

• Carefully choose probe positions. It's best to position probes close to the restriction enzyme sites, even overlapping when possible (Hughes et al., 2014).

• Keep libraries complex. Maintaining library complexity is the top priority. A complex library means more high-quality interactions in the output. For this reason, anything that could decrease library complexity should be avoided, such as a Hi-C biotin capture (Hughes et al., 2014).

• Watch for false interaction in duplicated regions. The mapping process can stimulate strong interactions between these regions (such as pseudogenes) that are actually artefacts (Hughes et al., 2014)

References

1. Belton JM,, McCord RP,, Gibcus JH,, et al. Hi-C: a comprehensive technique to capture the conformation of genomes Methods 58 ,268-76 (2012)
2. Dekker J,, Rippe K,, Dekker M,, et al. Capturing chromosome conformation Science 295 ,1306-1311 (2002)
3. Dekker J The three ‘C’ s of chromosome conformation capture: controls, controls, controls Nat Methods 3 ,17-21 (2006)
4. Dostie J,, Dekker J. Mapping networks of physical interactions between genomic elements using 5C technology Nat Protoc 2 ,988-1002 (2007)
5. Dostie J,, Zhan Y,, Dekker J. Chromosome conformation capture carbon copy technology Curr Protoc Mol Biol , (2007)
6. Horike S,, Cai S,, Miyano M,, et al. Loss of silent-chromatin looping and impaired imprinting of DLX5 in Rett syndrome Nat Genet 37 ,31-40 (2005)
7. Fullwood MJ,, et al. An oestrogen-receptor-alpha-bound human chromatin interactome Nature 462 ,58-64 (2009)
8. Lieberman-Aiden E,, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome Science 326 ,289-293 (2009)
9. Hughes JR, et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment Nat Genet 46 ,205-212 (2014)
10. Simonis M,, Kooren J,, de Laat W. An evaluation of 3C-based methods to capture DNA interactions Nat Methods 11 ,895-901 (2007)
11. Van de Werken H,, de Vree PJ,, Splinter E,, et al. 4C technology: protocols and data analysis Methods Enzymol 513 ,89-112 (2012)