All tags Epigenetics Getting started with Chromatin Conformation Capture (3C)

Getting started with Chromatin Conformation Capture (3C)

This guide will summarize current 3C methods, help you to choose the best option, and provide expert tips on optimizing the process in your lab. 

The concept of chromatin contact mapping, or determining the three-dimensional structure conformation and interactions of chromatin domains, is now a reality because of Chromatin Conformation Capture (3C) and subsequent methods born out of that approach. Several new 3C-based techniques have emerged, each with particular strengths and applications, but the sheer variety creates a challenge when selecting the best method for a specific situation. 

Emergence of Chromatin Conformation Capture (3C)

In 2002, Job Dekker and colleagues published 3C as a novel approach for determining 3D chromatin structures and interactions in vivo (Dekker et al., 2002).

3C analyzes excised DNA fragments generated from formaldehyde cross-linked, then restriction enzyme digested chromatin to find points where selected DNA regions are connected through a protein complex. The frequency and identity of these fragments are then determined by quantitive PCR (qPCR).

Not only is 3C an important technology on its own, but it has also become the foundation for a host of related techniques that have been developed to achieve greater scale, throughput or specificity. These techniques are discussed below with helpful hints for getting started.



Circularized Chromosome Conformation Capture (4C)

4C enables identification of previously unknown DNA regions that interact with a locus of interest, which makes 4C especially well suited to discover novel interactions with a specific region that is being investigated (Dekker et al.​, 2006). 

4C helpful hits: 

  • Choose the right restriction enzymes. More frequent cutters (i.e. four bp recognition sites) are better for local interactions between the region of interest and nearby sequences on the same chromosome (van de Werken et al., 2012).
  • Find the appropriate crosslinking stringency for your experiment. Lower formaldehyde concentrations promote undesirable region-of-interest self-ligations, but also prevent DNA "hairballs" that hinder restriction enzyme cutting. Higher 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)

This technique generates a library of any ligation products from DNA regions that associate with the target loci, which are then analyzed by next-generation sequencing.

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 particular regions (Dotsie and Dekker, 2007).

5C helpful hints:

  • Select the right restriction enzyme. This is a much simpler task for 5C than 4C, since 5C does not need to consider the distribution of restriction sites around the region-of-interest. However, choosing an enzyme that functions efficiently under your specific experimental conditions is important. For example, BamHI is not recommended for most experiments due to inefficiency under 3C conditions (Dotsie et al., 2007). 
  • Optimize primer design. This is a key for 5C, and a little different to other 3C techniques. 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.
  • Try to 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. However, in a large-scale study, this may not be reasonable, and most researchers opt to skip it. If this library is not constructed, then researchers should be aware that interaction frequencies would be less precise.

Fig 1. Schematic of the 3C-based technologies
(Adapted from original graphic in Trends in Cell Biology)



Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET)

ChIA-PET takes aspects of chromatin immunoprecipitation (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 in order for the interaction to be called (Fullwood et al., 2009).

ChIA-PET helpful hits:

  • Overlap PET tags to reduce background. Like most 3C technologies, background noise is a big technical challenge. In ChIA-PET particularly, noise can make it difficult to find true 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 true long-range interaction (http://chiapet.gis.a-star.edu.sg/protocols).




ChIP-loop

ChIP-loop is a mixture chromatin immunoprecipitation (ChIP) and 3C that employs antibodies targeted to proteins that are 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. It is well suited to confirmation of suspected interactions, but not 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. Random interactions are often captured by 3C technologies, especially ChIP-loop. 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 that interact with the desired DNA locus, and then assesses their frequencies by high-throughput sequencing. Hi-C is a great choice when broad coverage of the entire genome is required, but resolution is not of great concern. For example, mapping the genome-wide changes in chromosome structure in tumor cells (Lieberman-Aiden et al., 2009).

Hi-C helpful hints:

  • Optimize library amplification.​ Hi-C library amplification must generate enough product for analysis, while avoiding PCR artifacts. To do this, the PCR cycle number should be optimized (in the range of 9-15 cycles). If enough product can't 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 a proper 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 perfect 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 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 artifacts (Hughes et al., 2014).



Selecting a 3C method

The abundance of available 3C related techniques can make it a chore to choose just one of the many options, but it also means that there is likely one that is ideally suited for any experimental scenario. The following table can help point out where each method really shines.

As the C methods continue to evolve, become more refined and their use expands, they will be a valuable tool in understanding of how chromatin structure, protein interactions and DNA sequence all work together to control gene expression for years to come.

3C methodUnique benefitBest applicationExpert tips
4CDetects interactions of unknown DNA regions.Searching for novel associations with a particular region of interest.
  • Choose the right restriction enzymes.
  • Find the appropriate crosslinking 
    ​stringency
5C Specially designed primers allow for interaction analysis of a specific locus in great detailCreating detailed interaction matrices and mapping 3D structures of a DNA region.
  • Select the right restriction enzyme.
  • Optimize primer design.
  • Try to use a control template.
ChIA-PETCaptures distant DNA fragments associate through a specific protein.Identifying DNA regions that interact with a protein and each other. For example: transcription factor binding sites.
  • Overlap PET tags to reduce background.
ChIP-loopReduced background and improved specificity over standard 3C. Leveraging antibody specificity to determine DNA loci interacting with a particular protein.
  • Avoid non-native loops.
  • Validate ChIP-loop interactions.
Hi-CHarnesses advanced sequencing for genome-wide capability.Forming a broad snapshot of chromatin structure across the entire genome, with moderate resolution.
  • Optimize library amplification.
  • Balance read lengths.
  • Choose a proper bin size.
Capture-C Oligonucleotide capture technology (OCT) and sequencing make this approach both high-throughput and high resolution.Characterizing hundreds of loci in one experiment, while still maintaining very high resolution.
  • Carefully choose probe positions.
  • Keep libraries complex.
  • Watch for false interactions in duplicated regions.




​Additional reading

Chen X, Shi C, Yammine S, Gö​ndör A, Rönnlund D, Fernandez-Woodbridge A, Sumida N, Widengren J and Ohlsson R (2014). Chromatin in situ proximity (ChrISP): single-cell analysis of chromatin proximities at a high resolution. Biotechniques, 56, 117-124.

Kolovos P, van de Werken HJ, Kepper N, Zuin J, Brouwer RW, Kockx CE, Wendt KS, van IJcken WF, Grosveld F and Knoch TA (2014). Targeted Chromatin Capture (T2C): a novel high resolution high-throughput method to detect genomic interactions and regulatory elements. Epigenetics Chromatin, 7, 10.

These two new methods, ChrISP and T2C, offer novel approaches to answer many of the same questions that the 3C variants have been designed to interrogate. It bears monitoring how well they perform in the hands of researchers in comparison to more established protocols.



References

  • Belton JM, McCord RP, Gibcus JH, Naumova N, Zhan Y and Dekker J (2012). Hi-C: a comprehensive technique to capture the conformation of genomes. Methods, 58, 268-76.
  • Dekker J, Rippe K, Dekker M and Kleckner N (2002). Capturing chromosome conformation. Science, 295, 1306-1311.
  • Dekker J. (2006). The three ‘C’ s of chromosome conformation capture: controls, controls, controls. Nat Methods, 3, 17-21.
  • Dostie J and Dekker J (2007). Mapping networks of physical interactions between genomic elements using 5C technology. Nat Protoc, 2, 988-1002.
  • Dostie J, Zhan Y and Dekker J (2007). Chromosome conformation capture carbon copy technology. Curr Protoc Mol Biol, Chapter 21, Unit 21.14.
  • Horike S, Cai S, Miyano M, Cheng JF and Kohwi-Shigematsu T (2005). Loss of silent-chromatin looping and impaired imprinting of DLX5 in Rett syndrome. Nat Genet, 37, 31-40.
  • Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, Orlov YL, Velkov S, Ho A, Mei PH, Chew EGY, Huang PYH, Welboren WJ, Han Y, Ooi HS, Ariyaratne PN, Vega VB, Luo Y, Tan PY, Choy PY, Wansa KD, Zhao B, Lim KS, Leow SC, Yow JS, Joseph R, Li H, Desai KV, Thomsen JS, Lee YK, Karuturi RK, Herve T, Bourque G, Stunnenberg HG, Ruan X, Cacheux-Rataboul V, Sung WK, Liu ET, Wei CL, Cheung E and Ruan Y (2009). An oestrogen-receptor-alpha-bound human chromatin interactome. Nature, 462, 58-64.
  • Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, Sandstrom R, Bernstein B, Bender MA, Groudine M, Gnirke A, Stamatoyannopoulos J, Mirny LA, Lander ES and Dekker J (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326, 289-293.
  • Hughes JR (August 2014). Email interview.
  • Hughes JR, Roberts N, McGowan S, Hay D, Giannoulatou E, Lynch M, De Gobbi M, Taylor S, Gibbons R and Higgs DR (2014). Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat Genet, 46, 205-212.
  • Simonis M, Kooren J and de Laat W (2007). An evaluation of 3C-based methods to capture DNA interactions. Nat Methods, 11, 895-901.
  • van de Werken H, de Vree PJ, Splinter E, Holwerda SJ, Klous P, de Wit E and de Laat W (2012). 4C technology: protocols and data analysis. Methods Enzymol, 513, 89-112.
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