We thank Christl Gaubitz and Kathleen Stewart-Morgan for their critical reading of the manuscript. We thank Anja Groth and the Groth lab for their support in establishing our lab. We thank the staff of the CPR/reNEW Genomics Platform for support: H. Wollmann, M. Michaut, and A. Kalvisa. The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW) is supported by the Novo Nordisk Foundation grant number NNF21CC0073729. The Novo Nordisk Foundation Center for Protein Research (CPR) is supported by the Novo Nordisk Foundation grant number NNF14CC0001. We thank the Brickman lab at the Novo Nordisk Center for Stem Cell Medicine, reNEW Copenhagen, for the mouse ES cells.

<ol><li>Lieberman-Aiden, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comprehensive+mapping+of+long-range+interactions+reveals+folding+principles+of+the+human+genome%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Comprehensive mapping of long-range interactions reveals folding principles of the human genome.</a> Science. 326 (5950), 289-293 (2009).</li>
<li>Hsieh, T. -H. S., Fudenberg, G., Goloborodko, A., Rando, O. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Micro-C+XL%3A+Assaying+chromosome+conformation+from+the+nucleosome+to+the+entire+genome%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Micro-C XL: Assaying chromosome conformation from the nucleosome to the entire genome.</a> Nature Methods. 13 (12), 1009-1011 (2016).</li>
<li>Hsieh, T. -H. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mapping+nucleosome+resolution+chromosome+folding+in+yeast+by+Micro-C%5BTitle%5D)+AND+%22Cell%22%5BJournal%5D)">Mapping nucleosome resolution chromosome folding in yeast by Micro-C.</a> Cell. 162 (1), 108-119 (2015).</li>
<li>Krietenstein, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ultrastructural+details+of+mammalian+chromosome+architecture%5BTitle%5D)+AND+%22Molecular+Cell%22%5BJournal%5D)">Ultrastructural details of mammalian chromosome architecture.</a> Molecular Cell. 78 (3), 554-565 (2020).</li>
<li>Hsieh, T. -H. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Resolving+the+3D+landscape+of+transcription-linked+mammalian+chromatin+folding%5BTitle%5D)+AND+%22Molecular+Cell%22%5BJournal%5D)">Resolving the 3D landscape of transcription-linked mammalian chromatin folding.</a> Molecular Cell. 78 (3), 539-553 (2020).</li>
<li>Belaghzal, H., Dekker, J., Gibcus, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hi-C+2.0%3A+An+optimized+Hi-C+procedure+for+high-resolution+genome-wide+mapping+of+chromosome+conformation%5BTitle%5D)+AND+%22Methods%22%5BJournal%5D)">Hi-C 2.0: An optimized Hi-C procedure for high-resolution genome-wide mapping of chromosome conformation.</a> Methods. 123, 56-65 (2017).</li>
<li>van Holde, K. E. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3avan+Holde%2C+KE+%22Chromatin%22">Chromatin</a>. , Springer. New York, NY. (1989).</li>
<li>Oksuz, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Systematic+evaluation+of+chromosome+conformation+capture+assays%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Systematic evaluation of chromosome conformation capture assays.</a> Nature Methods. 18, 1046-1055 (2021).</li>
<li>Lafontaine, D. L., Yang, L., Dekker, J., Gibcus, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Hi-C+3.0%3A+Improved+protocol+for+genome-wide+chromosome+conformation+capture%5BTitle%5D)+AND+%22Current+Protocols%22%5BJournal%5D)">Hi-C 3.0: Improved protocol for genome-wide chromosome conformation capture.</a> Current Protocols. 1 (7), 198(2021).</li>
<li>Goloborodko, A., Venev, S., Abdennur, N., Di Tommaso, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mirnylab%2Fdistiller-nf%3B+v033%5BTitle%5D)+AND+%22Zenodo%22%5BJournal%5D)">Mirnylab/distiller-nf; v033.</a> Zenodo. , (2019).</li>
<li>Venev, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Open2c%2Fcooltools%3A+v0.4.1%5BTitle%5D)+AND+%22Zenodo%22%5BJournal%5D)">Open2c/cooltools: v0.4.1.</a> Zenodo. , (2021).</li>
<li>Abdennur, N., Mirny, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cooler%3A+Scalable+storage+for+Hi-C+data+and+other+genomically+labeled+arrays%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Cooler: Scalable storage for Hi-C data and other genomically labeled arrays.</a> Bioinformatics. 36 (1), 311-316 (2019).</li>
<li>Zhang, S., Übelmesser, N., Barbieri, M., Papantonis, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhancer-promoter+contact+formation+requires+RNAPII+and+antagonizes+loop+extrusion%5BTitle%5D)+AND+%22BioRxiv%22%5BJournal%5D)">Enhancer-promoter contact formation requires RNAPII and antagonizes loop extrusion.</a> BioRxiv. , (2022).</li>
<li>Barshad, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA+polymerase+II+and+PARP1+shape+enhancer-promoter+contacts%5BTitle%5D)+AND+%22BioRxiv%22%5BJournal%5D)">RNA polymerase II and PARP1 shape enhancer-promoter contacts.</a> BioRxiv. , (2022).</li>
<li>Hansen, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Distinct+classes+of+chromatin+loops+revealed+by+deletion+of+an+RNA-binding+region+in+CTCF%5BTitle%5D)+AND+%22Molecular+Cell%22%5BJournal%5D)">Distinct classes of chromatin loops revealed by deletion of an RNA-binding region in CTCF.</a> Molecular Cell. 76 (3), 395-411 (2019).</li>
<li>Hua, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Defining+genome+architecture+at+base-pair+resolution%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Defining genome architecture at base-pair resolution.</a> Nature. 595 (7865), 125-129 (2021).</li>
<li>Downes, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-resolution+targeted+3C+interrogation+of+cis-regulatory+element+organization+at+genome-wide+scale%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">High-resolution targeted 3C interrogation of cis-regulatory element organization at genome-wide scale.</a> Nature Communications. 12, 531(2021).</li>
<li>Goel, V. Y., Huseyin, M. K., Hansen, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Region+capture+Micro-C+reveals+coalescence+of+enhancers+and+promoters+into+nested+microcompartments%5BTitle%5D)+AND+%22BioRxiv%22%5BJournal%5D)">Region capture Micro-C reveals coalescence of enhancers and promoters into nested microcompartments.</a> BioRxiv. , (2022).</li>
</ol>

The authors have nothing to disclose.

The success of a Micro-C experiment depends on a few critical steps in the protocol that need to be carefully executed. First, crosslinking with the additional crosslinker DSG or EGS can lead to the aggregation of cells, depending on the cell type. Adding 0.1%-0.5% BSA to the crosslinking reaction significantly reduces the aggregation without affecting the crosslinking efficiency. Inefficient crosslinking can result in increased rates of trans-chromosomal interactions that are indicative of random ligations. The second, but most crucial, step in this protocol is the digestion of chromatin with MNase. Suboptimal chromatin digestion leads to inefficient proximity ligation (over-digestion) or increased rates of non-proximity-ligated di-nucleosomes (under-digestion). The efficiency of the ligation reaction can be evaluated by agarose gel electrophoresis (Figure 1B) and is additionally best estimated by low-input sequencing. If the low-input sequencing reveals either a high duplication rate (inefficient ligation) or increased di-nucleosome rates, the MNase digestion degree should be re-evaluated. Notably, the loss of sample when executing the protocol can lead to reduced library complexity. The concentration of a sample is best evaluated after DNA purification (step 5.3) or by minimal PCR (step 8). The total yield of DNA from 5 x 106 mammalian cells after DNA purification is typically &#62;2 &#181;g. The DNA concentration should be controlled after the MNase digestion, ExoIII digestion, and DNA purification. Endogenous nucleases, the abundance of which is cell type-specific and species-specific, can be a source of DNA degradation. In addition, column-based DNA purification can lead to sample loss due to incompatibility with the SDS from deproteination reactions. Ethanol precipitation can be considered if the DNA concentration is low at this step.
As Micro-C requires sample-specific MNase titration, it is challenging to apply Micro-C to small cell populations, such as with small organs from various model organisms, embryos and single cells, organoids, or patient biopsies. Here, Hi-C 3.0 offers a well-established alternative using an endpoint reaction by sequence-specific restriction endonucleases8,9.
Micro-C is a widely applicable high-resolution chromosome conformation technology with a high dynamic range and a low signal-to-noise ratio, which makes it particularly suitable for investigating short-range chromosome features4,5,8, such as chromosome loops. The resolution of Micro-C allows for capturing promoter-enhancer loops, which are beyond the detection limit of Hi-C, efficiently enabling a more detailed analysis of the relationship between genome organization and regulation13,14,15. Furthermore, DNA capture strategies have recently been combined with Micro-C to increase the locus-specific resolution of the targeted genomic loci to unprecedented levels, revealing novel insights into the ultrastructure of the 3D genome16,17,18. In summary, we envision that Micro-C and its derivates will be a key technology for dissecting the role of the 3D genome in transcriptional regulation and, consequently, cell type differentiation and maintenance.

Micro-C-XL is a genome-wide technique to measure 3D genome conformation with nucleosome resolution. Micro-C-XL builds on the widely used proximity ligation-based Hi-C technology, which has transformed our understanding of how 3D genomes are organized1. Micro-C-XL and its first iteration, Micro-C, were initially developed in Saccharomyces cerevisiae2,3 and later adapted to mammalian cell systems, for which the protocol has demonstrated its full potential in detecting short-range features of the 3D genome, such as chromosome loops and insulation sites. This version is based on recent mammalian Micro-C-XL publications4,5. As Micro-C-XL supersedes Micro-C, Micro-C-XL is henceforth referred to as Micro-C in the manuscript.
The major differences between Micro-C and Hi-C6 are as follows: 1) genome fragmentation with micrococcal nuclease (MNase) compared to restriction enzymes and 2) additional crosslinkers with larger atomic spacing between the reactive groups compared to only formaldehyde. Both steps contribute significantly to the improved signal-to-noise ratio of Micro-C compared to conventional Hi-C. The fragmentation size limits the resolution to which the 3D genome organization can be resolved during the proximity ligation protocol. MNase is a nuclease that preferentially digests accessible DNA and leaves nucleosomal-protected DNA intact. Nucleosome footprinting using MNase-sequencing has shown that nucleosomes fully cover most eukaryotic genomes7. As nucleosomes are distributed throughout the genome with an average spacing of 160-220 bp, depending on species and cell type, MNase is the ideal enzyme for high-resolution mapping of the genome architecture.
The use of an additional crosslinker in combination with formaldehyde (FA) in the Micro-C method additionally improves the signal-to-noise ratio2,8. Amine-specific crosslinkers with longer atomic spacers between the reactive groups facilitate protein-protein crosslinks. These are typically disuccinimidyl glutarate (DSG) or ethylene glycol bis-succinimidyl succinate (EGS) with 7.7 &#197; and 16.1 &#197; spacers, respectively. The reduction of noise through EGS or DSG is particularly apparent in experiments with high fragmentation rates, such as Micro-C, and presumably occurs due to a reduction in the rate of random ligation events8.
A recently developed Hi-C 3.0 protocol that utilizes ESG/DSG crosslinking and multiple combinations of restriction enzymes reduces noise in Hi-C experiments and significantly improves the detection of chromosome loops and insulation sites8,9. Still, a site-by-site comparison of various interaction data features found that Micro-C had superior detection of short-range features, such as chromosome loops and insulation sites, compared to both Hi-C 3.0 and conventional Hi-C8. However, Hi-C 3.0 does improve the detection of short-range features and maintains strong detection of genome compartmentalization compared to conventional Hi-C. In summary, the choice of a chromosome conformation capture method should be determined by the objective and biological question.
Here, we provide a step-by-step protocol for successful Micro-C experiments that can unravel 3D genome organization.

<table><tbody><tr><td>1 mM Biotin dATP</td><td>Jenna Bioscience&amp;nbsp;</td><td>NU-835-Bio14-S</td><td></td></tr><tr><td>1 mM Biotin dCTP</td><td>Jenna Bioscience</td><td>NU-809-BioX-S</td><td></td></tr><tr><td>10 mM dGTP</td><td>NEB</td><td>N0442S</td><td></td></tr><tr><td>10 mM dTTP&amp;nbsp;</td><td>NEB</td><td>N0443S</td><td></td></tr><tr><td>10 U/ml T4 PNK</td><td>NEB</td><td>M0201L</td><td></td></tr><tr><td>100 U/L Exonuclease III</td><td>NEB</td><td>M0206L</td><td></td></tr><tr><td>10x NEBuffer 1.1</td><td>NEB</td><td>B7001S</td><td></td></tr><tr><td>10x NEBuffer 2.1</td><td>NEB</td><td>B7202S</td><td></td></tr><tr><td>10x T4 DNA Ligase buffer&amp;nbsp;</td><td>NEB</td><td>B0202A</td><td></td></tr><tr><td>1x DPBS w/o Mg2&lt;sup&gt;+&lt;/sup&gt; and Ca2&lt;sup&gt;+&lt;/sup&gt;</td><td>ThermoFisher&amp;nbsp;</td><td>14190144</td><td></td></tr><tr><td>1x LIF</td><td></td><td></td><td></td></tr><tr><td>2_Mercaptoethanol 50 mM&amp;nbsp;</td><td>Gibco</td><td>31350010</td><td>0.1 mM b-mercaptoethanol&amp;nbsp;</td></tr><tr><td>37% Formaldehyde&amp;nbsp;</td><td>Sigma Aldrich</td><td>252549-500ML</td><td>Caution. See manufactures MSDS</td></tr><tr><td>400 U/ml T4 DNA Ligase&amp;nbsp;</td><td>NEB</td><td>M0202L</td><td></td></tr><tr><td>5 U/ml Klenow Fragment</td><td>NEB</td><td>M0210L</td><td></td></tr><tr><td>Agarose</td><td>BIO-RAD</td><td>1613102</td><td>Caution. See manufactures MSDS</td></tr><tr><td>BSA 20mg/ml</td><td>NEB</td><td>B9000S</td><td></td></tr><tr><td>CaCl&lt;sub&gt;2&lt;/sub&gt;</td><td></td><td></td><td></td></tr><tr><td>cell counter</td><td></td><td></td><td></td></tr><tr><td>Dimethyl Sulfoxide (DMSO)</td><td>Sigma Aldrich</td><td>D8418-100ML</td><td>Caution. See manufactures MSDS</td></tr><tr><td>Dynabeads MyOne Streptavidin C1</td><td>Invitrogen</td><td>65001</td><td></td></tr><tr><td>DynaMag-2 Magnet</td><td>Invitrogen</td><td>12321D</td><td>refered to as: magnet magnet for 1.5 ml tubes</td></tr><tr><td>DynaMag-PCR Magnet</td><td>Invitrogen</td><td>492025</td><td>refered to as: magnet magnet for PCR tubes</td></tr><tr><td>EDTA Ultrapure 0.5M pH 8.0</td><td>Invitrogen</td><td>15575-038</td><td></td></tr><tr><td>EGTA Ultrapure 0.5M pH 8.0</td><td>BioWorld</td><td>40121266-1</td><td></td></tr><tr><td>Ethanol 96%&amp;nbsp;</td><td>VWR Chemicals</td><td>20824365</td><td>quality control system&amp;nbsp;</td></tr><tr><td>Ethidium Bromide&amp;nbsp;</td><td>Invitrogen</td><td>15585-011</td><td></td></tr><tr><td>Ethylene glycol bis(succinimidyl succinate) (EGS)</td><td>ThermoFisher</td><td>21565</td><td></td></tr><tr><td>Fetl Bovin Serum</td><td>Sigma Aldrich</td><td>F7524</td><td>15% FBS</td></tr><tr><td>Gel Loading dye purple (6X)</td><td>NEB</td><td>B7024S</td><td></td></tr><tr><td>Glycine&amp;nbsp;</td><td>PanReac AppliChem</td><td>A1067.0500</td><td></td></tr><tr><td>Halt Proteinase inhibitor (100x)</td><td>ThermoFisher</td><td>78430</td><td>Caution. See manufactures MSDS</td></tr><tr><td>IGEPAL CA-630 (NP-40)</td><td>Sigma Aldrich</td><td>18896-50ML</td><td></td></tr><tr><td>MgCl 1 M</td><td>Invitrogen</td><td>AM9530G</td><td></td></tr><tr><td>Micrococcal Nuclease (MNase)</td><td>Worthington</td><td>LS004798</td><td></td></tr><tr><td>mouse embryonic stem cells&amp;nbsp;</td><td></td><td></td><td></td></tr><tr><td>NaCl</td><td>Sigma Aldrich</td><td>S9888-1KG</td><td></td></tr><tr><td>NEBNext Multiplex Oligos for Illumina (Dual Index primers)</td><td>NEB</td><td>E7600S</td><td>amplification primers for sequencing libraries</td></tr><tr><td>NEBNext Ultra II DNA library prep kit for Illumina</td><td>NEB</td><td>E7645L</td><td>sequencing library preparation kit&amp;nbsp;</td></tr><tr><td>NEBNext Ultra II Q5 Master mix&amp;nbsp;</td><td>NEB</td><td>M0544S</td><td>Caution. See manufactures MSDS</td></tr><tr><td>Non-Essential Amino Acids Solution</td><td>Gibco</td><td>11140050</td><td>1x NEAA</td></tr><tr><td>Penicillin-Streptomycin (10,000 U/mL)</td><td>Gibco</td><td>15140148</td><td>1% Pen-Strep</td></tr><tr><td>Proteinase K (40 mg/ml)</td><td>GoldBio</td><td>P-480-1</td><td>Caution. See manufactures MSDS</td></tr><tr><td>QIAquick Gel extraction kit</td><td>QIAgen</td><td>28706</td><td>refered to as: DNA gel elution kit&amp;nbsp;</td></tr><tr><td>QIAquick PCR purification kit</td><td>QIAgen</td><td>28106</td><td>refered to as: commercial DNA purification kit&amp;nbsp;</td></tr><tr><td>Qubit dsDNA HS Assay kit</td><td>Invitrogen</td><td>Q32854</td><td>high sensitivity DNA quantification instrument&amp;nbsp;</td></tr><tr><td>Quick load purple 1kb plus DNA Ladder</td><td>NEB</td><td>N0550S</td><td></td></tr><tr><td>SPRIselect size selection beads</td><td>Beckman Coulter</td><td>B23319</td><td>paramagnetic beads&amp;nbsp;</td></tr><tr><td>ThermoMixer C</td><td>Eppendorf&amp;nbsp;</td><td>5382000015</td><td>refered to as: thermomixer</td></tr><tr><td>Tris</td><td>Merck</td><td>10708976001</td><td></td></tr><tr><td>Trypsin</td><td></td><td></td><td></td></tr><tr><td>Tween20</td><td>Sigma Aldrich</td><td>P7949-100ML</td><td></td></tr><tr><td>Ultrapure 10% SDS</td><td>Invitrogen</td><td>15553-035</td><td></td></tr><tr><td>Ultrapure Phenol Chloroform Isoamyl Alcohol (PCI)</td><td>Invitrogen</td><td>15593-031</td><td></td></tr><tr><td>Fragment Analyzer</td><td></td><td></td><td></td></tr></tbody></table>

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.

The successful preparation of Micro-C libraries can be evaluated in several steps of the protocol. The most important step is the choice of a proper MNase digestion degree. Therefore, the MNase concentration must be titrated to consistently yield 70%-90% mono-nucleosomes over di-nucleosomes for every sample. It is important to note that chromatin digestion is different for eu- and hetero-chromatin, with MNase digesting heterochromatin less efficiently. Thus, the optimal digestion degree depends on the chromatin region of interest and the cell type studied since the relative proportion of eu- and hetero-chromatin is cell type-specific. Therefore, it is advisable to carefully titrate the MNase concentration required and to first evaluate the Micro-C experiment&#39;s success by low-input sequencing.
A typical MNase titration pattern of chromatin treated with decreasing amounts of MNase is shown in Figure 1A. Here, chromatin from 250,000 cells per reaction is digested with a four-fold dilution of MNase. The highest concentration (10 U of MNase, Lane 2) shows over-digested chromatin almost exclusively consisting of mono-nucleosomal DNA (~150 bp). Notably, the center of the mono-nucleosomal band runs lower in the agarose gel compared to the corresponding bands in the samples with reduced MNase concentrations, indicating an over-digestion of nucleosomal DNA. Over-digested nucleosomes are inefficiently ligated in the proximity ligation reaction; therefore, the sample in Lane 2 is suboptimal for Micro-C experiments. Lane 3 (2.5 U of MNase) displays an almost appropriate digestion degree for Micro-C experiments. Here, the mono-nucleosomal band is the dominant species, and the subnucleosomal smear, indicative of over-digested nucleosomes, is reduced; however, it is still present. The digestion degree in Lane 4 (0.635 U of MNase) is an ideal condition for a Micro-C experiment in this titration example. A clear mono-nucleosomal band without sub-nucleosomal DNA is present. The band intensity for the mono- and di-nucleosome DNA is almost equal, indicating a mono-nucleosome yield of 66% or higher. It is worth noting that the di-nucleosomal DNA is approximately twice the size of the mono-nucleosomal DNA (~320 bp vs. ~150 bp), so its band intensity per mole of DNA is twice as high compared to its mono-nucleosomal counterpart. The digestion degree in Lane 5 (0.156 U of MNase) shows under-digested chromatin with almost no nucleosomal DNA, and this, therefore, represents a suboptimal sample.
In conclusion, in this example, the digestion of 2.5 x 105 mouse ES cells with 0.625 U of MNase (corresponding to 2.5 U of MNase for 1 x 106 cells in 200 &#181;L) offers the most promising starting point for preparative digestions in Micro-C experiments. However, an intermediate MNase concentration between the conditions used for the samples in Lane 3 and Lane 4 (corresponding to 5 U of MNase for 1 x 106 cells in 200 &#181;L) should also be considered. Importantly, chromatin digestion with MNase cannot be scaled linearly, and it is not recommended to upscale the preparative digestion more than 4x. To prepare Micro-C libraries from more than 1 x 106 cells, it is advisable to digest the chromatin in aliquots of 1 x 106 cells and pool them after MNase inactivation.
To assess the success of the proximity ligation protocol, the input control, which is MNase-digested and not proximity ligated (step 3.8), should be compared to the proximity-ligated sample (step 5.3) by 1.5% agarose gel electrophoresis (Figure 1B). The proximity-ligated mono-nucleosome band has an approximate size of 300 bp, similar to that of di-nucleosomes. Therefore, the mono- to di- nucleosomal band signal ratio should shift from predominantly mono-nucleosomes (Lane 1) toward di-nucleosomes (Lane 3 and Lane 4). As the agarose gel in this step is the di-nucleosomal DNA that is excised and purified, splitting the samples into multiple lanes is advisable to avoid over-loading.
Assessing the quality and quantity of the prepared sequencing library by minimal PCR is recommended. Here, DNA from 1 &#181;L of beads (1/20 of the total sample) is amplified for 16 cycles in 10 &#181;L of PCR reaction. The total concentration of the minimal PCR library typically ranges from 50-500 ng after 16 PCR cycles. In theory, this corresponds to a 1-10 &#181;g library from the remaining 19 &#181;L sample if it were also amplified for 16 cycles. It is recommended to use the minimum number of PCR cycles needed to generate a library of approximately 100 ng from the total DNA. Assuming logarithmic amplification in the PCR, the theoretical concentration of the DNA obtained from the 19 &#181;L input at 16 cycles can be divided successively by two to calculate the number of PCR cycles required to generate a 100 ng library. For example, a 100 ng yield from 1 &#181;L after 16 cycles corresponds to a 1,900 ng yield amplified from 19 &#181;L. In this scenario, 12 cycles should ideally generate a 118 ng sequencing library from the total DNA (1,900 ng/[2 &#215; 2 &#215; 2 &#215; 2] = 118 ng). The remaining 9 &#181;L sample from the minimal PCR can then be used to assess the quality of the library by agarose gel electrophoresis (Figure 1C). Visualization should show one distinct band at 420 bp and no bands for adaptor dimers (120 bp). Smaller fragments may also appear, and these correspond to unused PCR primers.
Next, analyzing and confirming successful Micro-C sample preparation by low-input sequencing is recommended before committing to resource-intensive deep sequencing. Typically, libraries are sequenced to a read depth of 5 x 106 to 1 x 107 and evaluated based on the following criteria: the sequencing read duplication rate, the cis- versus trans-chromosomal interaction rate, and the sequencing read orientation frequency. The Micro-C libraries are processed with Distiller, a full-service pipeline that processes the data from sequencing read files (Fastq format) to read-pairs files (Bedpe format) and scalable interaction matrices (Cool and Mcool formats) using cooler, pairtools, and cooltools10,11,12. The pipeline also generates a summary file that is ideal for assessing the quality of the Micro-C libraries10 (https://github.com/open2c/distiller-nf). The PCR duplication rate provides information on the sequencing library complexity and can be extracted from the *.stats file generated. High-quality Micro-C libraries have less than 5%-10% PCR duplication rates when generated from 5 million or more cells. Notably, some sequencing platforms generate PCR duplicates during cluster formation independent of the sequencing library complexity. Figure 2A shows the relative duplication rates of two experiments: one that we consider a good sample and one bad sample. In this example, both samples displayed acceptable map rates. The next criterias to assess the quality of Micro-C libraries is the cis versus trans ratio and read orientation frequencies. Within the nucleus, chromosomes inhabit individual chromosome territories and, thus, rarely interact with other chromosomes. A high rate of detected trans-chromosomal interactions indicates a high rate of random ligations. It should be noted that at this level of analysis, the bad sample showed a high rate of trans-chromosomal interactions compared to the good sample (Figure 2B). For Micro-C, a 70% or higher cis-chromosomal interaction rate is desirable.
A Micro-C library has a fragment size similar to the di-nucleosomal DNA band, which can co-purify with the proximity ligated sample and contaminate the experiment. These contaminates are always cis-chromosomal interactions. Therefore, it is important to also evaluate the read orientation rates. The rate of di-nucleosomal contamination can be estimated by low-input sequencing. Di-nucleosomal DNA stems from two neighboring nucleosomes that have not been cleaved by MNase. Thus, the resulting sequencing reads will always display a forward-reverse read orientation (F and R), and the distance between the read pairs will be around 320 bp. Proximity-ligated fragments, in comparison, can be ligated in four orientations, yielding read-pairs with F-R, R-R, R-F, and F-F, ideally with equal abundance (Figure 2C). In addition, they display various distances between the two read pairs. To estimate the quantity of di-nucleosomal contaminates, the frequency of read orientations can be calculated from the *stats files generated by distiller (Figure 2D). Notably, in this work, the fraction of F-R reads (red) was higher in the bad sample compared to the good sample, and this became more apparent when the read orientations were stratified by distance (Figure 2E). The F-R fraction is dominated by di-nucleosomal fragments compared to Micro-C libraries when the read pairs are stratified into reads with distances &#60;562 bp or &#8805;562 bp. Here, the fraction of reads with distance &#60;562 bp are dominated by F-R reads, whereas the fraction with distances &#8805;562 bp displays an even distribution between the four possible orientations, indicating that the global over-representation of F-R reads stems from di-nucleosomal contaminants. The choice of 562 bp as the threshold for subsetting is defined by the binning in the *stats file generated. Although not necessary for this quality control, more defined subsetting can be achieved by extracting the distances from the *pairs file, which is also generated by distiller. It is important to note that di-nucleosomal reads do not decrease the quality of the Micro-C sample as they can be identified and ignored during data processing. However, they do not contain valuable information about 3D interactions, and they dilute the informative reads.
Thus, careful MNase titration and thorough quality control with low-input sequencing are the best tools to optimize the quality of Micro-C experiments.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64579/64579fig01.jpg" /> 
Figure 1: Intermediate stages of the Micro-C protocol. (A) Agarose gel electrophoresis of chromatin from 2.5 x 105 mouse ES cells digested with varying MNase concentrations. The mono-, di-, and tri-nucleosomal bands are indicated by arrows. M: DNA ladder (Lane 1/6); 10 U of MNase per 250,000 cells (Lane 2); 2.5 U of MNase per 250,000 cells (Lane 3); 0.625 U of MNase per 250,000 cells (Lane 4); 0.156 U of MNase per 250,000 cells (Lane 2). (B) The 1.0% agarose gel electrophoresis of the Micro-C prepared samples (Lane 3 and Lane 4) and the MNase digested input control (Lane 1). Lane 1 and Lane 2 (M: DNA ladder) are enhanced to emphasize the relative change in mono- to di-nucleosomal fragment intensity. The mono- and di-nucleosomal bands are indicated by arrows. The di-nucleosomal band in the proximity-ligated sample combines di-nucleosomal and Micro-C library DNA. (C) The 1.0% agarose gel electrophoresis of the Micro-C sequencing libraries amplified from 1 &#181;L sample to evaluate the quality. Lane 1 (M): DNA ladder; Lane 2 (S): Mirco-C library. (D) Fragment Analyzer trace of the final Micro-C library. <a href="https://www.jove.com/files/ftp_upload/64579/64579fig01large.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/64579/64579fig02.jpg" /> 
Figure 2: Sample statistics for the low-input sequencing of a good sample and a bad sample. (A) Bar graph of the percentage mapped (green) and unmapped (red) reads. (B) Normalized fraction of reads mapping cis and trans-chromosomal interactions. The data sets were normalized to the cis-mapping read. The cis-mapping reads were stratified by the distance between the first and the second reads of the paired-end sequenced samples: &#8804;1 kbp (yellow), &#62;1 kbp and &#8804;10 kbp (orange), and &#62;10 kbp (red). (C) Schematic of the potential molecular species with the di-nucleosomal sizes. (D) Percentages of read-pair orientations of all the reads of the good sample and the bad sample. (E) Same as panel (D) but stratified by distances (left, &#60;562 bp and right, &#8805;562 bp). <a href="https://www.jove.com/files/ftp_upload/64579/64579fig02large.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>Components</td>
			<td>1x</td>
			<td>4.4x</td>
		</tr>
		<tr>
			<td>10x NEBuffer 2.1,</td>
			<td>10 &#181;L</td>
			<td>44 &#181;L</td>
		</tr>
		<tr>
			<td>2 &#181;L 100 mM ATP</td>
			<td>2 &#181;L</td>
			<td>8.8 &#181;L</td>
		</tr>
		<tr>
			<td>100 mM DTT</td>
			<td>5 &#181;L</td>
			<td>22 &#181;L</td>
		</tr>
		<tr>
			<td>H2O</td>
			<td>68 &#181;L</td>
			<td>299.2 &#181;L</td>
		</tr>
		<tr>
			<td>10 U/&#181;L T4 PNK</td>
			<td>5 &#181;L</td>
			<td>22 &#181;L</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>90 &#181;L</td>
			<td>396 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 1: Micro-C master mix 1. Composition of the master mix for the end chewing reaction.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Components</td>
			<td>1x</td>
			<td>4.4x</td>
		</tr>
		<tr>
			<td>1 mM Biotin-dATP</td>
			<td>10 &#181;L</td>
			<td>44 &#181;L</td>
		</tr>
		<tr>
			<td>1 mM Biotin-dCTP</td>
			<td>10 &#181;L</td>
			<td>44 &#181;L</td>
		</tr>
		<tr>
			<td>10 mM mix of dTTP and dGTP</td>
			<td>1 &#181;L</td>
			<td>4.4 &#181;L</td>
		</tr>
		<tr>
			<td>10x T4 DNA Ligase Buffer</td>
			<td>5 &#181;L</td>
			<td>22 &#181;L</td>
		</tr>
		<tr>
			<td>200x BSA</td>
			<td>0.25 &#181;L</td>
			<td>1.1 &#181;L</td>
		</tr>
		<tr>
			<td>H2O</td>
			<td>23.75 &#181;L</td>
			<td>104.5 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 2: Micro-C master mix 2. Composition of the master mix for the end labeling reaction.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Components</td>
			<td>1x</td>
			<td>4.4x</td>
		</tr>
		<tr>
			<td>10x NEB T4 Ligase reaction buffer</td>
			<td>50 &#181;L</td>
			<td>220 &#181;L</td>
		</tr>
		<tr>
			<td>H2O</td>
			<td>422,5 &#181;L</td>
			<td>1859 &#181;L</td>
		</tr>
		<tr>
			<td>T4 DNA Ligase</td>
			<td>25 &#181;L</td>
			<td>110&#181;L</td>
		</tr>
	</tbody>
</table>
Table 3: Micro-C master mix 3. Composition of the master mix for the proximity ligation reaction.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Components</td>
			<td>1x</td>
			<td>4.4x</td>
		</tr>
		<tr>
			<td>10x NEBuffer 1.1</td>
			<td>20 &#181;L</td>
			<td>88 &#181;L</td>
		</tr>
		<tr>
			<td>H2O</td>
			<td>180 &#181;L</td>
			<td>792 &#181;L</td>
		</tr>
		<tr>
			<td>ExoIII nuclease</td>
			<td>10 &#181;L</td>
			<td>44 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 4: Micro-C master mix 4. Composition of the master mix for the biotin removal reaction.

Watch this Scientific Journal Video about Mapping Mammalian 3D Genome Interactions with Micro-C-XL at JoVE.com

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

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, ...

mapping-mammalian-3d-genome-interactions-with-micro-c-xl

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Genetics

2.3K Views.  University of Copenhagen. 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. 

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

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a protocol for performing the chromatin conformation capture technique in situ Hi-C on staged Drosophila melanogaster embryo populations. The result is a sequencing library that allows the mapping of all chromatin interactions that occur in the nucleus in a single experiment. Embryo sorting is done manually using a fluorescent stereo microscope and a transgenic fly line containing a nuclear marker. Using this technique, embryo populations from each nuclear division cycle, and with defined cell cycle status, can be obtained with very high purity. The protocol may also be adapted to sort older embryos beyond gastrulation. Sorted embryos are used as inputs for in situ Hi-C. All experiments, including sequencing library preparation, can be completed in five days. The protocol has low input requirements and works reliably using 20 blastoderm stage embryos as input material. The end result is a sequencing library for next generation sequencing. After sequencing, the data can be processed into genome-wide chromatin interaction maps that can be analyzed using a wide range of available tools to gain information about topologically associating domain (TAD) structure, chromatin loops, and chromatin compartments during Drosophila development.

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

This work describes a protocol for the generation of high resolution in situ Hi-C libraries from tightly staged pre-gastrulation Drosophila melanogaster embryos.

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a ...

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

A major question in cell biology is genomic organization within the nuclear space and how chromatin architecture can influence processes such as gene expression, cell identity and differentiation. Many approaches developed to study the 3D architecture of the genome can be divided into two complementary categories: chromosome conformation capture based technologies (C-technologies) and imaging. While the former is based on capturing the chromosome conformation and proximal DNA interactions in a population of fixed cells, the latter, based on DNA fluorescence in situ hybridization (FISH) on 3D-preserved nuclei, allows contemporary visualization of multiple loci at a single cell level (multicolor), examining their interactions and distribution within the nucleus (3D multicolor DNA FISH). The technique of 3D multicolor DNA FISH has a limitation of visualizing only a few predetermined loci, not permitting a comprehensive analysis of the nuclear architecture. However, given the robustness of its results, 3D multicolor DNA FISH in combination with 3D-microscopy and image reconstruction is a possible method to validate C-technology based results and to unambiguously study the position and organization of specific loci at a single cell level. Here, we propose a step by step method of 3D multicolor DNA FISH suitable for a wide range of human primary cells and discuss all the practical actions, crucial steps, notions of 3D imaging and data analysis needed to obtained a successful and informative 3D multicolor DNA FISH within different biological contexts.

3D multicolor DNA FISH represents a tool to visualize multiple genomic loci within 3D preserved nuclei, unambiguously defining their reciprocal interactions and localization within the nuclear space at a single cell level. Here, a step by step protocol is described for a wide spectrum of human primary cells.

A major question in cell biology is genomic organization within the nuclear space and how chromatin architecture can influence processes such as gene ...

High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease (ChIP-Exo)

Identification of specific protein-DNA interactions on the genome is important for understanding gene regulation. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) is widely used to identify genome-wide binding locations of DNA-binding proteins. However, the ChIP-seq method is limited by its heterogeneity in length of sonicated DNA fragments and non-specific background DNA, resulting in low mapping resolution and uncertainty in DNA-binding sites. To overcome these limitations, the combination of ChIP with exonuclease digestion (ChIP-exo) utilizes 5&#8217; to 3&#8217; exonuclease digestion to trim the heterogeneously sized immunoprecipitated DNA to the protein-DNA crosslinking site. Exonuclease treatment also eliminates non-specific background DNA. The library-prepared and exonuclease-digested DNA can be sent for high-throughput sequencing. The ChIP-exo method allows for near base-pair mapping resolution with greater detection sensitivity and reduced background signal. An optimized ChIP-exo protocol for mammalian cells and next-generation sequencing is described below.

High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease ChIP-Exo

Precise determination of protein-binding locations across the genome is important for understanding gene regulation. Here we describe a&#160;genomic mapping method that treats chromatin-immunoprecipitated DNA with exonuclease digestion (ChIP-exo) followed by high-throughput sequencing. This method detects protein-DNA interactions with near base-pair mapping resolution and high signal-to-noise ratio in mammalian neurons.

Identification of specific protein-DNA interactions on the genome is important for understanding gene regulation. Chromatin immunoprecipitation coupled ...

Capturing Chromosome Conformation Across Length Scales

Chromosome conformation capture (3C) is used to detect three-dimensional chromatin interactions. Typically, chemical crosslinking with formaldehyde (FA) is used to fix chromatin interactions. Then, chromatin digestion with a restriction enzyme and subsequent religation of fragment ends converts three-dimensional (3D) proximity into unique ligation products. Finally, after reversal of crosslinks, protein removal, and DNA isolation, DNA is sheared and prepared for high-throughput sequencing. The frequency of proximity ligation of pairs of loci is a measure of the frequency of their colocalization in three-dimensional space in a cell population.
A sequenced Hi-C library provides genome-wide information on interaction frequencies between all pairs of loci. The resolution and precision of Hi-C relies on efficient crosslinking that maintains chromatin contacts and frequent and uniform fragmentation of the chromatin. This paper describes an improved in situ Hi-C protocol, Hi-C 3.0, that increases the efficiency of crosslinking by combining two crosslinkers (formaldehyde [FA] and disuccinimidyl glutarate [DSG]), followed by finer digestion using two restriction enzymes (DpnII and DdeI). Hi-C 3.0 is a single protocol for the accurate quantification of genome folding features at smaller scales such as loops and topologically associating domains (TADs), as well as features at larger nucleus-wide scales such as compartments.

Hi-C 3.0 is an improved Hi-C protocol that combines formaldehyde and disuccinimidyl glutarate crosslinkers with a cocktail of DpnII and DdeI restriction enzymes to increase the signal-to-noise ratio and the resolution of chromatin interaction detection.

Chromosome conformation capture (3C) is used to detect three-dimensional chromatin interactions. Typically, chemical crosslinking with formaldehyde (FA) ...

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 ...

Chromosome Conformation Capture (3C) for Undergraduate Research and Teaching

Getting an A with the 3Cs: Chromosome Conformation Capture for Undergraduates

Chromosome conformation capture (3C) is a powerful tool that has spawned a family of similar techniques (e.g., Hi-C, 4C, and 5C, referred to here as 3C techniques) that provide detailed information of the three-dimensional organization of chromatin. The 3C techniques have been used in a wide range of studies, from monitoring the changes in chromatin organization in cancer cells to identifying enhancer contacts made with gene promoters. While many of the studies using these techniques are asking big genome-wide questions with intricate sample types (i.e., single-cell analysis), what is often lost is that the 3C techniques are grounded in basic molecular biology methods that are applicable to a broad range of studies. By addressing tightly focused questions of chromatin organization, this cutting-edge technique can be used to enhance the undergraduate research and teaching lab experience. This paper presents a 3C protocol and provides adaptations and points of emphasis for implementation at primarily undergraduate institutions in undergraduate research and teaching experiences.

Author Spotlight: Getting an A with the 3Cs: Chromosome Conformation Capture for Undergraduates  

Here, we present an adaption of the chromosome conformation capture (3C) technique in detail with an emphasis on undergraduate involvement and learning.

Chromosome conformation capture (3C) is a powerful tool that has spawned a family of similar techniques (e.g., Hi-C, 4C, and 5C, referred to here as 3C ...

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

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Genetic information encoded by DNA is organized in a complex and highly regulated chromatin structure. Each chromosome occupies a specific territory, that may change according to stage of development or cell cycle. Gene expression can occur in specialized transcriptional factories where chromatin segments may loop out from various chromosome territories, leading to co-localization of DNA segments which may exist on different chromosomes or far apart on the same chromosome. The Associated Chromosome Trap (ACT) assay provides an effective methodology to identify these long-range DNA associations in an unbiased fashion by extending and modifying the chromosome conformation capture technique. The ACT assay makes it possible for us to investigate mechanisms of transcriptional regulation in trans, and can help explain the relationship of nuclear architecture to gene expression in normal physiology and during disease states.

The associated chromosome trap (ACT) assay is a novel unbiased method for identifying long-range DNA interactions. The characterization of long range DNA interactions will allow us to determine the relationship of nuclear architecture to gene expression in both normal physiology and in diseased states.

Genetic information encoded by DNA is organized in a complex and highly regulated chromatin structure.  Each chromosome occupies a specific territory, ...

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to dose-limiting toxicity and many adverse side effects. Targeting the genome with sequence-specific small molecules may enable molecules with increased therapeutic index and fewer off-target effects. N-methylpyrrole/N-methylimidazole polyamides are molecules that can be rationally designed to target specific DNA sequences with exquisite precision. And unlike most natural transcription factors, polyamides can bind to methylated and chromatinized DNA without a loss in affinity. The sequence specificity of polyamides has been extensively studied in vitro with cognate site identification (CSI) and with traditional biochemical and biophysical approaches, but the study of polyamide binding to genomic targets in cells remains elusive. Here we report a method, the crosslinking of small molecules to isolate chromatin (COSMIC), that identifies polyamide binding sites across the genome. COSMIC is similar to chromatin immunoprecipitation (ChIP), but differs in two important ways: (1) a photocrosslinker is employed to enable selective, temporally-controlled capture of polyamide binding events, and (2) the biotin affinity handle is used to purify polyamide&#8211;DNA conjugates under semi-denaturing conditions to decrease DNA that is non-covalently bound. COSMIC is a general strategy that can be used to reveal the genome-wide binding events of polyamides and other genome-targeting chemotherapeutic agents.

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Identifying the direct targets of genome-targeting molecules remains a major challenge. To understand how DNA-binding molecules engage the genome, we developed a method that relies on crosslinking of small molecules to isolate chromatin (COSMIC).

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to ...

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

The DNA adenine methyltransferase identification (DamID) assay is a powerful method to detect protein-DNA interactions both locally and genome-wide. It is an alternative approach to chromatin immunoprecipitation (ChIP). An expressed fusion protein consisting of the protein of interest and the E. coli DNA adenine methyltransferase can methylate the adenine base in GATC motifs near the sites of protein-DNA interactions. Adenine-methylated DNA fragments can then be specifically amplified and detected. The original DamID assay detects the genomic locations of methylated DNA fragments by hybridization to DNA microarrays, which is limited by the availability of microarrays and the density of predetermined probes. In this paper, we report the detailed protocol of integrating high throughput DNA sequencing into DamID (DamID-seq). The large number of short reads generated from DamID-seq enables detecting and localizing protein-DNA interactions genome-wide with high precision and sensitivity. We have used the DamID-seq assay to study genome-nuclear lamina (NL) interactions in mammalian cells, and have noticed that DamID-seq provides a high resolution and a wide dynamic range in detecting genome-NL interactions. The DamID-seq approach enables probing NL associations within gene structures and allows comparing genome-NL interaction maps with other functional genomic data, such as ChIP-seq and RNA-seq.

We describe herein an assay by coupling DNA adenine methyltransferase identification (DamID) to high throughput sequencing (DamID-seq). This improved method provides a higher resolution and a wider dynamic range, and allows analyzing DamID-seq data in conjunction with other high throughput sequencing data such as ChIP-seq, RNA-seq, etc.

The DNA adenine methyltransferase identification (DamID) assay is a powerful method to detect protein-DNA interactions both locally and genome-wide. It is ...

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

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Przeglądaj więcej filmów

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease (ChIP-Exo)

Capturing Chromosome Conformation Across Length Scales

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

Getting an A with the 3Cs: Chromosome Conformation Capture for Undergraduates

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

Associated Chromosome Trap for Identifying Long-range DNA Interactions

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments