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>

<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功地应用于各种其他肿瘤类型, 但重要的是要确定抗体特异性, 如果研究不同因素的兴趣27。
成功实验的另一个关键方面包括优化超声波条件。超声波时间在样品和组织类型之间可能变化, 并且必须相应地调整。为了进行适当的优化, 通过增量增加超声波时间并不断检查片段大小, 对每个示例执行试用运行。对于最初的芯片实验, 染色质碎片应介于 200-1000 bp 之间的最佳免疫沉淀, 并在进行图书馆准备之前量化纯化的芯片 DNA。此片段大小确保了将被用于多路复用和的扩增 DNA (200 到 600 bp) 的最佳生成和保留。如果以高通量的方式进行这些实验, 则多路复用的一个好的起点是每条车道上的8个样本进行测序。虽然这一量的样品可能会产生一个浅的测序深度, 它是有用的测试抗体质量, 然后再继续。汇集多个样本的瓶颈之一是不平等覆盖后测序。通常使用一种基于定量的 fluoremetric 方法, 但是, 许多次它并不等同于每个库中的测序就绪分子。最好的方法是定量的图书馆 DNA 是利用 qPCR 的标准-预先确定的测序-就绪 DNA 分子。
芯片序列数据集分析的另一个关键步骤是排序后数据处理。重要的是, 在进行下游分析的任何方面之前, 都应该首先满足各种质量控制指标。诸如 FastQC 之类的程序将提供有关原始 fastq 顺序读取的质量信息 (通过或失败测试的形式)。虽然 fastq 读取应该通过大部分的指标, 一些更重要的测试包括基本统计, 每序列基础质量, 每序列质量分数, 和适配器污染。FastQC 处理的读数与人类基因组对齐, 并应以 50-80% 的速率映射, 最大值为一个不匹配。低于50% 的比率可能表明芯片、库准备或测序中的问题, 可能包括污染、低质量的芯片 DNA 或在 PCR 过程中过度放大。在将 SAM 转换为 BAM 文件期间, 应首先标记重复读取, 然后在继续向下取样之前使用 SAMBLASTER 删除。虽然在 PCR 过程中某种程度的重复是正常的, 但高水平的复制通常预示着低质量的芯片 DNA, 或者可能是图书馆准备的问题。FastQC、领结和 samblaster (组合在 flagstat 中) 的输出都可以通入 MultiQC, 这提供了所有质量控制结果的完整交互式摘要。MultiQC 从 fastqc、领结和 samblaster 的输出文件中显示质量控制指标, 分别提供有关基本统计信息、对齐百分比和 PCR 复制率的资料。
在下采样规范化之后, 下一个可能最重要的步骤是使用基因组浏览器 (如 IGV 或 UCSC) 进行数据可视化。每个组蛋白修饰都代表了表观遗传学调控景观的一个关键组成部分, 包括 H3K27ac (促动剂)、H3K4me1 (活性和促动剂)、H3K4me3 (促进者)、H3K79me2 (转录区)、H3K27me3 (polycomb 压制) 和H3K9me3 (异色镇压)。通过使用这些标记单独或组合, 这些组蛋白可以识别活跃, 准备或被压抑的促进剂和促进剂, 以及编码区域的转录状态。这是使用 ChromHMM 表明, 其中功能分明的染色质状态表示压制和活跃的领域被确定。这些状态由单数标记定义, 例如 polycomb 镇压 (H3K27me3), hetrochromatic 镇压 (H3K9me3) 和活跃转录 (H3K79me2), 以及组合标记, 例如活跃促进剂 (H3K4me1 和 H3K27ac) 和转录促进剂 (H3K4me1, H3K27ac 和 H3K79me2)。
所提出的综合平台非常适合于测定组织样本中组蛋白或转录因子的占用情况。我们已经成功地利用这一协议, 以确定上述六标记的占用, 从活检大小黑色素瘤样本。然而, 我们仍然没有确定成功的芯片序列应用所需的最低数量的组织, 因为它是 depenedent 的组织类型和可用的抗体。此外, 虽然我们经常使用这个协议的新鲜, 闪光冷冻和 OCT 冷冻样本, 我们没有优化这个协议的 FFPE 组织。最近的一些研究报告了此类组织的协议46、47和建议的更改可以在这个平台的框架内合并, 以测试所提出的平台是否会对 FFPE 组织有用。我们相信, 这将是非常有用的, 以确定的染色质状态模式从临床材料得到的病人在临床试验。总的来说, 本协议描述了一个完整的和全面的芯片序列模块, 用于全基因组在人类肿瘤组织中的染色质状态的映射, 并易于遵循指导, 包括所有必要的组成部分, 成功的实验。

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&#160;</td>
			<td>Roche Diagnostics</td>
			<td>11836153001</td>
			<td>protease inhibitor tablets</td>
		</tr>
		<tr>
			<td>Dynabeads Protein G&#160;</td>
			<td>Invitrogen</td>
			<td>10009D</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioruptor NGS tubes 0.65 mL</td>
			<td>Diagenode</td>
			<td>C30010011</td>
			<td>sonication tubes</td>
		</tr>
		<tr>
			<td>DynaMag - 96 Side Skirted</td>
			<td>Invitrogen</td>
			<td>120.27</td>
			<td>96-well magnetic stand</td>
		</tr>
		<tr>
			<td>TE buffer</td>
			<td>Promega</td>
			<td>V6231</td>
			<td></td>
		</tr>
		<tr>
			<td>RNase A</td>
			<td>Invitrogen</td>
			<td>12091021</td>
			<td></td>
		</tr>
		<tr>
			<td>Proteinase K</td>
			<td>Invitrogen</td>
			<td>100005393</td>
			<td></td>
		</tr>
		<tr>
			<td>AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63882</td>
			<td>paramagnetic beads</td>
		</tr>
		<tr>
			<td>Ethanol</td>
			<td>Sigma-Aldrich</td>
			<td>E7023</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit ds DNA High Sensitivity Assay Kit</td>
			<td>Invitrogen</td>
			<td>Q32854</td>
			<td>high sensitivity DNA reagents</td>
		</tr>
		<tr>
			<td>NEBNext Ultra II DNA Library Prep Kit</td>
			<td>New England BioLabs</td>
			<td>E7645L</td>
			<td>DNA Library Prep Kit</td>
		</tr>
		<tr>
			<td>Nuclease-free water</td>
			<td>Ambion</td>
			<td>AM9932</td>
			<td></td>
		</tr>
		<tr>
			<td>High sensitivity D1000 ScreenTape</td>
			<td>Agilent Technologies</td>
			<td>5067-5584</td>
			<td>high sensitivity DNA reagents</td>
		</tr>
		<tr>
			<td>High sensitivity D1000 reagents</td>
			<td>Agilent Technologies</td>
			<td>5067-5585</td>
			<td>high sensitivity DNA reagents</td>
		</tr>
		<tr>
			<td>Multiplex Oligos (Index primers- Set 1)</td>
			<td>New England BioLabs</td>
			<td>E7335L</td>
			<td>Multiplex Oligos&#160;</td>
		</tr>
		<tr>
			<td>Multiplex Oligos (Index primers- Set 2)</td>
			<td>New England BioLabs</td>
			<td>E7500L</td>
			<td>Multiplex Oligos&#160;</td>
		</tr>
		<tr>
			<td>TapeStation 4200</td>
			<td>Agilent Technologies</td>
			<td>G2991AA</td>
			<td>high sensitivity DNA electropherogram instrument</td>
		</tr>
		<tr>
			<td>Bioruptor Pico sonication device&#160;</td>
			<td>Diagenode</td>
			<td>B01060001</td>
			<td>water bath disruputor</td>
		</tr>
		<tr>
			<td>Mixer</td>
			<td>Nutator</td>
			<td>421105</td>
			<td></td>
		</tr>
		<tr>
			<td>Bio-Rad C1000 Touch Thermal Cycler</td>
			<td>Bio-Rad</td>
			<td>1851196</td>
			<td>PCR Thermal cycler</td>
		</tr>
		<tr>
			<td>Water Bath</td>
			<td>Fisher Scientific</td>
			<td>2322</td>
			<td></td>
		</tr>
		<tr>
			<td>Multichannel Pipet</td>
			<td>Denville</td>
			<td>1003123</td>
			<td></td>
		</tr>
		<tr>
			<td>Tube Revolver</td>
			<td>Thermo-Scientific</td>
			<td>88881001</td>
			<td></td>
		</tr>
		<tr>
			<td>96-Well Skirted Plate</td>
			<td>Eppendorf</td>
			<td>47744-110</td>
			<td></td>
		</tr>
		<tr>
			<td>Allegra X-12R Centrifuge&#160;</td>
			<td>Beckman Coulter</td>
			<td>A99464</td>
			<td>benchtop centrifuge</td>
		</tr>
		<tr>
			<td>Centrifuge 5424</td>
			<td>Eppendorf</td>
			<td>22620461</td>
			<td>tabletop centrifuge</td>
		</tr>
		<tr>
			<td>Optical tube strips (8x Strip)</td>
			<td>Agilent Technologies</td>
			<td>401428</td>
			<td></td>
		</tr>
		<tr>
			<td>Optical tube strip caps (8x strip)</td>
			<td>Agilent Technologies</td>
			<td>401425</td>
			<td></td>
		</tr>
		<tr>
			<td>Loading Tips, 10 Pk</td>
			<td>Agilent Technologies</td>
			<td>5067-5599</td>
			<td></td>
		</tr>
		<tr>
			<td>IKA MS3 vortex</td>
			<td>IKA</td>
			<td>3617000</td>
			<td>vortex</td>
		</tr>
	</tbody>
</table>

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

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 (图 2A)。适用于多路复用和脂肪酸的已完成库应介于 200-600 bp 之间, 并且不含任何入门级的(图 2B).在后测序后, 在继续执行管线的进一步步骤 (如 mac 峰值调用或 ChromHMM 分析(图 3))之前, 需要满足许多质量控制指标。许多这些指标都可以使用诸如 FastQC 和 MultiQC 这样的程序来确定。质量 fastq 读数与人类基因组一致, 速度为 50-80%, 最大值为一个不匹配。建议的排序深度为 "点源" 因子的 10-1500万唯一映射的读取, 而 ~ 20-3000万唯一映射的读取为 "广义源" 因子27,41,42,43。已排序和索引的 BAM 文件用于生成可使用基因组浏览器 (如 IGV 或 UCSC) 可视化的要人文件。芯片样本应该丰富的输入 DNA 和每个组蛋白修改应该显示一个不同的配置文件, 代表表观的特定组成部分(图 4A)。在某些组织中, 可能会有更高的背景噪声, 从而掩盖了芯片序列信号。如果发生这种情况, 建议在基因组浏览器中生成输入减去的要人文件并重新可视化。为了确定染色质状态剖面, 我们利用 ChromHMM 算法24。ChromHMM 使用多元隐马尔可夫模型来识别基于组蛋白修饰的最突出的再发生组合和空间染色质模式(图 5)。使用这六修改 ChromHMM 识别功能上不同的染色质状态, 代表压制性和活跃的领域, 如 polycomb 镇压 (状态 1), 异色镇压 (状态 5), 活跃转录 (状态8和 9) 和活性增强剂 (状态13和 14)。ChromHMM 的输出包括每个状态的分段床文件, 可进一步用于下游分析。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56972/56972fig1.jpg"> 图 1: 芯片模块的工作流.活检肿瘤组织首先被分离并交叉连接, 以捕获蛋白质-DNA 相互作用。组织然后微气泡获得的染色质片段适合免疫沉淀和组蛋白-DNA 配合物是沉淀使用六表示的抗体。样品被洗涤, 交叉链接被逆转, 并且芯片脱氧核糖核酸被纯化和定量。图书馆是由纯化的 DNA 生成的, 并根据独特的索引进行复用。<a href="/files/ftp_upload/56972/56972fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56972/56972fig2.jpg"> 图 2: 电泳图谱纯化芯片 DNA 的分析和完成的库准备。(A) 由纯化芯片-含有染色质碎片的 DNA 电泳图谱的代表, 适用于图书馆准备的 200-600 bp。(B) 有代表性的电泳图谱从一个已完成的图书馆扩增和双面顺磁尺寸选择, 适用于多路复用和。<a href="/files/ftp_upload/56972/56972fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56972/56972fig3.jpg"> 图 3: 用于芯片序列数据处理模块的工作流.工作流显示芯片序列数据处理和下游分析准备的关键步骤。此协议中使用的管线是使用snakemake生成的, 但是, 每个步骤也可以按所述单独执行。<a href="/files/ftp_upload/56972/56972fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56972/56972fig4.jpg"> 图 4: 组蛋白修饰芯片-序列轨迹的基因型浏览器视图。a) deeptools 产生的芯片序列轨迹, 它显示有代表性的黑色素瘤肿瘤标本周围 NRAS 轨迹的活跃和受压抑区域。基因, 如 NRAS, CSDE1 是 SIKE1 与所有主动组蛋白修饰 (H3K27ac, H3K4me3 和 H3K4me1) 和主动转录 (H3K79me2), 而侧翼基因 AMPD1, DENND2C 和 SYCP1 包含 polycomb 压制标记 H3K27me3, 并没有包含活动转录。B) 由 deeptools 在代表性黑色素瘤肿瘤标本中显示 ZNF 基因的染色质的芯片序列轨迹。<a href="/files/ftp_upload/56972/56972fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56972/56972fig5.jpg"> 图 5: 基于黑色素瘤肿瘤样本的 ChromHMM 模型.15态 LearnModel 的发射剖面根据六组蛋白修饰研究。ChromHMM 识别出功能分明的染色质状态, 代表压制性和活跃的领域, 如 polycomb 镇压 (状态 1), 异色镇压 (状态 5), 活跃转录 (状态8和 9) 和活跃促进剂 (State13 和14).<a href="/files/ftp_upload/56972/56972fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

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

Nanyang Technological University

Research

JoVE Journal

Cancer Research

10.2K 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

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

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

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

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

该<em&gt;果蝇</em&gt;成虫盘瘤模型:基因的表达与肿瘤侵袭性的可视化和定量遗传马赛克

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

科研

癌症研究

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

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

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

肺定植法在肺部组织循环肿瘤细胞可视化中的建立

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

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

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

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

全基因组的 rna 干扰筛查以确定调节溶瘤病毒治疗的宿主因素

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

Native Chromatin Immunoprecipitation Using Murine Brain Tumor Neurospheres

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

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

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 ...

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

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

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

高通量三维肿瘤球体药物筛选的生成

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

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

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

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

数字聚合酶链反应法测定零星家族性腺瘤性息肉病患者的基因变异

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

In Vitro Assay to Study Tumor-macrophage Interaction

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

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

体外测定研究肿瘤-巨噬菌体相互作用

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

Establishment and Characterization of Small Bowel Neuroendocrine Tumor Spheroids

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

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

小肠神经内分泌肿瘤类球体的建立与特征

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

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

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

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

胃肠癌DNA甲基化基因组全谱分析

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

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

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

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

患者源性肿瘤外植体作为预测患者耐药性的"实时"临床前平台

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