我们感谢马库斯科伊尔, 柯蒂斯 Gumbs, SMF 核心在 MDACC 的排序支持。本文所述的工作得到了 NIH 赠款 (CA016672) 对 SMF 核心和1K99CA160578 奖 (R00CA160578) 的赠款的支持, 以 k.r。

所有临床标本都是按照机构审查委员会的指导方针获得的。
1. 缓冲准备
<ol><li>使200毫升的 TE 缓冲器 (10 毫米三盐酸, 10 毫米乙二胺乙酸 (EDTA) pH 值 8.0)。</li>
	<li>使200毫升的圣凯瑟缓冲 (10 毫米三盐酸, 10 毫米 EDTA pH 值 8.0, 140 毫米氯化钠)。</li>
	<li>使200毫升2.0 米甘氨酸 (37.52 克甘氨酸在200毫升水) 和加热它到65°c。</li>
	<li>使200毫升的5% 脱氧钠 (doc) 溶液 (10 克的 doc 在200毫升水)。</li>
	<li>制作500毫升的芯片收获缓冲器 (12 毫米三氯, 0.1x 磷酸盐缓冲盐水 (PBS), 6 毫米 EDTA, 0.5% 月桂酸钠 (SDS))。</li>
	<li>使500毫升的芯片稀释缓冲 (10 毫米三氯, 140 毫米氯化钠, 0.1% DOC, 1% 海卫 x, 1 毫米 EDTA)。</li>
	<li>制作500毫升的马国贤洗涤缓冲器 (x-100, 1%, 0.1% SDS, 0.1% DOC)。</li>
	<li>使500毫升的 RIPA/500 洗涤缓冲器 (马国贤缓冲 + 360 毫米氯化钠)。</li>
	<li>制作500毫升的 LiCL 洗涤缓冲器 (TE, 250 毫米 LiCL, 0.5% NP-40, 0.5% DOC)。</li>
	<li>使500毫升直接洗脱缓冲:10 毫米三氯 pH 8.0, 5 毫米 EDTA, 300 毫米氯化钠, 0.5% SDS。</li>
	<li>制作50毫升抗体结合/阻断缓冲 (PBS + 0.1% TWEEN-20 + 0.2% 无 IgG BSA)。</li>
</ol>2. 组织/细胞系的加工和交联
<ol><li>如果组织是闪光冷冻, dethaw 它在冰在离解之前。</li>
	<li>使用无菌刀片将50毫克组织 (每组蛋白修饰抗体的8毫克) 与2毫升的汉克斯平衡盐溶液 (HBSS) 进行人工分离。在无菌组织培养皿中, 将组织切成3到4毫米, 大约5分钟。</li>
	<li>将组织和 HBSS 溶液转移到 dissociator 管中, 再加入8毫升的 HBSS。进一步解除组织使用 dissociator 直到匀质。</li>
	<li>对于黑色素瘤肿瘤, 把管子放在一个 dissociator 中, 每一次依次运行以下周期: h_tumor_01.01、h_tumor_02.01、h_tumor_03.01 和 m_heart_02.01。</li>
	<li>刮 21 x 106细胞在10毫升中等 (~ 3 x 106每组蛋白的修改和输入; 它可以降低到10万细胞每标记为罕见的人口) 生长在一个标准的组织培养皿, 并收集他们在15毫升圆锥管。 注: 用于代表性黑色素瘤细胞系 WM115 的培养基 DMEM 补充10% 胎牛血清和5% 青霉素/链霉素。</li>
	<li>通过增加200µL 16% 甲醛每3毫升 HBBS 为组织 (或3毫升培养基为细胞) 交联的组织/细胞使用1% 最终甲醛浓度。在37摄氏度的时候, 用搅拌机在 10 rpm 处摇动混合物。 注意: 甲醛是有毒的, 应该在适当的通风罩中处理。</li>
	<li>添加200µL 2.0 米甘氨酸每3毫升的样品, 并继续晃动在 10 rpm 另5分钟37°c。 注: 甘氨酸起淬火作用。每个月都要做新鲜的甘氨酸溶液, 因为溶液的 pH 值随着时间的推移而逐渐趋于。</li>
	<li>使用台式离心机将样品在4摄氏度的 934 x g 处旋转5分钟。取出上清液, 加入5毫升冰冷 PBS, 再离心样品 934 x g, 取出上清液。</li>
	<li>闪光冷冻颗粒和存储在-80 °c, 以进一步处理。</li>
</ol>3. 组织裂解、超声波和抗体制备
<ol><li>溶解1蛋白酶抑制剂片剂每10毫升的芯片收获缓冲。</li>
	<li>添加300µL 的芯片收获缓冲液, 每50毫克的组织蛋白酶抑制剂, 并允许30分钟的裂解冰。 注: 添加300µL 的芯片收获缓冲液, 每 1 X 107 WM115 黑色素瘤细胞系的蛋白酶抑制剂。</li>
	<li>当细胞株溶藻时, 打开水浴器干扰装置和相关的冷却系统, 使温度达到4摄氏度。将 sonicator 管放置在水浴器的干扰和油脂实验黑色素瘤组织60周期在三十年代和三十年代关闭, 以获得 200-600 基对 (bp) 的染色质碎片。 注: 超声波时间可以不同组织类型, 并应相应调整。这是一个关键的步骤, 应在试点试验中进行优化, 然后再继续免疫沉淀。</li>
	<li>在超声波期间, 使用1000µL 的绑定/阻塞缓冲 (PBS + 0.1% TWEEN-20 + 0.2% BSA), 每样抗体三次清洗20µL 的蛋白质 G 磁性珠子。</li>
	<li>对于8毫克的黑色素瘤组织, 孵化3µg 的每个组蛋白抗体100µL 的捆绑/阻塞缓冲区 2 h 4 °c 与旋转使用管左轮手枪。12个单一抗体的样本包含240µL 的珠子, 36 µg 的抗体和1200µL 的捆绑/阻塞缓冲器。 注: 对于 3 x 106单元格, 每种抗体使用5µg 的每个抗体和30µL 蛋白 G 磁性珠子。如果使用不同数量的组织, 则应滴定抗体和蛋白 G 珠。该协议说明了所描述的六组蛋白修饰的免疫沉淀条件 (材料表), 但是, 抗体浓度应针对其他修改和转录因子进行优化。</li>
	<li>要确定碎片大小, 请删除20µL 的 sonciated 染色质溶液, 并添加一个洗脱缓冲主混合 (室温), 其中包含44µL 的直接洗脱缓冲器, 1 µL RNase (20 毫克/毫升) 和5µL 蛋白酶 K (20 毫克/毫升) 每样。在 PCR 热循环仪中孵育样品, 至少在50摄氏度时为2小时。根据制造商的指示, 使用 PCR 纯化试剂盒净化样品。</li>
	<li>确保染色质得到充分的剪切通过确定碎片大小使用高灵敏度的 DNA 电泳图谱仪器, 然后再进行步骤3.6。打开电泳图谱仪器。</li>
	<li>将高灵敏度缓冲剂2µL 到8井光管条中的每个井中。将高灵敏度梯的2µL 在剩余井中的第一井和2µL 纯化样品中。</li>
	<li>将光学管帽放在管 stips 和涡流上, 样品在2000转每分钟1分钟. 打开盖子, 插入一个新的高灵敏度的屏幕磁带和加载提示框。取下带帽并将样品装入电泳图谱仪器。</li>
	<li>打开分析软件, 突出显示屏幕磁带上的前十三个空格, 然后按右下角的 "开始" 选项卡。染色质碎片介于 200-1000 bp 之间, 适用于芯片。</li>
	<li>将微气泡溶液转移到无菌管上, 用台式离心机在4摄氏度处旋转 21130 x g 的管子, 15 分钟。将上清液转移到新管上。</li>
	<li>确定上清的总容积, 并删除输入控制的总解决方案的 10%, 并将其存储在4摄氏度。</li>
</ol>4. 染色质免疫沉淀
<ol><li>溶解2蛋白酶抑制剂片剂每20毫升的芯片稀释缓冲。</li>
	<li>在超声波稀释剩余的样品5次使用芯片稀释缓冲器使 SDS 水平降低到0.1%。稀释270µL 的剩余上清与1080µL 芯片稀释缓冲器, 以获得最终总容积1350年µL。</li>
	<li>当抗体和蛋白 G 磁性珠子的孵化完成后, 将管子放在磁铁上, 取出上清。</li>
	<li>当管子仍然坐在磁铁上时, 用蛋白酶抑制剂加入750µL 的芯片稀释缓冲液来洗涤珠子。 注意: 在这个步骤中, 不要打扰珠子或旋转管子是很重要的。</li>
	<li>去除芯片稀释缓冲器洗涤和并用重悬在240µL 相同的芯片稀释缓冲器的珠子。整除将20µL 的珠子分成12个单独的管, 每根都用于一个单独的组织样本。</li>
	<li>整除微气泡材料均匀地对蛋白 G 珠与特定抗体和翻滚使用管左轮手枪过夜4摄氏度。</li>
</ol>5. Immunoprecipitated DNA 蛋白复合物的洗涤和反向交联
<ol><li>第二天早上反向交联后, 将抗体蛋白溶液转移到96井板上, 将其放在磁支架上。允许珠子坚持至少三十年代和删除上清。</li>
	<li>使用多通道吸管, 用150µL 的冰冷马国贤洗涤缓冲器清洗珠子5次。在洗涤步骤中, 不要吸管珠子, 但每次清洗时从右向左移动磁铁。</li>
	<li>用150µL 的冰冷 RIPA-500 洗涤缓冲器清洗样品两次。</li>
	<li>用150µL 的冰冷 LiCl 洗涤缓冲器清洗样品两次。</li>
	<li>用150µL 的冰冷 TE 洗涤缓冲器清洗样品一次, 立即取出 TE 缓冲器。对于低输入应用程序, 可以省略此步骤。</li>
	<li>将输入控制从步骤3.7 添加到包含芯片 DNA 样本的96井板的新鲜井中。</li>
	<li>洗涤步骤后, 添加一个洗脱缓冲主混合 (室温), 其中包含44µL 直接洗脱缓冲器, 1 µL RNase (20 毫克/毫升) 和5µL 蛋白酶 K (20 毫克/毫升) 每芯片和输入样品的反向交联。</li>
	<li>用 PCR 热循环仪在37°c, 4 小时在50°c 和 8-16 h 在65°c 孵化样品。</li>
</ol>6. 沉淀 DNA 的纯化和定量化
<ol><li>第二天早上, 把样品放回磁铁上, 转移到上清到一个新的96井 PCR 板, 其中含有 immunoprecipitated 的 DNA。</li>
	<li>添加2.3x 顺磁珠到解决方案 (115 µL 50 µL 的解决方案), 并小心吸管上下25次使用多通道吸管。如果适当地混合, 溶液将变为均匀。</li>
	<li>将溶液在室温下从磁铁中孵化4分钟. 将样品放回磁铁上, 在室温下孵化4分钟. 放弃上清。</li>
	<li>在将样品留在磁铁上时, 添加150µL 70% 乙醇 (卷/卷), 并在室温下孵化, 而不干扰珠子。</li>
	<li>取出乙醇并重复清洗。允许顺磁珠在磁铁上风干5分钟。 注: 除去所有的乙醇后, 第二次清洗, 因为剩余的乙醇将干扰净化。</li>
	<li>从磁铁中取出样品, 并添加30µL 10 毫米三氯 pH 8.0。混合吹打25次, 在室温下将样品从磁铁上孵化4分钟。</li>
	<li>将样品放回磁铁上, 在室温下孵化4分钟. 将每个样品的20µL 转移到一个新的96井板上, 供图书馆准备。存储剩余的10µL 的芯片 DNA, 以防任何可能的问题与生成的库。</li>
	<li>在这个阶段, 在图书馆准备之前量化芯片 DNA。用高灵敏度的 dna 试剂测定 dna 浓度。用高灵敏度的 dna 电泳图谱仪对步骤7.1 进行测定, 确定沉淀 dna 的大小分布。</li>
</ol>7. 使用 NEBNext 超 II DNA 库准备套件的库生成
<ol><li>根据制造商推荐的协议, 使用 DNA 库准备生成用于自定序列的库。所有反应在96井板和孵化执行在 PCR 热循环仪与盖子温度集 > 100 °c 和容量设置在50µL。</li>
	<li>对于索引, 使用了寡核苷酸平台的多路复用。使用以下设置执行总共10x 个周期的 PCR 扩增: 初始变性在98°c 三十年代, 变性在98°c 十年代, 退火/延长在65°c 为三十年代 (10x 周期), 最后的延长65°c 为5分钟和举行在4°c。 注: 表1包含用于 PCR 放大的引物。</li>
	<li>PCR 扩增后, 取出样品, 用核酸酶水将总容积µL 100。执行双面顺磁尺寸选择 (0.55x/0.8x) 先加入55µL 的珠子, 并混合吹打25倍。在室温下将样品从磁铁中孵化4分钟。</li>
	<li>将样品放回磁铁上, 再在室温下孵化4分钟。此步骤用于保留不适合排序的珠子上的大块碎片。</li>
	<li>将上清液转移到新的96井 PCR 板上。不要丢弃上清, 因为这包含部分纯化的 DNA。</li>
	<li>再添加25µL 顺磁珠, 混合吹打25次, 在室温下从磁铁中孵化4分钟。</li>
	<li>将样品放回磁铁上, 再在室温下孵化4分钟, 然后丢弃上清。此步骤用于保留将用于最终库的200到 600 bp 的片段。</li>
	<li>在将样品留在磁铁上时, 添加150µL 70% 乙醇 (v/v), 并允许在三十年代孵化, 而不干扰珠子 (不要在磁铁上移动)。取出乙醇, 再重复一次洗涤。</li>
	<li>允许顺磁珠在磁铁上晾干5分钟. 在第二次洗涤后, 除去所有的乙醇, 因为剩余的乙醇会干扰纯化。</li>
	<li>从磁铁中取出样品, 并添加25µL 10 毫米三氯 pH 8.0。混合吹打25次, 在室温下从磁铁中孵化4分钟. 将样品放回磁铁上, 在室温下孵育另4分钟。</li>
	<li>在多路复用前使用高灵敏度的 DNA 电泳图谱仪器量化库, 以确保尺寸适合测序。完成的图书馆范围在 200-600 bp 之间并且缺乏所有底漆脂肪酸。 注: 如有必要, 另进行0.7x 顺磁珠提纯, 以去除不需要的底漆脂肪酸, 其存在于 130 bp。</li>
	<li>根据浓度以独特的指数引物为基础, 建立量化的 DNA 库。对于包含六个样品的水池在 1 ng/µL 和 6 ng/µL 之间, 分布是15µL 的最低样品和2.5 µL 最高的样品。这样做是为了确保读取计数将均匀分布在每个通道的流单元。</li>
</ol>8. 后芯片序列数据处理
<ol><li>基于snakemake38、39的管线用于在单个命令中处理以下所有步骤。重要的是, 每个步骤都与相关命令单独讨论。</li>
	<li>确定原始 fastq 排序的质量使用 FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/): 打开 unix 终端并更改目录 (cd) 到包含每个六组蛋白修改的原始 fastq 文件的文件夹。在 unix 终端类型中, fastqc * fastq 和按 [返回]。这将对文件夹中的所有 fastq 文件运行质量控制指标。</li>
	<li>将质量读数与参考基因组对齐, 并在压缩到 bam 文件之前删除重复项。这是使用领结版本 1.1.239、SAMBLASTER 版本 0.1.2140和 SAMTOOLS 版本 1.3.141执行的: 在 unix 终端类型中, 领结-m 1-n 1-最佳-地层 s-q/path_to/hg19 histone1.fastq.gz |samblaster-removeDups |samtools 查看-> histone1 和按 [返回]。 注: path_to 表示包含 hg19 人类基因组组件的文件夹。这将根据感兴趣的有机体而改变。</li>
	<li>使用以下命令对 bam 文件进行排序: 在 unix 终端类型中, SAMTOOLS 排序 histone1 SAMTOOLS 2G @ 5 T histone1. 排序并按 [返回]。</li>
	<li>使用 SAMTOOLS 在以下命令中索引 bam 文件: 在 unix 终端类型中, SAMTOOLS 索引 histone1.sorted.bam histone1.sorted.bam.bai 并按 [返回]。</li>
	<li>使用以下命令确定唯一映射的和齐的读取 SAMTOOLS flagstat: 在 unix 终端类型中, SAMTOOLS flagstat histone1.sorted.bam 并按 [返回]。</li>
	<li>通过使用以下命令对 bam 文件进行标准化: 在 unix 终端类型中, sambamba 视图 f bam-subsampling-seed=3 0.X histone1.sorted.bam |samtools 排序-m 2G-@ 5 吨 histone1.downsample.temp histone1.downsample.sorted.bam 和按 [返回]。</li>
	<li>使用 SAMTOOLS 以下列命令再次索引 bam 文件: 在 unix 终端类型中, SAMTOOLS 索引 histone.downsample.sorted.bam histone.downsample.sorted.bam.bai 并按 [返回]。</li>
	<li>在基因组浏览器 (i. e) 上可视化芯片序列库。UCSC 或 IGV), 使用 deepTools 版本 2.4.040生成要人文件, 方法是将 bam 文件缩放为每百万 (kilobase) 的每 RPKM 读取。可以使用以下命令执行此操作:<ol>
<li>对于标准的要人文件: 在 unix 终端类型中, bamCoverage-b histone1。downsample.sorted.bam--normalizeUsingRPKM--binSize 30-smoothLength 300-extendReads 200 o histone1, 并按 {返回]。</li>
		<li>对于输入减去的大人物文件: 在 unix 终端类型中, bamCompare--bamfile1 histone1.downsample.sorted.bam bamfile2 input1.downsample.sorted.bam--normalizeUsingRPKM--binSize 30-smoothLength 300-extendReads 200-ohistone1.subtracted.bw 和按 [返回]。</li>
	</ol>
</li>
	<li>用基于模型的芯片序列分析 (mac) 版本 1.4.225,26和2.1.0 版本, 识别 "输入" 背景下的芯片序列信号富集。使用 macs1 的 "点源" 因素 H3K4me3, H3K4me1 和 H3K27ac 和 macs2 的 "广泛来源" 因素 H3K79me2, H3K27me3 和 H3K9me3。这是使用以下命令执行的:<ol>
<li>对于点源: 在 unix 终端类型中, macs14 histone1.downsample.sorted.bam-input1.downsample.sorted.bam 1e-5-nomodel-保持所有 n histone1. 文件并按 [返回]。</li>
		<li>对于广义源: 在 unix 终端类型中, macs2 callpeak histone1.downsample.sorted.bam-c input1.downsample.sorted.bam-g 1e-6-宽-宽截止 1e-6-保持所有-nomodel n histone1. 文件和按 [返回]。</li>
	</ol>
</li>
	<li>使用 ChromHMM24可以根据使用以下命令研究的组蛋白修改来识别组合染色质状态模式:<ol>
<li>在 unix 终端类型中, cd/path_to/ChromHMM_folder 和按 [返回]。</li>
		<li>一旦在 ChromHMM 目录类型, java mx2G jar ChromHMM. jar BinarizeBam-b 1000 hg19.txt/path_to/bam_directory/path_to/CellMarkFile .txt/path_to/BinarizeBam_output 和按 [返回]。</li>
		<li>类型, mx2G ChromHMM 罐 LearnModel-b 1000/path_to/BinarizeBam/path_to/output_directory 15 hg19 和按 [返回]。</li>
	</ol>
</li>
</ol>

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作者声明没有冲突。

本协议描述了一种完整和全面的高通量芯片序列模块, 用于在人类肿瘤组织和细胞系中染色质状态的全基因图谱。在任何芯片序列协议中, 最重要的步骤之一是抗体特异性。在这里, 此方法说明了所描述的六组蛋白修改的免疫沉淀条件, 所有这些都是芯片级的, 以前在我们和其他实验室中验证过 42, 44, 45.虽然相同的抗体浓度已成?...

大多数哺乳动物基因组 (98-99%) 由非编码序列组成, 这些 nocoding 区域包含已知参与控制基因表达和染色质组织 1, 2 的调控元素.在正常细胞中, 基因组 DNA 的特定组装进入致密的染色质结构, 对于各种 DNA 相关进程的空间组织、调控和精确定时至关重要, 这些过程3,4,5。然而, 在癌细胞中, 异常表观遗传机制的染色质修饰可能导致染色质结构的不正确组织, 包括获得调控元素、染色体循环系统和基因表达模式6, 7,8,9,10。
尽管最近取得了进展, 我们对与肿瘤进展或治疗反应相关的表观变化的理解有限。表观基因由一系列的修改组成, 包括组蛋白标记和 DNA 甲基化, 它们共同形成一个动态状态 (称为染色质状态), 它会影响基因表达网络和其他关键的过程来维持细胞身份。最近, 通过研究 H3K27Ac 配置文件11, 增强剂的改变已显示在多种恶性肿瘤中。虽然这些研究提供了对孤立的表观遗传标记的相关性的洞察力, 但100多种表观修改已被识别为12,13 , 而没有清楚地了解它们的生物学角色和相互 依赖。此外, 还有更多的可能组合模式的这些组蛋白和 DNA 的修改, 它是这些组合模式-而不是个别的修改-听写表观遗传学的状态14。因此, 在癌症进展或对治疗的反应中, 有必要确定这些染色质状态的变化.由于技术 (例如, 从少量临床材料/单细胞生成大规模数据) 和分析 (例如算法来定义, 癌症中表观基因改变的综合知识已经滞后于部分原因。组合状态) 的挑战。因此, 迫切需要强健的高通量方法来分析大量的组蛋白修饰标记, 从临床材料和易于实现的计算方法来预测组合模式, 这将有助于不同肿瘤分期和治疗耐药性相关的表观遗传状态的测定。此外, 从最近的表观基因分析研究中获得的数据15, 16, 17, 18, 19, 20, 21, 22、23正常组织和细胞系可与肿瘤染色质剖面结合, 进一步深入了解表观基因对肿瘤生物学的贡献。
染色质分析已成为识别各种染色质修改的全局绑定模式的强大工具15,24。近年来, 芯片序列已成为研究全球尺度上的 DNA-蛋白质相互作用的 "金标准"25,26,27。对于任何芯片序列实验, 有必要的关键步骤, 它的成功, 包括组织处理和解除, 确定最佳超声波条件, 确定的最佳抗体浓度的降水, 图书馆准备,后测序数据处理和下游分析。这些步骤中的每一个都包含关键的质量控制检查点, 当两者结合在一起时, 对于正确识别功能验证的潜在目标至关重要。通过这些步骤的创新, 一些先前的研究已经开发出从少量组织执行芯片或芯片序列的方法28,29,30,31,32.此外, 一些研究还提出了高通量芯片实验的协议, 其次是基于 PCR 的定量33,34。最后, 一些公开可用的芯片序列数据分析平台现在可用, 如 Easeq35和 Galaxy36。然而, 在高吞吐量的方式下, 结合计算管道来执行单标记和染色质状态分析, 一个集成的平台来执行芯片序列。
本协议描述了一个完整和全面的芯片序列工作流程, 用于全基因组在肿瘤组织和细胞系中的染色质状态图谱, 并易于遵循指导原则, 包括成功实验所需的所有步骤。通过采用以前由 Blecher Gonen et描述的高通量方法。37, 此协议可在多个样本中并行执行, 并已成功应用于癌细胞系和人类肿瘤, 如黑色素瘤、结肠、前列腺和胶质瘤多形性。我们演示了六组核心组蛋白修饰的方法, 它代表了人类黑色素瘤细胞系和肿瘤样本中表观遗传调控景观的关键组成部分。这些修改包括 H3K27ac (增强剂)、H3K4me1 (积极和有活力的增强剂)、H3K4me3 (促进者)、H3K79me2 (转录区)、H3K27me3 (polycomb 镇压) 和 H3K9me3 (异色镇压)。这些标记可以单独或组合使用, 以确定代表压制性和活动域的功能上不同的染色质状态。

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

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

组蛋白修饰是表观基因的主要组成部分, 在确定相关基因座的转录状态方面起着重要的调控作用。此外, 还使用特定的修改来确定位置和身份非编码功能元素, 如增强剂。近年来, 染色质免疫沉淀其次是下一代测序 (芯片序列) 已成为一个强有力的工具, 以确定基因组范围内的个别蛋白修饰的轮廓。然而, 越来越清楚的是, 染色质修饰的组合模式, 称为染色质状态, 决定了相关基因组轨迹的特征和性质。因此, 由健壮的高通量 (HT) 方法组成的工作流, 用于分析许多组蛋白修饰标记, 以及能够处理无数芯片序列分析数据集的计算分析管线, 因此需要大量样品中 epigenomic 状态的综合测定。这里提出的 HT 芯片序列工作流包括两个模块: 1) 一个实验性的协议, 用于分析从少量肿瘤样本和细胞系的96井格式的几个组蛋白修饰;和 2) 一个计算数据分析管道, 结合现有的工具, 以计算单独的标记占用和组合染色质状态模式。这两个模块结合在一起, 便于快速、高效地处理数以百计的芯片序列样本。本文介绍的工作流用于从黑色素瘤肿瘤和细胞系中的6组蛋白标记剖面中提取染色质状态模式。总的来说, 我们提出了一个全面的芯片序列的工作流程, 可用于许多人肿瘤标本和癌细胞株, 以确定 epigenomic 畸变在各种恶性肿瘤。

该协议允许免疫沉淀从冰冻肿瘤组织和细胞系, 可以在许多样本并行使用高通量方法 ( 图1A)。染色质碎片应介于 200-1000 bps 之间, 以达到最佳免疫沉淀。我们注意到, 达到相同的剪切长度所需的时间不同的组织和细胞类型。芯片的成功, 从少量的组织依赖于保持裂解冷, 特别是在超声波期间。在进行库准备之前, 应量化纯化的芯片 DN Ashould (<s...

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

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

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

在这里, 我们描述了一个优化的高通量芯片排序协议和计算分析管道, 以确定从冷冻肿瘤组织和细胞系的全基因组的染色质状态模式。

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

an-integrated-platform-for-genome-wide-mapping-chromatin-states-using

Universidad de Cartagena UDEC

Research

JoVE Journal

Cancer Research

10.3K 次观看.  University of Texas MD Anderson Cancer Center. 在这里, 我们描述了一个优化的高通量芯片排序协议和计算分析管道, 以确定从冷冻肿瘤组织和细胞系的全基因组的染色质状态模式。

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

Native Chromatin Immunoprecipitation Using Murine Brain Tumor Neurospheres

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

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

利用小鼠脑肿瘤球的原发染色质沉淀

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

科研

癌症研究

CometChip: A High-throughput 96-Well Platform for Measuring DNA Damage in Microarrayed Human Cells

DNA damaging agents can promote aging, disease and cancer and they are ubiquitous in the environment and produced within human cells as normal cellular metabolites. Ironically, at high doses DNA damaging agents are also used to treat cancer. The ability to quantify DNA damage responses is thus critical in the public health, pharmaceutical and clinical domains. Here, we describe a novel platform that exploits microfabrication techniques to pattern cells in a fixed microarray. The &#8216;CometChip&#8217; is based upon the well-established single cell gel electrophoresis assay (a.k.a. the comet assay), which estimates the level of DNA damage by evaluating the extent of DNA migration through a matrix in an electrical field. The type of damage measured by this assay includes abasic sites, crosslinks, and strand breaks. Instead of being randomly dispersed in agarose in the traditional assay, cells are captured into an agarose microwell array by gravity. The platform also expands from the size of a standard microscope slide to a 96-well format, enabling parallel processing. Here we describe the protocols of using the chip to evaluate DNA damage caused by known genotoxic agents and the cellular repair response followed after exposure. Through the integration of biological and engineering principles, this method potentiates robust and sensitive measurements of DNA damage in human cells and provides the necessary throughput for genotoxicity testing, drug development, epidemiological studies and clinical assays.

We describe here a platform that allows comet assay detection of DNA damage with unprecedented throughput. The device patterns mammalian cells into a microarray and enables parallel processing of 96 samples. The approach facilitates analysis of base level DNA damage, exposure-induced DNA damage and DNA repair kinetics.

CometChip:一种高通量的96孔平台的微阵列人类细胞测量DNA损伤

DNA damaging agents can promote aging, disease and cancer and they are ubiquitous in the environment and produced within human cells as normal cellular ...

生物工程

High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease (ChIP-Exo)

Identification of specific protein-DNA interactions on the genome is important for understanding gene regulation. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) is widely used to identify genome-wide binding locations of DNA-binding proteins. However, the ChIP-seq method is limited by its heterogeneity in length of sonicated DNA fragments and non-specific background DNA, resulting in low mapping resolution and uncertainty in DNA-binding sites. To overcome these limitations, the combination of ChIP with exonuclease digestion (ChIP-exo) utilizes 5&#8217; to 3&#8217; exonuclease digestion to trim the heterogeneously sized immunoprecipitated DNA to the protein-DNA crosslinking site. Exonuclease treatment also eliminates non-specific background DNA. The library-prepared and exonuclease-digested DNA can be sent for high-throughput sequencing. The ChIP-exo method allows for near base-pair mapping resolution with greater detection sensitivity and reduced background signal. An optimized ChIP-exo protocol for mammalian cells and next-generation sequencing is described below.

High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease ChIP-Exo

Precise determination of protein-binding locations across the genome is important for understanding gene regulation. Here we describe a&#160;genomic mapping method that treats chromatin-immunoprecipitated DNA with exonuclease digestion (ChIP-exo) followed by high-throughput sequencing. This method detects protein-DNA interactions with near base-pair mapping resolution and high signal-to-noise ratio in mammalian neurons.

使用色素免疫沉淀-外核酶（ChIP-Exo）对小鼠干细胞衍生神经元中的蛋白质-DNA相互作用进行高分辨率映射

Identification of specific protein-DNA interactions on the genome is important for understanding gene regulation. Chromatin immunoprecipitation coupled ...

遗传学

研究稀缺细胞中启动子相互作用组的综合方法

An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Spatiotemporal gene transcription is tightly regulated by distal regulatory elements, such as enhancers and silencers, which rely on physical proximity with their target gene promoters to control transcription. Although these regulatory elements are easy to identify, their target genes are difficult to predict, since most of them are cell-type specific and may be separated by hundreds of kilobases in the linear genome sequence, skipping over other non-target genes. For several years, Promoter Capture Hi-C (PCHi-C) has been the gold standard for the association of distal regulatory elements to their target genes. However, PCHi-C relies on the availability of millions of cells, prohibiting the study of rare cell populations such as those commonly obtained from primary tissues. To overcome this limitation, low input Capture Hi-C (liCHi-C), a cost-effective and customizable method to identify the repertoire of distal regulatory elements controlling each gene of the genome, has been developed. liCHi-C relies on a similar experimental and computational framework as PCHi-C, but by employing minimal tube changes, modifying&#160;the reagent concentration and volumes, and swapping or eliminating steps, it accounts&#160;for minimal material loss during library construction. Collectively, liCHi-C enables the study of gene regulation and spatiotemporal genome organization in the context of developmental biology and cellular function.

作者聚焦:研究稀缺细胞群中以启动子为中心的时空基因组结构的集成工作流程  

Gene expression is regulated by interactions of gene promoters with distal regulatory elements. Here, we descirbe how low input Capture Hi-C (liCHi-C) allows the identification of these interactions in rare cell types, which were previously unmeasurable.

研究稀缺细胞群中以启动子为中心的时空基因组结构的集成工作流程

Spatiotemporal gene transcription is tightly regulated by distal regulatory elements, such as enhancers and silencers, which rely on physical proximity ...

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 ...

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

The three-dimensional folding of chromosomes compartmentalizes the genome and and can bring distant functional elements, such as promoters and enhancers, into close spatial proximity 2-6. Deciphering the relationship between chromosome organization and genome activity will aid in understanding genomic processes, like transcription and replication. However, little is known about how chromosomes fold. Microscopy is unable to distinguish large numbers of loci simultaneously or at high resolution. To date, the detection of chromosomal interactions using chromosome conformation capture (3C) and its subsequent adaptations required the choice of a set of target loci, making genome-wide studies impossible 7-10.
We developed Hi-C, an extension of 3C that is capable of identifying long range interactions in an unbiased, genome-wide fashion. In Hi-C, cells are fixed with formaldehyde, causing interacting loci to be bound to one another by means of covalent DNA-protein cross-links. When the DNA is subsequently fragmented with a restriction enzyme, these loci remain linked. A biotinylated residue is incorporated as the 5' overhangs are filled in. Next, blunt-end ligation is performed under dilute conditions that favor ligation events between cross-linked DNA fragments. This results in a genome-wide library of ligation products, corresponding to pairs of fragments that were originally in close proximity to each other in the nucleus. Each ligation product is marked with biotin at the site of the junction. The library is sheared, and the junctions are pulled-down with streptavidin beads. The purified junctions can subsequently be analyzed using a high-throughput sequencer, resulting in a catalog of interacting fragments.
Direct analysis of the resulting contact matrix reveals numerous features of genomic organization, such as the presence of chromosome territories and the preferential association of small gene-rich chromosomes. Correlation analysis can be applied to the contact matrix, demonstrating that the human genome is segregated into two compartments: a less densely packed compartment containing open, accessible, and active chromatin and a more dense compartment containing closed, inaccessible, and inactive chromatin regions. Finally, ensemble analysis of the contact matrix, coupled with theoretical derivations and computational simulations, revealed that at the megabase scale Hi-C reveals features consistent with a fractal globule conformation.

The Hi-C method allows unbiased, genome-wide identification of chromatin interactions (1). Hi-C couples proximity ligation and massively parallel sequencing. The resulting data can be used to study genomic architecture at multiple scales: initial results identified features such as chromosome territories, segregation of open and closed chromatin, and chromatin structure at the megabase scale.

嗨 - C：一个方法研究基因组的三维架构。

The three-dimensional folding of chromosomes compartmentalizes the genome and and can bring distant functional elements, such as promoters and enhancers, ...

生物学

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using bioinformatics pipelines (&#34;dry bench&#34;). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible &#34;tag&#34; PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.

低质量和低数量肿瘤活检丰枯台过程的集成优化了有针对性的下一代测序

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using ...

医学

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 ...

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

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

嗨 - C：一个方法研究基因组的三维架构。

低质量和低数量肿瘤活检丰枯台过程的集成优化了有针对性的下一代测序

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）