This work was supported by grant NIH GM117446 to A.B.

1. Preparation of Cells for Flow Cytometry Sorting
<ol>
	<li>Inoculate 15 mL test tubes containing 8 mL each of YEPD broth such that the cultures reach a density of 5 x 106 to 1.5 x 107 cells per mL after overnight growth (see discussion note 1).</li>
	<li>Spin down yeast cells (1,400 x g, at room temperature or 4 &#176;C) in a 15 mL screw cap centrifuge tube for 5 min, resuspend cells in 1.5 mL 70% ethanol, and transfer to a 1.6 mL microfuge tube, letting this sit for 1 h at room temperature or at least 3 h on ice (can be stored indefinitely at 4 &#176;C) (see Discussion point (2)).</li>
	<li>Spin down yeast cells in the microfuge (14,000 x g, 4 &#176;C) for 1 min, resuspend in 1 mL 50 mM sodium citrate, pH 7.2, spin down (14,000 x g, 4 &#176;C) for 1 min, aspirate supernatant, resuspend in 1 mL 50 mM sodium citrate, pH 7.2, containing 0.25 mg/mL RNAse solution for 1 h at 50 &#176;C or overnight at 37 &#176;C in heat block or water bath.</li>
	<li>Add 50 &#181;L proteinase K solution (20 mg/mL resuspended in 10 mM Tris pH 7.5, 2 mM CaCl2, 50% weight to volume glycerol) and incubate for 1 h at 50 &#176;C.</li>
	<li>Spin down cells (14,000 x g, 4 &#176;C), resuspend cells in 1 mL 50 mM sodium citrate, pH 7.2, spin down (14,000 x g, 4 &#176;C), aspirate supernatant with vacuum, resuspend in 1 mL 1 &#181;M Green nucleic acid strain resuspended in 50 mM sodium citrate, pH 7.2 and incubate in the dark for 1 h at room temperature, sonicate 2x for 2 s each (output power 2 watts).</li>
</ol>
2. Cell Sorting
<ol>
	<li>Using a cell sorter, sort cells according to DNA content into phases G1, S, &#34;early G2,&#34; and &#34;late G2&#34;, collecting at least 1.6 million haploid cells from each cell cycle phase as in Figure 1 (see Discussion point (3)).</li>
	<li>Transfer cells to microfuge tubes and spin down cells for 20 min at 14,000 x g at 4 &#176;C in the microfuge and aspirate supernatant with vacuum (the pellet can be stored frozen at -20 &#176;C indefinitely; see discussion point (4)).</li>
</ol>
3. DNA Extraction and the Preparation of Sequencing Libraries
NOTE: The following steps are based on &#34;protocol I&#34; in the Yeast Genomic DNA extraction Kit (see Table of Materials).
<ol>
	<li>Add 120 &#181;L of &#34;Digestion Buffer,&#34;&#160;a proprietary buffer that allows yeast lytic enzyme to digest the cell wall, and 5 &#181;L of yeast lytic enzyme, resuspend by vortexing, and incubate at 37 &#176;C for 40 - 60 min.</li>
	<li>Add 120 &#181;L of &#34;Lysis Buffer,&#34;&#160;a proprietary buffer that contains detergent, and vortex hard for 10 - 20 s.</li>
	<li>Add 250 &#181;L chloroform - mix thoroughly for 1 min.</li>
	<li>Spin at maximum speed in a microfuge for 2 min.</li>
	<li>Load the supernatant onto the DNA binding column and centrifuge at maximum speed in a microfuge for 1 min.</li>
	<li>Add 300 &#181;L of &#34;DNA Washing Buffer,&#34;&#160;a proprietary buffer that contains ethanol, and centrifuge for 1 min at maximum speed in a microfuge. Discard the flow through, add 300 &#181;L of DNA Wash Buffer, and repeat the wash process.</li>
	<li>Transfer the spin column to a fresh microfuge tube, add 60 &#181;L of water (not TE) - wait 1 - 3 min and then centrifuge for 10 s to elute the DNA.</li>
	<li>Measure concentration by adding 5 &#181;L of DNA to 195 &#181;L of dsDNA high sensitivity reagent that has been diluted 1:200 in dsDNA high sensitivity buffer and then measure fluorescence in a fluorometer, using 10 &#181;L of supplied standards for calibration; expect between 10 and 50 ng of DNA total yield.</li>
	<li>Sonicate (Peak Incident Power 450, Duty Factor 30%, Cycles per Burst 200, Treatment Time 60 s, Water Level 6) to break up DNA into 250 - 350 bp fragments.</li>
	<li>Prepare a library using DNA sample preparation kit and sequence it (at least 10 million 50 bp single-end reads) on the DNA sequencing instrument in rapid mode (see discussion note (5)).</li>
</ol>
4. Analysis of Sequencing Data and Generation of Replication Profiles
<ol>
	<li>Align sequences to Saccharomyces cerevisiae sacCer3 genome using Gsnap12 (see discussion note (6)).</li>
	<li>Determine per-base pair read depths using Bedtools&#39; &#34;genomeCoverageBed&#34; software13.</li>
	<li>Remove artefactual spikes in read depths, if present (see discussion note (7)).</li>
	<li>Smooth read depths by chromosome using medians in a 20 kb sliding window using &#34;runMean&#34; function in R package &#34;caTools&#34; (see discussion note (8)).</li>
	<li>Plot read depths in S phase, early G2, and late G2 as a percentage of completely replicated read depths (see discussion note (9)).</li>
</ol>

<ol>
	<li>Fragkos, M., Ganier, O., Coulombe, P., Mechali, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+replication+origin+activation+in+space+and+time.">DNA replication origin activation in space and time.</a> Nat Rev Mol Cell Biol. 16, 360-374 (2015).</li><li>Santocanale, C., Diffley, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Mec1-+and+Rad53-dependent+checkpoint+controls+late-firing+origins+of+DNA+replication.">A Mec1- and Rad53-dependent checkpoint controls late-firing origins of DNA replication.</a> Nature. 395, 615-618 (1998).</li><li>Brewer, B. J., Fangman, W. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+replication+fork+barrier+at+the+3'+end+of+yeast+ribosomal+RNA+genes.">A replication fork barrier at the 3' end of yeast ribosomal RNA genes.</a> Cell. 55, 637-643 (1988).</li><li>Raghuraman, M. K., 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=Replication+dynamics+of+the+yeast+genome.">Replication dynamics of the yeast genome.</a> Science. 294, 115-121 (2001).</li><li>Muller, C. A., Nieduszynski, C. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Conservation+of+replication+timing+reveals+global+and+local+regulation+of+replication+origin+activity.">Conservation of replication timing reveals global and local regulation of replication origin activity.</a> Genome Res. 22, 1953-1962 (2012).</li><li>Koren, A., Soifer, I., Barkai, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MRC1-dependent+scaling+of+the+budding+yeast+DNA+replication+timing+program.">MRC1-dependent scaling of the budding yeast DNA replication timing program.</a> Genome Res. 20, 781-790 (2010).</li><li>Lang, G. I., Murray, A. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mutation+rates+across+budding+yeast+chromosome+VI+are+correlated+with+replication+timing.">Mutation rates across budding yeast chromosome VI are correlated with replication timing.</a> Genome Biol Evol. 3, 799-811 (2011).</li><li>Durkin, S. G., Glover, T. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chromosome+fragile+sites.">Chromosome fragile sites.</a> Annu Rev Genet. 41, 169-192 (2007).</li><li>Dillon, L. W., Burrow, A. A., Wang, Y. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNA+instability+at+chromosomal+fragile+sites+in+cancer.">DNA instability at chromosomal fragile sites in cancer.</a> Curr Genomics. 11, 326-337 (2010).</li><li>Widrow, R. J., Hansen, R. S., Kawame, H., Gartler, S. M., Laird, C. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Very+late+DNA+replication+in+the+human+cell+cycle.">Very late DNA replication in the human cell cycle.</a> Proc Natl Acad Sci U S A. 95, 11246-11250 (1998).</li><li>Foss, E. J., 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=SIR2+suppresses+replication+gaps+and+genome+instability+by+balancing+replication+between+repetitive+and+unique+sequences.">SIR2 suppresses replication gaps and genome instability by balancing replication between repetitive and unique sequences.</a> Proc Natl Acad Sci U S A. 114, 552-557 (2017).</li><li>Wu, T. D., Reeder, J., Lawrence, M., Becker, G., Brauer, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GMAP+and+GSNAP+for+Genomic+Sequence+Alignment:+Enhancements+to+Speed,+Accuracy,+and+Functionality.">GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality.</a> Methods Mol Biol. 1418, 283-334 (2016).</li><li>Quinlan, A. R., Hall, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BEDTools:+a+flexible+suite+of+utilities+for+comparing+genomic+features.">BEDTools: a flexible suite of utilities for comparing genomic features.</a> Bioinformatics. 26, 841-842 (2010).</li><li>Langmead, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aligning+short+sequencing+reads+with+Bowtie.">Aligning short sequencing reads with Bowtie.</a> Curr Protoc Bioinformatics. , 17 (2010).</li><li>Li, H., Durbin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+and+accurate+short+read+alignment+with+Burrows-Wheeler+transform.">Fast and accurate short read alignment with Burrows-Wheeler transform.</a> Bioinformatics. 25, 1754-1760 (2009).</li><li>Kellis, M., Birren, B. W., Lander, E. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proof+and+evolutionary+analysis+of+ancient+genome+duplication+in+the+yeast+Saccharomyces+cerevisiae.">Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae.</a> Nature. 428, 617-624 (2004).</li><li>Goodwin, S., McPherson, J. D., McCombie, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coming+of+age:+ten+years+of+next-generation+sequencing+technologies.">Coming of age: ten years of next-generation sequencing technologies.</a> Nat Rev Genet. 17, 333-351 (2016).</li></ol>

The authors have nothing to disclose.

While this technique is robust and relatively straight forward, particular attention should be devoted to the following:
(1) We recommend that cultures grow for at least 12 h before they reach log phase, since differences manifest in cell cycle distributions if cultures are harvested after having reached the desired density just 4 h after inoculation. Our assumption is that a cell cycle distribution that has reached a relatively stable equilibrium better represents a &#34;real&#34; log phase distribution than a distribution that is still in flux when a saturated culture has been transferred more recently to fresh medium.
(2) Instead of spinning down the cells before fixing, cells can be also fixed by directly adding 100% ethanol to the yeast culture so that the final concentration of ethanol is 70%. Cells can be grown in either synthetic or rich (YEPD) medium.
(3) Cell cycle phases are visualized for sorting by plotting cell number as a function of 530 nm emission area. We have found that the inflection points between G1 and S and between S and G2 serve as robust landmarks for delineating S phase, and we use the G2 maximum as the boundary to separate early and late G2. We use the terms &#34;early G2&#34; and &#34;late G2&#34; for clarity and brevity, perhaps at the expense of accuracy in the sense that cells are technically in S phase until they have completed DNA replication. Although the goal of G2-seq is to identify very late-replicating sites, which are expected to be revealed in the late G2 fraction, we collect S phase and early G2 cells to follow replication through all phases of the cell cycle.
(4) It is critically important that cells be spun down promptly after sorting. Do not let cells sit overnight before spinning them down. We have had problems recovering cells by centrifugation if they have been stored at 4 &#176;C, suggesting that they become fragile upon sorting and lyse with time.
(5) As few as 5 million reads is sufficient, but plots become less smooth with lower numbers of reads. Data sets with even fewer reads may be able to be accommodated by smoothing with windows larger than 20 kb, but we have not tried this.
(6) Any widely-used alignment software (e.g., Bowtie14, Novalign (http://www.novocraft.com), BWA15, or Gsnap12) will work.
(7) High throughput sequencing data from yeast show occasional spikes in read depths, which often reflect Ty elements. We eliminate these by substituting 2.5x median sample read depths for read depths outside of regions known to contain repetitive stretches (e.g., the rDNA) that are greater than 2.5x median. The rDNA and the ends of chromosomes are excluded because they are known to contain repetitive elements. An example of this &#34;despiking&#34; using the R language is the following:
# create a data frame called &#34;reads&#34; containing data: 
chr &#60;- rep(&#39;chrI&#39;, 10) 
pos &#60;- seq(1, 10) 
rd &#60;- c(3, 4, 6, 5, 88, 2, 9, 10, 900, 1) 
 
reads &#60;- data.frame(&#39;chromosome&#39; = chr, 
&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#39;position&#39; = pos, 
&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#39;read_depth&#39; = rd) 
 
# &#34;despike&#34; the read depths: 
reads[,&#39;despiked&#39;] &#60;- ifelse(reads[,3] &#62; (2.5 * median(reads[,3])), 
&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;(2.5 * median(reads[,3])), 
&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;reads[,3])
&#34;Capping&#34; or &#34;despiking&#34; can also be achieved by excluding reads that map to more than one location, but this will eliminate reads that map to, for example, just two locations. Instances of reads that map to more than one location, but still provide valuable information, are not uncommon when working with S. cerevisiae, whose genome resulted from an ancestral duplication16.
(8) We have obtained comparable results with both the &#34;rollmedian&#34; function in the R package &#34;zoo&#34; (https://cran.r-project.org/web/packages/zoo/index.html) and the &#34;runmean&#34; function in the R package &#34;caTools&#34; (https://cran.r-project.org/web/packages/caTools/index.html).
(9) We have experimented with numerous methods to normalize read depths, all of which gave comparable results. Our favored approach is based on the assumption that at least some regions of the genome are fully replicated in our S phase sample. We determined the read depth that represented full replication as the median read depth in our data set at sites corresponding to the 12 earliest firing high confidence (&#34;confidence&#34; score &#62;= 7) origins in a publicly available data set. (http://cerevisiae.oridb.org/data_output.php?main=sc_ori_studies&#38;table=Raghu2001_ori&#38;ext_format=&#38;format=tab). For most applications, the approach taken for normalization is not critical and there are many reasonable options, most simply just normalizing to read count totals. However, it is worth noting that if there are large differences in the numbers of repetitive sequences between samples, normalization to read count totals can be misleading. For example, when comparing two strains whose rDNA arrays differ by several fold, normalizing to total read counts can make certain regions of the genome of the strain with the larger rDNA array artefactually appear to replicate later than the strain with the smaller array.
In summary, G2-seq represents a logical extension of existing techniques for replication profiling. Like other sequencing- and microarray-based techniques, G2-seq has advantages over 2D gel analysis of genome wide coverage and the lack of requirement of knowledge of the location of replication origins. The latter is particularly relevant in mammalian cells where, unlike in budding yeast, replication origins are not limited to specific sequences. On the other hand, 2D gels provide a level of resolution of molecular intermediates in DNA replication completely unattainable by G2-seq. Extending G2-seq to other organisms whose cells can be sorted according to DNA content should be relatively straight forward, however, the highly repetitive nature of the genomes of higher eukaryotes makes alignment of short read sequences much more challenging. Emerging sequencing technologies that provide significantly longer read length may prove useful in this regard, especially since G2-seq should be relatively robust to the higher error rates that can be associated with longer reads17. In our experience, the technical problem that is most likely to cause problems is letting cells sit after they have been sorted and prior to isolation of DNA. These cells appear to be particularly fragile and to lyse with time, and DNA should be isolated within at most an hour or two of sorting. Promising future directions for this technique include dividing both S and G2 phases into increasing numbers of fractions to obtain higher resolution of the dynamics of genome replication, from early initiation to late completion.

Eukaryotic genome replication is initiated at multiple discrete sites, called origins of replication, from which replication forks proceed in both directions (reviewed in Fragkos et al., 20151). Origins vary in both their timing and efficiency of firing, and several techniques have been developed to monitor replication origin activity and elucidate the causes of this variation. The activity of individual origins can be inferred from levels of single-stranded DNA2, which forms around active origins, or by using 2D gel electrophoresis to monitor specific replication intermediates3, both of which can be detected with radioactive probes. Both of these techniques are more easily applied in S. cerevisiae than in mammalian cells, because origins are limited to specific known sequences in the former. With the advent of microarrays, it became possible to assess origin firing globally. This was first done by labeling DNA with heavy isotopes, releasing cells from a G1 block into medium containing light isotopes, and then monitoring the formation of heavy-light hybrid DNA across the genome4. The introduction of high throughput sequencing allowed similar genome-wide monitoring of origin activity without the requirement of expensive isotopic labeling. Cells were sorted in a flow cytometer according to DNA content and their DNA subjected to deep sequencing. Because sequence coverage proceeds from 1x to 2x over the course of S phase, relative replication timing can be assessed by comparing read depths of cells in S phase to those in G1 or G25,6. These techniques, particularly applied to yeast, led to a deeper understanding of how DNA sequence, chromatin structure, and DNA replication proteins regulate origin timing and efficiency.
Faithful transmission of genetic information during cellular proliferation requires not only successful initiation of DNA replication, which takes place at origins, but also successful completion of replication, which occurs where replication forks meet. Like initiation of replication, the timing of completion of replication varies across the genome with certain regions remaining unreplicated even late in the cell cycle. Such regions may be particularly distant from active replication origins or may contain sequences or chromatin structures that impede DNA polymerases. Late replicating regions can manifest themselves as fragile sites, which are associated with chromosomal breakage and higher mutation rates, and have been implicated in cancer and aging7,8,9. However, despite the importance of proper completion of DNA replication in maintenance of genome stability, our understanding of where and how this takes place has lagged far behind that of replication initiation. And while individual genes whose late replication has been associated with disease have been studied with, for example, qPCR10, global studies directed at elucidating the locations and underlying causes of late replication have been lacking. Here we describe a technique we refer to as &#34;G2 seq&#34; in which we combine flow cytometry with high throughput sequencing to shed light on the completion of genome replication in yeast11. With minor changes, this protocol can be adapted to any cells that can be flow-sorted according to DNA content.

<table><tbody><tr><td>YeaStar Genomic DNA kit</td><td>Genesee Scientific</td><td>11-323</td><td></td></tr><tr><td>1 &amp;micro;M SYTOX Green</td><td>ThermoFisher</td><td>57020</td><td>resuspend in 50 mM sodium citrate, pH 7.2</td></tr><tr><td>50 mM sodium citrate, pH 7.2</td><td></td><td></td><td></td></tr><tr><td>RNase solution (0.25 mg / mL)</td><td>Sigma</td><td>R6513</td><td>0.25 mg / mL RNaseA resuspended in 50 mM sodium citrate, pH 7.2, boil RNase solution for 10 minutes before the first use only, and from then on store at -20&amp;deg;</td></tr><tr><td>proteinase K solution (20 mg / mL)</td><td>ThermoFisher / Invitrogen</td><td>25530-015</td><td>resuspend in 10 mM Tris, pH 7.5, 2 mM CaCl2, 50% glycerol, store at -20&amp;deg;C</td></tr><tr><td>Model 50 Sonic Dismembrator</td><td>Fisher Scientific</td><td>FB50A220</td><td></td></tr><tr><td>BD Biosciences FACSAria II</td><td>BD Biosciences</td><td>644832</td><td></td></tr><tr><td>Zymo-spin III columns</td><td>Zymo Research</td><td>C1005</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>ThermoFisher</td><td>Q32851</td><td></td></tr><tr><td>Qubit 3.0 Fluorometer</td><td>ThermoFisher</td><td>Q33216</td><td></td></tr><tr><td>Covaris Model LE220 Focused-Ultrasonicator</td><td>Covaris</td><td>500219</td><td></td></tr><tr><td>Illumina TruSeq DNA LT Sample Prep Kit</td><td>Illumina</td><td>15026486</td><td></td></tr><tr><td>Illumina HiSeq 2500 instrument</td><td>Illumina</td><td>SY&amp;ndash;401&amp;ndash;2501</td><td></td></tr><tr><td>gsnap alignment software</td><td>open source software / Genentech</td><td></td><td>http://research-pub.gene.com/gmap</td></tr></tbody></table>

g2-seq: a high throughput sequencing-based technique for identifying late replicating regions of the genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have been directed toward elucidation of the location and timing of initiation of genome duplication rather than its completion. However, it is critical that we understand regions of the genome that are last to complete replication, because these regions suffer elevated levels of chromosomal breakage and mutation, and they have been associated with both disease and aging. Here we describe how we have extended a technique that has been used to monitor replication initiation to instead identify those regions of the genome last to complete replication. This approach is based on a combination of flow cytometry and high throughput sequencing. Although this report focuses on the application of this technique to yeast, the approach can be used with any cells that can be sorted in a flow cytometer according to DNA content.

We have used the procedure described above to identify late replicating sites in budding yeast. Testing this approach using a known late replicating region on an artificial chromosome proved the technique to be accurate and reliable. Our results have also demonstrated the biological importance of timely completion of replication by showing that a late replicating region on chromosome 7, which we identified as late replicating on the basis of our G2-seq results, was lost approximately three-fold more frequently than a comparable control region11.
A specific example of results with G2-seq is shown in Figure 2. We had hypothesized that there would be regions of the genome that replicated particularly late in cells lacking a protein deacetylase called Sir2. Figure 2A shows the progression of data analysis for wild type (blue) and sir2 (red). The raw data (upper rows) contain large spikes, most of which are due to the presence of Ty elements. These spikes are removed by capping read depths at 2.5x the median read depth for each sample (middle rows). The despiked data are then smoothed with a 20 kb sliding window (bottom row). Finally, data are normalized and plotted as ratios to the levels in G1. A typical late-replicating region that appears in the sir2 mutant is highlighted in Figure 2C.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56286/56286fig1.jpg" /> 
Figure 1. Flow cytometry profile indicating cell cycle fractions used; outer limits of all gates marked. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56286/56286fig1large.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/56286/56286fig2.jpg" /> 
Figure 2. (A-B) Progression of data processing from raw data (top rows), despiked data (middle rows) and smoothed data (bottom rows) for wild type (A) and sir2 (B). (C) Ratios of S phase-, early G2-, and late G2-to G1 read depths. Wild type in blue and sir2 in red. The highlighted region in the upper right shows a late replicating region specific to sir2 mutant. Scale indicates fraction of complete (2N) replication; a completely unreplicated cell would appear at 0.5, a completely replicated cell at 1. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56286/56286fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome at JoVE.com

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

g2-seq-high-throughput-sequencing-based-technique-for-identifying

Nanyang Technological University

Research

JoVE Journal

Genetics

5.8K Views.  Fred Hutchinson Cancer Research Center. We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Video: G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

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 ...

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

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

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

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

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

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

Generation of Cancer Cell Clones to Visualize Telomeric Repeat-containing RNA TERRA Expressed from a Single Telomere in Living Cells

Telomeres are transcribed, giving rise to telomeric repeat-containing long noncoding RNAs (TERRA), which have been proposed to play important roles in telomere biology, including heterochromatin formation and telomere length homeostasis. Recent findings revealed that TERRA molecules also interact with internal chromosomal regions to regulate gene expression in mouse embryonic stem (ES) cells. In line with this evidence, RNA fluorescence in situ hybridization (RNA-FISH) analyses have shown that only a subset of TERRA transcripts localize at chromosome ends. A better understanding of the dynamics of TERRA molecules will help define their function and mechanisms of action. Here, we describe a method to label and visualize single-telomere TERRA transcripts in cancer cells using the MS2-GFP system. To this aim, we present a protocol to generate stable clones, using the AGS human stomach cancer cell line, containing MS2 sequences integrated at a single subtelomere. Transcription of TERRA from the MS2-tagged telomere results in the expression of MS2-tagged TERRA molecules that are visualized by live-cell fluorescence microscopy upon co-expression of a MS2 RNA-binding protein fused to GFP (MS2-GFP). This approach enables researchers to study the dynamics of single-telomere TERRA molecules in cancer cells, and it can be applied to other cell lines.

Here, we present a protocol to generate cancer cell clones containing a MS2 sequence tag at a single subtelomere. This approach, relying on the MS2-GFP system, enables visualization of the endogenous transcripts of telomeric repeat-containing RNA (TERRA) expressed from a single telomere in living cells.

Telomeres are transcribed, giving rise to telomeric repeat-containing long noncoding RNAs (TERRA), which have been proposed to play important roles in ...

2D Gel Electrophoresis for Analyzing DNA Replication and Fork Stalling Events

Electrophoretic Analysis of Replication Through Structure-Prone DNA Repeats Within the SV40-Based Human Episome

Two-dimensional neutral/neutral gel-electrophoresis (2DGE) emerged as a benchmark technique to analyze DNA replication through natural impediments. This protocol describes how to analyze replication fork progression through structure-prone, expandable DNA repeats within the simian virus 40 (SV40)-based episome in human cells. In brief, upon plasmid transfection into human cells, replication intermediates are isolated by the modified Hirt protocol and treated with the DpnI restriction enzyme to remove non-replicated DNA. Intermediates are then digested by appropriate restriction enzymes to place the repeat of interest within the origin-distal half of a 3-5 kb-long DNA fragment. The replication intermediates are separated into two perpendicular dimensions, first by size and then by shape. Following Southern blot hybridization, this approach allows researchers to observe fork stalling at various structure-forming repeats on the descending half of the replication Y-arc. Furthermore, this positioning of the stall site allows the visualization of various outcomes of repeat-mediated fork stalling, such as fork reversal, the advent of a converging fork, and recombinational fork restart.

Author Spotlight: Characterizing DNA Replication of Pathogenic Repeats to Uncover Mechanisms of Replication Fork Stalling and Expansion

Here, we outline the procedure for analyzing replication progression through pathogenic, structure-prone repeats using 2-dimensional gel electrophoresis.

Two-dimensional neutral/neutral gel-electrophoresis (2DGE) emerged as a benchmark technique to analyze DNA replication through natural impediments. This ...

Gel-seq: A Method for Simultaneous Sequencing Library Preparation of DNA and RNA Using Hydrogel Matrices

The ability to amplify and sequence either DNA or RNA from small starting samples has only been achieved in the last five years. Unfortunately, the standard protocols for generating genomic or transcriptomic libraries are incompatible and researchers must choose whether to sequence DNA or RNA for a particular sample. Gel-seq solves this problem by enabling researchers to simultaneously prepare libraries for both DNA and RNA starting with 100 - 1000 cells using a simple hydrogel device. This paper presents a detailed approach for the fabrication of the device as well as the biological protocol to generate paired libraries. We designed Gel-seq so that it could be easily implemented by other researchers; many genetics labs already have the necessary equipment to reproduce the Gel-seq device fabrication. Our protocol employs commonly-used kits for both whole-transcript amplification (WTA) and library preparation, which are also likely to be familiar to researchers already versed in generating genomic and transcriptomic libraries. Our approach allows researchers to bring to bear the power of both DNA and RNA sequencing on a single sample without splitting and with negligible added cost.

Gel-seq enables researchers to simultaneously prepare libraries for both DNA- and RNA-seq at negligible added cost starting from 100 - 1000 cells using a simple hydrogel device. This paper presents a detailed approach for the fabrication of the device as well as the biological protocol to generate paired libraries.

The ability to amplify and sequence either DNA or RNA from small starting samples has only been achieved in the last five years. Unfortunately, the ...

Bioengineering

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization

Mammalian DNA replication initiates at multiple sites along chromosomes at different times during S phase, following a temporal replication program. The specification of replication timing is thought to be a dynamic process regulated by tissue-specific and developmental cues that are responsive to epigenetic modifications. However, the mechanisms regulating where and when DNA replication initiates along chromosomes remains poorly understood. Homologous chromosomes usually replicate synchronously, however there are notable exceptions to this rule. For example, in female mammalian cells one of the two X chromosomes becomes late replicating through a process known as X inactivation1. Along with this delay in replication timing, estimated to be 2-3 hr, the majority of genes become transcriptionally silenced on one X chromosome. In addition, a discrete cis-acting locus, known as the X inactivation center, regulates this X inactivation process, including the induction of delayed replication timing on the entire inactive X chromosome. In addition, certain chromosome rearrangements found in cancer cells and in cells exposed to ionizing radiation display a significant delay in replication timing of &gt;3 hours that affects the entire chromosome2,3. Recent work from our lab indicates that disruption of discrete cis-acting autosomal loci result in an extremely late replicating phenotype that affects the entire chromosome4. Additional 'chromosome engineering' studies indicate that certain chromosome rearrangements affecting many different chromosomes result in this abnormal replication-timing phenotype, suggesting that all mammalian chromosomes contain discrete cis-acting loci that control proper replication timing of individual chromosomes5.
Here, we present a method for the quantitative analysis of chromosome replication timing combined with fluorescent in situ hybridization. This method allows for a direct comparison of replication timing between homologous chromosomes within the same cell, and was adapted from6. In addition, this method allows for the unambiguous identification of chromosomal rearrangements that correlate with changes in replication timing that affect the entire chromosome. This method has advantages over recently developed high throughput micro-array or sequencing protocols that cannot distinguish between homologous alleles present on rearranged and un-rearranged chromosomes. In addition, because the method described here evaluates single cells, it can detect changes in chromosome replication timing on chromosomal rearrangements that are present in only a fraction of the cells in a population.

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization

A quantitative method for the analysis of chromosome replication timing is described. The method utilizes BrdU incorporation in combination with fluorescent in situ hybridization (FISH) to assess replication timing of mammalian chromosomes. This technique allows for the direct comparison of rearranged and un-rearranged chromosomes within the same cell.

Mammalian DNA replication initiates at multiple sites along chromosomes at different times during S phase, following a temporal replication program. The ...

Biology

gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair

The widespread use of Next Generation Sequencing has opened up new avenues for cancer research and diagnosis. NGS will bring huge amounts of new data on cancer, and especially cancer genetics. Current knowledge and future discoveries will make it necessary to study a huge number of genes that could be involved in a genetic predisposition to cancer. In this regard, we developed a Nextera design to study 11 complete genes involved in DNA damage repair. This protocol was developed to safely study 11 genes (ATM, BARD1, BRCA1, BRCA2, BRIP1, CHEK2, PALB2, RAD50, RAD51C, RAD80, and TP53) from promoter to 3&#39;-UTR in 24 patients simultaneously. This protocol, based on transposase technology and gDNA enrichment, gives a great advantage in terms of time for the genetic diagnosis thanks to sample multiplexing. This protocol can be safely used with blood gDNA.

gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair

gDNA enrichment for NGS sequencing is an easy and powerful tool for the study of constitutional mutations. In this article, we present the procedure to analyse simply the complete sequence of 11 genes involved in DNA damage repair.

The widespread use of Next Generation Sequencing has opened up new avenues for cancer research and diagnosis. NGS will bring huge amounts of new data on ...

3' End Sequencing Library Preparation with A-seq2

Studies in the last decade have revealed a complex and dynamic variety of pre-mRNA cleavage and polyadenylation reactions. mRNAs with long 3&#39; untranslated regions (UTRs) are generated in differentiated cells whereas proliferating cells preferentially express transcripts with short 3&#39;UTRs. We describe the A-seq protocol, now at its second version, which was developed to map polyadenylation sites genome-wide and study the regulation of pre-mRNA 3&#39; end processing. Also this current protocol takes advantage of the polyadenylate (poly(A)) tails that are added during the biogenesis of most mammalian mRNAs to enrich for fully processed mRNAs. A DNA adaptor with deoxyuracil at its fourth position allows the precise processing of mRNA 3&#39; end fragments for sequencing. Not including the cell culture and the overnight ligations, the protocol requires about 8 h hands-on time. Along with it, an easy-to-use software package for the analysis of the derived sequencing data is provided. A-seq2 and the associated analysis software provide an efficient and reliable solution to the mapping of pre-mRNA 3&#39; ends in a wide range of conditions, from 106 or fewer cells.

This protocol describes a method for mapping pre-mRNA 3&#39; end processing sites.

Studies in the last decade have revealed a complex and dynamic variety of pre-mRNA cleavage and polyadenylation reactions. mRNAs with long 3&#39; ...

TChIP-Seq: Cell-Type-Specific Epigenome Profiling

Epigenetic regulation plays central roles in gene expression. Since histone modification was discovered in the 1960s, its physiological and pathological functions have been extensively studied. Indeed, the advent of next-generation deep sequencing and chromatin immunoprecipitation (ChIP) via specific histone modification antibodies has revolutionized our view of epigenetic regulation across the genome. Conversely, tissues typically consist of diverse cell types, and their complex mixture poses analytic challenges to investigating the epigenome in a particular cell type. To address the cell type-specific chromatin state in a genome-wide manner, we recently developed tandem chromatin immunoprecipitation sequencing (tChIP-Seq), which is based on the selective purification of chromatin by tagged core histone proteins from cell types of interest, followed by ChIP-Seq. The goal of this protocol is the introduction of best practices of tChIP-Seq. This technique provides a versatile tool for tissue-specific epigenome investigation in diverse histone modifications and model organisms.

We describe a step-by-step protocol for tandem chromatin immunoprecipitation sequencing (tChIP-Seq) that enables the analysis of cell-type-specific genome-wide histone modification.

Epigenetic regulation plays central roles in gene expression. Since histone modification was discovered in the 1960s, its physiological and pathological ...

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Summary

Explore More Videos

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

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

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

Generation of Cancer Cell Clones to Visualize Telomeric Repeat-containing RNA TERRA Expressed from a Single Telomere in Living Cells

Electrophoretic Analysis of Replication Through Structure-Prone DNA Repeats Within the SV40-Based Human Episome

Gel-seq: A Method for Simultaneous Sequencing Library Preparation of DNA and RNA Using Hydrogel Matrices

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization

gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair

3' End Sequencing Library Preparation with A-seq2

TChIP-Seq: Cell-Type-Specific Epigenome Profiling