JQW、XD、RCDD 和 YY 得到了国立卫生研究院 R01 NS088353 的赠款的支持;NIH 赠款 1R21AR071583-01;Staman 奥格尔维基金-纪念赫尔曼基金会;UTHealth 脑主动和 CTSA UL1 TR000371;还有德克萨斯大学神经系统科学和 Neurotechnology 研究所的补助金 (赠款 #362469)。

所有动物的使用和实验性的协议都是按照《动物护理和使用指南》进行的, 并由德克萨斯大学卫生科学中心的机构生物安全委员会和动物福利委员会批准。休斯顿。
1. 从小鼠脑中纯化 PDGFRα阳性少突胶质谱系细胞 (从先前描述的 immunopanning 协议修改为5, 6, 7)
<ol><li>
PDGFRα阳性细胞选择 immunopanning 板的制备及2片用于内皮细胞和小胶质的损耗。 注: 请注意, 培养皿而不是细胞培养皿为 immunopanning 实验工作;如果纯化细胞将用于培养, immunopanning 步骤必须在生物安全柜中进行<ol>
<li>10厘米培养板与30µL 山羊抗鼠 IgG 在10毫升 pH 9.5, 50 毫米三 HCl 隔夜在4°c。搅拌板, 确保板材表面均匀, 完全覆盖的涂层解决方案。</li>
		<li>用12毫升 Dulbecco 磷酸盐缓冲盐水 (DPBS) 稀释40µL 抗 PDGFRα抗体, 制备 PDGFRα抗体溶液, 含 0.2% BSA。</li>
		<li>用10毫升 1x DPBS 的 igg 涂敷板3洗净后, 室温下用 PDGFRα抗体溶液孵育4小时的 igg 涂层。</li>
		<li>用10毫升 1x DPBS 的 PDGFRα抗体涂覆钢板3次。在板侧壁上轻轻地添加 DPBS 溶液, 不干扰涂层表面。</li>
		<li>涂层2新的 15 cm 培养板为内皮细胞和小胶质的耗尽与20毫升 DPBS 包含2.3 µg/毫升的Banderiaea 单叶蔓荆凝集素 1 (BSL-1) 2 h。</li>
		<li>用20毫升 1x DPBS BSL-1 涂层板3次。在板侧壁上轻轻地添加 DPBS 溶液, 不要扰乱涂层表面。</li>
	</ol>
</li>
	<li>
从以前发布的方法中修改 PDGFRα正少突胶质沿袭单元格的纯化 6,7, 8.
	<ol>
<li>根据以前发布的协议5,6, 从2出生日 7 (P7) 鼠脑中解剖皮质组织。</li>
		<li>根据详细制造商的说明, 将组织分离产生单细胞悬浮体, 并采用神经组织离解试剂盒 (P)。</li>
		<li>简要地, 用手术刀把解剖过的皮质组织切成片, 并将其与酶消化37摄氏度。消化后, 手工将工件与火抛光玻璃巴斯德吸管分离成单细胞悬浮。</li>
		<li>离心机的单细胞悬浮在 300 x g 10 分钟室温和悬浮细胞颗粒使用15毫升的 immunopanning 缓冲 (immunopanning 缓冲 DPBS 与 0.02% BSA 和5µg/毫升胰岛素)。</li>
		<li>在室温下, 每5分钟将2只小鼠脑细胞悬浮在 2 BSL-1 涂覆板上, 以温和的搅拌板, 以确保小胶质细胞和内皮干细胞的更好的耗竭。</li>
		<li>轻轻地旋转盘子, 收集细胞悬浮液中的非黏附细胞, 在室温下将其孵化在大鼠 PDGFRα抗体涂层板上45分钟。</li>
		<li>在对大鼠 PDGFRα抗体涂层板进行细胞悬浮培养后, 轻轻地旋转盘子收集细胞悬浮液, 用 DPBS 冲洗盘子8次以去除非黏附细胞。轻轻地在板侧壁上加洗液, 搅拌板几次, 以摆脱非黏附细胞。</li>
		<li>用4毫升细胞脱离溶液治疗10分钟37摄氏度, 从大鼠 PDGFRα抗体涂层板中分离出细胞。摇动盘子以去除黏附的细胞。</li>
		<li>在室温下以 300 x g 离心法收集纯化 OPCs, 用2毫升 OPC 细胞培养基悬浮细胞, 用台盼蓝和 hemocytometer (500 毫升细胞培养培养基 DMEM/F12, 含5毫升的培养基来计数细胞;青霉素-链霉素溶液 (P/S), 5 毫升 N2, 10 毫升 B27, 5 µg/毫升胰岛素, 0.1% BSA, 20 ng/毫升 bFGF 和 10 ng/毫升 PDGFRα)。</li>
	</ol>
</li>
	<li>
immunopanning 后 OPCs 纯度的验证。
	<ol>
<li>为了评价 immunopanning 后 OPCs 的富集情况, 应根据制造商的指示, 使用一些纯化的 OPCs 进行 RNA 提取, 以 guanidium 硫氰酸盐为基础提取。</li>
		<li>采用荧光绿色染料 qRT 进行 PCR 检测, 以检查纯化 OPCs 中 PDGFRα表达的富集度, 并根据先前发布的材料4与离解脑细胞进行比较。</li>
		<li>另外, 用 anti-NG2 硫酸软骨素蛋白多糖 (NG2) 抗体作为先前发布的材料9, 将一些纯化的 OPCs 加入聚 d-赖氨酸涂层的24井板中染色。</li>
	</ol>
</li>
</ol>2. 高通量测序的低细胞芯片制备和芯片库建设
<ol><li>
Olig2 低细胞芯片的准备与商业上可用的超声波系统和一个商业上可用的低细胞数芯片套件 (见材料表), 按照详细的标准程序, 从制造商的指示。
	<ol>
<li>分离后的大鼠抗 PDGFRα抗体涂层板和细胞计数的台盼蓝和 hemocytometer 投入2万纯化 OPCs 1 毫升 OPC 细胞培养基为每个芯片的反应。</li>
		<li>添加27µL 36.5% 甲醛固定细胞悬浮在室温下10分钟。 警告: 请注意, 由于安全原因, 必须在化学油烟机中使用甲醛。</li>
		<li>在室温下停止 DNA 蛋白交联, 50 µL 2.5 米甘氨酸5分钟。 注: 所有步骤必须在冰或4摄氏度冷室从这一点上进行。</li>
		<li>将1毫升的冰冷汉克斯平衡盐溶液 (HBSS) 与蛋白酶抑制剂鸡尾酒洗净, 用 300 x 克4摄氏度的预冷离心机把细胞颗粒化。</li>
		<li>溶解细胞颗粒在25µL 完全溶解缓冲5分钟冰。 注: 搅动管在裂解缓冲液中悬浮细胞。</li>
		<li>用75µL 冰冷 HBSS 含蛋白酶抑制剂鸡尾酒补充细胞裂解物, 用预冷超声波系统剪切细胞裂解物的染色质, 并在三十年代和三十年代关闭程序。总是油脂实验6管一起, 并确保平衡管包含100µL 水。</li>
		<li>剪切后, 离心机在 1.4万 x g 10 分钟4°c, 离心后在新管中收集上清。</li>
		<li>用蛋白酶抑制剂稀释100µL 的剪切染色质, 同体积的冰冷完全芯片缓冲器。</li>
		<li>保存20µL 稀释剪切染色质作为输入控制样品。 注: 输入控制样本也需要作为比较 Olig2 immunoprecipitated 样本, 以识别 Olig2 抗体 immunoprecipitated DNA。</li>
		<li>添加1µL 兔 anti-olig2 抗体180µL 稀释剪切染色质和孵化反应管在一个旋转轮 40 rpm 16 小时在4°c。 注: 在冷室中执行此步骤。</li>
		<li>对于每个芯片的反应, 洗涤11µL 磁性蛋白 A 涂层珠与55µL 珠洗涤缓冲器, 并把珠丸在11µL 的珠洗涤缓冲区后2洗涤。 注: 在冷室中执行此步骤。</li>
		<li>对于每个芯片的反应, 添加10µL 的预水洗蛋白 A 涂层珠, 以芯片反应管。在寒冷的房间里执行这一步骤。</li>
		<li>在旋转轮上将芯片反应管孵化为4摄氏度, 另2小时。</li>
		<li>将芯片反应管放在磁架上1分钟。 注: 在冷室中执行此步骤。</li>
		<li>移除上清, 不要将珠粒取出。</li>
		<li>用100µL 为4个洗涤缓冲器分别在4摄氏度的旋转轮上清洗珠粒。</li>
		<li>洗净后, 将200µL 的洗脱缓冲液添加到珠状颗粒中, 在65°c 处孵化出芯片反应管4小时, 以逆转交联蛋白 DNA。</li>
		<li>另外, 添加180µL 的洗脱缓冲到20µL 输入控制样本和孵化65°c 4 小时, 以逆转交叉链蛋白 DNA 以及。</li>
		<li>添加200µL 25:24:1 (v/v) 混合苯酚, 氯仿和异戊醇的每个芯片反应管。</li>
		<li>涡流为1分钟, 离心机在室温下以 1.3万 x g 为15分钟。将上相转移到新管上。</li>
		<li>添加40µL 3 米醋酸钠溶液, 1000 µL 100% 乙醇和2µL 糖原沉淀共价键链接到每个芯片反应管隔夜在-20 °c。</li>
		<li>离心机在 1.3万 x g 20 分钟在4摄氏度。</li>
		<li>丢弃上清液, 用500µL 冷70% 乙醇和离心机在 1.3万 x g 处洗涤20分钟4摄氏度。</li>
		<li>丢弃上清液, 保持颗粒空气干燥10分钟, 用50µL 水溶解颗粒。</li>
	</ol>
</li>
	<li>
用于高通量测序的芯片库构建
	<ol>
<li>根据制造商的指示, 清洁和浓缩芯片 DNA 和清洁套件。</li>
		<li>根据制造商的说明, 用微微绿色量化芯片样品。</li>
		<li>根据制造商的说明, 使用一个芯片序列套件, 用于 T 尾跟踪、复制和跟踪、模板切换和扩展、添加适配器和放大、库大小选择和纯化。<ol>
<li>dsDNA 变性后, dephosphorylate 3 ' 末端 ssDNA 虾碱性磷酸酶, 并添加一吨 (T) 尾到 ssDNA 由终端 deoxynucleotidyl 转移转移。</li>
			<li>将 dna 聚 (dA) 底漆退火到 ssDNA 模板进行 dna 复制和模板交换。</li>
			<li>模板切换后, 用正向和反向引物进行 PCR 放大芯片序列库, 进行索引。</li>
			<li>选择 PCR 放大芯片序列库, 其片段范围从250到 500 bp 由顺磁珠的选择2为双尺寸选择。</li>
			<li>使用微流控芯片-毛细管电泳装置检查所选芯片序列库的质量。</li>
		</ol>
</li>
	</ol>
</li>
</ol>3. 数据分析
<ol><li>准备目录结构。
	<ol>
<li>创建一个用于执行芯片序列数据分析的目录。在新创建的目录内, 创建六个具有以下名称的子目录: 原始文件、fastqc 输出、领结输出、荷马输出、macs2 输出、主题分析和报告。</li>
	</ol></li>
	<li>准备数据并对原始序列数据进行质量控制分析。
	<ol>
<li>移动到 "原始文件" 目录并下载芯片序列数据文件。</li>
		<li>验证文件名。如果排序是配对结束, 将有4个文件 (两个用于治疗和两个输入) 具有相似的基本名称: PDGFRα_Olig2. read1 fastq, PDGFRα_Olig2 read2. fastq, PDGFRα_input. read1 fastq, PDGFRα_input. read2. fastq。如果文件名不同, 请使用 "mv" 命令重命名它们。</li>
		<li>移动到 "fastqc 输出" 目录。</li>
		<li>使用 fastq 文件质量控制软件10从原始读取中获取质量控制指标。使用图 S1A中的命令分别为每个 fastq 文件运行质量控制过程。该软件将在 html 文件中显示几个质量控制指标。</li>
		<li>打开 html 质量控制文件并验证读取次数、读取长度、每基序列质量、每个序列质量分数、序列 GC 内容、序列复制级别、适配器内容和 kmer 内容。</li>
	</ol></li>
	<li>修剪尾随的低质量读取和适配器内容。
	<ol>
<li>观察 "每基序列质量" 和 "适配器内容" 地块。确定每个读取的头部和尾部的修剪长度。修剪的适当长度是基础质量低于 30, 并且有适配器内容的证据。</li>
		<li>移动到 "原始文件" 目录。</li>
		<li>下载并安装用于修剪读取11的软件。使用图 S1B中的命令分别修剪处理和输入。参数 "裁剪" 指示从末尾修剪基础后的剩余读取长度, "HEADCROP" 指定从读取开始时要消除的基数。</li>
		<li>修剪命令还包括在参数 "MINLEN" 中筛选后接受的最小读取长度。如果读数至少为 50 bp, 请使用 35 bp 作为读取长度阈值。</li>
		<li>移动到 "fastqc 输出" 目录。</li>
		<li>在修剪读取后执行 fastq 文件质量控制分析。验证质量控制问题是否已解决。使用图 S1C中的命令获取质量控制指标。</li>
	</ol></li>
	<li>将单端或配对端的芯片序列读数与鼠标参考基因组对齐。
	<ol>
<li>移动到 "领结输出" 目录进行映射。</li>
		<li>下载 GENCODE mm10 鼠标参考基因组。将 mm10 鼠标参考基因组重命名为 "mm10"。</li>
		<li>下载读取映射器12并在系统中安装它。</li>
		<li>使用图 S2A中的命令创建下载的参考基因组的索引文件。将自动创建六个文件: mm10.1 bt2、mm10.2 bt2、mm10.3 bt2、mm10.4 bt2、mm10. 启 1. bt2 和 mm10. 启 2. bt2。</li>
		<li>使用图 S2B中的命令执行对齐方式, 并根据系统设置将参数 "-p" 调整为处理内核的数目。如果运行配对结束模式, 参数 "-1" 和 "-2" 表示修剪的 fastq 文件的名称。 注意: 为处理和输入示例分别执行映射, 并为每个示例创建输出度量日志文件。</li>
		<li>下载用于操作 SAM 格式13中的文件并将其安装到系统中的软件。使用图 S2C中的命令将对齐的 SAM 文件转换为 BAM 文件。</li>
	</ol></li>
	<li>获得对配对端处理和控制样本的峰值调用前映射读取的质量控制指标。
	<ol>
<li>要解决的最重要的质量控制指标之一是排序深度。在领结. 输出目录中打开文件 "领结 PDGFRα_Olig2 .txt" 和 "日志领结. .txt", 并验证每个示例中唯一映射的读对数是否大于1000万。</li>
		<li>移动到 "荷马输出" 目录。</li>
		<li>下载用于主题发现和下一代排序分析的软件14并将其安装在系统中。</li>
		<li>创建名为 "tagDir" 的目录。</li>
		<li>要验证标记克隆, 请使用图 S3A中描述的命令。"makeTagDirectory" 命令将生成四质量控制输出 files:tagAutocorrelation.txt、tagCountDistribution.txt、tagInfo.txt 和 tagLengthDistribution.txt。</li>
		<li>移动到 "tagDir" 目录。</li>
		<li>将 "tagCountDistribution.txt" 文件复制到 "报告" 目录中。</li>
		<li>移动到 "报告" 目录。</li>
		<li>使用电子表格程序打开 tagCountDistribution.txt 文件, 并创建每个基因组位置标记数的条形图。存储直方图文件的名称为 "tag.clonality.xlsx"。</li>
		<li>在系统中安装 R 编程语言15 。</li>
		<li>在终端中, 键入 "r", 然后按 enter 键访问 R 编程环境。</li>
		<li>下载用于处理芯片序列数据16的 R 包, 并使用图 S3B中描述的命令安装它。</li>
		<li>使用图 S3C中的 R 脚本绘制链的交叉关联。</li>
	</ol></li>
	<li>使用峰值映射器检测峰值。
	<ol>
<li>移动到 "macs2 输出" 目录。在系统中下载并安装峰值映射器17 。</li>
		<li>使用函数 "callpeak" 与处理和控制在步骤3.4.6 中生成的 BAM 文件。 注: 其他参数包括 "-f" 输入文件格式, "-g" 用于基因组大小, "-名称" 表示所有输出文件的基名称, "-B" 用于存储 bedgraph 格式的碎片堆积。完整的峰值调用命令包括在图 S4A中。对配对端样品使用 BEDPE。</li>
		<li>验证峰值映射程序生成了六个文件: PDGFRα_Olig2_vs_PDGFRα_input_peaks、PDGFRα_Olig2_vs_PDGFRα_input_peaks narrowPeak、PDGFRα_Olig2_vs_PDGFRα_input_summits 床、PDGFRα_Olig2_vs_PDGFRα_input_model r、PDGFRα_Olig2_vs _PDGFRα_input_control_lambda. bdg 和 PDGFRα_Olig2_vs_PDGFRα_input_treat_pileup bdg。</li>
		<li>打开文件 PDGFRα_Olig2_vs_PDGFRα_input_peaks. xls 查看被称为峰值, 位置, 长度, 山顶位置, 交通, 和峰值浓缩指标:-log10 (pvalue), 折叠浓缩, 和-log10 (q 值)。</li>
	</ol></li>
	<li>筛选和注释称为峰值。
	<ol>
<li>使用上一步中打开的文件, 根据折叠富集、p 值和/或 q 值对生成的峰值进行筛选。要确定适当的过滤阈值, 请制作一个直方图图来决定每个指标的密度。筛选出选定阈值之下的数值以获得显著峰值。</li>
		<li>下载 mm10 黑名单 (已知有人为地高信号) 和过滤出芯片序列峰值, 这是在任何一个工件区域内。</li>
		<li>创建包含以下列的筛选峰值的床文件: 染色体、开始、结束、peakID、模拟和链。用点 "." 填充模拟列, 因为它没有使用。在 "链" 列中, 将所有值设置为 "+"。将床文件保存为 "filtered.peakData2.bed"。</li>
		<li>在 "报告" 目录中复制 "filtered.peakData2.bed" 文件。</li>
		<li>移动到 "报告" 目录。</li>
		<li>若要将筛选的峰值批注到特定的基因区域, 请使用在上一步中创建的床文件的函数 "annotatePeaks"。使用的命令显示在图 S5A中。</li>
		<li>使用统计软件打开 "filtered.annotatedPeaks.txt" 文件。</li>
		<li>过滤产生的峰值位于基因间区域的距离大于 5 kb, 从注释的 TSS。使用文件中的 "距离到 TSS" 列作为筛选距离。将已筛选的峰值存储在名为 "5 kbup geneBody peakData .txt" 的 excel 文件中。</li>
		<li>将生成的筛选峰值存储在具有列的 bedGraph 文件中: 染色体、开始、结束和-log10 (q 值)。命名文件 "5 kbup geneBody peakData bedGraph"。</li>
	</ol></li>
	<li>为浏览器可视化生成要人文件。
	<ol>
<li>下载并安装用于基因组算术18的软件。</li>
		<li>下载用于将 bedgraph 转换为要人并安装在系统中的工具。下载 "mm10.chrom.sizes" 文件。</li>
		<li>使用图 S6A中的命令生成要人文件。</li>
		<li>在系统中下载并安装一个基因组浏览器19 。</li>
		<li>打开基因组浏览器并加载文件 "5 kbup, geneBody, peakData", 以可视化的显著峰值和他们的位置有关已知的基因。</li>
	</ol></li>
	<li>主题搜索。
	<ol>
<li>移动到 "主题分析" 目录。</li>
		<li>下载一个从头主题扫描和主题浓缩程序, 并将其安装到您的系统20中。</li>
		<li>下载一个包含 Olig2 主题的综合主题数据库。</li>
		<li>获得500个以重大高峰峰会为中心的 bp 地区的基因组序列。将序列存储为 peak.sequences.txt。使用图 S7中的命令在500个 bp 峰值区域中搜索 Olig2 图案。</li>
		<li>使用适当的电子值筛选生成的图案 (默认为 0.05)。</li>
	</ol></li>
</ol>

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作者声明他们没有竞争的财政利益。

哺乳动物基因调控网络是非常复杂的。芯片序列是研究全基因组蛋白质-DNA 相互作用的有力方法。该协议包括如何使用小鼠大脑中的少量纯化 OPCs (每反应低2万细胞) 来执行 Olig2 芯片序列。该协议的第一个关键步骤是用 PDGFRα抗体 immunopanning 纯化小鼠大脑中的 OPCs。对于 PDGFRα涂覆板的 OPCs 的阳性选择, OPCs 通常与 PDGFRα涂层板紧密结合。即使经过几次洗涤, 仍然有一些非黏附的细胞留下。在显微镜下,...

重要的是研究蛋白质 (或蛋白质复合物) DNA 绑定和表观遗传标记建立转录调控网络参与各种生物过程。特别是转录因子与基因组 DNA 的绑定, 在基因调控、细胞分化、组织发育等方面都有重要作用。研究转录调节和表观遗传学机制的有力工具是染色质免疫沉淀 (芯片)。由于下一代测序技术的飞速进步, 芯片结合高通量 dna 测序 (芯片序列) 被用于蛋白质 DNA 绑定和表观遗传标记的分析1。然而, 一个标准的芯片序列协议需要大约2000万细胞每反应, 这使得这种技术的应用困难, 当细胞数量有限, 如孤立的原生细胞和稀有细胞群体。
少突胶质谱系细胞包括少突胶质前体细胞 (OPCs) 和少突胶质广泛分布于整个大脑, 对大脑的发育和功能至关重要。OPCs 作为前体细胞的一种, 具有自我更新和分化的能力。OPCs 不仅作为少突胶质的始祖, 而且在神经元信号的传播中起着重要作用, 通过与其他类型的脑细胞进行交流2。以前的研究表明, 少突胶质的发展是由沿袭特定的转录因子 (如 Olig2 和Sox103, 4) 调节的.这些转录因子被发现绑定到一些关键基因的启动子或增强区, 以影响其表达在少突胶质规范和分化。然而, 要确定蛋白质 (或蛋白质复合物) 在急性纯化的初级 OPCs 中的 DNA 结合有很大的挑战性, 细胞数量非常有限。
本协议描述了如何利用芯片序列技术对纯化小鼠 OPCs 中 Olig2 基因组 DNA immunoprecipitated 进行系统的研究。OPCs 从小鼠的大脑被 immunopanning 的敏锐纯化, 并用于芯片实验, 不扩散在体外。有限数量的 OPCs 可以得到 immunopanning, 是不够的标准芯片序列实验。本文介绍了一种低细胞芯片序列协议, 其每片反应的转录因子为2万细胞。简而言之, 交联细胞裂解和微气泡由超声波装置剪切染色质。用 Olig2 抗体和蛋白 A 包覆的微珠对剪切的染色质进行孵化, 沉淀 Olig2 抗体绑定基因组 DNA。从蛋白 A 包膜珠和反向交联洗脱后, 用苯酚-氯仿萃取纯化 Olig2 抗体沉淀的基因组 DNA。该产品经过定量化, 并受到 T 尾矿、底漆退火模板的切换和扩展, 增加了适配器和放大器, 库的大小选择和净化步骤, 为芯片序列库的建设。
在测序后, 分析了用 Olig2 转录因子抗体和对照样品制备的样品的原始读数质量。剪裁了包含读取片段的低质量基对和适配器。接下来, 修剪的读数与鼠标参考基因组对齐。与对照样品相比, 对芯片读数显著丰富的基因组区域被检测为峰值。显著的峰值, 代表潜在的转录因子结合点, 被过滤和可视化的基因组浏览器。
值得注意的是, 本协议中描述的方法可以广泛地用于其他转录因子的芯片序列, 并具有任何细胞类型的有限数目。

<table><tbody><tr><td>&lt;strong&gt;Reagent/ Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Banderiaea simplicifolia lectin 1</td><td>Vector Laboratories</td><td># L-1100</td><td></td></tr><tr><td>Rat anti-PDGFRa antibody</td><td>BD Bioscience</td><td># 558774</td><td></td></tr><tr><td>Neural tissue dissociation Kit (P)</td><td>MACS Miltenyi Biotec</td><td># 130-092-628</td><td></td></tr><tr><td>Accutase</td><td>STEMCELL technologies</td><td># 07920</td><td></td></tr><tr><td>TRIzol</td><td>Thermo Fisher</td><td># 15596026</td><td></td></tr><tr><td>Anti-NG2 Chondroitin Sulfate Proteoglycan Antibody</td><td>Millipore</td><td># AB5320</td><td></td></tr><tr><td>Bioruptor Pico sonication device</td><td>Diagenode</td><td># B01060001</td><td></td></tr><tr><td>True MicroChIP kit</td><td>Diagenode</td><td># C01010130</td><td></td></tr><tr><td>Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v)</td><td>Thermo Fisher</td><td># 15593031</td><td></td></tr><tr><td>NucleoSpin Gel and PCR Clean-Up kit</td><td>MACHEREY-NAGEL</td><td># 740609</td><td></td></tr><tr><td>Quant-iT PicoGreen dsDNA Assay Kit</td><td>Thermo Fisher</td><td># P11496</td><td></td></tr><tr><td>DNA SMART ChIP-Seq kit</td><td>Clontech Laboratories</td><td># 634865</td><td></td></tr><tr><td>Agencourt AMPure XP</td><td>Beckman Voulter</td><td># A63880</td><td></td></tr><tr><td>GlycoBlue</td><td>Thermo Fisher</td><td># AM9516</td><td></td></tr><tr><td>pico-green</td><td>Thermo Fisher</td><td># P11496</td><td></td></tr><tr><td>D-PBS</td><td>Thermo Fisher</td><td># 14190-144</td><td></td></tr><tr><td>SYBR Green master mix</td><td>Bio-Rad Laboratories</td><td># 1725124</td><td></td></tr><tr><td>DMEM/F12</td><td>Fisher Scientific</td><td># 11-320-033</td><td></td></tr><tr><td>Penicillin-Streptomycin (P/S)</td><td>Fisher Scientific</td><td># 15140122</td><td></td></tr><tr><td>N-2 Supplement</td><td>Fisher Scientific</td><td># 17502048</td><td></td></tr><tr><td>B-27 Supplement</td><td>Fisher Scientific</td><td># 17504044</td><td></td></tr><tr><td>insulin</td><td>Sigma</td><td># I6634</td><td>Prepare 0.5 mg/ml insulin solution by dissolving 5 mg insulin in 10 ml water and 50 &amp;mu;l of 1 N HCl.</td></tr><tr><td>Bovine Serum Albumin, suitable for cell culture (BSA)</td><td>Sigma</td><td># A4161</td><td>Prepare 4% BSA solution by dissolving 4 g BSA in 100 ml D-PBS and adjust the pH to 7.4</td></tr><tr><td>Fibroblast Growth Factor basic Protein, Human recombinant (bFGF)</td><td>EMD Millipore</td><td># GF003</td><td></td></tr><tr><td>HUMAN PDGF-AA</td><td>VWR</td><td># 102061-188</td><td></td></tr><tr><td>poly-D-lysine</td><td>VWR</td><td># IC15017510</td><td>Prepare 1 mg/ml poly-D-lysine solution by dissolving 10 mg poly-D-lysine in 10 ml water and dilute 100 times when using.</td></tr><tr><td>Falcon Disposable Petri Dishes, Sterile, Corning, 100x15mm</td><td>VWR</td><td># 25373-100</td><td></td></tr><tr><td>Falcon Disposable Petri Dishes, Sterile, Corning, 150x15mm</td><td>VWR</td><td># 25373-187</td><td></td></tr><tr><td>HBSS, 10X, no Calcium, no Magnesium, no Phenol Red</td><td>Fisher Scientific</td><td># 14185-052</td><td></td></tr><tr><td>Trypan Blue</td><td>Stemcell Technologies</td><td># 07050</td><td></td></tr><tr><td>protease inhibitor cocktail</td><td>Sigma</td><td># 11697498001</td><td></td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>FastQC</td><td>[10] http://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td><td></td><td>Obtaining quality control metrics of raw and trimmed reads</td></tr><tr><td>Trimmomatic 0.33</td><td>[11] http://www.usadellab.org/cms/?page=trimmomatic</td><td></td><td>Trimming and filtering raw reads</td></tr><tr><td>GENCODE mm10</td><td>ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_mouse/release_M15/GRCm38.primary_assembly.genome.fa.gz</td><td></td><td>Mouse reference genome</td></tr><tr><td>Bowtie2 2.2.4</td><td>[12] http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</td><td></td><td>Aligning reads to a reference genome</td></tr><tr><td>SAMTools 1.5</td><td>[13] http://samtools.sourceforge.net/</td><td></td><td>Converting SAM file into BAM format</td></tr><tr><td>HOMER 4.9.1</td><td>[14] http://homer.ucsd.edu/homer/</td><td></td><td>Creating a tag directory and annotating enriched genomic regions with gene symbols</td></tr><tr><td>R 3.2.2</td><td>[15] https://www.R-project.org/</td><td></td><td>Programming scripts and running functions</td></tr><tr><td>SPP 1.13</td><td>[16] https://github.com/hms-dbmi/spp</td><td></td><td>Creating a strand cross-correlation plot</td></tr><tr><td>MACS2 2.2.4</td><td>[17] https://github.com/taoliu/MACS</td><td></td><td>Finding regions of ChIP enrichment over control</td></tr><tr><td>BEDTools 2.25</td><td>[18] http://bedtools.readthedocs.io/en/latest/</td><td></td><td>Genome arithmetics</td></tr><tr><td>bedGraphToBigWig</td><td>http://hgdownload.cse.ucsc.edu/admin/exe/</td><td></td><td>Converting bedGraph file into bigwig</td></tr><tr><td>IGV browser 2.3.58</td><td>[19] http://software.broadinstitute.org/software/igv/</td><td></td><td>Visualization and browsing of significant ChIP-seq peaks</td></tr><tr><td>Microsoft Excel</td><td></td><td></td><td>Spreasheet program</td></tr><tr><td>ENCODE&#039;s blacklist</td><td>https://sites.google.com/site/anshulkundaje/projects/blacklists</td><td></td><td>Filtering peaks</td></tr><tr><td>mm10.chrom.sizes</td><td>http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/mm10.chrom.sizes.</td><td></td><td>Converting bedGraph file into bigwig</td></tr><tr><td>ENCODE&#039;s motif database</td><td>[25] http://compbio.mit.edu/encode-motifs/</td><td></td><td>Comprehensive motif database required for motif enrichment</td></tr><tr><td>MEME-ChIP</td><td>[20] http://meme-suite.org/index.html</td><td></td><td>Motif enrichment analysis and motif discovery</td></tr><tr><td>&lt;strong&gt;Primer names used for qPCR&lt;/strong&gt;</td><td></td><td></td><td>&lt;strong&gt;Primer sequences used for qPCR&lt;/strong&gt;</td></tr><tr><td>Mbp-F:</td><td></td><td></td><td>CTATAAATCGGCTCACAAGG</td></tr><tr><td>Mbp-R:</td><td></td><td></td><td>AGGCGGTTATATTAAGAAGC</td></tr><tr><td>Iba1-F:</td><td></td><td></td><td>ACTGCCAGCCTAAGACAACC</td></tr><tr><td>Iba1-R:</td><td></td><td></td><td>GCTTTTCCTCCCTGCAAATCC</td></tr><tr><td>Mog-F:</td><td></td><td></td><td>GGCTTCTTGGAGGAAGGGAC</td></tr><tr><td>Mog-R:</td><td></td><td></td><td>TGAATTGTCCTGCATAGCTGC</td></tr><tr><td>GAPDH-F</td><td></td><td></td><td>ATGACATCAAGAAGGTGGTG</td></tr><tr><td>GAPDH-R</td><td></td><td></td><td>CATACCAGGAAATGAGCTTG</td></tr><tr><td>Tuj1-F</td><td></td><td></td><td>TTTTCGTCTCTAGCCGCGTG</td></tr><tr><td>Tuj1-R</td><td></td><td></td><td>GATGACCTCCCAGAACTTGGC</td></tr><tr><td>PDGFR&amp;alpha;-F</td><td></td><td></td><td>AGAGTTACACGTTTGAGCTGTC</td></tr><tr><td>PDGFR&amp;alpha;-R</td><td></td><td></td><td>GTCCCTCCACGGTACTCCT</td></tr></tbody></table>

identifying transcription factor olig2 genomic binding sites in acutely purified pdgfr&#945;+ cells by low-cell chromatin immunoprecipitation sequencing analysis

在哺乳动物细胞中, 基因转录通过转录因子与基因组 DNA 的相互作用来调节细胞类型的特定方式。谱系特异转录因子被认为在细胞规格和分化过程中发挥重要作用。芯片结合高通量 DNA 测序 (芯片序列) 被广泛用于分析基因组范围内转录因子 (或其关联的复合体) 与基因组 DNA 的结合点。然而, 一个标准的芯片反应需要大量的细胞, 这使得研究孤立的原生细胞或稀有细胞群体的数量有限是困难的。为了了解 Olig2 在急性纯化小鼠 OPCs 中少突胶质谱系特异转录因子的调控机制, 本文给出了用芯片序列识别 Olig2 (或 Olig2 复合体) 全基因组结合点的详细方法。首先, 该协议解释了如何从小鼠脑中纯化血小板衍生生长因子受体α (PDGFRα) 阳性 OPCs。其次, 对 Olig2 抗体介导芯片和库结构进行了实验。最后一部分介绍了用于 Olig2 芯片序列分析的生物信息学软件和程序。总之, 本文报道了一种分析急性纯化脑 OPCs 转录因子 Olig2 的全基因组绑定的方法。

采用低细胞芯片序列进行生物信息学分析, 研究转录因子 Olig2 与基因组 DNA 在急性纯化脑 OPCs 中的潜在相互作用。图 1显示了实验和数据分析过程的一般工作流。在这个协议中, 产后老鼠的大脑被分离成单细胞悬浮。组织分离后, 采用 PDGFRα抗体对 OPCs 进行纯化, immunopanning。图 2显示了 PDGFRα的显著丰富性、神经元标记 Tuj1 的小表?...

Watch this Scientific Journal Video about Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRÎ±+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysi

通过低细胞染色质免疫沉淀测序分析鉴定急性纯化的 PDGFRα+ 细胞中的转录因子 Olig2 基因组结合位点 |神经科学 |视频

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

在这里, 我们提出一个协议, 旨在分析少突胶质转录因子 2 (Olig2) 在急性纯化脑少突胶质前体细胞 (OPCs) 的全基因组结合执行低细胞染色质免疫沉淀 (芯片)、库准备、高通量测序和生物信息学数据分析。

低细胞染色质免疫沉淀测序分析 Olig2 基因组结合位点在急性纯化 PDGFRα + 细胞中的转录因子鉴定

In mammalian cells, gene transcription is regulated in a cell type specific manner by the interactions of transcriptional factors with genomic DNA. ...

identifying-transcription-factor-olig2-genomic-binding-sites-acutely

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Unit 1

DNA Structure and Function

Gene Expression

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9.2K 次观看.  University of Texas Health Science Center. 在这里, 我们提出一个协议, 旨在分析少突胶质转录因子 2 (Olig2) 在急性纯化脑少突胶质前体细胞 (OPCs) 的全基因组结合执行低细胞染色质免疫沉淀 (芯片)、库准备、高通量测序和生物信息学数据分析。

视频: Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Genome-wide Snapshot of Chromatin Regulators and States in Xenopus Embryos by ChIP-Seq

The recruitment of chromatin regulators and the assignment of chromatin states to specific genomic loci are pivotal to cell fate decisions and tissue and organ formation during development. Determining the locations and levels of such chromatin features in vivo will provide valuable information about the spatio-temporal regulation of genomic elements, and will support aspirations to mimic embryonic tissue development in vitro. The most commonly used method for genome-wide and high-resolution profiling is chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq). This protocol outlines how yolk-rich embryos such as those of the frog Xenopus can be processed for ChIP-Seq experiments, and it offers simple command lines for post-sequencing analysis. Because of the high efficiency with which the protocol extracts nuclei from formaldehyde-fixed tissue, the method allows easy upscaling to obtain enough ChIP material for genome-wide profiling. Our protocol has been used successfully to map various DNA-binding proteins such as transcription factors, signaling mediators, components of the transcription machinery, chromatin modifiers and post-translational histone modifications, and for this to be done at various stages of embryogenesis. Lastly, this protocol should be widely applicable to other model and non-model organisms as more and more genome assemblies become available.

Genome-wide Snapshot of Chromatin Regulators and States in Xenopus Embryos by ChIP-Seq

The question of how chromatin regulators and chromatin states affect the genome in vivo is key to our understanding of how early cell fate decisions are made in the developing embryo. ChIP-Seq&#8212;the most popular approach to investigate chromatin features at a global level&#8212;is outlined here for Xenopus embryos.

染色质监管机构和各国的全基因组快照<em&gt;爪</em&gt;通过胚胎的ChIP-SEQ

The recruitment of chromatin regulators and the assignment of chromatin states to specific genomic loci are pivotal to cell fate decisions and tissue and ...

科研

发育生物学

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

转录因子结合、转录、翻译和营业额的实时分析在细胞激活过程中显示全球事件

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

遗传学

A Chromatin Immunoprecipitation Assay to Identify Novel NFAT2 Target Genes in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant B cell clones and represents the most common leukemia in western countries. The majority of CLL patients show an indolent course of the disease as well as an anergic phenotype of their leukemia cells, referring to a B cell receptor unresponsive to external stimulation. We have recently shown that the transcription factor NFAT2 is a crucial regulator of anergy in CLL. A major challenge in the analysis of the role of a transcription factor in different diseases is the identification of its specific target genes. This is of&#160;great significance for the elucidation of pathogenetic mechanisms and potential therapeutic interventions. Chromatin immunoprecipitation (ChIP) is a classic technique to demonstrate protein-DNA interactions and can, therefore, be used to identify direct target genes of transcription factors in mammalian cells. Here, ChIP was used to identify LCK as a direct target gene of NFAT2 in human CLL cells. DNA and associated proteins are crosslinked using formaldehyde and subsequently sheared by sonication into DNA fragments of approximately 200-500 base pairs (bp). Cross-linked DNA fragments associated with NFAT2 are then selectively immunoprecipitated from cell debris using an &#945;NFAT2 antibody. After purification, associated DNA fragments are detected via quantitative real-time PCR (qRT-PCR). DNA sequences with evident enrichment represent regions of the genome which are targeted by NFAT2 in vivo. Appropriate shearing of the DNA and the selection of the required antibody are particularly crucial for the successful application of this method. This protocol is ideal for the demonstration of direct interactions of NFAT2 with target genes. Its major limitation is the difficulty to employ ChIP in large-scale assays analyzing the target genes of multiple transcription factors in intact organisms.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. NFAT transcription factors are important regulators of development and activation in numerous cell types. Here, we present a protocol for the use of chromatin immunoprecipitation (ChIP) in human CLL cells to identify novel target genes of NFAT2.

一种检测慢性淋巴细胞白血病新 nfat2 靶基因的染色质免疫沉淀法

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant B cell clones and represents the most common leukemia in western ...

癌症研究

Discovering CsgD Regulatory Targets in Salmonella Biofilm Using Chromatin Immunoprecipitation and High-Throughput Sequencing (ChIP-seq)

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique that can be used to discover the regulatory targets of transcription factors, histone modifications, and other DNA-associated proteins. ChIP-seq data can also be used to find differential binding of transcription factors in different environmental conditions or cell types. Initially, ChIP was performed through hybridization on a microarray (ChIP-chip); however, ChIP-seq has become the preferred method through technological advancements, decreasing financial barriers to sequencing, and massive amounts of high-quality data output. Techniques of performing ChIP-seq with bacterial biofilms, a major source of persistent and chronic infections, are described in this protocol. ChIP-seq is performed on Salmonella enterica serovar Typhimurium biofilm and planktonic cells, targeting the master biofilm regulator, CsgD, to determine differential binding in the two cell types. Here, we demonstrate the appropriate amount of biofilm to harvest, normalizing to a planktonic control sample, homogenizing biofilm for cross-linker access, and performing routine ChIP-seq steps to obtain high quality sequencing results.

Discovering CsgD Regulatory Targets in Salmonella Biofilm Using Chromatin Immunoprecipitation and High-Throughput Sequencing ChIP-seq

Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) is a method used to establish interactions between transcription factors and the genomic sequences they control. This protocol outlines techniques for performing ChIP-seq with bacterial biofilms, using Salmonella enterica serovar Typhimurium bacterial biofilm as an example.

使用染色质免疫沉淀和高通量测序（ChIP-seq）发现沙门氏菌生物膜中的CsgD监管目标

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a technique that can be used to discover the regulatory targets of transcription ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

采用芯片识别亚型特定的靶基因的全基因组分析

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

生物学

Repressing Gene Transcription by Redirecting Cellular Machinery with Chemical Epigenetic Modifiers

Regulation of chromatin compaction is an important process that governs gene expression in higher eukaryotes. Although chromatin compaction and gene expression regulation are commonly disrupted in many diseases, a locus-specific, endogenous, and reversible method to study and control these mechanisms of action has been lacking. To address this issue, we have developed and characterized novel gene-regulating bifunctional molecules. One component of the bifunctional molecule binds to a DNA-protein anchor so that it will be recruited to an allele-specific locus. The other component engages endogenous cellular chromatin-modifying machinery, recruiting these proteins to a gene of interest. These small molecules, called chemical epigenetic modifiers (CEMs), are capable of controlling gene expression and the chromatin environment in a dose-dependent and reversible manner. Here, we detail a CEM approach and its application to decrease gene expression and histone tail acetylation at a Green Fluorescent Protein (GFP) reporter located at the Oct4 locus in mouse embryonic stem cells (mESCs). We characterize the lead CEM (CEM23) using fluorescent microscopy, flow cytometry, and chromatin immunoprecipitation (ChIP), followed by a quantitative polymerase chain reaction (qPCR). While the power of this system is demonstrated at the Oct4 locus, conceptually, the CEM technology is modular and can be applied in other cell types and at other genomic loci.

Regulation of the chromatin environment is an essential process required for proper gene expression. Here, we describe a method for controlling gene expression through the recruitment of chromatin-modifying machinery in a gene-specific and reversible manner.

用化学表观修饰因子重定向细胞机械抑制基因转录

Regulation of chromatin compaction is an important process that governs gene expression in higher eukaryotes. Although chromatin compaction and gene ...

生物工程

Chromatin Isolation by RNA Purification (ChIRP)

Long noncoding RNAs are key regulators of chromatin states for important biological processes such as dosage compensation, imprinting, and developmental gene expression 1,2,3,4,5,6,7. The recent discovery of thousands of lncRNAs in association with specific chromatin modification complexes, such as Polycomb Repressive Complex 2 (PRC2) that mediates histone H3 lysine 27 trimethylation (H3K27me3), suggests broad roles for numerous lncRNAs in managing chromatin states in a gene-specific fashion 8,9. While some lncRNAs are thought to work in cis on neighboring genes, other lncRNAs work in trans to regulate distantly located genes. For instance, Drosophila lncRNAs roX1 and roX2 bind numerous regions on the X chromosome of male cells, and are critical for dosage compensation 10,11. However, the exact locations of their binding sites are not known at high resolution. Similarly, human lncRNA HOTAIR can affect PRC2 occupancy on hundreds of genes genome-wide 3,12,13, but how specificity is achieved is unclear. LncRNAs can also serve as modular scaffolds to recruit the assembly of multiple protein complexes. The classic trans-acting RNA scaffold is the TERC RNA that serves as the template and scaffold for the telomerase complex 14; HOTAIR can also serve as a scaffold for PRC2 and a H3K4 demethylase complex 13.
Prior studies mapping RNA occupancy at chromatin have revealed substantial insights 15,16, but only at a single gene locus at a time. The occupancy sites of most lncRNAs are not known, and the roles of lncRNAs in chromatin regulation have been mostly inferred from the indirect effects of lncRNA perturbation. Just as chromatin immunoprecipitation followed by microarray or deep sequencing (ChIP-chip or ChIP-seq, respectively) has greatly improved our understanding of protein-DNA interactions on a genomic scale, here we illustrate a recently published strategy to map long RNA occupancy genome-wide at high resolution 17. This method, Chromatin Isolation by RNA Purification (ChIRP) (Figure 1), is based on affinity capture of target lncRNA:chromatin complex by tiling antisense-oligos, which then generates a map of genomic binding sites at a resolution of several hundred bases with high sensitivity and low background. ChIRP is applicable to many lncRNAs because the design of affinity-probes is straightforward given the RNA sequence and requires no knowledge of the RNA's structure or functional domains.

Chromatin Isolation by RNA Purification ChIRP

ChIRP is a novel and rapid technique to map genomic binding sites of long noncoding RNAs (lncRNAs). The method takes advantage of the specificity of anti-sense tiling oligonucleotides to allow the enumeration of lncRNA-bound genomic sites.

RNA纯化的染色质隔离（CHIRP）

Long noncoding RNAs are key regulators of chromatin states for important biological processes such as dosage compensation, imprinting, and developmental ...

Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass

Chromatin immunoprecipitation (ChIP) is a widely-used method for determining the interactions of different proteins with DNA in chromatin of living cells. Examples include sequence-specific DNA binding transcription factors, histones and their different modification states, enzymes such as RNA polymerases and ancillary factors, and DNA repair components. Despite its ubiquity, there is a lack of up-to-date, detailed methodologies for both bench preparation of material and for accurate analysis allowing quantitative metrics of interaction. Due to this lack of information, and also because, like any immunoprecipitation, conditions must be re-optimized for new sets of experimental conditions, the ChIP assay is susceptible to inaccurate or poorly quantitative results.
Our protocol is ultimately derived from seminal work on transcription factor:DNA interactions1,2 , but incorporates a number of improvements to sensitivity and reproducibility for difficult-to-obtain cell types. The protocol has been used successfully3,4 , both using qPCR to quantify DNA enrichment, or using a semi-quantitative variant of the below protocol.
This quantitative analysis of PCR-amplified material is performed computationally, and represents a limiting factor in the assay. Important controls and other considerations include the use of an isotype-matched antibody, as well as evaluation of a control region of genomic DNA, such as an intergenic region predicted not to be bound by the protein under study (or anticipated not to show changes under the experimental conditions). In addition, a standard curve of input material for every ChIP sample is used to derive absolute levels of enrichment in the experimental material. Use of standard curves helps to take into account differences between primer sets, regardless of how carefully they are designed, and also efficiency differences throughout the range of template concentrations for a single primer set. Our protocol is different from others that are available5-8 in that we extensively cover the later, analysis phase.

We describe a robust method for chromatin immunoprecipitation using primary T cells. The method is founded on standard approaches, but uses a specific set of conditions and reagents that improve efficiency for limited a quantities of cells. Importantly, a detailed description of the data analysis phase is presented.

生物质的使用限制金额高效的染色质免疫沉淀

Chromatin immunoprecipitation (ChIP) is a  widely-used method for determining the interactions of different proteins with  DNA in chromatin of living ...

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 ...

Chromatin Immunoprecipitation to Identify Target Protein Binding Sites on Genomic DNA

Source: Dong, X., et.al., <a href="https://app.jove.com/v/57547">Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis.</a>&#160;J. Vis. Exp.&#160;(2018)
In this video, we demonstrate the chromatin immunoprecipitation technique to identify protein binding sites on specific regions of the genomic DNA of oligodendrocyte precursor cells via the selective immunoprecipitation of protein-bound chromatin fragments. This method helps to study the interaction of several regulatory proteins, including transcription factors involved in gene regulation.

染色质免疫沉淀鉴定基因组 DNA 上的靶蛋白结合位点

Source: Dong, X., et.al., Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin ...