This work was supported by the Institut Pasteur, INSERM, Universit&#233; Paris Diderot and by the Minist&#232;re de la Recherche (to S.C.); the Association pour la Recherche sur le Cancer (to S.C. and R.G.); the REVIVE Future Investment Program and the Agence Nationale de Recherche (ANR; grant ''Twothyme'' to A.C.); ANR grant ''Myeloten'' (to R.G.); and the Institut National du Cancer (Role of the immune microenvironment during liver carcinogenesis, to R.G.). We acknowledge the Center for Human Immunology and Cytometry platform at Institut Pasteur for their support.

所有的动物实验是由巴斯德研究所安全委员会根据法国农业部和欧盟的指导方针批准。 1.准备96孔的单细胞分选板<ol><li>通过加入5.0微升特定复古转录缓冲液，1.3微升低乙二胺四乙酸（EDTA）的TE缓冲液（10mM TE溶液，pH为8，和0.1mM EDTA的溶液的制备在1.5ml管扩增前混合; 0.2μM过滤），以及0.2微升Taq DNA聚合酶，每孔（ 图1a）的。 </li><li>在一个96孔板，分发6.5微升预扩增混合物的每孔中（最多48个孔）。该板是96-孔的单细胞分选板。 注意:使用电子移液器推荐此协议。它允许精确和可重复的体积测量并节省了时间。 </li></ol><img alt="图1" src="/files/ftp_upload/54858/54858fig1.jpg" /> <stroNG&gt;图1:96孔板加载过程。在96孔板的加载应非常小心地进行，因为它会影响试验的其余部分。 （a）在96孔的单细胞分选板，该预放大混合物第一分配，随后是0.2×测定混合物。 （ 二 ）每个单格位置的记录应保存在一个电子表格。良好无细胞被称为"无输入"好，并且可以作为对照。两行可以幸免于控制用cDNA稀释（顺序一个在十从10 5个细胞的相当于一个小区稀释）引物效率。 （c）在96孔测定板中，测定装载试剂第一分配，接着加入的引物。不要忘记保持每个引物位置的布局。 <a href="https://ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54858/54858fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> </一&gt; <ol start="3"><li>通过加入1.4微升的每种引物的制备在1.5ml管中的0.2×测定混合物（引物20倍;多达48个不同的探针; 见表1）。调节最终体积至140微升的低EDTA TE缓冲液。 </li><li>分发2.5微升0.2×测定混合物的对48孔中含有扩增前混合物中的96孔的单细胞分选板。 注:在96孔样品板的每个孔中的终体积应为9微升。 </li><li>密封用覆盖膜的板。涡板和自旋下来在280 XG 45秒。在-20℃保存96孔的单细胞分选板或立即使用。 </li></ol> 2.单细胞分离<ol><li>使用CO 2输送系统，由缺氧安乐死的小鼠。固定在夹层板鼠标和纵向打开腹腔用剪刀。 </li><li>从恢复肝脏的国际劳工公约。 <ol><li>此前肝脏隔离，同花顺的LIVER-循环细胞。 <ol><li>带出的门静脉，移动小和大肠到鼠标的左侧;门静脉出现在腹膜腔最大静脉。 </li><li>用10毫升注射器和0.45毫针刺，冲洗通过门静脉用磷酸盐缓冲的盐水中（PBS）的肝脏。为了促进肝冲洗，用剪刀剪断胃动脉。 </li><li>切除胆囊，捏钳胆囊的基部，它从用剪刀肝脏分离。 </li><li>为了分离肝脏，捏肝脏的基带钳，并用剪刀，肝脏从腹膜腔的其余部分分开。 </li><li>用5毫升罗斯韦尔园区纪念研究所培养基（RPMI）2％胎牛血清（FCS）的转移肝脏在波特管中。 </li></ol></li></ol></li><li>分离用杵技工分离肝脏。培养基转移到15毫升管。使总体积至14毫升的RPMI 2％FCS中。离开在冰上将细胞悬浮液15-20分钟到滗肝细胞。 </li><li>使用1毫升吸管回收在一个新的15ml试管上清液（无肝细胞）（12毫升上清液可以预期）。降速，在120×g离心7分钟，在10℃。丢弃离心后上清。 </li><li>悬浮于14ml密度梯度介质的在步骤2.4中得到的粒料（ 如，珀40％）。降速在600×g离心20分钟，在20℃。通过抽吸除去上清液。 </li><li>重悬在1ml的醋酸钾（ACK）沉淀。留在室温下1分钟。 1分钟后，使总体积至14毫升，汉克氏平衡盐渍溶液（HBSS）2％FCS中。降速，在120×g离心7分钟，在10℃。弃去上清液。 </li><li>细胞染色。 <ol><li>重悬300μl的生物素化抗体混合物（见材料表的沉淀;添加的所有生物素化抗体提编）和转移的细胞在1.5毫升的试管。离开它在黑暗中进行20分钟，4℃。 </li><li>使总体积为1毫升HBSS 2％FCS中。降速，在120×g离心7分钟，在10℃。重悬300微升荧光偶联抗体混合物和荧光标记的链霉的粒料（见材料表 ;增加所提到的所有荧光染料耦合抗体）。离开它在黑暗中进行20分钟，4℃。 </li></ol></li><li>使总体积为1毫升HBSS 2％FCS中。降速，在120×g离心7分钟，在10℃。使用1毫升吸管除去上清液。重悬在1毫升HBSS和碘化丙啶颗粒（PI; 1:4000）。 注:当使用广泛表达ILC细胞表面标记，3个种群的定义:NKP46 + IL-7Rα - ，+ NKP46 IL-7Rα+和NKP46 - IL-7Rα+。所有人群进行排序血统- CD3 - CD4 - CD45.2 +。 </li></ol> 3.单细胞荧光激活细胞分选（FACS） <ol><li>使用流式细胞仪对单个细胞13进行排序。 注意:不同的细胞类型可以在相同的96孔的单细胞分选板进行排序（ 图2）。 <ol><li>使用流式细胞仪对每孔含有0.2×测定混合物并在96孔的单细胞分选板的前置放大混音一个小区进行排序。使用新鲜制备的96孔的单细胞分选板或解冻，如果储存在-20℃。 <ol><li>把一个密封96孔板上的FACS板固定器。使用空96孔板进行测试。该板与位置A1孔的左侧和朝向实验者。 </li><li>调整板固定器，以获得在A1孔的中心与验证珠的下降。 </li><li>重复步骤3.1.1.2用线A.所有井</li><li>除去从96孔板的密封件和每孔排序100验证珠。检查ðROP形成在孔的中央。 </li><li>当适当调整，放置在板夹持器96孔的单细胞分选板。绘制板布局和每孔排序1细胞。 注:在96孔的单细胞分选板每个小区的正确定位是必不可少的。 A板布局应保持在一个电子表格软件。板布局将在接下来的几个步骤（ 图1b）14一起使用。留一个孔含有0.2×测定混合物和扩增前混合于96孔样品板没有细胞。此井用作无输入控制。离开6个孔的两行的cDNA稀释。这些孔被用作引物效率（ 图1b）的控制。 </li><li>在-20℃保存96孔的单细胞分选板或立即使用。 </li></ol></li></ol></li></ol> 4.预扩增<ol><li>使用新鲜或解冻的96孔的单细胞分选板OBTained步骤3.1.1.6。涡板和自旋下来（280 XG 45秒）。 </li><li>放置在热循环仪96孔的单细胞分选板。在图3和表2进行反转录和扩增前根据所提到的程序。 </li></ol><img alt="图3" src="/files/ftp_upload/54858/54858fig3.jpg" /> 图3:预扩增程序。为了有足够的材料，需要的对已排序单细胞特异性靶基因预扩增。 96孔的单细胞分选板被装载在热循环跟随前置放大方案。前置放大产物，然后用低EDTA TE缓冲液稀释，并可以立即使用或在-20℃冷冻。 <a href="https://ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54858/54858fig3large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <ol start="3"><li>稀前置放大器通过加入36微升低EDTA TE缓冲液到每个孔lified样品。 </li><li>密封用覆盖膜的板。涡板和自旋下来（280克45秒）。在-20℃下储存的预扩增，96孔的单细胞分选板或立即使用它。 </li></ol> 5.准备96孔样品盘<ol><li>通过添加定量PCR预混175微升和17.5微升样品装载试剂准备191μl反应混合液。 </li><li>上一个新的96孔板，分发3.6微升主混合物的各孔（最多48个孔）。这是在96孔样品板。 </li><li>从96孔样品盘传送2.9微升预扩增的cDNA的新96孔板中。保持相同的位置为96的单细胞分选板和96孔样品板之间的每个样品。 </li><li>密封用覆盖膜的板。涡板和自旋下来（280 XG 45秒）。放置在冰上的新96孔板避光。存放96孔小号在-20℃充足板或立即使用它。 </li></ol> 6.准备96孔测定板<ol><li>上一个新的96孔板，分发3微升测定加载试剂的每个孔中（最多48个孔）。该板被称为96孔测定板。 </li><li>加入3微升引物各孔中。保持在电子表格软件布局（ 图1c和表1;使用表1中列出的所有引物）。在96孔测定板的各引物的正确定位是在接下来的步骤是必不可少的。 <ol><li>如果采样的数目是低于48，加水代替的引物对未使用的孔中。 </li></ol></li><li>密封用覆盖膜的板。涡板和自旋下来（280 XG 45秒）。在-20存储96孔测定板℃或立即使用它。 注意:为了避免引物反复冻融，对于预放大混合分发的引物和用于96 WEL在同一时间L测定板。 </li></ol> 7.单细胞基因表达芯片<ol><li>放置在板凳上集成微流体通路（IFC），并使用帽注射器检查阀门。打开注射器，把它垂直于阀门，然后用力按压。 O形圈应该移动。填写与控制线流体芯片（0.3毫升结核菌素）。 </li><li>重复步骤7.1与第二阀。 注:没有液体应在芯片泻。 </li><li>从芯片的底部卸下蓝膜。加载芯片进入IFC控制器。在IFC控制器屏幕上，选择"素"，然后选择"运行"。微流体线路中的控制持续10分钟。 </li><li>弹出芯片和重新密封在芯片的底部的蓝色膜。 </li></ol><img alt="图4" src="/files/ftp_upload/54858/54858fig4.jpg" /> 图4:单细胞多重基因表达芯片加载。这些步骤需要摹REAT精度，尤其是在96孔板转移到单元多路复用基因表达芯片。为了避免加载错误和错位，强烈建议按顺序工作。对各传输量应在移液过程进行控制。最后，为了避免任何气泡，并在形成的情况下将其移除是很重要的。 <a href="https://ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54858/54858figfourlarge.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <ol start="5"><li>使用8通道移液器，传递从芯片的A1侧（左侧）的96孔测定板5微升。填充在芯片的左侧， 如图4所示。小心总是吸引相同体积入提示。 <ol><li>不要产生气泡。如果出现气泡，用10微升的提示删除它们。更改提示芯片的每口井。 </li></ol></li><li>右侧o重复步骤7.5F使用5微升从96孔样品板的芯片。 </li><li>从芯片的底部卸下蓝膜。加载芯片进入IFC控制器。在IFC控制器屏幕上，选择"运行脚本"，然后选择"运行"。微流体线路的负载持续45分钟。 </li><li>弹出芯片和重新密封在芯片的底部的蓝色膜。 </li></ol> 8.运行芯片<ol><li>在微流控的qPCR电脑，选择"数据收集"软件。一旦开始，选择"新建运行"。 </li><li>选择"弹出"，从芯片的底部取出蓝膜，并加载芯片。放置芯片，以获得A 1以及在左侧和朝向实验者。 </li><li>在"项目设置"，选择"无"，然后"下一步"。 </li><li>选择"新的芯片运行"，"新芯片目录"（创造实验的文件夹），和"下一步"。 </li><li>在"基因表达"中选择";被动引用= ROX"（羧基-X-罗丹明）和"单探头;"根据引物选择正确的过滤器，选择"下一步"。 </li><li>选择"浏览"，并选择根据所用的引物的正确的程序。 注:"默认-10分钟-Hotstart.pcl"为单细胞多重RT-qPCR的最常用的方案。 </li><li>选择"开始→运行"。反应需要大约90分钟。 </li><li>一旦完成后，选择"弹出完成任务。" </li></ol> 9.数据分析<ol><li>打开"实时荧光定量PCR分析"软件，选择"文件"和"打开"，找到实验文件夹，选择"ChipRun.bml文件"。 </li><li>点击"分析视图"，"成绩表"和"热图查看;"标有"X"的框是低于阈值检测水平和/或具有不良的扩增曲线。 </li><li>命名样本。进入"样本设置"和SEL等"新SBS 96."单击"映射"，然后选择"..."复制并粘贴从电子表格软件样本版式设计。定义粘贴布局为"样品名称"。 </li><li>命名检测。重复步骤9.3与底漆的名字"探测器安装"。定义粘贴布局"探测器名称。" </li><li>点击"视图分析"和"分析;"样品和引物名称将被自动归因于样品和引物（ 图7）。 </li><li>计算CT，三角洲CT和折叠每个样品变化。使用管家基因作为内部对照（这里，GAPDH）: ΔCT= 样品的Ct -的Ct 内部控制 倍数变化= 2 - （ΔCtsample-ΔCtnegative控制） <ol><li>点击"检测设定"，并选择与无输入控制阱（使用管家基因作为内部对照正常化的基因表达）。选择"Editor"，"定义参考"和"更新"。 </li><li>选择到的上述定义的内部控制将被应用于所有孔中。选择"编辑"和"定义为测试"。选择合适的参照基因，然后单击"更新"。 </li><li>选择"样本设置"，选择阴性对照好。选择"编辑"，并定义"参考"。点击"更新"。 </li><li>选择"分析视图"和"分析;"三角洲CT和倍数变化会自动进行计算。 </li></ol></li><li>导出数据。选择"文件"和"导出，另存为"表的结果，"选择目标文件夹，并点击"保存"和"退出"。 </li><li>重复步骤9.7，但不是"表的结果，"另存为"热图结果"。 </li></ol>

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	<li>Sun, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Bead-Based+Microfluidic+Approach+to+Integrated+Single-Cell+Gene+Expression+Analysis+by+Quantitative+RT-PCR.">A Bead-Based Microfluidic Approach to Integrated Single-Cell Gene Expression Analysis by Quantitative RT-PCR.</a> RSC Adv. 5, 4886-4893 (2015).</li><li>Kellogg, R. A., Berg, R. G., Leyrat, A. A., Tay, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+microfluidic+single-cell+analysis+pipeline+for+studies+of+signaling+dynamics.">High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics.</a> Nat. Protoc. 9 (7), 1713-1726 (2014).</li><li>Moignard, V., Macaulay, I. C., Swiers, G., Buettner, F., Sch&#252;tte, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterisation+of+transcriptional+networks+in+blood+stem+and+progenitor+cells+using+high-throughput+single+cell+gene+expression+analysis.">Characterisation of transcriptional networks in blood stem and progenitor cells using high-throughput single cell gene expression analysis.</a> Nat. Cell Biol. 15 (4), 363-372 (2013).</li><li>Chea, S., Schmutz, S., et al. <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+Gene+Expression+Analyses+Reveal+Heterogeneous+Responsiveness+of+Fetal+Innate+Lymphoid+Progenitors+to+Notch+Signaling.">Single-Cell Gene Expression Analyses Reveal Heterogeneous Responsiveness of Fetal Innate Lymphoid Progenitors to Notch Signaling.</a> Cell Rep. 14 (6), 1500-1516 (2016).</li><li>Ishizuka, I. E., Chea, S., et al. <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+analysis+defines+the+divergence+between+the+innate+lymphoid+cell+lineage+and+lymphoid+tissue+-+inducer+cell+lineage.">Single-cell analysis defines the divergence between the innate lymphoid cell lineage and lymphoid tissue - inducer cell lineage.</a> Nat. Immunol. 17, 1-9 (2016).</li><li>Spits, H., Artis, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innate+lymphoid+cells+-+a+proposal+for+uniform+nomenclature.">Innate lymphoid cells - a proposal for uniform nomenclature.</a> Nat.Rev. Immunol. 13 (2), 145-149 (2013).</li><li>Walker, J. A., Barlow, J. L., McKenzie, A. N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innate+lymphoid+cells-+how+did+we+miss+them.">Innate lymphoid cells- how did we miss them.</a> Nat. Rev. Immunol. 13 (2), 75-87 (2013).</li><li>Vallentin, B., Barlogis, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innate+Lymphoid+Cells+in+Cancer.">Innate Lymphoid Cells in Cancer.</a> Cancer Immunol. Res. 3 (10), 1109-1114 (2015).</li><li>Monticelli, L. a., Artis, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innate+lymphoid+cells+promote+lung+tissue+homeostasis+following+acute+influenza+virus+infection.">Innate lymphoid cells promote lung tissue homeostasis following acute influenza virus infection.</a> Nat. Immunol. 12 (11), 1045-1054 (2012).</li><li>Tang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+phenotypic+and+functional+properties+of+liver-resident+NK+cells+and+mucosal+ILC1s.">Differential phenotypic and functional properties of liver-resident NK cells and mucosal ILC1s.</a> J. Autoimmun. 67, 29-35 (2015).</li><li>Durum, K., Gilfillan, S., Colonna, M., Consortium, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptional+Programs+Define+Molecular+Characteristics+of+Innate+Lymphoid+Cell+Classes+and+Subsets.">Transcriptional Programs Define Molecular Characteristics of Innate Lymphoid Cell Classes and Subsets.</a> Nat. Immunol. 16 (3), 306-317 (2015).</li><li>Wang, H., Ramakrishnan, A., Fletcher, S., Prochownik, E. V., Genetics, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+dynamics+of+native+haematopoiesis.">Clonal dynamics of native haematopoiesis.</a> Nature. 2 (2), 322-327 (2015).</li><li>Samsa, L. A., Fleming, N., Magness, S., Qian, L., Liu, J. <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+Characterization+of+Single+Cells+from+Zebrafish+Embryos.">Isolation and Characterization of Single Cells from Zebrafish Embryos.</a> J. Vis. Exp. (109), e1-e10 (2016).</li><li>Klose, C., Flach, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differentiation+of+Type+1+ILCs+from+a+Common+Progenitor+to+All+Helper-like+Innate+Lymhpoid+Cell+Lineages.">Differentiation of Type 1 ILCs from a Common Progenitor to All Helper-like Innate Lymhpoid Cell Lineages.</a> Cell. 157, 340-356 (2014).</li><li>Dicle, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+Approaches+to+Single-Cell+Analysis+in+Developmental+Biology.">Bioinformatics Approaches to Single-Cell Analysis in Developmental Biology.</a> MHR. 22, 182-192 (2016).</li><li>Setty, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wishbone+identifies+bifurcating+developmental+trajectories+from+single-cell+data.">Wishbone identifies bifurcating developmental trajectories from single-cell data.</a> Nat Biotechnol. 34, 637-645 (2016).</li></ol>

The authors have nothing to disclose.

本协议描述了如何获取在克隆层面详尽的基因表达信息。在这里，我们调查了肝ILC车厢异质性。不同ILC种群（基于广​​泛表达ILC表面标记）的单细胞分选后，将样品预扩增特定预先选择的基因。然后，将得到的cDNA和引物装载到多重RT-qPCR的微流体芯片。最后，我们从48个单细胞获得的48个不同基因的表达。基因表达结果通过在线软件研究基因标记和细胞群的关系进行分析。 ?...

在过去的几年中，先天淋巴样细胞（劳工公约）已经被越来越多的研究。尽管他们缺乏抗原特异性受体，它们都属于淋巴谱系和代表组织稳态和炎症的重要哨兵。目前国际劳工公约分为基于他们的特异性转录因子组合，表达对他们产生细胞因子6,7能力三组。 国际劳工公约向众多稳态和病理情况在通过特定的细胞因子产生8,9多样器官。为了能够理解这些细胞的作用，重要的是要确定每个器官的各种ILC亚群，并确定其发展的关系。此外，不同的子集之间的可塑性现象已涉及。通过研究异质性存在于一个器官的细胞，有可能来分隔它们的成熟的阶段，并区分其特定的功能。 为了说明单细胞多重RT-qPCR的的技术，肝国际劳工公约被选择，特别强调他们的异质同ILC组（1型ILC）10中。首先，通过使用流式细胞仪的，三种不同的ILC种群特征肝脏。组1 ILC代表约80％的固有效应，而其他两个群体是罕见的肝ILC种群（先天效应小于5％）。这些人群采用ILC人群的广泛表达的细胞表面标记来分类的。其结果，排序在肝脏ILC种群看大致类似一个到另一个。 单细胞多重RT-qPCR的已经成为最好的技术之一迅速调查这些人群11的异质性</s了&gt;。两个主要特点是通过取单细胞多重RT-qPCR的技术的优点来确定。首先，通过观察克隆的水平，就有可能恢复细胞特异性基因表达，显然显示类似的发育阶段的细胞之间的比较。然后，通过观察基因表达的一个预先选择的组合中，我们将根据在一个时间点同时基因表达模式确定新的基因标记。这些方面允许的各种各样的表达数据收集的大量的细胞，甚至在稀有人群中，由于该技术是在克隆水平执行。从而，在肝脏ILC异质可以充分评估。 接着，通过排序具有全球ILC表型的所有细胞中，获得了肝ILC种群的多基因表达的广泛概述，即使它们代表极为罕见的人群。基于微流控芯片允许实验与连少量的细胞材料。作为结果，可以得到稀有细胞群的基因表达图谱。采用在线基因签名分析软件，细胞群簇和潜在细胞关系可以进行调查。因此，功能性测试可以被执行以验证在体内水平聚类数据。 基因表达的几十可能伴随在同一芯片3，11，12在数百或更多的单细胞进行评估。该测定的设计是在实验的最长的和最重要的部分。相关的假设要测试的基因的确定是非常重要的，以获得相关的结果。其次，需要内部控制（例如用于排序已知的表面标记）和具体的控制。这是至关重要的，以测试引物扩增特异性，扩增的效率，和叔他没有底漆的竞争。因此，与单细胞多重RT-qPCR的工作是一个省时的技术中，作为一个单元的多基因表达的同时进行评估。 使用试剂的相同的芯片和混合所有单元限制操作的可能的错误，并允许样品之间的再现性。总之，单细胞多重RT-qPCR的不同方面允许在克隆水平生产高度可靠的结果，具有灵敏度的为各种各样的样品和基因的大的水平。所得结果提供生物统计学测试功能强大且可靠的数据。 这可以是由于该方法，其允许在非常少量的物质的工作，并导致详尽结果的微流体方面来实现。最后，使用在线软件，它可以比较所需的人口。

<table><tbody><tr><td>Cells Direct One Step qRT-PCR kit</td><td>Applied Biosystems&amp;nbsp;</td><td>11753100</td><td>Primer probe detection kit.Contains 2x reaction mix, SSIII Platinium enzyme.</td></tr><tr><td>Low TE EDTA Buffer</td><td>Affymetrix</td><td>75793 100ML</td><td></td></tr><tr><td>96-well plates</td><td>Thermofisher Scientific</td><td>AB 1100</td><td>96-well plates adapted for cell sorting and thermocycling</td></tr><tr><td>Cover film</td><td>Dominique Dutscher</td><td>106570</td><td>aluminium cover film; avoid contamination and evaporation</td></tr><tr><td>Actb</td><td>Thermofisher Scientific</td><td>Mm00607939_s1</td><td>20x primer</td></tr><tr><td>Aes</td><td>Thermofisher Scientific</td><td>Mm01148854_s1</td><td>20x primer</td></tr><tr><td>Ahr</td><td>Thermofisher Scientific</td><td>Mm00478932_s1</td><td>20x primer</td></tr><tr><td>Bcl2</td><td>Thermofisher Scientific</td><td>Mm00477631_s1</td><td>20x primer</td></tr><tr><td>c-myc</td><td>Thermofisher Scientific</td><td>Mm00487804_s1</td><td>20x primer</td></tr><tr><td>Cbfb</td><td>Thermofisher Scientific</td><td>Mm01251026_s1</td><td>20x primer</td></tr><tr><td>Cd27</td><td>Thermofisher Scientific</td><td>Mm01185212_s1</td><td>20x primer</td></tr><tr><td>Cd49a</td><td>Thermofisher Scientific</td><td>Mm01306375_s1</td><td>20x primer</td></tr><tr><td>CD49b</td><td>Thermofisher Scientific</td><td>Mm00434371_s1</td><td>20x primer</td></tr><tr><td>Cxcr5</td><td>Thermofisher Scientific</td><td>Mm00432086_s1</td><td>20x primer</td></tr><tr><td>Cxcr6</td><td>Thermofisher Scientific</td><td>Mm02620517_s1</td><td>20x primer</td></tr><tr><td>Eomes</td><td>Thermofisher Scientific</td><td>Mm01351985_s1</td><td>20x primer</td></tr><tr><td>Ets1</td><td>Thermofisher Scientific</td><td>Mm01175819_s1</td><td>20x primer</td></tr><tr><td>Foxo1</td><td>Thermofisher Scientific</td><td>Mm00490672_s1</td><td>20x primer</td></tr><tr><td>Gapdh</td><td>Thermofisher Scientific</td><td>Mm03302249_s1</td><td>20x primer</td></tr><tr><td>Gata3</td><td>Thermofisher Scientific</td><td>Mm00484683_s1</td><td>20x primer</td></tr><tr><td>Gm-csf</td><td>Thermofisher Scientific</td><td>Mm01136644_s1</td><td>20x primer</td></tr><tr><td>Hes1</td><td>Thermofisher Scientific</td><td>Mm01342805_s1</td><td>20x primer</td></tr><tr><td>Hprt</td><td>Thermofisher Scientific</td><td>Mm00446968_s1</td><td>20x primer</td></tr><tr><td>Id2</td><td>Thermofisher Scientific</td><td>Mm01293217_s1</td><td>20x primer</td></tr><tr><td>Il-12rb2</td><td>Thermofisher Scientific</td><td>Mm00711781_s1</td><td>20x primer</td></tr><tr><td>Il-18r1</td><td>Thermofisher Scientific</td><td>Mm00515178_s1</td><td>20x primer</td></tr><tr><td>Il-1rl1</td><td>Thermofisher Scientific</td><td>Mm00434237_s1</td><td>20x primer</td></tr><tr><td>Il-22</td><td>Thermofisher Scientific</td><td>Mm001226722_s1</td><td>20x primer</td></tr><tr><td>Il-23r</td><td>Thermofisher Scientific</td><td>Mm00519943_s1</td><td>20x primer</td></tr><tr><td>Il-2ra</td><td>Thermofisher Scientific</td><td>Mm01340213_s1</td><td>20x primer</td></tr><tr><td>Il-2rb</td><td>Thermofisher Scientific</td><td>Mm01195267_s1</td><td>20x primer</td></tr><tr><td>IL-7r</td><td>Thermofisher Scientific</td><td>Mm00434295_s1</td><td>20x primer</td></tr><tr><td>Klr5</td><td>Thermofisher Scientific</td><td>Mm04207528_s1</td><td>20x primer</td></tr><tr><td>Lef1</td><td>Thermofisher Scientific</td><td>Mm00550265_s1</td><td>20x primer</td></tr><tr><td>Ncr1</td><td>Thermofisher Scientific</td><td>Mm01337324_s1</td><td>20x primer</td></tr><tr><td>Nfil3</td><td>Thermofisher Scientific</td><td>Mm01339838_s1</td><td>20x primer</td></tr><tr><td>Notch1</td><td>Thermofisher Scientific</td><td>Mm00435249_s1</td><td>20x primer</td></tr><tr><td>Notch2</td><td>Thermofisher Scientific</td><td>Mm00803069_s1</td><td>20x primer</td></tr><tr><td>Rora</td><td>Thermofisher Scientific</td><td>Mm01173766_s1</td><td>20x primer</td></tr><tr><td>Rorc</td><td>Thermofisher Scientific</td><td>Mm01261022_s1</td><td>20x primer</td></tr><tr><td>Runx3</td><td>Thermofisher Scientific</td><td>Mm00490666_s1</td><td>20x primer</td></tr><tr><td>Tbx21</td><td>Thermofisher Scientific</td><td>Mm01299453_s1</td><td>20x primer</td></tr><tr><td>Tcf3</td><td>Thermofisher Scientific</td><td>Mm01175588_s1</td><td>20x primer</td></tr><tr><td>Tcf7</td><td>Thermofisher Scientific</td><td>Mm00493445_s1</td><td>20x primer</td></tr><tr><td>Tle1</td><td>Thermofisher Scientific</td><td>Mm00495643_s1</td><td>20x primer</td></tr><tr><td>Tle3</td><td>Thermofisher Scientific</td><td>Mm00437097_s1</td><td>20x primer</td></tr><tr><td>Tsc22d3</td><td>Thermofisher Scientific</td><td>Mm01306210_s1</td><td>20x primer</td></tr><tr><td>Tnfrsf11a</td><td>Thermofisher Scientific</td><td>Mm00437132_s1</td><td>20x primer</td></tr><tr><td>Tox</td><td>Thermofisher Scientific</td><td>Mm00455231_s1</td><td>20x primer</td></tr><tr><td>Zbtb16</td><td>Thermofisher Scientific</td><td>Mm01176868_s1</td><td>20x primer</td></tr><tr><td>Zbtb7b</td><td>Thermofisher Scientific</td><td>Mm00784709_s1</td><td>20x primer</td></tr><tr><td>qPCR Master mix&amp;nbsp;</td><td>Applied BioSystems</td><td>P/N 4304437</td><td></td></tr><tr><td>2x Assay Loading Reagent</td><td>Fluidigm</td><td>P/N 85000736</td><td>Specific density medium to load assays in multiplex RT-qPCR microfluidic chip.&amp;nbsp;</td></tr><tr><td>2x Sample Loading Reagent</td><td>Fluidigm</td><td>P/N 85000735</td><td>Specific density medium to load samples in multiplex RT-qPCR microfluidic chip.&amp;nbsp;</td></tr><tr><td>48.48 mutliplex RT qPCR microfluidic chip</td><td>Fluidigm</td><td>BMK-M-48.48</td><td>48.48 Dynamic Array IFC for Gene Expression;chip for single cell multiplex RT-qPCR reaction</td></tr><tr><td>48.48 mutliplex RT qPCR microfluidic chip controller</td><td>Fluidigm</td><td>89000020</td><td>48.48 IFC Controller; control the chip internal fluidic system, load samples and assays in reaction chambers</td></tr><tr><td>mutliplex RT qPCR microfluidic thermocycler</td><td>Fluidigm</td><td>GE48.48</td><td>48.48 Dynamic Array IFC thermocycler</td></tr><tr><td>96-well plates</td><td>Thermofisher Scientific</td><td>AB 1100</td><td>96-well plates adapted for cell sorting and thermocycling</td></tr><tr><td>C57Bl/6 mice</td><td>Janvier</td><td>C57Bl/6 miceJ@RJ</td><td></td></tr><tr><td>10 ml syringe</td><td>BD Biosciences</td><td>309639</td><td></td></tr><tr><td>PBS</td><td>Life Technologies</td><td>14040174</td><td></td></tr><tr><td>HBSS</td><td>Life Technologies</td><td>24020133</td><td></td></tr><tr><td>RPMI</td><td>Life Technologies</td><td>61870044</td><td></td></tr><tr><td>FCS</td><td>CVFSVF000U</td><td>Eurobio Abcys</td><td>Standard fetal calf serum</td></tr><tr><td>Potter tube</td><td>N/A</td><td></td><td></td></tr><tr><td>15 ml tube</td><td>Corning</td><td>352097</td><td></td></tr><tr><td>1.5 ml tube</td><td>Sigma-Aldrich</td><td>T9661-1000EA</td><td></td></tr><tr><td>FACS machine</td><td>N/A</td><td></td><td></td></tr><tr><td>centrifuge&amp;nbsp;</td><td>Thermofisher Scientific</td><td>75004538</td><td></td></tr><tr><td>1,000 &amp;micro;l tips</td><td>Fisher Scientific</td><td>10313272</td><td></td></tr><tr><td>P1000&amp;nbsp;</td><td>Gilson</td><td>F123602</td><td></td></tr><tr><td>Percoll</td><td>Dominique Dutscher</td><td>17-0891-01</td><td></td></tr><tr><td>Facs tube</td><td>Falcon</td><td>352235</td><td></td></tr><tr><td>anti-CD8 Biotin mouse antibody</td><td>Sony</td><td>1103520</td><td>lineage antibody</td></tr><tr><td>anti-CD19 Biotin mouse antibody</td><td>Sony</td><td>1177520</td><td>lineage antibody</td></tr><tr><td>anti-TCRab Biotin mouse antibody</td><td>BioLegend</td><td>109204</td><td>lineage antibody</td></tr><tr><td>anti-TCRgd Biotin mouse antibody</td><td>BD Biosciences</td><td>553176</td><td>lineage antibody</td></tr><tr><td>anti-Ter119 Biotin mouse antibody</td><td>BD Biosciences</td><td>553672</td><td>lineage antibody</td></tr><tr><td>anti-Gr1 Biotin mouse antibody</td><td>BD Biosciences</td><td>553125</td><td>lineage antibody</td></tr><tr><td>anti-CD45.2 PerCPCy5.5 mouse antibody</td><td>BioLegend</td><td>109828</td><td></td></tr><tr><td>anti-IL7ra PeCy7 mouse antibody</td><td>ebioSciences</td><td>25-1271-82</td><td></td></tr><tr><td>anti-CD3 BV510 mouse antibody</td><td>BD Biosciences</td><td>563024</td><td></td></tr><tr><td>anti-CD4 BV786 mouse antibody</td><td>BD Biosciences</td><td>563727</td><td></td></tr><tr><td>anti-NKp46 PE mouse antibody</td><td>ebioSciences</td><td>12-3351-82</td><td></td></tr><tr><td>Streptavidin</td><td>Sony</td><td>2626025</td><td></td></tr><tr><td>Propidium Iodide</td><td>Sigma-Aldrich</td><td>P4864-10ML</td><td></td></tr><tr><td>Electronic pipette</td><td>Eppendorf</td><td>4986000017</td><td></td></tr><tr><td>Combitips 0.1 ml</td><td>Eppendorf</td><td>30089405</td><td></td></tr><tr><td>Multichannel pipette</td><td>Rainin</td><td>L8-10XLS+</td><td></td></tr><tr><td>Accudrop</td><td>BD Biosciences</td><td>345249</td><td>verification beads for FACS</td></tr></tbody></table>

single-cell gene expression using multiplex rt-qpcr to characterize heterogeneity of rare lymphoid populations

基因表达的异质性是一个有趣的功能在淋巴人群进行调查。在这些细胞中的基因表达细胞活化，应力，或刺激过程中变化。单细胞多路复用基因表达使几十基因1，2，3的的同时评估。在单细胞水平，多重基因表达的确定群体异质4,5。它允许人口异质性通过确定双方的成熟细胞中不同的前体阶段的可能搭配，也是细胞对刺激的多样性的区别。 先天淋巴样细胞（ILC）最近已描述为免疫应答6,7的固有效应的群体。在这个协议中，国际劳工大会，他的细胞的异质性稳态期间patic隔室进行了研究。 目前，最广泛使用的技术来评估基因表达，RT-qPCR的。这种方法的措施的基因表达在一个时间只有一个基因。此外，这种方法不能估计的基因表达的异质性，因为需要一个测试多个小区。这导致了人口的平均基因表达的测量。当评估大量基因，RT-qPCR的变成了时间，试剂 - 和样品消耗的方法。因此，折衷限制了可以被评估的基因或细胞群体的数量，增加丢失全局图像的风险。 该原稿描述单细胞多重RT-qPCR的如何可以被用来克服这些限制。这项技术已经从最近的微流控技术的进步1,2受益。在多重RT-qPCR的芯片发生的反应不EXCEED纳升一级。因此，单细胞基因表达，以及同时多个基因的表达，可以以试剂 - 进行的，样品 - 和成本效益的方式。有可能在不同的发育阶段，或在不同条件4下，5群内的细胞亚群之间的克隆水平检验细胞基因标记的异质性。在罕见的人群拥有大量的在单细胞水平的条件下工作不再是一种限制。

淋巴人群显示基因表达差异很大。在这个协议中，肝ILC室异质使用单细胞多重RT-qPCR的基因表达的影响。不像其他的基因表达技术，单细胞多重RT-qPCR的基因表达的允许工作在几个群体，甚至最稀有的，在同一时间。这种特异性，加上在克隆水平高灵敏度，允许对群体内的基因标记的差异调查和罕见群体之间。作为一个例子，从4周龄小鼠的肝脏中3 ILC种群的基因标记进行了?...

Watch this Scientific Journal Video about Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations at JoVE.com

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

这个协议描述如何评估在克隆水平大阵列的基因的表达。单细胞RT-qPCR的产生与数百个样品和基因的敏感性较强高度可靠的结果。

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 ...

single-cell-gene-expression-using-multiplex-rt-qpcr-to-characterize

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10.9K 次观看.  Institut Pasteur. 这个协议描述如何评估在克隆水平大阵列的基因的表达。单细胞RT-qPCR的产生与数百个样品和基因的敏感性较强高度可靠的结果。 

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

High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the research and pharmaceutical communities. To study miRNAs, it is essential that the quantification of microRNA levels is accurate and robust. By comparing wild-type to small RNA deficient mouse embryonic stem cells (mESC), we revealed a lack of accuracy and robustness in previous published multiplex qRT-PCR techniques. Here, we describe an optimized method, including purifying away excessive primers from previous multiplex steps before singleplex real time detection, which dramatically increases the accuracy and robustness of the technique. Furthermore, we explain how performing the technique on a microfluidic chip at nanoliter volumes significantly reduces reagent costs and permits time effective high throughput miRNA expression profiling.

Here we describe an optimized multiplex reverse transcriptase quantitative PCR (qRT-PCR) protocol in combination with a microfluidic platform as a cost and time effective high-throughput screening tool for microRNA (miRNA) expression levels, especially when working with limited amounts of sample.

高通量微RNA分析：优化复纳升秤定量RT - PCR Fluidig​​m公司动态ArrayTM国际金融中心

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the ...

科研

生物工程

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 ...

遗传学

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

一种组合单细胞法表征人茎和祖种群的分子和免疫表型异质性

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular phenotype diversity. Single-cell experiments investigating the network dynamics of biological systems demonstrate cellular and tissue responses to various conditions at biologically meaningful resolution. Herein, we explain our methods for gathering single cells from anatomically specific locations and accurately measuring a subset of their gene expression profiles. We combine laser capture microdissection (LCM) with microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). We also use this microfluidic RT-qPCR platform to measure the microbial abundance of gut contents.

This protocol explains how to collect single neurons, microglia, and astrocytes from the central nucleus of the amygdala with high accuracy and anatomic specificity using laser capture microdissection. Additionally, we explain our use of microfluidic RT-qPCR to measure a subset of the transcriptome of these cells.

将激光捕获微切片和微流体qPCR相结合，分析单细胞的转录曲线:阿片类药物依赖的系统生物学方法

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular ...

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental biology. It has been successfully used to identify rare cell populations, discover novel marker genes, and decode spatial and temporal developmental information. The single cell method has also evolved from the microfluidic based Fluidigm C1 technology to the droplet-based solutions in the last two to three years. Here we used the heart as an example to demonstrate how to profile the mouse embryonic tissue cells using the droplet based scRNA-Seq method. In addition, we have integrated two strategies into the workflow to profile multiple samples in a single experiment. Using one of the integrated methods, we have simultaneously profiled more than 9,000 cells from eight heart samples. These methods will be valuable to the developmental biology field by providing a cost-effective way to simultaneously profile single cells from different genetic backgrounds, developmental stages, or anatomical locations.

Here we presented a multiplexed single cell mRNA sequencing method to profile gene expression in mouse embryonic tissues. The droplet-based single cell mRNA sequencing (scRNA-Seq) method in combination with multiplexing strategies can profile single cells from multiple samples simultaneously, which significantly reduces reagent costs and minimizes experimental batch effects.

小鼠胚胎细胞的多路复用单细胞mRNA测序分析

Single cell mRNA sequencing has made significant progress in the last several years and has become an important tool in the field of developmental ...

发育生物学

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.

Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.

成年小鼠心肌细胞的单细胞转录谱

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation ...

生物学

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers easy identification and characterization of these cell types, prerequisites to study their specific functions. This article describes the multiplex single cell reverse transcription polymerase chain reaction (RT-PCR) protocol, which allows, after patch-clamp recording in slices, to detect simultaneously the expression of tens of genes in a single cell. This simple method can be implemented with morphological characterization and is widely applicable to determine the phenotypic traits of various cell types and their particular cellular environment, such as in the vicinity of blood vessels. The principle of this protocol is to record a cell with the patch-clamp technique, to harvest and reverse transcribe its cytoplasmic content, and to detect qualitatively the expression of a predefined set of genes by multiplex PCR. It requires a careful design of PCR primers and intracellular patch-clamp solution compatible with RT-PCR. To ensure a selective and reliable transcript detection, this technique also requires appropriate controls from cytoplasm harvesting to amplification steps. Although precautions discussed here must be strictly followed, virtually any electrophysiological laboratory can use the multiplex single cell RT-PCR technique.

This protocol describes the critical steps and precautions required to perform single cell multiplex reverse transcription polymerase chain reaction after patch-clamp. This technique is a simple and effective method to analyze the expression profile of a predetermined set of genes from a single cell characterized by patch-clamp recordings.

膜片钳后单细胞多重反转录聚合酶链反应

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers ...

神经科学

Determination of Immune Cell Identity and Purity Using Epigenetic-Based Quantitative PCR

Immune cell subtype population frequencies can have a large effect on the efficacy of T cell therapies. Current methods, like flow cytometry, have specific sample requirements, high sample input, are low throughput, and are difficult to standardize, all of which are detrimental to characterization of cell therapy products during their development and manufacturing.
The assays described herein accurately identify and quantify immune cell types in a heterogeneous mixture of cells using isolated genomic DNA (gDNA). DNA methylation patterns are revealed through bisulfite conversion, a process in which unmethylated cytosines are converted to uracils. Unmethylated DNA regions are detected through qPCR amplification using primers targeting converted areas. One unique locus per assay is measured and serves as an accurate identifier for a specific cell type. The assays are robust and identify CD8+, regulatory, and Th17 T cells in a high throughput manner. These optimized assays can potentially be used for in-process and product release testing for cell therapy process.

Here we describe a robust method of determining immune cell identity and purity through epigenetic signatures detected using quantitative PCR (qPCR). DNA demethylation at a specific locus serves as a unique identifier for a particular cell type and allows for identification of CD8+, regulatory, or Th17 T cells.

利用基于表观遗传学的定量PCR确定免疫细胞特性和纯度

Immune cell subtype population frequencies can have a large effect on the efficacy of T cell therapies. Current methods, like flow cytometry, have ...

免疫与感染

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different lineages. A single cell transcriptome assay can be a new approach for dissecting individual variation. We have developed the single cell qRT-PCR method, and confirmed that this method works well in several gene expression profiles. In single cell level, each human embryonic stem cell, sorted by OCT4::EGFP positive cells, has high expression in OCT4, but a different level of NANOG expression. Our single cell gene expression assay should be useful to interrogate population heterogeneities.

Single cell gene expression assay is needed for understanding stem cell heterogeneities.

分析单个人类胚胎干细胞通过定量RT-PCR

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different ...

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during development and in disease states. Single-cell RNA sequencing allows us to comprehensively profile gene expression changes observed in retinal development and disease at a cellular level, while single-cell ATAC-Seq allows analysis of chromatin accessibility and transcription factor binding to be profiled at similar resolution. Here the use of these techniques in the developing retina is described, and MULTI-Seq is demonstrated, where individual samples are labeled with a modified oligonucleotide-lipid complex, enabling researchers to both increase the scope of individual experiments and substantially reduce costs.

Here, the authors showcase the utility of MULTI-seq for phenotyping and subsequent paired scRNA-seq and scATAC-seq in characterizing the transcriptomic and chromatin accessibility profiles in retina.

使用 scRNA-Seq 和 scATAC-Seq 对视网膜基因表达和染色质可及性进行多重分析

Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during ...

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

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此视频中的章节

高通量微RNA分析：优化复纳升秤定量RT - PCR Fluidigm公司动态ArrayTM国际金融中心

单细胞基因表达谱使用流式细胞仪和qPCR内部标准

一种组合单细胞法表征人茎和祖种群的分子和免疫表型异质性

将激光捕获微切片和微流体qPCR相结合，分析单细胞的转录曲线:阿片类药物依赖的系统生物学方法

小鼠胚胎细胞的多路复用单细胞mRNA测序分析

成年小鼠心肌细胞的单细胞转录谱

膜片钳后单细胞多重反转录聚合酶链反应

利用基于表观遗传学的定量PCR确定免疫细胞特性和纯度

分析单个人类胚胎干细胞通过定量RT-PCR

使用 scRNA-Seq 和 scATAC-Seq 对视网膜基因表达和染色质可及性进行多重分析

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

摘要

探索更多视频

此视频中的章节

高通量微RNA分析：优化复纳升秤定量RT - PCR Fluidig​​m公司动态ArrayTM国际金融中心

单细胞基因表达谱使用流式细胞仪和qPCR内部标准

一种组合单细胞法表征人茎和祖种群的分子和免疫表型异质性

将激光捕获微切片和微流体qPCR相结合，分析单细胞的转录曲线:阿片类药物依赖的系统生物学方法

小鼠胚胎细胞的多路复用单细胞mRNA测序分析

成年小鼠心肌细胞的单细胞转录谱

膜片钳后单细胞多重反转录聚合酶链反应

利用基于表观遗传学的定量PCR确定免疫细胞特性和纯度

分析单个人类胚胎干细胞通过定量RT-PCR

使用 scRNA-Seq 和 scATAC-Seq 对视网膜基因表达和染色质可及性进行多重分析

高通量微RNA分析：优化复纳升秤定量RT - PCR Fluidigm公司动态ArrayTM国际金融中心