The work is supported by CPRIT Research Training Award RP140105, as well as partially supported by US National Institutes of Health (NIH) grants R01 GM114142 and by William &#38; Ella Owens Medical Research Foundation.

All tissues are obtained following approval of the Institutional Review Board committee and when all participants consented to both molecular analyses and follow-up studies. The protocols are approved by the Human Studies Committee at University of Texas Health Science Center at San Antonio.
1. Methyl-binding DNA Capture (MBDCap)
<ol>
	<li>Sample collection and DNA isolation
	<ol>
		<li>Collect bulk tumor or normal tissue samples from patient paraffin embedded tissue samples.</li>
		<li>Use a commercial DNA mini kit to isolate genomic DNA from the paraffin embedded tissue samples according to manufacturer&#39;s protocol.</li>
		<li>Use a methylated DNA enrichment kit with the procedure described here according to manufacturer&#39;s protocol.</li>
	</ol>
	</li>
	<li>Initial bead wash 
	Note: The beads are used to couple with MBD-Biotin protein, which detects DNA methylation on the genome. Beads are usually suspended in a stock solution. Use these steps to wash off this liquid.
	<ol>
		<li>Resuspend the stock of streptavidin beads (See Materials Table) by gently pipetting up and down to obtain a homogeneous suspension. Do not mix the beads by vortexing.</li>
		<li>For one &#181;g of input DNA, add10 &#181;L of beads to a tube with 90 &#181;L of 1x Bind/Wash Buffer. Rotate for 1 min. Do not mix by vortexing.</li>
		<li>Place the tube(s) on a magnetic rack for 1 min.</li>
		<li>Remove the liquid with a pipette and discard the liquid.</li>
		<li>Add 250 &#181;L of 1x Bind/Wash Buffer to the beads and rotate for 1 min.</li>
		<li>Repeat steps 1.2.3 - 1.2.5 twice.</li>
	</ol>
	</li>
	<li>Coupling the MBD-Biotin protein to the beads
	<ol>
		<li>For 1.2 &#181;g of input DNA, add 7 &#181;L (3.5 &#181;g) of MBD-Biotin protein to each tube.</li>
		<li>Add 93 &#181;L of 1x Bind/Wash Buffer to the protein to make a final volume of 100 &#181;L.</li>
		<li>Mix the beads-protein mixture on a rotating mixer at RT&#160;for 1 h.</li>
	</ol>
	</li>
	<li>Fragmented DNA (input)
	<ol>
		<li>Sonicate DNA by adding 1.2 &#181;g of total genomic DNA in 96 &#181;L of TE&#160;buffer and 24 &#181;L of 5x Bind/wash buffer to make a 120 &#181;L solution. Use 30 s&#160;power ON and 30 s&#160;power OFF during sonication, 20 - 25 times.</li>
		<li>Run 1 &#181;L of sonicated DNA solution using a bioanalyzer system with a commercial high sensitivity DNA kit to check the size of the fragments to be in the range of 150 - 350 bp according to manufacturer&#39;s protocol.</li>
	</ol>
	</li>
	<li>Wash the MBD-beads
	<ol>
		<li>Place the tube containing MBD-beads on a magnetic rack for 1 min.</li>
		<li>Remove and discard the liquid with a pipette without touching the beads.</li>
		<li>Resuspend the beads with 250 &#181;L of 1x Bind/Wash Buffer.</li>
		<li>Mix the beads on a rotating mixer at RT for 5 min.</li>
		<li>Repeat steps 1.5.1 - 1.5.4 twice.</li>
	</ol>
	</li>
	<li>Fragmented DNA capture reaction (1 &#181;g input DNA)
	<ol>
		<li>Transfer the DNA/Buffer mixture to the tube containing the MBD-beads.</li>
		<li>Mix the MBD-beads with the DNA on a rotating mixer for 1 h&#160;at RT. Alternatively, mix O/N at 4&#160;oC.</li>
	</ol>
	</li>
	<li>Removing non-captured DNA from the Beads
	<ol>
		<li>After mixing the DNA and MBD-beads, place the tube on the magnetic rack for 1 min to concentrate all of the beads on the inner wall of the tube.</li>
		<li>Remove the supernatant liquid with a pipette and save it in a clean DNase-free microcentrifuge tube as nonmethylated DNA. Store this sample on ice.</li>
		<li>Add 200 &#181;L of 1x Bind/Wash Buffer to the beads to wash the beads.</li>
		<li>Mix the beads on a rotating mixer for 3 min.</li>
		<li>Place the tube on the magnetic rack for 1 min.</li>
		<li>Remove the liquid with a pipette and save it in a microcentrifuge tube.</li>
		<li>For capture reactions of &#8804;1 &#181;g of input DNA, repeat steps 1.7.3 - 1.7.6 for 2 more wash fractions in total.</li>
	</ol>
	</li>
	<li>Single fraction elution
	<ol>
		<li>Mix low-salt buffer with high-salt buffer (1:1) to get elution buffer (1,000 mM NaCl).</li>
		<li>Resuspend the beads in 200 &#181;L of elution buffer (1,000 mM NaCl).</li>
		<li>Incubate the beads on a rotating mixer for 3 min.</li>
		<li>Place the tube on the magnetic rack for 1 min.</li>
		<li>With the tube in place on the magnetic rack, remove the liquid with a pipette without touching the beads with the pipette tip, and save it in a clean DNase-free 1.5 mL microcentrifuge tube. Store this sample on ice.</li>
		<li>Repeat steps 1.8.1 - 1.8.4 once, collecting the second sample in the same tube (total volume will be 400 &#181;L). Store the pooled sample on ice.</li>
	</ol>
	</li>
	<li>Ethanol precipitation (DNA cleanup)
	<ol>
		<li>To each noncaptured, wash, and elution fraction from the previous steps, add 1 &#181;L glycogen (20 &#181;g/&#181;L, included in kit), 1/10 sample volume of 3M sodium acetate, pH 5.2 (e.g., 40 &#181;L per 400 &#181;L of sample) and 2 sample volumes of 100% ethanol (e.g., 800 &#181;L per 400 &#181;L of sample). The total volume is 1,241 &#181;L.</li>
		<li>Mix well and incubate at -80&#160;oC for at least 2 h.</li>
		<li>Centrifuge the tube for 15 min at 11,363 x g at 4&#160;oC.</li>
		<li>Carefully discard the supernatant without disturbing the pellet.</li>
		<li>Add 500 &#181;L of cold 70% ethanol.</li>
		<li>Centrifuge the tube for 5 min at 11,363 x g at 4&#160;oC.</li>
		<li>Carefully discard the supernatant without disturbing the pellet.</li>
		<li>Repeat steps 1.9.6 - 1.9.7 once and remove any remaining residual supernatant.</li>
		<li>Air-dry the pellet for ~5 min. Do not completely dry the pellet.</li>
		<li>Resuspend the DNA pellet in 37.5 &#181;L of DNase-free water or other appropriate volume of buffer.</li>
		<li>Place the DNA on ice or store the DNA at -20&#160;oC or below until further use.</li>
		<li>Check that the total amount of DNA is over 20 ng, (e.g., use fluorometric quantitation system, see Materials Table).</li>
	</ol>
	</li>
</ol>
2. Sequencing
<ol>
	<li>Use the DNA fragments recovered from MBDCap procedure for sequencing. For each sample to be sequenced, a total amount of 20&#160;- 40 ng DNA is required for library preparation.</li>
	<li>For DNA-seq library preparation use a fully automated library construction system (See Materials Table) and follow the standard protocol supplied by the manufacturer. Select DNA fragments of size 200 - 400 bp for library preparation. The automation library preparation system allows standardizing the library preparation procedure and minimizing the variation across the samples due to manual preparation.</li>
	<li>Use adaptor primers and dilute to 100x concentration. Of this, use 10 &#181;L for each sample.</li>
	<li>Following step 2.2, take out the reaction, and quantify the DNA-seq library using a fluorometric quantitation system (See Materials Table).
	<ol>
		<li>Set up PCR amplification procedure. The choice of PCR master mix is to improve the PCR efficiency of GC enriched region of genome. In a PCR tube, add 15 &#181;L DNA-seq library, 25&#160;&#181;L PCR master mix (See Materials Table), 2 &#181;L PCR primer mix (See Materials Table), and 8 &#181;L H2O.&#160;</li>
	</ol>
	</li>
	<li>Follow PCR recommendation from manufacturer of the PCR master mix, altering cycling times and temperatures for differing starting materials: 98&#160;oC for 45 s, X cycles of 98&#160;oC for 15 s, 65&#160;oC for 30 s, 72&#160;oC for 30 s, and then 72&#160;oC for 1 min. Hold at 4&#160;oC.
	<ol>
		<li>Perform enough cycles to end up with 2 -&#160;10 nM of library DNA (8 - 10 cycles).</li>
	</ol>
	</li>
	<li>Clean up PCR reaction with 1:1 ratio of PCR purification beads according to manufacturer&#39;s protocol (See Materials Table).</li>
	<li>Elute with 20 - 30 &#181;L elution buffer.</li>
	<li>Barcode each sample and pool 4 samples together into one lane of a flow cell. Use the standard 50 bp single read protocol for sequencing (See Materials Table).</li>
</ol>
3. Bioinformatics Analysis
Note: Further process the raw fastq files obtained from the sequencing to perform quality control and mapping the short DNA sequences (reads) to the genome.
<ol>
	<li>Use Bowtie short read aligner, genome mapping tool on each sample fastq file to map the reads to the reference genome. Many read mapping tools are available to perform this step and can be used based on individual preference.
	<ol>
		<li>Allow up to 2 mismatches in the whole reads while mapping to the reference genome. Report up to 20 best alignments for each read. For example, use bowtie -v 2 --best -k 20 --chunkmbs 200 &#60;ebwt&#62;&#60;fastqFile&#62;&#60;alignFile&#62;.</li>
	</ol>
	</li>
	<li>Use LONUT9 to recover the multiply mapped reads to improve the detection of the enriched regions. For each read, at most one alignment would be recovered when it is close enough to any of the above called peaks. For example, use: perl lonut.pl -g hg19 -w 250 -p 99 &#60;alignFile&#62;&#60;outputPath&#62;. LONUT will perform the following steps.
	<ol>
		<li>Split alignments into uniquely mapped reads and multiply mapped reads. Call peak on the uniquely mapped reads using peak caller BELT11. The option -w 250 and -p 99 in the example command are for BELT.</li>
		<li>Combine uniquely mapped reads with the recovered reads obtained from LONUT, and the combined reads, in BED format, will be used in further analysis. Calculate the number of combined reads for future normalization.</li>
	</ol>
	</li>
	<li>Bin the reads. Extract reads in region of interest using perl script: perl methyPipeline.pl &#60;sampleInfoFile&#62;&#60;refGeneFile&#62; -up &#60;upstream extending length&#62; -dn&#60;downstream extending length&#62;. For each bed file listed in the sampleInfoFile, the perl script performs the below tasks. 
	Note: See supplementary coding file for the perl script.
	<ol>
		<li>Bin the reads into fixed bin size, e.g., 100 bp, for 24 chromosomes.</li>
		<li>For each gene specified in refGeneFile, extract the bins around transcription start site (TSS), up to the extending length specified by option -up and -dn. For example, extract the data in the region that is up and downstream 4 kb of TSS. At bin size 100 bp, this extracted data have 81 bins, and the center bin corresponds to the TSS.</li>
		<li>For the gene on the antisense strand, flip the extracted region from left to right so that upstream bins are always placed on the left with the perl script.</li>
		<li>Further split the TSS &#177;4 kb region into three sub regions: Left, upstream 4 kb to upstream 2 kb; Middle, up and downstream 2 kb; Right, downstream 2 kb to 4 kb.</li>
		<li>For each sample, divide the count of reads in each bin by the total number of combined mapped reads calculated in 3.3.3. The normalization will eliminate the sample specific read differences and allows to compare the reads across different samples.</li>
	</ol>
	</li>
	<li>Run the bash script (as indicated in the script file) to perform statistical test on the normalized reads obtained from the left region, to detect differential methylation between normal and case group in the regions described as in the flank region. 
	Note: See the supplementary coding file for the bash script. Based on the regions that are differentially methylated, there are seven combinations: 
	Whole region: differential methylation throughout the TSS &#177;4 kb region 
	MiddleLeft: differential methylation in both middle and left region 
	MiddleRight: differential methylation in both middle and right region 
	Flank: differential methylation in both left and right region 
	Left: differential methylation in left region only 
	Right: differential methylation in right region only 
	Middle: differential methylation in middle region only</li>
	<li>Use the same bash script to visualize the differential methylation in a tornado plot. Plot the hyper and hypo methylated genes on a separate panel, and sort in the order of the region combination as described in 3.4, respectively.</li>
</ol>

<ol>
	<li>Trimarchi, M. P., Mouangsavanh, M., Huang, T. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+epigenetics:+a+perspective+on+the+role+of+DNA+methylation+in+acquired+endocrine.">Cancer epigenetics: a perspective on the role of DNA methylation in acquired endocrine.</a> Chin. J. Cancer. 30, 749-756 (2011).</li><li>Nair, S. 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=Comparison+of+methyl-DNA+immunoprecipitation+(MeDIP)+and+methyl-CpG+binding+domain+(MBD)+protein+capture+for+genome-wide+DNA+methylation+analysis+reveal+CpG+sequence+coverage+bias.">Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpG sequence coverage bias.</a> Epigenetics. 6, 34-44 (2011).</li><li>Zuo, T., Tycko, B., Liu, T. M., Lin, J. J., Huang, T. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+in+DNA+methylation+profiling.">Methods in DNA methylation profiling.</a> Epigenomics. 1, 331-345 (2009).</li><li>Clark, C., 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=A+comparison+of+the+whole+genome+approach+of+MeDIP-seq+to+the+targeted+approach+of+the+Infinium+HumanMethylation450+BeadChip((R))+for+methylome+profiling.">A comparison of the whole genome approach of MeDIP-seq to the targeted approach of the Infinium HumanMethylation450 BeadChip((R)) for methylome profiling.</a> PLoS One. 7, e50233 (2012).</li><li>Walker, D. L., 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=DNA+methylation+profiling:+comparison+of+genome-wide+sequencing+methods+and+the+Infinium+Human+Methylation+450+Bead+Chip.">DNA methylation profiling: comparison of genome-wide sequencing methods and the Infinium Human Methylation 450 Bead Chip.</a> Epigenomics. , 1-16 (2015).</li><li>Huang, Y. W., Huang, T. H., Wang, L. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Profiling+DNA+methylomes+from+microarray+to+genome-scale+sequencing.">Profiling DNA methylomes from microarray to genome-scale sequencing.</a> Technol. Cancer Res. Treat. 9, 139-147 (2010).</li><li>Serre, D., Lee, B. H., Ting, A. 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This protocol is developed in the laboratories of Dr. Tim Huang and Dr. Victor Jin at the University of Texas Health Science Center at San Antonio.

The MBDCap-seq technique is an affinity enrichment approach3, considered as a cost effective alternative when investigating cohorts with a large number of patients15. The pipeline presented here describes a comprehensive approach from sample procurement to data analysis and interpretation. One of the most important steps is setting up a PCR amplification procedure to improve the PCR efficiency of the GC enriched regions in the genome as this is where DNA methylation occurs. Also, it is essential to ensure that after seq...

Epigenetic regulation of genes through DNA methylation is one of the essential mechanisms required to determine the cell fate by stable differentiation of different tissue types in the body1. Dysregulation of this process has been known to cause various diseases including cancer2.
This process mainly involves the addition of methyl groups on the cytosine residue in the CpG dinucleotides of DNA3. There are a few different techniques currently used to investigate this mechanism, each having their own advantages as outlined in many studies2-8. Here we will discuss one of these techniques called Methyl-Binding DNA Capture&#160;sequencing (MBDCap-seq), where we use an affinity enrichment technique to identify methylated regions of the DNA. This technique builds upon the methyl-binding ability of the MBD2 protein to enrich for the genomic DNA fragments containing methylated CpG sites. We utilize a commercial methylated DNA enrichment kit for the isolation of these methylated regions. Our laboratory has screened hundreds of patient samples using this technique and here we provide a comprehensive optimized protocol, which can be used to investigate large patient cohorts.
As evident with any next-generation sequencing technology, MBDCap-seq also requires a specific bioinformatics approach in order to accurately quantify the levels of methylation across the samples. There have been many recent studies in an effort to optimize the normalization and analysis process of the sequencing data9,10. In this protocol, we demonstrate one of these methods implementing a unique read recovery approach &#8212; LONUT &#8212; followed by linear normalization of each sample in order to enable unbiased comparisons across large number of patient samples.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Methylminer DNA enrichment Kit</td>
			<td>Invitrogen</td>
			<td>ME10025</td>
			<td></td>
		</tr>
		<tr>
			<td>Dynabeads M-280 Streptavidin</td>
			<td>Invitrogen</td>
			<td>112-05D</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioruptor Plus Sonication Device</td>
			<td>diagenode</td>
			<td>B01020001</td>
			<td></td>
		</tr>
		<tr>
			<td>3M sodium acetate pH 5.2</td>
			<td>Sigma</td>
			<td>S7899</td>
			<td>100ml</td>
		</tr>
		<tr>
			<td>SPRIworks Fragment Library System I</td>
			<td>Beckman Coulter</td>
			<td>A50100</td>
			<td>Fully automated library construction system</td>
		</tr>
		<tr>
			<td>Adapter Primers</td>
			<td>Bioo Scientific</td>
			<td>514104</td>
			<td>PCR primer mix</td>
		</tr>
		<tr>
			<td>Qubit</td>
			<td>Invitrogen</td>
			<td>Q32854</td>
			<td>Fluorometric Quantitation System</td>
		</tr>
		<tr>
			<td>PCR master mix</td>
			<td>KAPA scientific</td>
			<td>KK2621</td>
			<td>PCR master mix</td>
		</tr>
		<tr>
			<td>AMPure XP</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td>PCR Purification beads</td>
		</tr>
		<tr>
			<td>EB Buffer</td>
			<td>Qiagen</td>
			<td>19086</td>
			<td></td>
		</tr>
		<tr>
			<td>HiSeq 2000 Sequencing System</td>
			<td>Illumina</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

methyl-binding dna capture sequencing for patient tissues

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable development and differentiation of different tissue types. Dysregulation of this process is often the hallmark of various diseases like cancer. Here, we outline one of the recent sequencing techniques, Methyl-Binding DNA Capture sequencing (MBDCap-seq), used to quantify methylation in various normal and disease tissues for large patient cohorts. We describe a detailed protocol of this affinity enrichment approach along with a bioinformatics pipeline to achieve optimal quantification. This technique has been used to sequence hundreds of patients across various cancer types as a part of the 1,000 methylome project (Cancer Methylome System).

We have used MBDCap-seq to study DNA methylation alterations in a large number of patients from diverse cancer types including breast12, endometrial13, prostate14, and liver cancers among others. Here we demonstrate some information from the breast cancer study published recently12. In this instance, we used the whole genome sequencing approach to identify CpG islands that are differentially methylated in tumor with respect to normal across different genomic regions. The investigation revealed that...

Watch this Scientific Journal Video about Methyl-binding DNA capture Sequencing for Patient Tissues at JoVE.com

Methyl-binding DNA capture Sequencing for Patient Tissues

Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable ...

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8.5K Views.  University of Texas Health Science Center at San Antonio. Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

Video: Methyl-binding DNA capture Sequencing for Patient Tissues

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Purification of High Molecular Weight Genomic DNA from Powdery Mildew for Long-Read Sequencing

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics of these pathogens, in part due to a lack of well-developed genetic and genomic resources. These organisms have large, repetitive genomes, which have made genome sequencing and assembly prohibitively difficult. Here, we describe methods for the collection, extraction, purification and quality control assessment of high molecular weight genomic DNA from one powdery mildew species, Golovinomyces cichoracearum. The protocol described includes mechanical disruption of spores followed by an optimized phenol/chloroform genomic DNA extraction. A typical yield was 7 &#181;g DNA per 150 mg conidia. The genomic DNA that is isolated using this procedure is suitable for long-read sequencing (i.e., &#62; 48.5 kbp). Quality control measures to ensure the size, yield, and purity of the genomic DNA are also described in this method. Sequencing of the genomic DNA of the quality described here will allow for the assembly and comparison of multiple powdery mildew genomes, which in turn will lead to a better understanding and improved control of this agricultural pathogen.

Described here is a method for the extraction, purification, and quality control of genomic DNA from the obligate biotrophic fungal pathogen, powdery mildew, for use in long-read genome sequencing.

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics ...

Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic fragments. Organellar genomes also exist in admixtures within a given cell or tissue type (heteroplasmy), and an abundance of subtypes may change throughout development or when under stress (sub-stoichiometric shifting). Next-generation sequencing (NGS) technologies are required to obtain deeper understanding of organellar genome structure and function. Traditional sequencing studies use several methods to obtain organellar DNA: (1) If a large amount of starting tissue is used, it is homogenized and subjected to differential centrifugation and/or gradient purification. (2) If a smaller amount of tissue is used (i.e., if seeds, material, or space is limited), the same process is performed as in (1), followed by whole-genome amplification to obtain sufficient DNA. (3) Bioinformatics analysis can be used to sequence the total genomic DNA and to parse out organellar reads. All these methods have inherent challenges and tradeoffs. In (1), it may be difficult to obtain such a large amount of starting tissue; in (2), whole-genome amplification could introduce a sequencing bias; and in (3), homology between nuclear and organellar genomes could interfere with assembly and analysis. In plants with large nuclear genomes, it is advantageous to enrich for organellar DNA to reduce sequencing costs and sequence complexity for bioinformatics analyses. Here, we compare a traditional differential centrifugation method with a fourth method, an adapted CpG-methyl pulldown approach, to separate the total genomic DNA into nuclear and organellar fractions. Both methods yield sufficient DNA for NGS, DNA that is highly enriched for organellar sequences, albeit at different ratios in mitochondria and chloroplasts. We present the optimization of these methods for wheat leaf tissue and discuss major advantages and disadvantages of each approach in the context of sample input, protocol ease, and downstream application.

The comparison and optimization of two plant organellar DNA enrichment methods are presented: traditional differential centrifugation and fractionation of the total gDNA based on methylation status. We assess the resulting DNA quantity and quality, demonstrate performance in short-read next-generation sequencing, and discuss the potential for use in long-read single-molecule sequencing.

Plant organellar genomes contain large, repetitive elements that may undergo pairing or recombination to form complex structures and/or sub-genomic ...

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

The role of long noncoding RNAs (lncRNAs) in cancer is coming to the forefront due to growing interest in understanding their mechanistic functions during cancer development and progression. Despite this, the global epigenetic regulation of lncRNAs and repetitive sequences in cancer has not been well investigated, particularly in chronic lymphocytic leukemia (CLL). This study focuses on a unique approach: the immunoprecipitation-based capture of double-stranded, methylated DNA fragments using methyl-binding domain (MBD) proteins, followed by next-generation sequencing (MBD-seq). CLL patient samples belonging to two prognostic subgroups (5 IGVH mutated samples + 5 IGVH unmutated samples) were used in this study. Analysis revealed 5,800 hypermethylated and 12,570 hypomethylated CLL-specific differentially methylated genes (cllDMGs) compared to normal healthy controls. Importantly, these results identified several CLL-specific, differentially methylated lncRNAs, repetitive elements, and protein-coding genes with potential prognostic value. This work outlines a detailed protocol for an MBD-seq and bioinformatics pipeline developed for the comprehensive analysis of global methylation profiles in highly CpG-rich regions using CLL patient samples. Finally, a protein-coding gene and an lncRNA were validated using pyrosequencing, which is a highly quantitative method to analyze CpG methylation levels to further corroborate the findings from the MBD-seq protocol.

This work describes an optimized methyl-CpG-binding domain (MBD) sequencing protocol and a computational pipeline to identify differentially methylated CpG-rich regions in chronic lymphocytic leukemia (CLL) patients.

The role of long noncoding RNAs (lncRNAs) in cancer is coming to the forefront due to growing interest in understanding their mechanistic functions during ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

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

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

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

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

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

Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of cancer development. By using non-invasive or pre-malignant lesions from patients who later develop invasive disease, gene expression analyses may help identify early molecular alterations that predispose to cancer risk. It has been well described that nucleic acids recovered from FFPE tissues have undergone severe physical damage and chemical modifications, which make their analysis difficult and generally requires adapted assays. MicroRNAs (miRNAs), however, which represent a small class of RNA molecules spanning only up to ~18&#8211;24 nucleotides, have been shown to withstand long-term storage and have been successfully analyzed in FFPE samples. Here we present a 3&#39; barcoded complementary DNA (cDNA) library preparation protocol specifically optimized for the analysis of small RNAs extracted from archived tissues, which was recently demonstrated to be robust and highly reproducible when using archived clinical specimens stored for up to 35 years. This library preparation is well adapted to the multiplex analysis of compromised/degraded material where RNA samples (up to 18) are ligated with individual 3&#39; barcoded adapters and then pooled together for subsequent enzymatic and biochemical preparations prior to analysis. All purifications are performed by polyacrylamide gel electrophoresis (PAGE), which allows size-specific selections and enrichments of barcoded small RNA species. This cDNA library preparation is well adapted to minute RNA inputs, as a pilot polymerase chain reaction (PCR) allows determination of a specific amplification cycle to produce optimal amounts of material for next-generation sequencing (NGS). This approach was optimized for the use of degraded FFPE RNA from specimens archived for up to 35 years and provides highly reproducible NGS data.

Formalin-fixed paraffin-embedded specimens represent a valuable source of molecular biomarkers of human diseases. Here we present a laboratory-based cDNA library preparation protocol, initially designed with fresh frozen RNA, and optimized for the analysis of archived microRNAs from tissues stored up to 35 years.

&#8211;Archived, clinically classified formalin-fixed paraffin-embedded (FFPE) tissues can provide nucleic acids for retrospective molecular studies of ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near full-length (intact and defective), HIV-1 proviruses. FLIPS allows determination of the genetic composition of integrated HIV-1 within a cell population. Through identifying defects within HIV-1 proviral sequences that arise during reverse transcription, such as large internal deletions, deleterious stop codons/hypermutation, frameshift mutations, and mutations/deletions in cis acting elements required for virion maturation, FLIPS can identify integrated proviruses incapable of replication. The FLIPS assay can be utilized to identify HIV-1 proviruses that lack these defects and are&#160;therefore potentially replication-competent. The FLIPS protocol involves: lysis of HIV-1 infected cells, nested PCR of near full-length HIV-1 proviruses (using primers targeted to the HIV-1 5&#39; and 3&#39; LTR), DNA purification and quantification, library preparation for Next-generation Sequencing (NGS), NGS, de novo assembly of proviral contigs, and a simple process of elimination for identifying replication-competent proviruses. FLIPS provides advantages over traditional methods designed to sequence integrated HIV-1 proviruses, such as single-proviral sequencing. FLIPS amplifies and sequences near full-length proviruses enabling replication competency to be determined, and also uses fewer amplification primers, preventing the consequences of primer mismatches. FLIPS is a useful tool for understanding the genetic landscape of integrated HIV-1 proviruses, especially within the latent reservoir, however, its utilization can extend to any application in which the genetic composition of integrated HIV-1 is required.

Full-length individual proviral sequencing (FLIPS) provides an efficient and high-throughput method for the amplification and sequencing of single, near full-length (intact and defective) HIV-1 proviruses and allows for determination of their potential replication-competency. FLIPS overcomes limitations of previous assays designed to sequence the latent HIV-1 reservoir.

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near ...

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 ...