Name: detection of copy number alterations using single cell sequencing
Uploaded: 2017-02-17T14:00:00.000+00:00
Duration: 9 min 45 s
Description: Watch this Scientific Journal Video about Detection of Copy Number Alterations Using Single Cell Sequencing at JoVE.com

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>

<ol>
	<li>Kahlem, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcript+Level+Alterations+Reflect+Gene+Dosage+Effects+Across+Multiple+Tissues+in+a+Mouse+Model+of+Down+Syndrome.">Transcript Level Alterations Reflect Gene Dosage Effects Across Multiple Tissues in a Mouse Model of Down Syndrome.</a> Genome Res. 14 (7), 1258-1267 (2004).</li><li>Torres, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+Aneuploidy+on+Cellular+Physiology+and+Cell+Division+in+Haploid+Yeast.">Effects of Aneuploidy on Cellular Physiology and Cell Division in Haploid Yeast.</a> Science. 317 (5840), 916-924 (2007).</li><li>Itsara, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+Analysis+of+Large+Copy+Number+Variants+and+Hotspots+of+Human+Genetic+Disease.">Population Analysis of Large Copy Number Variants and Hotspots of Human Genetic Disease.</a> Am. J. Human Gen. 84 (2), 148-161 (2009).</li><li>Sudmant, P. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+diversity,+population+stratification,+and+selection+of+human+copy-number+variation.">Global diversity, population stratification, and selection of human copy-number variation.</a> Science. 349 (6253), aab3761 (2015).</li><li>Navin, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inferring+tumor+progression+from+genomic+heterogeneity.">Inferring tumor progression from genomic heterogeneity.</a> Genome Res. 20 (1), 68-80 (2010).</li><li>Torres, L., Ribeiro, F. R., Pandis, N., Andersen, J. A., Heim, S., Teixeira, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intratumor+genomic+heterogeneity+in+breast+cancer+with+clonal+divergence+between+primary+carcinomas+and+lymph+node+metastases.">Intratumor genomic heterogeneity in breast cancer with clonal divergence between primary carcinomas and lymph node metastases.</a> Breast cancer res and treat. 102 (2), 143-155 (2007).</li><li>Yates, L. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subclonal+diversification+of+primary+breast+cancer+revealed+by+multiregion+sequencing.">Subclonal diversification of primary breast cancer revealed by multiregion sequencing.</a> Nat. Med. 21 (7), 751-759 (2015).</li><li>Forsberg, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-related+somatic+structural+changes+in+the+nuclear+genome+of+human+blood+cells.">Age-related somatic structural changes in the nuclear genome of human blood cells.</a> Am. J. Human Gen. 90 (2), 217-228 (2012).</li><li>Laurie, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detectable+clonal+mosaicism+from+birth+to+old+age+and+its+relationship+to+cancer.">Detectable clonal mosaicism from birth to old age and its relationship to cancer.</a> Nat. Gen. 44 (6), 642-650 (2012).</li><li>Jacobs, K. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detectable+clonal+mosaicism+and+its+relationship+to+aging+and+cancer.">Detectable clonal mosaicism and its relationship to aging and cancer.</a> Nat. Gen. 44 (6), 651-658 (2012).</li><li>Knouse, K. A., Wu, J., Whittaker, C. A., 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=Single+cell+sequencing+reveals+low+levels+of+aneuploidy+across+mammalian+tissues.">Single cell sequencing reveals low levels of aneuploidy across mammalian tissues.</a> Proc. Natl. Acad. Sci. 111 (37), 13409-13414 (2014).</li><li>Knouse, K. 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. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+and+short-term+culture+of+primary+keratinocytes,+hair+follicle+populations+and+dermal+cells+from+newborn+mice+and+keratinocytes+from+adult+mice+for+in+vitro+analysis+and+for+grafting+to+immunodeficient+mice.">Isolation and short-term culture of primary keratinocytes, hair follicle populations and dermal cells from newborn mice and keratinocytes from adult mice for in vitro analysis and for grafting to immunodeficient mice.</a> Nat. Protoc. 3 (5), 799-810 (2008).</li><li>Brewer, G. J., Torricelli, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+and+culture+of+adult+neurons+and+neurospheres.">Isolation and culture of adult neurons and neurospheres.</a> Nat. Protoc. 2 (6), 1490-1498 (2007).</li><li>Navin, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumour+evolution+inferred+by+single-cell+sequencing.">Tumour evolution inferred by single-cell sequencing.</a> Nature. 472 (7341), 90-94 (2011).</li><li>Szulwach, K. E., Chen, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-Cell+Genetic+Analysis+Using+Automated+Microfluidics+to+Resolve+Somatic+Mosaicism.">Single-Cell Genetic Analysis Using Automated Microfluidics to Resolve Somatic Mosaicism.</a> PLoS ONE. 10 (8), e0135007 (2015).</li><li>Zong, C., Lu, S., Chapman, A. R., Xie, X. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Detection+of+Single-Nucleotide+and+Copy-Number+Variations+of+a+Single+Human+Cell.">Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell.</a> Science. 338 (6114), 1622-1626 (2012).</li><li>Wang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+evolution+in+breast+cancer+revealed+by+single+nucleus+genome+sequencing.">Clonal evolution in breast cancer revealed by single nucleus genome sequencing.</a> Nature. 512 (7513), 155-160 (2014).</li><li>Zhang, C. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chromothripsis+from+DNA+damage+in+micronuclei.">Chromothripsis from DNA damage in micronuclei.</a> Nature. 522 (7555), 179-184 (2015).</li><li>Macaulay, I. C., Voet, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single+Cell+Genomics:+Advances+and+Future+Perspectives.">Single Cell Genomics: Advances and Future Perspectives.</a> PLoS Genetics. 10 (1), e1004126 (2014).</li><li>Gawad, C., Koh, W., Quake, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+genome+sequencing:+current+state+of+the+science.">Single-cell genome sequencing: current state of the science.</a> Nat. Rev. Gen. , 1-14 (2016).</li></ol>

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><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&quot;, OD 5/16&quot;, ~1 foot)</td><td>VWR</td><td>89068-500</td><td></td></tr><tr><td>PVC tubing (ID 5/16&quot;, OD 7/16&quot;, ~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 &amp;micro;m)</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&amp;nbsp;Full-Height 96-Well Semi-Skirted PCR&amp;nbsp;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 &#039;A&#039; 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&amp;nbsp;</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&amp;nbsp;</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 ...

detection-of-copy-number-alterations-using-single-cell-sequencing

Research

JoVE Journal

Genetics

11.6K 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

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Often cellular behavior and cellular responses are analyzed at the population level where the responses of many cells are pooled together as an average result masking the rich single cell behavior within a complex population. Single cell protein detection and quantification technologies have made a remarkable impact in recent years. Here we describe a practical and flexible single cell analysis platform based on addressable droplet microarrays. This study describes how the absolute copy numbers of target proteins may be measured with single cell resolution. The tumor suppressor p53 is the most commonly mutated gene in human cancer, with more than 50% of total cancer cases exhibiting a non-healthy p53 expression pattern. The protocol describes steps to create 10 nL droplets within which single human cancer cells are isolated and the copy number of p53 protein is measured with single molecule resolution to precisely determine the variability in expression. The method may be applied to any cell type including primary material to determine the absolute copy number of any target proteins of interest.

Here we present addressable droplet microarrays (ADMs), a droplet array based method able to determine absolute protein abundance in single cells. We demonstrate the capability of ADMs to characterize the heterogeneity in expression of the tumor suppressor protein p53 in a human cancer cell line.

用可寻址滴微阵列计算单细胞中的蛋白质

Often cellular behavior and cellular responses are analyzed at the population level where the responses of many cells are pooled together as an average ...

科研

癌症研究

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. However, the application of this method is challenging due to its low sensitivity in analyzing the trace amount of ctDNA in the blood. Furthermore, the method may generate false positive and negative results from this sequencing and subsequent analysis. To improve the feasibility and reliability of ctDNA detection in the clinic, here we present a technique which enriches rare mutations for sequencing, Enrich Rare Mutation Sequencing (ER-Seq). ER-Seq can distinguish a single mutation out of 1 x 107 wild-type nucleotides, which makes it a promising tool to detect extremely low frequency genetic alterations and thus will be very useful in studying disease heterogenicity. By virtue of the unique sequencing adapter&#39;s ligation, this method enables an efficient recovery of ctDNA molecules, while at the same time correcting for errors bidirectionally (sense and antisense). Our selection of 1021 kb probes enriches the measurement of target regions that cover over 95% of the tumor-related driver mutations in 12 tumors. This cost-effective and universal method enables a uniquely successful accumulation of genetic data. After efficiently filtering out background error, ER-seq can precisely detect rare mutations. Using a case study, we present a detailed protocol demonstrating probe design, library construction, and target DNA capture methodologies, while also including the data analysis workflow. The process to carry out this method typically takes 1-2 days.

This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement.

利用下一代测序检测 CtDNA 中的稀有突变

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. ...

Serum and Plasma Copy Number Detection Using Real-time PCR

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, and prediction of treatment resistance. Starting from the hypothesis that androgen receptor (AR) gene copy number (CN) gain is a frequent event in metastatic castration resistance prostate cancer (mCRPC), we propose to analyze this event in cfDNA as a potential predictive biomarker.
We evaluated AR CN in cfDNA using 2 different real-time PCR assays and 2 reference genes (RNaseP and AGO1). DNA amount of 60 ng was used for each assay combination. AR CN gain was confirmed using Digital PCR as a more accurate method. CN variation analysis has already been demonstrated to be informative for the prediction of treatment resistance in the setting of mCRPC, but it could be useful also for other purposes in different patient settings. CN analysis on cfDNA has several advantages: it is non-invasive, rapid and easy to perform, and it starts from a small volume of serum or plasma material.

This manuscript describes the copy number variation analysis performed in serum or plasma DNA using real-time PCR approach. This method is suitable for the prediction of drug resistance in castration resistant prostate cancer patients, but it could be informative also for other diseases.

应用实时 PCR 技术检测血清和血浆拷贝数

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, ...

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal gastric mucosa. Recently, we detected CDH1a transcript variant in fresh-frozen tumor tissues obtained from patients with GC. The expression of this variant in tissue samples was investigated by the chip-based digital PCR (dPCR) approach presented here. dPCR offers the potential for an accurate, robust, and highly sensitive measurement of nucleic acids and is increasingly utilized for many applications in different fields. dPCR is capable of detecting rare targets; in addition, dPCR offers the possibility for absolute and precise quantification of nucleic acids without the need for calibrators and standard curves. In fact, the reaction partitioning enriches the target from the background, which improves amplification efficiency and tolerance to inhibitors. Such characteristics make dPCR an optimal tool for the detection of the CDH1a rare transcript.

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

The manuscript describes a chip-based digital PCR assay to detect a rare CDH1 transcript variant (CDH1a) in fresh-frozen normal and tumor tissues obtained from patients with gastric cancer.

基于芯片的数字 PCR 检测新鲜冷冻胃癌组织中的一个CDH1罕见转录变体

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal ...

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 ...

遗传学

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Assessing intra-tumoral heterogeneity (ITH) is of paramount importance to anticipate failure of targeted therapies and design accordingly effective anti-tumor strategies. Although concerns are frequently raised due to differences in sample processing and depth of coverage, next-generation sequencing of solid tumors have unraveled a highly variable degree of ITH across tumor types. Capturing the genetic relatedness between primary and metastatic lesions through the identification of clonal and subclonal populations is critical to the design of therapies for advance-stage diseases. Here, we report a method for comparative lesions analysis that allows for the identification of clonal and subclonal populations among different specimens from the same patient. The experimental approach described here integrates three well-established approaches: histological analysis, high-coverage multi-lesion sequencing, and immunophenotypic analyses. In order to minimize the effects on the detection of subclonal events by inappropriate sample processing, we subjected tissues to careful pathological examination and neoplastic cell enrichment. Quality controlled DNA from neoplastic lesions and normal tissues was then subjected to high coverage sequencing, targeting the coding regions of 409 relevant cancer genes. While only looking at a limited genomic space, our approach enables evaluating the extent of heterogeneity among somatic alterations (single-nucleotide mutations and copy-number variations) in distinct lesions from a given patient. Through comparative analysis of sequencing data, we were able to distinguish clonal vs. subclonal alterations. The majority of ITH is often ascribed to passenger mutations; therefore, we also used immunohistochemistry to predict functional consequences of mutations. While this protocol has been applied to a specific tumor type, we anticipate that the methodology described here is broadly applicable to other solid tumor types.

This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.

通过有针对性的测序方法进行比较病变分析

Assessing intra-tumoral heterogeneity (ITH) is of paramount importance to anticipate failure of targeted therapies and design accordingly effective ...

行为学

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing &gt;50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Single-cell sequencing reveals genotypic heterogeneity in biological systems, but current technologies lack the throughput necessary for the deep profiling of community composition and function. Here, we describe a microfluidic workflow for sequencing &gt;50,000 single-cell genomes from diverse cell populations.

单细胞基因组测序的超高通量微流控平台

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of ...

生物工程

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Efforts to detect and investigate key oncogenic mutations have proven valuable to facilitate the appropriate treatment for cancer patients. The establishment of high-throughput, massively parallel "next-generation" sequencing has aided the discovery of many such mutations. To enhance the clinical and translational utility of this technology, platforms must be high-throughput, cost-effective, and compatible with formalin-fixed paraffin embedded (FFPE) tissue samples that may yield small amounts of degraded or damaged DNA. Here, we describe the preparation of barcoded and multiplexed DNA libraries followed by hybridization-based capture of targeted exons for the detection of cancer-associated mutations in fresh frozen and FFPE tumors by massively parallel sequencing. This method enables the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus conferring high sensitivity for detecting low frequency mutations.

We describe the preparation of barcoded DNA libraries and subsequent hybridization-based exon capture for detection of key cancer-associated mutations in clinical tumor specimens by massively parallel "next generation" sequencing. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus yielding high sensitivity for detecting low frequency mutations.

由外显子捕获和大规模平行测序检测体细胞遗传学改变在肿瘤标本

Efforts  to detect and investigate key oncogenic mutations have proven valuable to  facilitate the appropriate treatment for cancer patients. The ...

生物学

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a clinical diagnostic service laboratory. DNA extracted from a patient&#8217;s sample (blood, saliva or other tissue types) is labeled with a fluorochrome (either cyanine 5 or cyanine 3). A reference DNA sample is labeled with the opposite fluorochrome. There follows a cleanup step to remove unincorporated nucleotides before the labeled DNAs are mixed and resuspended in a hybridization buffer and applied to an array comprising ~60,000 oligonucleotide probes from loci across the genome, with high probe density in clinically important areas. Following hybridization, the arrays are washed, then scanned and the resulting images are analyzed to measure the red and green fluorescence for each probe. Software is used to assess the quality of each probe measurement, calculate the ratio of red to green fluorescence and detect potential copy number variants.

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its application in a diagnostic service laboratory.

阵列比较基因组杂交（CGH阵列），用于检测基因拷贝数变异的

Array CGH for the detection of genomic copy number variants has replaced G-banded karyotype analysis. This paper describes the technology and its ...

Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction

The mammalian mitochondrial (mt)DNA is a small, circular, double-stranded, intra-mitochondrial DNA molecule, encoding 13 subunits of the electron transport chain. Unlike the diploid nuclear genome, most cells contain many more copies of mtDNA, ranging from less than 100 to over 200,000 copies depending on cell type. MtDNA copy number is increasingly used as a biomarker for a number of age-related degenerative conditions and diseases, and thus, accurate measurement of the mtDNA copy number is becoming a key tool in both research and diagnostic settings. Mutations in the mtDNA, often occurring as single nucleotide polymorphisms (SNPs) or deletions, can either exist in all copies of the mtDNA within the cell (termed homoplasmy) or as a mixture of mutated and WT mtDNA copies (termed heteroplasmy). Heteroplasmic mtDNA mutations are a major cause of clinical mitochondrial pathology, either in rare diseases or in a growing number of common late-onset diseases such as Parkinson's disease. Determining the level of heteroplasmy present in cells is a critical step in the diagnosis of rare mitochondrial diseases and in research aimed at understanding common late-onset disorders where mitochondria may play a role. MtDNA copy number and heteroplasmy have traditionally been measured by quantitative (q)PCR-based assays or deep sequencing. However, the recent introduction of ddPCR technology has provided an alternative method for measuring both parameters. It offers several advantages over existing methods, including the ability to measure absolute mtDNA copy number and sufficient sensitivity to make accurate measurements from single cells even at low copy numbers. Presented here is a detailed protocol describing the measurement of mtDNA copy number in single cells using ddPCR, referred to as droplet generation PCR henceforth, with the option for simultaneous measurement of heteroplasmy in cells with mtDNA deletions. The possibility of expanding this method to measure heteroplasmy in cells with mtDNA SNPs is also discussed.

Here we present a protocol for measuring absolute mitochondrial (mt)DNA copy number and mtDNA deletion heteroplasmy levels in single cells.