Chromosome conformation capture

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Chromosome Conformation Capture Technologies

Chromosome conformation capture techniques (often abbreviated to 3C technologies or 3C-based methods[1]) are a set of molecular biology methods used to analyze the organization of chromatin in a cell. They quantify the number of interactions between genomic loci that are nearby in 3-D space, but may be separated by many nucleotides in the linear genome.[2] Such interactions may result from biological functions, such as promoter-enhancer interactions, or from random polymer looping, where undirected physical motion of chromatin causes loci to collide.[3] These methods assume that the observed interaction frequency between two loci is inversely proportional to the average distance between them.[4] Interaction frequencies may be analyzed directly,[5] or they may be converted to distances and used to reconstruct 3-D structures.[4]

All of these methods begin with cross-linking, which introduces bonds that "freeze" interactions between genomic loci. The genome is then cut into fragments. Next, random ligation is performed. This quantifies the proximity of fragments, because fragments are more likely to be ligated to nearby fragments.

Subsequently, the ligated fragments are quantified using one of a number of techniques. For example, in 3C, the ligations between two specific fragments are quantified. In contrast, Hi-C quantifies ligation events between all possible pairs of fragments simultaneously.


The Chromosome conformation capture (3C) methodology was originally developed by Dekker in the Kleckner lab in 2002 at Harvard University.[6] It aimed at identifying, locating, and mapping physical interactions between genetic elements located throughout the human genome.[citation needed] This technology was described[by whom?] as giving possible beneficial insights into the complex interplay of genetic factors that contribute to such debilitating disorders such as cancer, Duchenne muscular dystrophy (DMD), Rett syndrome and Alzheimer's disease.[citation needed]

3C is based on proximity ligation, which had been used previously to determine circularization frequencies of DNA in solution, and the effect of protein-mediated DNA bending on circularization.[citation needed] Seyfred and colleagues developed proximity ligation in nuclei: restriction enzyme digestion of unfixed nuclei and ligation in situ without diluting the chromatin, which they termed the "Nuclear Ligation Assay."[7]


Original method[edit]


The chromosome conformation capture (3C) experiment quantifies interactions between a single pair of genomic loci, which can be used, for example, to detect promoter-enhancer interactions. Ligated fragments are detected using PCR with known primers.[2][8]

Enrichment for specific loci[edit]


Chromosome conformation capture-on-chip (4C) captures interactions between one locus and all other genomic loci. It involves a second ligation step, to create self-circularized DNA fragments, which are used to perform inverse PCR. Inverse PCR allows the known sequence to be used to amplify the unknown sequence ligated to it.[2][9] In contrast to 3C and 5C, the 4C technique does not require the prior knowledge of both interacting chromosomal regions. Results obtained using 4C are highly reproducible with most of the interactions that are detected between regions proximal to one another. On a single microarray, approximately a million interactions can be analyzed.[citation needed]


Capture-C combines 3C library manufacture with oligonucleotide capture to enrich for specific loci.[10] A high-throughput version of this method has also been developed.[11]


ChIP-loop combines 3C with ChIP-seq to detect interactions between two loci of interest mediated by a protein of interest.[2][12] The ChIP-loop may be useful in identifying long-range cis-interactions and trans interaction mediated through proteins since frequent DNA collisions will not occur.[citation needed]

Enrichment for all loci[edit]


Chromosome conformation capture carbon copy (5C) detects interactions among all loci by ligating universal primers to all fragments. However, it has relatively low coverage.[2][13] The 5C technique overcomes the junctional problems at the intramolecular ligation step and is useful for constructing complex interactions of specific loci of interest. This approach is unsuitable for conducting genome-wide complex interactions since that will require millions of 5C primers to be used.[citation needed]


Hi-C uses high-throughput sequencing to find the nucleotide sequence of fragments.[2][14] The original protocol used paired end sequencing - that is, a short sequence from each end of a ligated fragment is retrieved. As such, the two sequences ideally represent two different restriction fragments that were ligated together in the random ligation step. The pair of sequences are individually aligned to the genome, thus determining the fragments involved in that ligation event.

Hence, as per 5C, all possible pairwise interactions between fragments are tested.


ChIA-PET combines Hi-C with ChIP-seq to detect all interactions mediated by a protein of interest.[2][15]

Biological function[edit]

Studying the structural properties and spatial organization of chromosomes is important for the understanding and evaluation of the regulation of gene expression, DNA replication and repair, and recombination.[not verified in body] 3C methods have enabled researchers to study the influences of chromosomal activity on the aforementioned cellular mechanisms.[citation needed]

3C methods have demonstrated the importance of spatial proximity of regulatory elements to the genes that they regulate. For example, in tissues that express globin genes, the β-globin locus control region forms a loop with these genes. This loop is not found in tissues where the gene is not expressed.[16] This technology has further aided the genetic and epigenetic study of chromosomes both in model organisms and in humans.[not verified in body]

These methods have revealed large-scale organization of the genome into topologically associating domains (TADs), which correlate with epigenetic markers. Some TADs are transcriptionally active, while others are repressed.[17]

Data analysis[edit]

Hi-C data is often used to analyze genome-wide chromatin organization, such as topologically associating domains (TADs), linearly contiguous regions of the genome that are associated in 3-D space.[17] Several algorithms have been developed to identify TADs from Hi-C data.[5][18]

The 3-D organization of the genome can also be analyzed via eigendecomposition of the contact matrix. Each eigenvector corresponds to a set of loci, which are not necessarily linearly contiguous, that share structural features.[19]

A significant confounding factor in 3C technologies is the frequent non-specific interactions between genomic loci that occur due to random polymer behavior. An interaction between two loci must be confirmed as specific through statistical significance testing.[3]


  1. ^ de Wit, E.; de Laat, W. (3 January 2012). "A decade of 3C technologies: insights into nuclear organization". Genes & Development 26 (1): 11–24. doi:10.1101/gad.179804.111. 
  2. ^ a b c d e f g Hakim, Ofir (2012). "SnapShot: Chromosome confirmation capture". Cell 148 (5): 1068. Retrieved 30 May 2016. 
  3. ^ a b Ay, Ferhat (2014). "Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts". Genome Research 24 (6): 999–1011. Retrieved 30 May 2016. 
  4. ^ a b Varoquaux, Nelle (2014). "A statistical approach for inferring the 3D structure of the genome". Bioinformatics 30 (12): i26–i33. Retrieved 30 May 2016. 
  5. ^ a b Rao, Suhas (2014). "A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping". Cell 159 (7): 1665–1680. Retrieved 30 May 2016. 
  6. ^ Dekker, J; Rippe, K; Dekker, M; Kleckner, N (15 February 2002). "Capturing chromosome conformation.". Science (New York, N.Y.) 295 (5558): 1306–11. PMID 11847345. 
  7. ^ Cullen et al. Science. 1993 Jul 9;261(5118):203-6; Gothard LQ et al. Mol Endocrinol. 1996 Feb;10(2):185-95).
  8. ^ Dekker, Job (2002). "Capturing chromosome conformation". Science 295 (5558): 1306–1311. Retrieved 30 May 2016. 
  9. ^ Simonis, Marieke (2006). "Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)". Nature Genetics 38 (11): 1348–1354. Retrieved 30 May 2016. 
  10. ^ Hughes, Jim (2014). "Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment". Nature Genetics 46 (2): 205–212. Retrieved 6 June 2016. 
  11. ^ Davies, James (2016). "Multiplexed analysis of chromosome conformation at vastly improved sensitivity". Nature Methods 13: 74–80. 
  12. ^ Horike, Shin-ichi (2005). "Loss of silent-chromatin looping and impaired imprinting of DLX5 in Rett syndrome". Nature Genetics 37 (1): 31–40. Retrieved 30 May 2016. 
  13. ^ Dostie, Josee (2006). "Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements". Genome Research 16 (10): 1299–1309. 
  14. ^ Lieberman-Aiden, Erez (2009). "Comprehensive mapping of long-range interactions reveals folding principles of the human genome". Science 326 (5950): 289–293. Retrieved 30 May 2016. 
  15. ^ Fullwood, Melissa (2009). "An oestrogen-receptor-alpha-bound human chromatin interactome". Nature 462 (7269): 58–64. Retrieved 30 May 2016. 
  16. ^ Tolhuis B, Palstra RJ, Splinter E, Grosveld F, de Laat W (2002). "Looping and interaction between hypersensitive sites in the active beta-globin locus". Mol. Cell. 10 (6): 1453–1465. doi:10.1016/S1097-2765(02)00781-5. PMID 12504019. 
  17. ^ a b Cavalli, Giacamo (2013). "Functional implications of genome topology". Nature Structural & Molecular Biology 20 (3): 290–299. Retrieved 12 June 2016. 
  18. ^ Dixon, Jesse (2012). "Topological domains in mammalian genomes identified by analysis of chromatin interactions". Nature 485 (7398): 376–380. Retrieved 19 April 2016. 
  19. ^ Mirny, Leonid (2012). "Iterative correction of Hi-C data reveals hallmarks of chromosome organization". Nature Methods 9 (10): 999–1003. Retrieved 7 June 2016. 

Further reading[edit]

See also[edit]