We thank Valeriya Malysheva for critical reading of the manuscript and expert help with Figure 1. This work was supported by the Medical Research Council, UK (MR/L007150/1) and the UK Biotechnology and Biological Sciences Research Council, UK (BB/J004480/1).

Authors have nothing to disclose.

Modular design of Promoter Capture Hi-C
Promoter Capture Hi-C is designed to specifically enrich Hi-C libraries for interactions involving promoters. These interactions comprise only a subset of ligation products present in a Hi-C library.
Capture Hi-C can easily be modified to enrich Hi-C libraries for any genomic region or regions of interest by changing the capture system. Capture regions can be continuous genomic segments44,45,46,48, enhancers that have been identified in PCHi-C (&#39;Reverse Capture Hi-C&#39;35), or DNase I hypersensitive sites49. The size of the capture system can be adjusted depending on the experimental scope. For example, Dryden et al. target 519 bait fragments in three gene deserts associated with breast cancer44. The capture system by Martin et al. targets both continuous genomic segments (&#39;Region Capture&#39;: 211 genomic regions in total; 2,131 restriction fragments) and selected promoters (3,857 gene promoters)45.
SureSelect libraries are available in different size ranges: 1 kb to 499 kb (5,190&#8211;4,806), 500 kb to 2.9 Mb (5,190&#8211;4,816), and 3 Mb to 5.9 Mb (5,190&#8211;4,831). As each individual capture biotin-RNA is 120 nucleotides long, these capture systems accommodate a maximum of 4,158, 24,166 and 49,166 individual capture probes, respectively. This corresponds to 2,079, 12,083, and 24,583 targeted restriction fragments, respectively (note that the numbers for restriction fragments are lower bounds based on the assumption that two individual capture probes can be designed for every restriction fragment &#8212; in reality due to repetitive sequences this will not be the case for every restriction fragment (see also Figure 1B, C), resulting in a higher number of targetable restriction fragments for a constant number of available capture probes).
The protocol described here is based on the use of a restriction enzyme with a 6 bp recognition site to uncover long-range interactions. Using a restriction enzyme with a 4 bp recognition site for greater resolution of more proximal interactions is also possible40,49.
Limitations of PCHi-C 
One inherent limitation of all chromosome conformation capture assays is that their resolution is determined by the restriction enzyme used for the library generation. Interactions that occur between DNA elements located on the same restriction fragment are invisible to &#39;C-type&#39; assays. Further, in PCHi-C, in some cases more than one transcription start site can be located on the same promoter-containing restriction fragment, and PIRs in some cases harbor both active and repressive histone marks, making it difficult to pinpoint which regulatory elements mediate the interactions, and to predict the regulatory output of promoter interactions. Using restriction enzymes with 4 bp recognition sites mitigates this issue but comes at the expense of vastly increased Hi-C library complexity (Hi-C libraries generated with 4 bp recognition site restriction enzymes are at least 100 times more complex than Hi-C libraries generated with 6 bp recognition site restriction enzymes), and the associated costs for next generation sequencing.
Another limitation is that the current PCHi-C protocol requires millions of cells as starting material, precluding the analysis of promoter interactions in rare cell types. A modified version of PCHi-C to enable the interrogation of promoter contacts in cell populations with 10,000 to 100,000 cells (for example cells during early embryonic development or hematopoietic stem cells) would therefore be a valuable addition to the Capture Hi-C toolbox.
Finally, like all methods that rely on formaldehyde fixation, PCHi-C only records interactions that are &#39;frozen&#39; at the time point of fixation. Thus, to study the kinetics and dynamics of promoter interactions, methods such as super-resolution live cell microscopy are required alongside PCHi-C.
Methods to dissect spatial chromosome organization at high resolution
The vast complexity of chromosomal interaction libraries prohibits the reliable identification of interaction products between two specific restriction fragments with statistical significance. To circumvent this problem, sequence capture has been used to enrich either Hi-C33,34,40,44 or 3C50,51 libraries for specific interactions. The major advantage of using Hi-C libraries over 3C libraries for the enrichment step is that Hi-C, unlike 3C, includes an enrichment step for genuine ligation products. As a consequence, the percentage of valid reads in PCHi-C libraries is approximately 10-fold higher than in Capture-C libraries50, which contained around 5&#8211;8% valid reads after HiCUP filtering. Sahlen et al. have directly compared Capture-C to HiCap, which like PCHi-C uses Hi-C libraries for capture enrichment, in contrast to Capture-C which uses 3C libraries. Consistent with our findings, they found that Capture-C libraries are mainly composed of un-ligated fragments40. In addition, HiCap libraries had a higher complexity than Capture-C libraries40.
A variant of Capture-C, called next-generation Capture-C52 (NG Capture-C) uses one oligo per restriction fragment end, as previously established in PCHi-C33,34, instead of overlapping probes used in the original Capture-C protocol50. This increases the percentage of valid reads compared to Capture-C modestly, but NG Capture-C employs two sequential rounds of capture enrichment, and a relatively high number of PCR cycles (20 to 24 cycles in total, compared to 11 cycles typically for PCHi-C), which inevitably results in higher numbers of sequence duplicates and lower library complexity. In trial experiments during the optimization of PCHi-C, we found that the percentage of unique (i.e., not duplicated) read pairs was only around 15% when we used 19 PCR cycles (13 cycles pre-capture + 6 cycles post-capture; data not shown), however optimization to a lower number of PCR cycles, typically yields 75&#8211;90% unique read pairs. Thus, reducing the number of PCR cycles substantially increases the amount of informative sequence data.
A recent method combines ChIP with Hi-C to focus on chromosomal interactions mediated by a specific protein of interest (HiChIP53). Compared to ChIA-PET54, which is based on a similar rationale, HiChIP data contains a higher number of informative sequence reads, allowing for higher-confidence interaction calling53. It will be very interesting to directly compare the corresponding HiChIP and Capture Hi-C data sets once they become available (for example HiChIP using an antibody against the cohesin unit Smc1a53 with Capture Hi-C for all Smc1a bound restriction fragments) side by side. One inherent difference between these two approaches is that Capture Hi-C does not rely on chromatin immunoprecipitation, and therefore is capable of interrogating chromosomal interactions irrespective of protein occupancy. This enables comparison of 3D genome organization in the presence or absence of specific factor binding, as has been used to identify PRC1 as a key regulator of mouse ESC spatial genome architecture7.
PCHi-C and GWAS
Genome-wide association studies (GWAS) have revealed that greater than 95% of disease-associated sequence variants are located in non-coding regions of the genome, often at great distances to protein-coding genes55. GWAS variants are often found in close proximity to DNase I hypersensitive sites, which is a hallmark of sequences with potential regulatory activity. PCHi-C and Capture Hi-C have been used extensively to link promoters to GWAS risk loci implicated in breast cancer44, colorectal cancer48, and autoimmune disease35,45,46. A PCHi-C study on 17 different human hematopoietic cell types found SNPs associated with autoimmune disease were enriched in PIRs in lymphoid cells, whereas sequence variants associated with platelet and red blood cell specific traits were predominantly found in the macrophages and erythroblasts, respectively35,56. Thus, tissue-type specific promoter interactomes uncovered by PCHi-C may help to understand the function of non-coding disease-associated sequence variants and identify new potential disease genes for therapeutic intervention.
Characteristics of promoter-interacting regions
Several lines of evidence link promoter interactomes to gene expression control. First, several PCHi-C studies have demonstrated that genomic regions interacting with promoters of (highly) expressed genes are enriched in marks associated with enhancer activity, such as H3K27 acetylation and p300 binding33,34,37. We found a positive correlation between gene expression level and the number of interacting enhancers, suggesting that additive effects of enhancers result in increased gene expression levels34,35. Second, naturally occurring expression quantitative trait loci (eQTLs) are enriched in PIRs that are connected to the same genes whose expression is affected by the eQTLs35. Third, by integrating TRIP57 and PCHi-C data, Cairns et al. found that TRIP reporter genes mapping to PIRs in mouse ESCs show stronger reporter gene expression than reporter genes at integration sites in non-promoter-interacting regions58, indicating that PIRs possess transcriptional regulatory activity. Together, these findings suggest that promoter interactomes uncovered by PCHi-C in various mouse and human cell types include key regulatory modules for gene expression control.
It is worth noting that enhancers represent only a small fraction (~20%) of all PIRs uncovered by PCHi-C33,34. Other PIRs could have structural or topological roles rather than direct transcriptional regulatory functions. However, there is also evidence that PCHi-C may uncover DNA elements with regulatory function that do not harbor classical enhancer marks. In a human lymphoid cell line, the BRD7 promoter was found to interact with a region devoid of enhancer marks that was shown to possess enhancer activity in reporter gene assays33. Regulatory elements with similar characteristics may be more abundant than currently appreciated. For example, a CRISPR-based screen for regulatory DNA elements identified unmarked regulatory elements (UREs) that control gene expression but are devoid of enhancer marks59.
In other cases, PIRs have been shown to harbor chromatin marks associated with transcriptional repression. PIRs and interacting promoters bound by PRC1 in mouse ESCs were engaged in an extensive spatial network of repressed genes bearing the repressive mark H3K27me37. In human lymphoblastoid cells, a distant element interacting with the BCL6 promoter repressed transgene reporter gene expression33, suggesting that it may function to repress BCL6 transcription in its native context.
PIRs enriched for occupancy of the chromatin insulator protein CTCF in human ESCs and NECs37 may represent yet another class of PIRs. Collectively, these results suggest that PIRs harbor a collection of gene regulatory activities yet to be functionally characterized.

Accumulating evidence suggests that the three-dimensional organization of the genome plays an important functional role in a range of nuclear processes, including gene activation1,2,3, repression4,5,6,7,8, recombination9,10, DNA repair11, DNA replication12,13, and cellular senescence14. Distant enhancers are found in close spatial proximity to the promoters they regulate15,16,17, which is essential for proper spatio-temporal gene expression control. Enhancer deletions show that distal enhancers are essential for target gene transcription18,19,20,21,22, and &#39;forced chromatin looping&#39; demonstrates that engineered tethering between an enhancer and its target promoter in the Hbb locus is sufficient to drive transcriptional activation23. Further, genome rearrangements that bring genes under the control of ectopic enhancers can result in inappropriate gene activation and disease24,25,26. Together, these examples illustrate that promoter-enhancer interactions are essential for gene control and require tight regulation to ensure appropriate gene expression. The human and mouse genomes are each estimated to harbor around one million enhancers. For the vast majority of these enhancers, target genes are unknown, and the &#39;rules of engagement&#39; between promoters and enhancers are poorly understood. Assigning transcriptional enhancers to their target genes thus remains a major challenge in deciphering mammalian gene expression control.
Our understanding of three-dimensional genome architecture has been revolutionized by the introduction of 3C27 (chromosome conformation capture) and its variants28,29,30,31. The most powerful of these techniques, Hi-C (high throughput chromosome conformation capture) is designed to identify the entire ensemble of chromosomal interactions within a cell population. Hi-C libraries, typically generated from millions of cells, are highly complex with an estimated 1011 independent ligation products between ~4 kb fragments in the human genome32. As a consequence, reliable and reproducible identification of interactions between individual restriction fragments (such as those containing a promoter or enhancer) from Hi-C data is not feasible unless Hi-C libraries are subjected to ultra-deep sequencing, which is not an economically viable solution for laboratories preparing Hi-C libraries routinely. To circumvent this shortcoming, we developed Promoter Capture Hi-C to specifically enrich promoter-containing ligation products from Hi-C libraries. We focused on promoters for two reasons. First, promoter-enhancer contacts have been shown to be crucial for proper gene expression levels in numerous studies (see references above), and second, as promoters are largely invariant between cell types, the same capture bait system can be used to interrogate the regulatory circuitry across multiple cell types and conditions. Our approach relies on in-solution hybridization of Hi-C libraries with tens of thousands of biotinylated RNA 120mers complementary to promoter-containing Hi-C ligation products and subsequent capture on streptavidin-coated magnetic beads. This results in PCHi-C libraries with much reduced complexity compared to the original Hi-C library, focusing only on the identification of fragments that are ligated to promoters at significantly high frequencies.
We have used PCHi-C in a number of human and mouse cell types to contribute to a better understanding of gene expression control by uncovering long-range distal promoter interacting regions with putative regulatory function, as well as non-random promoter-promoter contacts in the three-dimensional space of the nucleus. The studies have mapped hundreds of thousands of promoter-enhancer contacts across numerous cell types33,34,35,36,37,38,39, identified Polycomb Repressive Complex-mediated spatial genome organization in mouse embryonic stem cells7, demonstrated large-scale rewiring of promoter interactomes during cellular differentiation37,38,39, and linked non-coding disease-associated sequence variants to gene promoters35.
PCHi-C is an ideally suited method to map the genome-wide ensemble of DNA sequences interacting with promoters. Related approaches, such as Capture Hi-C of continuous genomic regions (see Discussion) are the method of choice to obtain high-resolution interaction profiles for selected genomic regions. PCHi-C and Capture Hi-C are extremely similar from an experimental point of view (the only difference is the choice of capture system), so that the advice and guidelines we provide are applicable to both approaches. Here, we present a detailed description of PCHi-C. We outline the rationale and design of a PCHi-C experiment, provide a step-by-step PCHi-C library generation protocol, and illustrate how the quality of PCHi-C libraries can be monitored at various steps in the protocol to yield high-quality data.

<table><tbody><tr><td>16% (vol/vol) paraformaldehyde solution</td><td>Agar Scientific</td><td>R1026</td><td></td></tr><tr><td>Dulbecco&#039;s Modified Eagle Medium (DMEM) 1x</td><td>Life Technologies</td><td>41965-039</td><td></td></tr><tr><td>Fetal bovine serum (FBS) sterile filtered</td><td>Sigma&amp;nbsp;</td><td>F9665</td><td></td></tr><tr><td>Low-retention filter tips&amp;nbsp;</td><td>Starlab</td><td>S1180-3810, S1180-1810, S1180-8810 and S1182-1830</td><td></td></tr><tr><td>10x PBS pH 7.4</td><td>Life Technologies</td><td>70011-036</td><td></td></tr><tr><td>Molecular biology grade water</td><td>Sigma-Aldrich</td><td>W4502</td><td></td></tr><tr><td>1 M Tris-HCl pH 8.0</td><td>Life Technologies</td><td>15568-025</td><td></td></tr><tr><td>IGEPAL CA-630&amp;nbsp;</td><td>Sigma-Aldrich</td><td>I8896</td><td></td></tr><tr><td>5 M NaCl&amp;nbsp;</td><td>Life Technologies</td><td>24740-011</td><td></td></tr><tr><td>Protease inhibitor cocktail (EDTA-free)&amp;nbsp;</td><td>Roche Diagnostics</td><td>11873580001</td><td></td></tr><tr><td>Restriction buffer 2 (10x NEBuffer 2)</td><td>New England Biolabs</td><td>B7002</td><td></td></tr><tr><td>DNA LoBind tube, 1.5 mL&amp;nbsp;</td><td>Eppendorf</td><td>0030 108.051</td><td></td></tr><tr><td>DNA LoBind tube, 2 mL</td><td>Eppendorf</td><td>30108078</td><td></td></tr><tr><td>20% (wt/vol) SDS&amp;nbsp;&amp;nbsp;</td><td>Bio-Rad Laboratories</td><td>161-0418</td><td></td></tr><tr><td>20% (vol/vol) Triton X-100&amp;nbsp;</td><td>Sigma-Aldrich</td><td>T8787</td><td></td></tr><tr><td>HindIII, 100 U/uL</td><td>New England Biolabs</td><td>R0104</td><td></td></tr><tr><td>10 mM dCTP&amp;nbsp;</td><td>Life Technologies</td><td>18253-013</td><td></td></tr><tr><td>10 mM dGTP</td><td>Life Technologies</td><td>18254-011</td><td></td></tr><tr><td>10 mM dTTP&amp;nbsp;</td><td>Life Technologies</td><td>18255-018</td><td></td></tr><tr><td>0.4 mM Biotin-14-dATP</td><td>Life Technologies</td><td>19524-016</td><td></td></tr><tr><td>DNA polymerase I large (Klenow) fragment 5000 units/mL</td><td>New England Biolabs</td><td>M0210</td><td></td></tr><tr><td>10x T4 DNA ligase reaction buffer&amp;nbsp;</td><td>New England Biolabs</td><td>B0202</td><td></td></tr><tr><td>100x 10mg/ml Bovine Serum Albumin&amp;nbsp;</td><td>New England Biolabs</td><td>B9001</td><td></td></tr><tr><td>T4 DNA ligase, 1 U/&amp;mu;L&amp;nbsp;</td><td>Invitrogen</td><td>15224-025</td><td></td></tr><tr><td>RNase A&amp;nbsp;</td><td>Roche</td><td>10109142001</td><td></td></tr><tr><td>Proteinase K, recombinant, PCR grade&amp;nbsp;</td><td>Roche</td><td>3115836001</td><td></td></tr><tr><td>20 000&amp;times;g 50 ml centrifuge tube</td><td>VWR</td><td>525-0156</td><td></td></tr><tr><td>0.5 M EDTA pH 8.0&amp;nbsp;</td><td>Life Technologies</td><td>15575-020</td><td></td></tr><tr><td>Phenol pH 8.0&amp;nbsp;</td><td>Sigma</td><td>P4557</td><td></td></tr><tr><td>Phenol: Chloroform: Isoamyl Alcohol 25:24:1&amp;nbsp;</td><td>Sigma</td><td>P3803</td><td></td></tr><tr><td>Sodium acetate pH 5.2&amp;nbsp;</td><td>Sigma</td><td>S7899</td><td></td></tr><tr><td>Quant-iT PicoGreen&amp;nbsp;</td><td>Invitrogen</td><td>P7589</td><td></td></tr><tr><td>QIAquick Gel Extraction Kit</td><td>Qiagen</td><td>28704</td><td></td></tr><tr><td>QIAquick PCR Purification Kit</td><td>Qiagen</td><td>28104</td><td></td></tr><tr><td>Restriction buffer 2.1 (10x NEBuffer 2.1)</td><td>New England Biolabs</td><td>B7202</td><td></td></tr><tr><td>NheI, 100U/uL</td><td>New England Biolabs</td><td>R0131</td><td></td></tr><tr><td>Micro TUBE AFA Fiber Pre-slit snap cap 6x16mm vials&amp;nbsp;</td><td>Covaris</td><td>520045</td><td>For sonication</td></tr><tr><td>SPRI beads (Agencourt AMPure XP)&amp;nbsp;</td><td>Beckman Coulter</td><td>A63881</td><td></td></tr><tr><td>Dynabeads MyOne Streptavidin C1 beads&amp;nbsp;</td><td>Invitrogen</td><td>65001</td><td></td></tr><tr><td>Tween 20&amp;nbsp;</td><td>Sigma</td><td>P9416</td><td></td></tr><tr><td>10 mM dATP&amp;nbsp;</td><td>Life Technologies</td><td>18252-015</td><td></td></tr><tr><td>T4 DNA polymerase 3000 units/mL</td><td>New England Biolabs</td><td>M0203</td><td></td></tr><tr><td>T4 PNK 10000 units/mL</td><td>New England Biolabs</td><td>M0201</td><td></td></tr><tr><td>Klenow exo minus 5000 units/mL&amp;nbsp;</td><td>New England Biolabs</td><td>M0212</td><td></td></tr><tr><td>Quick ligation reaction buffer&amp;nbsp;</td><td>New England Biolabs</td><td>B6058</td><td></td></tr><tr><td>NEB DNA Quick ligase&amp;nbsp;</td><td>New England Biolabs</td><td>M2200</td><td></td></tr><tr><td>PE adapter 1.0 (5&#039;-P-GATCGGAAGAGCGGTTCAGC&lt;br/&gt; AGGAATGCCGAG-3&#039;)</td><td>Illumina</td><td></td><td></td></tr><tr><td>PE adapter 2.0 (5&#039;-ACACTCTTTCCCTACACGACGCT&lt;br/&gt; CTTCCGATCT-3&#039;)</td><td>Illumina</td><td></td><td></td></tr><tr><td>NEB Phusion PCR kit&amp;nbsp;</td><td>New England Biolabs</td><td>M0530</td><td></td></tr><tr><td>PE PCR primer 1.0 (5&#039;-AATGATACGGCGACCACCGA&lt;br/&gt; GATCTACACTCTTTCCCTAC&lt;br/&gt; ACGACGCTCTTCCGATCT-3&#039;)</td><td>Illumina</td><td></td><td></td></tr><tr><td>PE PCR primer 2.0 (5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GATCGGTCTCGGCATTCCT&lt;br/&gt; GCTGAACCGCTCTTCCGATCT-3&#039;)&amp;nbsp;</td><td>Illumina</td><td></td><td></td></tr><tr><td>PCR strips&amp;nbsp;</td><td>Agilent Technologies</td><td>410022 and 401425</td><td></td></tr><tr><td>SureSelect SSEL TE Reagent ILM PE full adaptor kit&amp;nbsp;</td><td>Agilent Technologies</td><td>931108</td><td></td></tr><tr><td>SureSelect custom 3-5.9 Mb library&amp;nbsp;</td><td>Agilent Technologies</td><td>5190-4831</td><td>custom design mouse or human PCHi-C system</td></tr><tr><td>Dynabeads MyOne Streptavidin T1 beads&amp;nbsp;</td><td>Invitrogen</td><td>65601</td><td></td></tr><tr><td>E220&amp;nbsp; high-performance focused ultra-sonicator</td><td>Corvaris</td><td>E220</td><td></td></tr></tbody></table>

promoter capture hi-c: high-resolution, genome-wide profiling of promoter interactions

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the spatio-temporal expression of their target genes through physical contact, often bridging considerable (in some cases hundreds of kilobases) genomic distances and bypassing nearby genes. The human genome harbors an estimated one million enhancers, the vast majority of which have unknown gene targets. Assigning distal regulatory regions to their target genes is thus crucial to understand gene expression control. We developed Promoter Capture Hi-C (PCHi-C) to enable the genome-wide detection of distal promoter-interacting regions (PIRs), for all promoters in a single experiment. In PCHi-C, highly complex Hi-C libraries are specifically enriched for promoter sequences through in-solution hybrid selection with thousands of biotinylated RNA baits complementary to the ends of all promoter-containing restriction fragments. The aim is to then pull-down promoter sequences and their frequent interaction partners such as enhancers and other potential regulatory elements. After high-throughput paired-end sequencing, a statistical test is applied to each promoter-ligated restriction fragment to identify significant PIRs at the restriction fragment level. We have used PCHi-C to generate an atlas of long-range promoter interactions in dozens of human and mouse cell types. These promoter interactome maps have contributed to a greater understanding of mammalian gene expression control by assigning putative regulatory regions to their target genes and revealing preferential spatial promoter-promoter interaction networks. This information also has high relevance to understanding human genetic disease and the identification of potential disease genes, by linking non-coding disease-associated sequence variants in or near control sequences to their target genes.

Promoter Capture Hi-C has been used to enrich mouse7,34,36,39 and human33,35,37,38 Hi-C libraries for promoter interactions. A similar protocol (named HiCap) has been described by the Sandberg group40. Figure 1A shows the schematic workflow for Promoter Capture Hi-C. In the protocol described here, Hi-C libraries are generated using in-nucleus ligation41, which results in a significantly reduced number of spurious ligation products42. For PCHi-C, highly complex mouse or human Hi-C libraries are subjected to in-solution hybridization and capture using 39,021 biotinylated RNAs complementary to 22,225 mouse promoter-containing HindIII restriction fragments, or 37,608 biotinylated RNAs targeting 22,076 human promoter-containing HindIII restriction fragments, respectively. Promoter containing restriction fragments can be targeted at either or both ends by individual biotinylated RNAs (Figure 1B). We found that capture of both ends improved coverage of individual promoters (Figure 1C; raw sequence reads) nearly two-fold, as expected. Thus, whenever possible (i.e., in non-repetitive regions), we advise to use biotinylated RNAs complementary to both ends of a restriction fragment to be captured.
To assess PCHi-C library quality at an early stage during library preparation, we perform two controls after DNA ligation and purification, as previously described31. The first is to use specific primer pairs to amplify ligation products as in 3C27. We use primer pairs (Table 1) to amplify cell-type invariant long-range ligation products, such as between the Myc gene and its known enhancers located approximately 2 Mb away (Figure 2A) or between genes of the Hist1 locus (separated by 1.5 Mb), and between two regions located in close linear proximity (&#39;short-range control&#39;).
The second quality control is carried out to determine the efficiency of biotin incorporation during Klenow-mediated fill-in of restriction site overhangs with biotin-dATP. Successful Klenow fill-in and subsequent blunt-end ligation results in the disappearance of the original restriction site between the DNA molecules of a ligation product, and in the case of HindIII in the formation of a new NheI recognition site (Figure 2B). The ratio of the HindIII to NheI digested ligation product is a direct readout of biotin incorporation efficiency. A poor quality Hi-C library will show a high level of HindIII digestion, whereas high-quality libraries have near-complete NheI digestion of ligation products (Figure 2B).
After Hi-C library preparation (i.e., after biotin-streptavidin pull down of size-selected Hi-C ligation products, adapter ligation and pre-capture PCR), the integrity and size distribution of the Hi-C library is assessed by Bioanalyzer (Figure 2C). The same control is carried out at the end of PCHi-C library preparation (i.e., after hybridization capture of promoter-containing ligation products and post-capture PCR). Comparison of the Hi-C and PCHi-C Bioanalyzer profiles shows that as expected, Hi-C libraries are much more concentrated than the corresponding PCHi-C libraries, but the size distribution of the libraries is highly similar, indicating that the capture step in PCHi-C does not introduce a size bias (Figure 2C, D).
After paired-end sequencing, the PCHi-C reads are mapped, quality controlled and filtered using the HiCUP pipeline43. High-quality PCHi-C libraries contain between 70-90% &#39;valid pairs&#39; (i.e., paired-end sequence reads between two restriction fragments that are not neighboring on the linear genomic map; Figure 3A, B). Using the in-nucleus ligation protocol41,42, the percentage of trans read pairs (i.e., paired-end sequence reads between two restriction fragments that are located on different chromosomes) are usually low, between 5 and 25%, reflecting the existence of chromosome territories, and indicating good library quality. Direct comparison of the percentage of &#39;valid pairs&#39; between Hi-C libraries and their corresponding PCHi-C libraries35, shows that in all cases the percentage of valid pairs is higher in the PCHi-C libraries (Figure 3B). This is accompanied by a reduction in the percentage of non-valid &#39;same fragment internal&#39; reads in PCHi-C (Figure 3C). This is expected, as the capture step not only enriches for promoter-containing ligation products, but also for restriction fragment ends, due to the position of the capture oligos on the restriction fragments (see Figure 1B).
After HiCUP filtering, we determine the capture efficiency. PCHi-C libraries contain three types of valid sequence reads after HiCUP filtering: 
1.) Promoter: genome reads (i.e., reads between a captured promoter fragment and a non-promoter HindIII restriction fragment anywhere in the genome) 
2.) Promoter: promoter reads (reads between two captured promoter fragments) 
3.) Genome: genome reads (background Hi-C ligation products where neither of the ligation product partners maps to a captured promoter). These are discarded prior to downstream analyses.
High-quality PCHi-C libraries have capture efficiencies (sum of categories 1 and 2 above) between 65&#8211;90% (Figure 3D). A direct comparison to Hi-C libraries shows that PCHi-C results in a ~15-fold enrichment for promoter-containing ligation products (Figure 3D), in some cases 17-fold. This is close to the hypothetical maximum (19.6-fold) enrichment for PCHi-C, which is dependent on the percentage of the genome restriction fragments covered by the capture system. Greater enrichment can be achieved by designing capture systems targeting fewer restriction fragments44,45,46.
Analysis of promoter interactomes demonstrates cell type and lineage-specificity33,34,35, with pronounced changes during cellular differentiation37,38,39. Figures 4 and 5 show examples of lineage specificity and differentiation dynamics at specific promoters. For example, ALAD is constitutively expressed in all cells but its expression is upregulated in erythroblasts47. The ALAD promoter contacts several distal fragments in all hematopoietic cells and engages in additional interactions specifically in erythroblasts (Figure 4). IL-8 shows no statistically significant interactions in B cells, very few interactions in T cells, but dozens of interactions in cells of the myeloid lineage, including cell-type specific interactions in monocytes, neutrophils and megakaryocytes (Figure 5). These examples demonstrate how PCHi-C can be used to unravel cell-type specific interactomes and identify promoter-interacting regions with regulatory potential.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57320/57320fig1.jpg" /> 
Figure 1: Promoter Capture Hi-C rationale and capture bait design. (A) Schematic workflow of PCHi-C. In-nucleus ligation Hi-C41,42 (I) is followed by in-solution hybridization with biotinylated RNA baits (II) targeting the restriction fragments of all human (depicted here) or mouse gene promoters (III). (B) Bait design for PCHi-C. Biotinylated RNA capture baits (red curved lines) are designed against the ends of promoter-containing restriction fragments (grey; note that the promoter sequences themselves (red) are only targeted by the RNA capture baits if they are located at restriction fragment ends). Ligation products consisting of promoter-containing restriction fragments (grey) and their interacting restriction fragments (yellow and green) are isolated through sequence-complementarity hybridization between RNA bait and DNA target, and subsequent biotin-streptavidin pulldown, as shown in A. (C) Comparison of PCHi-C capture efficiency for promoter-containing restriction fragments targeted by one RNA bait capture probe vs two RNA bait capture probes (see schematic in B). <a href="/files/ftp_upload/57320/57320fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57320/57320fig2.jpg" /> 
Figure 2: PCHi-C pre-sequencing quality controls. (A) Left, schematic of spatial juxtaposition between promoter and PIR, resulting in a Hi-C ligation product consisting of a promoter-containing restriction fragment (grey; promoter sequence in red) and a PIR restriction fragment (yellow). Right, DNA gel electrophoresis showing examples of Hi-C ligation products amplified using specific primer pairs (as depicted in schematic on the left). (B) Left, representative examples of HindIII, NheI and HindIII/NheI restriction digests of Hi-C ligation products (PCR products shown in A). Right, schematic of DNA sequence after Hi-C ligation following unsuccessful (top) or successful (bottom) dNTP Klenow fill-in of restriction junctions and subsequent ligation. (C) Representative Hi-C library bioanalyzer profile (1/5 dilution). (D) Representative PCHi-C library bioanalyzer profile (no dilution). <a href="/files/ftp_upload/57320/57320fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57320/57320fig3.jpg" /> 
Figure 3: PCHi-C post-sequencing quality controls. (A) Comparison of percentage valid sequence read pairs after HiCUP43 processing in PCHi-C vs corresponding Hi-C libraries (data from Javierre et al., 201635). (B) Representative HiCUP PCHi-C result showing valid read pairs, and other sequence categories that are discarded prior to downstream analyses (data from Javierre et al., 201635). (C) Comparison of percentage &#39;same fragment internal&#39; reads after HiCUP processing in PCHi-C vs corresponding Hi-C libraries (data from Javierre et al., 201635). (D) Comparison of percentage sequence reads involving baited promoter fragments (capture efficiency) in PCHi-C vs corresponding Hi-C libraries (data from Javierre et al., 201635). <a href="/files/ftp_upload/57320/57320fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57320/57320fig4.jpg" /> 
Figure 4: ALAD PCHi-C profile in human hematopoietic cells. Promoter interactions of myeloid cell types are shown as blue arches, and promoter interactions of lymphoid cell types are shown as purple arches. Erythroblast-specific interactions are indicated by red arrows (data from Javierre et al., 201635). <a href="/files/ftp_upload/57320/57320fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57320/57320fig5.jpg" /> 
Figure 5: IL8 PCHi-C profile in human hematopoietic cells. Promoter interactions of myeloid cell types are shown as blue arches, and promoter interactions of lymphoid cell types are shown as purple arches. Monocyte-specific interactions are indicated by green arrows, neutrophil-specific interactions are indicated by red arrows, and a megakaryocyte-specific interaction is indicated by a brown arrow (data from Javierre et al., 201635). <a href="/files/ftp_upload/57320/57320fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="9">Human</td>
		</tr>
		<tr>
			<td>Name</td>
			<td colspan="3">Sequence&#160;</td>
			<td>Chromosome</td>
			<td>Strand</td>
			<td>Start GRCh38/hg38</td>
			<td>End GRCh38/hg38</td>
			<td>Primer combinations to test 3C interactions and biotin incorporation</td>
		</tr>
		<tr>
			<td>hs AHF64 Dekker&#160;</td>
			<td colspan="3">GCATGCATTAGCCTCTGCTGTTCTCTGAAATC</td>
			<td>11</td>
			<td>+</td>
			<td>116803960</td>
			<td>116803991</td>
			<td>use in combination with hs AHF66 Dekker&#160;</td>
		</tr>
		<tr>
			<td>hs AHF66 Dekker</td>
			<td colspan="3">CTGTCCAAGTACATTCCTGTTCACAAACCC</td>
			<td>11</td>
			<td>+</td>
			<td>116810219</td>
			<td>116810248</td>
			<td>use in combination with hs AHF64 Dekker&#160;</td>
		</tr>
		<tr>
			<td>hs MYC locus</td>
			<td colspan="3">GGAGAACCGGTAATGGCAAA</td>
			<td>8</td>
			<td>-</td>
			<td>127733814</td>
			<td>127733833</td>
			<td>use in combination with hs MYC +1820 or hs MYC -538</td>
		</tr>
		<tr>
			<td>hs MYC +1820</td>
			<td colspan="3">AAAATGCCCATTTCCTTCTCC</td>
			<td>8</td>
			<td>+</td>
			<td>129554527</td>
			<td>129554547</td>
			<td>use in combination with hs MYC locus</td>
		</tr>
		<tr>
			<td>hs MYC -538</td>
			<td colspan="3">TGCCTGATGGATAGTGCTTTC</td>
			<td>8</td>
			<td>-</td>
			<td>127195696</td>
			<td>127195716</td>
			<td>use in combination with hs MYC locus</td>
		</tr>
		<tr>
			<td>hs HIST1 F</td>
			<td colspan="3">AAGCAGGAAAAGGCATAGCA</td>
			<td>6</td>
			<td>+</td>
			<td>26207174</td>
			<td>26207193</td>
			<td>use in combination with hs HIST1 R</td>
		</tr>
		<tr>
			<td>hs HIST1 R</td>
			<td colspan="3">TCTTGGGTTGTGGGACTTTC</td>
			<td>6</td>
			<td>+</td>
			<td>27771575</td>
			<td>27771594</td>
			<td>use in combination with hs HIST1 F</td>
		</tr>
		<tr>
			<td colspan="9">Mouse</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">Sequence</td>
			<td>Chromosome</td>
			<td>Strand</td>
			<td>Start GRCm38/mm10</td>
			<td>End GRCm38/mm10</td>
			<td>Primer combinations to test 3C interactions and biotin incorporation</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">TCATGAGTTCCCCACATCTTTG</td>
			<td>8</td>
			<td>+</td>
			<td>84841090</td>
			<td>84841111</td>
			<td>use in combination with mm Calr2</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">CTGTGGGCACCAGATGTGTAAAT</td>
			<td>8</td>
			<td>+</td>
			<td>84848519</td>
			<td>84848541</td>
			<td>use in combination with mm Calr1</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">TATCAAGGGTGCCCGTCACCTTCAGC</td>
			<td>6</td>
			<td>+</td>
			<td>125163098</td>
			<td>125163123</td>
			<td>use in combination with Gapdh4 Dekker</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">GGGCTTTTATAGCACGGTTATAAAGT</td>
			<td>6</td>
			<td>+</td>
			<td>125163774</td>
			<td>125163799</td>
			<td>use in combination with Gapdh3 Dekker</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">GGAGGAGGGAAAAGGAGTGATT</td>
			<td>6</td>
			<td>+</td>
			<td>52212829</td>
			<td>52212850</td>
			<td>use in combination with mm Hoxa13</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">CAGGCATTATTTGCTGAGAACG</td>
			<td>6</td>
			<td>-</td>
			<td>52253490</td>
			<td>52253511</td>
			<td>use in combination with mm Hoxa7</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">GGGTAATGGTGTCACTAACTGG</td>
			<td>13</td>
			<td>+</td>
			<td>23571284</td>
			<td>23571305</td>
			<td>use in combination with mm Hist1h3e or mm Hist1h4i</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">GGGTTTGATGAGTTGGTGAAG</td>
			<td>13</td>
			<td>+</td>
			<td>23566541</td>
			<td>23566561</td>
			<td>use in combination with mm Hist1h2ae</td>
		</tr>
		<tr>
			<td></td>
			<td colspan="3">TTGGGCCAAAGCCTATATGA</td>
			<td>13</td>
			<td>+</td>
			<td>22043085</td>
			<td>22043104</td>
			<td>use in combination with mm Hist1h2ae</td>
		</tr>
	</tbody>
</table>
Table 1: Primer sequences for quality control of human and mouse Hi-C libraries.

Watch this Scientific Journal Video about Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions at JoVE.com

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

DNA regulatory elements, such as enhancers, control gene expression by physically contacting target gene promoters, often through long-range chromosomal interactions spanning large genomic distances. Promoter Capture Hi-C (PCHi-C) identifies significant interactions between promoters and distal regions, enabling the assignment of potential regulatory sequences to their target genes.

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the ...

promoter-capture-hi-c-high-resolution-genome-wide-profiling-promoter

Nanyang Technological University

Research

JoVE Journal

Genetics

32.3K Views.  Babraham Research Campus. DNA regelgevende elementen, zoals versterkers, kwaliteitsbeheersing genexpressie door fysiek contact met target gene initiatiefnemers, vaak door middel van lange-afstands chromosomale interacties verspreid over grote genomic afstanden. Promotor vangen Hi-C (PCHi-C) identificeert belangrijke interacties tussen initiatiefnemers en distale regio's, waardoor de toewijzing van potentiële regulerende sequenties aan hun doelgenen.

Video: Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic region is replicated at a distinct time during S phase through the simultaneous activation of multiple origins of replication. Time of replication (ToR) correlates with many genomic and epigenetic features and is linked to mutation rates and cancer. Comprehending the full genomic view of the replication program, in health and disease is a major future goal and challenge.
This article describes in detail the &#34;Copy Number Ratio of S/G1 for mapping genomic Time of Replication&#34; method (herein called: CNR-ToR), a simple approach to map the genome wide ToR of mammalian cells. The method is based on the copy number differences between S phase cells and G1 phase cells. The CNR-ToR method is performed in 6 steps: 1. Preparation of cells and staining with propidium iodide (PI); 2. Sorting G1 and S phase cells using fluorescence-activated cell sorting (FACS); 3. DNA purification; 4. Sonication; 5. Library preparation and sequencing; and 6. Bioinformatic analysis. The CNR-ToR method is a fast and easy approach that results in detailed replication maps.

We describe here a relatively fast and simple approach for mapping genome-wide mammalian replication timing, from cell isolation to the basic analysis of the sequencing results. A genomic map of a representative replication program will be provided following the protocol.

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic ...

Analysis of the c-KIT Ligand Promoter Using Chromatin Immunoprecipitation

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. Therefore, DNA-protein interactions regulate multiple physiological, pathophysiological, and biological functions, such as cell differentiation, cell proliferation, cell cycle control, chromosome stability, epigenetic gene regulation, and cell transformation. In eukaryotic cells, the DNA interacts with histone and nonhistone proteins and is condensed into chromatin. Several technical tools can be used to analyze DNA-protein interactions, such as the Electrophoresis (gel) Mobility Shift Assay (EMSA) and DNase I footprinting. However, these techniques analyze the protein-DNA interaction in vitro, not within the cellular context. Chromatin immunoprecipitation (ChIP) is a technique that captures proteins at their specific DNA binding sites, thereby allowing for the identification of DNA-protein interactions within their chromatin context. It is done by fixation&#160;of the DNA-protein interaction, followed by&#160;immunoprecipitation of the protein of interest. Subsequently, the genomic site that the protein was bound to is characterized. Here, we describe and discuss ChIP and demonstrate its analytical value for the identification of the Transforming Growth Factor-&#946; (TGF-&#946;)-induced binding of the transcription factor SMAD2 to SMAD Binding Elements (SBE) within the promoter region of the tyrosine-protein kinase Kit (c-KIT) receptor ligand Stem Cell Factor (SCF).

DNA-protein interactions are essential for multiple biological processes. During the evaluation of cellular functions, the analysis of DNA-protein interactions is indispensable for understanding gene regulation. Chromatin immunoprecipitation (ChIP) is a powerful tool to analyze such interactions in vivo.

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

Cancer Research

Adaptation of Hybridization Capture of Chromatin-associated Proteins for Proteomics to Mammalian Cells

The hybridization capture of chromatin-associated proteins for proteomics (HyCCAPP) technology was initially developed to uncover novel DNA-protein interactions in yeast. It allows analysis of a target region of interest without the need for prior knowledge about likely proteins bound to the target region. This, in theory, allows HyCCAPP to be used to analyze any genomic region of interest, and it provides sufficient flexibility to work in different cell systems. This method is not meant to study binding sites of known transcription factors, a task better suited for Chromatin Immunoprecipitation (ChIP) and ChIP-like methods. The strength of HyCCAPP lies in its ability to explore DNA regions for which there is limited or no knowledge about the proteins bound to it. It can also be a convenient method to avoid biases (present in ChIP-like methods) introduced by protein-based chromatin enrichment using antibodies. Potentially, HyCCAPP can be a powerful tool to uncover truly novel DNA-protein interactions. To date, the technology has been predominantly applied to yeast cells or to high copy repeat sequences in mammalian cells. In order to become the powerful tool we envision, HyCCAPP approaches need to be optimized to efficiently capture single-copy loci in mammalian cells. Here, we present our adaptation of the initial yeast HyCCAPP capture protocol to human cell lines, and show that single-copy chromatin regions can be efficiently isolated with this modified protocol.

This is a method to identify novel DNA-interacting proteins at specific target loci, relying on sequence-specific capture of crosslinked chromatin for subsequent proteomic analyses. No prior knowledge about potential binding proteins, nor cell modifications are required. Initially developed for yeast, the technology has now been adapted for mammalian cells.

The hybridization capture of chromatin-associated proteins for proteomics (HyCCAPP) technology was initially developed to uncover novel DNA-protein ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Identification of Enhancer-Promoter Contacts in Embryoid Bodies by Quantitative Chromosome Conformation Capture (4C)

During mammalian development, cell fates are determined through the establishment of regulatory networks that define the specificity, timing, and spatial patterns of gene expression. Embryoid bodies (EBs) derived from pluripotent stem cells have been a popular model to study the differentiation of the main three germ layers and to define regulatory circuits during cell fate specification. Although it is well-known that tissue-specific enhancers play an important role in these networks by interacting with promoters, assigning them to their relevant target genes still remains challenging. To make this possible, quantitative approaches are needed to study enhancer-promoter contacts and their dynamics during development. Here, we adapted a 4C method to define enhancers and their contacts with cognate promoters in the EB differentiation model. The method uses frequently cutting restriction enzymes, sonication, and a nested-ligation-mediated PCR protocol compatible with commercial DNA library preparation kits. Subsequently, the 4C libraries are subjected to high-throughput sequencing and analyzed bioinformatically, allowing detection and quantification of all sequences that have contacts with a chosen promoter. The resulting sequencing data can also be used to gain information about the dynamics of enhancer-promoter contacts during differentiation. The technique described for the EB differentiation model is easy to implement.

Identification of Enhancer-Promoter Contacts in Embryoid Bodies by Quantitative Chromosome Conformation Capture 4C

We report the application of quantitative chromosome conformation capture followed by high-throughput sequencing in embryoid bodies generated from embryonic stem cells. This technique allows to identify and quantitate the contacts between putative enhancers and promoter regions of a given gene during embryonic stem cell differentiation.

During mammalian development, cell fates are determined through the establishment of regulatory networks that define the specificity, timing, and spatial ...

A Comprehensive Approach to Studying the Promoter Interactome in Scarce Cells

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Spatiotemporal gene transcription is tightly regulated by distal regulatory elements, such as enhancers and silencers, which rely on physical proximity with their target gene promoters to control transcription. Although these regulatory elements are easy to identify, their target genes are difficult to predict, since most of them are cell-type specific and may be separated by hundreds of kilobases in the linear genome sequence, skipping over other non-target genes. For several years, Promoter Capture Hi-C (PCHi-C) has been the gold standard for the association of distal regulatory elements to their target genes. However, PCHi-C relies on the availability of millions of cells, prohibiting the study of rare cell populations such as those commonly obtained from primary tissues. To overcome this limitation, low input Capture Hi-C (liCHi-C), a cost-effective and customizable method to identify the repertoire of distal regulatory elements controlling each gene of the genome, has been developed. liCHi-C relies on a similar experimental and computational framework as PCHi-C, but by employing minimal tube changes, modifying&#160;the reagent concentration and volumes, and swapping or eliminating steps, it accounts&#160;for minimal material loss during library construction. Collectively, liCHi-C enables the study of gene regulation and spatiotemporal genome organization in the context of developmental biology and cellular function.

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations  

Gene expression is regulated by interactions of gene promoters with distal regulatory elements. Here, we descirbe how low input Capture Hi-C (liCHi-C) allows the identification of these interactions in rare cell types, which were previously unmeasurable.

Spatiotemporal gene transcription is tightly regulated by distal regulatory elements, such as enhancers and silencers, which rely on physical proximity ...

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Three-dimensional (3D) chromosome organization is a major factor in genome regulation and cell-type specification. For example, cis-regulatory elements, known as enhancers, are thought to regulate the activity of distal promoters via interaction in 3D space. Genome-wide chromosome conformation capture (3C)-technologies, such as Hi-C, have transformed our understanding of how genomes are organized in cells. The current understanding of 3D genome organization is limited by the resolution with which the topological organization of chromosomes in 3D space can be resolved. Micro-C-XL measures chromosome folding with resolution at the level of the nucleosome, the basic unit of chromatin, by utilizing micrococcal nuclease (MNase) to fragment genomes during the chromosome conformation capture protocol. This results in an improved signal-to-noise ratio in the measurements, thus facilitating the better detection of insulation sites and chromosome loops compared to other genome-wide 3D technologies. A visually supported, detailed, step-by-step protocol for preparing high-quality Micro-C-XL samples from mammalian cells is presented in this article.

A protocol for mapping the three-dimensional genome organization with nucleosome resolution using the genome-wide chromosome conformation capture method Micro-C-XL is presented here.

Three-dimensional (3D) chromosome organization is a major factor in genome regulation and cell-type specification. For example, cis-regulatory elements, ...

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

The three-dimensional folding of chromosomes compartmentalizes the genome and and can bring distant functional elements, such as promoters and enhancers, into close spatial proximity 2-6. Deciphering the relationship between chromosome organization and genome activity will aid in understanding genomic processes, like transcription and replication. However, little is known about how chromosomes fold. Microscopy is unable to distinguish large numbers of loci simultaneously or at high resolution. To date, the detection of chromosomal interactions using chromosome conformation capture (3C) and its subsequent adaptations required the choice of a set of target loci, making genome-wide studies impossible 7-10.
We developed Hi-C, an extension of 3C that is capable of identifying long range interactions in an unbiased, genome-wide fashion. In Hi-C, cells are fixed with formaldehyde, causing interacting loci to be bound to one another by means of covalent DNA-protein cross-links. When the DNA is subsequently fragmented with a restriction enzyme, these loci remain linked. A biotinylated residue is incorporated as the 5' overhangs are filled in. Next, blunt-end ligation is performed under dilute conditions that favor ligation events between cross-linked DNA fragments. This results in a genome-wide library of ligation products, corresponding to pairs of fragments that were originally in close proximity to each other in the nucleus. Each ligation product is marked with biotin at the site of the junction. The library is sheared, and the junctions are pulled-down with streptavidin beads. The purified junctions can subsequently be analyzed using a high-throughput sequencer, resulting in a catalog of interacting fragments.
Direct analysis of the resulting contact matrix reveals numerous features of genomic organization, such as the presence of chromosome territories and the preferential association of small gene-rich chromosomes. Correlation analysis can be applied to the contact matrix, demonstrating that the human genome is segregated into two compartments: a less densely packed compartment containing open, accessible, and active chromatin and a more dense compartment containing closed, inaccessible, and inactive chromatin regions. Finally, ensemble analysis of the contact matrix, coupled with theoretical derivations and computational simulations, revealed that at the megabase scale Hi-C reveals features consistent with a fractal globule conformation.

The Hi-C method allows unbiased, genome-wide identification of chromatin interactions (1). Hi-C couples proximity ligation and massively parallel sequencing. The resulting data can be used to study genomic architecture at multiple scales: initial results identified features such as chromosome territories, segregation of open and closed chromatin, and chromatin structure at the megabase scale.

The three-dimensional folding of chromosomes compartmentalizes the genome and and can bring distant functional elements, such as promoters and enhancers, ...

Biology

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

The spatial organization of the genome contributes to its function and regulation in many contexts, including transcription, replication, recombination, and repair. Understanding the exact causality between genome topology and function is therefore crucial and increasingly the subject of intensive research. Chromosome conformation capture technologies (3C) allow inferring the 3D structure of chromatin by measuring the frequency of interactions between any region of the genome. Here we describe a fast and simple protocol to perform Capture Hi-C, a 3C-based target enrichment method that characterizes the allele-specific 3D organization of megabased-sized genomic targets at high-resolution. In Capture Hi-C, target regions are captured by an array of biotinylated probes before downstream high-throughput sequencing. Thus, higher resolution and allele-specificity are achieved while improving the time-effectiveness and affordability of the technology. To demonstrate its strengths, the Capture Hi-C protocol was applied to the mouse X-inactivation center (Xic), the master regulatory locus of X-chromosome inactivation (XCI).

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

This protocol describes the Capture Hi-C method used to characterize the 3D organization of megabased-sized targeted genomic regions at high-resolution, including boundaries of topologically associating domains (TADs) and long-range chromatin interactions between regulatory and other DNA sequence elements.

The spatial organization of the genome contributes to its function and regulation in many contexts, including transcription, replication, recombination, ...

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

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