这份出版物是由赠款 T32ES019851 (Fritsch)、R01AG054641 和一位遥远的青年调查员赠款 (m. Vermulst) 提供的资金所实现的。

1. 准备
<ol><li>RNases 无处不在;因此, 通过喷洒净化试剂 (如 RNase) 和70% 乙醇, 彻底清洁工作空间。喷洒任何吸管, 钢笔, 或管架, 以消除潜在的 RNase 污染来源。</li>
	<li>为了进一步防止 RNases 污染样品, 在试验期间穿上实验室外套或长袖 t恤衫。避免接触手套和任何将使用的管子的内部, 特别是当从盒子或袋子中取回时。</li>
	<li>在反向转录之前, 经常更换手套。 注: 为了获得最佳结果, 请不要在反向转录之前暂停实验。</li>
</ol>2.细胞和动物的文化和收藏
<ol><li>
酿酒酵母的生长和收集
	<ol>
<li>如果要从酵母中提取 C 序列库, 首先接种3毫升的培养基, 其中含有酵母萃取物、腺嘌呤、蛋白胨和葡萄糖 (YAPD), 并在一夜之间在30摄氏度的滚动鼓中孵化。</li>
		<li>早晨, 重新接种50毫升 YAPD 培养基中的培养到0.1 的光密度 (OD), 并将细胞生长直到达到 0.5–0.7 (~ 7 x 106细胞/毫升) 的 OD。</li>
		<li>收集整个50毫升培养 (大约 350 x 106细胞) 在50毫升圆锥管和旋转的细胞下降3分钟 2100 x g。小心地取出 YAPD 介质, 用 1x PBS 清洗一次颗粒。</li>
		<li>然后, 并用重悬480µL 裂解缓冲液中的颗粒, 48 µL 10% SDS, 480 µL Phenol:Chloroform:Isoamyl 乙醇 (25:24:1), pH 6.8。涡流样品在最大速度为 5 s, 并且转移它到1.5 毫升螺丝盖帽管与750µL 冷氧化锆珠 (提供与套件)。继续执行本协议的步骤3以进行细胞裂解和总 RNA 提取。 注: 在推荐的酵母 RNA 提取试剂盒中提供了步骤2.1.4 所需的所有试剂。这些试剂同样适用于酵母、蠕虫和苍蝇, 因此在酵母 (步骤 2.1)、蠕虫 (步骤 2.2) 或苍蝇 (步骤 2.3) 收集之后, 可以使用统一的方法生成 C 序列库 (步骤 3–10)。</li>
	</ol>
</li>
	<li>
秀丽线虫文化和收集
	<ol>
<li>如果一个 C 序列库是希望从蠕虫, 存入30成人蠕虫在新鲜的一盘琼脂发现与 OP50 细菌。 注: 五板足够1复制。</li>
		<li>培养蠕虫4天, 并收集蠕虫通过轻轻地洗涤的盘子与5毫升的 M9 介质, 并池蠕虫成一个单一的50毫升锥管。</li>
		<li>旋转蠕虫在 100 x g 2 分钟, 以创建一个小球。以尽可能低的速度减速离心机, 以免干扰颗粒。</li>
		<li>将蠕虫轻轻地移动到1.5 毫升管中, 在1.5 毫升的 M9 介质中清洗两次, 并将管子离心在 100 x g处, 以去除细菌并生成干净的100µL 颗粒。</li>
		<li>并用重悬480µL 裂解缓冲液中的蠕虫, 48 µL 10% SDS, 480 µL Phenol:Chloroform:Isoamyl 醇。将样品以最大速度涡旋5秒, 并将其转移到1.5 毫升螺钉盖管中, 其中含有750µL 的冷氧化锆珠。继续执行本协议的步骤 3, 对蠕虫和总 RNA 提取进行裂解。</li>
	</ol>
</li>
	<li>
果蝇养殖与收藏
	<ol>
<li>如果一个 C 序列的图书馆是渴望从苍蝇, 文化20–25苍蝇的瓶子, 其中含有葡萄糖或基于糖蜜的培养基。整除苍蝇超过3瓶, 所以每个瓶子包含约8苍蝇。</li>
		<li>收集苍蝇, 把瓶子放到冰桶里, 直到苍蝇被镇静。然后, 用刷子在1.5 毫升的管子里收集它们。不要用2的治疗方法收集苍蝇。</li>
		<li>让苍蝇温暖室温 (RT), 并迅速添加一个预制混合物500µL 裂解缓冲, 50 µL 10% SDS, 500 µL Phenol:Chloroform:Isoamyl 酒精的管。使用 micropestle 研磨的苍蝇约15冲程, 伴随着坚定的扭转运动时, 杵到达底部的管。</li>
		<li>将苍蝇和裂解介质的混合物倒入1.5 毫升螺钉盖管中, 其中含有750µL 的冷氧化锆珠, 并以最大速度将其涡旋5秒. 继续执行本议定书的步骤 3, 以使苍蝇的裂解和总 RNA 提取。</li>
	</ol>
</li>
</ol>3. 总 RNA 纯化
注意: 在这一点上, 所有三协议都会聚在一起, 一个统一的方法可以用来生成 C 序列库。
<ol><li>拧紧瓶盖, 并将管连接到带有适配器或胶带的 vortexer。以最大速度涡流管10分钟溶解样品。然后, 离心样品在 16100 x g 5 分钟, 以分离有机溶剂从水相。</li>
	<li>小心地去除水相中的400–500µL, 不干扰白色, 多云相间, 并将其添加到15毫升圆锥管中, 包含1.9 毫升的结合缓冲器。用涡流彻底混合。</li>
	<li>加入1.25 毫升100% 乙醇, 并通过涡流混合样品。将样品的700µL 转移到收集管中组装的滤筒中, 并在三十年代将样品旋转 14500 x g . 重复, 直到所有的样品通过墨盒。 注意: 在这一点上, 样品可能会变的有点不透明。如果太多的细胞、蠕虫或苍蝇被裂解, 可能会出现沉淀, 堵塞过滤器。</li>
	<li>洗涤过滤器一次与700µL 洗涤液 1, 并且两次与500µL 洗涤液2或3。将样品在 14500 x g处旋转2分钟以去除多余的乙醇, 完成洗涤。</li>
	<li>将过滤盒转移到1.5 毫升海豚管, 并添加108µL prewarmed 洗脱液 (65 °c)。 注: 在反向转录之前, 切勿将 RNA 样品暴露在温度高于65摄氏度的情况下, 因为这样做会引起胞嘧啶尿脱氨作用事件, 无法区分转录错误。</li>
	<li>降低分离 RNA 中的 DNA 污染 (见步骤 2.1–3.4), 添加11µL 10x DNase i 缓冲液, 2 µL RNase 抑制剂, 4 µL DNase I (8 U), 并孵化他们在37°c 30 分钟。</li>
	<li>消化后, 加入11µL 的 DNase 灭活试剂。漩涡的混合物, 让它坐在 RT 5 分钟。然后, 离心样品在 1.5万 x g 3 分钟, 以颗粒的灭活试剂和收集110µL 的上清, 不干扰颗粒。将上清液转移至1.5 毫升管。 注: DNase 灭活试剂可能很难吸管, 所以在每次吹打行动后, 目视检查吸管尖端。如果失活试剂变得太粘性到吸管, 添加10毫米三 HCl (pH 7.4) 等于剩余体积的1/5。</li>
	<li>(质量检查)用分光光度计测量总 RNA 的浓度。然后预留1µL 的总 RNA 和稀释它到集中 10 ng/µL 在核酸酶无 (NF) 水。用高灵敏度的 rna 胶带在合适的核苷酸分析仪器上运行 rna, 以确保 rna 不退化, 并具有至少8.5 或更高的艾琳值 (图 2B)。</li>
</ol>4. mRNA 浓缩
<ol><li>用 mrna miniprep 试剂盒 (见材料表) 进行 mrna 浓缩。将140µL 的 NF 水添加到样品中, 总容积为250µL。然后, 添加250µL 的2x 绑定缓冲区, 混合的样本涡流, 并添加15µL 的寡核苷酸 (dT) 珠子。</li>
	<li>通过吹打, 将样品混合在一起, 在65摄氏度的2分钟内孵化。然后, 将样品放在 RT 上10分钟。最后, 离心样品在 16100 x g 2 分钟的颗粒珠: mRNA 复合物。</li>
	<li>在500µL 洗涤液中丢弃上清和并用重悬颗粒。将解决方案传输到旋转列, 并在 1.5万 x g处旋转1分钟。用另外500µL 的洗涤液, 丢弃并洗涤样品。然后, 在 1.5万 x g处旋转样品2分钟, 从自旋过滤器中取出多余的乙醇。</li>
	<li>将自旋过滤器转移到海豚管, 并将该柱中的颗粒并用重悬80µL 的 prewarmed 洗脱液 (65 °c)。在65摄氏度的时候孵化样品1.5 分钟, 然后在 1.5万 x g处旋转1分钟洗脱。</li>
	<li>使用分光光度计或类似的工具测量纯化 rna 的浓度, 使 rna 浓度和质量得到量化。</li>
</ol>5. RNase III. 碎片和 RNA 清理
注意: (重要) 要准备适合生成 C 序列库的圆形 rna 分子, rna 必须被分割成大致60–80的基础。虽然以前的方法已经使用了化学碎片的片段 RNA, 化学碎片与重金属引入损害的 RNA 样本, 可以被误解为转录错误在最后的分析。为了规避此问题, 请改用使用 RNase III 生成小碎片的碎片。这种酶法的另一个优点是, 它创造了结扎所需的相容端, 不需要在化学破碎后进行末端修复。
<ol><li>用1µg 的纯化 rna、10µL 反应缓冲器、5µL RNase III 和足够的 NF 水来建立 rna 碎片反应 (见材料表)。最后加入酶, 将混合物向上和向下的吸管搅拌至少10倍。将样品孵化为37摄氏度, 25 分钟, 这往往产生大约 60-80 基的 RNA 片段长度。</li>
	<li>一旦25分钟孵化完成, 立即清理反应与寡糖清理和选矿套件 (见材料表), 以防止任何进一步消化。在样品中加入200µL 的寡聚缓冲液, 将其完全混合涡流, 并添加800µL 100% 乙醇。通过涡流混合样品并将其转移到收集管中提供的旋转柱上。</li>
	<li>离心样品在 1万 x g三十年代和洗涤它与750µL 洗涤缓冲器去除 RNase iii。离心样品在 1万 x g在三十年代, 放弃流动, 并且再洗涤样品以750µL 洗涤缓冲器如上所述。在第二次流经被丢弃后, 离心机的柱在 1.5万 x g 2 分钟, 以消除任何过量的乙醇。</li>
	<li>将该柱转移到一个新鲜的1.5 毫升管, 并洗脱24µL 的 NF 水中的样品, 旋转 1万 x g 1 分钟。</li>
	<li>
质量检查:用2µL 的纯化 RNA 片段, 并通过添加2µL 的 NF 水稀释它2倍。在屏幕磁带上运行碎片 rna, 以确保碎片 rna 位于50–100基的长度范围内 (图 2c)。</li>
</ol>6. RNA Circularization 和滚动圈子反向转录
<ol><li>为了通报 RNA 片断, 热变性20µL 样品在65°c 为1分钟并且立刻放置它在冰2分钟。然后, 添加4µL 10x T4 RNA 连接酶缓冲器, 4 µL 10 毫米 ATP, 1 µL 的 RNase 抑制剂, 1 µL 的 NF 水, 8 µL 50% 聚乙二醇 (PEG)。</li>
	<li>通过涡流将样品彻底混合, 然后添加2µL T4 RNA 连接酶 i 的 10 U/µL, 再通过吹打将样品再混合, 并在 thermocycler 25 °c 时将其孵化为2小时, 在30摄氏度的盖子温度下进行。</li>
	<li>2小时后, 将10µL 的 NF 水添加到样品中, 并根据制造商的规格, 用寡聚清洁和浓缩试剂盒清洁样品。洗脱在20µL 的纳滤水中取样。</li>
	<li>要反转转录循环 RNA 分子, 添加4µL 10 毫米 dNTPs, 4 µL 50 ng/µL 随机 hexamers, 9 µL 的 NF 水到9µL 的 rna。混合所有的东西彻底的吹打, 变性样品在65°c 1 分钟, 并把它放在冰上2分钟. 将剩余的 RNA 存储为备份。</li>
	<li>添加8µL 5x 第一条链合成缓冲器, 2 µL 0.1 毫米, 和4µL 200 U/µL 逆转录酶, 并混合所有的吹打。在25摄氏度孵化样品10分钟, 其次是20分钟孵化在42°c 与盖子设置在5°c 高于孵化温度。 注: 在这个步骤中必须使用带股位移能力的反向逆转录酶, 以允许滚动圈反向转录。</li>
	<li>将10µL 的 NF 水添加到样品中, 并根据制造商的建议, 将其与寡糖清理和浓缩试剂盒一起清理干净。洗脱42µL 洗脱液中的样品。</li>
	<li>
质量检查:稀释2µL 的单链 cDNA 在2µL 的 NF 水和运行此样本的核苷酸分析仪器与高灵敏度的 RNA 磁带。如果反向转录工作正常, 一个 cDNA 分子库, 从大致60–1,500 的长度, 应该是可见的。理想情况下, 大多数 cDNA 在500–1,000 基的范围内 (图 2d)。</li>
</ol>7. 第二链 cDNA 合成和末端修复
<ol><li>使用第二链合成试剂盒生成双链 cDNA 文库。将样品放在冰上, 将30µL 的 NF 水添加到38µL 的样品中。然后, 添加8µL 的10x 第二链缓冲和4µL 的第二链酶, 并混合样品的温和吹打之前, 其孵化16°c 2.5 小时。</li>
	<li>根据制造商对100µL 反应的建议, 在38µL 的 NF 水中洗脱样品, 用寡糖清理和浓缩试剂盒清理样品。</li>
	<li>通过将20µL 的 NF 水添加到35.5 µL 的双链 cDNA、6.5 µL 末端修复反应缓冲液和3µL 的末端准备酶组合, 建立最终修复反应。仔细检查反应缓冲的白色沉淀和溶解他们的手变暖如果存在。</li>
	<li>混合吹打的最终修复反应和孵化它在20°c 30 分钟, 其次是30分钟孵化在65摄氏度。将 thermocycler 盖的温度设置在5摄氏度以上, 高于孵化温度。</li>
</ol>8. 适配器结扎和准备库的大小选择
<ol><li>为最终的图书馆准备工作使用多步骤库准备模块 (见材料表)。首先, 稀释适配器为下一代测序10倍在10毫米三 HCl 到最后浓度的1.5 µM。然后, 添加2.5 µL 稀释适配器和1µL 结扎增强剂的每个样本, 并混合涡流。</li>
	<li>添加15µL 的钝/TA 连接酶主混合, 混合它的吹打和孵化它在20°c 15 分钟。然后, 添加3µL 尿特异性切除试剂酶, 并在37摄氏度孵育15分钟。结扎完成后, 将13.5 µL 的 NF 水添加到样品中, 总容积为100µL, 并进行尺寸选择。 注意: 最好是在600–800基的长度范围内选择用于 cDNA 分子的尺寸。如果分裂的 RNA 分子近似地60–80基地, 选择为600–800基础的分子, 将确保大多数选定的分子包含至少三串联重复, 这是理想的下游处理和分析。</li>
	<li>要选择 600–800 bp 的分子长度, 添加30µL 的悬浮磁性珠 (见材料表), 适应 RT, 每个样本和混合吹打。将样品转移到1.5 毫升管, 在 RT 上孵化5分钟。</li>
	<li>将样品放在磁性支架上5分钟, 将珠子与上清的珠隔开。将上清 (包含所需的 cDNA 分子) 转移到一个新的1.5 毫升管中, 并处理磁性珠子 (绑定到大的 cdna 分子)。</li>
	<li>添加一个新鲜的整除15µL 磁性珠的每个样本, 完全混合吹打, 并孵化5分钟的 rt. 将样品放在磁性架子上, 在 rt 上孵化5分钟。然后, 小心地删除和处置的上清液, 确保不打扰珠子。 注: 不要处置磁性珠子, 它现在包含了所需的目标 cDNA。</li>
	<li>将200µL 80% 新鲜制备的乙醇添加到每个样品中, 而不干扰颗粒状磁性珠子和孵化三十年代. 用80% 乙醇将乙醇和样品再洗一次。然后, 完全清除样品中的任何微量乙醇, 风干珠5分钟, 但要小心不要过度干燥的珠子。如果珠子 overdried, 小球中就会出现裂缝。</li>
	<li>要从珠子中洗脱样品, 从磁支架上取出管子, 再加入19µL 10 毫米的三盐酸. 吸管它上下多次悬浮珠和孵化样品5分钟的 RT。如果珠子是 overdried, 孵化他们 > 10 分钟。</li>
	<li>将样品放回磁性支架上, 孵化5分钟, 将珠从上清中分离出来。小心去除15µL 的上清, 其中含有纯化的, 大小选择的 cDNA 文库从试管和转移到一个新鲜的1.5 毫升管。</li>
</ol>9. PCR 扩增和终珠纯化
<ol><li>
添加5µL 的通用底漆和5µL 一个独特的索引底漆 (见材料表) 到每个纯化 cDNA 文库, 并彻底混合吹打。仔细检查聚合酶主混合物的晶体和其他沉淀, 并温暖的主混合的手, 直到沉淀溶解。
	<ol>
<li>添加25µL 的聚合酶主混合物的样本, 混合他们的吹打, 并遵循循环条件下的 PCR 放大。运行最初变性在98°c 三十年代, 然后执行双步 pcr 反应通过 denaturating 样品在98°c 十年代和 PCR 产品的退火或引伸在65°c 为七十五年代 (12x), 跟随最后的引伸在65°c 为5分钟和无限期保持在4摄氏度。</li>
	</ol>
</li>
	<li>通过将45µL 悬浮磁珠直接添加到 PCR 反应中, 将其进行清理, 并将其与吹打混合, 并在 RT 中孵化5分钟. 将样品转移到1.5 毫升管并将其置于磁性支架中。孵化5分钟, 丢弃上清, 并清洗两次与新鲜准备的80% 乙醇三十年代。</li>
	<li>第二次清洗后, 从样品中完全去除乙醇, 将微珠颗粒干燥5分钟, 洗脱35µL 0.1x TE。并用重悬珠子吹打, 然后在 RT 上孵化5分钟. 将样品放在磁性支架上, 在5分钟后, 将上清的30µL 转移到新鲜的1.5 毫升管上。将库存储在-80 摄氏度。</li>
	<li>
质量检查:稀释1µL 的最终图书馆在9µL 的 NF 水和运行的样本在一个专用的 DNA 分析仪器与高灵敏度芯片;大多数 cDNA 应该在 600–800 bp 的范围内 (图 2e)。</li>
	<li>洗脱后, 通过 250 bp 配对端读取, 或 300 bp 单端读取, 将样品发送到大规模并行测序平台上进行排序。</li>
</ol>10. 循环测序数据的生物信息化分析
注: 分析和解释原始数据从 C 序列化验需要一个专用的生物信息化管道。图 3描述了用于分析的管线示意图。请在 https://github.com/LynchLab/MAPGD 下载此管线。
<ol><li>首先, 用 cutadapt18来修剪读取以删除适配器序列和低质量的基本调用, 使用质量分数阈值为20。</li>
	<li>然后, 用适当的算法来识别 concatemerized RNA 分子编码的重复值, 以检测30核苷酸最小大小的任何重复, 并在重复18之间最大2不匹配。从这些重复中推导出一个一致的序列, 并跟踪每个位置的基本调用和相关质量分数。 注: 由于反向转录可以从发送的 rna 分子的任何位置开始, 重复的开始不一定对应于碎片 RNA 分子的 5′ (以下简称结扎点)。因此, 一致的序列不会连续映射到相应的转录, 这就需要识别结扎点。</li>
	<li>
要识别结扎点, 请使用类似于转录 (BLAT)19的 concatemer 对引用进行映射。
	<ol>
<li>将协商一致序列的两个实例串联起来, 以确保映射序列至少包含初始 RNA 片段序列的一个完全不间断的出现。然后, 旋转一致序列以在预测的结扎点位置开始。</li>
	</ol>
</li>
	<li>使用带着它20将旋转的一致性序列映射到基因组, 并使用对齐来检测任何单核苷酸多态性 (SNPs), 这可能被误认为是转录错误。</li>
	<li>
从映射的旋转一致序列中调用转录错误, 并测试候选错误是否通过四附加测试。
	<ol>
<li>首先, 确保转录错误不会与 SNP 重叠 (典型的要求是至少有20读取需要覆盖特定的基, 而不超过5% 的读取可能支持备用基调用)。</li>
		<li>第二, 检查一致序列是从至少3串联重复, 每一个调用相同的基对;第三, 确保该职位的质量分数的总和必须超过100。</li>
	</ol>
</li>
	<li>对于第四和最终检查, 旋转一致序列的所有可能的组合 (模拟所有可能的位置的结扎点), 并映射每个版本反对参考基因组使用带着它。 注: 如果其中一个旋转序列找到一个完美匹配的基因组, 相应的结扎点现在被认为是正确的, 和转录错误调用应该被拒绝。</li>
</ol>

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作者没有什么可透露的。

在这里, 我们描述了一个优化的协议, 以准备 C 序列库, 以检测转录错误的酿酒酵母,果蝇, 和秀丽线虫。该协议比现有的协议和替代技术具有许多优点。
在过去的15年中, 许多记者系统已经开发, 依靠荧光素酶7,8或创可氧合酶3,4,15,21 , 以检测转录错误。这些记者系统对研究人员对转录保真的理解是无价的, 因为它们允许基因、等位基因和分子机制直接调节转录的真实性。然而, 他们只报告人为损坏的模板的错误, 或在高度特定的基因环境中, 这限制了他们可以回答的科学问题。C 序列协议的一个重要优点是它监视整个转录5中转录的真实性, 极大地扩展了对基因信息表达的准确性的科学认识。其次, 由于 C 序列分析利用 RNA 作为其源材料, 这一分析很可能可以适应任何选择的有机体, 不需要为每个转基因模型生成复杂的记者构造。该协议还优于现有的大规模并行排序方法17,22。最值得注意的是, 这些方法大多利用重金属来片段 RNA 库。然而, 这些碎片方法将工件引入到 RNA 中, 这与真实的转录错误5不区分。为了解决这一问题, 该协议片段 RNA 酶与 RNase III, 这有额外的优势, 它留下了兼容的末端为自结扎, 大大简化 RNA circularization 和增加的灵敏度10倍。
同时, C 序列法也有其局限性。首先, 尽管这一检测方法在整个基因组中都有转录错误, 但是数据的一个很大的组成部分往往来源于高度表达的基因, 因为这些基因的记录往往主宰着大多数 RNA 库。另一个影响高度表达基因分析的因素是生物信息化管道中的阈值, 它可以防止基因突变和 RNA 编辑事件混淆最终测量结果。这个阈值规定, 只有被测序20倍或以上的碱基才能用于最终分析。第二个限制涉及图书馆的多样性。与大多数用于 RNA 提取的试剂盒一样, 这里使用的试剂盒不能有效地净化小 rna 分子, 这限制了从 RNAP iii 合成的分子中提取的读数的数量。在这里使用的 mRNA 纯化步骤进一步限制了最终测序库的多样性。尽管 RNAP 产生的分子可以有效地测序, 但在这个步骤中, 大多数剩余的线粒体 rna、tRNAs 和非编码 rna 都丢失了。为了更频繁地捕获这些分子, 应该使用不同的试剂盒, 专门净化小的 RNA 分子, 或者细胞类型应该被浓缩为这些分子。不过, 请注意, 大多数这些问题都可以通过比往常更深的顺序来解决, 或者同时对同一细胞的 DNA 进行排序, 尽管这两种解决方案都需要调查人员进行更大的金融投资。
此外, 未来的技术发展正准备在基本水平上改进 C 序列分析法。例如, 通过延长现有测序技术的读长, 就有可能对较长的分子进行序列化, 这意味着可以使用更多的重复来确定转录的保真度。这种改进将自动导致更大的测序深度和增加可用于下游分析的读取百分比。对于任何测序技术, 都可以进行类似的论证, 使更多的分子能够并行测序。此外, C 序列分析的许多组成部分仍有待优化, 包括 RNA circularization 的效率, 大小选择的严格性, 以及试验本身的耗时性质。最后, 强烈建议所有用户都可以获得用于核酸分析和潜在故障诊断的专用、高灵敏度的工具 (见材料表)。如果没有这个工具, 就很难确定潜在问题出现的程度, 从而使它们无法修复。
虽然整个协议是相当无情的 (小错误往往有重大的后果), 有几个步骤, 是绝对关键的成功的 c-序列化验。其中一个最重要的要求是, 孤立的 RNA 被视为尽可能温和。大多数 rna 提取方法和下游处理套件不关心 rna 的化学成分超过基本的工业标准, 这是足够的大多数分子分析。然而, C 序列分析的任务是确定一个单一的转录误差在成千上万的野生碱基, 大大增加了减少分子压力的需要。即使压力仅影响1万个基地的 1, 也会引入完全无效结果的工件。例如, 用户绝不能避免/限制将 RNA 暴露在65摄氏度以上的任何温度。其次, 每个用户必须仔细优化的时间为 RNA 碎片与 RNase iii。这是绝对关键的化验的成功, 最终分子是大约60–80的基础长度。较短的分子可能难以映射到基因组, 更长的分子将不允许3独立重复在 250 bp 读取的顺序。最后, 建议不要在整个协议中多次冻结和解冻 RNA。相反, 在一天内分离 RNA 并合成 cDNA。由于这一承诺所涉及的时间和精力、协议本身的复杂性以及经常处理的样本数量, 建议两名调查员共同努力生成这些库。
一旦成功, 这些实验将使它能够准确地检测出任何生物体的转录错误, 在任何实验条件下。例如, 通过比较对照组和患病个体之间的关系, 也许可以确定某些疾病是否与转录错误相关, 这可能揭示许多人类病理的病因的新成分。其他可以探讨的参数是生物体老化、营养、基因型和环境因素, 如接触有毒化学物质。此外, 由于这种检测检测到整个转录的转录错误, 它将允许研究人员解剖不同的机制, 有助于对 RNA 聚合酶 I, II 和 III 的保真度, 以及线粒体 RNA 聚合酶。因此, 我们期望这项议定书将开辟一个新的领域的诱变, 广泛的实验, 其特点是研究突变的 RNA, 而不是在 DNA。

基因组提供了精确的生物生命蓝图。为了正确地实施这一蓝图, 对基因组进行精确的转录是很重要的。然而, 转录不太可能是错误的自由。例如, 长期以来已知的 RNA 聚合酶在体外1、2中很容易出错, 最近发现它们在体内犯下错误, 以及3、5、6,特别是面对 DNA 损伤7,8,9,10。这些观察结果表明, 在所有活细胞中, 转录错误不断发生, 这表明它们可能是突变蛋白的有力来源。
这一过程, 称为转录诱变, 不同于经典诱变的两种方式。首先, 与基因突变相比, 转录错误影响有丝分裂和有丝分裂后细胞, 因为它们不依赖 DNA 复制。因此, 研究影响转录保真度的机制, 将对有丝分裂和有丝分裂后细胞的突变负荷提供有价值的见解。有趣的是, 转录错误最近被牵涉到促进蛋白质聚集11,12,13 , 并已被推测为两种致癌作用10和细菌耐药性的发展14。
第二, 与基因突变相比, 转录错误是暂时性的。它们的暂时存在特别具有挑战性, 因为它使转录错误极其难以检测。例如, 虽然几个实验室已经为研究转录诱变设计了有价值的记者化验, 但这些化验只能够测量在有限数量的上下文和模型有机体4,15的转录误差。为了克服这些局限性, 许多研究人员已经转向 rna 测序技术 (rna 序列), 理论上允许在任何物种的转录中记录转录错误。然而, 这些研究容易混淆的图书馆建设工件, 如反向转录错误, PCR 放大错误, 和容易出错的性质排序本身。例如, 反向 transcriptases 每隔2万个基元就会提交大约一个错误, 而 RNA 聚合酶 (RNAPs) 则每30万个基5、6将只进行一个错误。由于反向转录的错误率只会使 rna 聚合酶在细胞内的错误率相形见绌, 所以在传统的 rna 序列数据中, 很难区分真实的转录错误和由图书馆准备产生的伪影。图 1a)。
为了解决这个问题, 我们开发了一个优化版本的圆排序 (Cirseq, 或 C 序列从今以后) 化验5,16。这种检测允许用户检测转录错误和 RNA 中的其他罕见变种在整个转录5。循环测序法具有这个名称, 因为这个检测的关键步骤围绕 RNA circularization。一旦 RNA 目标被发送, 他们是反向转录在一个滚动圈的方式, 以产生线性 cDNA 分子, 包含许多副本相同的 RNA 模板。如果其中一个模板中存在错误, 则该错误也会出现在 cDNA 分子中包含的每一个重复项中。相反, 由反向转录、PCR 放大或测序引入的错误倾向于随机产生, 因此只会出现一次或两次重复。因此, 通过为每个 cDNA 分子生成一个一致的序列, 并区分随机错误和所有重复发生的错误, 库构造工件可以有效地与真实的转录错误分离 (图 1b)。
如果使用得当, C 序列分析可以用来准确地检测 RNA 的基替换, 插入和删除的速率, 在整个转录的任何物种 (例如, 见遍历和 Ochman17)。例如, 我们使用了 C 序列分析法, 提供了全基因组的测量在酵母、果蝇和秀丽线虫中转录的错误率, 单碱分辨率为5。(未发表的意见)。最初用于精确序列化 RNA 病毒的人群, 这种优化版本的 C 序列分析已经简化, 以尽量减少苛刻的条件, 在图书馆的准备工作, 有助于图书馆建设工件。此外, 通过使用一些商业上可用的套件, 该检测的吞吐量大大提高, 以及其用户友好性。如果使用得当, 这种检测可以准确地检测到数以千计的转录错误每复制, 从而大大提高了以前的研究6。总的来说, 这种方法提供了一个强大的工具, 研究转录突变, 并将允许用户获得新的洞察力的机制, 控制转录的真实性在广泛的有机体。

<table>
	<tbody>
		<tr>
			<td>RiboPure RNA purification kit</td>
			<td>ThermoFisher</td>
			<td>AM1926</td>
			<td>Total RNA purification</td>
		</tr>
		<tr>
			<td>Genelute mRNA purification kit</td>
			<td>Sigma-Aldrich</td>
			<td>MRN70-1KT</td>
			<td>mRNA purification</td>
		</tr>
		<tr>
			<td>Nuclease-free Water</td>
			<td>Ambion</td>
			<td>AM9937</td>
			<td>Elution and dilution</td>
		</tr>
		<tr>
			<td>Ambion RNase III</td>
			<td>ThermoFisher</td>
			<td>AM2290</td>
			<td>RNA fragmentation</td>
		</tr>
		<tr>
			<td>T4 RNA Ligase 1 (ssRNA Ligase)</td>
			<td>New England Biolabs</td>
			<td>M0204S</td>
			<td>RNA circularization</td>
		</tr>
		<tr>
			<td>Ribolock</td>
			<td>ThermoFisher</td>
			<td>EO0381</td>
			<td>RNase inhibitor</td>
		</tr>
		<tr>
			<td>SuperScript III Reverse Transcriptase</td>
			<td>ThermoFisher</td>
			<td>18080044</td>
			<td>Rolling circle reverse transcription</td>
		</tr>
		<tr>
			<td>10 mM dNTP mix</td>
			<td>ThermoFisher</td>
			<td>18427013</td>
			<td>Rolling circle reverse transcription</td>
		</tr>
		<tr>
			<td>Random hexamers (50 ng/&#181;l)</td>
			<td>ThermoFisher</td>
			<td>N8080127</td>
			<td>Rolling circle reverse transcription</td>
		</tr>
		<tr>
			<td>NEB Second Strand Synthesis Module</td>
			<td>New England Biolabs</td>
			<td>E6111S</td>
			<td>Second Strand Synthesis</td>
		</tr>
		<tr>
			<td>NEBNext Ultra DNA Library Prep Kit for Illumina</td>
			<td>New England Biolabs</td>
			<td>E7370S</td>
			<td>cDNA library preparation</td>
		</tr>
		<tr>
			<td>NEB Next index primers</td>
			<td>NEB</td>
			<td>E7335S</td>
			<td>Multiplex PCR primers</td>
		</tr>
		<tr>
			<td>Oligo Clean &#38; Concentrator</td>
			<td>Zymo Research</td>
			<td>D4061</td>
			<td>Clean up of RNA and DNA samples</td>
		</tr>
		<tr>
			<td>DynaMag-2 Magnet</td>
			<td>ThermoFisher</td>
			<td>12321D</td>
			<td>Magnetic bead purification</td>
		</tr>
		<tr>
			<td>AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td>Magnetic bead purification</td>
		</tr>
		<tr>
			<td>Eppendorf 5424 Microcentrifuge</td>
			<td>FisherScientific</td>
			<td>05-403-93</td>
			<td>centrifugation</td>
		</tr>
		<tr>
			<td>INCU-Shaker 10L</td>
			<td>Benchmark Scientific</td>
			<td>H1010</td>
			<td>Cell culture</td>
		</tr>
		<tr>
			<td>T100 Thermal Cycler</td>
			<td>BIO RAD</td>
			<td>1861096</td>
			<td>Medium to High temperature cycling conditions</td>
		</tr>
		<tr>
			<td>PTC-200 Thermal Cycler</td>
			<td>GMI</td>
			<td>8252-30-0001&#160;</td>
			<td>Low temperature cycling conditions</td>
		</tr>
		<tr>
			<td>RNase Away</td>
			<td>Molecular Bioproducts</td>
			<td>700S-11</td>
			<td>Sterilization</td>
		</tr>
		<tr>
			<td>50 ml Centrifuge Tube</td>
			<td>Corning</td>
			<td>430290</td>
			<td>Nuclease-free</td>
		</tr>
		<tr>
			<td>15 ml Centrifuge Tube</td>
			<td>Corning</td>
			<td>430052</td>
			<td>Nuclease-free</td>
		</tr>
		<tr>
			<td>Eppendorf tubes</td>
			<td>USA Scientific</td>
			<td>1615-5500</td>
			<td>Nuclease-free</td>
		</tr>
		<tr>
			<td>4200 Tapestation System</td>
			<td>Agilent</td>
			<td>G2991AA</td>
			<td>Nucleotide analysis instrument for quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>High Sensitivity RNA Screen Tape</td>
			<td>Agilent</td>
			<td>5067-5579</td>
			<td>Quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>RNA ScreenTape Sample Buffer</td>
			<td>Agilent</td>
			<td>5067-5577</td>
			<td>Quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>RNA ScreenTape Ladder</td>
			<td>Agilent</td>
			<td>5067-5578</td>
			<td>Quality control of RNA and single stranded DNA samples</td>
		</tr>
		<tr>
			<td>2100 Bioanalyzer Instrument</td>
			<td>Agilent</td>
			<td>G2939BA</td>
			<td>Double stranded DNA quality control</td>
		</tr>
		<tr>
			<td>High Sensitivity DNA Kit</td>
			<td>Agilent</td>
			<td>5067-4626</td>
			<td>Quality control for double stranded cDNA samples</td>
		</tr>
		<tr>
			<td>Water Bath</td>
			<td>VWR</td>
			<td>462-0244</td>
			<td>Incubation</td>
		</tr>
		<tr>
			<td>NanoDrop 2000/2000C Spectrophotometer</td>
			<td>ThermoFisher</td>
			<td>ND-2000C</td>
			<td>Determination of RNA concentration</td>
		</tr>
	</tbody>
</table>

genome-wide surveillance of transcription errors in eukaryotic organisms

基因信息的忠实表达需要准确的转录。令人惊讶的是, 对控制转录的真实性的机制知之甚少。为了填补这一科学知识的空白, 我们最近优化了循环测序检测, 以检测整个转录的酿酒酵母、果蝇和秀丽的转录错误. 线虫。该协议将为研究人员提供一个强大的新工具, 用于映射真核细胞中转录错误的景观, 以便能够在前所未有的细节上阐明控制转录的真实性的机制。

与所有大规模并行排序方法一样, 每个 C 序列实验都产生一个笨重的大数据集。对于第一次使用者来说, 处理这些数据集可能会很困难;因此, 建议所有用户在实验之前与经验丰富的生物 informatician 联系。平均来说, 人们的期望是, 用户将产生大约55–70千兆基 (Gbases) 每运行在大多数大规模并行排序平台。对于此协议, 通常情况下, 12–30示例在每次运行时进行复用, 以便为每个样本约 2–6 Gbases 获取。从该数据集修整适配器序列和低质量的基调用 (< 20) 后, 原始数据大小的70% 仍保持不变。
然后对这些碱基进行进一步分析, 通过确定生成的重复的大小来研究 RNA 碎片和 circularization 的效率。大多数重复的长度往往是45–80的基础 (图 4A), 并且大约50% 的碱基被测序是这些重复的一部分 (图 4B)。由于这些基的大部分都存在于包含3个重复或更多的读取中, 因此排序的唯一基的数目约为基序列的总数目的1/3。平均来说, 这些共识序列的 > 75% 可以映射回参考基因组。最后, 大约有25% 的基础被20读取或更多的覆盖, 这最终意味着大约10% 的数据可以用于错误检测。图 4给出了这一分析的一个例子, 它代表了在本协议的设置过程中获取的序列信息, 用于1复制单个 C 序列库, 该数据库的排序相对较浅, 代表较低的用户可以期望获取的数据的限制。
每个运行大约5,000–25,000 错误往往被识别, 虽然这些数字可能会有很大的差异, 取决于错误率本身 (错误率越高, 检测到的错误越多), 转录的大小 (更大的转录, 较少的基数将覆盖20读取, 限制可用于错误检测的测序数据), 以及测序的深度 (更深层次的测序将使给定的基底更有可能覆盖20读或更多)。这些错误倾向于分布在整个基因组, 因此, 平均约47% 的错误是由 RNA 聚合酶 ii (RNAP ii) 产生的 mRNA 分子, 49% 位于 rRNA 分子产生的 RNAP I, 其余的 ~ 3% 位于 rna由 RNAP III 和线粒体 RNA 聚合酶产生。然而, 这些比率可能会有很大的差异, 取决于细胞类型或受调查的有机体 (图 4C)。例如, 大量依赖线粒体功能的细胞类型, 如心肌细胞, 含有比其他细胞更高的线粒体 rna, 极大地增加了序列化的线粒体 rna 分子数量, 从而检测到的错误。
一旦一个错误列表被编译, 并且它们在基因组中的位置是已知的, 这些错误就可以用来识别控制每个生物体中转录错误率的参数。例如, 这些转录错误的位置可能与基因组的许多特征相关, 例如 DNA 重复的存在、特定的基因上下文或表达率, 以了解这些特征如何改变其保真度。转录5。人们的期望是, 在未来, 用户将能够确定多少额外的功能影响转录保真度, 包括表观遗传学标记, 3 维组织的基因组, 养分的可用性, 年龄, 或暴露于有毒化合物, 以阐明遗传和环境因素对转录的保真度的贡献。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57731/57731fig1.jpg"> 图 1: RNA 序列与C 型的图示(A) 传统的 rna 序列实验将 rna 从感兴趣的样本中分离出来, 片段 rna, 并在最终的图书馆准备和测序之前反转转录。但是, 这些准备程序将许多技术工件引入到图书馆中, 其形式是反向转录错误、PCR 放大错误和排序错误。(B) 这种优化的 c-序列分析法允许通过在反转转录之前 circularizing 碎片 RNA 分子来纠正这些技术工件, 从而使它们能够在滚动圈中反向转录以产生线性包含原始 RNA 模板的几个拷贝的 cDNA 分子串联重复。这些串联重复可以用来区分真实的转录错误从工件, 因为真正的转录错误 (星) 将出现在所有重复在同一地点, 而伪影, 如反向转录错误 (正方形) 和 PCR放大错误 (圆圈) 只存在于任何给定的 cDNA 分子的一个或两个重复。<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig1large.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/57731/57731fig2.jpg"> 图 2: 图书馆准备工作的代表性结果.这些面板显示了一个电泳 (a) 高灵敏度的 rna 屏幕胶带梯子 (见材料表), (B) 从酿酒酵母纯化的总 RNA, (C) RNase 三碎片 rna, 和 (D) 滚动圈反向转录 cDNA。(E) 最终大小选择的 cDNA 文库在高灵敏度的双链 DNA 分析芯片上运行 (见材料表)。<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig2large.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/57731/57731fig3.jpg"> 图 3: 用于分析圆测序数据的生物信息化管道示意图.修剪顺序读取后, 将识别重复项, 并在任何给定位置使用最可能的基调用生成协商一致序列。然后, 确定了初始 RNA 模板的结扎点, 并将一致序列与参考基因组对齐, 从而可以识别出可能的转录误差。<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig3large.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/57731/57731fig4.jpg"> 图 4: C 序列管道的结果示例.(A) 这个小组显示了从典型的 C 序列实验中获得的一致数列的大小分布。(B) 本小组显示 C 序列生物信息学管道每一个步骤中的读数的基数、读数和百分比。(C) 本小组显示检测到的转录错误数量, 以及由于 rna 聚合酶 i、rna 聚合酶 II、rna 聚合酶 III 和线粒体 rna 聚合酶而导致的误差百分比。<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57731/57731fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms at JoVE.com

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

该协议为研究人员提供了一种新的工具来监测多模型生物体中转录的真实性。

全基因组对真核生物转录误差的监测

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

genome-wide-surveillance-transcription-errors-eukaryotic

Nanyang Technological University

Research

JoVE Journal

Genetics

9.4K 次观看.  Children's Hospital of Philadelphia. 该协议为研究人员提供了一种新的工具来监测多模型生物体中转录的真实性。

视频: Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

In Vitro Transcription Assays and Their Application in Drug Discovery

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process requires multi-subunit DNA-dependent RNA polymerase (RNAP) and a series of transcription factors that act to modulate the activity of RNAP during gene expression. Sequencing gel electrophoresis of radiolabeled transcripts is used to provide detailed mechanistic information on how transcription proceeds and what parameters can affect it. In this paper we describe the protocol to study how the essential elongation factor NusA regulates transcriptional pausing, as well as a method to identify an antibacterial agent targeting transcription initiation through inhibition of RNAP holoenzyme formation. These methods can be used a as platform for the development of additional approaches to explore the mechanism of action of the transcription factors which still remain unclear, as well as new antibacterial agents targeting transcription which is an underutilized drug target in antibiotic research and development.

In Vitro Transcription Assays and Their Application in Drug Discovery

In this manuscript, we describe a protocol to functionally examine transcription and the inhibitory activity of antibacterial agents targeting bacterial transcription.

体外转录测定及其在药物研究中的应用

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process ...

科研

遗传学

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

利用CyVerse资源<em&gt; De Novo</em&gt;不合格（非模型）生物的比较转录组学

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic region is replicated at a distinct time during S phase through the simultaneous activation of multiple origins of replication. Time of replication (ToR) correlates with many genomic and epigenetic features and is linked to mutation rates and cancer. Comprehending the full genomic view of the replication program, in health and disease is a major future goal and challenge.
This article describes in detail the &#34;Copy Number Ratio of S/G1 for mapping genomic Time of Replication&#34; method (herein called: CNR-ToR), a simple approach to map the genome wide ToR of mammalian cells. The method is based on the copy number differences between S phase cells and G1 phase cells. The CNR-ToR method is performed in 6 steps: 1. Preparation of cells and staining with propidium iodide (PI); 2. Sorting G1 and S phase cells using fluorescence-activated cell sorting (FACS); 3. DNA purification; 4. Sonication; 5. Library preparation and sequencing; and 6. Bioinformatic analysis. The CNR-ToR method is a fast and easy approach that results in detailed replication maps.

We describe here a relatively fast and simple approach for mapping genome-wide mammalian replication timing, from cell isolation to the basic analysis of the sequencing results. A genomic map of a representative replication program will be provided following the protocol.

通过DNA含量测定的哺乳动物复制时序的全基因组的测定

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic ...

Analysis of Termination of Transcription Using BrUTP-strand-specific Transcription Run-on (TRO) Approach

This manuscript describes a protocol for detecting transcription termination defect in vivo. The strand-specific TRO protocol using BrUTP described here is a powerful experimental approach for analyzing the transcription termination defect under physiological conditions. Like the traditional TRO assay, it relies on the presence of a transcriptionally active polymerase beyond the 3&#8242; end of the gene as an indicator of a transcription termination defect1. It overcomes two major problems encountered with the traditional TRO assay. First, it can detect if the polymerase reading through the termination signal is the one that initiated transcription from the promoter-proximal region, or if it is simply representing a pervasively transcribing polymerase that initiated non-specifically from somewhere in the body or the 3&#8242; end of the gene. Secondly, it can distinguish if the transcriptionally active polymerase signal beyond the terminator region is truly the readthrough sense mRNA transcribing polymerase or a terminator-initiated non-coding anti-sense RNA signal. Briefly, the protocol involves permeabilizing the exponentially growing yeast cells, allowing the transcripts that initiated in vivo to elongate in the presence of the BrUTP nucleotide, purifying BrUTP-labelled RNA by the affinity approach, reverse transcribing the purified nascent RNA and amplifying the cDNA using strand-specific primers flanking the promoter and the terminator regions of the gene2.

Analysis of Termination of Transcription Using BrUTP-strand-specific Transcription Run-on TRO Approach

We describe a basic experimental approach for analysis of termination of transcription by RNA polymerase II in vivo using BrUTP by the strand-specific transcription run-on (TRO) approach in budding yeast. This protocol can be extended to study transcription termination by other RNA polymerases both in yeast and higher eukaryotes.

使用BrUTP链特异性转录运行的（TRO）方法终止转录分析

This manuscript describes a protocol for detecting transcription termination defect in vivo. The strand-specific TRO protocol using BrUTP described here ...

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident processes. Formaldehyde crosslinking followed by chromatin immunoprecipitation and high-throughput sequencing (X-ChIP-seq) has been used to gain many valuable insights into genome biology. However, X-ChIP-seq has notable limitations linked to crosslinking and sonication. Native ChIP avoids these drawbacks by omitting crosslinking, but often results in poor recovery of chromatin-bound proteins. In addition, all ChIP-based methods are subject to antibody quality considerations. Enzymatic methods for mapping protein-DNA interactions, which involve fusion of a protein of interest to a DNA-modifying enzyme, have also been used to map protein-DNA interactions. We recently combined one such method, chromatin endogenous cleavage (ChEC), with high-throughput sequencing as ChEC-seq. ChEC-seq relies on fusion of a chromatin-associated protein of interest to micrococcal nuclease (MNase) to generate targeted DNA cleavage in the presence of calcium in living cells. ChEC-seq is not based on immunoprecipitation and so circumvents potential concerns with crosslinking, sonication, chromatin solubilization, and antibody quality while providing high resolution mapping with minimal background signal. We envision that ChEC-seq will be a powerful counterpart to ChIP, providing an independent means by which to both validate ChIP-seq findings and discover new insights into genomic regulation.

Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

We describe chromatin endogenous cleavage coupled with high-throughput sequencing (ChEC-seq), a chromatin immunoprecipitation (ChIP)-orthogonal method for mapping protein binding sites genome-wide with micrococcal nuclease (MNase) fusion proteins.

与ChEC-seq的蛋白质-DNA相互作用的全基因组映射<em&gt;酿酒酵母（Saccharomyces cerevisiae）</em

Genome-wide mapping of protein-DNA interactions is critical for understanding gene regulation, chromatin remodeling, and other chromatin-resident ...

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

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

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

ATAC 基因组在原发性人类 T 淋巴细胞中的染色体全易接近染色质

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

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ours, have demonstrated that quiescence is associated with widespread changes in gene expression. Some of these changes occur through changes in the level or activity of proliferation-associated transcription factors, such as E2F and MYC. We have demonstrated that mRNA decay can also contribute to changes in gene expression between proliferating and quiescent cells. In this protocol, we describe the procedure for establishing proliferating and quiescent cultures of human dermal foreskin fibroblasts. We then describe the procedures for inhibiting new transcription in proliferating and quiescent cells with Actinomycin D (ActD). ActD treatment represents a straightforward and reproducible approach to dissociating new transcription from transcript decay. A disadvantage of ActD treatment is that the time course must be limited to a short time frame because ActD affects cell viability. Transcript levels are monitored over time to determine transcript decay rates. This procedure allows for the identification of genes and isoforms that exhibit differential decay in proliferating versus quiescent fibroblasts.

We describe a protocol for generating proliferating and quiescent primary human dermal fibroblasts, monitoring transcript decay rates, and identifying differentially decaying genes.

增殖和静止人成纤维细胞的全基因组转录衰变率的测定

Quiescence is a temporary, reversible state in which cells have ceased cell division, but retain the capacity to proliferate. Multiple studies, including ...

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 ...

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a protocol for performing the chromatin conformation capture technique in situ Hi-C on staged Drosophila melanogaster embryo populations. The result is a sequencing library that allows the mapping of all chromatin interactions that occur in the nucleus in a single experiment. Embryo sorting is done manually using a fluorescent stereo microscope and a transgenic fly line containing a nuclear marker. Using this technique, embryo populations from each nuclear division cycle, and with defined cell cycle status, can be obtained with very high purity. The protocol may also be adapted to sort older embryos beyond gastrulation. Sorted embryos are used as inputs for in situ Hi-C. All experiments, including sequencing library preparation, can be completed in five days. The protocol has low input requirements and works reliably using 20 blastoderm stage embryos as input material. The end result is a sequencing library for next generation sequencing. After sequencing, the data can be processed into genome-wide chromatin interaction maps that can be analyzed using a wide range of available tools to gain information about topologically associating domain (TAD) structure, chromatin loops, and chromatin compartments during Drosophila development.

Generation of Genome-wide Chromatin Conformation Capture Libraries from Tightly Staged Early Drosophila Embryos

This work describes a protocol for the generation of high resolution in situ Hi-C libraries from tightly staged pre-gastrulation Drosophila melanogaster embryos.

大范围早期果蝇胚胎的全基因组染色质构象捕获库的生成

Investigating the three-dimensional architecture of chromatin offers invaluable insight into the mechanisms of gene regulation. Here, we describe a ...

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the spatio-temporal expression of their target genes through physical contact, often bridging considerable (in some cases hundreds of kilobases) genomic distances and bypassing nearby genes. The human genome harbors an estimated one million enhancers, the vast majority of which have unknown gene targets. Assigning distal regulatory regions to their target genes is thus crucial to understand gene expression control. We developed Promoter Capture Hi-C (PCHi-C) to enable the genome-wide detection of distal promoter-interacting regions (PIRs), for all promoters in a single experiment. In PCHi-C, highly complex Hi-C libraries are specifically enriched for promoter sequences through in-solution hybrid selection with thousands of biotinylated RNA baits complementary to the ends of all promoter-containing restriction fragments. The aim is to then pull-down promoter sequences and their frequent interaction partners such as enhancers and other potential regulatory elements. After high-throughput paired-end sequencing, a statistical test is applied to each promoter-ligated restriction fragment to identify significant PIRs at the restriction fragment level. We have used PCHi-C to generate an atlas of long-range promoter interactions in dozens of human and mouse cell types. These promoter interactome maps have contributed to a greater understanding of mammalian gene expression control by assigning putative regulatory regions to their target genes and revealing preferential spatial promoter-promoter interaction networks. This information also has high relevance to understanding human genetic disease and the identification of potential disease genes, by linking non-coding disease-associated sequence variants in or near control sequences to their target genes.

DNA regulatory elements, such as enhancers, control gene expression by physically contacting target gene promoters, often through long-range chromosomal interactions spanning large genomic distances. Promoter Capture Hi-C (PCHi-C) identifies significant interactions between promoters and distal regions, enabling the assignment of potential regulatory sequences to their target genes.

启动子捕获高分辨率, 全基因组的启动子交互分析

The three-dimensional organization of the genome is linked to its function. For example, regulatory elements such as transcriptional enhancers control the ...