We thank Marcus Coyle, Curtis Gumbs, SMF core at MDACC for sequencing support. The work described in this article was supported by grants from the NIH grant (CA016672) to SMF Core and NCI awards (1K99CA160578 and R00CA160578) to K. R.

All clinical specimens were obtained following the guidelines of Institutional Review Board.
1. Buffer Preparation
<ol>
	<li>Make 200 mL of TE buffer (10 mM Tris-HCl, 10 mM ethylenediaminetetraacetic acid (EDTA) pH 8.0).</li>
	<li>Make 200 mL of STE buffer (10 mM Tris-HCl, 10 mM EDTA pH 8.0, 140 mM NaCl).</li>
	<li>Make 200 mL of 2.0 M glycine (37.52 g of glycine in 200 mL of water) and heat it to 65 &#176;C.</li>
	<li>Make 200 mL of 5% sodium deoxycholate (DOC) solution (10 g of DOC in 200 mL of water).</li>
	<li>Make 500 mL of ChIP harvest buffer (12 mM Tris-Cl, 0.1x phosphate buffered saline (PBS), 6 mM EDTA, 0.5% sodium dodecyl sulfate (SDS)).</li>
	<li>Make 500 mL of ChIP dilution buffer (10 mM Tris-Cl, 140 mM NaCl, 0.1% DOC, 1% Triton-X, 1 mM EDTA).</li>
	<li>Make 500 mL of RIPA wash buffer (STE, 1% Triton x-100, 0.1% SDS, 0.1% DOC).</li>
	<li>Make 500 mL of RIPA/500 wash buffer (RIPA buffer + 360 mM NaCl).</li>
	<li>Make 500 mL of LiCL wash buffer (TE, 250 mM LiCl, 0.5% NP-40, 0.5% DOC).</li>
	<li>Make 500 mL of Direct Elution buffer: 10 mM Tris-Cl pH 8.0, 5 mM EDTA, 300 mM NaCl, 0.5% SDS.</li>
	<li>Make 50 mL of Antibody Binding/Blocking buffer (PBS + 0.1% TWEEN-20 + 0.2% IgG-free BSA).</li>
</ol>
2. Tissue/cell Line Processing and Cross-linking
<ol>
	<li>If tissue is flash frozen, dethaw it on ice prior to dissociation.</li>
	<li>Disassociate 50 mg of tissue (~8 mg per histone modification antibody) manually in 2 mL of Hanks&#39; Balanced Salt Solution (HBSS) using a sterile razor blade. Mince the tissue into 3 to 4 mm pieces for approximately 5 min in a sterile tissue culture dish.</li>
	<li>Transfer the tissue and HBSS solution into a dissociator tube and add another 8 mL of HBSS. Further disassociate the tissue using a dissociator until homogenized.</li>
	<li>For melanoma tumors, place the tubes in a dissociator and run the following cycles each one time in this order: h_tumor_01.01, h_tumor_02.01, h_tumor_03.01 and m_heart_02.01.</li>
	<li>Scrape 21 &#215; 106 cells in 10 mL of medium (~3 &#215; 106 per histone modification and input; it can be lowered to 100,000 cells per mark for rare populations) grown in a standard tissue culture dish and collect them in a 15 mL conical tube. 
	Note: The media used for representative melanoma cell line WM115 is DMEM supplemented with 10% Fetal Bovine Serum and 5% penicillin/streptomycin.</li>
	<li>Crosslink the tissue/cells using a 1% final formaldehyde concentration by adding 200 &#181;L of 16% formaldehyde per 3 mL HBBS for tissue (or 3 mL of medium for cells). Shake the mixture at 10 rpm using a mixer for exactly 10 min at 37 &#176;C. 
	Caution: Formaldehyde is toxic and should be handled in an appropriate fume hood.</li>
	<li>Add 200 &#181;L of 2.0 M glycine per 3 mL of sample and continue shaking at 10 rpm for another 5 min at 37 &#176;C. 
	NOTE: Glycine acts as a quencher. Make fresh glycine solution every month as the pH pf the solution tends over time.</li>
	<li>Spin the samples at 934 x g for 5 min at 4 &#176;C using a benchtop centrifuge. Remove the supernatant, add 5 mL of ice cold PBS, centrifuge the samples again at 934 x g and remove the supernatant.</li>
	<li>Flash freeze the pellet and store it at -80 &#176;C for further processing.</li>
</ol>
3. Tissue Lysis, Sonication, and Antibody Preparation
<ol>
	<li>Dissolve 1 protease inhibitor tablet per 10 mL of ChIP harvest buffer.</li>
	<li>Add 300 &#181;L of ChIP harvest buffer with protease inhibitors per 50 mg of tissue and allow 30 min of lysis on ice. 
	NOTE: Add 300 &#181;L of ChIP harvest buffer with protease inhibitors per 1 X 107 of WM115 melanoma cell lines.</li>
	<li>While the cells are lysing, turn on the the waterbath disruptor and associated cooling system and allow temperature to reach 4 &#176;C. Place the sonicator tubes in the waterbath disruptor and sonicate the melanoma tissues for 60 cycles at 30 s on and 30 s off to obtain chromatin fragments of ~200 - 600 base pairs (bp). 
	NOTE: Sonication time can differ between tissue type and should be adjusted accordingly. This is a critical step and should be optimized in pilot experiment before proceeding to immunoprecipitation.</li>
	<li>During sonication, wash 20 &#181;L of protein G magnetic beads per antibody per sample three times using 1000 &#181;L of binding/blocking buffer (PBS + 0.1% TWEEN-20 + 0.2% BSA).</li>
	<li>For ~8 mg of melanoma tissue, incubate 3 &#181;g of each histone antibody in 100 &#181;L of binding/ blocking buffer for 2 h at 4 &#176;C with rotation using a tube revolver. 12 samples for a singular antibody contain 240 &#181;L of beads, 36 &#181;g of antibody and 1200 &#181;L of binding/blocking buffer. 
	NOTE: For 3 &#215; 106 cells, use 5 &#181;g of each antibody and 30 &#181;L of Protein G magnetic beads per antibody. Antibody and Protein-G beads should be titrated if different amount of tissue is being used. This protocol illustrates immunoprecipitation conditions for the described six histone modifications (Table of Materials), however, antibody concentrations should be optimized for other modifications and transcription factors.</li>
	<li>To determine fragment size, remove 20 &#181;L of sonciated chromatin solution and add an elution buffer master mix (room temperature) containing 44 &#181;L of direct elution buffer, 1 &#181;L of RNase (20mg/mL), and 5 &#181;L Proteinase K (20mg/mL) per sample. Incubate the samples in a PCR thermal cycler for at least 2 h at 50 &#176;C. Purify the sample using a PCR purification kit following the manufacturers instructions.</li>
	<li>Ensure chromatin is sufficiently sheared by determining fragment size using a high sensitivity DNA electropherogram instrument before proceeding to step 3.6. Turn on the electropherogram instrument.</li>
	<li>Load 2 &#181;L of high sensitivity buffer reagent into each well of an 8-well optical tube strip. Load 2 &#181;L of high sensitivity ladder into the first well and 2 &#181;L of purified samples in the remaining wells.</li>
	<li>Place optical tube caps on tube stips and vortex the samples at 2000 rpm for 1 min. Open the lid and insert a new high sensitivity screen tape and box of loading tips. Remove the strip caps and load the samples into electropherogram instrument.</li>
	<li>Open analysis software, highlight the first thirteen spaces on the screen tape and press the Start tab in the bottom right corner. Chromatin fragments ranging between ~200 - 1000 bp are suitable for ChIP.</li>
	<li>Transfer the sonicated solution to sterile tubes and spin the tubes at 21,130 x g using a table top centrifuge for 15 min at 4 &#176;C. Transfer the supernatant to new tubes.</li>
	<li>Determine the total volume of the supernatant and remove 10% of the total solution for Input control and store it at 4 &#176;C.</li>
</ol>
4. Chromatin Immunoprecipitation
<ol>
	<li>Dissolve 2 protease inhibitor tablets per 20 mL of ChIP dilution buffer.</li>
	<li>After sonication dilute the remaining sample 5 times using ChIP dilution buffer to bring the SDS levels down to 0.1%. Dilute 270 &#181;L of remaining supernatant with 1080 &#181;L of ChIP dilution buffer to obtain a final total volume of 1350 &#181;L.</li>
	<li>When the incubation of antibody and protein G magnetic beads finishes, place the tube on a magnet and remove the supernatant.</li>
	<li>With the tube still sitting on the magnet, wash the beads once by adding 750 &#181;L of ChIP dilution buffer with protease inhibitors. 
	NOTE: It is important not to disturb the beads or rotate the tubes during this step.</li>
	<li>Remove ChIP dilution buffer wash and resuspend the beads in 240 &#181;L of the same ChIP dilution buffer. Aliquot 20 &#181;L of beads into 12 separate tubes, each of which is used for a separate tissue sample.</li>
	<li>Aliquot the sonicated material evenly to protein G beads with specific antibody and tumble using a tube revolver overnight at 4 &#176;C.</li>
</ol>
5. Washing and Reverse Crosslinking of Immunoprecipitated DNA-protein Complexes
<ol>
	<li>The next morning after reverse crosslinking, transfer the antibody-protein solution to a 96-well plate and place it on the magnetic stand. Allow the beads to adhere for at least 30 s and remove the supernatant.</li>
	<li>Wash the beads 5 times with 150 &#181;L of ice-cold RIPA wash buffer using a multi-channel pipet. During the wash steps, do not pipet the beads, but move the magnet continuously from right to left for 30 s per wash.</li>
	<li>Wash the samples twice with 150 &#181;L of ice-cold RIPA-500 wash buffer.</li>
	<li>Wash the samples twice with 150 &#181;L of ice-cold LiCl wash buffer.</li>
	<li>Wash the samples once with 150 &#181;L of ice-cold TE wash buffer and remove TE buffer immediately. This step can be omitted for low input applications.</li>
	<li>Add Input control from step 3.7 into fresh wells of the 96-well plate containing the ChIP DNA samples.</li>
	<li>After the washing steps, add an elution buffer master mix (room temperature) containing 44 &#181;L of direct elution buffer, 1 &#181;L of RNase (20mg/mL) and 5 &#181;L Proteinase K (20mg/mL) per ChIP and Input sample for reverse crosslinking.</li>
	<li>Incubate the samples using a PCR thermal cycler for 4 h at 37 &#176;C, 4 h at 50 &#176;C and 8 - 16 h at 65 &#176;C.</li>
</ol>
6. Purification and Quantification of Precipitated DNA
<ol>
	<li>The next morning, place the samples back on the magnet and transfer supernatant to a new 96-well PCR plate which contains the immunoprecipitated DNA.</li>
	<li>Add 2.3x paramagnetic beads to solution (115 &#181;L for 50 &#181;L of solution) and carefully pipet up and down 25 times using a multichannel pipet. The solution will become homogenous if properly mixed.</li>
	<li>Incubate the solution at room temperature off the magnet for 4 min. Place the samples back on the magnet and incubate at room temperature for another 4 min. Discard the supernatant.</li>
	<li>While leaving the samples on the magnet, add 150 &#181;L of 70% ethanol (vol/vol) and incubate at room temperature for 30 s without disturbing the beads.</li>
	<li>Remove the ethanol and repeat the wash. Allow the paramagnetic beads to air dry on the magnet for 5 min. 
	NOTE: Remove all the ethanol after the second wash as remaining ethanol will interfere with purification.</li>
	<li>Remove the samples from the magnet and add 30 &#181;L of 10 mM Tris-Cl pH 8.0. Mix by pipetting 25 times and incubate the samples at room temperature off the magnet for 4 min.</li>
	<li>Place the samples back on the magnet and incubate at room temperature for another 4 min. Transfer 20 &#181;L of each sample to a new 96-well plate for library preparation. Store the remaining 10 &#181;L of ChIP DNA in case of any potential issues with generation of the libraries.</li>
	<li>At this stage, quantify ChIP DNA before library preparation. The DNA concentration is measured with high-sensitivity DNA reagents. Determine the size distribution of precipitated DNA using a high sensitivity DNA electropherogram instrument proceeding to step 7.1.</li>
</ol>
7. Library Generation using the NEBNext Ultra II DNA Library Preparation Kit
<ol>
	<li>Generate libraries for NGS sequencing using DNA Library Preparation following the recommended protocol from the manufacturer. All the reactions are performed in 96-well plates and incubations are performed in a PCR thermal cycler with the lid temperature set &#62; 100 &#176;C and the volume set at 50 &#181;L.</li>
	<li>For indexes, Multiplex Oligos for NGS platform are used. Perform a total of 10x cycles for PCR amplification using the following settings: initial denaturation at 98 &#176;C for 30 s, denaturation at 98 &#176;C for 10 s, annealing/extension at 65 &#176;C for 30 s (10x cycles), final extension 65 &#176;C for 5 min and hold at 4&#176;C. 
	NOTE: Table 1 contains the primers used for PCR amplification.</li>
	<li>After PCR amplification, remove the samples and bring total volume to 100 &#181;L using nuclease-free water. Perform double-sided paramagnetic size selection (0.55x/0.8x) by first adding 55 &#181;L of beads and mix by pipetting 25 times. Incubate the samples at room temperature off the magnet for 4 min.</li>
	<li>Place the samples back on a magnet and incubate at room temperature for another 4 min. This step is used to retain large fragments on the beads that are unsuitable for sequencing.</li>
	<li>Transfer the supernatant to a new 96-well PCR plate. Do not discard supernatant as this contains partially purified DNA.</li>
	<li>Add another 25 &#181;L of paramagnetic beads and mix by pipetting 25 times and incubate at room temperature off the magnet for 4 min.</li>
	<li>Place samples back on the magnet and incubate at room temperature for another 4 min and discard the supernatant. This step is used to retain fragments of ~200 to 600 bp that will be used for finalized libraries.</li>
	<li>While leaving the samples on the magnet, add 150 &#181;L of 70% ethanol (v/v) and allow incubating for 30 s without disturbing the beads (do not move while on the magnet). Remove the ethanol and repeat the wash a second time.</li>
	<li>Allow the paramagnetic beads to air dry on the magnet for 5 min. Remove all ethanol after the second wash as remaining ethanol will interfere with purification.</li>
	<li>Remove the samples from the magnet and add 25 &#181;L of 10 mM Tris-Cl pH 8.0. Mix by pipetting 25 times and incubate at room temperature off the magnet for 4 min. Place the samples back on the magnet and incubate at room temperature for another 4 min.</li>
	<li>Quantify libraries using a high sensitivity DNA electropherogram instrument before multiplexing to ensure sizes are suitable for sequencing. Completed libraries range between 200 - 600 bp and are devoid of all primer dimers. 
	NOTE: If necessary, another 0.7x paramagnetic bead purification is performed to remove unwanted primer dimers that are present at ~130 bp.</li>
	<li>Pool the quantified DNA based on unique index primers according to concentration. For a pool containing six samples with concentrations between 1 ng/&#181;L and 6ng/&#181;L, the distribution is 15 &#181;L of the lowest sample and 2.5 &#181;L of the highest sample. This is done to ensure read counts will be evenly distributed on each lane of the flow cell.</li>
</ol>
8. Post ChIP-seq Data Processing
<ol>
	<li>A pipeline based on snakemake38,39 is used to process of all of the following steps in a single command. Importantly, each of these steps are discussed individually with associated commands.</li>
	<li>Determine quality of raw fastq sequencing reads using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/): Open unix terminal and change directory (cd) to the folder containing the raw fastq files for each of the six histone modifications. In the unix terminal type, fastqc *fastq.gz and press [RETURN]. This will run quality control metrics on all the fastq files in the folder.</li>
	<li>Align quality reads to the reference genome and remove duplicates before compression to bam files. This is performed using bowtie version 1.1.239, SAMBLASTER version 0.1.2140, and SAMTOOLS version 1.3.141: In the unix terminal type, bowtie -m 1 -n 1 --best --strata -S -q /path_to/hg19 histone1.fastq.gz | samblaster -removeDups | samtools view -Sb - &#62;histone1.bam and press [RETURN]. 
	NOTE: path_to indicates the folder containing the hg19 human genome assembly. This will change depending on the organism of interest.</li>
	<li>Sort bam files using SAMTOOLS with the following command: In the unix terminal type, samtools sort histone1.bam -m 2G -@ 5 -T histone1.temp -o histone1.sorted and press [RETURN].</li>
	<li>Index bam files using SAMTOOLS with the following command: In the unix terminal type, samtools index histone1.sorted.bam histone1.sorted.bam.bai and press [RETURN].</li>
	<li>Determine uniquely mapped and unaligned reads using SAMTOOLS flagstat with the following command: In the unix terminal type, samtools flagstat histone1.sorted.bam and press [RETURN].</li>
	<li>Normalize bam files per read counts by performing random sampling with the following command: In the unix terminal type, sambamba view -f bam -subsampling-seed=3 -s 0.X histone1.sorted.bam | samtools sort -m 2G -@ 5 -T histone1.downsample.temp -o histone1.downsample.sorted.bam and press [RETURN].</li>
	<li>Index the bam files again using SAMTOOLS with the following command: In the unix terminal type, samtools index histone.downsample.sorted.bam histone.downsample.sorted.bam.bai and press [RETURN].</li>
	<li>To visualize ChIP-seq libraries on a genome browser (i.e. UCSC or IGV), generate bigwig files using deepTools version 2.4.040 by scaling the bam files to reads per kilobase per million (RPKM). This can be performed using the following commands:
	<ol>
		<li>For standard bigwig files: In the unix terminal type, bamCoverage -b histone1. downsample.sorted.bam --normalizeUsingRPKM --binSize 30 --smoothLength 300 --extendReads 200 -o histone1.bw and press {RETURN].</li>
		<li>For input subtracted bigwig files: In the unix terminal type, bamCompare --bamfile1 histone1.downsample.sorted.bam --bamfile2 input1.downsample.sorted.bam --normalizeUsingRPKM --ratio subtract --binSize 30 --smoothLength 300 --extendReads 200 -o histone1.subtracted.bw and press [RETURN].</li>
	</ol>
	</li>
	<li>Identify ChIP-seq signal enrichment over &#34;Input&#34; background using Model based analysis of ChIP-seq (MACS) version 1.4.225,26 and version 2.1.0. Use macs1 for &#34;point source&#34; factors H3K4me3, H3K4me1 and H3K27ac and macs2 for &#34;broad source&#34; factors H3K79me2, H3K27me3 and H3K9me3. This is performed using the following commands:
	<ol>
		<li>For Point Source: In the unix terminal type, macs14 -t histone1.downsample.sorted.bam -c input1.downsample.sorted.bam -g hs -f BAM -p 1e-5 --nomodel --keep-dup all -n histone1.file and press [RETURN].</li>
		<li>For Broad Source: In the unix terminal type, macs2 callpeak -t histone1.downsample.sorted.bam -c input1.downsample.sorted.bam -g hs -f BAM -p 1e-6 --broad --broad-cutoff 1e-6 --keep-dup all --nomodel -n histone1.file and press [RETURN].</li>
	</ol>
	</li>
	<li>Use ChromHMM24 to identify combinatorial chromatin state patterns based on the histone modifications studied using the following commands:
	<ol>
		<li>In the unix terminal type, cd /path_to/ChromHMM_folder and press [RETURN].</li>
		<li>Once in the ChromHMM directory type, java -mx2G -jar ChromHMM.jar BinarizeBam -b 1000 hg19.txt /path_to/bam_directory /path_to/CellMarkFile.txt /path_to/BinarizeBam_output and press [RETURN].</li>
		<li>Type, java -mx2G -jar ChromHMM.jar LearnModel -b 1000 /path_to/BinarizeBam /path_to/output_directory 15 hg19 and press [RETURN].</li>
	</ol>
	</li>
</ol>

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E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+NIH+Roadmap+Epigenomics+Mapping+Consortium.">The NIH Roadmap Epigenomics Mapping Consortium.</a> Nat Biotechnol. 28 (10), 1045-1048 (2010).</li><li>Roadmap Epigenomics, 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=Integrative+analysis+of+111+reference+human+epigenomes.">Integrative analysis of 111 reference human epigenomes.</a> Nature. 518 (7539), 317-330 (2015).</li><li>Ernst, J., Kellis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChromHMM:+automating+chromatin-state+discovery+and+characterization.">ChromHMM: automating chromatin-state discovery and characterization.</a> Nat Methods. 9 (3), 215-216 (2012).</li><li>Feng, J., Liu, T., Qin, B., Zhang, Y., Liu, X. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+ChIP-seq+enrichment+using+MACS.">Identifying ChIP-seq enrichment using MACS.</a> Nat Protoc. 7 (9), 1728-1740 (2012).</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>Landt, S. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ChIP-seq+guidelines+and+practices+of+the+ENCODE+and+modENCODE+consortia.">ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.</a> Genome Res. 22 (9), 1813-1831 (2012).</li><li>O'Neill, L. P., VerMilyea, M. D., Turner, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epigenetic+characterization+of+the+early+embryo+with+a+chromatin+immunoprecipitation+protocol+applicable+to+small+cell+populations.">Epigenetic characterization of the early embryo with a chromatin immunoprecipitation protocol applicable to small cell populations.</a> Nat Genet. 38 (7), 835-841 (2006).</li><li>Dahl, J. A., Collas, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Q2ChIP,+a+quick+and+quantitative+chromatin+immunoprecipitation+assay,+unravels+epigenetic+dynamics+of+developmentally+regulated+genes+in+human+carcinoma+cells.">Q2ChIP, a quick and quantitative chromatin immunoprecipitation assay, unravels epigenetic dynamics of developmentally regulated genes in human carcinoma cells.</a> Stem Cells. 25 (4), 1037-1046 (2007).</li><li>Zhang, B., 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=Allelic+reprogramming+of+the+histone+modification+H3K4me3+in+early+mammalian+development.">Allelic reprogramming of the histone modification H3K4me3 in early mammalian development.</a> Nature. 537 (7621), 553-557 (2016).</li><li>Dahl, J. A., 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=Broad+histone+H3K4me3+domains+in+mouse+oocytes+modulate+maternal-to-zygotic+transition.">Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition.</a> Nature. 537 (7621), 548-552 (2016).</li><li>Shen, 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=H3K4me3+epigenomic+landscape+derived+from+ChIP-Seq+of+1,000+mouse+early+embryonic+cells.">H3K4me3 epigenomic landscape derived from ChIP-Seq of 1,000 mouse early embryonic cells.</a> Cell Res. 25 (1), 143-147 (2015).</li><li>Flanagin, S., Nelson, J. D., Castner, D. G., Denisenko, O., Bomsztyk, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microplate-based+chromatin+immunoprecipitation+method,+Matrix+ChIP:+a+platform+to+study+signaling+of+complex+genomic+events.">Microplate-based chromatin immunoprecipitation method, Matrix ChIP: a platform to study signaling of complex genomic events.</a> Nucleic Acids Res. 36 (3), e17 (2008).</li><li>Nelson, J. D., Denisenko, O., Sova, P., Bomsztyk, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+chromatin+immunoprecipitation+assay.">Fast chromatin immunoprecipitation assay.</a> Nucleic Acids Res. 34 (1), e2 (2006).</li><li>Lerdrup, M., Johansen, J. V., Agrawal-Singh, S., Hansen, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+interactive+environment+for+agile+analysis+and+visualization+of+ChIP-sequencing+data.">An interactive environment for agile analysis and visualization of ChIP-sequencing data.</a> Nat Struct Mol Biol. 23 (4), 349-357 (2016).</li><li>Afgan, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Galaxy+platform+for+accessible,+reproducible+and+collaborative+biomedical+analyses:+2016+update.">The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.</a> Nucleic Acids Res. 44 (W1), W3-W10 (2016).</li><li>Blecher-Gonen, R., 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=High-throughput+chromatin+immunoprecipitation+for+genome-wide+mapping+of+in+vivo+protein-DNA+interactions+and+epigenomic+states.">High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein-DNA interactions and epigenomic states.</a> Nat Protoc. 8 (3), 539-554 (2013).</li><li>Tang, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=pyflow-ChIPseq:+a+snakemake+based+ChIP-seq+pipeline.">pyflow-ChIPseq: a snakemake based ChIP-seq pipeline.</a> Zenodo. , (2017).</li><li>Koster, J., Rahmann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Snakemake--a+scalable+bioinformatics+workflow+engine.">Snakemake--a scalable bioinformatics workflow engine.</a> Bioinformatics. 28 (19), 2520-2522 (2012).</li><li>Ramirez, F., 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=deepTools2:+a+next+generation+web+server+for+deep-sequencing+data+analysis.">deepTools2: a next generation web server for deep-sequencing data analysis.</a> Nucleic Acids Res. 44 (W1), W160-W165 (2016).</li><li>Bailey, 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=Practical+guidelines+for+the+comprehensive+analysis+of+ChIP-seq+data.">Practical guidelines for the comprehensive analysis of ChIP-seq data.</a> PLoS Comput Biol. 9 (11), e1003326 (2013).</li><li>Fiziev, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+Epigenomic+Analysis+Reveals+Chromatin+States+Associated+with+Melanoma+Progression.">Systematic Epigenomic Analysis Reveals Chromatin States Associated with Melanoma Progression.</a> Cell Rep. 19 (4), 875-889 (2017).</li><li>Liu, 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=Broad+chromosomal+domains+of+histone+modification+patterns+in+C.+elegans.">Broad chromosomal domains of histone modification patterns in C. elegans.</a> Genome Res. 21 (2), 227-236 (2011).</li><li>Egelhofer, T. 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Authors declare no conflicts.

This protocol describes a complete and comprehensive high-throughput ChIP-seq module for genome-wide mapping of chromatin states in human tumor tissues and cell lines. In any ChIP-seq protocol, one of the most important steps is antibody specificity. Here, this method illustrates immunoprecipitation conditions for the described six histone modifications, all of which are ChIP-grade and have been previously validated in our and other laboratories42,44,<...

The majority of mammalian genomes (98 - 99%) are comprised of noncoding sequence, and these nocoding regions contain regulatory elements known to participate in controlling gene expression and chromatin organization1,2. In a normal cell, the specific assembly of genomic DNA into compacted chromatin structure is critical for the spatial organization, regulation and precise timing of various DNA-associated processes3,4,5. In a cancer cell however, chromatin modifications by aberrant epigenetic mechanisms can lead to improper organization of chromatin structure, including access to regulatory elements, chromosomal looping systems, and gene expression patterns6,7,8,9,10.
Despite recent advances, we have limited understanding of epigenetic alterations that are associated with tumor progression or therapeutic response. The epigenome consists of an array of modifications, including histone marks and DNA methylation, which collectively form a dynamic state (referred to as chromatin state) that impinges upon gene expression networks and other processes critical for maintaining cellular identity. Recently, alterations in enhancers have been shown in multiple malignancies by studying H3K27Ac profiles11. Although such studies provide insight into the correlation of isolated epigenetic marks, more than 100 epigenetic modifications have been identified12,13 without clear understanding of their biological roles and interdependence. Furthermore, there are an even larger number of possible combinatorial patterns of these histone and DNA modifications, and it is these combinatorial patterns - not individual modifications - that dictate epigenetic states14. Hence, there is tremendous need to identify alterations in these chromatin states during cancer progression or responses to therapy. Comprehensive knowledge of epigenome alterations in cancers has been lagging in part due to technical (e.g. generation of large-scale data from small amount of clinical material/single cells) and analytical (e.g. algorithms to define combinatorial states) challenges. Therefore, there is critical need for robust high-throughput methods for profiling large number of histone modification marks from clinical material and easy-to-implement computational approaches to predict combinatorial patterns which will facilitate determination of epigenetic states associated with different stages of tumorigenesis and therapeutic resistance. Further, data available from recent epigenome profiling studies15,16,17,18,19,20,21,22,23 in normal tissues and cell lines can be integrated with chromatin profiles of tumors for further insights into epigenome contribution to tumor biology.
Chromatin profiling has become a powerful tool for identifying the global binding patterns of various chromatin modifications15,24. In recent years, ChIP-seq has become the &#34;gold standard&#34; for studying DNA-protein interactions on a global scale25,26,27. For any ChIP-seq experiment, there are critical steps necessary for its success, including tissue processing and disassociation, determing optimal sonication conditions, determing optimal antibody concentration for precipitation, library preparation, post-sequencing data processing, and downstream analysis. Each of these steps contain key quality control checkpoints, and when taken together, are crucial for properly identifying potential targets for functional validation. Through innovation in these steps, several prior studies have developed methodologies to perform ChIP or ChIP-Seq from small amount of tissues28,29,30,31,32. Further, some studies have suggested protocols for high-throughput ChIP experiments followed by PCR based quantitation33,34. Finally, some publically available analysis platforms for ChIP-Seq data are now available such as Easeq35 and Galaxy36. However, an integrated platform for performing ChIP-Seq in a high-throughput fashion in combination with a computational pipeline to perform single mark as well as chromatin state analyses has been lacking.
This protocol describes a complete and comprehensive ChIP-seq workflow for genome-wide mapping of chromatin states in tumor tissues and cell lines, with easy to follow guidelines encompassing all of the steps necessary for a successful experiment. By adopting a high-throughput method previously described by Blecher-Gonen et al.37, this protocol can be performed on dozens of samples in parallel and has been applied successfully on cancer cell lines and human tumors such as melanoma, colon, prostate, and glioblastoma multiforme. We demonstrate the methodology for six core histone modifications that represent key components of the epigenetic regulatory landscape in human melanoma cell lines and tumor samples. These modifications include H3K27ac (enhancers), H3K4me1 (active and poised enhancers), H3K4me3 (promoters), H3K79me2 (transcribed regions), H3K27me3 (polycomb repression), and H3K9me3 (heterochromatic repression). These marks can be used either alone or in combination to identify functionally distinct chromatin states representing both repressive and active domains.

<table>
	<tbody>
		<tr>
			<td>ChIP-grade H3K4me1 antibody</td>
			<td>Abcam</td>
			<td>ab8895</td>
			<td></td>
		</tr>
		<tr>
			<td>ChIP-grade H3K27ac antibody</td>
			<td>Abcam</td>
			<td>ab4729</td>
			<td></td>
		</tr>
		<tr>
			<td>ChIP-grade H3K4me3 antibody</td>
			<td>Abcam</td>
			<td>ab8580</td>
			<td></td>
		</tr>
		<tr>
			<td>ChIP-grade H3K79me2 antibody</td>
			<td>Abcam</td>
			<td>ab3594</td>
			<td></td>
		</tr>
		<tr>
			<td>ChIP-grade H3K27me3 antibody</td>
			<td>Abcam</td>
			<td>ab6002</td>
			<td></td>
		</tr>
		<tr>
			<td>ChIP-grade H3K9me3 antibody</td>
			<td>Abcam</td>
			<td>ab8898</td>
			<td></td>
		</tr>
		<tr>
			<td>1M Tris HCl, pH 8.0</td>
			<td>Teknova</td>
			<td>T1080</td>
			<td></td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>Sigma-Aldrich</td>
			<td>E9884</td>
			<td></td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>Sigma-Aldrich</td>
			<td>S7653</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycine</td>
			<td>Sigma-Aldrich</td>
			<td>G8898</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium deoxycholate</td>
			<td>Sigma-Aldrich</td>
			<td>30970</td>
			<td></td>
		</tr>
		<tr>
			<td>DPBS</td>
			<td>Sigma - Life Sciences</td>
			<td>D8537-500ML</td>
			<td></td>
		</tr>
		<tr>
			<td>SDS</td>
			<td>Sigma-Aldrich</td>
			<td>74255</td>
			<td></td>
		</tr>
		<tr>
			<td>Triton-X</td>
			<td>Sigma-Aldrich</td>
			<td>X100-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>LiCl</td>
			<td>Sigma-Aldrich</td>
			<td>746460</td>
			<td></td>
		</tr>
		<tr>
			<td>NP-40</td>
			<td>Calbiochem</td>
			<td>492016-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>1% TWEEN-20</td>
			<td>Fisher Bioreagents</td>
			<td>BP337-500</td>
			<td></td>
		</tr>
		<tr>
			<td>BSA - IgG-free</td>
			<td>Sigma - Life Sciences</td>
			<td>A2058-5G</td>
			<td></td>
		</tr>
		<tr>
			<td>HBSS</td>
			<td>Gibo</td>
			<td>14025092</td>
			<td></td>
		</tr>
		<tr>
			<td>GentleMACS C tube</td>
			<td>GentleMACS</td>
			<td>120-008-466</td>
			<td>disassociation tube</td>
		</tr>
		<tr>
			<td>16% Formaldehyde</td>
			<td>Peierce</td>
			<td>28906</td>
			<td></td>
		</tr>
		<tr>
			<td>miniProtease inhibitor&#160;</td>
			<td>Roche Diagnostics</td>
			<td>11836153001</td>
			<td>protease inhibitor tablets</td>
		</tr>
		<tr>
			<td>Dynabeads Protein G&#160;</td>
			<td>Invitrogen</td>
			<td>10009D</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioruptor NGS tubes 0.65 mL</td>
			<td>Diagenode</td>
			<td>C30010011</td>
			<td>sonication tubes</td>
		</tr>
		<tr>
			<td>DynaMag - 96 Side Skirted</td>
			<td>Invitrogen</td>
			<td>120.27</td>
			<td>96-well magnetic stand</td>
		</tr>
		<tr>
			<td>TE buffer</td>
			<td>Promega</td>
			<td>V6231</td>
			<td></td>
		</tr>
		<tr>
			<td>RNase A</td>
			<td>Invitrogen</td>
			<td>12091021</td>
			<td></td>
		</tr>
		<tr>
			<td>Proteinase K</td>
			<td>Invitrogen</td>
			<td>100005393</td>
			<td></td>
		</tr>
		<tr>
			<td>AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63882</td>
			<td>paramagnetic beads</td>
		</tr>
		<tr>
			<td>Ethanol</td>
			<td>Sigma-Aldrich</td>
			<td>E7023</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit ds DNA High Sensitivity Assay Kit</td>
			<td>Invitrogen</td>
			<td>Q32854</td>
			<td>high sensitivity DNA reagents</td>
		</tr>
		<tr>
			<td>NEBNext Ultra II DNA Library Prep Kit</td>
			<td>New England BioLabs</td>
			<td>E7645L</td>
			<td>DNA Library Prep Kit</td>
		</tr>
		<tr>
			<td>Nuclease-free water</td>
			<td>Ambion</td>
			<td>AM9932</td>
			<td></td>
		</tr>
		<tr>
			<td>High sensitivity D1000 ScreenTape</td>
			<td>Agilent Technologies</td>
			<td>5067-5584</td>
			<td>high sensitivity DNA reagents</td>
		</tr>
		<tr>
			<td>High sensitivity D1000 reagents</td>
			<td>Agilent Technologies</td>
			<td>5067-5585</td>
			<td>high sensitivity DNA reagents</td>
		</tr>
		<tr>
			<td>Multiplex Oligos (Index primers- Set 1)</td>
			<td>New England BioLabs</td>
			<td>E7335L</td>
			<td>Multiplex Oligos&#160;</td>
		</tr>
		<tr>
			<td>Multiplex Oligos (Index primers- Set 2)</td>
			<td>New England BioLabs</td>
			<td>E7500L</td>
			<td>Multiplex Oligos&#160;</td>
		</tr>
		<tr>
			<td>TapeStation 4200</td>
			<td>Agilent Technologies</td>
			<td>G2991AA</td>
			<td>high sensitivity DNA electropherogram instrument</td>
		</tr>
		<tr>
			<td>Bioruptor Pico sonication device&#160;</td>
			<td>Diagenode</td>
			<td>B01060001</td>
			<td>water bath disruputor</td>
		</tr>
		<tr>
			<td>Mixer</td>
			<td>Nutator</td>
			<td>421105</td>
			<td></td>
		</tr>
		<tr>
			<td>Bio-Rad C1000 Touch Thermal Cycler</td>
			<td>Bio-Rad</td>
			<td>1851196</td>
			<td>PCR Thermal cycler</td>
		</tr>
		<tr>
			<td>Water Bath</td>
			<td>Fisher Scientific</td>
			<td>2322</td>
			<td></td>
		</tr>
		<tr>
			<td>Multichannel Pipet</td>
			<td>Denville</td>
			<td>1003123</td>
			<td></td>
		</tr>
		<tr>
			<td>Tube Revolver</td>
			<td>Thermo-Scientific</td>
			<td>88881001</td>
			<td></td>
		</tr>
		<tr>
			<td>96-Well Skirted Plate</td>
			<td>Eppendorf</td>
			<td>47744-110</td>
			<td></td>
		</tr>
		<tr>
			<td>Allegra X-12R Centrifuge&#160;</td>
			<td>Beckman Coulter</td>
			<td>A99464</td>
			<td>benchtop centrifuge</td>
		</tr>
		<tr>
			<td>Centrifuge 5424</td>
			<td>Eppendorf</td>
			<td>22620461</td>
			<td>tabletop centrifuge</td>
		</tr>
		<tr>
			<td>Optical tube strips (8x Strip)</td>
			<td>Agilent Technologies</td>
			<td>401428</td>
			<td></td>
		</tr>
		<tr>
			<td>Optical tube strip caps (8x strip)</td>
			<td>Agilent Technologies</td>
			<td>401425</td>
			<td></td>
		</tr>
		<tr>
			<td>Loading Tips, 10 Pk</td>
			<td>Agilent Technologies</td>
			<td>5067-5599</td>
			<td></td>
		</tr>
		<tr>
			<td>IKA MS3 vortex</td>
			<td>IKA</td>
			<td>3617000</td>
			<td>vortex</td>
		</tr>
	</tbody>
</table>

an integrated platform for genome-wide mapping of chromatin states using high-throughput chip-sequencing in tumor tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

This protocol allows the immunoprecipitation from frozen tumor tissues and cell lines that can be performed on dozens of samples in parallel using a high-throughput method (Figure 1A). Chromatin fragments should range between ~200 - 1000 bps for optimal immunoprecipitation. We have noted that the time needed to achieve same shearing length differs for different tissue and cell types. The success of ChIP from small amounts of tissue depends on...

Watch this Scientific Journal Video about An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues at JoVE.com

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

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Research

JoVE Journal

Cancer Research

10.2K Views.  University of Texas MD Anderson Cancer Center. Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Video: An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the context of a whole organism. Sophisticated techniques to generate genetic mosaics facilitate induction of visually marked, genetically defined clones surrounded by normal tissue. The clones can be analyzed through diverse molecular, cellular and omics approaches. This study describes how to generate fluorescently labeled clonal tumors of varying malignancy in the eye/antennal imaginal discs (EAD) of Drosophila larvae using the Mosaic Analysis with a Repressible Cell Marker (MARCM) technique. It describes procedures how to recover the mosaic EAD and brain from the larvae and how to process them for simultaneous imaging of fluorescent transgenic reporters and antibody staining. To facilitate molecular characterization of the mosaic tissue, we describe a protocol for isolation of total RNA from the EAD. The dissection procedure is suitable to recover EAD and brains from any larval stage. The fixation and staining protocol for imaginal discs works with a number of transgenic reporters and antibodies that recognize Drosophila proteins. The protocol for RNA isolation can be applied to various larval organs, whole larvae, and adult flies. Total RNA can be used for profiling of gene expression changes using candidate or genome-wide approaches. Finally, we detail a method for quantifying invasiveness of the clonal tumors. Although this method has limited use, its underlying concept is broadly applicable to other quantitative studies where cognitive bias must be avoided.

The Drosophila Imaginal Disc Tumor Model: Visualization and Quantification of Gene Expression and Tumor Invasiveness Using Genetic Mosaics

This protocol demonstrates how to generate fluorescently marked, genetically defined clonal tumors in the Drosophila eye/antennal imaginal discs (EAD). It describes how to dissect the EAD and brain from the third instar larvae and how to process them to visualize and quantify gene expression changes and tumor invasiveness.

Drosophila melanogaster has emerged as a powerful experimental system for functional and mechanistic studies of tumor development and progression in the ...

The Establishment of a Lung Colonization Assay for Circulating Tumor Cell Visualization in Lung Tissues

Metastasis is the major cause of cancer death. The role of circulating tumor cells (CTCs) in promoting cancer metastasis, in which lung colonization by CTCs critically contributes to early lung metastatic processes, has been vigorously investigated. As such, animal models are the only approach that captures the full systemic process of metastasis. Given that problems occur in previous experimental designs for examining the contributions of CTCs to blood vessel extravasation, we established an in vivo lung colonization assay in which a long-term-fluorescence cell-tracer, carboxyfluorescein succinimidyl ester (CFSE), was used to label suspended tumor cells and lung perfusion was performed to clear non-specifically trapped CTCs prior to lung removal, confocal imaging, and quantification. Polymeric fibronectin (polyFN) assembled on CTC surfaces has been found to mediate lung colonization in the final establishment of metastatic tumor tissues. Here, to specifically test the requirement of polyFN assembly on CTCs for lung colonization and extravasation, we performed short term lung colonization assays in which suspended Lewis lung carcinoma cells (LLCs) stably expressing FN-shRNA (shFN) or scramble-shRNA (shScr) and pre-labeled with 20 &#956;M of CFSE were intravenously inoculated into C57BL/6 mice. We successfully demonstrated that the abilities of shFN LLC cells to colonize the mouse lungs were significantly diminished in comparison to shScr LLC cells. Therefore, this short-term methodology may be widely applied to specifically demonstrate the ability of CTCs within the circulation to colonize the lungs.

An animal model is needed to decipher the role of circulating tumor cells (CTCs) in promoting lung colonization during cancer metastasis. Here, we established and successfully performed an in vivo assay to specifically test the requirement of polymeric fibronectin (polyFN) assembly on CTCs for lung colonization.

Metastasis is the major cause of cancer death. The role of circulating tumor cells (CTCs) in promoting cancer metastasis, in which lung colonization by ...

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

Here we describe a protocol for employing high-throughput RNAi screening to uncover host targets that can be manipulated to enhance oncolytic virus therapy, specifically rhabodvirus and vaccinia virus therapy, but it can be readily adapted to other oncolytic virus platforms or for discovering host genes that modulate virus replication generally.

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus ...

Native Chromatin Immunoprecipitation Using Murine Brain Tumor Neurospheres

Epigenetic modifications may be involved in the development and progression of glioma. Changes in methylation and acetylation of promoters and regulatory regions of oncogenes and tumor suppressors can lead to changes in gene expression and play an important role in the pathogenesis of brain tumors. Native chromatin immunoprecipitation (ChIP) is a popular technique that allows the detection of modifications or other proteins tightly bound to DNA. In contrast to cross-linked ChIP, in native ChIP, cells are not treated with formaldehyde to covalently link protein to DNA. This is advantageous because sometimes crosslinking may fix proteins that only transiently interact with DNA and do not have functional significance in gene regulation. In addition, antibodies are generally raised against unfixed peptides. Therefore, antibody specificity is increased in native ChIP. However, it is important to keep in mind that native ChIP is only applicable to study histones or other proteins that bind tightly to DNA. This protocol describes the native chromatin immunoprecipitation on murine brain tumor neurospheres.

Epigenetic mechanisms are frequently altered in glioma. Chromatin immunoprecipitation could be used to study the consequences of genetic alterations in glioma that result from changes in histone modifications which regulate chromatin structure and gene transcription. This protocol describes native chromatin immunoprecipitation on murine brain tumor neurospheres.

Epigenetic modifications may be involved in the development and progression of glioma. Changes in methylation and acetylation of promoters and regulatory ...

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is exposed to in vivo. Solid tumors grow not as a sheet attached to plastic, but instead as a collection of clonal cells in a three-dimensional (3D) space interacting with their neighbors, and with distinct spatial properties such as the disruption of normal cellular polarity. These interactions cause 3D-cultured cells to acquire morphological and cellular characteristics which are more relevant to in vivo tumors. Additionally, a tumor mass is in direct contact with other cell types such as stromal and immune cells, as well as the extracellular matrix from all other cell types. The matrix deposited is comprised of macromolecules such as collagen and fibronectin.
In an attempt to increase the translation of research findings in oncology from bench to bedside, many groups have started to investigate the use of 3D model systems in their drug development strategies. These systems are thought to be more physiologically relevant because they attempt to recapitulate the complex and heterogeneous environment of a tumor. These systems, however, can be quite complex, and, although amenable to growth in 96-well formats, and some now even in 384, they offer few choices for large-scale growth and screening. This observed gap has led to the development of the methods described here in detail to culture tumor spheroids in a high-throughput capacity in 1536-well plates. These methods represent a compromise to the highly complex matrix-based systems, which are difficult to screen, and conventional 2D assays. A variety of cancer cell lines harboring different genetic mutations are successfully screened, examining compound efficacy by using a curated library of compounds targeting the Mitogen-Activated Protein Kinase or MAPK pathway. The spheroid culture responses are then compared to the response of cells grown in 2D, and differential activities are reported. These methods provide a unique protocol for testing compound activity in a high-throughput 3D setting.

Across a wide variety of disease indications, more physiologically relevant models are being developed and implemented into drug discovery programs. The new model system described here demonstrates how three-dimensional tumor spheroids can be cultured and screened in a high-throughput 1536-well plate-based system to search for new oncology drugs.

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as tumors. Because digital polymerase chain reactions (PCR) enable the precise quantification of DNA copy number variants, they are becoming an essential tool for detecting rare genetic variations, such as drug-resistant mutations. It is expected that molecular diagnoses using digital PCR (dPCR) will be available in clinical practice in the near future; thus, how to efficiently conduct dPCR with human genetic material is a hot topic. Here, we introduce a method to detect Adenomatous polyposis coli (APC) somatic mosaicism using dPCR with the chip-in-a-tube format, which allows eight dPCR reactions to be simultaneously conducted. Care should be taken when filling and sealing the reaction mixture on the chips. This article demonstrates how to avoid the over- and underestimation of positive partitions. Furthermore, we present a simple procedure for collecting the dPCR product from the partitions on the chips, which can then be used to confirm the specific amplification. We hope that this methods report will help promote the dPCR with the chip-in-a-tube method in genetic research.

Digital polymerase chain reaction (PCR) is a useful tool for the high-sensitivity detection of single nucleotide variants and DNA copy number variants. Here, we demonstrate key considerations for measuring rare variants in the human genome using digital PCR with the chip-in-a-tube format.

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

In Vitro Assay to Study Tumor-macrophage Interaction

Tumor associated macrophages (TAMs) account for a large percentage of cells in the tumor mass for different types of cancers. Glioblastoma (GBM), a malignant brain tumor with no cure, has up to a half the tumor mass TAMs. TAMs can be pro-tumoral or anti-tumoral, depending on the activation of specific genes in the cells. Genetic mutations in the tumors, through regulating cytokine expression, can affect recruitment of TAMs to the tumor microenvironment. Here, we describe a quantitative cell-based assay to assess macrophage recruitment by the conditioned medium from the tumor cells. This assay uses the human macrophage cell line MV-4-11 to study macrophage attraction by the conditioned medium from glioblastoma, allowing for high reproducibility and low variability. Data generated with this assay can contribute to a better understanding of the interaction between the tumor and the tumor microenvironment. Similar assay can be used to assess interaction between the tumor cells and other immune cells, including T cells and natural killer (NK) cells.

This article represents a useful in vitro assay to evaluate the capability of conditioned medium from tumor cells to attract macrophages.

Tumor associated macrophages (TAMs) account for a large percentage of cells in the tumor mass for different types of cancers. Glioblastoma (GBM), a ...

Establishment and Characterization of Small Bowel Neuroendocrine Tumor Spheroids

Small bowel neuroendocrine tumors (SBNETs) are rare cancers originating from enterochromaffin cells of the gut. Research in this field has been limited because very few patient derived SBNET cell lines have been generated. Well-differentiated SBNET cells are slow growing and are hard to propagate. The few cell lines that have been established are not readily available, and after time in culture may not continue to express characteristics of NET cells. Generating new cell lines could take many years since SBNET cells have a long doubling time and many enrichment steps are needed in order to eliminate the rapidly dividing cancer-associated fibroblasts. To overcome these limitations, we have developed a protocol to culture SBNET cells from surgically removed tumors as spheroids in extracellular matrix (ECM). The ECM forms a 3-dimensional matrix that encapsulates SBNET cells and mimics the tumor micro-environment for allowing SBNET cells to grow. Here, we characterized the growth rate of SBNET spheroids and described methods to identify SBNET markers using immunofluorescence microscopy and immunohistochemistry to confirm that the spheroids are neuroendocrine tumor cells. In addition, we used SBNET spheroids for testing the cytotoxicity of rapamycin.

Neuroendocrine tumors (NETs) originate from neuroendocrine cells of the neural crest. They are slow growing and challenging to culture. We present an alternative strategy to grow NETs from the small bowel by culturing them as spheroids. These spheroids have small bowel NET markers and can be used for drug testing.

Small bowel neuroendocrine tumors (SBNETs) are rare cancers originating from enterochromaffin cells of the gut. Research in this field has been limited ...

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 ...

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable preclinical models that can accurately predict patient responses. One of the disadvantages of existing preclinical models is the inability to contextually preserve the human tumor microenvironment (TME) and accurately represent intratumoral heterogeneity, thus limiting the clinical translation of data. By contrast, by representing the culture of live fragments of human tumors, the patient-derived explant (PDE) platform allows drug responses to be examined in a three-dimensional (3D) context that mirrors the pathological and architectural features of the original tumors as closely as possible. Previous reports with PDEs have documented the ability of the platform to distinguish chemosensitive from chemoresistant tumors, and it has been shown that this segregation is predictive of patient responses to the same chemotherapies. Simultaneously, PDEs allow the opportunity to interrogate molecular, genetic, and histological features of tumors that predict drug responses, thereby identifying biomarkers for patient stratification as well as novel interventional approaches to sensitize resistant tumors. This paper reports PDE methodology in detail, from collection of patient samples through to endpoint analysis. It provides a detailed description of explant derivation and culture methods, highlighting bespoke conditions for particular tumors, where appropriate. For endpoint analysis, there is a focus on multiplexed immunofluorescence and multispectral imaging for the spatial profiling of key biomarkers within both tumoral and stromal regions. By combining these methods, it is possible to generate quantitative and qualitative drug response data that can be related to various clinicopathological parameters and thus potentially be used for biomarker identification.

This paper describes methods for the generation, drug treatment, and analysis of patient-derived explants for assessing tumor drug responses in a live, patient-relevant, preclinical model system.

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable ...