我们感谢亚当·科斯特和Altschuler领导和吴实验室进行有益的反馈和讨论的所有其他成员。这项研究是由美国国家卫生研究所的支持下授予R01 GM085442和CA133253（SJA），R01 GM081549（LFW），CPRIT RP10900（LFW）和韦尔奇基金会的I-1619（SJA）和I-1644（LFW）。

1。准备样品和成像<ol><li>细胞培养<ol><li>分裂的文化常规，并在20-70％汇合维护。一天的实验之前，生长的HeLa细胞在DMEM（Dulbecco改良的Eagle培养基，见表材料）和补充有10％FBS（胎牛血清）和1x青霉素/链霉素。 </li><li>在实验的当天，细胞分裂生长在文化（约50％汇合）。 </li><li>用PBS（磷酸盐缓冲盐水）洗涤细胞，暴露在几秒钟约2毫升胰蛋白酶/ EDTA（乙二胺四乙酸）的。吸出多余的胰蛋白酶/ EDTA和保持细胞沐浴在胰蛋白酶/ EDTA的在罩在RT〜3分钟的薄膜。 </li><li>机械摇瓶两次，分离细胞与血清的培养基中。计数细胞，并稀释至5,000 cells/50微升的媒体，并立即板50μL/孔到384孔的玻璃底板使用自动化液体处理机器人（只有30以及该板的S是用在这里）。 </li><li>培养板在室温下30分钟，以尽量减少在井5的边缘效应。后室温孵育地方板在37℃/ 5％CO 2培养箱中过夜。 </li><li>每种药物将被测定一式两份。加入25微升药物对上盘已经包含媒体相应的孔中。 </li><li>固定板在24小时后药物治疗的PBS 4％多聚甲醛溶液。 </li></ol></li><li>染色法 具体的初级抗体，荧光标记的第二抗体和其他荧光染料这里所使用的列于表材料。 <ol><li>通透固定的细胞用在TBS中的0.2％的Triton X-100溶液（Tris缓冲盐水）持续3分钟，洗净，用TBST（Tris缓冲盐水+吐温20）3倍。加5％BSA（牛血清白蛋白）溶液在TBST中阻断。 </li><li>培养板在室温下2小时，然后用TBST 3倍。 </li><li>使初级抗体dilut在TBST 5％BSA的离子（见表材料），并加入10微升/孔。培养板在室温下搅拌2小时，然后用TBST洗3次。 </li><li>使在TBST二级抗体dilutionin 5％的BSA（见表材料），并加入10微升/孔。 </li><li>在黑暗中于室温搅拌2小时孵育板，然后用TBST洗涤两次。 </li><li>准备稀释的赫斯特和鬼笔环肽（见表材料）的PBS，加入10微升/孔。 </li><li>将培养板在黑暗中在室温30分钟。 </li><li>经过2 TBST洗，盖板箔及金属储存于4°CO / N。 </li></ol></li><li>成像 使用20X物镜与1x1的相机分档使用落射荧光显微镜采集图像。获得16图像各孔中，一共有480图像（×3信道）。 </li></ol> 2。分析图像下载和phenoripper.org安装PhenoRipper，并启动PhenoRipper开始伊马分析GES。 PhenoRipper目前支持TIFF，PNG和JPEG和利用MATLAB支持的所有其他格式（显微镜软件通常支持导出成TIFF）。 2.1加载数据<ol><li>没有标准命名/组织惯例存在显微镜图像，它可以使加载图像和相关联的元数据（ 即实验注解，如治疗型，井数等）的工作流程中最具挑战性的部分。选择所呈现的一种方法（下面描述）。 <ol><li> PhenoLoader:选择此选项可半自动自动加载数据时，文件名 ​​和/或目录结构包含足够的信息以图像组，然后随意（ 如 B16 HeLa_Drug5-DAPI.png可能表明该图像是从井B16，细胞治疗用其ID为5，并且图像捕捉核DAPI染色的药物 - 与此命名惯例，从A1孔或药物5将所有的图像可以很容易地进行分组）。该向导将引导一步将元数据与图像组相关联，并定义标记如下用户的步骤: <ol><li>点击"文件结构"访问到PhenoLoader窗口。 </li><li>点击"定义根目录"，选择包含图像数据集的目录。然后筛选未使用的文件（未使用的文件）进行分析（可选项，可以留空）。 </li><li>点击"定义标记"，以指定在实验中所用的生物标记物，并将其与图像相关联。具体来说，点击一个图像文件，然后选择文件名的一部分唯一标识的生物标志物。最后，输入每个生物标志物的名称。 </li><li>点击"定义元数据"信息与每个图像相关联。具体来说，点击一个图像文件，然后选择文件名 ​​的识别图像信息（ 如 "B16"的井名）的一部分。一个窗口，要求输入群组名称（ 例如 "我们LL"）会弹出，输入一个名称。从文件名 ​​中提取的组的成员将显示在组表（ 如好名的完整列表）。要添加不存在的文件名 ​​的其他信息，请单击" （可选）下一步"按钮，点击"添加元数据组"，输入其他组的名称（ 如 "药"）和预定义组的每个值（ 如 "井"），其数值输入（ 如 "紫杉醇"）有是没有限制的，可以添加额外的基团的数目。 </li><li>最后，点击"创建Metadatafile"生成元数据文件，并继续分析。如果一个元数据文件之前已经产生，可避免使用选项"元数据文件"（在3以下说明）直接加载文件以上步骤。 </li></ol></li><li>基于正则表达式的模板:正则表达式匹配模式的标准方法在文本。选择此选项时，文件名和/或目录结构包含足够的信息以图像组和命名约定相匹配的预先存在的模板之一。基于正则表达式的自定义模板也可以手动定义和重用。仅建议用户熟悉正则表达式这个高级选项。 </li><li>元数据文件:选择此选项为最终定制。定义和加载一个逗号分隔值包含与每个图像相关的文件名和元数据（CSV）文件。文件格式的一个例子是可在网站（phenoripper.org）的下载部分。只需通过接口加载该文件，以显示其包含的信息。 </li></ol></li><li>选择元数据字段定义了重复实验，以避免重复抽样。对于包含重复（ 例如，在一个良好的所有图像应该是相似的），将它们组合在一起（ 例如，通过井）可以展示次数的数据集奥雅纳性能。在大多数情况下是合理的选择默认选项"随机（不知道）"，它会随机选择一个子集。 </li></ol> 2.2设置分析参数点击"设置参数"定义来处理数据集所需的参数。定义的参数是在左侧面板上。右侧面板显示的数据集的合并范例。点击左/右箭头来不同的图像之间进行切换。 <ol><li>阈浓度:选择一个适当的阈值来大致确定图像的细胞部分（而不是背景，非细胞区域）。阈值是由PhenoRipper预先计算，但可以调整使用滚动条可在图像的顶部，以提高蜂窝区域高亮。如果选择的门槛并不在所有影像工作，降低门槛，以包括非细胞区域，而不是下降的细胞区域。 </li><li> S座IZE:选择一个合适的块大小（以像素为单位），以网格图像。指定大小的块将被叠加在图像上。调整块尺寸，以获得平均20-30块/单元。叠加网格在图像上点击，在"块大小"字段前面的复选框。 </li><li>可选参数:如果自动缩放的图像不显示具有期望的相对强度的标记（ 例如，一种颜色是饱和的），或者如果标记需要被从分析中删除，则使用"调节通道强度"选项。来识别标记的一个子集蜂窝区域使用选项"使用门槛通道"。默认选择的其他参数通常就足够了，但如果需要的话，使用"高级分析选项"进行调整。 </li><li>按"PhenoRip"在主应用程序面板上的运行分析。 PhenoRipper会自动识别图像中的经常性的块类型。表型的配置文件将用于生成根据它的不同的块类型的分数每幅图像。 </li></ol> 3。研究结果<ol><li>探索使用PhenoBrowser（ 图1）的图像的表型分布之间的关系。该PhenoBrowser接口由四个面板:在面板左上角的是不同的图像/条件之间的相对相似的图形表示（散点图），在选定的条件下正确显示样本图像和最后一个面板（条形图的两个面板）比较它们的表型分布。 </li><li> "丢弃空帧"使用"数据处理"菜单。 </li><li>除去质量差的图像:首先，要研究单个图像关闭分组从"数据处理"菜单。接下来，通过点击散点图的离群点和丢弃低质量的图像（ 如对焦，染色神器）。 </li><li>探索数据:决定哪些元数据字段定义比较的基本单元（ 例如 ，比较药物，与元数据字段的值相同的"药物"的图像需要被组合在一起）。收起这个元数据字段的值相同的所有图像与"分组依据"单个数据点。例如，通过药物分组将每种药物内平均的图像，得到每药1点。在散点图附近（或远）点反映引​​起类似的（或不同）的细胞表型的条件。颜色或标注点（数据显示菜单）的基础上可用的元数据，以帮助解释性。 3D图是可旋转通过点击"旋转"复选框，并单击左键并拖动鼠标时。 </li><li>选择两个不同的点，它们直接比较。图像的面板将显示样本图像的每个点，和条形图面板将显示块类型，其发生频率是两个点之间的最不同。 </li><li>启动了Clustergram从"Clustergram菜单"提供的块类型和实验条件之间的关系更具全球性的观点。通过将两种块类型和条件，这代表强调了细胞表型最能区分条件组（ 图5）。 </li></ol>

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作者什么都没有透露。

这里所描述的工作流程允许快速和容易表征和显微镜图像的对比。我们首先演示了如何这个工作流程可以帮助实验优化，例如通过快速执行质量控制的显微镜图像。接下来，我们展示了其潜力分析的高通量筛选数据:我们可以根据它们的作用机制组药物。药物的分组是可比较的，使用更复杂的方法6中发现，即使PhenoRipper是快几个数量级。 虽然我们已经发现这种方法对?...

在过去的十年中，在成像技术的快速发展已经给许多实验室进行高通量显微镜的能力。我们面临的挑战已经转移从成像的一个分析之一。我们如何表征，比较，并探讨通过一个典型的高通量成像屏幕产生的复杂而微妙的表型的洪流？ 许多图像分析和信息平台，给用户先进的工具箱1-3从图像的大集合中提取的生物信息。然而，许多显著障碍仍然在分析来自高通量基于图像的画面数据。涉及选择的图像和算法参数微调的适当表征（对感兴趣的系统）的困难常常导致长的设置时间，尤其是对于用户不进行图像分析的专门知识。特别是，小区分割，识别单个细胞的过程中N个图像，就是这方面的一个主要瓶颈。分割可以是对实验参数如细胞类型，细胞密度和生物标志物高度敏感的，并经常需要对大数据集的重复调节。由于这些原因，图像分析常常是由专家进行的，而不是实验性的工作流程的一个组成部分是独立的，终端的步骤。这排除了分析和实验，检验发展的一个关键部分之间的快速反馈循环。 在这里，我们描述了被优化用于高通量荧光显微镜和避免了许多与上述困难的替代的工作流。为此，我们利用PhenoRipper 4，迅速 ​​地探索和显微图像分析的开源软件平台。显著，PhenoRipper避免许多由不尝试识别单个细胞参与细胞分割的挑战。相反，PhenoRipper把图像分为像素亚细胞大小的块和图像特征在它们所包含的块为单位。具体来说，PhenoRipper型材的标记共定位为基础的功能方面的块，并自动识别4在训练图像中最有代表性的块类型。 PhenoRipper然后分配给每个块最相似的块代表性的类型，最后表征根据它们所包含的块的类型的图像。 较传统的单细胞​​为基础的方法，该方法需要很少的微调和专业知识。这样，用户只需要设置两个参数:块大小和阈值强度（丢弃的图像的非细胞部分），这两者都设置有图形的反馈。重要的是，这里所描述的工作流程已被证明4可以轻松地扩展到更大的数据集，其中的速度和最小的微调提供了大量的时间和可靠性的收益。在实践中，我们发现，这个程序蟑螂降低了对安装和运行了4级的多个订单所需的时间。 PhenoRipper的主要输出是图像相似的视觉表示，从而使用户能够识别的，导致细胞显示相似的表型的实验条件组。 PhenoRipper发现典型的分组具有可比的"生物可解释性"的那些由比较费时，基于细胞的方法4产生。在实践中，这意味着它们通常，在执行一个典型的96 - 孔荧光显微术实验的半小时内，不另行图像分析经验实验者可以得到关于他或她的实验的性能可靠的读出。然后实验者可以开始探索不同的实验条件之间的关系，以及与这些关系到图像的表型。 在这里，我们证明这一点的工作流程，通过分析影响药物在不同的机械类对HeLa细胞染色的DNA和细胞骨架标记。与同一类药物处理的细胞的图像进行组合在一起，质量差的图像（染色文物，失焦的细胞等等）表现为异常值，使它们易于识别和潜在的丢弃。 而此工作流是为大量的基于图像的检测有用的，明显有许多情况下，不同的方法可能是更有益。例如，从PhenoRipper输出为主要的荧光模式跨越的实验条件下的相对比较。如果要求特定的单细胞特征的绝对表征，而不是（在细胞中的斑点的例如数），将需要一个单细胞为基础的分析。然而，即使在这种类型的情况，PhenoRipper是可能检测到的变化在这些单细胞的表型，因此可用于测定OPTI一个有用的工具mization。

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	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DMEM, High Glucose</td>
			<td style="width:116px;">Invitrogen</td>
			<td align="right" style="width:119px;">11965</td>
			<td style="width:404px;">High Glucose, L-Glutamine</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;"></td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Marker Hoechst 33342</td>
			<td style="width:116px;">Invitrogen&#160;</td>
			<td style="width:119px;">H1399</td>
			<td style="width:404px;">DNA stain, dilution 1:2000 of 5mg/ml stock</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Marker phalloidin-Alexa488</td>
			<td style="width:116px;">Invitrogen&#160;</td>
			<td style="width:119px;">A12379</td>
			<td style="width:404px;">Conjugated phallotoxin, dilution 1:200</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Primary Antibody &#945;-tubulin</td>
			<td style="width:116px;">Sigma&#160;</td>
			<td style="width:119px;">T9026</td>
			<td style="width:404px;">Primary Ab, mouse monoclonal, dilution 1:200</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Secondary Antivody anti-Mouse TRITC</td>
			<td style="width:116px;">Jackson&#160;</td>
			<td style="width:119px;">115-025-166</td>
			<td style="width:404px;">Conjugated secondary Ab, 0.5mg in 1ml PBS + 1ml glycerol</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Aldosterone</td>
			<td></td>
			<td></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Apicidin</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :20 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Colchicine</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.16 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Cortisol</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Dexamethasone</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Docetaxel</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">M344</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :20 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MS-275</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :5 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Nocodazole</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.3 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Prednisolone</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">RU486</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Scriptaid</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :70 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Taxol</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.3 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Trichostatin A</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.2 &#956;M</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Vinorelbine</td>
			<td style="width:116px;"></td>
			<td style="width:119px;"></td>
			<td style="width:404px;">concentration used :0.3 &#956;M</td>
		</tr>
	</tbody>
</table>

rapid analysis and exploration of fluorescence microscopy images

尽管在高通量显微镜的快速发展，定量图像为基础的检测依然构成显著的挑战。虽然各种专门的图像分析工具可用，最传统的图像分析为基础的工作流程，有陡峭的学习曲线（用于分析参数进行微调），并导致成像和分析之间的长期周转时间。特别是，小区分割，识别单个细胞的图像的过程中，是在这方面的一个主要瓶颈。 在这里，我们提出了一个替代，细胞分割自由的基础上PhenoRipper，一个开源软件平台设计的快速分析和显微镜图像的勘探工作流程。这里介绍的管道进行了优化细胞培养免疫荧光显微镜图像，需要最少的用户干预。不到半小时，PhenoRipper可以从一个典型的96孔实验分析数据并生成图像轮廓。用户可以然后直观地探索他们的数据，他们的实验进行质量控制，确保应对扰动和重复检查的可重复性。这有利于分析和实验，这是至关重要的过程中检测优化之间的快速反馈循环。这个协议是有用的不只是作为一个第一遍分析的质量控制，而且还可以被用作一个终端到终端的解决方案，特别是用于筛选。此处所描述的工作流扩展到大的数据集，如由高通量筛选产生，并且已显示出组实验条件下通过表型准确在宽范围的生物系​​统。该PhenoBrowser界面提供了直观的框架，探讨表型的空间和与图片属性生物注解。总而言之，这里所描述的协议将降低门槛采用的基于图像的屏幕定量分析。

在这里，我们测试了这个工作流的分组药物根据其作用机制的能力。将HeLa细胞接种在30孔384 - 孔板的和染色的DNA /肌动蛋白/α-微管蛋白。具体的初级抗体，荧光标记的第二抗体，并且所使用的其他荧光染料列于表材料。井均采用属于三个机制类（组蛋白去乙酰化酶抑制，微管定位和DNA损伤），24小时，并使用萤光显微镜20X物镜放大成像15药物。购入十六画面各孔，共480图像（×3信道）。这里所描?...

Watch this Scientific Journal Video about Rapid Analysis and Exploration of Fluorescence Microscopy Images at JoVE.com

Rapid Analysis and Exploration of Fluorescence Microscopy Images

在这里，我们描述了快速分析和探索使用PhenoRipper，最近开发的图像分析平台的荧光显微镜图像的集合的工作流。

快速分析和荧光显微镜图像的探索

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized ...

rapid-analysis-and-exploration-of-fluorescence-microscopy-images

Research

JoVE Journal

Biology

12.1K 次观看.  UT Southwestern Medical Center. 在这里，我们描述了快速分析和探索使用PhenoRipper，最近开发的图像分析平台的荧光显微镜图像的集合的工作流。 

视频: Rapid Analysis and Exploration of Fluorescence Microscopy Images

Time-lapse Imaging of Mitosis After siRNA Transfection

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and cellular content. Hallmark events of mitosis, such as chromosome movement, can be readily tracked on an individual cell basis using time-lapse fluorescence microscopy of mammalian cell lines expressing specific GFP-tagged proteins. In combination with RNAi-based depletion, this can be a powerful method for pinpointing the stage(s) of mitosis where defects occur after levels of a particular protein have been lowered. In this protocol, we present a basic method for assessing the effect of depleting a potential mitotic regulatory protein on the timing of mitosis. Cells are transfected with siRNA, placed in a stage-top incubation chamber, and imaged using an automated fluorescence microscope. We describe how to use software to set up a time-lapse experiment, how to process the image sequences to make either still-image montages or movies, and how to quantify and analyze the timing of mitotic stages using a cell-line expressing mCherry-tagged histone H2B. Finally, we discuss important considerations for designing a time-lapse experiment. This strategy is complementary to other approaches and offers the advantages of 1) sensitivity to changes in kinetics that might not be observed when looking at cells as a population and 2) analysis of mitosis without the need to synchronize the cell cycle using drug treatments. The visual information from such imaging experiments not only allows the sub-stages of mitosis to be assessed, but can also provide unexpected insight that would not be apparent from cell cycle analysis by FACS.

Here we describe a basic protocol to image and quantify the mitotic timing of live mammalian tissue culture cells after siRNA transfection.

有丝分裂的siRNA转染后的时间推移成像

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and ...

科研

生物学

Cell Electrofusion Visualized with Fluorescence Microscopy

Cell electrofusion is a safe, non-viral and non-chemical method that can be used for preparing hybrid cells for human therapy. Electrofusion involves application of short high-voltage electric pulses to cells that are in close contact. Application of short, high-voltage electric pulses causes destabilization of cell plasma membranes. Destabilized membranes are more permeable for different molecules and also prone to fusion with any neighboring destabilized membranes. Electrofusion is thus a convenient method to achieve a non-specific fusion of very different cells in vitro. In order to obtain fusion, cell membranes, destabilized by electric field, must be in a close contact to allow merging of their lipid bilayers and consequently their cytoplasm. In this video, we demonstrate efficient electrofusion of cells in vitro by means of modified adherence method. In this method, cells are allowed to attach only slightly to the surface of the well, so that medium can be exchanged and cells still preserve their spherical shape. Fusion visualization is assessed by pre-labeling of the cytoplasm of cells with different fluorescent cell tracker dyes; half of the cells are labeled with orange CMRA and the other half with green CMFDA. Fusion yield is determined as the number of dually fluorescent cells divided with the number of all cells multiplied by two.

In this video we demonstrate efficient electrofusion of cells in vitro by means of modified adherence method using electroporation and the subsequent detection of fused cells visualization with fluorescence microscopy.

荧光显微镜下细胞电融合的可视化

Cell electrofusion is a safe, non-viral and non-chemical method that can be used for preparing hybrid cells for human therapy. Electrofusion involves ...

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using fluorochromes like Green Fluorescent Protein (GFP) directly attached to the protein of interest has become increasingly popular 1. Using GFP and similar fluorochromes the subcellular localisations and movements of proteins can be detected in a fluorescent microscope. Moreover, also the subnuclear localisation of a certain region of a chromosome can be studied using this technique. GFP is fused to the Lac Repressor protein (LacR) and ectopically expressed in the cell where tandem repeats of the lacO sequence has been inserted into the region of interest on the chromosome2. The LacR-GFP will bind to the lacO repeats and that area of the genome will be visible as a green dot in the fluorescence microscope. Yeast is especially suited for this type of manipulation since homologous recombination is very efficient and thereby enables targeted integration of the lacO repeats and engineered fusion proteins with GFP 3. Here we describe a quantitative method for live cell analysis of fission yeast. Additional protocols for live cell analysis of fission yeast can be found, for example on how to make a movie of the meiotic chromosomal behaviour 4. In this particular experiment we focus on subnuclear organisation and how it is affected during gene induction. We have labelled a gene cluster, named Chr1, by the introduction of lacO binding sites in the vicinity of the genes. The gene cluster is enriched for genes that are induced early during nitrogen starvation of fission yeast 5. In the strain the nuclear membrane (NM) is labelled by the attachment of mCherry to the NM protein Cut11 giving rise to a red fluorescent signal. The Spindle Pole body (SPB) compound Sid4 is fused to Red Fluorescent Protein (Sid4-mRFP) 6. In vegetatively growing yeast cells the centromeres are always attached to the SPB that is embedded in the NM 7. The SPB is identified as a large round structure in the NM. By imaging before and 20 minutes after depletion of the nitrogen source we can determine the distance between the gene cluster (GFP) and the NM/SPB. The mean or median distances before and after nitrogen depletion are compared and we can thus quantify whether or not there is a shift in subcellular localisation of the gene cluster after nitrogen depletion.

The fission yeast, Schizosaccharomyces pombe, is a good model system to study basic cellular processes. Here we describe a method to perform quantitative live cell analysis of fission yeast. In this particular experiment we focus on organisation of the genome within the cell nucleus, but the method can also be used to study cytosolic factors.

裂殖酵母的活细胞定量荧光显微镜分析

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using ...

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology1 even in complex tissue sections2. Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. 
Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells3, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgen&ouml;ssische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). 
We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

We developed a software platform that utilizes Imaris Neuroscience, ImarisXT and MATLAB to measure the changes in morphology of an undefined shape taken from three-dimensional confocal fluorescence of single cells. This novel approach can be used to quantify changes in cell shape following receptor activation and therefore represents a possible additional tool for drug discovery.

细胞形态变化，从三维荧光图像量化的分析工具，

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

采用先进的3D荧光显微镜定量分析自噬

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells possess several lines of defense against damage to biologically relevant molecules like nucleic acids, lipids and proteins. In addition to organelle dynamics (fusion/fission/motility/inheritance) and tightly controlled protease activity, the degradation of surplus, damaged or compromised organelles by autophagy (cellular &#8216;self-eating&#8217;) has received much attention from the scientific community. The regulation of autophagy is quite complex and depends on genetic and environmental factors, many of which have so far not been elucidated. Here a novel method is presented that allows the convenient study of autophagy in the filamentous fungus Penicillium chrysogenum. It is based on growth of the fungus on so-called &#8216;starvation pads&#8217; for stimulation of autophagy in a reproducible manner. Samples are directly assayed by microscopy and evaluated for autophagy induction / progress. The protocol presented here is not limited for use with P. chrysogenum and can be easily adapted for use in other filamentous fungi.

Analysis of Autophagy in Penicillium chrysogenum by Using Starvation Pads in Combination With Fluorescence Microscopy

A convenient and powerful method for studying autophagy in Penicillium chrysogenum by using starvation pads (mixtures of agarose and tap water in a microscope slide containing a central cavity) is presented here.

自噬的分析<em&gt;产黄青霉</em&gt;通过结合使用饥饿垫荧光显微镜

The study of cellular quality control systems has emerged as a highly dynamic and relevant field of contemporary research. It has become clear that cells ...

Identification of Kinesin-1 Cargos Using Fluorescence Microscopy

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear transcription factor c-MYC for proteosomal degradation in the cytoplasm. The method described here involves the study of a motorless KIF5B mutant for fluorescence microscopy. The wild-type and motorless KIF5B proteins are tagged with the fluorescent protein tdTomato. The wild-type tdTomato-KIF5B appears homogenously in the cytoplasm, while the motorless tdTomato-KIF5B mutant forms aggregates in the cytoplasm. Aggregation of the motorless KIF5B mutant induces aggregation of its cargo c-MYC in the cytoplasm. Hence, this method provides a visual means to identify the cargos of Kinesin-1. A similar strategy can be utilized to identify cargos of other motor proteins.

Here, a protocol is presented to identify Kinesin-1 cargos. A motorless mutant of the Kinesin-1 heavy chain (KIF5B) aggregates in the cytoplasm and induces aggregation of its cargos. Both aggregates are detected under fluorescence microscopy. A similar strategy can be employed to identify cargos of other motor proteins.

使用荧光显微镜驱动蛋白-1的货物的识别

Fluorescence microscopy is employed to identify Kinesin-1 cargos. Recently, the heavy chain of Kinesin-1 (KIF5B) was shown to transport the nuclear ...

Visualizing Stromule Frequency with Fluorescence Microscopy

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but whose functions remain mysterious. Alongside growing attention on the role of chloroplasts in coordinating plant responses to stress, interest in stromules and their relationship to chloroplast signaling dynamics has increased in recent years, aided by advances in fluorescence microscopy and protein fluorophores that allow for rapid, accurate visualization of stromule dynamics. Here, we provide detailed protocols to assay stromule frequency in the epidermal chloroplasts of Nicotiana benthamiana, an excellent model system for investigating chloroplast stromule biology. We also provide methods for visualizing chloroplast stromules in vitro by extracting chloroplasts from leaves. Finally, we outline sampling strategies and statistical approaches to analyze differences in stromule frequencies in response to stimuli, such as environmental stress, chemical treatments, or gene silencing. Researchers can use these protocols as a starting point to develop new methods for innovative experiments to explore how and why chloroplasts make stromules.

Protocols to investigate the dynamics of chloroplast stromules, the stroma-filled tubules that extend from the surface of chloroplasts, are described.

可视化Stromule频率与荧光显微镜

Stromules, or "stroma-filled tubules", are narrow, tubular extensions from the surface of the chloroplast that are universally observed in plant cells but ...

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 ...

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of clustering. Here, we describe an imaging approach to determine the average oligomeric state of mEGFP-tagged-receptor homocomplexes in the membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis. N&#38;B is a method similar to fluorescence-correlation spectroscopy (FCS) and photon counting histogram (PCH), which are based on the statistical analysis of the fluctuations of the fluorescence intensity of fluorophores diffusing in and out of an illumination volume during an observation time. In particular, N&#38;B is a simplification of PCH to obtain information on the average number of proteins in oligomeric mixtures. The intensity fluctuation amplitudes are described by the molecular brightness of the fluorophore and the average number of fluorophores within the illumination volume. Thus, N&#38;B considers only the first and second moments of the amplitude distribution, namely, the mean intensity and the variance. This is, at the same time, the strength and the weakness of the method. Because only two moments are considered, N&#38;B cannot determine the molar fraction of unknown oligomers in a mixture, but it only estimates the average oligomerization state of the mixture. Nevertheless, it can be applied to relatively small time series (compared to other moment methods) of images of live cells on a pixel-by-pixel basis, simply by monitoring the time fluctuations of the fluorescence intensity. It reduces the effective time-per-pixel to a few microseconds, allowing acquisition in the time range of seconds to milliseconds, which is necessary for fast oligomerization kinetics. Finally, large cell areas as well as sub-cellular compartments can be explored.

We describe an imaging approach for the determination of the average oligomeric state of mEGFP-tagged-receptor oligomers induced by ligand binding in the plasma membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis.

通过总内部反射荧光显微镜与数量和亮度分析相结合，活细胞中细胞表面受体的显微化动力学

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of ...