This study was supported by the Swedish Research Council, the Swedish Cancer Society, the Knut and Alice Wallenberg Foundation (KAW), and FoU V&#228;straG&#246;talandsregionen.

The ethical approval for collecting the CLL samples is from 2007-05-21, with the following registration number: EPN Gbg dnr 239/07. All CLL patients were diagnosed according to recently revised criteria8, and the samples were collected at the time of diagnosis. The patients in the study were included from different hematology departments in the western part of Sweden after written consent had been obtained. Only CLL peripheral blood mononuclear cell (PBMC) samples with a tumor percentage of leukemic cells &#8805;70% were selected in this study.
1. Preparations
<ol>
	<li>Isolate PBMCs for DNA extractions from CLL and normal, healthy peripheral blood samples using a commercial isolation kit (see the Materials Table), according to the manufacturer&#39;s instructions.</li>
	<li>Autoclave 1.5 mL tubes. Thaw all reagents in the methylated DNA binding kit (see the Materials Table).</li>
	<li>Use DNase-free water to prepare 10 mL of 1x bead wash buffer from the 5x stock wash buffer provided by the kit to wash beads and dilute the MBD protein provided by the kit.</li>
	<li>Obtain or prepare the following items in advance: 3 M sodium acetate, absolute ethanol, and 70% ethanol for DNA precipitation (Materials Table).</li>
</ol>
2. Genomic DNA Extraction and Sonication
<ol>
	<li>Use a commercially available DNA extraction kit (Materials Table) to isolate genomic DNA from patient and normal PBMC samples according to the manufacturer&#39;s protocol. Quantify the genomic DNA using a spectrophotometer at 260 nm. 
	NOTE: The DNA extraction column capacity is 5-6 million cells, maximum; hence, it is important to use more than one column for samples with more than 5 million cells. Elute the DNA twice in equal volumes totaling 100 &#181;L of 10 mM Tris EDTA (TE) buffer. The MBD-biotin protein used in the methyl miner kit for MBD sequencing will not bind to single-stranded DNA. Thus, it is very important to keep the eluted DNA either frozen or at 4 &#176;C to preserve its double-stranded nature.</li>
	<li>Dilute 5 &#181;g of genomic DNA from each sample to a total of 200 &#181;L using TE buffer (pH 8), giving a final concentration of 25 ng/&#181;L.</li>
	<li>Perform sonication in specially designed tubes using a sonicator (Materials Table) for a total of 30 cycles (30 s on and 30 s off per cycle; after every 5 cycles, briefly spin the tubes to collect the samples to the bottom). 
	NOTE: This will produce fragmented DNA ranging between 150 bp and 300 bp, which is optimal for this protocol.</li>
	<li>Check the sonication range for all samples before proceeding to the next step of MBD sequencing by running 1 &#181;L of the fragmented DNA samples and a DNA size ladder on a commercially available, pre-cast 2% agarose gel using DNA electrophoresis imaging equipment. Visualize the DNA using a standard UV transilluminator.</li>
</ol>
3. Bead Preparation Prior to Binding with MBD-biotin Protein
<ol>
	<li>Resuspend the magnetic streptavidin beads from the stock tube provided by the kit, gently pipetting up and down to obtain a homogenous suspension. Do not vortex the beads or allow them to dry.</li>
	<li>Place 50 &#181;L of beads (for each 5 &#181;g of fragmented DNA sample) in separate, clean, and labelled 1.5 mL tubes. Add 50 &#181;L of 1x bead wash buffer to reach the final volume of 100 &#181;L. 
	NOTE: When using small 0.5 mL polymerase chain reaction (PCR) stripes for washing beads, around 150-200 &#181;L of wash buffer per tube is used, as mentioned in the kit protocol. However, in the case of 1.5 mL tubes, add at least 250 &#181;L to 300 &#181;L of wash buffer per tube for the bead wash.</li>
	<li>Place the tubes on a magnetic stand for one minute to allow all the magnetic beads to concentrate on the inner wall of the tube facing the magnet. Remove the liquid, without touching the beads, using a 200 &#181;L pipette.</li>
	<li>Remove the tubes from the magnetic stand, add 250 &#181;L of 1x bead wash buffer, and mix the beads gently with a pipette.</li>
	<li>Repeat steps 3.3 and 3.4 at least 4-5 times for all samples and finally resuspend in 250 &#181;L of 1x bead wash buffer. Keep them on ice.</li>
</ol>
4. Binding the MBD-biotin Protein to the Washed Beads
<ol>
	<li>Add 35 &#181;L of MBD protein (7 &#181;L for 1 &#181;g of DNA sample) to separate tubes and bring the total volume up to 250 &#181;L using 1x bead wash buffer.</li>
	<li>Add 250 &#181;L of diluted MBD protein to the 250 &#181;L of washed beads and leave them on end-to-end rotation at room temperature for 1 h.</li>
	<li>After mixing the beads and protein for 1 h, wash the MBD protein biotin bound with magnetic streptavidin beads.
	<ol>
		<li>Place the tubes on the magnetic stand for 1 min and remove the liquid without touching the beads using a pipette. Add 250 &#181;L of 1x bead wash buffer and place the tubes on a rotation mixer for 5 min at room temperature.</li>
	</ol>
	</li>
	<li>Repeat step 4.3.1 two more times and finally resuspend the washed MBD-biotin beads in 200 &#181;L of 1x bead wash buffer, making the beads ready for methylated DNA capture. 
	NOTE: Washing 2-3 times in this manner removes all the background unbound MBD protein beads and improves the efficient binding of MBD protein-coupled beads with fragmented genomic DNA.</li>
</ol>
5. Binding MBD-biotin Beads with Fragmented Genomic DNA
<ol>
	<li>In a clean 1.5 mL DNase-free tube, add 100 &#181;L of 5x bead wash buffer and 180 &#181;L of the fragmented genomic DNA (step 2.3). Bring the final volume to 500 &#181;L using DNase free water.</li>
	<li>Add 380 &#181;L of DNase-free water to the remaining 20 &#181;L of the fragmented genomic DNA and freeze them. Use these samples as input DNA controls and precipitate them later along with the final eluted methylated DNA samples (see step 7.1).</li>
	<li>Place the tubes containing washed MBD-biotin beads (step 4.4) on a magnetic stand for 1 min and remove the liquid without disturbing the beads. Add 500 &#181;L of fragmented genomic DNA diluted in bead wash buffer.</li>
	<li>Seal all the tubes tightly with paraffin film and leave them overnight at 4 &#176;C on an end-to-end rotation stand at 8-10 rpm. 
	NOTE: The DNA and biotin-bead binding reaction can be done for 1 h at room temperature, but leaving it at 4 &#176;C overnight can improve the recovery of the final methylated DNA.</li>
</ol>
6. Removing the Unbound DNA and Eluting the Methylated DNA from the Beads
<ol>
	<li>After the DNA and MBD-bead binding reaction, place the tubes on the magnetic rack for 1 min to concentrate all the beads on the inner wall of the tube.</li>
	<li>Remove the supernatant liquid with a pipette without touching the beads and save this unbound DNA sample fraction on ice.</li>
	<li>Add 200 &#181;L of 1x bead wash buffer to the beads and place the tubes on the rotating stand for 3 min at room temperature. Place the tubes on the magnetic stand and remove the liquid. Repeat the wash another two times to remove the residual unbound DNA.</li>
	<li>After the final wash, add 200 &#181;L of high-salt elution buffer (2,000 mM NaCl), provided in the kit to elute the DNA.</li>
	<li>Place the tubes on the rotating stand for 15 min at room temperature. Place them on the magnetic stand for 1 min and use a pipette to carefully transfer the supernatant to a new, clean 1.5 mL tube.</li>
	<li>Add 200 &#181;L of high-salt elution buffer and repeat the elution by rotating the tubes at room temperature for 15 min. Add the second elute to the same tube containing the 200 &#181;L of the first elute. 
	NOTE: The final 400 &#181;L of total eluted DNA is now ready for ethanol precipitation to extract the purified DNA suitable for next-generation sequencing.</li>
</ol>
7. Ethanol Precipitation and the Enrichment of Methylated DNA
<ol>
	<li>Add 1 &#181;L of glycogen (20 &#181;g/&#181;L; included in the kit); 40 &#181;L of 3 M sodium acetate, pH 5.2; and 800 &#181;L of ice-cold absolute ethanol to 400 &#181;L of eluted DNA and also to the 400 &#181;L of input DNA samples prepared in step 5.2.</li>
	<li>Mix the tubes well by vortexing and incubate them at -80 &#176;C overnight.</li>
	<li>Centrifuge the tubes at 12,000 x g (maximum speed) and 4 &#176;C. Discard the supernatant carefully, without disturbing the pellet, add 500 &#181;L of 70% ethanol, and vortex the tubes.</li>
	<li>Centrifuge again at maximum speed for 15 min at 4 &#176;C and remove the supernatant by carefully using a pipette.Centrifuge the tubes at maximum speed for 1 min at room temperature and remove the residual ethanol completely using a pipette tip.</li>
	<li>Air-dry the pellet for 5 min at room temperature. Add 10 &#181;L of DNase-free water to the DNA pellet. Proceed to the methylated DNA quantification (step 7.6), MBD sequencing (step 7.7), and analysis (sections 8 and 9).</li>
	<li>Quantify the final recovered methylated DNA samples using a commercially available fluorometric quantitation kit (see the Materials Table), according the manufacturer&#39;s instructions.</li>
	<li>Note: Using this protocol, 30-50 ng of final DNA was recovered from each sample. DNA samples are ready to send on dry ice for downstream library construction and high-throughput MBD sequencing</li>
	<li>Perform DNA library constructionand high-throughput MBD sequencing using a commercial platform (Materials Table), as described in reference9. 
	NOTE: For the initial quality control of the final methylated DNA, perform library preparations using 50-bp pair-end sequencing for all samples. Process the raw data for post-sequencing quality control, as elaborated in step 8.1, and further process it using bioinformatics approaches and statistical methods, as described below (steps 8.2-9.6).</li>
</ol>
8. Bioinformatics Analysis Method 1: Identifying CLL-associated Differentially Methylated Regions (cllDMRs)
<ol>
	<li>Clean the obtained 49-bp reads (FASTQ format) for adaptors using available quality-control tools, such as Trimmomatic10 or Cutadapt. Crosscheck the quality of the trimmed reads using the FastQC toolkit: 
	java -jar trimmomatic.jar SE SAMPLE_uncleaned.fastq SAMPLE.fastq ILLUMINACLIP:adapters.fasta:2:30:10</li>
	<li>Align the cleaned FASTQ files with short-read genome aligner Bowtie against a reference genome11. Specify the parameters as below:
	<ol>
		<li>Allow up to two mismatches (Bowtie parameter: -v 2).</li>
		<li>Limit the reporting to the six best alignments per read (Bowtie parameter: -m 6) to control for multi-mapping reads. 
		bowtie -v 2 -a -m 6 HG19_INDEX -S SAMPLE.fastq &#62; SAMPLE.sam 
		NOTE: Convert the SAM files generated for individual samples after alignment into BAM using SAMtools. 
		samtools view -bS -o SAMPLE.bam SAMPLE.sam</li>
	</ol>
	</li>
	<li>Use the model-based analysis of ChIP-Seq (MACS) peak caller on aligned samples (BAM) to predict enriched/methylated regions from the CLL subgroups12. 
	NOTE: Comparison I: Use the Input sample as the control group and both the Normal and CLL patient samples as treatment groups. This step is to collect all negative peaks (peaks enriched in Input/background). Comparison II: Use the Normal sample group as the control group and the CLL patient sample group as the treatment group. Obtained positive peaks are CLL hyper-methylated regions, and negative peaks are CLL hypomethylated regions over normal or differentially methylated regions (DMRs). 
	macs14 -t SAMPLE_TREATMENT.bam -c SAMPLE_CONTROL.bam --format BAM -g hs</li>
	<li>Remove Comparison I background peaks from differentially methylated regions (DMRs) using BEDtools13. 
	bedtools subtract -a &#60;CLL_enriched_regions&#62; -b &#60;Input_enriched_regions&#62;</li>
	<li>Predict the percentage of repeat elements (SINE-Alu, LINE, etc.) enriched in DMRs.
	<ol>
		<li>Use fastacmd to extract the FASTA sequence of DMRs by the chromosomal coordinates from reference genome HG19. 
		fastacmd -d HG19_genome.fa -s Chromosome -L Start,End -l 50000 &#62; DMRs.fasta</li>
		<li>Use the RepeatMasker commandline tool to predict the percentage of repeat elements present in teh FASTA sequence of DMRs. 
		RepeatMasker -gc -gccalc -s -species human -html DMRs.fasta</li>
	</ol>
	</li>
	<li>Annotate the predicted DMRs using Homer &#34;annotatePeaks.pl&#34; with the available Ensembl [PMC4919035] or Gencode [PMID 22955987] transcript annotation (protein-coding and noncoding transcripts). 
	annotatePeaks.pl DMRs.bed &#60;GENOME&#62; -gtf &#60;Ensembl or Gencode GTF&#62; 
	NOTE: This provides information on the distribution of peaks over different genic regions (i.e., promoter, exon, intron, 3&#39; UTRs, and 5&#39; UTRs) and intergenic regions. Transcripts or genes associated with DMRs are called differentially methylated genes (DMGs).</li>
	<li>Use the enrichment tool with the updated or current functional database to find functions enriched by CLL DMGs (only protein-coding genes)14. 
	NOTE: Gene Set Clustering based on Functional Annotation (GeneSCF)14 is a real-time-based tool for functional enrichment analysis that uses updated KEGG and Gene Ontology as a reference database.</li>
</ol>
9. Bioinformatics Analysis Method 2: Identifying CLL-associated Significantly Differentially Methylated Regions (cll sigDMR&#39;s)
<ol>
	<li>Follow steps 8.1-8.5 from method I (section 8) and use read-count-based differential enrichment analysis by utilizing the available tools, as elaborated in steps 9.2 - 9.6, to add one more level of statistics to the analysis pipeline.</li>
	<li>Quantify the number of reads mapped to individual peaks or DMRs in Normal and CLL patient samples using &#34;featureCounts&#34; from the &#34;Subread&#34; package15. 
	subread/bin/featureCounts -Q 30 -F SAF -a DMRs.SAF -o DMRs_counts.table SAMPLE_TREATMENT.bam SAMPLE_CONTROL.bam 
	NOTE: A mapping quality filter can be introduced to avoid quantifying bad-quality reads (e.g.,-Q 30). For this step, prepare SAF files for the obtained DMRs. For more information on SAF file format, please use this link http://bioinf.wehi.edu.au/featureCounts/.</li>
	<li>Use a RAW read-count table containing the number of reads for the individual peaks in Normal and CLL patient sample groups in edgeR as the input16. 
	NOTE: Compare normal versus CLL patient sample groups to find sigDMRs. For differential enrichment analysis, follow the guide from edgeR for detailed step-by-step instructions (https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf).</li>
	<li>Filter the sigDMRs using the false discovery rate (FDR) and log-fold change predicted by edgeR16.</li>
	<li>Annotate the predicted sigDMR&#39;s using Homer &#34;annotatePeaks.pl&#34; with the available Ensembl or Gencode transcript annotation (protein-coding and noncoding transcripts). 
	annotatePeaks.pl sigDMRs.bed HG19_genome.fa -gtf &#60;Ensembl or Gencode GTF&#62; -CpG 
	NOTE: This provides information on the distribution of peaks over different genic regions (i.e., promoter, exon, intron, 3&#39; UTRs, and 5&#39; UTRs) and intergenic regions. Transcripts or genes associated with sigDMRs are called sigDMGs.</li>
	<li>Use an enrichment tool with the updated or current functional database to find functions enriched by CLL sigDMGs (only protein-coding genes)14. 
	NOTE: GeneSCF, one of the real-time tools for functional enrichment analysis, uses KEGG and Gene Ontology as reference databases.</li>
</ol>

<ol>
	<li>Kanduri, M., 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=Differential+genome-wide+array-based+methylation+profiles+in+prognostic+subsets+of+chronic+lymphocytic+leukemia.">Differential genome-wide array-based methylation profiles in prognostic subsets of chronic lymphocytic leukemia.</a> Blood. 115 (2), 296-305 (2010).</li><li>Cahill, 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=450K-array+analysis+of+chronic+lymphocytic+leukemia+cells+reveals+global+DNA+methylation+to+be+relatively+stable+over+time+and+similar+in+resting+and+proliferative+compartments.">450K-array analysis of chronic lymphocytic leukemia cells reveals global DNA methylation to be relatively stable over time and similar in resting and proliferative compartments.</a> Leukemia. 27 (1), 150-158 (2013).</li><li>Kanduri, M., 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+transcriptional+control+in+major+immunogenetic+subsets+of+chronic+lymphocytic+leukemia+exhibiting+subset-biased+global+DNA+methylation+profiles.">Distinct transcriptional control in major immunogenetic subsets of chronic lymphocytic leukemia exhibiting subset-biased global DNA methylation profiles.</a> Epigenetics. 7 (12), 1435-1442 (2012).</li><li>Kulis, M., 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=Epigenomic+analysis+detects+widespread+gene-body+DNA+hypomethylation+in+chronic+lymphocytic+leukemia.">Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia.</a> Nat Genet. 44 (11), 1236-1242 (2012).</li><li>Booth, M. 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=Quantitative+sequencing+of+5-methylcytosine+and+5-hydroxymethylcytosine+at+single-base+resolution.">Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution.</a> Science. 336 (6083), 934-937 (2012).</li><li>Yu, M., 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=Base-resolution+analysis+of+5-hydroxymethylcytosine+in+the+mammalian+genome.">Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome.</a> Cell. 149 (6), 1368-1380 (2012).</li><li>Subhash, S., Andersson, P. O., Kosalai, S. T., Kanduri, C., Kanduri, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+DNA+methylation+profiling+reveals+new+insights+into+epigenetically+deregulated+protein+coding+and+long+noncoding+RNAs+in+CLL.">Global DNA methylation profiling reveals new insights into epigenetically deregulated protein coding and long noncoding RNAs in CLL.</a> Clin Epigenetics. 8, 106 (2016).</li><li>Hallek, M., 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=Guidelines+for+the+diagnosis+and+treatment+of+chronic+lymphocytic+leukemia:+a+report+from+the+International+Workshop+on+Chronic+Lymphocytic+Leukemia+updating+the+National+Cancer+Institute-Working+Group+1996+guidelines.">Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia updating the National Cancer Institute-Working Group 1996 guidelines.</a> Blood. 111 (12), 5446-5456 (2008).</li><li>De Meyer, T., 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=Quality+evaluation+of+methyl+binding+domain+based+kits+for+enrichment+DNA-methylation+sequencing.">Quality evaluation of methyl binding domain based kits for enrichment DNA-methylation sequencing.</a> PLoS One. 8 (3), e59068 (2013).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics. 30 (15), 2114-2120 (2014).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biol. 10 (3), R25 (2009).</li><li>Zhang, Y., 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=Model-based+analysis+of+ChIP-Seq+(MACS).">Model-based analysis of ChIP-Seq (MACS).</a> Genome Biol. 9 (9), R137 (2008).</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 (6), 841-842 (2010).</li><li>Subhash, S., Kanduri, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GeneSCF:+a+real-time+based+functional+enrichment+tool+with+support+for+multiple+organisms.">GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.</a> BMC Bioinformatics. 17 (1), 365 (2016).</li><li>Liao, Y., Smyth, G. K., Shi, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=featureCounts:+an+efficient+general+purpose+program+for+assigning+sequence+reads+to+genomic+features.">featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.</a> Bioinformatics. 30 (7), 923-930 (2014).</li><li>Robinson, M. D., McCarthy, D. J., Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26 (1), 139-140 (2010).</li><li>Martinelli, 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=ANGPT2+promoter+methylation+is+strongly+associated+with+gene+expression+and+prognosis+in+chronic+lymphocytic+leukemia.">ANGPT2 promoter methylation is strongly associated with gene expression and prognosis in chronic lymphocytic leukemia.</a> Epigenetics. 8 (7), 720-729 (2013).</li><li>Kopparapu, P. 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=Epigenetic+silencing+of+miR-26A1+in+chronic+lymphocytic+leukemia+and+mantle+cell+lymphoma:+Impact+on+EZH2+expression.">Epigenetic silencing of miR-26A1 in chronic lymphocytic leukemia and mantle cell lymphoma: Impact on EZH2 expression.</a> Epigenetics. 11 (5), 335-343 (2016).</li><li>Robinson, M. D., 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=Evaluation+of+affinity-based+genome-wide+DNA+methylation+data:+effects+of+CpG+density,+amplification+bias,+and+copy+number+variation.">Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation.</a> Genome Res. 20 (12), 1719-1729 (2010).</li></ol>

The authors have nothing to disclose.

MBD-seq is a cost-effective, immunoprecipitation-based technique that can be used to study methylation patterns with complete genome-wide coverage. Both MeDIP seq (methylated DNA immunoprecipitation followed by sequencing) and MBD-seq result in the enrichment of CpG-rich methylated DNA. However, MBD-seq shows more affinity towards binding to highly CpG-rich regions when compared to MeDIP seq19. Using a methyl-binding enrichment kit, one can fractionate the DNA into high-CpG- and low-CpG-rich regio...

The use of next-generation sequencing techniques to analyze global DNA methylation profiles has been increasingly popular during recent years. Genome-wide methylation assays, including microarray- and non-microarray-based methods, were developed based on the following: the bisulfite conversion of genomic DNA, methylation-sensitive restriction enzyme digestions, and the immunoprecipitation of methylated DNA using methyl CpG-specific antibodies.
Aberrant DNA methylation is one of the hallmarks of leukemia and lymphomas, includingchronic lymphocytic leukemia (CLL). Earlier, several groups including ours characterized the DNA methylation profiles of different CLL prognostic subgroups and normal, healthy B-cell controls using the bisulfite conversion of genomic DNA, followed by micro-array-based methods or whole-genome sequencing1,2,3,4. The bisulfite conversion of genomic DNA leads to the deamination of unmodified cytosines to uracil, leaving the modified methylated cytosines in the genome. Once converted, the methylation status of the DNA can be determined by PCR amplification and sequencing using different quantitative or qualitative methods, such as micro-array-based or whole-genome bisulfite sequencing (WGBS). Although bisulfite conversion-based methods have many advantages and are widely used in different cancer types to analyze DNA methylation levels, there are a few drawbacks associated with this technique. WGBS sequencing allows single-base-pair resolution with lower amounts of DNA and is the best suitable option for analyzing a large number of samples. However, this method fails to differentiate the modifications between the 5 mC and 5 hmC levels in the genome5,6. Additionally, microarray-based methods do not offer complete coverage of the genome.
In a recent study from our laboratory7, immunoprecipitation based methods, rather than bisulfite conversion, were used to identify highly CpG-rich, differentially methylated regions on a global scale in CLL patients and normal healthy controls. Inmethyl-CpG-binding domain (MBD)next-generation sequencing (MBD-seq), the enrichment of double-stranded fragmented DNA depends upon the degree of CpG methylation. This method can overcome the drawbacks of the bisulfite conversion method and can also provide genome-wide coverage of CpG methylation in an unbiased and PCR-independent manner. Additionally, unlike bisulfite conversion-based microarray methods, MBD-seq can be used to analyze the methylation status of repetitive elements, such as long interspersed nuclear elements (LINEs), short interspersed nuclear elements (SINEs), long terminal repeats (LTRS), etc. However, compared to bisulfite conversion methods, an MBD-seq protocol requires a relatively large amount of input DNA. Also, the quality of the sequencing reads and the data depend on the specificity, affinity, and quality of the antibodies used.
The current study explains a detailed MBD-seq protocol to enrich methylated DNA for next-generation sequencing. It uses a commercial methylated DNA binding enrichment kit (listed in the Materials Table), as well as a computational pipeline to visualize and interpret methylation sequencing data to identify CLL-specific hyper- and hypomethylated regions compared to normal healthy controls. Basically, this method makes use of the ability of the MBD of the human MBD2 protein interaction with methylated CpGs to extract DNA enriched with methylated CpGs, and this is followed by the high-throughput sequencing of methylated DNA.

<table><tbody><tr><td>Dneasy Blood and tissue kit</td><td>Qaigen</td><td>69504</td><td></td></tr><tr><td>Lymphoprep solution</td><td>A X I S-S H I E L D</td><td>1114544</td><td></td></tr><tr><td>Nano drop 2000</td><td>Thermo Fischersceintific</td><td></td><td></td></tr><tr><td>TE buffer pH 8</td><td>Sigma aldrich</td><td>93283</td><td></td></tr><tr><td>Bioruptor standard sonication device</td><td>Diagenode</td><td>UCD-200</td><td></td></tr><tr><td>TPX bioruptor tubes 1.5 mL</td><td>Diagenode</td><td>C30010010-300</td><td></td></tr><tr><td>3 M Sodium acetate</td><td>Diagenode</td><td>C03030002</td><td></td></tr><tr><td>E-gel iBase safe imager combo kit</td><td>Thermo Fischersceintific</td><td>G6465EU</td><td></td></tr><tr><td>E-gel 2% Agarose gels</td><td>Thermo Fischersceintific</td><td>G441002</td><td></td></tr><tr><td>Methylminer Methylated DNA enrichment kit</td><td>Thermo Fischersceintific</td><td>ME10025</td><td></td></tr><tr><td>Labquake Tube Shaker/Rotators</td><td>Thermo Fischersceintific</td><td>415110</td><td></td></tr><tr><td>Dynal MPC-S</td><td>Thermo Fischersceintific</td><td>A13346</td><td></td></tr><tr><td>Vortex mixer</td><td>VWR</td><td>12620-848</td><td></td></tr><tr><td>Absolute Ethanol</td><td>Any company</td><td></td><td></td></tr><tr><td>70% Ethanool</td><td>Any company</td><td></td><td></td></tr><tr><td>DNAse free water</td><td>Milli Q</td><td></td><td></td></tr><tr><td>DNA precipitant (3M sodium acetate)</td><td>Diagenode</td><td>C03030002</td><td></td></tr><tr><td>Safe seal 1.5 mL eppendorf tubes</td><td>Eppendorf</td><td>4036-3204</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>Thermo Fischersceintific</td><td>Q32851</td><td></td></tr><tr><td>Qubit 0.5 mL tubes</td><td>Thermo Fischersceintific</td><td>Q32856</td><td></td></tr><tr><td>Qubit</td><td>Thermo Fischersceintific</td><td>Q32866</td><td></td></tr><tr><td>Illumina Hiseq2000 Platform</td><td>Illumina</td><td></td><td></td></tr><tr><td>Water&amp;nbsp; Bath</td><td>Grant</td><td></td><td></td></tr><tr><td>Heat block</td><td>grant</td><td></td><td></td></tr><tr><td>Tube rotater</td><td>Labquake</td><td></td><td></td></tr></tbody></table>

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.

MBD-seq was recently performed on CLL patients and matched, normal, healthy controls to identify CLL-specific differentially hyper- and hypomethylated genes7. The experimental and bioinformatic pipeline used for analyzing the data generated from CLL and normal healthy samples are shown in Figure 1A and 1B. These analyses identified several CLL-specific differentially methylated regions (cllDMRs), which were significant...

Watch this Scientific Journal Video about Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients at JoVE.com

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

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 ...

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Genetics

9.8K Views.  Gothenburg University. 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.

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

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).

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 ...

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of enzymatic oxidation of 5mC to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) as well as detection of N6-methyladenine (6mA) in the DNA of multicellular organisms provided additional degrees of complexity to the epigenetic research. According to a growing body of experimental evidence, these novel DNA modifications may play specific roles in different cellular and developmental processes. Importantly, as some of these marks (e. g. 5hmC, 5fC and 5caC) exhibit tissue- and developmental stage-specific occurrence in vertebrates, immunochemistry represents an important tool allowing assessment of spatial distribution of DNA modifications in different biological contexts. Here the methods for computational analysis of DNA modifications visualized by immunostaining followed by confocal microscopy are described. Specifically, the generation of 2.5 dimension (2.5D) signal intensity plots, signal intensity profiles, quantification of staining intensity in multiple cells and determination of signal colocalization coefficients are shown. Collectively, these techniques may be operational in evaluating the levels and localization of these DNA modifications in the nucleus, contributing to elucidating their biological roles in metazoans.

Newly discovered oxidized forms of 5-methylcytosine (oxi-mCs), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) may represent distinct DNA modifications with unique functional roles. Here a semi-quantitative workflow for visualization of oxi-mCs' spatial distribution, signal intensity profiling and colocalization is described.

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of ...

LINE-1 Methylation Analysis in Mesenchymal Stem Cells Treated with Osteosarcoma-Derived Extracellular Vesicles

Methylation-specific probe amplification (MSPA) is a simple and robust technique that can be used to detect relative differences in methylation levels of DNA samples. It is resourceful, requires small amounts of DNA, and takes around 4&#8211;5 h of hands-on work. In the presented technique, DNA samples are first denatured then hybridized to probes that target DNA at either methylated or reference sites as a control. Hybridized DNA is separated into parallel reactions, one undergoing only ligation and the other undergoing ligation followed by HhaI-mediated digestion at unmethylated GCGC sequences. The resultant DNA fragments are amplified by PCR and separated by capillary electrophoresis. Methylated GCGC sites are not digested by HhaI and produce peak signals, while unmethylated GCGC sites are digested and no peak signals are generated. Comparing the control-normalized peaks of digested and undigested versions of each sample provides the methylation dosage ratio of a DNA sample. Here, MSPA is used to detect the effects of osteosarcoma-derived extracellular vesicles (EVs) on the methylation status of long interspersed nuclear element-1 (LINE-1) in mesenchymal stem cells. LINE-1s are repetitive DNA elements that typically undergo hypomethylation in cancer and, in this capacity, may serve as a biomarker. Ultracentrifugation is also used as a cost-effective method to separate extracellular vesicles from biological fluids (i.e., when preparing EV-depleted fetal bovine serum [FBS] and isolating EVs from osteosarcoma conditioned media [differential centrifugation]). For methylation analysis, custom LINE-1 probes are designed to target three methylation sites in the LINE-1 promoter sequence and seven control sites. This protocol demonstrates the use of MSPA for LINE-1 methylation analysis and describes the preparation of EV-depleted FBS by ultracentrifugation.

Described here is the use of a methylation-specific probe amplification method to analyze methylation levels of LINE-1 elements in mesenchymal stem cells treated with osteosarcoma-derived extracellular vesicles. Ultracentrifugation, a popular procedure for separating extracellular vesicles from fetal bovine serum, is also demonstrated.

Methylation-specific probe amplification (MSPA) is a simple and robust technique that can be used to detect relative differences in methylation levels of ...

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is a useful measure to provide an accurate analysis of the methylation status of gastrointestinal (GI) malignancies. Given the multiple potential translational uses of DNA methylation analysis, practicing clinicians and others new to DNA methylation studies need to be able to understand step by step how these genome-wide analyses are performed. The goal of this protocol is to provide a detailed description of how this method is used for the biomarker identification in GI malignancies. Importantly, we describe three critical steps that are needed to obtain accurate results during genome-wide analysis. Clearly and concisely written, these three methods are often lacking and not noticeable to those new to epigenetic studies. We used 48 samples of a GI malignancy (gastric cancer) to highlight practically how genome-wide DNA methylation analysis can be performed for GI malignancies.

Herein, we describe a procedure for genome-wide analysis of DNA methylation in gastrointestinal cancers. The procedure is of relevance to studies that investigate relationships between methylation patterns of genes and factors contributing to carcinogenesis in gastrointestinal cancers.

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is ...

Cancer Research

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid storage processes contributing to the development of the disease. We demonstrate a complete protocol from selection to epigenetic data analysis of patients with and without obesity. All steps from the protocol were tested and validated in a pilot study. 32 women participated in the study, in which 15 individuals were classified with obesity according to Body Mass Index (BMI) (45.1 &#177; 5.4 kg/m2); and 17 individuals were classified without obesity according to BMI (22.6 &#177; 1.8 kg/m2). In the group with obesity, 564 CpG sites related to fat mass were identified by linear regression analysis. The CpG sites were in the promoter regions. The differential analysis found 470 CpGs hypomethylated and 94 hypermethylated sites in individuals with obesity. The most hypomethylated enriched pathwayswere in the RUNX, WNT signaling, and response to hypoxia. The hypermethylated pathways were related to insulin secretion, glucagon signaling, and Ca2+. We conclude that the protocol effectively identified DNA methylation patterns and trait-related DNA methylation. These patterns could be associated with altered gene expression, affecting adipogenesis and lipid storage. Our results confirmed that an obesogenic lifestyle could promote epigenetic changes in human DNA.

The present study describes the workflow to manage DNA methylation data obtained by microarray technologies. The protocol demonstrates steps from sample preparation to data analysis. All procedures are described in detail, and the video shows the significant steps.

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid ...

DNA Methylation: Bisulphite Modification and Analysis

Epigenetics describes the heritable changes in gene function that occur independently to the DNA sequence. The molecular basis of epigenetic gene regulation is complex, but essentially involves modifications to the DNA itself or the proteins with which DNA associates. The predominant epigenetic modification of DNA in mammalian genomes is methylation of cytosine nucleotides (5-MeC). DNA methylation provides instruction to gene expression machinery as to where and when the gene should be expressed. The primary target sequence for DNA methylation in mammals is 5'-CpG-3' dinucleotides (Figure 1). CpG dinucleotides are not uniformly distributed throughout the genome, but are concentrated in regions of repetitive genomic sequences and CpG "islands" commonly associated with gene promoters (Figure 1). DNA methylation patterns are established early in development, modulated during tissue specific differentiation and disrupted in many disease states including cancer. To understand the biological role of DNA methylation and its role in human disease, precise, efficient and reproducible methods are required to detect and quantify individual 5-MeCs.
This protocol for bisulphite conversion is the "gold standard" for DNA methylation analysis and facilitates identification and quantification of DNA methylation at single nucleotide resolution. The chemistry of cytosine deamination by sodium bisulphite involves three steps (Figure 2). (1) Sulphonation: The addition of bisulphite to the 5-6 double bond of cytosine (2) Hydrolic Deamination: hydrolytic deamination of the resulting cytosine-bisulphite derivative to give a uracil-bisulphite derivative (3) Alkali Desulphonation: Removal of the sulphonate group by an alkali treatment, to give uracil. Bisulphite preferentially deaminates cytosine to uracil in single stranded DNA, whereas 5-MeC, is refractory to bisulphite-mediated deamination. Upon PCR amplification, uracil is amplified as thymine while 5-MeC residues remain as cytosines, allowing methylated CpGs to be distinguished from unmethylated CpGs by presence of a cytosine "C" versus thymine "T" residue during sequencing.
DNA modification by bisulphite conversion is a well-established protocol that can be exploited for many methods of DNA methylation analysis. Since the detection of 5-MeC by bisulphite conversion was first demonstrated by Frommer et al.1 and Clark et al.2, methods based around bisulphite conversion of genomic DNA account for the majority of new data on DNA methylation. Different methods of post PCR analysis may be utilized, depending on the degree of specificity and resolution of methylation required. Cloning and sequencing is still the most readily available method that can give single nucleotide resolution for methylation across the DNA molecule.

The gold standard for DNA methylation analysis is genomic sequencing of bisulphite converted DNA. This method takes advantage of the increased sensitivity of cytosine compared with 5-methylcytosine (5-MeC) to bisulphite deamination under acidic conditions. Unmethylated cytosines can be distinguished from methylated cytosines after PCR amplification of the target genomic DNA.

Epigenetics describes the heritable changes in gene function that occur independently to the DNA sequence. The molecular basis of epigenetic gene ...

Biology

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine methylation patterns in cells. Reduced representation of whole genome bisulfite sequencing was developed to detect quantitative base pair resolution cytosine methylation patterns at GC-rich genomic loci. This is accomplished by combining the use of a restriction enzyme followed by bisulfite conversion. Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) increases the biologically relevant genomic loci covered and has been used to profile cytosine methylation in DNA from human, mouse and other organisms. ERRBS initiates with restriction enzyme digestion of DNA to generate low molecular weight fragments for use in library preparation. These fragments are subjected to standard library construction for next generation sequencing. Bisulfite conversion of unmethylated cytosines prior to the final amplification step allows for quantitative base resolution of cytosine methylation levels in covered genomic loci. The protocol can be completed within four days. Despite low complexity in the first three bases sequenced, ERRBS libraries yield high quality data when using a designated sequencing control lane. Mapping and bioinformatics analysis is then performed and yields data that can be easily integrated with a variety of genome-wide platforms. ERRBS can utilize small input material quantities making it feasible to process human clinical samples and applicable in a range of research applications. The video produced demonstrates critical steps of the ERRBS protocol.

Enhanced Reduced Representation Bisulfite Sequencing is a method for the preparation of sequencing libraries for DNA methylation analysis based on restriction enzyme digestion combined with cytosine bisulfite conversion. This protocol requires 50 ng of starting material and yields base pair resolution data at GC-rich genomic regions.

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine ...

Targeted DNA Methylation Analysis by Next-generation Sequencing

The role of epigenetic processes in the control of gene expression has been known for a number of years. DNA methylation at cytosine residues is of particular interest for epigenetic studies as it has been demonstrated to be both a long lasting and a dynamic regulator of gene expression. Efforts to examine epigenetic changes in health and disease have been hindered by the lack of high-throughput, quantitatively accurate methods. With the advent and popularization of next-generation sequencing (NGS) technologies, these tools are now being applied to epigenomics in addition to existing genomic and transcriptomic methodologies. For epigenetic investigations of cytosine methylation where regions of interest, such as specific gene promoters or CpG islands, have been identified and there is a need to examine significant numbers of samples with high quantitative accuracy, we have developed a method called Bisulfite Amplicon Sequencing (BSAS). This method combines bisulfite conversion with targeted amplification of regions of interest, transposome-mediated library construction and benchtop NGS. BSAS offers a rapid and efficient method for analysis of up to 10 kb of targeted regions in up to 96 samples at a time that can be performed by most research groups with basic molecular biology skills. The results provide absolute quantitation of cytosine methylation with base specificity. BSAS can be applied to any genomic region from any DNA source. This method is useful for hypothesis testing studies of target regions of interest as well as confirmation of regions identified in genome-wide methylation analyses such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing, and methylated DNA immunoprecipitation sequencing.

Bisulfite amplicon sequencing (BSAS) is a method for quantifying cytosine methylation in targeted genomic regions of interest. This method uses bisulfite conversion paired with PCR amplification of target regions prior to next-generation sequencing to produce absolute quantitation of DNA methylation at a base-specific level.

The role of epigenetic processes in the control of gene expression has been known for a number of years. DNA methylation at cytosine residues is of ...

DNA Methylation Analysis

Methylation at CpG dinucleotides is a chemical modification of DNA hypothesized to play important roles in regulating gene expression. In particular, the methylation of clusters of methylation sites, called &#8220;CpG islands&#8221;, near promoters and other gene regulatory elements may contribute to the stable silencing of genes, for example, during epigenetic processes such as genomic imprinting and X-chromosome inactivation. At the same time, aberrant CpG methylation has been shown to be associated with cancer.In this video, the biological functions and mechanisms of DNA methylation will be presented, along with various techniques used to identify methylation sites in the genome. We will then examine the steps of bisulfite analysis, one of the most commonly used methods for detecting DNA methylation, as well as several applications of this technique.

Methylation at CpG dinucleotides is a chemical modification of DNA hypothesized to play important roles in regulating gene expression. In particular, the ...

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5-Methylcytosine Dot Blot to Determine the Extent of DNA Methylation

Source: Jia, Z. et al., <a href="https://app.jove.com/v/55565">A 5-mC Dot Blot Assay Quantifying the DNA Methylation Level of Chondrocyte Dedifferentiation In Vitro.</a>&#160;J. Vis. Exp.&#160;(2017)
This video describes the technique of dot-blot to determine the level of DNA methylation in human chondrocytes in vitro via the blotting of DNA samples on a nylon membrane and the subsequent visualization using monoclonal antibodies. This helps to determine the chondrocytes&#39; phenotype at various stages in culture.