- Open Access
A cautionary note on the use of chromosome conformation capture in plants
© The Author(s) 2017
Received: 24 March 2017
Accepted: 8 November 2017
Published: 16 November 2017
The chromosome conformation capture (3C) technique is a method to study chromatin interactions at specific genomic loci. Initially established for yeast the 3C technique has been adapted to plants in recent years in order to study chromatin interactions and their role in transcriptional gene regulation. As the plant scientific community continues to implement this technology, a discussion on critical controls, validations steps and interpretation of 3C data is essential to fully benefit from 3C in plants.
Here we assess the reliability and robustness of the 3C technique for the detection of chromatin interactions in Arabidopsis. As a case study, we applied this methodology to the genomic locus of a floral integrator gene SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1), and demonstrate the need of several controls and standard validation steps to allow a meaningful interpretation of 3C data. The intricacies of this promising but challenging technique are discussed in depth.
The 3C technique offers an interesting opportunity to study chromatin interactions at a resolution infeasible by microscopy. However, for interpretation of 3C interaction data and identification of true interactions, 3C technology demands a stringent experimental setup and extreme caution.
Perception and response to internal and external stimuli is the fundamental nature of cellular life. The transcriptional regulatory system plays an integral role in fulfilling the needs of the cell and organism by ensuring proper gene activity. In comparison to bacteria, transcriptional control of an eukaryotic cell is far more complex, involving several layers of regulation inside the nucleus. It takes more than just the action and sufficient quantity of activator or repressor proteins to modulate gene expression.
Many modules, such as transcription factors (TFs), RNA polymerase, chromatin remodellers and associated proteins, and regulatory DNA sequences, are determinants of eukaryotic transcription [1, 2]. All together these factors create an open chromatin structure, which is essential to initiate eukaryotic gene transcription. General TFs recognize and bind to discrete DNA sequences (also referred to as cis-elements) located in the core promoter region close to the transcription start site (TSS). For instance, the TATA box is one such evolutionarily conserved core promoter cis-acting element found upstream of most eukaryotic genes [3–5]. Upon association of the general TFs to cis-elements, they interact with other proteins and form complexes to recruit RNA polymerase II, thereby initiating transcription. Examples of these other proteins include specific TFs, which can bind to cis-elements more distantly located from the core promoter elements [6, 7]. When these distant cis-elements are involved in the specific activation of gene expression they are called transcriptional enhancers. These enhancers can be found upstream, downstream, or within introns of coding regions and are reported to be located as far as several hundred kilo bases (kb) from the TSS [8, 9]. These distant enhancers can come into close proximity of their target sequences by protein-mediated chromatin interaction. In this respect, transcriptional gene regulation relies to a great extent on proteins that bind to DNA, not only close to the genes that they regulate, but also at distal DNA sites that can interact with the transcription initiation site by looping the intervening DNA. Thus DNA looping is speculated to be crucial to allow multiple proteins to regulate the core transcriptional machinery, resulting in a correct and controlled transcriptional response [10–12].
Over the last two decades much attention has been paid to the role of chromatin and its conformation in the regulation of gene expression. Various processes, including the differential deposition of histone variants, histone modifications such as methylation and acetylation, DNA methylation, and the activity of other non-histone architectural proteins are known to regulate the structure of chromatin [13–15]. Empirical evidence add to the notion that the dynamics of higher-order chromatin conformation plays a crucial role not just in transcription, but also in other nuclear processes inherent to DNA (DNA replication, DNA repair, chromosome transmission etc.). Therefore understanding the conformation of the chromatin within the cell nucleus has become a fundamental topic in biology.
Over the years, different imaging methods have been deployed to study chromosome conformation [16, 17]. However, detailed and local analysis of chromatin contacts with these methods has been complicated due to technical constraints. For instance, scanning electron microscopy (SEM) provides high resolution, but this technology is laborious, and most importantly, not suitable to study specific loci. Light microscopy has a limited resolution (200 nm), and therefore is inadequate to define local chromosome conformation. Direct in vitro evidence of DNA looping has been shown using very-high resolution three-dimensional atomic force microscopy (AFM) . Nevertheless, this method is labour intensive and an in vitro based approach. Artificial TFs fused with fluorescent proteins such as GFP do allow to spatially visualize and temporally track repetitive genome sequences in vivo, but the method still needs optimization in order to visualize unique individual loci and to detect chromatin interactions . Fluorescence in situ hybridisation (FISH) is another alternative. However, this method involves stringent preparation treatments that can influence the chromatin organization itself and it was originally only suitable for the visualization of repetitive sequences . Though, recent improvements and coupling of FISH with rolling-circle amplification of gene-specific circularizable oligonucleotides makes it possible to visualize the dynamics of individual loci . Overall, microscopy studies have been crucial in defining chromosome territories and nuclear architecture at a single-cell level and new developments will probably allow to image individual chromatin contacts in vivo in the near future. Additionally, a new molecular approach has become available in recent years to study spatial organization of chromosomes at a high resolution, and this molecular tool is called Chromosome Conformation Capture (3C) .
In 3C, chromatin in the intact nucleus is cross-linked by formaldehyde, followed by digestion with a restriction enzyme (RE) and intramolecular ligation . The 3D conformation of the region or locus of interest is then studied by detecting ligation events occurring between non-neighbouring restriction sites. Possible interactions occurring between different chromosomal locations within the nucleus can be quantified as fused sequences by quantitative PCR . The 3C method is cell population based and results in information about the relative frequency of interactions. The 3C method was initially developed for yeast by Dekker and co-workers and has been widely adapted to different model organisms shortly after. For plants, this method was also successfully applied to study chromatin conformation  and, since then it has become a powerful method to study gene looping in plants, as summarized below.
The first report exploring 3C in plants comes from a study in maize (Zea mays) that describes the role of a distant enhancer sequence at the b1 locus. At the b1 Locus, a hepta-repeat around 100 kb upstream of the transcription start site (TSS) appeared to interact with the TSS region in a tissue and epiallele-specific manner . Since then a number of studies have highlighted the occurrence of chromosomal interactions in Thale Cress, Arabidopsis thaliana.
Crevillen et al. reported the presence and condition-dependent disruption of a chromatin loop at the FLOWERING LOCUS C (FLC) locus upon vernalization . FLC, a potent floral repressor and a polycomb target gene, is under tight control of winter cold. Vernalization is a classical epigenetic process in which prolonged cold exposure quantitatively affects the time of flowering. A robust gene loop, due to an interaction between the 5′ and 3′ flanking sequences of the FLC locus, has been reported and this interaction is independent of the level of FLC transcript in different genetic backgrounds and genomic contexts. However, upon vernalization, within the first 2 weeks of cold exposure, the loop is disrupted and it has been proposed that this disruption is an early event in the transition of the FLC locus to an epigenetically silent stage. Subsequently, other DNA contacts in the FLC locus increase in frequency under control of the COLDAIR and COLDWRAP long non-coding RNAs (LncRNAs), giving rise to polycomb-dependent and stable repression of FLC expression .
Another study by Liu et al. in 2013 reported the occurrence of a conformational change in chromosome looping at the TERMINAL FLOWER1 (TFL1) locus that appears to be regulating TFL1 transcription . In this case, disruption of the gene loop between the TSS and 3′ distal region of the TFL1 locus results in TFL1 suppression.
Likewise, two independent studies have identified gene loops at another flowering related gene, FLOWERING LOCUS T (FT), that are associated with the photoperiod-dependent flowering response [28, 29]. FT, a floral integrator, can unite signals from multiple pathways to induce flowering. The first study reports the occurrence of multiple loops between a distal enhancer element (that contains CCAAT boxes) and core cis regulatory sites located in the promoter of FT (pFT) . Nuclear Factor-Y (NF-Y) is known to bind CCAAT-boxes and these CCAAT-bound NF-Y complexes are hypothesised to come into close proximity with core pFT sites, enabling improved recruitment and stabilized binding of CONSTANS (CO), together initiating photoperiod-dependent flowering in Arabidopsis . A second study showed the folding of the FT locus into a three-dimensional structure, favouring interactions between two regulatory regions (named as Block A and Block C, ~ 5.6 kb apart) with another region called Block ID, an intermittent promoter region between Block C and A . An introduced change in the promoter length of FT, i.e., an increase in the distance between Block C and Block ID, by a T-DNA insertion, abolished the C-ID interaction and resulted in reduced chromatin interactions of Block C with Block A. It is noteworthy to mention that the chromatin interactions identified in these two studies do not overlap.
Together, the 3C studies discussed above provide intriguing insights into the possible roles of chromatin interactions to regulate gene expression in plants, similar to the studies reported in yeast and other model organisms [30, 31].
3C provides an interesting opportunity to study in vivo chromatin interactions at a high-resolution and thus has become a standard method for studying chromatin contacts at single gene loci [8, 24]. However, like every other method, 3C has its own shortcomings. The challenges and technical issues of this method can at times outweigh its advantages. Therefore a good experimental setup, rigorous controls, and unbiased data analysis are crucial for meaningful interpretation of 3C data. This is clearly evident from studies performed in other model organism (mammalians, yeast etc.), where several papers have highlighted the importance of necessary 3C controls and appropriate data analysis [23, 32–35]. However, cautionary notes are largely missing in the plant science community. In this study we assessed the reliability and robustness of the qPCR-based 3C method in Arabidopsis. Based on this investigation, we provide detailed guidelines on necessary controls and how interaction data should be interpreted in a 3C experiment. Intricacies of this promising but challenging technique are further discussed.
Results and discussion
Chromosome conformation capture (3C) in Arabidopsis
In a 3C experiment the fragment(s) that show(s) the highest interaction frequency with the bait fragment is (are) considered as chromatin contact(s). For the TSS region in the SOC1 locus the highest interaction frequencies were observed with fragments X, XI and XII, all downstream of the VII-bait (Fig. 1). Furthermore, a potential contact with a promoter region, Fragment IV, was identified.
Cross-validation of chromatin contacts
The key difference between the 3C and r3C experiment is the fragment that is selected as bait and consequently, the combinations of qPCR primers used to detect the relative interaction frequency. For the SOC1 locus we identified in this way e.g., an interaction between bait VII and fragment X (Fig. 1) and therefore fragment X was used as bait in the r3C experiment (Fig. 2). In any PCR-based 3C experiment, the primer of the bait is kept constant and is combined with a unique primer annealing specifically to one of the fragments that is tested for interaction . It is good to realize that in a 3C and r3C experiment the combination of primers to test the interactions between one specific combination of fragments (VII and X in the example of SOC1), is identical. Performing an r3C experiment is certainly of value, since a comprehensive profile of chromatin interaction of the locus will be obtained from yet another viewpoint. However, it is good to realize that the outcome of 3C experiments might be biased due to different characteristics of the used REs or technical constraints of PCR . Therefore, it is desirable to perform another independent type of validation, besides the r3C experiment.
Validation of 3C interaction profiles with another restriction enzyme
Overall, upon performing 3C at the SOC1 locus independently with two different REs, we found various similarities, but also differences in the generated 3C profiles. One clear trend observed was the interaction of the TSS-spanning bait region with a region close to the 3′ UTR of the SOC1 locus for both restriction profiles. A striking discrepancy were the interaction patterns identified for the TSS bait fragment with the promoter region. Hence, our data show the potential subjectivity of 3C results and prompt for cautiousness when interpreting 3C interaction patterns.
Classification of 3C interactions
All the above discussed results reveal the subjectivity of 3C data interpretation. Furthermore, in a 3C experiment, for regions in close proximity to the bait fragment under study, usually a high occurrence of ligations due to random (non-specific) collisions is observed [32, 38]. Thus, mere identification of ligation events does not necessarily mean the occurrence of an existing in vivo interaction. In order to be able to detect interactions above the background of random interactions, it’s important to carefully consider the proximity between the bait and the supposed interacting fragments. The Arabidopsis genome in nature is very small and compact , hence studying regulatory interactions at individual gene loci is complex, due to the small distances between neighbouring restriction fragments thereby resulting in high potential for random interactions . In conclusion, the combination of compact genome of Arabidopsis and the flexible nature of chromatin fibres makes interpretation of 3C interaction on individual loci challenging and demands high caution.
Reviewing published literature we found only a limited number of 3C studies that have been performed in plants. Among these studies we observed considerable shortcomings within the 3C experimental set up (e.g., the PCR method, lack of endogenous normalization and random ligation libraries as controls, use of only one enzyme and no r3C), and differences in the way the 3C interaction data have been interpreted and represented. Furthermore, comparing 3C results across experiments and laboratories is complicated due to differences in the experimental set up, such as PCR method, normalization method, plant growth conditions, and the way the interaction frequency is determined. For example, in literature, one can find chromatin interactions determined using 3C by either semi-quantitative PCR or by qPCR-based approaches. Only the latter provide numeric peak interactions and is therefore a much more trusted and widely adopted method in recent years. A recent publication manually curated more than 3000 interactions from 5000 publications across 17 species into a database called 3C database (3CDB) . This 3CDB classified the strongest 3C interactions into four distinct classes based on their reliability. Class I and II cover the semi-quantitative PCR data, which nowadays is not an accepted detection method, whereas Class III and IV refer to numeric peak interactions. According to the 3CDB classification, interactions belonging to class IV are considered to be the most reliable, due to the fact that they are validated with an r3C experiment . The results we described in this study are all numeric peak interactions and fall into class III and IV. However, based on our interaction profiles from independent 3C experiments that used different REs, we see a need to further extend this set of classifications. We suggest introducing a class V for interactions that have been identified and r3C-validated using two different REs. Taking into account that even in this set up the experimental procedure is the same and provides relative and hence, subjective data, 3C experiments should be confirmed by an alternative and independent method to get full proof for a potential chromosome interaction.
Challenges of the 3C method
Ligation based methods, such a 3C, heavily rely on a sound experimental design. Many technical biases may be introduced if the design and set up of the experiment is not optimal. For example, optimization of crosslinking conditions is necessary, as over-fixed chromatin often renders digestion with REs inefficient. Similarly, biases may arises from the choice of RE and its digestion efficiency. One of the prerequisites for REs in 3C is their ability to digest crossed-linked chromatin efficiently, but at the same time providing the desired resolution at the locus of interest. By far not all REs behave optimally in 3C, e.g., because buffering conditions during digestion are sub-optimal (e.g., due to presence of detergents). Hence, optimization of several parameters is essential in order to overcome technical biases in a PCR-based 3C experiment. Most of these technical aspects and their optimization procedures have been very-well addressed in literature [23, 24, 32, 41–43]. Besides these technical issues, biological variation may arise from differences in the growing conditions, the time of tissue collection in relation to the developmental age of the plant, and due to sampling itself. These aspects are very critical, especially when the goal is to study dynamics in chromatin interactions in time or upon a change in condition, as was e.g., done for the FLC locus . To exemplify this, we performed a 3C assay on a developmentally different tissue where SOC1 is known to be actively expressed . For this experiment we sampled the same type of plant material exactly 1 week later, but upon transfer of the plants to flowering-inducing long day (LD) conditions and after growth at these conditions for 7 days (7dai). At this time point, we observed a distinct chromatin interaction profile at SOC1 locus, when using the NlaIII RE (Additional file 1: Fig. S1), suggesting dynamics in relation to SOC1’s transcriptional state. However, when 3C was repeated on this sample using FspBI (Additional file 1: Fig. S2), we obtained a pattern resembling the pattern after 3 weeks growth under SD conditions (Fig. 4b), suggesting lack of dynamics. This example reveals that extra caution is required when studying dynamic interactions and that it is of utmost importance to keep biological variation at minimum.
Beyond the traditional 3C
Overview of existing and recently developed 3C-based methods
Ligation based chromatin capture method
Hi-C, Dnase Hi-C, Micro-C, Micro-CXL,
3C is a powerful tool when it comes to studying chromatin interactions at a gene specific locus. However, identification of valid interactions via PCR-based 3C demands multiple controls and validation steps. Only when the results are consistent across the proper control and validation experiments, an interaction can be considered of high confidence. Subsequently, it is of interest to unveil for every high confidence interaction whether it exists because of the regulation of gene expression, a particular nuclear or chromatin organisation, or spatial restrictions in the nucleus or the flexible nature of chromatin. Hence, identified interactions do not reveal the underlying mechanism behind its co-localization, neither do they distinguishing if it’s a functional or non-functional interaction. To shed more light on the functionality of an observed interaction, genetic studies are essential. For instance, making use of T-DNA insertion lines, or targeted disruption of the DNA regions involved in the observed interactions with CRISPR-Cas9 genome editing , can aid further functional characterization of identified in vivo interactions.
Since the establishment of the 3C technique, hundreds of potential interactions have been reported supporting the potential role of chromatin interactions in transcriptional control. As the plant scientific field is gaining momentum in deciphering this new layer of transcriptional regulation of intricate gene regulatory networks, the 3C technique will play a prominent role in expanding our knowledge on this new fundamental topic of plant biology. Nevertheless, utmost care should be taken in assigning meaningful 3C interactions, as described here.
Plant material, growth conditions and tissue collection
Col-0 wild type plants were grown on rock-wool for 3 weeks at 20 °C under short day (SD) conditions (8 h light, 16 h dark). Two grams of seedling material (above ground tissues) per biological sample were collected during the afternoon hour of the day. In addition, material was sampled 7 days later and after transfer and growth of the plants for a week at long day photoperiod conditions (16 h light, 8 h dark).
Chromatin conformation capture (3C)
3C was performed on the SOC1 locus using the previously described protocol with some adaptations for Arabidopsis . Two grams of Arabidopsis above-ground seedling material was crosslinked with 2% paraformaldehyde PBS buffer under vacuum for 30 min (mins) on ice. The cross-linking reaction was stopped by addition of ice-cold 2 M glycine (final concentration = 0.125 M) under vacuum for 5 min on ice. The crosslinked tissue was ground and nuclei were isolated and purified using nuclei extraction (NE) buffer. Before digestion, the purified nuclei pellets were re-suspended in 1.2 × restriction buffer and treated with 0.2% SDS at 65 °C for 20 min. Later, SDS was sequestered by incubating with 2%Triton X-100 for 30 min. 3C analysis was performed on the SOC1 locus using two different REs (namely FspBI and NlaIII) independently. 400 U of RE was used for overnight digestion at 37 °C. Digestion was stopped by incubation at 65 °C for 20 min. Ligation was performed using 100 U of T4 DNA ligase, initially at 16 °C for 5 h, followed by room temperature for 45 min. Reverse cross-linking was done overnight with a treatment of proteinase K at 65 °C. After reversal of the crosslinks, phenol/chloroform extraction and ethanol precipitation was performed for recovery of the DNA.
3C primers, controls and quantification
3C primers for FspBI restriction profile
Gene locus and restriction enzyme
Seq 5′ to 3′
Fragment VII (Bait)
− 500 bp
3C primers for NlaIII restriction profile
Gene locus and restriction enzyme
Seq 5′ to 3′
SJ, RGHI and GCA designed the experiments. SJ performed the experiments and acquired the data. SJ and RGHI analysed the data. SJ, MS, RGHI, GCA critically discussed the outcomes of the experiments and wrote the manuscript. All authors read and approved the final manuscript.
We kindly thank Iris Hovel and Blaise Weber for their advice on the methodology. We thank the PDS group members at Wageningen University & Research and the EpiTRAITS community, for useful ideas and fruitful discussions.
The authors declare that they have no competing interests.
Availability of data and materials
The dataset(s) supporting the conclusions of this article are included in this article.
Ethics approval and consent to participate
We gratefully acknowledge the support from the European Commission Seventh Framework-People-2012-ITN Project EpiTRAITS, GA-316965 (Epigenetic regulation of economically important plant traits) to SBJ, MES and GCA.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis 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.
- Coulon A, Chow CC, Singer RH, Larson DR. Eukaryotic transcriptional dynamics: from single molecules to cell populations. Nat Rev Genet. 2013;14:572–84.View ArticlePubMedGoogle Scholar
- Urnov FD, Wolffe AP. Chromatin remodeling and transcriptional activation: the cast (in order of appearance). Oncogene. 2001;20:2991–3006.View ArticlePubMedGoogle Scholar
- Burley SK, Roeder RG. Biochemistry and structural biology of transcription factor IID (TFIID). Annu Rev Biochem. 1996;65:769–99.View ArticlePubMedGoogle Scholar
- Patikoglou GA, Kim JL, Sun L, Yang SH, Kodadek T, Burley SK. TATA element recognition by the TATA box-binding protein has been conserved throughout evolution. Genes Dev. 1999;13:3217–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Weber B, Zicola J, Oka R, Stam M. Plant enhancers: a call for discovery. Trends Plant Sci. 2016;21(11):974–87. https://doi.org/10.1016/j.tplants.2016.07.013.View ArticlePubMedGoogle Scholar
- Krivega I, Dean A. Enhancer and promoter interactions-long distance calls. Curr Opin Genet Dev. 2012;22:79–85.View ArticlePubMedGoogle Scholar
- Marsman J, Horsfield JA. Long distance relationships: enhancer–promoter communication and dynamic gene transcription. Biochim Biophys Acta. 2012;1819:1217–27.View ArticlePubMedGoogle Scholar
- Louwers M, Bader R, Haring M, van Driel R, de Laat W, Stam M. Tissue- and expression level-specific chromatin looping at maize b1 epialleles. Plant Cell. 2009;21:832–42.View ArticlePubMedPubMed CentralGoogle Scholar
- Marand AP, Zhang T, Zhu B, Jiang J. Towards genome-wide prediction and characterization of enhancers in plants. Biochim Biophys Acta. 2017;1860(1):131–9.View ArticlePubMedGoogle Scholar
- Ptashne M. Gene regulation by proteins acting nearby and at a distance. Nature. 1986;322:697–701.View ArticlePubMedGoogle Scholar
- Schleif R. DNA looping. Science. 1988;240:127–8.View ArticlePubMedGoogle Scholar
- Matthews KS. DNA looping. Microbiol Rev. 1992;56:123–36.PubMedPubMed CentralGoogle Scholar
- Rosa S, Shaw P. Insights into chromatin structure and dynamics in plants. Biology (Basel). 2013;2:1378–410.Google Scholar
- Jiang D, Berger F. Histone variants in plant transcriptional regulation. Biochim Biophys Acta. 2017;1860(1):123-30. https://doi.org/10.1016/j.bbagrm.2016.07.002.View ArticlePubMedGoogle Scholar
- Rodriguez-Granados NY, Ramirez-Prado JS, Veluchamy A, Latrasse D, Raynaud C, Crespi M, Ariel F, Benhamed M. Put your 3D glasses on: plant chromatin is on show. J Exp Bot. 2016;67:3205–21.View ArticlePubMedGoogle Scholar
- Daban JR. Electron microscopy and atomic force microscopy studies of chromatin and metaphase chromosome structure. Micron. 2011;42:733–50.View ArticlePubMedGoogle Scholar
- Rapkin LM, Anchel DR, Li R, Bazett-Jones DP. A view of the chromatin landscape. Micron. 2012;43:150–8.View ArticlePubMedGoogle Scholar
- Puranik S, Acajjaoui S, Conn S, Costa L, Conn V, Vial A, Marcellin R, Melzer R, Brown E, Hart D, et al. Structural basis for the oligomerization of the MADS domain transcription factor SEPALLATA3 in Arabidopsis. Plant Cell. 2014;26:3603–15.View ArticlePubMedPubMed CentralGoogle Scholar
- Fujimoto S, Sugano SS, Kuwata K, Osakabe K, Matsunaga S. Visualization of specific repetitive genomic sequences with fluorescent TALEs in Arabidopsis thaliana. J Exp Bot. 2016;67:6101–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Fransz P, de Jong JH, Lysak M, Castiglione MR, Schubert I. Interphase chromosomes in Arabidopsis are organized as well defined chromocenters from which euchromatin loops emanate. Proc Natl Acad Sci USA. 2002;99:14584–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Feng CM, Qiu YJ, Van Buskirk EK, Yang EJ, Chen M. Light-regulated gene repositioning in Arabidopsis. Nat Commun. 2014;5:3027.PubMedPubMed CentralGoogle Scholar
- Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science. 2002;295:1306–11.View ArticlePubMedGoogle Scholar
- Hagege H, Klous P, Braem C, Splinter E, Dekker J, Cathala G, de Laat W, Forne T. Quantitative analysis of chromosome conformation capture assays (3C-qPCR). Nat Protoc. 2007;2:1722–33.View ArticlePubMedGoogle Scholar
- 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;4:1216–29.View ArticlePubMedGoogle Scholar
- Crevillen P, Sonmez C, Wu Z, Dean C. A gene loop containing the floral repressor FLC is disrupted in the early phase of vernalization. EMBO J. 2013;32:140–8.View ArticlePubMedGoogle Scholar
- Kim DH, Sung S. Vernalization-triggered intragenic chromatin loop formation by long noncoding RNAs. Dev Cell. 2017;40(302–312):e304.Google Scholar
- Liu C, Teo ZW, Bi Y, Song S, Xi W, Yang X, Yin Z, Yu H. A conserved genetic pathway determines inflorescence architecture in Arabidopsis and rice. Dev Cell. 2013;24:612–22.View ArticlePubMedGoogle Scholar
- Cao S, Kumimoto RW, Gnesutta N, Calogero AM, Mantovani R, Holt BF 3rd. A distal CCAAT/NUCLEAR FACTOR Y complex promotes chromatin looping at the FLOWERING LOCUS T promoter and regulates the timing of flowering in Arabidopsis. Plant Cell. 2014;26:1009–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu L, Adrian J, Pankin A, Hu J, Dong X, von Korff M, Turck F. Induced and natural variation of promoter length modulates the photoperiodic response of FLOWERING LOCUS T. Nat Commun. 2014;5:4558.PubMedPubMed CentralGoogle Scholar
- Pombo A, Dillon N. Three-dimensional genome architecture: players and mechanisms. Nat Rev Mol Cell Biol. 2015;16:245–57.View ArticlePubMedGoogle Scholar
- Levine M, Cattoglio C, Tjian R. Looping back to leap forward: transcription enters a new era. Cell. 2014;157:13–25.View ArticlePubMedPubMed CentralGoogle Scholar
- Dekker J. The three ‘C’ s of chromosome conformation capture: controls, controls, controls. Nat Methods. 2006;3:17–21.View ArticlePubMedGoogle Scholar
- Gavrilov AA, Golov AK, Razin SV. Actual ligation frequencies in the chromosome conformation capture procedure. PLoS ONE. 2013;8:e60403.View ArticlePubMedPubMed CentralGoogle Scholar
- Miele A, Dekker J. Mapping cis- and trans- chromatin interaction networks using chromosome conformation capture (3C). Methods Mol Biol. 2009;464:105–21.View ArticlePubMedGoogle Scholar
- O’Sullivan JM, Hendy MD, Pichugina T, Wake GC, Langowski J. The statistical-mechanics of chromosome conformation capture. Nucleus. 2013;4:390–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee J, Lee I. Regulation and function of SOC1, a flowering pathway integrator. J Exp Bot. 2010;61:2247–54.View ArticlePubMedGoogle Scholar
- Frohler S, Dieterich C. 3PD: rapid design of optimal primers for chromosome conformation capture assays. BMC Genom. 2009;10:635.View ArticleGoogle Scholar
- Dekker J, Marti-Renom MA, Mirny LA. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet. 2013;14:390–403.View ArticlePubMedPubMed CentralGoogle Scholar
- Arabidopsis Genome Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature. 2000;408:796–815.View ArticleGoogle Scholar
- Yun X, Xia L, Tang B, Zhang H, Li F, Zhang Z. 3CDB: a manually curated database of chromosome conformation capture data. Database (Oxford). 2016;2016:baw044. https://doi.org/10.1093/database/baw044.View ArticleGoogle Scholar
- Naumova N, Smith EM, Zhan Y, Dekker J. Analysis of long-range chromatin interactions using chromosome conformation capture. Methods. 2012;58:192–203.View ArticlePubMedGoogle Scholar
- Hovel I, Louwers M, Stam M. 3C technologies in plants. Methods. 2012;58:204–11.View ArticlePubMedGoogle Scholar
- Splinter E, Grosveld F, de Laat W. 3C technology: analyzing the spatial organization of genomic loci in vivo. Methods Enzymol. 2004;375:493–507.View ArticlePubMedGoogle Scholar
- Immink RG, Pose D, Ferrario S, Ott F, Kaufmann K, Valentim FL, de Folter S, van der Wal F, van Dijk AD, Schmid M, Angenent GC. Characterization of SOC1’s central role in flowering by the identification of its upstream and downstream regulators. Plant Physiol. 2012;160:433–49.View ArticlePubMedPubMed CentralGoogle Scholar
- de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 2012;26:11–24.View ArticlePubMedPubMed CentralGoogle Scholar
- Denker A, de Laat W. The second decade of 3C technologies: detailed insights into nuclear organization. Genes Dev. 2016;30:1357–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R, de Wit E, van Steensel B, de Laat W. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat Genet. 2006;38:1348–54.View ArticlePubMedGoogle Scholar
- Schmid MW, Grob S, Grossniklaus U. HiCdat: a fast and easy-to-use Hi-C data analysis tool. BMC Bioinformatics. 2015;16:277.View ArticlePubMedPubMed CentralGoogle Scholar
- Sato T, Suyama M. ChromContact: a web tool for analyzing spatial contact of chromosomes from Hi-C data. BMC Genom. 1060;2015:16.Google Scholar
- Yardimci GG, Noble WS. Software tools for visualizing Hi-C data. Genome Biol. 2017;18:26.View ArticlePubMedPubMed CentralGoogle Scholar
- Paulsen J, Sandve GK, Gundersen S, Lien TG, Trengereid K, Hovig E. HiBrowse: multi-purpose statistical analysis of genome-wide chromatin 3D organization. Bioinformatics. 2014;30:1620–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Lajoie BR, Dekker J, Kaplan N. The Hitchhiker’s guide to Hi-C analysis: practical guidelines. Methods. 2015;72:65–75.View ArticlePubMedGoogle Scholar
- Ay F, Noble WS. Analysis methods for studying the 3D architecture of the genome. Genome Biol. 2015;16:183.View ArticlePubMedPubMed CentralGoogle Scholar
- 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;336:1448–51.View ArticlePubMedPubMed CentralGoogle Scholar
- 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;55:694–707.View ArticlePubMedPubMed CentralGoogle Scholar
- Grob S, Schmid MW, Grossniklaus U. Hi-C analysis in Arabidopsis identifies the KNOT, a structure with similarities to the flamenco locus of Drosophila. Mol Cell. 2014;55:678–93.View ArticlePubMedGoogle Scholar
- 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. 2015;25:246–56.View ArticlePubMedPubMed CentralGoogle Scholar
- Grob S, Schmid MW, Luedtke NW, Wicker T, Grossniklaus U. Characterization of chromosomal architecture in Arabidopsis by chromosome conformation capture. Genome Biol. 2013;14:R129.View ArticlePubMedPubMed CentralGoogle Scholar
- Soler E, Andrieu-Soler C, de Boer E, Bryne JC, Thongjuea S, Stadhouders R, Palstra RJ, Stevens M, Kockx C, van Ijcken W, et al. The genome-wide dynamics of the binding of Ldb1 complexes during erythroid differentiation. Genes Dev. 2010;24:277–89.View ArticlePubMedPubMed CentralGoogle Scholar
- 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;9:969–72.View ArticlePubMedGoogle Scholar
- Dostie J, Dekker J. Mapping networks of physical interactions between genomic elements using 5C technology. Nat Protoc. 2007;2:988–1002.View ArticlePubMedGoogle Scholar
- 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;462:58–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Stadhouders R, Thongjuea S, Andrieu-Soler C, Palstra RJ, Bryne JC, van den Heuvel A, Stevens M, de Boer E, Kockx C, van der Sloot A, et al. Dynamic long-range chromatin interactions control Myb proto-oncogene transcription during erythroid development. EMBO J. 2012;31:986–99.View ArticlePubMedGoogle Scholar
- de Almeida CR, Stadhouders R, de Bruijn MJ, Bergen IM, Thongjuea S, Lenhard B, van Ijcken W, Grosveld F, Galjart N, Soler E, Hendriks RW. The DNA-binding protein CTCF limits proximal Vkappa recombination and restricts kappa enhancer interactions to the immunoglobulin kappa light chain locus. Immunity. 2011;35:501–13.View ArticleGoogle Scholar
- Dryden NH, Broome LR, Dudbridge F, Johnson N, Orr N, Schoenfelder S, Nagano T, Andrews S, Wingett S, Kozarewa I, et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 2014;24:1854–68.View ArticlePubMedPubMed CentralGoogle Scholar
- Sahlen P, Abdullayev I, Ramskold D, Matskova L, Rilakovic N, Lotstedt B, Albert TJ, Lundeberg J, Sandberg R. Genome-wide mapping of promoter-anchored interactions with close to single-enhancer resolution. Genome Biol. 2015;16:156.View ArticlePubMedPubMed CentralGoogle Scholar
- 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.View ArticlePubMedPubMed CentralGoogle Scholar
- 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;46:205–12.View ArticlePubMedGoogle Scholar
- Kolovos P, van de Werken HJ, Kepper N, Zuin J, Brouwer RW, Kockx CE, Wendt KS. van IJcken WF, Grosveld F, Knoch TA: Targeted Chromatin Capture (T2C): a novel high resolution high throughput method to detect genomic interactions and regulatory elements. Epigenetics Chromatin. 2014;7:10.View ArticlePubMedPubMed CentralGoogle Scholar
- Ramani V, Cusanovich DA, Hause RJ, Ma WX, Qiu RL, Deng XX, Blau CA, Disteche CM, Noble WS, Shendure J, Duan ZJ. Mapping 3D genome architecture through in situ DNase Hi-C. Nat Protoc. 2016;11:59–76.View ArticleGoogle Scholar
- Hsieh THS, Weiner A, Lajoie B, Dekker J, Friedman N, Rando OJ. mapping nucleosome resolution chromosome folding in yeast by micro-C. Cell. 2015;162:108–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Hsieh THS, Fudenberg G, Goloborodko A, Rando OJ. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat Methods. 2016;13:1009–11.View ArticlePubMedGoogle Scholar
- Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Kalhor R, Tjong H, Jayathilaka N, Alber F, Chen L. Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat Biotechnol. 2012;30:90-U139.Google Scholar
- Woo JW, Kim J, Kwon SI, Corvalan C, Cho SW, Kim H, Kim SG, Kim ST, Choe S, Kim JS. DNA-free genome editing in plants with preassembled CRISPR-Cas9 ribonucleoproteins. Nat Biotechnol. 2015;33:1162–4.View ArticlePubMedGoogle Scholar
- Weber B, Jamge S, Stam M. 3C in Maize and Arabidopsis. Methods Mol Biol. 2018;1675:247–70.View ArticlePubMedGoogle Scholar