我们感谢莱文斯图尔特对这个手稿的意见。这项工作是由卫生格兰特GM056800全国学院和凯西和科特大理石癌症研究基金，以安格阿蒙，并在由科赫研究所支援津贴P30-CA14051的部分支持。安格阿蒙也是霍华德休斯医学研究所和格伦生物医学研究基金会的调查员。 KAK由NIGMS培训资助T32GM007753支持。

1.单细胞分离<ol><li>准备microaspirator <ol><li>拆下吸气管组件的透明塑料端插入窄端到1英尺长的PVC管的一端3/16"内径。 </li><li>插入一个0.2微米的注射器过滤器的出口到1英尺长的PVC管的与3/6"内径的另一端。 </li><li>插入0.2微米的注射器过滤器的入口与5/16"内径的6英寸长的PVC管的一端。 </li><li>可选:切割5/16"内径的6英寸长的PVC管的一半，然后插入两半之间在自来水陷阱。 </li><li>打破在1毫升刻度5毫升塑料血清吸管并插入断头插入PVC管的开放端与5/16"内径。 </li><li>插入5毫升塑料血清吸管的出口进入吸气管组件的柔性端（那里的透明塑料端已被移除）。 </li><li>对于存储，覆盖吸气管组件用无菌塑料管红色的喉舌。 </li><li>通过使邻近本生灯火焰管的中间和在管的任一端施加张力抽出的玻璃毛细管的10-30微米的内径。打破拉制管中间创建两个潜在的抽吸针。重复此步骤，以几个管作为只有一些管将最终的内径是适当用于拾取单细胞。 </li><li>对于存储，放置橡皮泥的两个带在15厘米的培养皿中，安全跨越条抽吸针。 </li></ol></li><li>挑选细胞<ol><li>准备以适合的细胞类型和实验的单个细胞悬浮液。例如，为了制备贴壁细胞如人成纤维细胞系，收获细胞通过胰蛋白酶消化和转移细胞到含有合适培养基的锥形管中。 </li><li>之前，期间，或压脚提升准备单细胞悬液（取决于需要多长时间来准备单细胞悬液），设置了罩全基因组扩增。 <ol><li>喷向下罩，枪头盒，并用10％的漂白剂枪头的表面并用纸巾擦干。重复此步骤，以70％的乙醇。 </li><li>从全基因组扩增（WGA）包到96孔PCR平板的各个孔中加水8微升，对于每个小区中的一个孔，以进行测序。覆盖从96孔组织培养板中，并置于冰上的盖的96孔PCR板。 </li></ol></li><li>添加1,000个细胞至10毫升介质或磷酸盐缓冲盐水（PBS）在15厘米的培养皿中，并将培养皿在冰上，以防止细胞附着在培养皿中。 </li><li>带来在培养皿的细胞和96孔PCR平板在冰上盖用10X物镜光学显微镜。 </li><li>轻轻拍打日的拉出端增加抽吸针的开口在坚硬的表面Ë抽吸针，使得尖折断。将抽吸针的宽端插入microaspirator的明确结束。 </li><li>将包含在显微镜舞台细胞培养皿。放置在口腔中抽吸的红色吸嘴。用一只手来移动抽吸针和另​​一只手移动含有细胞培养皿。识别单个细胞待测序。 </li><li>用口吸气，绘制一个单细胞到抽吸针头的媒体或PBS〜1-2μL一起。细胞转移到水的8微升的96孔PCR板的单个孔中。避免传输单元时产生气泡。 </li><li>重复步骤1.2.7至细胞的期望数量的已被分离。保持冰的PCR板和采摘细胞间盖上盖子。标记已经接收的细胞的孔中。 <ol><li>虽然采摘单细胞，解冻从整体克10X单细胞裂解，破碎缓冲enome扩增试剂盒。拾取单元的期望数量后，立即进行全基因组扩增。 </li></ol></li></ol></li></ol> 2.全基因组扩增<ol><li>为防止污染全基因组扩增过程中，加入组织培养罩内的所有试剂，用枪头有过滤器，并改变枪头井之间。 </li><li>准备从全基因组扩增（WGA）工具包的工作裂解，破碎缓冲液。对于每一组多达32个细胞，结合32微升蛋白酶K溶液10倍的单细胞裂解和破碎缓冲液和2微升的微量离心管中。涡管混合的解决方案。 </li><li>加入1微升的工作裂解和片段化缓冲液到每个孔中并吸取上下混合。 </li><li>覆盖板和密封所有孔用塑料薄膜。短暂离心在迷你板微调板。 </li><li>热周期FOLL流入:50℃，1小时，99℃，4分钟。 </li><li>在冰上冷却板，短暂离心在迷你板微调板。 </li><li>准备从全基因组扩增试剂盒工作文库制备缓冲溶液。对于每个小区，结合1个单细胞文库制备缓冲液2微升和在微量离心管文库稳定溶液1μL。准备几个小区在相同的离心管中的工作液。 </li><li>除去塑料膜，并添加工作文库制备缓冲液到每个孔中的3微升。吸取井上下的内容混合。更换塑料薄膜。 注:塑料薄膜可以遍及全基因组扩增过程，直到井开始在膜中钻孔被重用。当这种情况发生时，切换到新的塑料膜。 </li><li>短暂离心在微型板喷丝板，并在95℃下孵育2分钟。 </li><li>冷却板置于冰上并简要离心机迷你板微调板。 </li><li>除去塑料膜，添加1μL的文库制备酶至每个孔，并吸取井的内容上下混合。保持冰或经过该步骤感冒块库制剂酶。更换塑料薄膜。 </li><li>短暂离心在微型板喷丝和热循环的板如下:16℃，20分钟，24℃，20分钟，37℃，20分钟，75℃，5分钟，并保持在4℃。 </li><li>在冰上冷却板，短暂离心在迷你板微调板。 </li><li>准备从全基因组扩增试剂盒工作扩增混合液。对于每个小区，结合4​​8.5微升水，7.5微升10X放大主混合物，并在离心管5微升的WGA DNA聚合酶。准备工作组合为几个小区在相同的离心管中。保持冰工作组合。 </li><li>取下塑料薄膜，加入61 mL工作扩增混合液以良好上下各有好，吸管内容物混合。保持冰或经过该步骤感冒块工作扩增混合液。更换塑料薄膜。 </li><li>短暂离心在微型板喷丝板和热循环如下:95℃，3分钟，94℃25个循环，30秒和65℃，5分钟，并保持在4℃。 </li><li>转移60微升各样品到一个单独的离心管中并储存在 - 20℃下，在步骤3中使用。 </li><li> DNA上样染料添加到每个样品（ 即 3 6×μL的DNA上样染料样品的15μL）的剩余量，并从在1％琼脂糖凝胶上各反应运行5微升。 注:成功扩增将显示为涂片从250到1000 bp的细胞。样品不显示一个涂抹或只显示一个微弱涂抹不大可能产生有用的测序数据。 </li></ol> 3.测序<ol><li>净化与顺珠样品。 <ol><li> THA在冰上瓦特DNA样本。涡旋顺磁性珠粒直到溶液是均匀的孵育在室温下将珠粒至少30分钟。 </li><li>转移每一个DNA样本的20微升到一个干净的离心管中。该样品的其余部分可被储存在-20℃。 </li><li>添加的顺磁珠30μL（1.5倍），以每一个DNA样本，涡旋混合。在室温下孵育样品10分钟。留在室温下，在步骤3.4的珠的原液使用。 </li><li>放置在磁管条管2分钟，或直到珠粒形成沉淀，上清液是明确的。 </li><li>使用P200移液器，去掉尽可能多上清尽可能不干扰珠。 </li><li>添加80％乙醇180微升的DNA珠混合物。旋转管相对于所述磁体通过乙醇溶液"冲洗"珠数次。 </li><li>使用P200移液器，去掉尽可能多的乙醇洗为possi的竹叶提取不干扰珠子。 </li><li>重复3.1.6和3.1.7。 </li><li>空气干燥该珠为约10分钟，或直到没有更多的乙醇是可见的。继续进行到下一步骤时，珠出现破裂或脱落管的壁。 </li><li>从磁条取出样品。添加10毫摩尔Tris pH 8.0的40微升洗脱从珠的DNA和涡样品重悬珠子。孵育在室温下2分钟。 </li><li>短暂离心样品以收集在试管底部的液体，然后将管上的磁性管条用于至少两个分钟。转移洗脱液成清洁离心管，而不会干扰珠。 </li></ol></li><li>样品正常化<ol><li>定量使用分光光度计每个样品的浓度。浓度应为10至30毫微克/微升。 </li><li>每个样品稀释至0.2纳克/μL使用10毫米的Tris pH值8.0。下面的步骤将需要最少我从这种稀释5微升NPUT。 </li></ol></li><li>图书馆准备<ol><li>由以下的文库制备试剂盒13的说明准备测序文库。 </li></ol></li><li>最后的清理<ol><li>根据步骤3.1清洗样品。对于50个基点，75基点，或150基点的读长，使用珠的比例分别为1.5倍采样，1个或0.6倍的量。用10mM的Tris pH为8.0的15微升洗脱。 </li><li>上运行的片段分析器样本以检查库14的粒度分布。粒度分布应均匀地分布从150至900基点。 </li></ol></li><li>量化使用qPCR样品<ol><li>使用库定量试剂盒，根据试剂盒的说明15设置了定量PCR反应。留下一个空白栏添加正的标准（从工具包DNA标准）和负标准（水或其他空白溶液）。 </li><li>热周期FOL低点:95℃，5分钟（4.8℃/秒的升温速度），并在95℃，30秒（4.8℃/秒的斜升速率）和60℃，45秒（2.5爬坡速率35次循环℃/秒）。 </li></ol></li><li>池<ol><li>确定如何，还需要在流动池很多小巷和划分样本分成组，每个通道一组。 </li><li>对于每一个样品组，选择与基于从步骤3.5.2的qPCR数据的最低浓度的样品和该组中的正常化所有样品使用的10mM Tris pH 8.0的浓度。 </li><li>池中的每个组中的样本。 </li></ol></li><li>测序<ol><li>按照标准程序16新一代测序机上载池。 </li></ol></li></ol> 4.数据分析注:基于Unix的环境下才能运行本节中的程序和脚本。安装其在下面的协议中提到的软件<html> stallation指南。所有的脚本可以在https://sourceforge.net/projects/singlecellseqcnv/找到。 <ol><li>修剪读取使用fastx_trimmer从FASTX-工具包版本0.0.13 17 40-NT。 fastx_trimmer -Q33 -i example.fastq -l 40 -o example.trim.fastq </li><li>对准读取使用BWA版0.6.1使用默认选项18适当的参考基因组（MM9鼠标，hg19人类）。 BWA的AlN mm9.fa example.trim.fastq&gt; example.sai BWA samse mm9.fa example.sai example.trim.fastq&gt; example.sam </li><li>删除对齐到CHRM和随机染色体，然后进行排序，并使用SAMTools版本0.1.19 19指数产生的BAM文件。 grep的-v -w CHRM example.sam | grep的-v随机&gt; example.filtered.sam samtools查看-uSh example.filtered.sam | samtools排序 - example.filtered samtools指数example.filtered.bam </li><li>运行HMMcopy检测拷贝数变异="外部参照"&gt; 20 <ol><li>使用gcCounter在HMMcopy产生用于基因组中的GC百分比参考文件。使用选项"-w 500000"来指定窗口的大小。使用步骤4.2中使用的FASTA参考文件的版本相同，但要确保CHRM和随机染色体从FASTA文件中删除。 gcCounter -w 500000 mm9.fa&gt; mm9_gc.wig </li><li>使用generateMap.pl在HMMcopy生成mappability一个摆动文件。使用选项"-w 40"指定阅读的长度。使用步骤4.4.1使用的相同的fasta参考文件。 generateMap.pl -b mm9.fa generateMap.pl -w 40 -i mm9.fa mm9.fa -o mm9.bigwig </li><li>使用mapCounter在HMMcopy产生用于基因组中的mappability参考文件。使用选项"-w 500000"来指定窗口的大小。 mapCounter -w 500000 mm9.bigwig&gt; mm9_map.wig </li><li>使用readCounter在HMMcopy生成每个BAM文件蠕动文件。 readCounter -w 500000 example.filtered.bam&gt; input.wig </l我&gt; <li>修改在R脚本路径参考文件（run_hmmcopy.mm9.r或run_hmmcopy.hg19.r），使用4.4.1（ 如 mm9_gc.wig）和4.4.3（ 如上面mm9_map生成的文件。假发）为变量GFILE和MFILE，其分别是指由GC百分比参考文件和mappability参考文件。然后运行提供ř剧本"run_hmmcopy.mm9.r"或"run_hmmcopy.hg19.r"。 - [R CMD批run_hmmcopy.mm9.r 要么 - [R CMD批run_hmmcopy.hg19.r </li><li>一组BAM文件的批处理，把BAM文件在一个文件夹中，并运行提供的脚本"HMMpipe.pl"在包中调用段和计算变异的分数（VS）。按照格式: perl的HMMpipe.pl Folder_to_BAMfiles MM9 </li></ol></li><li>运行DNAcopy检测CNV的21。 <ol><li>使用SAMtools版本0.1.19中提取唯一映射每个BAM文件读取。 samtools查看-h -F 0x0004 example.filtered.bam | egrep的-i"^ @ | XT:A:U"| samtools查看姝达 - &gt; example.mapped.bam </li><li>使用皮卡德版本1.94 MarkDuplicates，以纪念在BAM PCR重复文件22。 Java的罐子MarkDuplicates.jar INPUT = example.mapped.bam输出= example.nondup.bam METRICS_FILE = example.dup REMOVE_DUPLICATES =真</li><li>在bedtools版本2.17.0来计算映射使用coveragebed读取所提供的床档"mm9.500k.dynamic.win.bed"或"hg19.500k.dynamic.win.bed"23每个预定义的动态500KB可映射窗口。 coverageBed -abam example.nondup.bam -b mm9.500k.dynamic.win.bed -COUNTS |排序-k1,1 -k2,2n&gt; example.nondup.counts </li><li>使用所提供的Perl脚本"normalizeGC.pl"正常化基于所提供的GC含量的参考文件"mm9.500k.dynamic.win.fa.gc.txt"或"hg19.500k.dynamic.win.fa读计数。 gc.txt"。 perl的normalizeGC。PL mm9.500k.dynamic.win.fa.gc.txt example.nondup.counts&gt; example.bam.norm.counts </li><li>使用所提供的脚本 - [R"dnacopy.r"来称呼段。 - [R CMD批dnacopy.r </li></ol></li><li>确定拷贝数变异<ol><li>排除这在4.4.6计算VS超过0.26细胞。 </li><li>筛选4.4.6通过HMMcopy称为段只包括那些的量，中位数日志2比大于0.4（推定的增益）或小于-0.35（推定的损失）。 </li><li>筛选由4.5.5叫DNAcopy段，只包括那些该段的意思是小于0.6大于1.32。 </li><li>从4.6.2和4.6.3重叠部分，只有在地区其中两个HMMcopy和DNAcopy确定一个假定的利得或损失假定调用拷贝数变异。 </li></ol></li></ol>

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A., Wu, J., Amon, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+megabase-scale+somatic+copy+number+variation+using+single-cell+sequencing.">Assessment of megabase-scale somatic copy number variation using single-cell sequencing.</a> Genome Res. 26 (3), 376-384 (2016).</li><li>. KAPA Library Quantification Kit Available from: <a target="_blank" href="https://www.kapabiosystems.com/product-applications/products/next-generation-sequencing-2/library-quantification/">https://www.kapabiosystems.com/product-applications/products/next-generation-sequencing-2/library-quantification/</a> (2016)</li><li>. DNAcopy Available from: <a target="_blank" href="https://bioconductor.org/packages/release/bioc/html/DNAcopy.html">https://bioconductor.org/packages/release/bioc/html/DNAcopy.html</a> (2016)</li><li>Lichti, U., Anders, J., Yuspa, S. 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作者什么都没有透露。

传统上，确定拷贝数变异和非整倍体在所需的细胞学方法如FISH和SKY单个细胞的水平。现在，单细胞测序已经作为这些问题的另一种方法。单细胞测序拥有FISH和SKY优点它既是全基因组和高分辨率。此外，当施加适当的质量控制方法，单细胞测序能提供拷贝数变异更可靠的评估和非整倍体，因为它是不容易固有FISH和SKY杂交和传播文物。然而，许多最近的单细胞测序的应用中还没有被证实的灵敏度的全面评估和特异性的方法和分析。事实上，一些由其它研究中使用的分析方法均与假阳性的CNV调用12高的频率（&gt; 50％）相关联。我们介绍的方法已经通过KN细胞经过严格测试自己的CNV负担，以确定真正的和错误发现率和优化的CNV检测12的灵敏度和特异性。使用的质量控制，并在此协议中描述的分析方法，5兆增益约20％，5兆损失75％，而所有的CNV超过10兆可以被检测出来。虽然确定单细胞测序的假发现率是困难的，我们已经估计它是小于25％。该协议可从各种来源被应用到细胞中，脚本可以被修改，以调整CNV检测的分辨率，并且该协议可以适于识别其它类型的基因组改变的。 有各种各样的离解新鲜组织成单个细胞的装置，并且许多出版物描述程序特定的组织进行了优化，如皮肤24和脑25。我们愿意通过向误吸单个细胞分离，因为它允许FO<html>待测序的每个细胞中的R视觉评估。但是，它也可以通过荧光激活细胞分选（FACS）26和微流体装置27至单个细胞分离。如果单细胞分离和全基因组扩增手动执行，这是合理的分离和扩增多达四十细胞在长疮。为了获得高品质的单细胞测序数据，这是至关重要的单细胞的基因组的扩增是均匀和完整。我们发现，单细胞的分离以及裂解和扩增效率的质量对测序数据的质量有显著影响。因此，细胞应该从其天然环境分离和全基因组扩增之前，为了收获应该开始的细胞分离之后。此外，裂解和片段化步骤应完全一样的步骤2.3-2.6中所述执行。 "&gt;该算法可以调整，以改变CNV检测的分辨率，具有相对的灵敏度和特异性12的效果。另外，也可以调整阈值，以检测的四倍体11设置全染色体非整倍体。然而，我们发现我们的方法被限制于检测到的CNV大于5兆，如全基因组扩增过程中引入的噪声较小的变体12的检测复杂化。在全基因组扩增的方法今后改进应最终提高单细胞测序CNV检测的分辨率。 单细胞测序不仅允许拷贝数改变的调查，但也有单核苷酸变异28,29和结构变异30。我们的单细胞分离方案可以应用到回答这些其他阙stions。然而，全基因组扩增方法的选择取决于具体的应用。在这个协议中，这是基于聚合酶链反应的方法中，最适合用于检测拷贝数改变，因为它是用较低水平的扩增偏差32相关联。调查其它类型的基因组改变，如单核苷酸多态性，全基因组扩增的其它方法被认为是更适合的31,32。

在DNA拷贝数变化的大小可以从几个碱基对范围（拷贝数变异）（拷贝数变异）到整个染色体（非整倍体）。影响的基因组中的大区域拷贝数改变可通过改变多达数千种基因1,2的表达有显著的表型的影响。 CNV的存在于一个种群的所有细胞可以通过本体测序或基于微阵列的方法3,4被检测。然而，种群也可以是遗传异质性，与现有的在群体或甚至单个细胞的子集的CNV。遗传异质性是在癌症常见，驾驶肿瘤演变，并且也存在于正常组织，具有未知后果5，6，7，8，9 <suP&gt;，10。 传统上，遗传异质性或者通过细胞学方法或批量测序评估。细胞学方法，如原位杂交（FISH），染色体利差和光谱核型分析（SKY）荧光，有目前在单个细胞识别改变的好处，但有由于杂交和蔓延11器物高错误率。这些方法也被限制在他们只露出分辨率的拷贝数变化跨越几兆。测序或散装的DNA微阵列，虽然更高的精度和分辨率，是较不敏感。为了通过基于人口的方法来检测遗传异质性，所述变体必须存在于群细胞的相当大一部分。的方法，以扩增来自单个细胞的基因组DNA的出现使得有可能进行测序的单细胞的基因组中。单细胞塞克通过实践具有高分辨率，高灵敏度，并且，当施加适当的质量控制方法，高精度12的优点。 在这里，我们描述了在单个细胞检测兆碱基的大规模拷贝数变化的方法。我们通过分离误吸单个细胞，用连接适配器PCR扩增基因组DNA，准备下一代测序文库，并检测拷贝数由双方隐马尔可夫模型和循环二元分割的变种。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Aspirator tube assemblies for calibrated microcapillary pipettes</td>
			<td>Sigma</td>
			<td>A5177</td>
			<td></td>
		</tr>
		<tr>
			<td>PVC tubing (ID 3/16&#34;, OD 5/16&#34;, ~1 foot)</td>
			<td>VWR</td>
			<td>89068-500</td>
			<td></td>
		</tr>
		<tr>
			<td>PVC tubing (ID 5/16&#34;, OD 7/16&#34;, ~6 inches)</td>
			<td>VWR</td>
			<td>89068-508</td>
			<td></td>
		</tr>
		<tr>
			<td>Acrodisc Syringe Filter with HT Tuffryn Membrane (diameter 25 mm, pore size 0.2 um)</td>
			<td>VWR</td>
			<td>28144-040</td>
			<td></td>
		</tr>
		<tr>
			<td>Serological pipette (5 ml)</td>
			<td>BioExpress</td>
			<td>P-2837-5</td>
			<td>Any plastic 5 mL pipette should suffice</td>
		</tr>
		<tr>
			<td>In-Line Water Trap for Oxygen Use</td>
			<td>Amazon.com</td>
			<td>700220210813</td>
			<td></td>
		</tr>
		<tr>
			<td>Capillary Melting Point Tubes (ID 0.8-1.1 mm, 100 mm length)</td>
			<td>VWR</td>
			<td>34502-99</td>
			<td></td>
		</tr>
		<tr>
			<td>Modeling clay</td>
			<td>VWR</td>
			<td>470156-850</td>
			<td></td>
		</tr>
		<tr>
			<td>Petri dish (150 mm diameter)</td>
			<td>VWR</td>
			<td>25384-326</td>
			<td>Surface should be non-adherent to facilitate aspiration of single cells</td>
		</tr>
		<tr>
			<td>Hard-Shell&#160;Full-Height 96-Well Semi-Skirted PCR&#160;Plates</td>
			<td>Bio-Rad</td>
			<td>HSS9601</td>
			<td>Any 96-well PCR plate should suffice</td>
		</tr>
		<tr>
			<td>96-well tissue culture plate</td>
			<td>VWR</td>
			<td>62406-081</td>
			<td>Any 96-well tissue culture plate should suffice</td>
		</tr>
		<tr>
			<td>GenomePlex Single Cell Whole Genome Amplification Kit</td>
			<td>Sigma</td>
			<td>WGA4</td>
			<td></td>
		</tr>
		<tr>
			<td>Microseal &#39;A&#39; Film</td>
			<td>Bio-Rad</td>
			<td>MSA5001</td>
			<td></td>
		</tr>
		<tr>
			<td>Mini plate spinner</td>
			<td>Thomas Scientific</td>
			<td>1225Z37</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermal cycler</td>
			<td>Bio-Rad</td>
			<td>1861096&#160;</td>
			<td>Any thermal cycler should suffice so long as it can accommodate a 96-well PCR plate</td>
		</tr>
		<tr>
			<td>Agencourt Ampure Beads</td>
			<td>Beckman Coulter</td>
			<td>A63880</td>
			<td></td>
		</tr>
		<tr>
			<td>Dynamag-2 Magnet</td>
			<td>Thermo Fisher Scientific</td>
			<td>12321D</td>
			<td>Any similar magnetic tube strip should suffice</td>
		</tr>
		<tr>
			<td>Nextera XT DNA Library Preparation Kit</td>
			<td>Illumina</td>
			<td>FC-131-1096</td>
			<td></td>
		</tr>
		<tr>
			<td>Nextera XT Index Kit</td>
			<td>Illumina</td>
			<td>FC-121-1012</td>
			<td></td>
		</tr>
		<tr>
			<td>Complete kit (optimized for Roche LightCycler 480)</td>
			<td>Kapa Biosystems</td>
			<td>KK4845&#160;</td>
			<td>This kit is optimized for the Roche LightCycler 480 real-time PCR machine. If using a different machine, substitute a kit optimized for your machine.</td>
		</tr>
	</tbody>
</table>

detection of copy number alterations using single cell sequencing

在单细胞分辨率的基因组的改变检测为在正常组织，癌症和微生物种群表征遗传异质性和进化重要。用于评估遗传异质性的传统方法已经由分辨率低，灵敏度低，和/或特异性较低的限制。单细胞测序已经成为用于检测与高分辨率，高灵敏度，并且当适当地进行分析，高特异性的遗传异质性的有力工具。这里我们提供了隔离，全基因组扩增，测序和分析单个细胞的一个协议。我们的方法允许在单个细胞兆碱基的大规模拷贝数变异的可靠识别。然而，该协议的各方面也可应用于研究其他类型的单细胞中的遗传改变的。

组装吸气应该会出现一个类似于图1A。针应绘制使得其足够宽以容纳单个细胞，但不那么广泛，大量体积绘制了单细胞。吸单个细胞时，有细胞在10X字段（ 图1B）一到五个之间是最容易的。 如果全基因组扩增是成功的，该样品将显示为在琼脂糖凝胶涂片（ 图2，泳道1，2，4，5，6，和7）。一个模糊或不存在涂抹指示失败扩增反应和样品不应被测序（ 图2，泳道3和8）。 以下库制备，样品的片段大小分布应通过在片段分析器毛细管电泳来评估。成功制备库将有一个相当甚至片段大小的分布从150到900碱基对（ 图3，A，B）。失败文库制备将导致歪斜片段大小分布和此类文库不应被测序（ 图3C）。 通过隐马尔可夫模型（HMMcopy）和圆形二进制分割（DNAcopy）处理测序数据将解析各细胞的基因组中估计的拷贝数的区段。然后，这些段可以被过滤，以确定那些具有在单个细胞（ 表1）的增益或损失一致的估计的拷贝数。然后从HMMcopy和DNAcopy这些过滤段应重叠，以确定高可信度的拷贝数变异。 <img alt="图1" src="/files/ftp_upload/55143/55143fig1.jpg" /> 图1.单细胞分离。 （<html> A）的组装microaspirator。 （B）10X域显示游离细胞（箭头）和microaspirator针（右下角）。吸单个细胞时，有细胞在10X字段中的一个和五个之间是最容易的。 <a href="http://ecsource.jove.com/files/ftp_upload/55143/55143fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图2" src="/files/ftp_upload/55143/55143fig2.jpg" /> 图2.全基因组扩增。 5微升的全基因组扩增产物的琼脂糖凝胶电泳。样品成​​功地扩增将从100bp的显示为明亮的涂片1kb的（泳道1，2，4，5，6，和7），并可以进行测序。样品不成功扩增会产生微弱的涂片或无污迹（泳道3和8），不应被测序。large.jpg"目标="_空白"&gt;点击此处查看该图的放大版本。 <img alt="图3" src="/files/ftp_upload/55143/55143fig3.jpg" /> 图3.图书馆准备。从一个片段分析器代表性结果。该图显示关于Y轴的X轴和相对荧光单位（RFU）片段的大小（以碱基对）。对于每个图的右侧是一个模拟凝胶泳道。 （A）结果的理想样本，与150甚至900基点之间没有尖锐峰或向一侧偏分布。此示例是排序可以接受的。 （B）从一个不错的样本结果，具有粒径分布向低片段大小倾斜。虽然这不是最佳的，样品可以仍然进行测序。 （C）从失败的样品结果，主要与小片段大小。这可能是由延长培养期间tagmentation步骤引起库准备。该样品不应当被测序。 <a href="http://ecsource.jove.com/files/ftp_upload/55143/55143fig3large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td colspan="7"> HMMcopy </td></tr><tr><td> 样品 </td><td> 分割 </td><td> CHR </td><td> 开始 </td><td> 结束 </td><td> 州 </td><td> 中位数 </td></tr><tr><td> D15-4998 </td><td> 23 </td><td> chr8 </td><td> 144500001 </td><td> 146500000 </td><td> 6 </td><td> 0.5008794 </td></tr><tr><td> D15-4998 </td><td> 29 </td><td> chr10 </td><td> 67000001 </td><td> 134500000 </td><td> 6 </td><td> 0.4031945 </td></tr><tr><td> D15-4998 </td><td> 52 </td><td> Chr19 </td><td> 1 </td><td> 20000000 </td><td> 6 </td><td> 0.4616884 </td></tr><tr><td> D15-4998 </td><td> 57 </td><td> CHRY </td><td> 1 </td><td> 59,500,000 </td><td> 2 </td><td> -1.506532 </td></tr><tr><td colspan="8"> DNAcopy </td></tr><tr><td> 样品 </td><td> CHROM </td><td> 启动斌 </td><td> 结束斌 </td><td> 开始 </td><td> 结束 </td><td> 赛格平均 </td><td> 赛格MED </td></tr><tr><td> D15-4998 </td><td> chr10 </td><td> 88 </td><td> 197 </td><td> 62612945 </td><td> 129971511 </td><td> 1.4688 </td><td> 0.157 </td></tr><tr><td> D15-4998 </td><td> chr19 </td><td> 0 </td><td> 31 </td><td> 0 </td><td> 28416392 </td><td> 1.4141 </td><td> 0.1674 </td></tr><tr><td> D15-4998 </td><td> chrX </td><td> 77 </td><td> 126 </TD&gt; <td> 51659160 </td><td> 95343369 </td><td> 1.3548 </td><td> 0.1874 </td></tr><tr><td> D15-4998 </td><td> CHRY </td><td> 0 </td><td> 14 </td><td> 0 </td><td> 23805358 </td><td> -2.7004 </td><td> 0.3591 </td></tr></tbody></table> 表1.数据分析。过滤段由一个单细胞通过HMMcopy和DNAcopy生成。重叠这两个结果揭示了从67至130兆10号染色体上的增益，以及从0到20兆19号染色体上的增益。我们已经发现，在19号染色体的近侧部分的收益是单细胞的测序12的工件。

Watch this Scientific Journal Video about Detection of Copy Number Alterations Using Single Cell Sequencing at JoVE.com

Detection of Copy Number Alterations Using Single Cell Sequencing

单细胞测序是一个日益流行的和可访问在高分辨率解决的基因组变化的工具。我们提供了一个使用单细胞测序，以确定在单个细胞拷贝数变化的协议。

单细胞测序拷贝数改变的检测

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

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Genetics

11.5K 次观看.  Massachusetts Institute of Technology. 单细胞测序是一个日益流行的和可访问在高分辨率解决的基因组变化的工具。我们提供了一个使用单细胞测序，以确定在单个细胞拷贝数变化的协议。 

视频: Detection of Copy Number Alterations Using Single Cell Sequencing

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell activation, stress, or stimulation. Single-cell multiplex gene expression enables the simultaneous assessment of tens of genes1,2,3. At the single-cell level, multiplex gene expression determines population heterogeneity4,5. It allows for the distinction of population heterogeneity by determining both the probable mix of diverse precursor stages among mature cells and also the diversity of cell responses to stimuli.
Innate lymphoid cells (ILC) have been recently described as a population of innate effectors of the immune response6,7. In this protocol, cell heterogeneity of the ILC hepatic compartment is investigated during homeostasis.
Currently, the most widely used technique to assess gene expression is RT-qPCR. This method measures gene expression only one gene at a time. Additionally, this method cannot estimate heterogeneity of gene expression, since multiple cells are needed for one test. This leads to the measurement of the average gene expression of the population. When assessing large numbers of genes, RT-qPCR becomes a time-, reagent-, and sample-consuming method. Hence, the trade-offs limit the number of genes or cell populations that can be evaluated, increasing the risk of missing the global picture.
This manuscript describes how single-cell multiplex RT-qPCR can be used to overcome these limitations. This technique has benefited from recent microfluidics technological advances1,2. Reactions occurring in multiplex RT-qPCR chips do not exceed the nanoliter-level. Hence, single-cell gene expression, as well as simultaneous multiple gene expression, can be performed in a reagent-, sample-, and cost-effective manner. It is possible to test cell gene signature heterogeneity at the clonal level between cell subsets within a population at different developmental stages or under different conditions4,5. Working on rare populations with large numbers of conditions at the single-cell level is no longer a restriction.

This protocol describes how to assess the expression of a large array of genes at the clonal level. Single-cell RT-qPCR produces highly reliable results with a strong sensitivity for hundreds of samples and genes.

单细胞基因表达利用多重RT-qPCR的表征罕见的淋巴人群的异质性

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell ...

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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

单细胞基因表达谱使用流式细胞仪和qPCR内部标准

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

Mutants are invaluable genetic resources for gene function studies. To generate mutant collections, three types of mutagens can be utilized, including biological such as T-DNA or transposon, chemical such as ethyl methanesulfonate (EMS), or physical such as ionization radiation. The type of mutation observed varies depending on the mutagen used. For ionization radiation induced mutants, mutations include deletion, duplication, or rearrangement. While T-DNA or transposon-based mutagenesis is limited to species that are susceptible to transformation, chemical or physical mutagenesis can be applied to a broad range of species. However, the characterization of mutations derived from chemical or physical mutagenesis traditionally relies on a map-based cloning approach, which is labor intensive and time consuming. Here, we show that a high-density genome array-based comparative genomic hybridization (aCGH) platform can be applied to efficiently detect and characterize copy number variations (CNVs) in mutants derived from fast neutron bombardment (FNB) mutagenesis in Medicago truncatula, a legume species. Whole genome sequence analysis shows that there are more than 50,000 genes or gene models in M. truncatula. At present, FNB-induced mutants in M. truncatula are derived from more than 150,000 M1 lines, representing invaluable genetic resources for functional studies of genes in the genome. The aCGH platform described here is an efficient tool for characterizing FNB-induced mutants in M. truncatula.

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

This protocol provides experimental steps and information about reagents, equipment, and analysis tools for researchers who are interested in carrying out whole genome array-based comparative genomic hybridization (CGH) analysis of copy number variations in plants.

一种基于阵列的比较基因组杂交平台, 有效检测快中子诱导的苜蓿蒺藜突变体的拷贝数变化

Mutants are invaluable genetic resources for gene function studies. To generate mutant collections, three types of mutagens can be utilized, including ...

Generating Transgenic Plants with Single-copy Insertions Using BIBAC-GW Binary Vector

When generating transgenic plants, generally the objective is to have stable expression of a transgene. This requires a single, intact integration of the transgene, as multi-copy integrations are often subjected to gene silencing. The Gateway-compatible binary vector based on bacterial artificial chromosomes (pBIBAC-GW), like other pBIBAC derivatives, allows the insertion of single-copy transgenes with high efficiency. As an improvement to the original pBIBAC, a Gateway cassette has been cloned into pBIBAC-GW, so that the sequences of interest can now be easily incorporated into the vector transfer DNA (T-DNA) by Gateway cloning. Commonly, the transformation with pBIBAC-GW results in an efficiency of 0.2&#8211;0.5%, whereby half of the transgenics carry an intact single-copy integration of the T-DNA. The pBIBAC-GW vectors are available with resistance to Glufosinate-ammonium or DsRed fluorescence in seed coats for selection in plants, and with resistance to kanamycin as a selection in bacteria. Here, a series of protocols is presented that guide the reader through the process of generating transgenic plants using pBIBAC-GW: starting from recombining the sequences of interest into the pBIBAC-GW vector of choice, to plant transformation with Agrobacterium, selection of the transgenics, and testing the plants for intactness and copy number of the inserts using DNA blotting. Attention is given to designing a DNA blotting strategy to recognize single- and multi-copy integrations at single and multiple loci.

Using a pBIBAC-GW binary vector makes generating transgenic plants with intact single-copy insertions, an easy process. Here, a series of protocols is presented that guide the reader through the process of generating transgenic Arabidopsis plants, and testing the plants for intactness and copy number of the inserts.

用 BIBAC 二元矢量生成单拷贝插入的转基因植物

When generating transgenic plants, generally the objective is to have stable expression of a transgene. This requires a single, intact integration of the ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

基于纠错 DNA 和 RNA 测序的稀有事件检测方法

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

单缓步动物标本的超低输入基因组测序库制备

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

纳米测序全血 mRNA 测序

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to normal and pathological conditions. The goal of this protocol is to generate high-quality, large-scale transcriptome data of each endocrine cell type with the use of a droplet-based microfluidic single-cell RNA sequencing technology. Such data can be utilized to build the gene expression profile of each endocrine cell type in normal or specific conditions. The process requires careful handling, accurate measurement, and rigorous quality control. In this protocol, we describe detailed steps for human pancreatic islets dissociation, sequencing, and data analysis. The representative results of about 20,000 human single islet cells demonstrate the successful application of the protocol.

Here, we present a protocol to generate high-quality, large-scale transcriptome data of single cells from isolated human pancreatic islets using a droplet-based microfluidic single-cell RNA sequencing technology.

人类胰腺胰岛的单细胞RNA测序与分析

Pancreatic islets comprise of endocrine cells with distinctive hormone expression patterns. The endocrine cells show functional differences in response to ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

使用RNA测序识别循环RNA

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

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 ...