This work is supported by the Israel Science Foundation (grant 748/14), Marie Curie Integration grant (CIG)- FP7-PEOPLE-20013-CIG-618763 and I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation grant no. 41/11.

All the procedures were approved by institutional review board of Bar Ilan University and the protocol follows guidelines provided by the committee approving the experiments.
1. Purification of Na&#239;ve Human CD4+ Cells and Polarization to T Helper 1 (Th1) and Th2 Cells
Note: Here we describe the procedure starting from frozen human peripheral blood mononuclear cells (PBMCs). The first step consists of isolating CD4+ cells using microbeads and columns that usually give us more than 95% of CD4+ cells. However, this step may vary according to the preferred protocol in each lab. The protocol for T cell activation and polarization was modified from Jenner et al. (2009)18. Isolation of CD4+ cells from 10 million PBMCs gives rise to 4 - 6 million of CD4+ cells. They are split into two flasks and grown under Th1 and Th2 polarizing conditions yielding 3-5 million Th1 and Th2 cells within just a week.
Note: Cool down the centrifuge to 4 &#176;C before starting.
<ol>
	<li>Thaw 1 mL of human PBMCs (107 cells) in a 50 mL tube containing 10 mL of RPMI medium supplemented with 1% Penicillin-Streptomycin, 2 mM L-glutamine and 10% heat-inactivated fetal bovine serum. Centrifuge at 500 x g for 5 min. Remove the supernatant and resuspend the cells with a sterile 25 mL pipette in 15 mL of supplemented RPMI medium. Transfer the cells (with sterile 25 mL pipette) to a T75 culture flask.</li>
	<li>Leave the cells overnight in a humidified incubator (37 &#176;C, 5% CO2).</li>
	<li>Transfer the floating cells with a sterile 25 mL pipette to 50 mL tube. Determine cell number and viability by trypan blue exclusion.</li>
	<li>Isolate CD4+ cells from 10 million live non-adherent PBMCs by positive selection using CD4+ microbeads and columns according to the manufacturer&#39;s recommendations (see Table of Materials/Equipment) with the following modifications: 107 PBMCs are labeled with 30 &#181;L of CD4 microbeads in 120 &#181;L of 0.5% BSA in PBS.</li>
	<li>Activate the CD4+ T cells for 72 h by rhIL-2 (10 ng/mL), plate-bound anti-CD3 (5 &#181;g/mL) and soluble anti-CD28 (2 &#181;g/mL). For Th1 polarization, add rhIL-12 (20 ng/mL) and anti-IL-4 (10 &#181;g/mL). For Th2 polarization, add rhIL-4 (40 ng/mL) and anti-IFN-&#947; (10 &#181;g/mL).</li>
	<li>Culture the cells for additional 7 days in the presence of rhIL-2 (10 ng/mL) and the same polarizing cytokines (rhIL-12 for Th1 and rhIL-4 for Th2).</li>
</ol>
2. Nuclei Isolation
NOTE: ATAC-seq is performed with intact nuclei. Lysis buffer containing 0.05% nonylphenyl polyethylene glycol (see Table of Materials/Equipment) was calibrated for isolating nuclei from primary human Th1 and Th2 cells. We recommend calibrating this step with the laboratory reagents and cells. An excess of intact cells from insufficient detergent decreases the efficiency of the transposition reaction. Cell lysis efficiency is determined by the number of nuclei (trypan blue positive cells) relative to the total number of cells. 
NOTE: Prepare the lysis buffer (10 mM Tris-HCl, pH 7.5, 10 mM NaCl, 3 mM MgCl2). Cool down a centrifuge with a swing-bucket rotor to 4 &#176;C. Pelleting the cells in a swing-bucket centrifuge instead of a fixed angle centrifuge reduces cell/nuclei loss. To avoid nuclei or cell loss, pipet carefully when discarding the supernatant.
<ol>
	<li>Add fresh nonylphenyl polyethylene glycol (to a final concentration of 0.05%) and 100x protease inhibitors (to a final concentration of 1x) to the cold lysis buffer immediately before use. Keep the buffer on ice.</li>
	<li>Count T cells to determine their amount and viability using trypan blue method. Viability that is lower than 90% results in higher non-specific digestion.</li>
	<li>Transfer 0.5 x 106 T cells (Th1 or Th2) to 1.5 mL microtubes. Spin down at 500 x g for 5 min at 4 &#176;C.</li>
	<li>Resuspend the cell pellet in 1 mL of cold phosphate-buffered saline (PBS) solution. Spin down at 500 x g for 5 min at 4 &#176;C.</li>
	<li>Resuspend the cell pellet in 1 mL of cold lysis buffer (containing nonylphenyl polyethylene glycol and protease inhibitors). Keep the tube on ice. Pipette gently to avoid disrupting the nuclei.</li>
	<li>Quickly take 10 &#181;L and count the cells with an automated cell counter while the microtube with lysed cells is on ice. This step should not exceed five minutes to avoid damaging the nuclei. At least 80% of the cells should be lysed.</li>
	<li>Continue immediately with the transposition reaction. Keep the prepared nuclei on the ice.</li>
</ol>
3. Transposition reaction
NOTE: In this step, isolated nuclei are incubated with prokaryotic Tn5 transposase (TDE1) loaded with adapters for NGS sequencing. Hyperactive Tn5 simultaneously fragments DNA and ligates adapters into accessible regions of the genome (tagmentation process). The ratio between nuclei and Tn5 transposase is critical for preferential cleavage at accessible chromatin. This protocol is calibrated for 100,000 nuclei in a 100 &#956;L reaction volume. However, the reaction can be scaled down.
<ol>
	<li>Set temperature in a thermal shaker to 37 &#176;C.</li>
	<li>Transfer 100,000 nuclei to a 1.5 mL microtube.</li>
	<li>Centrifuge at 500 x g for 10 min at 4 &#176;C and gently remove the supernatant.</li>
	<li>Add the transposition reaction components to the nuclei as specified in Table 1.</li>
	<li>Resuspend by gentle pipetting.</li>
	<li>Incubate the transposition reaction in a thermal shaker at 37 &#176;C for 30 min with gentle shaking (500 rpm). 
	Note: DNA Cleanup is performed by solid-phase reversible immobilization beads19 (see Table of Materials/Equipment) or PCR purification columns. At the end of the cleanup, elute the DNA fragments in 20 &#181;L of 10 mM Tris-HCl, pH 8. Avoid EDTA in the elution buffer.</li>
</ol>
4. PCR Enrichment of ATAC-seq Libraries
NOTE: This step is aimed to amplify the ATAC-seq library i.e., DNA fragments with inserted adapters. To allow mixing of several ATAC-seq libraries in the same next-generation sequencing lane (&#34;multiplexing&#34;) use unindexed Primer 1 (Ad1_noMx)1 for all samples, and a different indexed (barcoded) Primer 2 (Ad 2.1 - 2.24)1 for each sample. The primer sequences are provided in the supplemented Table of Materials/Equipment.
<ol>
	<li>Initial PCR amplification 
	NOTE: The working concentration of Primer 1 (Ad1_noMx) and Primer 2 is 25 &#181;M. All the primers are diluted from original stock of 100 &#181;M to 25 &#181;M. In all of the PCR reactions, use Primer 1 (Ad1_noMx) and only one of the indexed Primer 2.
	<ol>
		<li>Add the components of the PCR reaction as specified in Table 2 to a sterile PCR tube.</li>
		<li>Place PCR tube into a thermal cycler and perform PCR amplification using the cycling conditions detailed in Table 3.</li>
	</ol>
	</li>
	<li>Assessment of number of additional amplification cycles 
	NOTE: The number of additional PCR cycles should yield sufficient amount of library fragments for a successful next-generation sequencing run, while minimized to avoid GC and size bias20. The determination of the number of PCR cycles (N) required for optimal library fragment amplification is done by quantitative PCR (qPCR).
	<ol>
		<li>Dilute Primers 1 (Ad1_noMx) and 2 (used for initial library amplification) from 25 &#181;M to 6.25 &#181;M.</li>
		<li>Add the components to optical PCR tubes or a plate as stated in Table 4.</li>
		<li>Place in a qPCR instrument and cycle as specified in Table 5.</li>
		<li>To estimate the required number of additional amplification cycles (N), plot cycle number on the x-axis and relative fluorescence (RFU) on the y-axis.</li>
		<li>The number of additional amplification cycles (N) is 1/3 of the number of cycles at which the qPCR reaction reached the plateau. Figure 2 provides examples for three ATAC-seq libraries that reached plateau at ~2,350 relative fluorescence units, RFU (thick green line). The number of PCR cycles in which one third of the maximal amount (783 RFU, marked on y-axis) is amplified corresponds to 8 cycles for two of the libraries (red and blue amplification curves) and 9 PCR cycles for the third library (pink).</li>
	</ol>
	</li>
	<li>Final PCR amplification
	<ol>
		<li>Amplify the remaining 45 &#181;L of the PCR reaction. Place a PCR tube containing amplification reaction from step 4.1.2 in a thermal cycler. Run the PCR program described in Table 6. Use the previously determined (step 4.2.5) number of amplification cycles (N).</li>
	</ol>
	</li>
</ol>
5. Size Selection of ATAC-Seq Libraries
NOTE: In our experience, size selection of amplified ATAC-seq libraries improves next-generation sequencing results because it eliminates high molecular weight library fragments from the final ATAC-seq library. 
NOTE: Allow the magnetic beads to warm to room temperature 30 min before use. 
Prepare fresh 70% ethanol in nuclease-free water.
<ol>
	<li>Resuspend the magnetic beads by mixing.</li>
	<li>Add nuclease-free water to the ATAC-seq libraries (obtained in step 4.3.1.) and bring up to 100 &#181;L.</li>
	<li>Add 50 &#181;L (0.5x) of resuspended DNA-binding magnetic beads to 100 &#181;L of amplified libraries. Mix by pipetting up and down at least 10 times. Incubate samples for 5 min at room temperature. If necessary, quickly spin down the microtubes.</li>
	<li>Place the tube on an appropriate magnetic stand for 2 min to separate the magnetic beads from the supernatant. After 2 min, transfer the supernatant to a new microtube.</li>
	<li>Measure the volume of the supernatant by pipetting and add 0.7x of magnetic beads. Mix by pipetting up and down at least 10 times.</li>
	<li>Incubate 5 min at room temperature. Place on a magnetic stand for 2 min.</li>
	<li>After the 2 min incubation, discard the supernatant. Add 200 &#181;L freshly made 70% ethanol to wash the beads while the tubes are on the magnetic stand.</li>
	<li>Keep the microtube on the magnet for 30 s and then discard the ethanol.Repeat step 5.7.for two final ethanol washes.</li>
	<li>Completely remove the remaining ethanol and let the beads air-dry for 5 min while the tube is on the magnet. If necessary, briefly spin the microtube. Remove the traces of ethanol with a p10 pipette tip.</li>
	<li>Remove the microtube from the magnet and add 22 &#181;L of 10 mM Tris-HCl, pH 8. Do not elute the ATAC-seq libraries in buffer containing EDTA.</li>
	<li>Incubate the tube for 2 min at room temperature and then place on the magnetic stand.</li>
	<li>When the solution is clear, transfer 20 &#181;L of eluted libraries to a new sterile microtube.</li>
	<li>Store the size selected ATAC-seq libraries at -20 &#176;C.</li>
</ol>
6. Quality Analysis of the ATAC-Seq Libraries
<ol>
	<li>Validation of the quality of ATAC-seq libraries by Real-Time PCR 
	NOTE: It is important to assess the signal to noise ratio of the ATAC-seq libraries prior to next-generation sequencing. This is done by determining the relative amount of DNA fragments from accessible and inaccessible loci using quantitative PCR (qPCR). The inaccessible loci (negative control, chr1:48,137,860-48,137,934 and chr1:193,093,748-193,093,827) are amplified by primer pairs 1 and 2. The accessible loci (positive control, chr19:30,336,166-30,336,253 and chr19:11546154-11546237) are amplified by primer pairs 3 and 4. Positive and negative loci were defined from chromatin accessibility (DHS-seq) profiles of human CD4+ cells (ENCODE accessions ENCSR000EQE and ENCSR000EQG). Negative primer 1 is located in a large heterochromatic intergenic region (230 kb from TRADB2 and 88 kb from FOXD2). Negative control region 2 is in within the first intron of CDC73 gene. Positive primer pair 3 is within an open chromatin region downstream of the Cyclin E (CCNE1) gene while positive primer pair 4 is centered within the promoter of protein kinase C substrate 80K-H (PRKCSH). Importantly, these control loci show a similar pattern of accessibility in other human cell types from the ENCODE project3, suggesting that they can be applied to monitor ATAC-seq libraries from a broad spectrum of human cell types. Primer efficiency and specificity of all primer pairs was verified by qPCR on a serial dilution of genomic DNA (from human Th cells) and a melting curve analysis of obtained amplified products.
	<ol>
		<li>Isolate genomic DNA using a commercially available kit (see Table of Materials/Equipment).</li>
		<li>Dilute the amplified ATAC-seq library 1:10 (1 &#181;L library + 9 &#181;L of nuclease-free water) and genomic DNA to ~5 ng/&#181;L.</li>
		<li>Prepare reaction mixture (Table 7) for each positive and negative control primer pair, taking into account that the reactions are performed in triplicate.</li>
		<li>Incubate in qPCR thermal cycler according to the protocol recommended by qPCR master mix supplier.</li>
		<li>Analyze the results in qPCR instrument&#39;s software (see Table of Materials/Equipment). Choose genomic DNA as a control sample. The obtained values represent an enrichment of accessible regions (amplified by positive control primers). An example is shown in Table 8. 
		NOTE: Estimation of the average library size and concentration: the size distribution of DNA fragments from ATAC-seq libraries is determined by high-sensitivity automated electrophoresis systems according to the manufacturer&#39;s instructions. It is advisable to measure the sample concentration on a fluorometer using dsDNA high-sensitivity kit and at least 2 &#181;L of each DNA sample. 
		NOTE: Next-generation sequencing - prior to multiplexing the libraries, calculate the molarity of each ATAC-seq library by using the formula: (ng/&#181;L x 106)/(660 x average library fragment length). Aim for &#62; 30 million reads of each ATAC-seq library to assess open chromatin regions of human samples. If you wish to determine if the library is good enough for NGS sequencing, initially aim for ~10 million reads (5% of the sequencing lane on DNA sequencing instrument in rapid run mode). Keep in mind that to infer nucleosome positioning, paired-end sequencing is needed1.</li>
	</ol>
	</li>
</ol>
7. Analysis of the Obtained Next-Generation Sequencing Results
<ol>
	<li>Infer the quality of the sequencing reads by inspecting the FastQC files, separately for every library.</li>
	<li>Align the reads to human reference genome (hg19 assembly) using Bowtie21 software in the Unix/Linux environment. The command is &#39;bowtie -m 1 -q -S genome directory reads.fastq output_aligned.sam&#39;. Genome directory stands for the folder where the genome indexes of Bowtie are stored. The parameter -m 1 is for not allowing alignment of reads to more than one locus in the genome, -q is for the input file that should be in fastq format, -S is for the output that is in SAM format.</li>
	<li>Remove duplicate reads using SAMtools22 rmdup option in the Unix/Linux environment. The commands are &#39;samtools view -S output_aligned.sam -b | samtools sort -o -output_aligned &#62; output_aligned.bam&#39;, 
	samtools rmdup -s output_aligned.bam output_aligned _rmdup.bam&#39;. The first command, view, changes the SAM format into BAM format which is then sorted. The option of rmdup is then applied on the sorted BAM file. Optionally one can adjust the reads for the transposon insertion site, as described in the original ATAC-seq paper1.This is done using BEDtools commands23 in the Unix/Linux environment. The commands are &#39;bamToBed -i output_aligned _rmdup.bam &#62; output_aligned _rmdup.bed &#39;, &#39;shiftBed -i output_aligned _rmdup.bed -p 4 -m -5 -g genome &#62; output_aligned _rmdup_adjusted.bed&#39;. The first command, bamTobed, changes the BAM format into BED format which can then be used for the shiftBed command. The genome file is a tab delimited file that contains the length of each chromosome in the genome. The file is usually added in the BEDtools directory.</li>
	<li>Perform peak calling using model-based analysis of ChIP-seq (MACS2)24 software in the Unix/Linux environment on the shifted BED file with the following parameters: --nomodel --extsize 75 --shift -30. These parameters are used to adjust the reads so that the transposon insertion site is in the middle of each sequencing read.</li>
</ol>

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H., 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=Refined+DNase-seq+protocol+and+data+analysis+reveals+intrinsic+bias+in+transcription+factor+footprint+identification.">Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification.</a> Nat Methods. 11 (1), 73-78 (2013).</li><li>Madrigal, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+Accounting+for+Sequence-Specific+Bias+in+Genome-Wide+Chromatin+Accessibility+Experiments:+Recent+Advances+and+Contradictions.">On Accounting for Sequence-Specific Bias in Genome-Wide Chromatin Accessibility Experiments: Recent Advances and Contradictions.</a> Front Bioeng Biotechnol. 3, 1-4 (2015).</li><li>Qin, Q., 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=ChiLin:+a+comprehensive+ChIP-seq+and+DNase-seq+quality+control+and+analysis+pipeline.">ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline.</a> BMC Bioinformatics. 17 (1), (2016).</li><li>Bao, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+ATAC-seq+approach+reveals+lineage-specific+reinforcement+of+the+open+chromatin+landscape+via+cooperation+between+BAF+and+p63.">A novel ATAC-seq approach reveals lineage-specific reinforcement of the open chromatin landscape via cooperation between BAF and p63.</a> Genome Biol. 16 (1), 284 (2015).</li><li>Maurano, M. 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=Systematic+localization+of+common+disease-associated+variation+in+regulatory+DNA.">Systematic localization of common disease-associated variation in regulatory DNA.</a> Science. 337 (6099), 1190-1195 (2012).</li></ol>

The authors have nothing to disclose.

The ATAC-seq protocol described here has been successfully employed for the analysis of accessible chromatin in primary cells (human Th1, Th2 cells, and B cells) as well as the cultured cell lines (MCF10A human breast cancer cells and U261 glioblastoma cells). Applying ATAC-seq to other cell types may require some protocol optimization, especially in the lysis step. If the concentration of non-ionic detergent is too high, there may be a higher percentage of mitochondrial DNA contamination. This can be reduced by decreasi...

ATAC-seq1,2 is a robust method that enables identification of regulatory3 open chromatin regions and nucleosome positioning. This information is applied for inferring the location, identity, and activity of transcription factors. The method&#39;s sensitivity for measuring quantitative variations in chromatin structure allows the study of the activity of chromatin factors, including chromatin remodelers and modifiers, as well as the transcriptional activity of RNA polymerase II1. Thus ATAC-seq provides a powerful and unbiased approach for deciphering mechanisms that govern transcriptional regulation in any cell type of interest. We describe the adaptation of ATAC-seq to primary human Th1 and Th2 cells.
In ATAC-seq, hyperactive Tn5 transposase loaded with adaptors for next-generation sequencing (NGS) couples DNA fragmentation with tagging of DNA with adaptors (i.e., the &#34;tagmentation&#34; process)1. Following PCR amplification, the resulting DNA libraries are ready for next-generation sequencing (Figure 1). The preferential tagmentation of accessible chromatin is detected by the analysis of local enrichment of ATAC-seq sequencing reads.
The short experimental procedure and requirement for less starting material, relative to other methods for measuring chromatin accessibility and nucleosomal positioning such as DNase-seq4, FAIRE-seq5, and MNase-seq6, has promoted the use of ATAC-seq in multiple biological systems including human primary cells1,7 and clinical samples8, as well as unicellular organisms9, plants10, fruit flies11, and various mammals12.
The identity of transcription factors which are bound to accessible loci can be uncovered by analyzing the enrichment of their binding sequence motifs or combining ATAC-seq with chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq). This approach enabled the identification of lineage-specific transcription factors important for hematopoiesis in mouse13. The unbiased and global nature of ATAC-seq allows studying gene regulation in organisms for which reagents such as antibodies for ChIP analysis are not available. For example, evolutionary variations in cis-regulatory regions have been identified by studying cranial neural crest cells from humans and chimpanzees14, developmental variations in regulatory elements during early mouse embryogenesis15, changes in the regulatory landscape during a life cycle of unicellular C. owzarzaki9, and the evolution of promoters and enhancers across 20 mammal species12.
ATAC-seq has also been instrumental for measuring chromatin accessibility in single cells, thus revealing variability within cell populations, which usually evades genome-wide studies7,16. In addition, ATAC-seq can be used to study changes that occur in DNA regulatory regions in disease conditions, in which samples are rare. For example, ATAC-seq can be used to study changes in the regulatory landscape during the onset of acute myeloid leukemia (AML)17 or Ras-driven oncogenesis11.

<table><tbody><tr><td>50 mL tubes</td><td>Lumitron</td><td>LUM-CFT011500-P</td><td>Can be from other vendors.</td></tr><tr><td>Microtubes</td><td>Axygen Inc</td><td>MCT-175-C</td><td>Can be from other vendors.</td></tr><tr><td>25 mL serological pipettes</td><td>Corning Costar</td><td>4489</td><td>Can be from other vendors.</td></tr><tr><td>Tissue culture flask</td><td>Lumitron</td><td>LUM-TCF-012-250-P</td><td>Can be from other vendors.</td></tr><tr><td>Countes Automated Cell Counter</td><td>Invitrogen</td><td>C10227</td><td></td></tr><tr><td>NucleoSpin Tissue</td><td>MACHEREY-NAGEL</td><td>740952.5</td><td></td></tr><tr><td>Peripheral blood mononuclear cells&amp;nbsp;(PBMC)</td><td>ATCC</td><td>&amp;nbsp;PCS&amp;shy;800&amp;shy;011</td><td>Can be from other vendors.</td></tr><tr><td>RPMI 1640 Medium</td><td>Biological Industries</td><td>01-103-1A</td><td>Can be from other vendors.</td></tr><tr><td>L-Glutamine Solution (200 mM)</td><td>Biological Industries</td><td>03-020-1B</td><td>Can be from other vendors.</td></tr><tr><td>Penicillin-Streptomycin</td><td>Biological Industries</td><td>03-031-1B</td><td>Can be from other vendors.</td></tr><tr><td>Fetal Bovine Serum (FBS),&amp;nbsp;Heat Inactivated, European Grade</td><td>Biological Industries</td><td>04-127-1</td><td>Can be from other vendors.</td></tr><tr><td>MACS CD4 microbeads, human</td><td>Miltenyi Biotec</td><td>130-045-101</td><td></td></tr><tr><td>MACS MS columns</td><td>Miltenyi Biotec</td><td>130-042-201</td><td></td></tr><tr><td>Anti-Human CD4 FITC</td><td>Biogems</td><td>06121-50</td><td></td></tr><tr><td>Mouse IgG1 Isotype Control FITC</td><td>Biogems</td><td>44212-50</td><td></td></tr><tr><td>Anti-Human CD3 (OKT3)</td><td>Tonbo biosciences</td><td>40-0037</td><td></td></tr><tr><td>Anti-Human CD28 SAFIRE Purified</td><td>Biogems</td><td>10311-25</td><td></td></tr><tr><td>Recombinant Human IL2</td><td>Peprotech</td><td>200-02</td><td></td></tr><tr><td>Recombinant Human IL4</td><td>Peprotech</td><td>200-04</td><td></td></tr><tr><td>Recombinant Human IL12 p70</td><td>Peprotech</td><td>200-12</td><td></td></tr><tr><td>In Vivo Ready Anti-Human IL-4 (MP4-25D2)</td><td>Tonbo</td><td>40-7048</td><td></td></tr><tr><td>LEAF&amp;nbsp;Purified anti-human IFN-&amp;gamma;</td><td>BioLegend</td><td>506513</td><td></td></tr><tr><td>NaCl, analytical grade</td><td>Carlo Erba</td><td>479687</td><td>Can be from other vendors.</td></tr><tr><td>Magnesium chloride, Hexahydrate, molecular biology grade</td><td>Calbiochem</td><td>442611</td><td>Can be from other vendors.</td></tr><tr><td>EDTA</td><td>MP Biomedicals</td><td>800682</td><td>Can be from other vendors.</td></tr><tr><td>Tris, ultra pure, 99.9% pure</td><td>MP Biomedicals</td><td>819620</td><td>Can be from other vendors.</td></tr><tr><td>NP-40 alternative (Nonylphenyl Polyethylene Glycol)</td><td>Calbiochem</td><td>492016</td><td>Can be from other vendors.</td></tr><tr><td>Protease Inhibitors</td><td>Sigma</td><td>P2714</td><td>this protease inhibitor coctail is a powder. To make 100 x solution dilute in 1 mL of molecular-biology grade water.</td></tr><tr><td>Magnetic solid phase reverse immobilization beads: AMPure XP beads</td><td>Beckman</td><td>63881</td><td></td></tr><tr><td>PCR purification kit</td><td>HyLabs</td><td>EX-GP200</td><td>Can be from other vendors.</td></tr><tr><td>Nextera DNA Library Preparation Kit (TDE1 transposase and TD buffer)</td><td>Illumina</td><td>FC-121-1030</td><td></td></tr><tr><td>NEBNext High-Fidelity 2 x PCR Master Mix</td><td>New England BioLabs</td><td>M0541</td><td></td></tr><tr><td>NEBNext Q5 Hot Start HiFi PCR Master Mix</td><td>New England BioLabs</td><td>M0543</td><td></td></tr><tr><td>SYBR Green I&amp;nbsp;</td><td>Invitrogen</td><td>S7585</td><td></td></tr><tr><td>&amp;nbsp;CFX Connect Real-Time PCR&amp;nbsp;Detection&amp;nbsp;System</td><td>Bio-rad</td><td>185-5200</td><td>Can be from other vendors.</td></tr><tr><td>CFX Manager Software</td><td>Bio-rad</td><td>1845000</td><td></td></tr><tr><td>master mix for qPCR: iTaq Universal SYBR Green Supermix</td><td>Bio-rad</td><td>172-5124</td><td>Can be from other vendors.</td></tr><tr><td>Qubit fluorometer 2.0</td><td>Invitrogen</td><td>Q32866</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>Invitrogen</td><td>Q32854</td><td></td></tr><tr><td>Magnet for eppendorf tubes</td><td>Invitrogen</td><td>12321D</td><td>Can be from other vendors.</td></tr><tr><td>Swing bucket cooling centrifuge with the buckets for 15 mL falcon tubes and eppendorf tubes</td><td>Thermo Scientific</td><td>75004527</td><td>Could be from other vendors. It is important that it has buckets for eppendorf tubes.</td></tr><tr><td>Thermo-shaker</td><td>MRC</td><td></td><td>Can be from other vendors.</td></tr><tr><td>High Sensitivity D1000 ScreenTape</td><td>Agilent Technologies</td><td>5067-5584</td><td></td></tr><tr><td>High Sensitivity D1000 Reagents</td><td>Agilent Technologies</td><td>5067-5585</td><td></td></tr><tr><td>4200 TapeStation system</td><td>Agilent Technologies</td><td>G2991AA</td><td>Tape-based platform for&amp;nbsp; electrophoresis</td></tr><tr><td>High Sensitivity DNA kit</td><td>Agilent Technologies</td><td>5067-4626</td><td>Reagent for high-sensitivity TapeStation analysis</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>Primer name and sequence</td><td>Company</td><td></td><td></td></tr><tr><td>Ad1_noMX: 5&#039;-AATGATACGGCGACCACCGAGA&lt;br/&gt; TCTACACTCGTCGGCAGCGTC&lt;br/&gt; AGATGTG-3&#039;</td><td>IDT</td><td></td><td>Ad1-noMx: 5&#039;-P5 sequence-transposase sequence-3&#039;</td></tr><tr><td>Ad2.1_TAAGGCGA: 5&#039;-CAAGCAGAAGACGGCATACGAG&lt;br/&gt; AT[TCGCCTTA]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.1_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.2_CGTACTAG: 5&#039;-CAAGCAGAAGACGGCATACGAG&lt;br/&gt; AT[CTAGTACG]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.2_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.3_AGGCAGAA: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[TTCTGCCT]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.3_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.4_TCCTGAGC: 5&#039;-CAAGCAGAAGACGGCATACGAG&lt;br/&gt; AT[GCTCAGGA]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.4_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.5_GGACTCCT: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[AGGAGTCC]GTCTCGTGGG&lt;br/&gt; CTCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.5_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.6_TAGGCATG: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[CATGCCTA]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.6_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.7_CTCTCTAC: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[GTAGAGAG]GTCTCGTGGG&lt;br/&gt; CTCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.7_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.8_CAGAGAGG: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[CCTCTCTG]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.8_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.9_GCTACGCT: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[AGCGTAGC]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.9_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.10_CGAGGCTG: 5&#039;-CAAGCAGAAGACGGCATACG&lt;br/&gt; AGAT[CAGCCTCG]GTCTCGTGG&lt;br/&gt; GCTCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.10_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.11_AAGAGGCA: 5&#039;-CAAGCAGAAGACGGCATACG&lt;br/&gt; AGAT[TGCCTCTT]GTCTCGTGGG&lt;br/&gt; CTCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.11_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.12_GTAGAGGA: 5&#039;-CAAGCAGAAGACGGCATACG&lt;br/&gt; AGAT[TCCTCTAC]GTCTCGTGGG&lt;br/&gt; CTCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.12_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.13_GTCGTGAT: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[ATCACGAC]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.13_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.14_ACCACTGT: 5&#039;- CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[ACAGTGGT]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.14_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.15_TGGATCTG: 5&#039;- CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[CAGATCCA]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.15_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.16_CCGTTTGT: 5&#039;- CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[ACAAACGG]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.16_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>&amp;nbsp;Ad2.17_TGCTGGGT: 5&#039;- CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[ACCCAGCA]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.17_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>&amp;nbsp;Ad2.18_GAGGGGTT: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[AACCCCTC]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.18_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.19_AGGTTGGG: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[CCCAACCT]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.19_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>&amp;nbsp;Ad2.20_GTGTGGTG: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[CACCACAC]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.20_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>&amp;nbsp;Ad2.21_TGGGTTTC: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[GAAACCCA]GTCTCGTGGGC&lt;br/&gt; TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.21_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.22_TGGTCACA: 5&#039;- CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[TGTGACCA]GTCTCGTGGGC&lt;br/&gt;TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.22_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.23_TTGACCCT: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[AGGGTCAA]GTCTCGTGGGC&lt;br/&gt;TCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.23_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td>Ad2.24_CCACTCCT: 5&#039;-CAAGCAGAAGACGGCATACGA&lt;br/&gt; GAT[AGGAGTGG]GTCTCGTGGG&lt;br/&gt;CTCGGAGATGT-3&#039;</td><td>IDT</td><td></td><td>Ad2.24_expected index sequence read: 5&#039;-P7 sequence-[index sequence]-transposase sequence-3&#039;</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>F1: 5&#039;-CCTTTTTATTTGCCCATACACTC-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>R1: 5&#039;-CCCAGATAGAAAGTTGGAGAGG-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>F2: 5&#039;-TTGAGGGATGCCATAACAGTC-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>R2: 5&#039;-CTGCTGAACAACATCCTTCAC-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>F3: 5&#039;-GGTTTGCAGGTTGCGTTG-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>R3: 5&#039;-AGAGGAATCTGGGAGTGACG-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>F4: 5&#039;-TGCTCATTCCGTTTCCCTAC-3&#039;</td><td>IDT</td><td></td><td></td></tr><tr><td>R4: 5&#039;-AGCCGGAAAGAAAGTTCCTG-3&#039;</td><td>IDT</td><td></td><td></td></tr></tbody></table>

mapping genome-wide accessible chromatin in primary human t lymphocytes by atac-seq

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

The final outcome of this protocol is an ATAC-seq library of typically 3 - 20 ng/&#181;L. When run on a system for DNA integrity analysis (see Table of Materials/Equipment), they show ladder-like appearance2 (Figure 3A). The average size of DNA fragments is typically ~450 - 530 bp.
Proper quality control of the ATAC-seq libraries prior to performing next-gene...

Watch this Scientific Journal Video about Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq at JoVE.com

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

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

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

mapping-genome-wide-accessible-chromatin-primary-human-t-lymphocytes

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JoVE Journal

Genetics

18.0K Views.  Bar-Ilan University. Assay voor Transposase-toegankelijk chromatine in combinatie met high-throughput sequencing (ATAC-seq) is een genoom-brede methode aan het licht brengen van toegankelijke chromatine. Dit is een stapsgewijze ATAC-seq-protocol, van moleculaire aan computationele uiteindelijk geoptimaliseerd voor menselijke lymfocyten (Th1/Th2). Dit protocol kan worden aangenomen door onderzoekers zonder voorafgaande ervaring in volgorde van volgende-generatie methoden.

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

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.

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 ...

Cancer Research

ATAC-seq Assay with Low Mitochondrial DNA Contamination from Primary Human CD4+ T Lymphocytes

ATAC-seq has become a widely used methodology in the study of epigenetics due to its rapid and simple approach to mapping genome-wide accessible chromatin. In this paper we present an improved ATAC-seq protocol that reduces contaminating mitochondrial DNA reads. While previous ATAC-seq protocols have struggled with an average of 50% contaminating mitochondrial DNA reads, the optimized&#160;lysis buffer introduced in this protocol reduces mitochondrial DNA contamination to an average of 3%. This improved ATAC-seq protocol allows for a near 50% reduction in the sequencing cost. We demonstrate how these high-quality ATAC-seq libraries can be prepared from activated CD4+ lymphocytes, providing step-by-step instructions from CD4+ lymphocyte isolation from whole blood through data analysis. This improved ATAC-seq protocol has been validated in a wide range of cell types and will be of immediate use to researchers studying chromatin accessibility.

Here, we present a protocol to perform an assay for transposase-accessible chromatin sequencing (ATAC-seq) on activated CD4+ human lymphocytes. The protocol has been modified to minimize contaminating mitochondrial DNA reads from 50% to 3% through the introduction of a new lysis buffer.

ATAC-seq has become a widely used methodology in the study of epigenetics due to its rapid and simple approach to mapping genome-wide accessible ...

Adipocyte-Specific ATAC-Seq with Adipose Tissues Using Fluorescence-Activated Nucleus Sorting

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is a robust technique that enables genome-wide chromatin accessibility profiling. This technique has been useful for understanding the regulatory mechanisms of gene expression in a range of biological processes. Although ATAC-seq has been modified for different types of samples, there have not been effective modifications of ATAC-seq methods for adipose tissues. Challenges with adipose tissues include the complex cellular heterogeneity, large lipid content, and high mitochondrial contamination. To overcome these problems, we have developed a protocol that allows adipocyte-specific ATAC-seq by employing fluorescence-activated nucleus sorting with adipose tissues from the transgenic reporter Nuclear tagging and Translating Ribosome Affinity Purification (NuTRAP) mouse. This protocol produces high-quality data with minimal wasted sequencing reads while reducing the amount of nucleus input and reagents. This paper provides detailed step-by-step instructions for the ATAC-seq method validated for the use of adipocyte nuclei isolated from mouse adipose tissues. This protocol will aid in the investigation of chromatin dynamics in adipocytes upon diverse biological stimulations, which will allow for novel biological insights.

We present a protocol for assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) specifically on adipocytes using nucleus sorting with adipose tissues isolated from transgenic reporter mice with nuclear fluorescence labeling.

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is a robust technique that enables genome-wide chromatin ...

CATCH-UP: A High-Throughput Upstream-Pipeline for Bulk ATAC-Seq and ChIP-Seq Data

Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP), coupled with next-generation sequencing (NGS), have revolutionized the study of gene regulation. A lack of standardization in the analysis of the highly dimensional datasets generated by these techniques has made reproducibility difficult to achieve, leading to discrepancies in the published, processed data. Part of this problem is due to the diverse range of bioinformatic tools available for the analysis of these types of data. Secondly, a number of different bioinformatic tools are required sequentially to convert raw data into a fully processed and interpretable output, and these tools require varying levels of computational skills. Furthermore, there are many options for quality control that are not uniformly employed during data processing. We address these issues with a complete assay for transposase-accessible chromatin sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) upstream pipeline (CATCH-UP), an easy-to-use, Python-based pipeline for the analysis of bulk ChIP-seq and ATAC-seq datasets from raw fastq files to visualizable bigwig tracks and peaks calls. This pipeline is simple to install and run, requiring minimal computational knowledge. The pipeline is modular, scalable, and parallelizable on various computing infrastructures, allowing for easy reporting of methodology to enable reproducible analysis of novel or published datasets.

ATAC-seq and ChIP-seq allow detailed investigation of gene regulation; however, processing these data types is challenging and often inconsistent between research groups. We present CATCH-UP: an easy-to-use computational pipeline that allows standardized and reproducible data processing and analysis of new and published ATAC/ChIP-seq datasets.

Assay for transposase-accessible chromatin (ATAC) and chromatin immunoprecipitation (ChIP), coupled with next-generation sequencing (NGS), have ...

ATAC-Seq Optimization for Cancer Epigenetics Research

The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) probes deoxyribonucleic acid (DNA) accessibility using the hyperactive Tn5 transposase. Tn5 cuts and ligates adapters for high-throughput sequencing within accessible chromatin regions. In eukaryotic cells, genomic DNA is packaged into chromatin, a complex of DNA, histones, and other proteins, which acts as a physical barrier to the transcriptional machinery. In response to extrinsic signals, transcription factors recruit chromatin remodeling complexes to enable access to the transcriptional machinery for gene activation. Therefore, identifying open chromatin regions is useful when monitoring enhancer and gene promoter activities during biological events such as cancer progression. Since this protocol is easy to use and has a low cell input requirement, ATAC-seq has been widely adopted to define open chromatin regions in various cell types, including cancer cells. For successful data acquisition, several parameters need to be considered when preparing ATAC-seq libraries. Among them, the choice of cell lysis buffer, the titration of the Tn5 enzyme, and the starting volume of cells are crucial for ATAC-seq library preparation in cancer cells. Optimization is essential for generating high-quality data. Here, we provide a detailed description of the ATAC-seq optimization methods for epithelial cell types.

ATAC-seq is a DNA sequencing method that uses the hyperactive mutant transposase, Tn5, to map changes in chromatin accessibility mediated by transcription factors. ATAC-seq enables the discovery of the molecular mechanisms underlying phenotypic alterations in cancer cells. This protocol outlines optimization procedures for ATAC-seq in epithelial cell types, including cancer cells.

The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) probes deoxyribonucleic acid (DNA) accessibility using the ...

Nuclei Isolation from Mouse Cardiac Progenitor Cells at Single-Cell Resolution

Nuclei Isolation from Mouse Cardiac Progenitor Cells for Epigenome and Gene Expression Profiling at Single-Cell Resolution

The developing heart is a complex structure containing various progenitor cells controlled by complex regulatory mechanisms. The examination of the gene expression and chromatin state of individual cells allows the identification of the cell type and state. Single-cell sequencing approaches have revealed a number of important characteristics of cardiac progenitor cell heterogeneity. However, these methods are generally restricted to fresh tissue, which limits studies with diverse experimental conditions, as the fresh tissue must be processed at once in the same run to reduce the technical variability. Therefore, easy and flexible procedures to produce data from methods such as single-nucleus RNA sequencing (snRNA-seq) and the single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC-seq) are needed in this area. Here, we present a protocol to rapidly isolate nuclei for subsequent single-nuclei dual-omics (combined snRNA-seq and snATAC-seq). This method allows the isolation of nuclei from frozen samples of cardiac progenitor cells and can be combined with platforms that use microfluidic chambers.

Author Spotlight: Nuclei Isolation from Mouse Cardiac Progenitor Cells for Epigenome and Gene Expression Profiling at Single-Cell Resolution  

Here, we present a protocol describing cell nuclei preparation. After microdissection and enzymatic dissociation of cardiac tissue into single cells, the progenitor cells were frozen, followed by isolation of pure viable cells, which were used for single-nucleus RNA sequencing and the single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing analyses.

The developing heart is a complex structure containing various progenitor cells controlled by complex regulatory mechanisms. The examination of the gene ...

Developmental Biology

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing (ChIP-seq)

ChIP-sequencing (ChIP-seq) methods directly offer whole-genome coverage, where combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing can be utilized to identify the repertoire of mammalian DNA sequences bound by transcription factors in vivo. "Next-generation" genome sequencing technologies provide 1-2 orders of magnitude increase in the amount of sequence that can be cost-effectively generated over older technologies thus allowing for ChIP-seq methods to directly provide whole-genome coverage for effective profiling of mammalian protein-DNA interactions.
For successful ChIP-seq approaches, one must generate high quality ChIP DNA template to obtain the best sequencing outcomes. The description is based around experience with the protein product of the gene most strongly implicated in the pathogenesis of type 2 diabetes, namely the transcription factor transcription factor 7-like 2 (TCF7L2). This factor has also been implicated in various cancers.
Outlined is how to generate high quality ChIP DNA template derived from the colorectal carcinoma cell line, HCT116, in order to build a high-resolution map through sequencing to determine the genes bound by TCF7L2, giving further insight in to its key role in the pathogenesis of complex traits.

Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq

The combination of chromatin immunoprecipitation and ultra-high-throughput sequencing (ChIP-seq) can identify and map protein-DNA interactions in a given tissue or cell line. Outlined is how to generate a high quality ChIP template for subsequent sequencing, using experience with the transcription factor TCF7L2 as an example.

ChIP-sequencing (ChIP-seq) methods directly  offer whole-genome coverage, where combining chromatin immunoprecipitation  (ChIP) and massively parallel ...

Biology

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during development and in disease states. Single-cell RNA sequencing allows us to comprehensively profile gene expression changes observed in retinal development and disease at a cellular level, while single-cell ATAC-Seq allows analysis of chromatin accessibility and transcription factor binding to be profiled at similar resolution. Here the use of these techniques in the developing retina is described, and MULTI-Seq is demonstrated, where individual samples are labeled with a modified oligonucleotide-lipid complex, enabling researchers to both increase the scope of individual experiments and substantially reduce costs.

Here, the authors showcase the utility of MULTI-seq for phenotyping and subsequent paired scRNA-seq and scATAC-seq in characterizing the transcriptomic and chromatin accessibility profiles in retina.

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during ...

Neuroscience

Scalable ATAC-seq Protocol for Chromatin Accessibility in Rodent Neutrophils

ATAC-Seq Library Preparation of Murine Bone Marrow-Derived Neutrophils

Assay for Transposase-Accessible Chromatin with sequencing (ATAC-seq) is a powerful, high-throughput technique for assessing chromatin accessibility and understanding epigenomic regulation. Neutrophils, as a crucial leukocyte type in immune responses, undergo substantial chromatin architectural changes during differentiation and activation, which significantly impact the gene expression necessary for their functions. ATAC-seq has been instrumental in uncovering key transcription factors in neutrophil maturation, revealing pathogen-specific epigenomic signatures, and identifying therapeutic targets for autoimmune diseases. However, neutrophils' sensitivity to the external milieu complicates high-quality ATAC-seq data production. Here, we propose a scalable protocol for preparing ATAC-seq libraries from rodent bone marrow-derived neutrophils, featuring improved immunomagnetic separation to ensure optimal cell viability and high-quality libraries. The vital elements impacting the library quality and optimization principles for methodological extension are discussed in detail. This protocol will support the researchers who are willing to study the chromatin architecture and epigenomic reprogramming of neutrophils, advancing studies in basic and clinical immunology.

This manuscript outlines a protocol for preparing an ATAC-seq library of neutrophils from murine bone marrow, aiming to guarantee optimal neutrophil viability and high library quality. It offers step-by-step instructions on BMC preparation, immunomagnetic sorting, and library construction, serving as a valuable guide, especially for newcomers to studying neutrophils.

Assay for Transposase-Accessible Chromatin with sequencing (ATAC-seq) is a powerful, high-throughput technique for assessing chromatin accessibility and ...

Immunology and Infection

TChIP-Seq: Cell-Type-Specific Epigenome Profiling

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

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

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

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

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