- Open Access
3C and 3C-based techniques: the powerful tools for spatial genome organization deciphering
Molecular Cytogenetics volume 11, Article number: 21 (2018)
It is well known that the chromosomes are organized in the nucleus and this spatial arrangement of genome play a crucial role in gene regulation and genome stability. Different techniques have been developed and applied to uncover the intrinsic mechanism of genome architecture, especially the chromosome conformation capture (3C) and 3C-derived methods. 3C and 3C-derived techniques provide us approaches to perform high-throughput chromatin architecture assays at the genome scale. However, the advantage and disadvantage of current methodologies of C-technologies have not been discussed extensively. In this review, we described and compared the methodologies of C-technologies used in genome organization studies with an emphasis on Hi-C method. We also discussed the crucial challenges facing current genome architecture studies based on 3C and 3C-derived technologies and the direction of future technologies to address currently outstanding questions in the field. These latest news contribute to our current understanding of genome structure, and provide a comprehensive reference for researchers to choose the appropriate method in future application. We consider that these constantly improving technologies will offer a finer and more accurate contact profiles of entire genome and ultimately reveal specific molecular machines govern its shape and function.
During the higher eukaryotic cell cycle, the spatial volumes of each chromosome are not random but are organized into specific patterns, in which individual chromosomes occupy defined, mutually exclusive regions of the nuclear volume that represent a structural unit referred to as a chromosome territory (CT) [1,2,3,4]. With extensive effort, the spatial organizations of individual chromosomes and the entire genome, with resolutions down to 1kbp, have been described [5,6,7,8,9,10,11,12]. It has now been widely accepted that genome architecture is a crucial aspect of gene regulation and genome stability [1, 5, 13,14,15,16,17,18,19,20] because the highly ordered chromatin arrangement facilitates communication between genes and their regulatory elements [21,22,23,24,25,26].
Early studies of genomic conformation were largely based on cytological techniques, such as fluorescence in situ hybridization (FISH), which allows direct evaluation of the proximity between genetic loci using probes. Observations of genome architecture by FISH have revealed the existence of CTs [27,28,29], looping out from CTs [30,31,32], and the tendency for clustering of active chromatin domains [33, 34]. While this method has been a widely used tool to study topography of chromosomes or DNA fragments of interest in individual cells, and allow us to determine how the chromosomes are organized by directly viewing their position with microscopy [35, 36]. However, technical limitations such as low throughput, low resolution and probe sequence specificity make it unsuitable for elaborate genome-wide studies of chromosomal topology [37,38,39]. Recently, chromosome conformation capture (3C) and 3C-based techniques using high-throughput sequencing data have emerged as powerful tools to reconstruct the spatial topology at regional, whole chromosome and genome levels [40,41,42,43,44,45]. These techniques have become the most effective way to elucidate the functional impact and the potential mechanisms establishing and maintaining spatial genome organization. In this review, we describe and compare the methodologies used to study genome architecture, with an emphasis on recently developed key approaches including 3C and its derivatives. We discuss the crucial challenges facing current 3D studies based on 3C technologies and the direction of future technologies to address currently outstanding questions in the field.
The methodologies of 3C and 3C-derived technologies
The strategy of 3C to discover genomic architecture is based on quantifying the frequencies of contacts between distal DNA segments in cell populations . In contrast to cytogenetic approaches, 3C-based genomics strategies yield incomparable information-rich data describing genome topology at the genome-wide level, enabling more systematic genome topology studies at a higher resolution and throughput and providing deep insights into genome architecture and its impact on genome function. Thus, 3C technologies are revolutionizing our ability to explore genome organization from specific loci up to the whole genome.
The principal steps of 3C and 3C-based experiments are theoretically similar and have following principal steps: crosslink chromatin using a fixative agent in solution, most often formaldehyde, to create covalent bonds between DNA fragments bridged by proteins; isolate and digest the chromatin using a restriction enzyme such as HindIII , BglII , EcoRI , AciI , or DpnII [50, 51] at a low concentration to create pairs of crosslinked DNA fragments that are distant in linear distance but close in space; re-ligate the sticky ends of crosslinked DNA fragments to form chimeric molecules; reverse the crosslinks to obtain 3C templates; and finally, interrogate the rearranged DNA fragments by PCR or sequencing technologies (Fig. 1). Eventually, 3D conformations at the regional, chromosome and whole-genome levels can be inferred by calculating the number of ligation junctions between genomic loci (Fig. 1). To describe in more detail and to facilitate comparisons among different methods, we describe current 3C and 3C-based approaches below.
Chromosome conformation capture (3C)
3C technology was developed to detect ligation junctions by PCR followed by gel electrophoresis (Fig. 1) . The first 3C assay inferred the 3D conformation of yeast chromosome III and showed that it forms a contorted ring . Next, this method was adapted for mammalian systems. 3C technology confirmed the existence of chromatin loops, which confer spatial contact between DNA fragments such as regulatory DNA elements and their target genes [47, 52,53,54,55,56,57] or the start and end of a gene . Remarkably, this chromatin loop is dynamic based on transcriptional state changes [48, 58], implying that this structure is associated with genomic function.
Although 3C provides a method for visualizing the genome at high resolution, some shortcomings remain, including the requirement for PCR primers designed to amplify regions of interest. For this reason, 3C can be used only to detect spatial relationships between known DNA sequences. Obviously, the “one versus one” (Fig. 2a) throughput of this method limits its application to genome-scale assays. In addition, it can detect contact only in a limited range (not exceeding a few hundred kilobases) . To overcome these limitations, several 3C-derived methods have been developed to generate higher throughput chromatin interaction data.
Chromosome conformation capture-on-Chip (4C)
4C technology was developed by combining 3C with microarray [60, 61] or, more recently, next-generation sequencing (NGS) technologies [62, 63]. This method is able to assess chromatin interactions between one genomic locus of interest (referred to as bait or viewpoint) and all other genomic loci (one versus all) (Fig. 2b). In 4C experiments, small DNA circles are created by cleaving with a second restriction enzyme and re-ligating 3C DNA templates. Then, inverse PCR using bait-specific primers is applied to amplify any interacting fragments. Finally, the interacting fragments are evaluated using microarrays or NGS (Fig. 1).
4C was originally applied to elucidate the DNA contact maps of the β-globin and Rad23a genes , which showed that the housekeeping gene Rad23a tends to interact with other active regions on the chromosomes and that its contact maps were conserved in various tissues . By contrast, the contact maps for the erythroid-specific gene β-globin changed depending on its expression status. More specifically, β-globin contacts other active regions in erythroid cells, whereas it contacts inactive regions in the fetal brain, where this gene is silent. Subsequently, a series of 4C experiments were carried out and have shown changes in gene regulation and interaction profiles during differentiation and development  and that chromosome conformation is relatively stable in a given cell type . Furthermore, 4C has also been used to identify chromosomal rearrangements  and uncover disease mechanisms .
4C technology is an excellent strategy to survey the DNA contact profile of specific genomic sites. However, it is worth noting that the amplification of GC-rich fragments by inverse PCR during 4C library construction is inefficient, resulting in biases in the interaction profile . In addition, it is not possible to differentiate PCR duplication in 4C data.
Chromosome conformation capture carbon copy (5C)
Another variant of 3C is 5C [69, 70]. It is analogous to 3C technology but is a “many versus many” method (Fig. 2c), allowing the simultaneous detection of millions of interactions through the use of thousands of primers in a single assay. The main difference between 3C and 5C is the strategy for primer design. 5C primers have a universal sequence (usually T7 and T3) appended to the 5′ ends. This change, combined with multiplex PCR amplification and sequencing, allows researchers to detect contact events within a particular locus (Fig. 1). Thus, in contrast to 3C, 5C has a higher throughput and a lower bias. The improvements due to this higher-throughput scale have been demonstrated by its application in the study of the human β-globin locus , human α-globin locus , and human HOXA–D gene clusters [72,73,74]. In addition, 5C provided the first evidence of the existence of topologically associating domains (TADs) by X chromosome analysis . However, 5C is still limited in terms of the size of the genomic region that can be assayed because of the DNA sequence requirement of interested regions, as well as the quantitatively inestimable PCR duplication.
Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET)
Another 3C-based technology is ChIA-PET, which combines chromatin immunoprecipitation (ChIP) with 3C-type analysis to study genome-wide long-range chromatin interactions bound by one specific protein [76,77,78]. The key features of ChIA-PET technology are that the interaction sites are enriched by ChIP using a specific antibody after chromatin digestion, as in a ChIP experiment. Then, DNA sequences tethered together and to the protein of interest are connected through proximity ligation with oligonucleotide DNA linkers, the sequence of which contains restriction sites for digestion in the next step (Fig. 1). After high-throughput sequencing and bioinformatics analysis, an interactome map of the specific protein binding sites is achieved (Fig. 2d). Thus, ChIA-PET has been applied efficiently to study sites bound with specific transcription factors [76, 78, 79]. For example, some proteins, such as RAD21, SMC3, CTCF and ZNF143, that are important for the formation of a 3D chromatin structure were discovered with ChlA-PET analysis . Another advantage is that ChIA-PET has relatively low levels of library complexity compared with other 3C techniques; therefore, interactions that are identified with an extremely low number of reads are usually considered significant [76, 78]. Recently, an improved method, HiChIP was developed and it can improve over 10-fiold of the yield of chromatin interacting reads but with 100-fold lower requirement than that of ChIA-PET . Results generated from HiChIP of cohesin achieved multiscale genome intact map with greater signal-to-background rations than that of in situ Hi-c . More important is that the sustain high-confidence results are achieved from low cell number input, which will facilitate the investigation of chromatin conformation at cell- or tissue-specific aspects.
The development of high-throughput sequencing technology promoted the emergence of a series of “all versus all” methods (Fig. 2e). Of these, Hi-C was the first to be developed that does not depend on specific primers and generates genome-wide contact maps [82, 83]. In Hi-C experiments, the first step is to generate contact segments as with 3C, but the procedure is slightly different from that described above (Fig. 1). After restriction enzyme digestion, the sticky ends are filled in with biotin-labeled nucleotides followed by blunt-end ligation. The expected contacting DNAs are sheared and then purified in a biotin pull-down experiment using streptavidin beads to ensure that only biotinylated junctions are selected for further high-throughput sequencing and computational analysis (Fig. 1).
The strategy of Hi-C data analysis is thus different from above methods due to the massive parallel NGS data obtained. The basic data analysis typically involves the following aspects [84,85,86].
Paired-end reads are independently aligned to the corresponding reference genome. Given that effective sequence fragments used for sequencing are normally chimeras, which come from two or more chromatin loci, massive reads are discontinuous in the genome . Considering this situation, various methods have been proposed to improve data-use efficiency during the mapping stage, including a pre-truncation process , iterative mapping , or allowing split alignments .
First, the mapping results are filtered at the level of reads and fragments, and only reads with information about chromatin conformation are included. Generally, valid reads are a limited distance from the nearest restriction site, and valid pairs will fall within distinct restriction fragments, which correspond to an interaction between DNA fragments. Then, the remaining read pairs are further filtered to discard PCR duplicates. Here, all uninformative reads are excluded.
Establishment of the contact matrix
For this step, the genome is divided into non-overlapping bins, each filtered read pair is assigned to a specific bin pair, the count of read pairs in the corresponding bin pair aggregated, and eventually, a contact matrix is created. Rows and columns of the contact matrix represent bins across the genome, and each entry contains the number of read pairs that reflects bin–bin interactions.
Because of biases, such as GC content, the mappability, and the frequency of restriction sites, normalization (such as explicit-factor correction methods or matrix balancing-based methods) is essential to correct the raw contact matrix [89,90,91]. Once normalization is completed, one can generate a contact heatmap and infer genome-wide proximity information. Meanwhile, some tools have been developed to visualize Hi-C and other conformation capture data [92, 93]. These help researchers intuitively observe long-range genomic interactions.
In the original Hi-C study, two types of chromatin compartments (A and B) were identified, each of which has different functional and structural properties . The former is enriched in genes and sites with active histone marks, such as H3K36me3, or DNase I hypersensitivity. By contrast, B compartments are enriched with inactive histone marks, such as H3K27me3, and contain few genes and DNase I hypersensitivity sites. Thereafter, other domain types (TAD and sub-TAD) were also identified [87, 94]. In addition, several polymer models have been showed by Hi-C data, including the fractal globule model , random loop model , dynamic loop model  and strings and binders switch model , to explain the underlying biophysical principles governing chromatin packing. For chromosome positioning, the previously observed Rabl configuration of chromosomes was confirmed once again, with the results showing clustering of the centromeres and clustering of the telomeres . Due to its robust and powerful topology profiling at the genome scale, Hi-C has recently been applied extensively in genome conformation research [86, 99,100,101,102]. It is also important to mention that Hi-C can also assist in chromosomal rearrangements, genome assembly and haplotyping [16, 103,104,105,106,107,108,109,110,111].
Although Hi-C is a satisfactory technique for determining genome-wide chromatin interaction maps with relatively few biases compared with other existing C-methods, the acquisition of a reliable contact map with high-resolution still requires sufficient sequencing depth. Ordinarily, there is a direct relationship between mapping resolution and sequencing depth for Hi-C assay . For example, generating contact profiles with resolution from 40 kbp to 1 kbp in the human genome requires hundreds of millions to multiple billion paired-end reads . Thus, the cost and computational resources are certainly prohibitively expensive for most laboratories, which has seriously dampened the popularity of Hi-C.
Other 3C-based strategies
In addition to the above techniques, other seminal adaptations of 3C protocols have been developed for assessing proximity events between distal genomic elements. For example, UMI-4C uses a unique molecular identifier (UMI) to derive quantitative and targeted chromosomal contact profiling . In this approach, initial 3C ligation products are sonicated, and one end of each sonicated fragment is ligated to a sequencing adapter, generating a UMI. Using a bait-specific primer and a universal adaptor primer, physical linkage fragments can be amplified, sequenced and quantified. UMI-4C can eliminate PCR duplicates during data analysis based on the UMI and allows multiple baits (with a suggested number of 20–50 baits) to be selected .
Capture-C combines 3C, oligonucleotide capture technology and NGS to generate genome-wide contact profiles from hundreds of selected loci at a time . In this method, 3C DNAs are sonicated, and paired-end sequencing adaptors are added. The resulting library is then enriched for junction fragments of interest by hybridization to biotinylated capture probes and streptavidin pull-down. Finally, the captured DNAs are amplified and sequenced, and the interaction maps are produced by corresponding bioinformatics methods. The Capture-C strategy can be used to enrich Hi-C libraries, and accordingly a new technique, known as Capture Hi-C (CHi-C), was developed . This method enables deep sequencing of target fragments, excluding uninformative background [115, 116].
Subsequently, using an improved oligonucleotide capture strategy, Davies et al. re-designed the Capture-C protocol and developed another method, called next generation (NG) Capture-C . NG Capture-C has higher assay sensitivity and resolution than Capture-C, and its sensitivity allows the analysis of weak long-range interactions. In addition, multiple 3C libraries from different cell types or different experimental conditions can be processed simultaneously in a single reaction by pooling differently indexed samples, which significantly increases throughput, removes experimental variation and allows the subtractive analysis of chromosome conformation from different samples.
Researchers have also developed DNase Hi-C technology . Compared with Hi-C, the key difference is that DNase Hi-C uses DNase I rather than restriction enzymes for fragmenting crosslinked chromatin, leading to better genome coverage and resolution than Hi-C. Furthermore, the coupling of DNase Hi-C with DNA-capture technology, targeted DNase Hi-C has also been proposed and applied to characterize the 3D organization of large intergenic noncoding RNA promoters in different human cell lines . Similarly, micro-C  and an updated micro-C XL version  uses micrococcal nuclease (MNase) to fragment chromatin, enabling the analysis of chromosome conformation at nucleosome resolution.
Further considerations for 3C technologies
Researchers have been working to improve the resolution of 3C technologies to clarify the specific relationship between genomic conformation and function. However, some challenges have been encountered in this process, indicating that resolution can be affected by many factors . The most important limiting factor is the selection of the first restriction enzyme, which determines the maximum resolution of 3C experiments because contacts between DNA fragments can be detected only at restriction enzyme cut sites. If two restriction enzymes, four-cutter and six-cutter, are compared, the former will yield a 16-fold higher resolution library (256 bp vs. 4096 bp) in humans. However, we should also take into account that the distribution of restriction sites is not uniform in the genome, resulting in different resolutions at different genomic regions. A further increase in resolution can be achieved by substituting restriction enzymes with MNase  or DNase I , which can cut at any site along the genome and can theoretically generate single base pair resolution. After the restriction enzyme to be used in the 3C experiment is chosen, the resolution of the contact maps is further affected by sequencing depth. When it is insufficient to explore contacts at the level of individual restriction fragments, the resolution will be determined by an appropriate bin size. In addition, research on the 3D conformation of repeat regions in the genome is difficult and is mainly because sequence information in this region is often incomplete, and thus sequencing-based 3C methods cannot effectively handle data mapping in this area. Another experimental factor that may impact the output of 3C study is the bias caused potentially by the crosslinking agent. Because crosslinking treatment inappropriately will crosslink the fibers, which are in close physical proximity rather than directly interacting . Therefore, the consideration that to isolate native nuclei in an isotonic buffer to retain the native chromosome loops will properly present the native chromosome conformation [121, 122]. Moreover, comparative study of 3C-type experiments with FISH indicated that 3C-type experiments or FISH alone must be interpreted with caution when studying chromatin architecture , thus cross-validation of Hi-C with FISH  or visible data achieved with super-resolution microscope [10, 124] still need to be considered. Resemble the concept of native chromosome loops, that joint assays will present crucial information for fully understanding chromatin organization.
Theoretically, 3C experiments can capture all DNA fragments that make contact in space. However, studies have showed that capture probability present an exponential decrease as the linear distance between DNA fragments increases [82, 125]. Thus, the ligation junctions between sites that are far apart on the chromosome will be difficult to detect. Moreover, the capacity for 3C-based methods to efficiently detect simultaneous contacts between multiple genomic loci is still overrated . Most recently, Beagrie et al.  developed an all vs. all protocol described as Genome Architecture Mapping (GAM) to dissect the nuclear architecture . Instead of a strong reliance on digestion and ligation as above 3C technologies, this method combines ultrathin cryosectioning, laser microdissection, DNA sequencing, and statistical inference of co-segregation to detect interacting DNA fragments, which significantly expands our ability to explore chromatin spatial organization. Using this tool, they have succeeded in constructing the 3D chromatin structures of mouse embryonic stem cells, and unequivocally found some triplet contacts between super-enhancers .
Due to the heterogeneity between cells , it cannot be overlooked for the impact on data statistical processing regarding these data derived from millions of cells. Therefore, the established 3D genome modeling can represent only an average state of whole cell populations. Thus, one promising challenge is the development of strategies that analyze genomic 3D structure in single- or low-cell samples. Benefiting from the progresses of single-cell sequencing technology [129,130,131], Hi-C assays based on single or low-cell samples have been fulfilled and presented the chromatin structure diversities cell versus cell [132,133,134,135,136,137,138]. A prospective idea is that, by combining with other single-cell data of chromatin states, including transcriptome, DNA methylome, and histone modification, single-cell Hi-C assay will be possible to build a comprehensive picture for the interplay between chromatin folding and its states inside of single cell.
C-technologies, especially Hi-C have heralded the advent of other methods that together offer tantalizing prospects for visualizing the abstract 3D structure of the genome. With the advent of other aspects such as high-throughput and long sequencing reads, single cell sequencing and other type of epigenomics data might provide us more insights on the 3D genome and reveal fundamental principles underlying genome structure and function.
Chromosome conformation capture
Chromosome Conformation Capture-on-Chip
Chromosome conformation capture carbon copy
Chromatin Interaction Analysis by Paired-End Tag Sequencing
Fluorescence in situ hybridization
Genome architecture mapping
Topologically associating domains
Unique molecular identifier
Cremer T, Cremer C. Chromosome territories, nuclear architecture and gene regulation in mammalian cells. Nat Rev Genet. 2001;2(4):292–301.
Parada L, Misteli T. Chromosome positioning in the interphase nucleus. Trends Cell Biol. 2002;12(9):425–32.
Meaburn KJ, Tom M. Cell biology: chromosome territories. Nature. 2007;445(7126):379–81.
Grob S, Grossniklaus U. Chromosome conformation capture-based studies reveal novel features of plant nuclear architecture. Curr Opin Plant Biol. 2017;36:149–57.
Hakim O, Misteli T. SnapShot: chromosome confirmation capture. Cell. 2012;148(5):1068. e1061–1062
Fraser J, Williamson I, Bickmore WA, Dostie J. An overview of genome organization and how we got there: from FISH to hi-C. Microbiol Mol Biol Rev. 2015;79(3):347–72.
Heard E. 3D solutions to complex gene regulation. Nat Rev Mol Cell Biol. 2016;17(12):739.
Krijger PH, de Laat W. Regulation of disease-associated gene expression in the 3D genome. Nat Rev Mol Cell Biol. 2016;17(12):771–82.
Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol. 2016;17(12):743–55.
Schmid VJ, Cremer M, Cremer T. Quantitative analyses of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. Methods. 2017;123:33–46.
Bonev B, Cavalli G. Organization and function of the 3D genome. Nat Rev Genet. 2016;17(11):661–78.
Feng S, Cokus SJ, Schubert V, Zhai J, Pellegrini M, Jacobsen SE. Genome-wide hi-C analyses in wild-type and mutants reveal high-resolution chromatin interactions in Arabidopsis. Mol Cell. 2014;55(5):694–707.
Bickmore W, Vansteensel B. Genome architecture: domain Organization of Interphase Chromosomes. Cell. 2013;152(6):1270–84.
Osorio J. Chromatin: moving a TAD closer to unravelling chromosome architecture. Nat Rev Genet. 2016;17(1):3–3.
Dekker J, Mirny L. The 3D genome as moderator of chromosomal communication. Cell. 2016;164(6):1110–21.
Makova KD, Hardison RC. The effects of chromatin organization on variation in mutation rates in the genome. Nat Rev Genet. 2015;16(4):213–23.
Dixon JR, Jung I, Selvaraj S, Shen Y, Antosiewicz-Bourget JE, Lee AY, Ye Z, Kim A, Rajagopal N, Xie W, et al. Chromatin architecture reorganization during stem cell differentiation. Nature. 2015;518(7539):331–6.
Grob S, Schmid MW, Luedtke NW, Wicker T, Grossniklaus U. Characterization of chromosomal architecture in Arabidopsis by chromosome conformation capture. Genome Biol. 2013;14(11):R129.
Shen Y, Yue F, McCleary DF, Ye Z, Edsall L, Kuan S, Wagner U, Dixon J, Lee L, Lobanenkov VV, et al. A map of the cis-regulatory sequences in the mouse genome. Nature. 2012;488(7409):116–20.
Francastel C, Schübeler D, Martin DI, Groudine M. Nuclear compartmentalization and gene activity. Nat Rev Mol Cell Biol. 2000;1(2):137–43.
Wasserman WW, Sandelin A. Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet. 2004;5(4):276–87.
Branco MR, Pombo A. Chromosome organization: new facts, new models. Trends Cell Biol. 2007;17(3):127–34.
Won H, De la Torre-Ubieta L, Stein JL, Parikshak NN, Huang J, Opland CK, Gandal MJ, Sutton GJ, Hormozdiari F, Lu D, et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature. 2016;538(7626):523–7.
Taberlay PC, Achinger-Kawecka J, Lun AT, Buske FA, Sabir K, Gould CM, Zotenko E, Bert SA, Giles KA, Bauer DC, et al. Three-dimensional disorganisation of the cancer genome occurs coincident with long range genetic and epigenetic alterations. Genome Res. 2016;26(6):719–31.
Dekker J. Mapping the 3D genome: aiming for consilience. Nat Rev Mol Cell Biol. 2016;17(12):741–2.
Moissiard G, Cokus SJ, Cary J, Feng S, Billi AC, Stroud H, Husmann D, Zhan Y, Lajoie BR, McCord RP, et al. MORC family ATPases required for heterochromatin condensation and gene silencing. Science. 2012;336(6087):1448–51.
Cremer T, Cremer C. Rise, fall and resurrection of chromosome territories: a historical perspective. Part I. The rise of chromosome territories. Eur J Histochem. 2006;50(3):161–76.
Muller I, Boyle S, Singer RH, Bickmore WA, Chubb JR. Stable morphology, but dynamic internal reorganisation, of interphase human chromosomes in living cells. PLoS One. 2010;5(7):e11560.
Edelmann P, Bornfleth H, Zink D, Cremer T, Cremer C. Morphology and dynamics of chromosome territories in living cells. Biochim Biophys Acta. 2001;1551(1):M29–39.
Mahy NL, Perry PE, Bickmore WA. Gene density and transcription influence the localization of chromatin outside of chromosome territories detectable by FISH. J Cell Biol. 2002;159(5):753–63.
Chambeyron S, Bickmore WA. Chromatin decondensation and nuclear reorganization of the HoxB locus upon induction of transcription. Genes Dev. 2004;18(10):1119–30.
Boyle S, Rodesch MJ, Halvensleben HA, Jeddeloh JA, Bickmore WA. Fluorescence in situ hybridization with high-complexity repeat-free oligonucleotide probes generated by massively parallel synthesis. Chromosom Res. 2011;19(7):901–9.
Shopland LS, Lynch CR, Peterson KA, Thornton K, Kepper N, Hase J, Stein S, Vincent S, Molloy KR, Kreth G, et al. Folding and organization of a contiguous chromosome region according to the gene distribution pattern in primary genomic sequence. J Cell Biol. 2006;174(1):27–38.
Brown JM, Green J, das Neves RP, Wallace HAC, Smith AJH, Hughes J, Gray N, Taylor S, Wood WG, Higgs DR, et al. Association between active genes occurs at nuclear speckles and is modulated by chromatin environment. J Cell Biol. 2008;182(6):1083–97.
Cremer T, Cremer M, Dietzel S, Müller S, Solovei I, Fakan S. Chromosome territories--a functional nuclear landscape. Curr Opin Cell Biol. 2006;18(3):307–16.
van Steensel B, Dekker J. Genomics tools for unraveling chromosome architecture. Nat Biotechnol. 2010;28(10):1089–95.
Rego EH, Shao L, Macklin JJ, Winoto L, Johansson GA, Kamps-Hughes N, Davidson MW, Gustafsson MG. Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution. Proc Natl Acad Sci. 2011;109(3):E135–43.
Hu M, Deng K, Qin Z, Liu JS. Understanding spatial organizations of chromosomes via statistical analysis of hi-C data. Quant Biol. 2013;1(2):156–74.
Williamson I, Berlivet S, Eskeland R, Boyle S, Illingworth RS, Paquette D, Dostie J, Bickmore WA. Spatial genome organization: contrasting views from chromosome conformation capture and fluorescence in situ hybridization. Genes Dev. 2014;28(24):2778–91.
Sati S, Cavalli G. Chromosome conformation capture technologies and their impact in understanding genome function. Chromosoma. 2017;126(1):33–44.
Ramani V, Shendure J, Duan Z. Understanding spatial genome organization: methods and insights. Genomics Proteomics Bioinformatics. 2016;14(1):7–20.
Risca VI, Greenleaf WJ. Unraveling the 3D genome: genomics tools for multiscale exploration. Trends Genet. 2015;31(7):357–72.
Wang C, Liu C, Roqueiro D, Grimm D, Schwab R, Becker C, Lanz C, Weigel D. Genome-wide analysis of local chromatin packing in Arabidopsis thaliana. Genome Res. 2014;
Lesne A, Riposo J, Roger P, Cournac A, Mozziconacci J. 3D genome reconstruction from chromosomal contacts. Nat Meth. 2014;11(11):1141–3.
Louwers M, Splinter E, van Driel R, de Laat W, Stam M. Studying physical chromatin interactions in plants using chromosome conformation capture (3C). Nat Protoc. 2009;4(8):1216–29.
Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002;295(5558):1306–11.
Tolhuis B, Palstra RJ, Splinter E, Grosveld F, Laat WD. Looping and interaction between hypersensitive sites in the active beta-globin locus. Mol Cell. 2002;10(6):1453–65.
Robert-Jan P, Bas T, Erik S, Rian N, Frank G, Wouter DL. The ÃŸ-globin nuclear compartment in development and erythroid differentiation. Nat Genet. 2003;35(2):190–4.
Miele A, Bystricky K, Dekker J. Yeast silent mating type loci form heterochromatic clusters through silencer protein-dependent long-range interactions. PLoS Genet. 2009;5(5):e1000478.
Sue Mei TW, French JD, Proudfoot NJ, Brown MA. Dynamic interactions between the promoter and terminator regions of the mammalian BRCA1 gene. Proc Natl Acad Sci U S A. 2008;105(13):5160–5.
Comet I, Schuettengruber B, Sexton T, Cavalli G. A chromatin insulator driving three-dimensional Polycomb response element (PRE) contacts and Polycomb association with the chromatin fiber. Proc Natl Acad Sci U S A. 2011;108(6):2294–9.
Murrell A, Heeson S, Reik W. Interaction between differentially methylated regions partitions the imprinted genes Igf2 and H19 into parent-specific chromatin loops. Nat Genet. 2004;36(8):889–93.
Spilianakis CG, Lalioti MD, Town T, Lee GR, Flavell RA. Interchromosomal associations between alternatively expressed loci. Nature. 2005;435(7042):637–45.
Erik S, Helen H, Jurgen K, Robert-Jan P, Petra K, Frank G, Niels G, Wouter DL. CTCF mediates long-range chromatin looping and local histone modification in the beta-globin locus. Genes Dev. 2006;20(2):178.
Douglas V, De GM, Sloane-Stanley JA, Wood WG, Higgs DR. Long-range chromosomal interactions regulate the timing of the transition between poised and active gene expression. EMBO J. 2007;26(8):2041–51.
Nele G, Smith EM, Tabuchi TM, Koch CM, Ian D, Stamatoyannopoulos JA, Job D. Cell-type-specific long-range looping interactions identify distant regulatory elements of the CFTR gene. Nucleic Acids Res. 2010;38(13):4325–36.
Dekker J, Marti-Renom MA, Mirny LA. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet. 2013;14(6):390–403.
O'Sullivan JM, Tan-Wong SM, Morillon A, Lee B, Coles J, Mellor J, Proudfoot NJ. Gene loops juxtapose promoters and terminators in yeast. Nat Genet. 2004;36(9):1014–8.
Simonis M, Kooren J, De LW. An evaluation of 3C-based methods to capture DNA interactions. Nat Methods. 2007;4(11):895–901.
Marieke S, Petra K, Erik S, Yuri M, Rob W, Elzo DW, Bas VS, Wouter DL. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat Genet. 2006;38(11):1348–54.
Zhao Z, Tavoosidana G, Sjolinder M, Gondor A, Mariano P, Wang S, Kanduri C, Lezcano M, Sandhu KS, Singh U, et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat Genet. 2006;38(11):1341–7.
Splinter E, Wit ED, Nora EP, Klous P, Werken HJGVD, Zhu Y, Kaaij LJT, Ijcken WV, Gribnau J, Heard E. The inactive X chromosome adopts a unique three-dimensional conformation that is dependent on Xist RNA. Genes Dev. 2011;25(13):1371–83.
van de Werken HJ, Landan G, Holwerda SJ, Hoichman M, Klous P, Chachik R, Splinter E, Valdes-Quezada C, Oz Y, Bouwman BA, et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat Methods. 2012;9(10):969–72.
Andrey G, Montavon T, Mascrez B, Gonzalez F, Noordermeer D, Leleu M, Trono D, Spitz F, Duboule D. A switch between topological domains underlies HoxD genes collinearity in mouse limbs. Science. 2013;340(6137):1234167.
Daan N, Elzo DW, Petra K, Harmen VDW, Marieke S, Melissa LJ, Bert E, Annelies DK, Singer RH, Wouter DL. Variegated gene expression caused by cell-specific long-range DNA interactions. Nat Cell Biol. 2011;13(8):944–51.
Simonis M, Klous P, Homminga I, Galjaard RJ, Rijkers EJ, Grosveld F, Meijerink JP, de Laat W. High-resolution identification of balanced and complex chromosomal rearrangements by 4C technology. Nat Methods. 2009;6(11):837–42.
Groschel S, Sanders MA, Hoogenboezem R, de Wit E, Bouwman BA, Erpelinck C, van der Velden VH, Havermans M, Avellino R, van Lom K, et al. A single oncogenic enhancer rearrangement causes concomitant EVI1 and GATA2 deregulation in leukemia. Cell. 2014;157(2):369–81.
Stadhouders R, Kolovos P, Brouwer R, Zuin J, van den Heuvel A, Kockx C, Palstra RJ, Wendt KS, Grosveld F, van Ijcken W, et al. Multiplexed chromosome conformation capture sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions. Nat Protoc. 2013;8(3):509–24.
Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C, et al. Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 2006;16(10):1299–309.
Dostie J, Dekker J. Mapping networks of physical interactions between genomic elements using 5C technology. Nat Protoc. 2007;2(4):988–1002.
Baù D, Sanyal A, Lajoie BR, Capriotti E, Byron M, Lawrence JB, Dekker J, Marti-Renom MA. The three-dimensional folding of the a-globin gene domain reveals formation of chromatin globules. Nat Struct Mol Biol. 2011;18(1):107–14.
Fraser J, Rousseau M, Shenker S, Ferraiuolo MA, Hayashizaki Y, Blanchette M, Dostie J. Chromatin conformation signatures of cellular differentiation. Genome Biol. 2009;10(4):1–18.
Ferraiuolo MA, Mathieu R, Carol M, Solomon S, Wang XQD, Michelle N, Mathieu B, Josée D. The three-dimensional architecture of Hox cluster silencing. Nucleic Acids Res. 2010;38(21):7472–84.
Wang KC, Yang YW, Bo L, Amartya S, Ryan CZ, Yong C, Lajoie BR, Angeline P, Flynn RA, Gupta RA. A long noncoding RNA maintains active chromatin to coordinate homeotic gene expression. Nature. 2011;472(7341):120–4.
Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I, Servant N, Piolot T, van Berkum NL, Meisig J, Sedat J, et al. Spatial partitioning of the regulatory landscape of the X-inactivation Centre. Nature. 2012;485(7398):381–5.
Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, Orlov YL, Velkov S, Ho A, Mei PH, et al. An oestrogen-receptor-alpha-bound human chromatin interactome. Nature. 2009;462(7269):58–64.
Li G, Fullwood MJ, Han X, Mulawadi FH, Velkov S, Vega V, Ariyaratne PN, Mohamed YB, Ooi HS, Tennakoon C. ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol. 2010;11(2):1–13.
Li X, Luo OJ, Wang P, Zheng M, Wang D, Piecuch E, Zhu JJ, Tian SZ, Tang Z, Li G, et al. Long-read ChIA-PET for base-pair-resolution mapping of haplotype-specific chromatin interactions. Nat Protocols. 2017;12(5):899–915.
Handoko L, Xu H, Li G, Ngan CY, Chew E, Schnapp M, Lee CW, Ye C, Ping JL, Mulawadi F, et al. CTCF-mediated functional chromatin interactome in pluripotent cells. Nat Genet. 2011;43(7):630–8.
Heidari N, Phanstiel DH, He C, Grubert F, Jahanbani F, Kasowski M, Zhang MQ, Snyder MP. Genome-wide map of regulatory interactions in the human genome. Genome Res. 2014;24(12):1905–17.
Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA, Greenleaf WJ, Chang HY. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13(11):919–22.
Lieberman-Aiden E, Dekker J. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326(5950):289–93.
van Berkum NL, Lieberman-Aiden E, Williams L, Imakaev M, Gnirke A, Mirny LA, Dekker J, Lander ES. Hi-C: a method to study the three-dimensional architecture of genomes. J Vis Exp. 2010;39:e1869.
Ay F, Noble WS. Analysis methods for studying the 3D architecture of the genome. Genome Biol. 2015;16:183.
Lajoie BR, Dekker J, Kaplan N. The Hitchhiker’s guide to hi-C analysis: practical guidelines. Methods (San Diego, Calif). 2015;72:65–75.
Forcato M, Nicoletti C, Pal K, Livi CM, Ferrari F, Bicciato S. Comparison of computational methods for hi-C data analysis. Nat Meth. 2017;14(7):679–85.
Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, et al. A 3D map of the human genome at Kilobase resolution reveals principles of chromatin looping. Cell. 2014;159(7):1665–80.
Wingett S, Ewels P, Furlan-Magaril M, Nagano T, Schoenfelder S, Fraser P, Andrews S. HiCUP: pipeline for mapping and processing hi-C data. F1000Res. 2015;4:1310.
Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA. Iterative correction of hi-C data reveals hallmarks of chromosome organization. Nat Methods. 2012;9(10):999–1003.
Yaffe E, Tanay A. Probabilistic modeling of hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet. 2011;43(11):1059–65.
Hu M, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS. HiCNorm: removing biases in hi-C data via Poisson regression. Bioinformatics. 2012;28(23):3131–3.
Asbury TM, Mitman M, Tang J, Zheng WJ. Genome3D: a viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome. BMC Bioinformatics. 2010;11:444.
Zhou X, Lowdon RF, Li D, Lawson HA, Madden PA, Costello JF, Wang T. Exploring long-range genome interactions using the WashU epigenome browser. Nat Methods. 2013;10(5):375–6.
Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, Hu M, Liu JS, Ren B. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485(7398):376–80.
Mateos-Langerak J, Bohn M, de Leeuw W, Giromus O, Manders EM, Verschure PJ, Indemans MH, Gierman HJ, Heermann DW, van Driel R, et al. Spatially confined folding of chromatin in the interphase nucleus. Proc Natl Acad Sci U S A. 2009;106(10):3812–7.
Bohn M, Heermann DW. Diffusion-driven looping provides a consistent framework for chromatin organization. PLoS One. 2010;5(8):521–6.
Barbieri M, Chotalia M, Fraser J, Lavitas LM, Dostie J, Pombo A, Nicodemi M. Complexity of chromatin folding is captured by the strings and binders switch model. Proc Natl Acad Sci U S A. 2012;109(40):16173–8.
Duan Z, Andronescu M, Schutz K, McIlwain S, Kim YJ, Lee C, Shendure J, Fields S, Blau CA, Noble WS. A three-dimensional model of the yeast genome. Nature. 2010;465(7296):363–7.
Le TB, Imakaev MV, Mirny LA, Laub MT. High-resolution mapping of the spatial organization of a bacterial chromosome. Science. 2013;342(6159):731–4.
Jin F, Li Y, Dixon JR, Selvaraj S, Ye Z, Lee AY, Yen CA, Schmitt AD, Espinoza CA, Ren B. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature. 2013;503(7475):290–4.
Ay F, Bunnik EM, Varoquaux N, Bol SM, Prudhomme J, Vert JP, Noble WS, Le Roch KG. Three-dimensional modeling of the P. Falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression. Genome Res. 2014;24(6):974–88.
Ma W, Ay F, Lee C, Gulsoy G, Deng X, Cook S, Hesson J, Cavanaugh C, Ware CB, Krumm A, et al. Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes. Nat Methods. 2015;12(1):71–8.
Burton JN, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat Biotechnol. 2013;31(12):1119–25.
Selvaraj S, RD J, Bansal V, Ren B. Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nat Biotechnol. 2013;31(12):1111–8.
Dudchenko O, Batra SS, Omer AD, Nyquist SK, Hoeger M, Durand NC, Shamim MS, Machol I, Lander ES, Aiden AP, et al. De novo assembly of the Aedes aegypti genome using hi-C yields chromosome-length scaffolds. Science. 2017;356(6333):92–5.
Avni R, Nave M, Barad O. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science. 2017;357(6346):93–7.
Jarvis DE, Ho YS, Lightfoot DJ, Schmockel SM, Li B, Borm TJ, Ohyanagi H, Mineta K, Michell CT, Saber N, et al. The genome of Chenopodium quinoa. Nature. 2017;542(7641):307–12.
Mascher M, Gundlach H, Himmelbach A, et al. A chromosome conformation capture ordered sequence of the barley genome. Nature. 2017;544(7651):427–433.
Wu P, Li T, Li R, Jia L, Zhu P, Liu Y, Chen Q, Tang D, Yu Y, Li C. 3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations. Nat Commun. 2017;8:1937.
Harewood L, Kishore K, Eldridge MD, Wingett S, Pearson D, Schoenfelder S, Collins VP, Fraser P. Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumours. Genome Biol. 2017;18(1):125.
Aymard F, Aguirrebengoa M, Guillou E, Javierre BM, Bugler B, Arnould C, Rocher V, Iacovoni JS, Biernacka A, Skrzypczak M, et al. Genome-wide mapping of long-range contacts unveils clustering of DNA double-strand breaks at damaged active genes. Nat Struct Mol Biol. 2017;24(4):353–61.
Servant N, Varoquaux N, Lajoie BR, Viara E, Chen CJ, Vert JP, Heard E, Dekker J, Barillot E. HiC-pro: an optimized and flexible pipeline for hi-C data processing. Genome Biol. 2015;16:259.
Schwartzman O, Mukamel Z, Oded-Elkayam N, Olivares-Chauvet P, Lubling Y, Landan G, Izraeli S, Tanay A. UMI-4C for quantitative and targeted chromosomal contact profiling. Nat Methods. 2016;13(8):685–91.
Hughes JR, Roberts N, McGowan S, Hay D, Giannoulatou E, Lynch M, De Gobbi M, Taylor S, Gibbons R, Higgs DR. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat Genet. 2014;46(2):205–12.
Mifsud B, Tavares-Cadete F, Young AN, Sugar R, Schoenfelder S, Ferreira L, Wingett SW, Andrews S, Grey W, Ewels PA, et al. Mapping long-range promoter contacts in human cells with high-resolution capture hi-C. Nat Genet. 2015;47(6):598–606.
Jager R, Migliorini G, Henrion M, Kandaswamy R, Speedy HE, Heindl A, Whiffin N, Carnicer MJ, Broome L, Dryden N, et al. Capture hi-C identifies the chromatin interactome of colorectal cancer risk loci. Nat Commun. 2015;6:6178.
Davies JO, Telenius JM, McGowan SJ, Roberts NA, Taylor S, Higgs DR, Hughes JR. Multiplexed analysis of chromosome conformation at vastly improved sensitivity. Nat Methods. 2016;13(1):74–80.
Hsieh TH, Weiner A, Lajoie B, Dekker J, Friedman N, Rando OJ. Mapping nucleosome resolution chromosome folding in yeast by micro-C. Cell. 2015;162(1):108–19.
Hsieh TS, Fudenberg G, Goloborodko A, Rando OJ. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat Methods. 2016;13(12):1009–11.
Davies JO, Oudelaar AM, Higgs DR, Hughes JR. How best to identify chromosomal interactions: a comparison of approaches. Nat Methods. 2017;14(2):125–34.
Brant L, Georgomanolis T, Nikolic M, Brackley CA, Kolovos P, van Ijcken W, Grosveld FG, Marenduzzo D, Papantonis A. Exploiting native forces to capture chromosome conformation in mammalian cell nuclei. Mol Syst Biol. 2016;12(12):891.
Rusk N. Genomics: native chromosome conformation. Nat Meth. 2017;14(2):105.
Fudenberg G, Imakaev M. FISH-ing for captured contacts: towards reconciling FISH and 3C. Nat Meth. 2017;14(7):673–8. advance online publication
Ou HD, Phan S, Deerinck TJ, Thor A, Ellisman MH, O’Shea CC. ChromEMT: visualizing 3D chromatin structure and compaction in interphase and mitotic cells. Science. 2017;357(6349):eaag0025.
Wijchers PJ, de Laat W. Genome organization influences partner selection for chromosomal rearrangements. Trends Genet. 2011;27(2):63–71.
Ay F, Vu TH, Zeitz MJ, Varoquaux N, Carette JE, Vert JP, Hoffman AR, Noble WS. Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C. BMC Genomics. 2015;16:121.
Beagrie RA, Scialdone A, Schueler M, Kraemer DC, Chotalia M, Xie SQ, Barbieri M, de Santiago I, Lavitas LM, Branco MR, et al. Complex multi-enhancer contacts captured by genome architecture mapping. Nature. 2017;543(7646):519–24.
Nitzan R, Young JW, Uri A, Swain PS, Elowitz MB. Gene regulation at the single-cell level. Science. 2005;307(5717):1962–5.
Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, Cook K, Stepansky A, Levy D, Esposito D, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90–4.
De Souza N. Single-cell methods. Nat Methods. 2011;9(1):35.
Chi KR. Singled out for sequencing. Nat Methods. 2014;11(1):13–7.
Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, Laue ED, Tanay A, Fraser P. Single-cell hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502(7469):59–64.
Stevens TJ, Lando D, Basu S, Atkinson LP, Cao Y, Lee SF, Leeb M, Wohlfahrt KJ, Boucher W, O'Shaughnessykirwan A. 3D structures of individual mammalian genomes studied by single-cell hi-C. Nature. 2017;544(7648):59.
Flyamer IM, Gassler J, Imakaev M, Brandão HB, Ulianov SV, Abdennur N, Razin SV, Mirny LA, Tachibana-Konwalski K. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature. 2017;544(7648):110–4. advance online publication
Nagano T, Lubling Y, Várnai C, Dudley C, Leung W, Baran Y, Mendelson Cohen N, Wingett S, Fraser P, Tanay A. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature. 2017;547(7661):61–7.
Du Z, Zheng H, Huang B, Ma R, Wu J, Zhang X, He J, Xiang Y, Wang Q, Li Y, et al. Allelic reprogramming of 3D chromatin architecture during early mammalian development. Nature. 2017;547(7662):232–5.
Nagano T, Lubling Y, Yaffe E, Wingett SW, Dean W, Tanay A, Fraser P. Single-cell hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat Protocols. 2015;10(12):1986–2003.
Ke Y, Xu Y, Chen X, Feng S, Liu Z, Sun Y, Yao X, Li F, Zhu W, Gao L, et al. 3D chromatin structures of mature gametes and structural reprogramming during mammalian embryogenesis. Cell. 2017;170(2):367–81. e320
Kim KD, Tanizawa H, Iwasaki O, Noma K. Transcription factors mediate condensin recruitment and global chromosomal organization in fission yeast. Nat Genet. 2016;48(10):1242–52.
The authors apologize to all those authors whose work they were unable to describe owing to space constraints. The authors thank members of the laboratory for their input and feedback on the manuscript.
KW was funded by the National Natural Science Foundation of China (31771862, 31471170 and 31628013).
Availability of data and materials
JH and KW conceived the study and drafted the manuscript. ZZ commented and revised the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Han, J., Zhang, Z. & Wang, K. 3C and 3C-based techniques: the powerful tools for spatial genome organization deciphering. Mol Cytogenet 11, 21 (2018). https://doi.org/10.1186/s13039-018-0368-2
- Chromosome conformation capture (3C)
- Topologically associating domains (TADs)
- Chromosome territory