这项工作得到以色列科学基金会 (赠款 748/14)、居里夫人综合赠款 (FP7-PEOPLE-20013-CIG-618763) 和计划和预算编制委员会的核心方案和以色列科学基金会赠款41/11 的支持。

所有的程序都是在宜兰大学的机构审查委员会批准的, 《议定书》遵循委员会批准实验的准则. 
 1. 纯化 Na 和 #239; ve 人类 CD4+ 细胞和极化到 T 帮助器 1 (Th1) 和 Th2 细胞 注意: 这里我们描述从冷冻人外周血单个核细胞 (PBMCs) 开始的过程。第一步包括使用微和列来隔离 CD4+ 细胞, 通常给我们提供超过95% 的 CD4+ 细胞。但是, 根据每个实验室的首选协议, 此步骤可能会有所不同。詹纳 et al. 修改了 T 细胞活化和极化的协议(2009) 18 . 从 1000万 PBMCs CD4+ 细胞的分离使 CD4+ 细胞产生 4-600万。它们被分成两个烧瓶, 生长在 Th1 和 Th2 极化条件下, 在短短一周内就能产生3-5 百万 Th1 和 Th2 细胞. 
 注意: 在启动前将离心机冷却到4和 #176; C. 
<ol> <li> 将1毫升的人类 PBMCs (10 7 细胞) 融化在含有10毫升 RPMI 培养基的50毫升管中, 并辅以1% 青霉素-链霉素、2毫米 l-谷氨酰胺和10% 热灭活胎牛血清。离心机在 500 x g 为5分钟, 去除上清和重在15毫升的补充 RPMI 培养基中的无菌25毫升吸管的细胞。将细胞 (无菌25毫升吸管) 转移到 T75 培养瓶中. </li>
	<li> 在湿恒温箱中过夜 (37 和 #176; C, 5% CO 2 ). </li>
	<li> 将无菌25毫升吸管移至50毫升管的浮动电池。通过台盼蓝排除确定细胞数和生存能力. </li>
	<li> 根据制造商和 #39 的建议, 使用 CD4+ 微和列将 CD4+ 单元从1000万活换药 PBMCs 中分离出来 (请参见 "材料/设备表" ), 其内容如下修改:10 7 PBMCs 标记为30和 #181; 120 和 #181 中的 CD4 微; l 0.5% BSA 在 PBS. </li>
	<li> rhIL-2 (10 ng/毫升)、anti-CD3 (5 和 #181; g/毫升) 和可溶性 anti-CD28 (2 和 #181; g/毫升) 激活 CD4+ T 细胞72小时。对于 Th1 极化, 添加 rhIL-12 (20 ng/毫升) 和 anti-IL-4 (10 和 #181; g/毫升)。对于 Th2 极化, 添加 rhIL-4 (40 ng/毫升) 和抗干扰素 #947; (10 和 #181; g/毫升). </li>
	<li> 在 rhIL-2 (10 ng/毫升) 和相同的极化细胞因子 (rhIL-12 Th1 和 rhIL-4 Th2) 的情况下, 将细胞培养为额外的7天. </li>
</ol> 2。细胞核隔离 注意: ATAC-seq 是用完整的细胞核进行的。裂解缓冲液含有0.05% 壬聚乙二醇 (见 材料表/设备 ) 被校准, 用于分离细胞核从主要人类 Th1 和 Th2 细胞。我们建议用实验室试剂和细胞来校准这一步。多余的完整的细胞从不足的洗涤剂降低了转移反应的效率。细胞裂解效率取决于细胞核 (台盼蓝阳性细胞) 相对于细胞总数的数量. 
注: 准备溶解缓冲液 (10 毫米三盐酸, pH 7.5, 10 毫米氯化钠, 3 毫米氯化镁 2 )。冷却的离心机与摆动斗转子到4和 #176; c. 将摇摆桶离心机中的细胞改为固定角度的离心机, 可以减少细胞/细胞核的损耗。为了避免细胞核或细胞丢失, 吸管在丢弃上清液时要小心. 
<ol> <li> 添加新鲜的壬聚乙二醇 (最终浓度为 0.05%) 和100x 蛋白酶抑制剂 (最终浓度为 1x), 并在使用前立即加入冷裂解缓冲液。把缓冲器放在冰上. </li>
	<li> 计数 T 细胞, 以确定其数量和可行性使用台盼蓝方法。低于90% 的存活率会导致非特异性消化能力的提高. </li>
	<li> 传输 0.5 x 10 6 T 单元格 (Th1 或 Th2) 到 1.5 mL 管内。在4和 #176 旋转 500 x g, 5 分钟; C. </li>
	<li> 重1毫升冷磷酸盐缓冲盐水 (PBS) 溶液中的细胞颗粒。在4和 #176 旋转 500 x g, 5 分钟; C. </li>
	<li> 重1毫升冷裂解缓冲液中的细胞颗粒 (含壬聚乙二醇和蛋白酶抑制剂)。把管子放在冰上。轻轻吸管以避免扰乱细胞核. </li>
	<li> 快速采取10和 #181; L 和计数的细胞与一个自动的细胞计数器, 而微与裂解细胞是在冰上。这一步骤不应超过五分钟, 以避免破坏原子核。至少有80% 的细胞应该是裂解的. </li>
	<li> 立即继续换位反应。把准备好的原子核留在冰上. </li>
</ol> 3。转位反应 注意: 在这一步中, 分离的细胞核与原核 Tn5 转 (TDE1) 一起被装上装有适配器的 NGS 测序。多动 Tn5 同时片段 DNA 和 ligates 适配器进入基因组 (tagmentation 过程) 的可及区域。细胞核和 Tn5 转之间的比值是在可获得的染色质上的优先劈裂的关键。该协议是校准10万核在一个100和 #956; L 反应量。但是, 反应可以缩小. 
<ol> <li> 将热振荡器中的温度设置为37和 #176; C. </li>
	<li> 将10万原子核转移到1.5 毫升微. </li>
	<li> 离心机在 500 x g 为10分钟, 在4和 #176; C 和轻轻地去除上清. </li>
	<li> 将转位反应元件添加到 表 1 中指定的原子核中. </li>
	<li> 重由柔和的移. </li>
	<li> 在37和 #176 的热振荡器中孵育换位反应; C 为30分钟, 轻柔摇动 (500 rpm). 
	注: DNA 清除是由固体相可逆固定化珠 19 (见材料/设备的 表 ) 或 PCR 纯化柱。在清理结束时, 洗20和 #181 的 DNA 片段; 10 毫米的 L-HCl, pH 值8。在洗脱缓冲液中避免 EDTA. </li>
</ol> 4。ATAC 序列库的 PCR 富集 注意: 这一步的目的是放大 ATAC-seq 库 即: 带有插入适配器的 DNA 片段。为了允许在相同的下一代测序车道 (和 #34; 多路传输和 #34;) 使用索引入门 1 (Ad1_noMx) 1 对所有示例和一个不同的索引 (扫描) 入门 2 (Ad 2.1-2.24) 的混合 ATAC每个示例的 1 。底漆序列在补充的 材料/设备表 中提供. 
<ol> <li> 初始 PCR 放大 注: 底漆 1 (Ad1_noMx) 和底漆2的工作浓度为25和 #181; m。所有底漆均由100和 #181 的原始库存稀释; m 到25和 #181; m。在所有的 PCR 反应中, 使用底漆 1 (Ad1_noMx) 和只有一个索引引物2。
	<ol> <li> 将在 表 2 中指定的 pcr 反应的组成部分添加到无菌 pcr 管中。
		<li> 将 pcr 管放入热循环, 并使用详细的循环条件进行 pcr 放大 表 3 . </li>
	</ol> </li> <li> 附加放大周期数的评估 注: 附加 PCR 周期的数量应产生足够数量的库片段 f或成功的下一代顺序运行, 同时最小化以避免 GC 和大小偏差 20 。通过定量 pcr (qPCR) 对最佳文库片段扩增所需 pcr 周期 ( N ) 的数量进行了测定。
	<ol> <li> 稀释底漆 1 (Ad1_noMx) 和 2 (用于初始库放大) 从25和 #181; m 到6.25 和 #181; m. </li>
		<li> 将组件添加到荧光 PCR 管或如 表 4 . </li>
		<li> 在 qPCR 的仪器和周期中指定的位置 表 5 . </li>
		<li> 估计所需的附加放大周期数 ( N )、x 轴上的绘图周期数和 y-axis 上的相对荧光 (RFU). </li>
		<li> 额外的放大倍数 ( N ) 是 qPCR 反应到达高原的周期数的1/3。 图 2 提供了三 ATAC-seq 库的示例, 它们到达了2350相对荧光单元 RFU (粗绿线)。pcr 周期的数量, 其中三分之一最大数量 (783 RFU, 标记在 y-axis) 被放大对应于8个周期为二图书馆 (红色和蓝色放大曲线) 和 9 pcr 周期为第三个图书馆 (粉红色). </li>
	</ol> </li> <li> 最终 pcr 放大 <ol> <li> 放大剩余的45和 #181; pcr 反应的 L。在热循环中, 放置一个含有步4.1.2 的扩增反应的 PCR 管。运行 表 6 中描述的 PCR 程序。使用先前确定的 (步骤 4.2.5) 扩增周期数 ( N ). </li>
	</ol> </li> </ol> 5。ATAC-seq 库的大小选择 注意: 在我们的经验中, 放大的 ATAC-seq 库的大小选择提高了下一代的测序结果, 因为它消除了高分子量库的碎片最后的 ATAC-seq 库. 
注意: 在使用前, 允许磁珠在室温下加热30分钟. 
在无核酸的水中准备新鲜的70% 乙醇. 
<ol> <li> 通过混合重磁珠. </li>
	<li> 将无核酸的水添加到 ATAC-seq 库 (在步骤4.3.1 中获得), 并使其达到100和 #181. </li>
	<li> 添加50和 #181; l (0.5x) 悬浮的 DNA 结合磁性珠子, 以100和 #181; l 的放大图书馆。混合移至少10次。室温下孵育样品5分钟。如有必要, 快速旋转管内. </li>
	<li> 将管放置在适当的磁性支架上2分钟, 将磁珠从上清液中分离出来。2分钟后, 将上清液转移到新的微. </li>
	<li> 通过移测量上清液的体积, 并加入0.7x 磁珠。混合移至少10次. </li>
	<li> 室温孵育5分钟。放置在一个磁性的立场为2分钟的 </li>
	<li> 孵育2分钟后, 丢弃上清液。添加200和 #181; 我刚做了70% 乙醇洗的珠子, 而管在磁性立场. </li>
	<li> 将微放在磁铁上, 三十年代再丢弃乙醇。重复步骤5.7.for 两个最后的乙醇洗涤. </li>
	<li> 完全清除剩余的乙醇, 并让珠子风干5分钟, 而管在磁铁上。如果必要, 简要地转动微。用 p10 吸管尖端除去乙醇的痕迹. </li>
	<li> 从磁铁中取出微, 并添加22和 #181; L 10 毫米的三盐酸, pH 8。不要洗包含 EDTA 的缓冲区中的 ATAC 序列库. </li>
	<li> 在室温下孵育2分钟的试管, 然后放置在磁性支架上. </li>
	<li> 当解决方案明确时, 将20和 #181 的洗库转换为新的无菌微. </li>
	<li> 在-20 和 #176 中存储选定的 ATAC seq 库的大小; C. </li>
</ol> 6。ATAC 序列库的质量分析 <ol> <li> 通过实时 PCR 技术验证 ATAC 序列库的质量 注意: 在下一代之前评估 ATAC 序列库的信噪比是很重要的序.这是通过定量 PCR (qPCR) 来确定从易接近和无法到达的基因组中的 DNA 片段的相对数量来完成的。无法到达的位点 (负控制, chr1:48,137,860-48,137,934 和 chr1:193,093,748-193,093,827) 被引物对1和2放大。可获得的基因座 (阳性对照, chr19:30,336,166-30,336,253 和 chr19:11546154-11546237) 被引物对3和4放大。阳性和阴性位点的定义从染色质可及性 (DHS-seq) 的人 CD4+ 细胞 (编码加入 ENCSR000EQE 和 ENCSR000EQG) 的轮廓。负引物1位于一个大的异基因区域 (230 kb 从 TRADB2 和 88 kb 从 FOXD2)。负控制区2位于 CDC73 基因的第一内含子内。正引物对3是在细胞周期蛋白 E (CCNE1) 基因的一个开放染色质区的下游, 而阳性底漆对4在蛋白质激酶 C 基板 80 k H (PRKCSH) 的启动子中居中。重要的是, 这些控制位点在编码项目 3 中显示了类似于其他人类细胞类型的可访问性模式, 这表明它们可以应用于从广泛的人类细胞类型中监视 ATAC 序列库。qPCR 对所有引物的引物效率和特异性进行了验证, 对基因组 DNA (从人类 Th 细胞) 进行了连续稀释, 并对获得的扩增产物进行了熔融曲线分析。
	<ol> <li> 使用商业上可用的工具包分离基因组 DNA (请参阅 材料/设备表 )。
		<li> 稀释放大的 ATAC-seq 库 1:10 (1 和 #181; l 库 + 9 和 #181; l 核酸) 和基因组 DNA 到 ~ 5 ng/和 #181; </li>
		<li> 准备反应混合物 ( 表 7 ) 对每个正和负控制底漆对, 考虑到反应是执行三个. </li>
		<li> 根据 qPCR 主混合供应商推荐的协议, 在 qPCR 热循环中孵化. </li>
		<li> 分析 qPCR 仪器和 #39 软件的结果 (请参阅 材料/设备表 )。选择基因组 DNA 作为对照样本。所获得的值表示可访问区域的富集 (由正控制引物放大)。 表 8 中显示了一个示例. 
		注: 平均库大小和浓度的估计: ATAC 序列库中 DNA 片段的大小分布是由基于制造商和 #39 的灵敏度自动电泳系统确定的。建议使用 dsDNA 灵敏度试剂盒和至少2和 #181 的荧光测量样品浓度. 
		注: 下一代测序-在对库进行多路复用之前, 使用公式计算每个 ATAC-seq 库的浓度: (ng/#181; x 10 6 )/(660 x 平均库片断长度)。瞄准和 #62; 3000万读取每个 ATAC-seq 库以评估开放染色质人类样本的区域。如果你想确定这个库是否足够 NGS 测序, 最初的目标是1000万读取 (在快速运行模式下 DNA 测序仪的5% 的测序车道)。请记住, 要推断核定位, 需要配对端排序 1 . </li>
	</ol> </li> </ol> 7。获得的下一代测序结果分析 <ol> <li> 通过检查 FastQC 文件, 分别为每个库推断顺序读取的质量. </li>
	<li> 在 Unix/Linux 环境中使用领结 21 软件将读取与人类参考基因组 (hg19 程序集) 对齐。命令是和 #39; 领结 1 q S 基因组目录读取. fastq output_aligned. 萨姆和 #39。基因组目录代表领结的基因组索引存储的文件夹。参数 m 1 是不允许对一个以上的基因组的读取对齐,-q 是为输入文件, 应以 fastq 格式,-s 是以 SAM 格式的输出. </li>
	<li> 在 Unix/Linux 环境中, 使用 SAMtools 22 rmdup 选项删除重复读取。命令是和 #39; samtools 视图 output_aligned. 山姆-b |samtools 排序-o-output_aligned 和 #62; output_aligned. bam 和 #39; samtools rmdup output_aligned. bam output_aligned _rmdup. bam 和 #39;。第一个命令 "视图" 将 SAM 格式更改为 BAM 格式, 然后对其进行排序。然后, 在已排序的 BAM 文件上应用 rmdup 选项。可以选择调整转插入站点的读取, 如原始 ATAC-seq 纸 1 中所述。这是在 Unix/Linux 环境中使用 BEDtools 命令 23 完成的。命令是和 #39; bamToBed output_aligned _rmdup. output_aligned _rmdup. 床和 #39;, #39; shiftBed-output_aligned _rmdup. 床-磷 4-m-5-g 基因组和 #62; output_aligned _rmdup_adjusted. 床和 #39;。第一个命令 bamTobed 将 BAM 格式更改为床格式, 然后可用于 shiftBed 命令。基因组文件是一个制表符分隔的文件, 它包含了基因组中每个染色体的长度。该文件通常在 BEDtools 目录中添加. </li>
	<li> 使用基于模型分析的芯片 seq (MACS2) 24 在移位床文件上的 Unix/Linux 环境中执行峰值调用, 具有以下参数:-nomodel-extsize 75-移位-30。这些参数用于调整读取, 以便转插入站点位于每个读取顺序的中间. </li>
</ol>

<ol>
	<li>Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., Greenleaf, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transposition+of+native+chromatin+for+fast+and+sensitive+epigenomic+profiling+of+open+chromatin,+DNA-binding+proteins+and+nucleosome+position.">Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.</a> Nat Methods. 10 (12), 1213-1218 (2013).</li><li>Buenrostro, J. D., Wu, B., Chang, H. Y., Greenleaf, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ATAC-seq:+A+method+for+assaying+chromatin+accessibility+genome-wide.">ATAC-seq: A method for assaying chromatin accessibility genome-wide.</a> Curr Protoc Mol Biol. , 21.29.1-21.29.9 (2015).</li><li>Thurman, R. E., Rynes, 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+accessible+chromatin+landscape+of+the+human+genome.">The accessible chromatin landscape of the human genome.</a> Nature. 489 (7414), 75-82 (2012).</li><li>Song, L., Crawford, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DNase-seq:+a+high-resolution+technique+for+mapping+active+gene+regulatory+elements+across+the+genome+from+mammalian+cells.">DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells.</a> Cold Spring Harb Protoc. (2), (2010).</li><li>Simon, J. M., Giresi, P. G., Davis, I. J., Lieb, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+formaldehyde-assisted+isolation+of+regulatory+elements+(FAIRE)+to+isolate+active+regulatory+DNA.">Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA.</a> Nat Protoc. 7 (2), 256-267 (2012).</li><li>Cui, K., Zhao, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Approaches+to+Determining+Nucleosome+Occupancy+in+Metazoans+Using+MNase-Seq.">Genome-Wide Approaches to Determining Nucleosome Occupancy in Metazoans Using MNase-Seq.</a> Methods Mol Biol. 833, 413-419 (2012).</li><li>Qu, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Individuality+and+Variation+of+Personal+Regulomes+in+Primary+Human+T+Cells.">Individuality and Variation of Personal Regulomes in Primary Human T Cells.</a> Cell Syst. 1 (1), 51-61 (2015).</li><li>Scharer, C. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ATAC-seq+on+biobanked+specimens+defines+a+unique+chromatin+accessibility+structure+in+na&#239;ve+SLE+B+cells.">ATAC-seq on biobanked specimens defines a unique chromatin accessibility structure in na&#239;ve SLE B cells.</a> Sci Rep. 6, 27030 (2016).</li><li>Seb&#233;-Pedr&#243;s, 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=The+Dynamic+Regulatory+Genome+of+Capsaspora+and+the+Origin+of+Animal+Multicellularity.">The Dynamic Regulatory Genome of Capsaspora and the Origin of Animal Multicellularity.</a> Cell. 165 (5), 1224-1237 (2016).</li><li>Lu, Z., Hofmeister, B. T., Vollmers, C., DuBois, R. M., Schmitz, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combining+ATAC-seq+with+nuclei+sorting+for+discovery+of+cis-regulatory+regions+in+plant+genomes.">Combining ATAC-seq with nuclei sorting for discovery of cis-regulatory regions in plant genomes.</a> Nucleic Acids Res. 45 (6), e41 (2016).</li><li>Davie, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discovery+of+Transcription+Factors+and+Regulatory+Regions+Driving+In+Vivo+Tumor+Development+by+ATAC-seq+and+FAIRE-seq+Open+Chromatin+Profiling.">Discovery of Transcription Factors and Regulatory Regions Driving In Vivo Tumor Development by ATAC-seq and FAIRE-seq Open Chromatin Profiling.</a> PLOS Genet. 11 (2), (2015).</li><li>Villar, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancer+Evolution+across+20+Mammalian+Species.">Enhancer Evolution across 20 Mammalian Species.</a> Cell. 160 (3), 554-566 (2015).</li><li>Lara-Astiaso, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chromatin+state+dynamics+during+blood+formation.">Chromatin state dynamics during blood formation.</a> Science. 345 (6199), 943-949 (2014).</li><li>Prescott, S. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancer+Divergence+and+cis-Regulatory+Evolution+in+the+Human+and+Chimp+Neural+Crest.">Enhancer Divergence and cis-Regulatory Evolution in the Human and Chimp Neural Crest.</a> Cell. 163 (1), 68-83 (2015).</li><li>Wu, 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=The+landscape+of+accessible+chromatin+in+mammalian+preimplantation+embryos.">The landscape of accessible chromatin in mammalian preimplantation embryos.</a> Nature. 534 (7609), 652-657 (2016).</li><li>Buenrostro, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+chromatin+accessibility+reveals+principles+of+regulatory+variation.">Single-cell chromatin accessibility reveals principles of regulatory variation.</a> Nature. 523 (7561), 486-490 (2015).</li><li>Corces, M. 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=Lineage-specific+and+single-cell+chromatin+accessibility+charts+human+hematopoiesis+and+leukemia+evolution.">Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.</a> Nat Genet. 48 (10), 1193-1203 (2016).</li><li>Jenner, R. 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=The+transcription+factors+T-bet+and+GATA-3+control+alternative+pathways+of+T-cell+differentiation+through+a+shared+set+of+target+genes.">The transcription factors T-bet and GATA-3 control alternative pathways of T-cell differentiation through a shared set of target genes.</a> Proc Natl Acad Sci USA. 106 (42), 17876-17881 (2009).</li><li>DeAngelis, M. M., Wang, D. G., Hawkins, T. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Solid-phase+reversible+immobilization+for+the+isolation+of+PCR+products.">Solid-phase reversible immobilization for the isolation of PCR products.</a> Nucleic Acids Res. 23 (22), 4742-4743 (1995).</li><li>Aird, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+and+minimizing+PCR+amplification+bias+in+Illumina+sequencing+libraries.">Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries.</a> Genome Biol. 12 (2), R18 (2011).</li><li>Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrafast+and+memory-efficient+alignment+of+short+DNA+sequences+to+the+human+genome.">Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.</a> Genome Biol. 10 (3), R25 (2009).</li><li>Li, H., Handsaker, 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=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25 (16), 2078-2079 (2009).</li><li>Quinlan, A. R., Hall, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BEDTools:+a+flexible+suite+of+utilities+for+comparing+genomic+features.">BEDTools: a flexible suite of utilities for comparing genomic features.</a> Bioinformatics. 26 (6), 841-842 (2010).</li><li>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>Meyer, C. A., Shirley Liu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+and+mitigating+bias+in+next-generation+sequencing+methods+for+chromatin+biology.">Identifying and mitigating bias in next-generation sequencing methods for chromatin biology.</a> Nat Rev Genet. 15 (11), 709-721 (2014).</li><li>He, H. 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>

作者没有什么可透露的。

本文所描述的 ATAC-seq 协议已经成功地用于对原细胞 (人 Th1、Th2 细胞和 B 细胞) 以及培养细胞系 (MCF10A 人乳腺癌细胞和 U261 胶质母细胞) 的染色质的分析。将 ATAC-seq 应用到其他细胞类型可能需要一些协议优化, 特别是在裂解步骤中。如果非离子洗涤剂浓度过高, 线粒体 DNA 污染的比例可能会更高。通过减少裂解液中的洗涤剂浓度而不降低细胞核的屈服率, 可以减少这种情况。在我们的经验, 裂解缓冲?...

ATAC-seq1,2是一种健壮的方法, 可以识别管理3开放染色质区域和核定位。此信息用于推断转录因子的位置、身份和活动。该方法的灵敏度测量染色质结构的数量变化, 允许研究染色质因子的活性, 包括染色质 remodelers 和修饰剂, 以及转录活性的 RNA 聚合酶 II1。因此, ATAC-seq 提供了一个强大的和无偏的方法来破译机制, 控制转录调控的任何细胞类型的利益。我们描述了 ATAC-seq 对初级人类 Th1 和 Th2 细胞的适应。
在 ATAC-seq, 多动 Tn5 转加载与适配器为下一代测序 (NGS) 夫妇的 dna 标记的 dna 与适配器 (即, "tagmentation" 进程)1。在 PCR 扩增后, 生成的 DNA 库就可以进行下一代测序 (图 1)。通过对 ATAC 序列测序的局部富集分析, 可以检测到可获得的染色质的优先 tagmentation。
相对于其他测量染色质可及性和小定位的方法, 例如 dnasei-seq4、自由 seq5和 MNase-seq6, 对较少起始材料的试验程序和要求, 具有促进在包括人类主要细胞在内的多个生物系统中使用 ATAC-seq1,7和临床样本8, 以及单细胞生物体9, 植物10, 果蝇11和各种哺乳动物12。
通过分析它们的结合序列图案的丰富性, 或者结合 ATAC 和染色质沉淀 (芯片), 再加上高通量 DNA 测序 (芯片-seq)。这种方法能够识别在小鼠13中对造血有重要影响的谱系特异转录因子。ATAC 的无偏和全球性质允许研究生物中的基因调控, 因为这些试剂如芯片分析的抗体无法获得。例如, 通过研究人类和黑猩猩的脑神经嵴细胞, 在cis调控区域中发现了进化变体14, 早期小鼠的调节元素发育变化胚胎发生15, 在单细胞C. owzarzaki9的生命周期中的调控环境的变化, 以及在20哺乳动物种类中的促进剂和促进剂的演变12。
ATAC-seq 也被用于测量单个细胞中染色质的可及性, 从而揭示细胞群中的变异性, 这通常会躲过基因组范围的研究7,16。此外, ATAC 可用于研究在疾病情况下 DNA 调控区发生的变化, 其中样本稀少。例如, ATAC-seq 可用于研究急性髓细胞白血病 (AML)17或Ras驱动的发生11的发病过程中的调节环境变化。

<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

用高通量测序法 (ATAC-seq) 测定转可接触染色质是一种用于鉴别染色质开放 (可接近) 区域的方法。这些区域代表调控的脱氧核糖核酸元素 (例如, 促进者, 促进者, 轨迹控制区域, 绝缘体) 转录因素束缚。绘制可访问的染色质景观是一个强有力的方法, 以揭示活动的调控元素的整个基因组。这一信息作为一种无偏见的方法, 以发现网络的相关转录因子和机制的染色质结构, 控制基因表达程序。ATAC-seq 是一个强大的和敏感的替代 dnasei I 超敏分析结合下一代测序 (dnasei-seq) 和甲醛辅助分离的调节元素 (自由放任) 染色体全基因组分析micrococcal 核酸敏感点 (MNase-seq) 的可访问性和测序, 以确定核定位。我们提出了一个详细的 ATAC-seq 协议优化人类原免疫细胞即CD4+ 淋巴细胞 (T 帮助 1 (Th1) 和 Th2 细胞)。这个全面的协议从细胞收获开始, 然后描述染色质 tagmentation 的分子过程, 样品准备为下一代测序, 并且也包括方法和考虑为用于的计算分析解释结果。此外, 为了节省时间和金钱, 我们引入了质量控制措施, 在排序前评估 ATAC 序列库。重要的是, 本协议中提出的原则允许它适应其他人类免疫和免疫的原细胞和细胞系。这些准则对于那些不精通下一代测序方法的实验室也将是有用的。

该协议的最终结果是一个典型的 3-20 ng/µL 的 ATAC-seq 库。当运行在一个系统的 DNA 完整性分析 (见材料表/设备), 他们显示类似梯形外观2 (图 3A)。DNA 片段的平均大小通常是 ~ 450-530 bp。
在执行下一代测序之前, 对 ATAC 序列库进行适当的质量控制对于节省时间和金钱是很重要的。...

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

转-可获得的染色质结合高通量测序 (ATAC-seq) 是一个基因组范围的方法, 以揭示可获得的染色质。这是一个 step-by 步骤的 ATAC-seq 协议, 从分子到最终的计算分析, 优化为人类淋巴细胞 (Th1/Th2)。这一协议可以被研究人员采用, 而无需事先经验的下一代测序方法。

ATAC 基因组在原发性人类 T 淋巴细胞中的染色体全易接近染色质

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

Universidad de Cartagena UDEC

Research

JoVE Journal

Genetics

17.9K 次观看.  Bar-Ilan University. 转-可获得的染色质结合高通量测序 (ATAC-seq) 是一个基因组范围的方法, 以揭示可获得的染色质。这是一个 step-by 步骤的 ATAC-seq 协议, 从分子到最终的计算分析, 优化为人类淋巴细胞 (Th1/Th2)。这一协议可以被研究人员采用, 而无需事先经验的下一代测序方法。

视频: 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.

肿瘤组织中高通量芯片测序的染色质状态全基因组图谱集成平台

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

科研

癌症研究

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.

原发性人类 CD4+ T 淋巴细胞低线粒体 DNA 污染的 ATAC-seq 检测

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 ...

遗传学

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.

使用荧光激活细胞核分选的脂肪细胞特异性ATAC-Seq与脂肪组织

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.

用于癌症表观遗传学研究的ATAC-Seq优化

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

以单细胞分辨率从小鼠心脏祖细胞中分离细胞核

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.

作者聚焦:从小鼠心脏祖细胞中分离细胞核，用于单细胞分辨率下的表观基因组和基因表达分析  

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.

从小鼠心脏祖细胞中分离细胞核，用于单细胞分辨率的表观基因组和基因表达谱分析

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

发育生物学

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.

代高通量测序技术（ChIP-seq的高品质的染色质免疫沉淀DNA模板）

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

生物学

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.

使用 scRNA-Seq 和 scATAC-Seq 对视网膜基因表达和染色质可及性进行多重分析

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

ATAC 基因组在原发性人类 T 淋巴细胞中的染色体全易接近染色质

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使用 scRNA-Seq 和 scATAC-Seq 对视网膜基因表达和染色质可及性进行多重分析