这项工作得到了补助CRUK 15043和CRUK 14251的支持。 我们感谢丹尼尔Wetterskog和嘉祺何为技术援助和手稿的批判性阅读。

1.实验扰动和细胞标记为响应标记（反向转染的siRNA屏幕） <ol><li>在无菌组织培养罩吸管70微升200nM的siRNA对1X siRNA的缓冲器在无菌，纯96孔板的孔中。稀释转染脂质成40体积的无血清DMEM培养基中，并分配105微升到每个孔中含有的siRNA。 注:稀释262.5微升脂质成10.5毫升无血清DMEM中产生一个主混合物适于整个96孔板的siRNA，提供2.6微升脂质的每孔。使用200nM的siRNA的起始浓度在该步骤将提供20纳米的工作浓度在步骤1.3中，但程序将工作工作浓度向下至5nm，与起始浓度相应的调整（ 即 50纳米）。低工作浓度可能会降低的脱靶假阳性分数，尽管它们可以减轻目标响应的幅度，从而导致增加-圆盾的吨假阴性率。 </li><li>混合板通过温和振动，在室温下十分钟。子划分所得175微升成三个50微升每个目标复制到治疗，96孔板用透明基底的不透明，组织培养。 </li><li>反向转染通过在直接含有10％血清到50微升脂质的siRNA复合物150微升DMEM中分配，每孔8000细胞。使用HCT116人类结肠细胞稳定表达GFP标记标记报告CDK2活性5,8。没有进一步的混合是必要的。用无菌，粘合剂透气膜密封该板，以控制湿度和防止板"边缘效应"，并将板放入培养箱，在37℃，5％CO 2的48小时。 </li><li>吸出介质，使得媒体小的残留量保持在孔中。通过加入100微升4％缓冲的甲醛至每个孔中固定细胞，并培育在通风橱中在室温吨10分钟emperature。 </li><li>由抽吸板拆下定影溶液。在这一点上要么停止实验通过洗涤该板3次用100μl磷酸盐缓冲盐水（PBS），然后储存密封，下在黑暗中加入100μl的PBS中，在4℃下进行长达一周，或继续进行透的细胞。 注意:我们尽快推荐处理板固定后，与一般喜欢存储完全处理的板。可以加入杀生物防腐剂如硫柳汞，叠氮化钠，或商业替代品，以防止micoroganismal增长。此外磷酸酶抑制剂的有利于维护磷酸表位，以及其它装置以保持蛋白质修饰状态可以是在相关检测上下文有用</li><li>从板除去PBS中，并通过加入100微升的透液透化细胞。孵育10分钟，在室温下不摇动。吸用多声道的通透的解决方案annel吸管。重复此步骤三次。 </li><li>通过加入每孔100μl块溶液中30分钟，在室温下阻断细胞。由抽吸板取下块溶液，然后探测用50μl抗P-S780 RB1抗体的稀释500倍的2小时，在黑暗中在室温下将块溶液。 </li><li>三次用100μl板洗涤溶液洗涤平板，离开上盘的溶液，每次5分钟。过夜，在黑暗中探测板在4℃下用50μl荧光标记的二级抗体稀释1000倍的块溶液补充有2微米的染色质特异性DNA染料双苯甲亚胺的。在4℃在黑暗中三次洗涤板作为前和储存密封，在100微升PBS中。图像中的两个星期之内的板。 </li></ol> 2.成像和图像分割<ol><li>使用共焦或转盘式荧光显微镜20倍的目标采取单独的16位的，在三个通道灰度TIFF图像对应的DNA染料，GFP和免疫荧光染色。捕获许多非重叠图像集，这里称为帧，将图像的大约1,000-2,000细胞每孔。 </li><li>系统地命名图像文件，以便每个文件的名称是"实验名&#39;，&#39;井地址"，"帧号"和"信道标识符"，以该顺序（ 图3）的独特组合。示例数据集使用"蓝"（染色质DNA染色）或"绿色"（GFP）或"红"（免疫染色的荧光团），为信道标识符。阱地址，帧号和信道标识符被进一步上称为图像元数据。使用下划线符号以避免混淆以及与框架的元数据。 </li><li>名称与指定的顺序，这些元数据元素的文件。这是必要的，以确保在随后的软件的步骤CORREctly组套图像进行分析。 </li><li>下载并安装免费的细胞探查，活动的Perl社区版，R统计编程环境和RStudio。安装期间接受所有默认选项; PC用户安装Active Perl中应使有关路径，文件扩展名关联和脚本映射在提示的所有选项。活动Perl是可选的Mac用户，但他们会以其他方式需要在3.2步从终端命令行，而不是使用图标点击运行Perl脚本。 </li><li>打开电池Profiler软件，点击"文件"，"导入管道"，然后"来自文件"，然后选择文件3_channels_pipeline.cppipe（ 图S1A和S1B）。该文件中包含必要的软件来从所描述的文件名称约定解释图像文件的元数据的指令。细胞探查现在涉及的图像，从提取日核DNA和抗体强度ESE并使用GFP通道来计算的核与细胞质强度的比率为每个细胞检测到的（ 图4和5）。 </li><li>单击单元格Profiler窗口的左下角的按钮&#39;查看输出设置"。在新屏幕的顶部是文本框标有"默认输入文件夹"和"默认输出文件夹"。一次一个，点击文件夹图标，以这些框的右边，并选择用于分析的图像文件的位置和目的地对所提取的数据，分别为（ 图S1C）。 </li><li>按"分析图像"按钮，在细胞探查的左下角开始进行图像分析。在屏幕的底部观察剩余时间的数据提取，则"停止分析"和"暂停"按钮。如果需要的话通过在任何时间，这是非常有用的选择的"暂停"按钮暂停分析当观看图像被分析（步骤2.8中所述）。 </li><li>任选地，通过点击上的程序窗口（ 图S1D）的极左面板眼图标打开窗户的任何的图像分析步骤。观察"IdentifyPrimaryObjects"窗口，那些"中等"和"第三Objects的检查，在细胞探查当前设置进行图像分割，适合（参见图1和讨论的意见修改这些设置）。 </li><li>点击" 确定 "的，当分析完成后出现的消息框。转到位置"默认输出文件夹"，所有的结果数据文件保存为逗号分隔值（.csv）文件（ 图S2A）。 </li></ol> 3.数据提取<ol><li>找到新的文件&#39;Nuclei.csv&#39;，它包含其中的从细胞探查输出。该文件包含单个单元格数据的荧光核抗体强度，核DNA的强度和GFP-CDK2记者比值（ 图6A和S2A）。 注意:不同的实验室将要处理这种类型的数据，以满足自己的测定法的性质。建议用于当前数据是从每个处理条件，根据该抗体的数据，并使用所提供的Perl脚本&#39;2_gate_classifier.pl&#39;将GFP-CDK2记者值的单元格的门控。 </li><li>所提供的Perl脚本文件&#39;2_gate_classifier.pl"复制到同一文件夹中的"Nuclei.csv"数据文件（ 图S2A）。双击该图标，Perl脚本，并出现提示时，键入跟着数据文件的全名由&#39;的.csv"文件名名称中的细胞被门终于为抗体的门值的文件荧光和GFP-CDK2记者的数据。 注意:如何确定主要设置闸门和应用这些数据的分析，下面的代表数据部分和图6讨论（分析提供的数据使用"0.004"和"1.5"，分别）。 Mac用户应当运行从输入终端的命令行Perl脚本:"perl的2_gate_classifier.pl"。 </li><li>观察从与亚群的标签的原始细胞探查数据显示如何从每个孔各小区执行对两个栅极（ 图6C）组合原始个体细胞测定值新创建的文件。 </li><li>绘制数据使用单独的细胞亚群的标签通过打开RStudio软件各实验条件。点击"文件"和"打开文件"，然后选择所提供的"analysis.r"文件。观察命令绘制图6B， <s仲&gt; 7和8中RStudio（ 图S2B）的左上窗口。在左上窗口中，在线路5和6的双引号符号之间，键入包含选通数据的文件夹的计算机地址。包括驱动器号和文件本身，分别的名称（ 如 "C:/分析文件夹/输出分析"和"nuclei_gated.csv"）。 注意:如果RStudio用于第一次的给定计算机上，R图形包&#39;ggplot2&#39;将需要首先安装。这是一种新的安装RStudio中，之后，该步骤变得多余一次仅一步。要安装"ggplot2"，单击叫RStudio的右下角的窗口上方"包"选项卡上，单击出现在这下面的"安装包"按钮。会出现一个新的窗口。键入"ggplot2"（省略报价）进入"包的步伐在这个新的窗口，最后单击"安装"按钮关闭该窗口，安装必要的ggplot2功能，并返回到主RStudio窗口从步骤3.6继续。 </li><li>在RStudio左上方窗口高亮线1到17，然后点击"运行"按钮。这将进入实验数据，阈值和井位的细节到R（ 图S2C）。 ř现在暂时持有的相关数据绘制。 </li><li>突出显示下方17行的其余代码的各个块，并像以前一样单击"运行"按钮，创建相应的地块。观察图中RStudio的右下角的窗口并保存格式数通过点击"导出"按钮（ 图S2D）。 </li><li>同时关闭RStudio，单击"不保存"提示时。这可以防止在下次使用RStudio的，这将否则保存数据混乱从以前的会话。 </li></ol>

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作者什么都没有透露

所描述的工作流构成的程序使用的siRNA，则后续的标记检测细胞的多孔扰动和最后使用的一系列软件支持的步骤，以便从所得的荧光显微镜图像提取的量化数据。该方法是集中交付核和细胞质强度值的单个细胞，其已在许多基于细胞的应用广泛的实际应用。这里使用的示例性数据是在一个siRNA的屏幕画面设定在其中两种荧光测定法G1期细胞周期中转进行测试和相关回核DNA含量的更直接的生物物理测量产生。 使用荧光DNA染色成像核DNA的是，在图像分割过程中不可缺少的步骤，因为它允许识别单个细胞，将所得&#39;核掩模"作为出发点，以确定相应cytoplasmiC区。的GFP标记CDK2记者，其稳定表达在细胞中，给出了一个可变但比通过此隔室可被划定的细胞质背景信号始终更高。同样的分析管道应该适用于使用其它合适的荧光联记者和其响应于扰动蛋白质易位事件的分析。此外，利用细胞质特异的荧光染料取代的GFP-CDK2记者将允许替代使用此算法来测量细胞质的尺寸和单元中的图像的相对尺寸。 另一种设计考虑在这里所描述的图像分割策略是利用细胞探查的提供集成的强度值的DNA定量。强度积分值的核DNA染色数据允许在细胞核大小可能的变化，代表了紧密匹配见过的量化概况为碘化丙啶染色FACS数据。然而，积分强度可能无法提供适当的手段来评估蛋白质的功能，其中平均浓度，通过抗原的荧光强度平均值例举，更生物学相关比一细胞区室中的集成总量蛋白（和相关的荧光）的。因此平均强度值被用于在P-S780 RB1和GFP的数据。可以选择两种模式（平均或集成）的数据评估的细胞Profiler软件的"ExportToSpreadsheet"面板上发现的​​改变。 在3_channels_pipeline.cppipe文件中的分析设置为在实施例的数据集的图像进行了优化。新的图像设置与此协议分析将需要的文件名 ​​采用命名约定上述（ 图3）。另外，感光度值，以满足核DNA染色和阈值背景的亮度在新的图像组的强度可能需要被内细胞探查设置调整。给定的DNA染色保持用于建立各种图像分割掩码的关键作用，正确的灵敏度设置为这个信道的应用程序的关键是与Cell Profiler软件新的图像数据的成功分析。提供的电池事件探查器设置文件（3_channels_pipeline.cppipe）包含了适应分析新的数据最常有用的参数说明。这些说明是在在细胞探查主窗口在屏幕的顶部的文本框，并且包括指导改变灵敏度设置和调整要被分析的信道数。至于起诉的协议第2.8节，以测试设置新的图像数据，可能有必要观察期间的图像分析图像分割点击打开"眼"的图标为每个"标识...... Objects的协议的步骤（ 图S1D </strong&gt;）。具体地，图像数据的通过"IdentifyPrimaryOjects&#39;可视化显示，如果核掩模被从DNA染色的图像正确识别。在为&#39;IdentifyPrimaryOjects"模块单元Profiler软件页面阈值​​修正系数。反复试验调整，这个值将解决大多数核识别错误。值平衡的映衬下强度为每个图像的DNA的通道。阈值校正因子值铰链周围1，其中大于这是更严格的（好为清晰的图像）和小于1的宽（适合于图像用染色和背景之间更少对比度）。 个别单元数据的自Cell探查的​​原始输出可以以不同的方式，以适应其他研究的需要进行分析。这里示出的是使用Perl脚本来门适用于每两个小区中度量的参数，以便协助从数据中提取的生物趋势ð允许交叉引用额外的测量确定的亚群的。尽管它同样可以包括细胞探查的框架内浇口元素，这里用的替代路线提供了更大的灵活性和速度，特别是如果大的数据集需要被评估。在当前协议后图像采集阶段最慢的阶段是Cell Profiler软件的运行。细胞分析器这里用不同栅值运行而不强加门，以产生一个未选通的原始数据集可以与随后的Perl脚本更快地重新分析和，如果需要的话，重复。不是所有的研究将预先知道的合适的门值，因为这可能对任何给定的一组数据用试剂而变化，并有可能随着时间的推移。正是因此，建议生成柱状图，其描绘的小区探查的阳性对照和模型扰动细胞，以获得原始数据分布来确定合适的门VALUES感兴趣的参数。 Perl脚本编写接受细胞探查数据的严格定义的列结构，如果用户修改参数，输出的细胞事件探查器使用"ExportToSpreadsheet"设置的数量可能会停止工作。为了帮助实现票据包括Perl脚本文件中的设置修改。要看到在文本编辑器查看这些脚本，最好是程序员的文本编辑器设置为颜色代码的Perl元素（ 例如 ，http://www.activestate.com/komodo-edit）。这些注释指明调整剧本，以适应变化中的数据格式。类似于Perl脚本，包含从绘制图像分析数据的数字说明中提供的R-代码文件（analysis.r），可以读取一个文本编辑器或RStudio软件，看看使用和适应附加说明。这些注释可以与正则表达式和Perl &lt;细节进行补充SUP&gt; 12和ggplot2 13包的R，这两者构成的基础数据是如何读，注释和作图，分别使用荧光显微镜新的研究，以及保藏公开的出版物的原始数据是适合于分析的方法，例如那些在这里描述的。高含量数据的本质适合于递归分析依赖于任何特定观察者的研究兴趣不同的分析重点。虽然可以提出的数据的问题，是由原本所使用的探针的限制，图像数据通常可以有意义地重新分析超出该生成它们的研究范围。

这里提出的工作描述了使用可免费获得的软件单元探查的执行的贴壁细胞，以确定个别的细胞和亚细胞限定区域的荧光显微镜图像算法引导击穿。这种方法被称为图像分割，使得成像单元的后续多参数分析通过定量定位于每个小区或亚细胞区（称为分割的对象）的荧光标记标记。此工作流构成用于使高含量的分析的基础，并意在作为该可进一步开发和修改，以适应多参数，在实验室中单个细胞的分析，而不访问专门的高含量的仪器或专有软件的工具。本稿件提供的文件包括一考定相关原始图像数据，算法设置和支持脚本生成描述的分析。所提供的算法设置˚F或细胞探查是为示例数据集和讨论部分细节的调整是什么可能是必要的，以便能够使用来自其他研究的图像数据进行了优化。 一旦量化数据已被使用Cell探查萃取，不同的实验室可以具有关于如何使用通过在原始数据的各个单元的值提供的信息不同的要求;这里示出的是由栅极被施加到原始数据的每个测定的一种方法。使用这些门中，数据被变换成响应二进制而言，允许联用的细胞发生反应由门限定的亚群不同处理的趋势可视化。栅极是基于为每个相关的测量获得适当的阴性和阳性对照的数据分布的观测置位。使用门的是如何管理原材料，基于细胞的测定的一个例子。这里还显示了使用核DNA强度我asurements在其原始形式中组合与门控的数据值的连续范围。其他的方法来管理的图像分析的数据，应考虑，这取决于研究的性质;统计替代使用门分配细胞亚群的报道策略2，系统的比较，总结了高含量的数据在大量的参数进行了报道3。 图像数据的高含量分析已经发现在细胞的药物-反应研究使用，反向遗传学和环境应激信号4 - 6。高内涵分析的优点源于以下事实荧光显微镜数据的算法分析可定量和空间参数可以同时跨越单个细胞7考虑。以这种方式，蜂窝结果对多个测定可以是测定-D交叉引用，差动行为efined细胞亚群可以在实验条件和测定法进行跟踪可以包括考虑形态变量。这里的策略和分析的工作流程所讨论的，作为其它的高含量的方法，是能够提供被交叉引用到单个细胞复用数据。产生的荧光显微镜图像和适用于数据从几十在低通量常规的基于荧光的显微镜产生的图像中的分析通过对成千上万的图像的高含量的方法西服研究使用自动化的高含量筛选平台制造。 工作流在这里示出与从其中分离测定法中任一核荧光标记物强度或荧光报告蛋白的核/细胞质易位，分别计测的例子的数据。工作流是灵活的，因为这些测定可单独考虑或联合取决于EACħ由不同的研究者给出的研究问题。该示例的数据产生作为RNA干扰（RNAi）实验（ 图1）的一部分。小干扰RNA寡核苷酸（RNA干扰）被用来拦截特定蛋白质在HCT116人结肠直肠癌细胞，其导致对细胞周期蛋白依赖性激酶（CDK）的活性的两种荧光记者的变化。核视网膜细胞瘤蛋白在丝氨酸780（P-S780 RB1）的CDK6依赖性磷酸化是通过抗体染色评估。在相同的细胞中，CDK2活性（GFP-CDK2记者）的绿色荧光蛋白标记的报告是由它的核评定，其中在不存在CDK2活性的记者驻留在细胞核中并在CDK2激活梭进入细胞质的细胞质比率8。此外，每个小区的核DNA用DNA-嵌入染料染色，双苯甲亚胺，作为识别细胞和细胞核中定义的边界中的图像的装置，以及一个SA测量脱氧核糖核酸丰提供对细胞的细胞周期的位置信息（ 图2）的。 CDK6和CDK2的活性是可检测从G1到细胞周期5的S期细胞中转和成功彼此9,10，正因为如此，在个体细胞中的两位记者之间的密切一致性的预期。此处所用的示范集分析，例如，siRNA的有效目标CDK6，视网膜母细胞瘤蛋白（RB1）和非靶向阴性对照（ 表1）。 CDK6击倒应引出既减少在P-S780 RB1表位和细胞在细胞周期的G1期的累积。将RB1击倒用作试剂控制的磷酸S780抗体的特异性。从福尔马林荧光显微镜图像固定11，荧光染色的HCT116组织培养细胞可用于算法的图像分析。将得到的数字数据，然后用于交叉引用的记者和衡量不同击倒状态的影响。 通过这种类型的分析产生的数据的电位的大小可以是一个挑战正常分析工具。例如，所述个体单元中的数据可以大于一些电子表格软件将容纳。包括的是数据而进行简单，高重复性，监督处理，以帮助大型数据集分析Perl脚本。 Perl脚本被用于通过细胞探查，处理与特定的文件命名约定（ 图3）的图像文件时生成的输出文件专门编写的，并允许每孔字段可变数量的分析中使用。它是将栅极个别细胞测定数据经常重要追踪细胞亚群5的趋势和这里示出的是使用的Perl脚本来标记根据一组预定的门对每个检测类型的每个单元。此外，还包括可选的Perl脚本这总结了各个孔（或条件）的数据的结果，提供:该组门中的细胞和原测定分数的平均值的百分比。查看数据的后者，更均匀的方式，是有效的，其中的反应影响所有或在井的大部分细胞。如上所讨论的，这种评估是比由单独的单元数据选通，其中响应局限于细胞群体内的一个子集，得到不太有用。 所描述的工作流的效用不被siRNA或描述的标志物分析限于扰动。研究已经用这种方法来测定使用的siRNA的组合，化学抑制剂和放射治疗组织培养实验和标记比其他CDK6和CDK2的活动5的评估反应。 在概念上，试验策略允许多种生物学有用的亚细胞区域中，以被自动注册在目前的荧光显微镜图像的单个细胞。因此，这种方法可以产生可通过技术，重点群体，而不是个别的细胞被错过的定量，多路复用的数据揭示的生物信息。稍作修改，所描述的方法和分析工作流程可以产生数量，单个单元格的数据对任何基于荧光检测输出和细胞生物反应，其中DNA含量的定量评估，核或细胞质荧光定量或这些标志物之间的穿梭两个隔室单独地或以多路复用方式是感兴趣。由于发布要求越来越朝着提交公开访问的原始数据，访问和熟悉的免费工具显微镜图像分析往往如这里所描述也将是直接有关的实验室寻求重新分析公布的数据。

<table border="0" cellpadding="0" cellspacing="0" width="604">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Name of Material/ Equipment</td>
			<td style="width:103px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td style="width:167px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">AllStars negative control siRNA</td>
			<td style="width:103px;">Qiagen</td>
			<td align="right" style="width:119px;">1027280</td>
			<td style="width:167px;">Negative control siRNA.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">CDK6 siRNA</td>
			<td style="width:103px;">Dharmacon/ Custom synthesis</td>
			<td style="width:119px;">NA</td>
			<td style="width:167px;">Antisense sequence: 5&#39; CUCUAGGCCAGUCUUCUUCUU</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">RB1 siRNA</td>
			<td style="width:103px;">Dharmacon/ Custom synthesis</td>
			<td style="width:119px;">NA</td>
			<td style="width:167px;">Antisense sequence: 5&#39; GGUUCAACUACGCGUGUAATT</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">5x siRNA buffer</td>
			<td style="width:103px;">Thermo Scientific</td>
			<td>B-002000-UB-100</td>
			<td style="width:167px;">To be diluted with nuclease-free, non-DEPC treated water.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Nulcease-free water (non-DEPC)</td>
			<td style="width:103px;">Applied Biosystems</td>
			<td>AM9937</td>
			<td style="width:167px;">For dilution of siRNA buffer.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Hiperfect</td>
			<td style="width:103px;">Qiagen</td>
			<td align="right">301705</td>
			<td style="width:167px;">Transfection lipid.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">DMEM</td>
			<td style="width:103px;">Life Technologies</td>
			<td align="right">41966052</td>
			<td style="width:167px;">Pyruvate and high-glucose supplemented tissue culture media.</td>
		</tr>
		<tr height="126">
			<td height="126" style="height:126px;width:216px;">96 well tissue culture plate</td>
			<td style="width:103px;">Falcon</td>
			<td align="right">3072</td>
			<td style="width:167px;">Plain, 96-well tissue culture plate for the parallel, compartmentalized mixing of transfection complexes.</td>
		</tr>
		<tr height="189">
			<td height="189" style="height:189px;width:216px;">Packard Viewplate</td>
			<td style="width:103px;">Perkin Elmer LAS</td>
			<td align="right">6005182</td>
			<td style="width:167px;">96 well TC plate with optical base on which cells are transfected, grown, fixed and eventually imaged. Supplied with opaque, adhesive plate seals, which are used during storage of used plates.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Breathable membrane</td>
			<td style="width:103px;">Alpha Labs</td>
			<td style="width:119px;">LW2783</td>
			<td style="width:167px;">Sterile, gas-permeable, adhesive membrane.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Neutral buffered formalin, 10%</td>
			<td style="width:103px;">Sigma-Aldrich</td>
			<td>HT5012-1CS</td>
			<td style="width:167px;">10% Formalin (4% formaldehyde) fixative used neat in protocol.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Triton X-100</td>
			<td style="width:103px;">Sigma-Aldrich</td>
			<td>X100PC</td>
			<td>Non-ionic detergent</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Tris</td>
			<td style="width:103px;">Sigma-Aldrich</td>
			<td style="width:119px;">T1503</td>
			<td style="width:167px;">Trizma base (tris(hydroxymethyl)aminomethane)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Tween 20</td>
			<td style="width:103px;">Sigma-Aldrich</td>
			<td style="width:119px;">P2287</td>
			<td style="width:167px;">For plate wash solution.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Anti-P-S780 RB1 antibody</td>
			<td style="width:103px;">Abcam</td>
			<td>ab32513</td>
			<td>Rabbit monoclonal</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">AlexaFluor647 Anti-rabbit</td>
			<td style="width:103px;">Invitrogen</td>
			<td>A21245</td>
			<td style="width:167px;">Highly cross-adsorbed, fluorescently labelled secondary antibody.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Hoechst 33342 (Bisbenzimide)</td>
			<td style="width:103px;">Sigma-Aldrich</td>
			<td>B2261</td>
			<td style="width:167px;">Fluorescent, chromatin-intercalating, DNA dye.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Permeabilization solution</td>
			<td style="width:103px;">NA</td>
			<td style="width:119px;">NA</td>
			<td style="width:167px;">0.1% Triton X-100 in 50 mM Tris-buffered saline, pH 8.0.</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Plate wash solution</td>
			<td style="width:103px;">NA</td>
			<td style="width:119px;">NA</td>
			<td style="width:167px;">Tris-buffered saline containing 0.1 % Tween-20.</td>
		</tr>
		<tr height="126">
			<td height="126" style="height:126px;width:216px;">Block solution</td>
			<td style="width:103px;">NA</td>
			<td style="width:119px;">NA</td>
			<td style="width:167px;">5% powdered milk in Tris-buffered saline and 0.1% Tween-20. For blocking plate prior to immuno-staining and dilution of antibodies.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Cell Profiler 2.1</td>
			<td style="width:103px;">Broad Institute</td>
			<td style="width:119px;"></td>
			<td>http://www.cellprofiler.org/download.shtml</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Active Perl Community Edition</td>
			<td style="width:103px;">ActiveState</td>
			<td style="width:119px;"></td>
			<td>http://www.activestate.com/activeperl/downloads</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">R programming environment</td>
			<td style="width:103px;">The R Foundation</td>
			<td style="width:119px;"></td>
			<td>http://www.r-project.org</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Rstudio</td>
			<td style="width:103px;">Rstudio</td>
			<td style="width:119px;"></td>
			<td>http://www.rstudio.com/</td>
		</tr>
	</tbody>
</table>

workflow for high-content, individual cell quantification of fluorescent markers from universal microscope data, supported by open source software

在了解治理细胞贴壁哺乳动物组织培养模式的行为控制机制的进步越来越依赖于单细胞分析模式。方法，它们提供的复合数据反映从细胞群体的生物标记的平均值可能失去了反映所研究的生物系统的异质性亚群动力学。符合此，传统的方法正在取代，或与开发，以允许评估由高含量显微镜细胞测定的更复杂的形式的支持。这些测定法可能产生大量的荧光标记物，其由所附的专有软件包启用的图像，使得每个细胞的多参数测量。然而，相对高的资金成本，许多这些装置的过分专业化阻止包括方便许多研究者。 这里描述的是一种普遍适用的工作流程，从适用于图像使用大多数荧光显微镜的单个细胞的特定亚地区的多种荧光标记强度的量化。关键这个工作流程是免费提供的细胞Profiler软件1实施分辨单个细胞中这些图像，他们将段定义亚地区，提供荧光标记的强度值具体到这些地区。单个细胞的强度值，从图像数据中提取的该工作流程的主要目的，将说明与控制数据从一个屏幕的siRNA用于粘附人体细胞的G1检查点调节器的分析。然而，这里提出的工作流程可以适用于从细胞扰动的其它装置的数据的分析（ 例如 ，化合物屏幕）和其它形式的基于荧光细胞标记，因此应为广泛的实验室是有用的。

Watch this Scientific Journal Video about Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software at JoVE.com

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

呈现是一个灵活的信息学的工作流程使得荧光标记的细胞的复用的基于图像的分析。工作流量化细胞核和细胞质的标记，并计算这些室之间的标记易位。提供了用于在96孔格式使用siRNA和可靠的方法为标记物的检测通过间接免疫荧光细胞的扰动过程。

工作流的高含量，荧光标记的万能工具显微镜数据，支持开源软件的单个细胞定量

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly ...

workflow-for-high-content-individual-cell-quantification-fluorescent

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

12.9K 次观看.  UCL Cancer Institute. 呈现是一个灵活的信息学的工作流程使得荧光标记的细胞的复用的基于图像的分析。工作流量化细胞核和细胞质的标记，并计算这些室之间的标记易位。提供了用于在96孔格式使用siRNA和可靠的方法为标记物的检测通过间接免疫荧光细胞的扰动过程。 

视频: Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

一个神经元和星形胶质细胞共培养检测神经毒性的高含量分析

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

科研

生物学

Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software

Cellular processes, such as chromosome assembly, segregation and cytokinesis,are inherently dynamic. Time-lapse imaging of living cells, using fluorescent-labeled reporter proteins or differential interference contrast (DIC) microscopy, allows for the examination of the temporal progression of these dynamic events which is otherwise inferred from analysis of fixed samples1,2. Moreover, the study of the developmental regulations of cellular processes necessitates conducting time-lapse experiments on an intact organism during development. The Caenorhabiditis elegans embryo is light-transparent and has a rapid, invariant developmental program with a known cell lineage3, thus providing an ideal experiment model for studying questions in cell biology4,5and development6-9. C. elegans is amendable to genetic manipulation by forward genetics (based on random mutagenesis10,11) and reverse genetics to target specific genes (based on RNAi-mediated interference and targeted mutagenesis12-15). In addition, transgenic animals can be readily created to express fluorescently tagged proteins or reporters16,17. These traits combine to make it easy to identify the genetic pathways regulating fundamental cellular and developmental processes in vivo18-21. In this protocol we present methods for live imaging of C. elegans embryos using DIC optics or GFP fluorescence on a compound epifluorescent microscope. We demonstrate the ease with which readily available microscopes, typically used for fixed sample imaging, can also be applied for time-lapse analysis using open-source software to automate the imaging process.

Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software

The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.

成像 C。线虫胚胎使用啶显微镜和开放源码软件

Cellular processes, such as chromosome assembly, segregation and cytokinesis,are inherently dynamic. Time-lapse imaging of living cells, using ...

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

定量健身分析工作流程

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

A High-content Imaging Workflow to Study Grb2 Signaling Complexes by Expression Cloning

Signal transduction by growth factor receptors is essential for cells to maintain proliferation and differentiation and requires tight control. Signal transduction is initiated by binding of an external ligand to a transmembrane receptor and activation of downstream signaling cascades. A key regulator of mitogenic signaling is Grb2, a modular protein composed of an internal SH2 (Src Homology 2) domain flanked by two SH3 domains that lacks enzymatic activity. Grb2 is constitutively associated with the GTPase Son-Of-Sevenless (SOS) via its N-terminal SH3 domain. The SH2 domain of Grb2 binds to growth factor receptors at phosphorylated tyrosine residues thus coupling receptor activation to the SOS-Ras-MAP kinase signaling cascade. In addition, other roles for Grb2 as a positive or negative regulator of signaling and receptor endocytosis have been described. The modular composition of Grb2 suggests that it can dock to a variety of receptors and transduce signals along a multitude of different pathways1-3.
	Described here is a simple microscopy assay that monitors recruitment of Grb2 to the plasma membrane. It is adapted from an assay that measures changes in sub-cellular localization of green-fluorescent protein (GFP)-tagged Grb2 in response to a stimulus4-6. Plasma membrane receptors that bind Grb2 such as activated Epidermal Growth Factor Receptor (EGFR) recruit GFP-Grb2 to the plasma membrane upon cDNA expression and subsequently relocate to endosomal compartments in the cell. In order to identify in vivo protein complexes of Grb2, this technique can be used to perform a genome-wide high-content screen based on changes in Grb2 sub-cellular localization. The preparation of cDNA expression clones, transfection and image acquisition are described in detail below. Compared to other genomic methods used to identify protein interaction partners, such as yeast-two-hybrid, this technique allows the visualization of protein complexes in mammalian cells at the sub-cellular site of interaction by a simple microscopy-based assay. Hence both qualitative features, such as patterns of localization can be assessed, as well as the quantitative strength of the interaction.

A high-content screening method for the identification of novel signaling competent transmembrane receptors is described. This method is amenable to large-scale automation and allows predictions about in vivo protein binding and the sub-cellular localization of protein complexes in mammalian cells.

一个高含量的成像流程研究Grb2的信号复合体的表达克隆

Signal  transduction by growth factor receptors is essential for cells to maintain  proliferation and differentiation and requires tight control. Signal  ...

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 ...

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here protocols are presented for preparing embryos and adult C. elegans animals for short- and long-term time-lapse microscopy and methods for tracking and quantification of developmental processes. The methods presented are all based on C. elegans strains available from the Caenorhabditis Genetics Center and on open-source software that can be easily implemented in any laboratory independently of the microscopy system used. A reconstruction of a 3D cell-shape model using the modelling software IMOD, manual tracking of fluorescently-labeled subcellular structures using the multi-purpose image analysis program Endrov, and an analysis of cortical contractile flow using PIVlab (Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB) are shown. It is discussed how these methods can also be deployed to quantitatively capture other developmental processes in different models, e.g., cell tracking and lineage tracing, tracking of vesicle flow.

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Here it is shown how to track and quantify developmental processes in C. elegans. The methods presented are based on open-source tools that can be easily implemented. It is demonstrated how to reconstruct 3D cell-shape models, how to manually track subcellular structures, and how to analyze cortical contractile flow.

跟踪和量化的发育过程<em&gt;℃。线虫</em&gt;使用开源工具

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here ...

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in &#181;Manager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

We present an open source high content analysis (HCA) instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using F&#246;rster resonance energy transfer (FRET) based readouts. Data acquisition for this openFLIM-HCA instrument is controlled by software written in &#181;Manager and data analysis is undertaken in FLIMfit.

开源高内涵分析利用自动荧光寿命成像显微镜

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions ...

Cellular Redox Profiling Using High-content Microscopy

Reactive oxygen species (ROS) regulate essential cellular processes including gene expression, migration, differentiation and proliferation. However, excessive ROS levels induce a state of oxidative stress, which is accompanied by irreversible oxidative damage to DNA, lipids and proteins. Thus, quantification of ROS provides a direct proxy for cellular health condition. Since mitochondria are among the major cellular sources and targets of ROS, joint analysis of mitochondrial function and ROS production in the same cells is crucial for better understanding the interconnection in pathophysiological conditions. Therefore, a high-content microscopy-based strategy was developed for simultaneous quantification of intracellular ROS levels, mitochondrial membrane potential (&#916;&#936;m) and mitochondrial morphology. It is based on automated widefield fluorescence microscopy and image analysis of living adherent cells, grown in multi-well plates, and stained with the cell-permeable fluorescent reporter molecules CM-H2DCFDA (ROS) and TMRM (&#916;&#936;m and mitochondrial morphology). In contrast with fluorimetry or flow-cytometry, this strategy allows quantification of subcellular parameters at the level of the individual cell with high spatiotemporal resolution, both before and after experimental stimulation. Importantly, the image-based nature of the method allows extracting morphological parameters in addition to signal intensities. The combined feature set is used for explorative and statistical multivariate data analysis to detect differences between subpopulations, cell types and/or treatments. Here, a detailed description of the assay is provided, along with an example experiment that proves its potential for unambiguous discrimination between cellular states after chemical perturbation.

This paper presents a high-content microscopy workflow for simultaneous quantification of intracellular ROS levels, as well as mitochondrial membrane potential and morphology &#8211; jointly referred to as mitochondrial morphofunction &#8211; in living adherent cells using the cell-permeant fluorescent reporter molecules 5-(and-6)-chloromethyl-2&#39;,7&#39;-dichlorodihydrofluorescein diacetate, acetyl ester (CM-H2DCFDA) and tetramethylrhodamine methylester (TMRM).

使用高含量显微镜的细胞氧化还原分析

Reactive oxygen species (ROS) regulate essential cellular processes including gene expression, migration, differentiation and proliferation. However, ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

与VirusMapper超分辨率显微镜开源单粒子分析

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts whose expression is associated with insecticide resistance phenotypes in Anopheles gambiae mosquitoes. The application can be used online or downloaded and used locally by anyone. The local application can be modified to add new insecticide resistance datasets generated from multiple -omics platforms. This guide demonstrates how to add new datasets and handle missing data. Furthermore, IR-TEx can be completely and easily recoded to use-omics datasets from any experimental data, making it a valuable resource to many researchers. The protocol illustrates the utility of IR-TEx in identifying new insecticide resistance candidates using the the microsomal glutathione transferase, GSTMS1, as an example. This transcript is upregulated in multiple pyrethroid resistant populations from C&#244;te D&#39;Ivoire and Burkina Faso. The identification of co-correlated transcripts provides further insight into the putative roles of this gene.

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any platform.

IR-TEx:专为疟疾病媒阿诺菲斯甘巴病设计的大数据转录组学的开源数据集成工具

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts ...