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.

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	<li>Lieberman-Aiden, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+mapping+of+long-range+interactions+reveals+folding+principles+of+the+human+genome.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro-C+XL:+Assaying+chromosome+conformation+from+the+nucleosome+to+the+entire+genome.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+nucleosome+resolution+chromosome+folding+in+yeast+by+Micro-C.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrastructural+details+of+mammalian+chromosome+architecture.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resolving+the+3D+landscape+of+transcription-linked+mammalian+chromatin+folding.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hi-C+2.0:+An+optimized+Hi-C+procedure+for+high-resolution+genome-wide+mapping+of+chromosome+conformation.">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://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Chromatin. , (1989).</li><li>Oksuz, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+evaluation+of+chromosome+conformation+capture+assays.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hi-C+3.0:+Improved+protocol+for+genome-wide+chromosome+conformation+capture.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mirnylab/distiller-nf;+v033.">Mirnylab/distiller-nf; v033.</a> Zenodo. , (2019).</li><li>Venev, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Open2c/cooltools:+v0.4.1.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cooler:+Scalable+storage+for+Hi-C+data+and+other+genomically+labeled+arrays.">Cooler: Scalable storage for Hi-C data and other genomically labeled arrays.</a> Bioinformatics. 36 (1), 311-316 (2019).</li><li>Zhang, S., &#220;belmesser, N., Barbieri, M., Papantonis, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancer-promoter+contact+formation+requires+RNAPII+and+antagonizes+loop+extrusion.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+polymerase+II+and+PARP1+shape+enhancer-promoter+contacts.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+classes+of+chromatin+loops+revealed+by+deletion+of+an+RNA-binding+region+in+CTCF.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Defining+genome+architecture+at+base-pair+resolution.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+targeted+3C+interrogation+of+cis-regulatory+element+organization+at+genome-wide+scale.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Region+capture+Micro-C+reveals+coalescence+of+enhancers+and+promoters+into+nested+microcompartments.">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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1 mM Biotin dATP</td>
			<td>Jenna Bioscience&#160;</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&#160;</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&#160;</td>
			<td>NEB</td>
			<td>B0202A</td>
			<td></td>
		</tr>
		<tr>
			<td>1x DPBS w/o Mg2+ and Ca2+</td>
			<td>ThermoFisher&#160;</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&#160;</td>
			<td>Gibco</td>
			<td>31350010</td>
			<td>0.1 mM b-mercaptoethanol&#160;</td>
		</tr>
		<tr>
			<td>37% Formaldehyde&#160;</td>
			<td>Sigma Aldrich</td>
			<td>252549-500ML</td>
			<td>Caution. See manufactures MSDS</td>
		</tr>
		<tr>
			<td>400 U/ml T4 DNA Ligase&#160;</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>CaCl2</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%&#160;</td>
			<td>VWR Chemicals</td>
			<td>20824365</td>
			<td>quality control system&#160;</td>
		</tr>
		<tr>
			<td>Ethidium Bromide&#160;</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&#160;</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&#160;</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&#160;</td>
		</tr>
		<tr>
			<td>NEBNext Ultra II Q5 Master mix&#160;</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&#160;</td>
		</tr>
		<tr>
			<td>QIAquick PCR purification kit</td>
			<td>QIAgen</td>
			<td>28106</td>
			<td>refered to as: commercial DNA purification kit&#160;</td>
		</tr>
		<tr>
			<td>Qubit dsDNA HS Assay kit</td>
			<td>Invitrogen</td>
			<td>Q32854</td>
			<td>high sensitivity DNA quantification instrument&#160;</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&#160;</td>
		</tr>
		<tr>
			<td>ThermoMixer C</td>
			<td>Eppendorf&#160;</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.

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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, ...

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1.9K 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

Conjugative Mating Assays for Sequence-specific Analysis of Transfer Proteins Involved in Bacterial Conjugation

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia coli, these transfer proteins make up a DNA transfer machinery known as the mating pair formation, or DNA transfer complex, which facilitates conjugative plasmid transfer. The objective of this paper is to provide a method that can be used to determine the role of a specific transfer protein that is involved in conjugation using a series of deletions and/or point mutations in combination with mating assays. The target gene is knocked out on the conjugative plasmid and is then provided in trans through the use of a small recovery plasmid harboring the target gene. Mutations affecting the target gene on the recovery plasmid can reveal information about functional aspects of the target protein that result in the alteration of mating efficiency of donor cells harboring the mutated gene. Alterations in mating efficiency provide insight into the role and importance of the particular transfer protein, or a region therein, in facilitating conjugative DNA transfer. Coupling this mating assay with detailed three-dimensional structural studies will provide a comprehensive understanding of the function of the conjugative transfer protein as well as provide a means for identifying and characterizing regions of protein-protein interaction.

Here, we present a protocol to knockout a gene of interest involved in plasmid conjugation and subsequently analyze the impact of its absence using mating assays. The function of the gene is further explored to a specific region of its sequence using deletion or point mutations.

The transfer of genetic material by bacterial conjugation is a process that takes place via complexes formed by specific transfer proteins. In Escherichia ...

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 ...

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA interactions such as microRNA-mRNA interactions have been shown to regulate gene expression, the full extent to which RNA interactions occur in the cell is still unknown. Previous methods to study RNA interactions have primarily focused on subsets of RNAs that are interacting with a particular protein or RNA species. Here, we detail a method named Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH) that allows genome-wide capture of RNA interactions in vivo in an unbiased manner. SPLASH utilizes in vivo crosslinking, proximity ligation, and high throughput sequencing to identify intramolecular and intermolecular RNA base-pairing partners globally. SPLASH can be applied to different organisms including bacteria, yeast and human cells, as well as diverse cellular conditions to facilitate the understanding of the dynamics of RNA organization under diverse cellular contexts. The entire experimental SPLASH protocol takes about 5 days to complete and the computational workflow takes about 7 days to complete.

Here, we detail the method of Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH), which enables genome-wide mapping of intramolecular and intermolecular RNA-RNA interactions in vivo. SPLASH can be applied to study RNA interactomes of organisms including yeast, bacteria and humans.

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA ...

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident processes. Formaldehyde crosslinking followed by chromatin immunoprecipitation and high-throughput sequencing (X-ChIP-seq) has been used to gain many valuable insights into genome biology. However, X-ChIP-seq has notable limitations linked to crosslinking and sonication. Native ChIP avoids these drawbacks by omitting crosslinking, but often results in poor recovery of chromatin-bound proteins. In addition, all ChIP-based methods are subject to antibody quality considerations. Enzymatic methods for mapping protein-DNA interactions, which involve fusion of a protein of interest to a DNA-modifying enzyme, have also been used to map protein-DNA interactions. We recently combined one such method, chromatin endogenous cleavage (ChEC), with high-throughput sequencing as ChEC-seq. ChEC-seq relies on fusion of a chromatin-associated protein of interest to micrococcal nuclease (MNase) to generate targeted DNA cleavage in the presence of calcium in living cells. ChEC-seq is not based on immunoprecipitation and so circumvents potential concerns with crosslinking, sonication, chromatin solubilization, and antibody quality while providing high resolution mapping with minimal background signal. We envision that ChEC-seq will be a powerful counterpart to ChIP, providing an independent means by which to both validate ChIP-seq findings and discover new insights into genomic regulation.

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

We describe chromatin endogenous cleavage coupled with high-throughput sequencing (ChEC-seq), a chromatin immunoprecipitation (ChIP)-orthogonal method for mapping protein binding sites genome-wide with micrococcal nuclease (MNase) fusion proteins.

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident ...

Retroviral Scanning: Mapping MLV Integration Sites to Define Cell-specific Regulatory Regions

Moloney murine leukemia (MLV) virus-based retroviral vectors integrate predominantly in acetylated enhancers and promoters. For this reason, mLV integration sites can be used as functional markers of active regulatory elements. Here, we present a retroviral scanning tool, which allows the genome-wide identification of cell-specific enhancers and promoters. Briefly, the target cell population is transduced with an mLV-derived vector and genomic DNA is digested with a frequently cutting restriction enzyme. After ligation of genomic fragments with a compatible DNA linker, linker-mediated polymerase chain reaction (LM-PCR) allows the amplification of the virus-host genome junctions. Massive sequencing of the amplicons is used to define the mLV integration profile genome-wide. Finally, clusters of recurrent integrations are defined to identify cell-specific regulatory regions, responsible for the activation of cell-type specific transcriptional programs.
The retroviral scanning tool allows the genome-wide identification of cell-specific promoters and enhancers in prospectively isolated target cell populations. Notably, retroviral scanning represents an instrumental technique for the retrospective identification of rare populations (e.g. somatic stem cells) that lack robust markers for prospective isolation.

Here, we describe a protocol for genome-wide mapping of the integration sites of Moloney murine leukemia virus-based retroviral vectors in human cells.

Moloney murine leukemia (MLV) virus-based retroviral vectors integrate predominantly in acetylated enhancers and promoters. For this reason, mLV ...

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions of chromatin. These regions represent regulatory DNA elements (e.g., promoters, enhancers, locus control regions, insulators) to which transcription factors bind. Mapping the accessible chromatin landscape is a powerful approach for uncovering active regulatory elements across the genome. This information serves as an unbiased approach for discovering the network of relevant transcription factors and mechanisms of chromatin structure that govern gene expression programs. ATAC-seq is a robust and sensitive alternative to DNase I hypersensitivity analysis coupled with next-generation sequencing (DNase-seq) and formaldehyde-assisted isolation of regulatory elements (FAIRE-seq) for genome-wide analysis of chromatin accessibility and to the sequencing of micrococcal nuclease-sensitive sites (MNase-seq) to determine nucleosome positioning. We present a detailed ATAC-seq protocol optimized for human primary immune cells i.e. CD4+ lymphocytes (T helper 1 (Th1) and Th2 cells). This comprehensive protocol begins with cell harvest, then describes the molecular procedure of chromatin tagmentation, sample preparation for next-generation sequencing, and also includes methods and considerations for the computational analyses used to interpret the results. Moreover, to save time and money, we introduced quality control measures to assess the ATAC-seq library prior to sequencing. Importantly, the principles presented in this protocol allow its adaptation to other human immune and non-immune primary cells and cell lines. These guidelines will also be useful for laboratories which are not proficient with next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin coupled with high-throughput sequencing (ATAC-seq) is a genome-wide method to uncover accessible chromatin. This is a step-by-step ATAC-seq protocol, from molecular to the final computational analysis, optimized for human lymphocytes (Th1/Th2). This protocol can be adopted by researchers without prior experience in next-generation sequencing methods.

Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method used for the identification of open (accessible) regions ...

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.

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 ...

Enhanced Genome Editing with Cas9 Ribonucleoprotein in Diverse Cells and Organisms

Site-specific eukaryotic genome editing with CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) systems has quickly become a commonplace amongst researchers pursuing a wide variety of biological questions. Users most often employ the Cas9 protein derived from Streptococcus pyogenes in a complex with an easily reprogrammed guide RNA (gRNA). These components are introduced into cells, and through a base pairing with a complementary region of the double-stranded DNA (dsDNA) genome, the enzyme cleaves both strands to generate a double-strand break (DSB). Subsequent repair leads to either random insertion or deletion events (indels) or the incorporation of experimenter-provided DNA at the site of the break.
The use of a purified single-guide RNA and Cas9 protein, preassembled to form an RNP and delivered directly to cells, is a potent approach for achieving highly efficient gene editing. RNP editing particularly enhances the rate of gene insertion, an outcome that is often challenging to achieve. Compared to the delivery via a plasmid, the shorter persistence of the Cas9 RNP within the cell leads to fewer off-target events. 
Despite its advantages, many casual users of CRISPR gene editing are less familiar with this technique. To lower the barrier to entry, we outline detailed protocols for implementing the RNP strategy in a range of contexts, highlighting its distinct benefits and diverse applications. We cover editing in two types of primary human cells, T cells and hematopoietic stem/progenitor cells (HSPCs). We also show how Cas9 RNP editing enables the facile genetic manipulation of entire organisms, including the classic model roundworm Caenorhabditis elegans and the more recently introduced model crustacean, Parhyale hawaiensis.

Utilizing a preassembled Cas9 ribonucleoprotein complex (RNP) is a powerful method for precise, efficient genome editing. Here, we highlight its utility across a broad range of cells and organisms, including primary human cells and both classic and emerging model organisms.

Site-specific eukaryotic genome editing with CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) systems has ...

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.

Here we present the proteogenomic tool PoGo and protocols for fast, quantitative, post-translational modification and variant enabled mapping of peptides identified through mass spectrometry onto reference genomes. This tool is of use to integrate and visualize proteogenomic and personal proteomic studies interfacing with orthogonal genomics data.

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly ...

CRISPR Guide RNA Cloning for Mammalian Systems

The outlined protocol describes streamlined methods for the efficient and cost-effective generation of Cas9-associated guide RNAs. Two alternative strategies for guide RNA (gRNA) cloning are outlined based on the usage of the Type IIS restriction enzyme BsmBI in combination with a set of compatible vectors. Outside of the access to Sanger sequencing services to validate the generated vectors, no special equipment or reagents are required aside from those that are standard to modern molecular biology laboratories. The outlined method is primarily intended for cloning one single gRNA or one paired gRNA-expressing vector at a time. This procedure does not scale well for the generation of libraries containing thousands of gRNAs. For those purposes, alternative sources of oligonucleotide synthesis such as oligo-chip synthesis are recommended. Finally, while this protocol focuses on a set of mammalian vectors, the general strategy is plastic and is applicable to any organism if the appropriate gRNA vector is available.

Here, a simple, efficient, and cost-effective method of sgRNA cloning is outlined.