作者承认了DFG（EXC-1003至MG）的资助。

细胞培养<ol><li>含有4.5g / L D-葡萄糖，L-丙氨酸-L-谷氨酰胺二肽，丙酮酸，10％胎牛血清和10单位/ ml青霉素/链霉素的Dulbecco改良Eagle培养基（DMEM）中的培养HeLa或COS细胞。补充有0.5mM L-丙氨酸-L-谷氨酰胺二肽，无血清神经元补充剂和10单位/ ml青霉素/链霉素的培养基中不含L-谷氨酰胺，谷氨酸或天冬氨酸的培养神经元。 </li><li>一旦达到40％汇合，按照制造商的说明书，使用转染试剂转染选择的构建体。将转染细胞保持在37℃和5％CO 2的培养箱中15-18小时。 </li><li>为了减少图像采集期间的pH和渗透压（由于蒸发）的变化，用含有20mM 4-（2-羟乙基）-1的37℃预热培养基将细胞培养室（成像前）填充至90％ - 哌嗪乙磺酸（HEPES）。密封盖子上盖上一层薄薄的真空润滑脂，轻轻按压在含有转染细胞的培养室上。 </li></ol>图像采集注意:丝状伪足的长度从2-10μm13变化。 Filopodia以0.05-0.1μm/ s的平均速度生长13,14 。 <ol><li>使用60X或100X物镜的显微镜拍摄图像，无需像素分类（这里是旋转盘共聚焦显微镜）。使用大于1赫兹（Hz）的采集率来跟踪斑点动力学。为了最小化离焦伪像，靠近基底膜（ 即衬底表面）的图像伪迹。 </li><li>为了保证平滑跟踪，调整摄像机的曝光时间和激光强度，使信噪比（SNR）大于4.避免个别通道饱和（ 即像素值255位为8位图像，65,535为16位图像），因为这将排除随后的图像分析。 </li><li>为了避免渗色，只能使用与激光线和显微镜的过滤器兼容的荧光标记（详见参考文献15 ）。 </li></ol>图像预处理注意:使用ImageJ或其他可用的软件预处理图像16,17 。 <ol><li>如果样品正在移动，则在分析之前使用可用的软件（ 例如 https://github.com/NMSchneider/fixTranslation-Macro_for_ImageJ）纠正横向漂移。排除具有轴向偏移的电影（即沿z方向移动）。 </li><li>正确的背景（ 即细胞外区域的灰度值），漂白（ 即如果荧光蛋白损伤引起的荧光强度持续损失）和可能的出血槽（ 即来自一种感染的信号）两个通道中的荧光探针）使用可用的软件（ 例如 http://imagej.net/Category:Plugins和16,17 ）。 注意:单个通道的荧光强度不会被软件改变。 </li><li>为了确保后续的图像分析，将工作文件夹中的".tiff"堆叠文件格式的灰度级特定蛋白质通道对应的电影保存。 注意:所有通道的堆栈的尺寸（ 即大小，长度）必须相同。 </li></ol> 4.图像分析 - 步骤1:加载图像注意:这里描述的软件是用Matlab（简称编程软件）编写的，只能运行这个程序。 <ol><li>从以下站点下载包含用于图像分析的所有必需文件的压缩文件夹:https://campus.uni-muenster.de/en/einrichtungen/impb0/nanoscale-forces-in-cells/softw是/。将文件解压缩并复制到工作文件夹中。 </li><li>安装完成后，打开编程软件并运行&#39;filopodiaAnalysisM3.fig&#39;。 GUI窗口＃1将打开， 如图2A所示。 </li><li>使用蛋白质A的&lt;1b&gt;，蛋白质B的&lt;1c&gt;，蛋白C的&lt;1d&gt;加载对应于GUI窗口＃1中的特定蛋白质的保存的堆积的&#39;.tiff&#39;文件。参见图2和表1详细信息。 注意:GUI窗口＃1中显示的蛋白A作为最终比例分析的参考通道。 </li><li>通过点击&lt;1a&gt;从蛋白质通道创建单元格的叠加图像。 </li><li>单击&lt;2a&gt;分配用于分析的最后一帧的第一个帧和&lt;2b&gt;。 </li><li>可选地，使用按钮&lt;2h&gt;裁剪包含感兴趣的感兴趣区域的感兴趣区域（ROI），旋转图像u唱按钮&lt;2i&gt;，或使用自由绘图工具&lt;2j&gt;删除不需要的区域。 注意:为了简化图像分析，建议使用ImageJ将ROI（ 即裁剪，旋转，删除）与其他预处理步骤（背景减除，漂白校正等）隔离开来。 </li><li>移动每个框架的滑块&lt;2c&gt;进行质量控制，并检查整个影片中是否保持清晰可见。 </li></ol> 5.图像分析 - 步骤2:生成跟踪<ol><li>点击GUI窗口＃1中的按钮&lt;3a&gt;打开GUI窗口＃2（ 见图2B ）。 </li><li>点击GUI窗口＃2中的按钮&lt;4&gt;，生成叠加单元格的蒙版（点击&lt;1a&gt;后在GUI窗口＃1中生成）。该程序还生成掩模的边界，其中实施了凸包以获得顶点。 </li><li> CLIck按钮&lt;5&gt;;将出现一个光标。使用光标选择基底（从其中测量细丝尖端的距离），然后在其首次出现的框架中的丝状伪足的尖端。为此，在窗口＃2中移动滑块。 注意:为了最大限度地减少数据输出中的错误，请将基准点沿着轴线垂直放置在光栅尖端的下方。将基点定位在其他位置（ 例如横向偏移）可能在细胞体内的荧光值中引入方向偏置。 </li><li>使用&lt;6c&gt;选择阈值长度（高于该长度的丝状伪足会弯曲）。 注意:该长度定义为基点（使用&lt;5&gt;选择）与细胞体的边界（ 即 ，丝状伪足开始生长的区域）之间的距离。测量的距离是以像素为单位。 </li><li>在&lt;6a&gt;框中指定用于近似丝状伪迹形状的段数。 注意:最少数量的片段取决于光合丝达到的最大长度，而且还如何弯曲。不要选择大于基座和尖端之间像素数的段数（ 即阈值长度）。选择比步骤5.4中定义的阈值更多的段。 （ 即基数和提示之间的像素数）将导致对光栅长度的过高估计。 </li><li>指定扫描宽度，作为放置节点的水平扫描仪，位于&lt;6b&gt;中。 注意:程序使用这些节点来将连接基座和尖端的线与弧形体的主体（ 即创建骨架）配合使用。作为起点，将像素值等于最大长度乘以因子<img alt="方程" src="/files/ftp_upload/55653/55653eq1.jpg" /> 。 </li><li>在&lt;6d&gt;框中指定扫描半径（以像素为单位）。使用比ob大约50％的值提供了跨框架尖端位移。 注意:使用非常大的扫描半径可能会在不存在真实尖端尖端（ 例如 ，平面外移动或低SNR）的帧中抓取不需要的凸包点。 </li><li>在&lt;6e&gt;框中指定弯曲角度。 注意:角度阈值由整个分析期间钢筋弯曲的最大角度决定。指定角度阈值可帮助软件排除跟踪丝状伪足向细胞体弯曲时从丝状伪足一侧生长的不需要的结构。该程序可靠地工作，具有与垂直轴小于45度的倾斜角度的丝眼伪装。 </li><li>要开始跟踪，请单击GUI窗口＃2中的&lt;跟踪和分析&gt;按钮。单击GUI窗口＃2中的"历史记录跟踪"框以保存整个跟踪协议，以备将来参考。 注意:跟踪过程完成后，每个框架中的圆顶的长度存储在"dynamics.xlsx"的"length_vel"表格供将来参考。同样，所有其他跟踪参数都存储在"dynamics.xlsx"的名为"parameters"的工作表中。 </li><li> 或者，如果在所有帧中没有自动检测到虚拟网元，请使用以下步骤手动更正它。 <ol><li>使用窗口＃2中的滑块访问相应的框架，并手动选择顶点。 注意:在GUI窗口＃2的跟踪窗口中的蓝色区域将表示没有检测到凸包点的框架。在启动跟踪程序以访问该帧中的节点坐标之前，必须先检查"历史记录跟踪"按钮。 </li><li>在GUI窗口＃2中使用&lt;6f&gt;选择参考点（基础后跟提示）。指定其他参数，如"扫描长度"，"精确测量后"和"最大弯曲角度";对于该帧，如步骤5.4-5.8所述。 </li><li>点击&lt;6i&gt;按钮保存新的参数（特定于该帧）。对由蓝色区域指示的所有帧重复这些步骤。 </li><li>完成后，使用&lt;7&gt;重新初始化跟踪程序。 注意:如果程序在一个电影中突然从一个文字转换到另一个文字，也可以使用此修正。另一种方法是考虑修剪包含多个信封的电影。 </li></ol></li></ol>图像分析 - 步骤3:时空蛋白质分析<ol><li>对于时空分析，选择由&lt;8b&gt;代表的框，然后选择感兴趣的蛋白质通道（&lt;8c&gt;和/或&lt;8d&gt;和/或&lt;8e&gt;）。 注意:由于使用蛋白A作为参考，因此在比例分析（&lt;9a&gt;，&lt;9b&gt;和/或&lt;9c&gt;）之前必须选择蛋白A。 </li><li>点击&lt;8a&gt;使用步骤5.9中生成的痕迹沿着丝状体长度启动蛋白质跟踪。 </li></ol> 7.图像分析 - 步骤4:比率 - 度量蛋白质分析<ol><li>复选框&lt;9b&gt;或&lt;9c&gt;，然后单击按钮&lt;9a&gt;获得时空比例图。 注意:为了避免相对蛋白质浓度的错误表示，比例图像不绘制为X / Y，而是作为log（X / Y）。为了将来使用，比例图将以".png"和".fig"格式文件导出，原始绘图数据存储在"dynamics.xlsx"文件中。 </li></ol> 8.图像分析 - 步骤5:虚拟提示分析<ol><li>复选框&lt;8f&gt;，并使用&lt;8g&gt;和&lt;8h&gt;从底部指定刀尖长度和阈值长度。点击&lt;按钮&gt;将数据保存到文件&#39;dynamics.xlsx&#39;。点击&lt;分析蛋白质强度&gt;来生成痕量蛋白质强度的斑点尖端和保存比例分析。 注意:提示长度决定了用于分析的尖端的像素数。该软件将返回每个帧顶端像素的平均强度值。阈值长度决定了从底部到尖端的最小距离，超过该最小距离开始生长（斑点尖端）（当程序开始跟踪尖端的强度值时）。所以尖端长度必须小于阈值长度。 </li><li>点击所需的比例，使用&lt;9d&gt;或&lt;9e&gt;进行分析。 </li><li>点击比较按钮&lt;9a&gt;生成比例数据。 </li></ol>

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<li>Nilufar, S., Morrow, A. A., Lee, J. M., Perkins, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((FiloDetect%3A+automatic+detection+of+filopodia+from+fluorescence+microscopy+images%5BTitle%5D)+AND+%22BMC+Syst+Biol%22%5BJournal%5D)">FiloDetect: automatic detection of filopodia from fluorescence microscopy images.</a> BMC Syst Biol. 7, 66(2013).</li>
</ol>

作者没有什么可以披露的。

在这里，我们提出了一个详细的协议，用于通过凸壳算法跟踪丝状体生长动力学和这些动态结构中相对蛋白质浓度的分析。使用该软件，可以在单次运行中将多达3个通道成对比较，从而在整个延伸/缩回循环中确定两个通道（ 即蛋白质）的相对浓度，并将其作为图像和数据文件存储在不同的文件夹中。除了常规操作之外，软件还提供了许多可以进行修改的参数，以优化相应实验的分析。这?...

肌动蛋白调节蛋白的时空控制与纤维素酶动力学1,2相关。随着时间的推移，跟踪沿着整个网络长度的空间分辨蛋白质浓度对于促进我们对这些动态结构的起始，伸长，稳定或崩溃的机理的理解至关重要3,4 。不同于细胞质中的蛋白质分析，其中许多细胞形状变化发生在更大的尺度，丝状伪足是持续弯曲5并弯曲的动态微结构，因此使用简单的方法（例如线扫描）排除分析。 可用于跟踪丝状体形状的不同软件解决方案6,7,8,9。 Likewise，细胞体内蛋白质动力学的比例跟踪软件已经开发10,11 。为了结合自动跟踪网格形状和时空蛋白质分析，我们最近开发了基于凸包算法的图像分析软件12 。这种通过图形用户界面（GUI）操作的新型分析方法首次将沿着丝状体长度和生长速度的相对蛋白质浓度相结合，从而允许精确测量时空蛋白质分布，而与这些蛋白质的运动无关动态结构12 。 软件背后的想法（源代码可以免费获得，见下文），凸包的顶点之一将与圆顶的尖端一致（ 图1A ）。通过查看后续框架for最近的凸包顶点，可以在整个影片中追踪移动的尖端。一旦在每个框架中检测到尖端，它的位置用于通过将尖端与光栅底部的参考点连接起来绘制轴（ 图1B ）。最后，使用等距节点，其位置由具有与轴正交的线上的最大强度的中值像素确定，用于确定跟随斑点形状的骨架。利用这种自适应骨架，产生一个色谱图 ，以沿着丝状体长度追踪多达三个通道的丝状体生长和蛋白质浓度（ 图1C ）。 <img alt="图1" class="xfigimg" src="/files/ftp_upload/55653/55653fig1.jpg" /> 图1:图像分析软件的工作原理。 （ A ）后面的算法该软件。在步骤1中，用户指定参考（基数）和顶点的顶点（tip）。在步骤2-1中，使用具有最大强度值的中值像素获得光栅的骨架。在步骤3中，骨架用于空间蛋白质强度分布。在步骤2-2中，软件会自动跟踪后续帧中的提示。整个过程迭代。 （ B ）算法的快照，其中包含真正的光纤引入重要元素，例如用于跟踪的凸包。 （ C ）使用算法可以测量的参数概述。这个数字已经从参考文献12中修改过。 <a href="http://ecsource.jove.com/files/ftp_upload/55653/55653fig1large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> 图像分析软件通过图形使用在Matlab（简称编程软件）中运行r接口。为了使特定实验设置的灵活性和鲁棒性达到最大化，用户可以调整一系列跟踪参数（ 例如允许的弯曲角度和帧间移动），并对电影进行一些修正（ 例如裁剪，旋转，删除不需要的对象） （ 图2A和表1） 。 <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td> GUI </td><td> 没有。 </td><td> 强制性 </td><td colspan="5"> 描述 </td><td> 名称（在GUI中） </td></tr><tr><td> ＃1 </td><td> 1A </td><td> ÿ </td><td colspan="5">加载堆叠的.tiff文件，表示单元格主体（框已检入）或从通道创建叠加的单元格体</td><td> CellBody </td></tr><tr><td> ＃1 </td><td> 1B </td><td> ÿ </td><td colspan="5"&gt;加载与蛋白质1相对应的堆叠图像文件</td><td>蛋白质1 </td></tr><tr><td> ＃1 </td><td> 1C </td><td> ÿ </td><td colspan="5">加载对应于蛋白质2的堆叠图像文件</td><td>蛋白质2 </td></tr><tr><td> ＃1 </td><td> 1D </td><td> ÿ </td><td colspan="5">加载对应于蛋白质3的堆叠图像文件</td><td>蛋白质3 </td></tr><tr><td> ＃1 </td><td> 1E </td><td> ñ </td><td colspan="5">将所有内容重置为预先加载的堆叠图像文件</td><td>重启</td></tr><tr><td> ＃1 </td><td> 2A </td><td> ÿ </td><td colspan="5">滚动条确定GUI窗口＃2中分析的初始帧</td><td> NA </td></tr><tr><td> ＃1 </td><td> 2B </td><td> ÿ </td><td colspan="5">滚动条，以确定在GUI窗口＃2中进行分析的最终框架</td><td> NA </td></tr><tr><td> ＃1 </td><td> 2C </td><td> ÿ </td><td colspan="5">代表卷曲的滚动条nt框架</td><td> NA </td></tr><tr><td> ＃1 </td><td> 2D </td><td> ñ </td><td colspan="5">所有像素将被设置为零以下的像素的灰度值</td><td> NA </td></tr><tr><td> ＃1 </td><td> 2E </td><td> ñ </td><td colspan="5">所有像素将被设置为最大值的像素的灰度值</td><td> NA </td></tr><tr><td> ＃1 </td><td> 2F </td><td> ñ </td><td colspan="5">设置由&lt;2e&gt;＆&lt;2f&gt;指定的像素的强度值</td><td>组</td></tr><tr><td> ＃1 </td><td> 2克</td><td> ñ </td><td colspan="5">播放强度调整的电影</td><td>玩</td></tr><tr><td> ＃1 </td><td> 2H </td><td> ñ </td><td colspan="5">作物图像</td><td>作物</td></tr><tr><td> ＃1 </td><td> 2I </td><td> ñ </td><td colspan="5">旋转图像</td><td>旋转</td></tr><tr><td> ＃1 </td><td> 2J </td><td> ñ </td><td colspan="5">删除整个堆栈中的区域</td><td>删除区域</td></tr><tr><td> ＃1 </td><td> 3A </td><td> ÿ </td><td colspan="5">点击打开"分析窗口"（GUI窗口＃2） </td><td>跟踪窗口</td></tr><tr><td> ＃1 </td><td> 3B </td><td> ÿ </td><td colspan="5">输入像素的大小（以微米为单位） </td><td>输入像素大小</td></tr><tr><td> ＃2 </td><td> 4 </td><td> ÿ </td><td colspan="5">单击以生成叠加单元体的边界/边缘图像</td><td>边界</td></tr><tr><td> ＃2 </td><td>五</td><td> ÿ </td><td colspan="5">点击以选择网点的基数和提示</td><td> Referernce </td></tr><tr><td> ＃2 </td><td> 6A </td><td> ÿ </td><td colspan="5">输入段或节点的数量</td><td>段数</td></tr><tr><td> ＃2 </td><td> 6B </td><td> ÿ </td><td colspan="5">输入扫描长度（垂直于轴） </td><td>扫描宽度</td></tr><tr><td> ＃2 </td><td> 6C </td><td> ÿ </td><td colspan="5">输入超过该长度的丝网眼开始弯曲的长度</td><td>准确测量</td></tr><tr><td> ＃2 </td><td> 6D </td><td> ÿ </td><td colspan="5">输入尖端检测圆的半径（即顶点可以在下一帧中进行本地化的区域） </td><td>尖端检测半径</td></tr><tr><td> ＃2 </td><td> 6E </td><td> ÿ </td><td colspan="5">输入光栅可以从垂直轴弯曲的最大角度</td><td>角度阈值</td></tr><tr><td> ＃2 </td><td> 1207米</td><td> ñ </td><td colspan="5">为该特定框架的基准和提示添加参考点</td><td>选择参考</td></tr><tr><td> ＃2 </td><td> 6克</td><td> ñ </td><td colspan="5">输入长度超过该长度的纤维纹身开始弯曲该特定框架</td><td>准确测量</td></tr><tr><td> ＃2 </td> &lt;TD&gt; 6H </td><td> ñ </td><td colspan="5">输入该特定帧的检测圆的半径</td><td>尖端检测半径</td></tr><tr><td> ＃2 </td><td> 6I </td><td> ñ </td><td colspan="5">输入特定框架的所有参数后，单击以将值存储到内存和文件以供进一步参考</td><td>加</td></tr><tr><td> ＃2 </td><td> 6J </td><td> ñ </td><td colspan="5">单击以删除该框架的设置手动参数</td><td>删除</td></tr><tr><td> ＃2 </td><td> 6K </td><td> ñ </td><td colspan="5">单击以删除手动存储的所有参数，使用所有帧的"可选功能面板" </td><td>重启</td></tr><tr><td> ＃2 </td><td> 6升</td><td> ñ </td><td colspan="5">在跟踪之前检入以将所有跟踪结果存储在内存中，以备将来参考</td><td>历史追踪</td></tr><tr><td> ＃2 </td><td> 7 </td><td> ÿ </td><td colspan="5">点击开始跟踪</td><td>跟踪及分析</td></tr><tr><td> ＃2 </td><td> 8A </td><td> ñ </td><td colspan="5">点击开始跟踪蛋白质通道强度</td><td>分析蛋白质浓度</td></tr><tr><td> ＃2 </td><td> 8B </td><td> ñ </td><td colspan="5">检查沿轨道长度跟踪蛋白质通道强度</td><td>全丝酵母</td></tr><tr><td> ＃2 </td><td> 8C </td><td> ñ </td><td colspan="5">检查参考蛋白或蛋白A的跟踪</td><td> A蛋白</td></tr><tr><td> ＃2 </td><td> 8D </td><td> ñ </td><td colspan="5">检查跟踪蛋白B </td><td> ProteinB </td></tr><tr><td> ＃2 </td><td> 8E </td><td> ñ </td><td colspan="5">检查跟踪蛋白质C </td><td> P53复合体蛋白</td></tr><tr><td> ＃2 </td><td> 8F </td><td> ñ </td><td colspan="5">检查以跟踪平均蛋白质强度提示</td><td>领先提示</td></tr><tr><td> ＃2 </td><td> 8克</td><td> ñ </td><td colspan="5">输入提示的长度</td><td>提示长度</td></tr><tr><td> ＃2 </td><td> 8小时</td><td> ñ </td><td colspan="5">输入从底部开始形成尖端的最小距离</td><td>阈</td></tr><tr><td> ＃2 </td><td> 8I </td><td> ñ </td><td colspan="5">点击将顶尖分析结果保存到文件</td><td>按钮</td></tr><tr><td> ＃2 </td><td> 9A </td><td> ñ </td><td colspan="5">点击启动比率蛋白质分析</td><td>比较</td></tr><tr><td> ＃2 </td><td> 9B </td><td> ñ </td><td colspan="5">入住比较蛋白B相对于A </td><td>日志10（B / A） </td></tr><tr><td> ＃2 </td><td> 9C </td><td> ñ </td><td colspan="5">入住以比较蛋白C相对于A </td><td>日志10（C / A） </td></tr><tr><td> ＃2 </td> &lt;TD&gt; 9D </td><td> ñ </td><td colspan="5">入住以比较蛋白B相对于A的尖端</td><td>日志10（B / A） </td></tr><tr><td> ＃2 </td><td> 9E </td><td> ñ </td><td colspan="5">入住以比较蛋白C相对于A的尖端</td><td>日志10（C / A） </td></tr><tr><td> ＃2 </td><td> 10A </td><td> ñ </td><td colspan="5">选择其他颜色地图（默认值:Jetplot） </td><td>彩色地图</td></tr><tr><td> ＃2 </td><td> 10B </td><td> ñ </td><td colspan="5">编辑色彩图</td><td>编辑色彩图</td></tr></tbody></table> 表1:GUI Windows＃1和＃2中存在的所有功能概述。 一旦完成，该程序将创建一个凸包，并自动跟踪整个电影中的提示。从电影中提取的参数，比如比例计算器，生长速度和光线长度a重新显示并存储在工作文件夹中作为图像和数据文件。然后可以从存储的数据文件中提取和进一步分析其他参数，如光合生存期，生长速率和收缩率（ 图2B ）。 <img alt="图2" class="xfigimg" src="/files/ftp_upload/55653/55653fig2.jpg" /> 图2:使用图像分析软件的图形用户界面。 （ A ）GUI窗口＃1用于加载和处理图像。该程序可以加载多达3个蛋白质通道，从而成对比较2个通道。窗口具有强制性（蓝色）和可选功能（绿色），用于在跟踪之前预处理图像（ B ）GUI窗口2用于跟踪光点以及时空和比率 - 度量蛋白质分析。再次，可选功能标记为绿色。这个数字已经被修改了参考文献12 。 <a href="http://ecsource.jove.com/files/ftp_upload/55653/55653fig2large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> 在这里，我们提供了一个详细的样品制备和软件处理协议。我们从培养细胞和获取优化图像分析的电影的详细说明开始。本部分数据采集之后是操作图像分析软件的详细说明。在整个协议中，我们介绍了在收集和处理数据时应该考虑的关键步骤和可选功能。最后，我们利用图像分析软件分析来自不同模型系统的虚拟障碍，然后将所描述的图像分析软件与可用于虚拟量化的其他程序进行比较，并讨论限制和未来方向。

<table><tbody><tr><td>DMEM</td><td>Life Technologies</td><td>31966-021</td><td></td></tr><tr><td>10% Fetal bovine serum</td><td>Biochrom AG</td><td>L11-044</td><td></td></tr><tr><td>Lipofectamine 2000</td><td>Life Technologies</td><td>11668-027</td><td></td></tr><tr><td>1% penicillin/streptomycin</td><td>Biochrom AG</td><td>12212</td><td></td></tr><tr><td>Neurobasal Medium</td><td>Life Technologies</td><td>21103-049</td><td></td></tr><tr><td>B27</td><td>Life Technologies</td><td>17504-044</td><td></td></tr><tr><td>HEPES (1M stock solution)</td><td>Life Technologies</td><td>15630</td><td></td></tr><tr><td>Citrine-N1</td><td>Addgene</td><td>54593</td><td></td></tr><tr><td>Labtech</td><td>Thermo</td><td>155411</td><td></td></tr><tr><td>Glutamax-I</td><td>Thermo</td><td>35050-061</td><td></td></tr><tr><td>Hela</td><td>Leibniz Institute DSMZ</td><td>ACC-57</td><td></td></tr><tr><td>COS 7</td><td>Leibniz Institute DSMZ</td><td>ACC-60</td><td></td></tr><tr><td>3T3 cells</td><td>Leibniz Institute DSMZ</td><td>ACC-59</td><td></td></tr><tr><td>Microscope</td><td>Nicon Eclipse</td><td></td><td></td></tr><tr><td>Camera</td><td>Andor</td><td>DU888 Ultra</td><td></td></tr><tr><td>Confocal Unit</td><td>Yokagawa</td><td>CSU-X1</td><td></td></tr><tr><td>Pyruvate</td><td>Gibco</td><td>31966-021</td><td></td></tr></tbody></table>

a graphical user interface for software-assisted tracking of protein concentration in dynamic cellular protrusions

Filopodia是与迁移和细胞 - 细胞通信相关联的动态的，指状的细胞突起。为了更好地了解潜伏期起始，延长和随后的稳定或收缩的复杂信号传导机制，确定这些动态结构中的时空蛋白质活性至关重要。为了分析丝状伪足中的蛋白质功能，我们最近开发了一种半自动跟踪算法，适应线形形状变化，从而允许平行分析突起动力学和沿整个长度的整个相对蛋白质浓度。在这里，我们提供了一个详细的逐步协议，用于优化单元处理，图像采集和软件分析。我们进一步提供在图像分析和数据表示期间使用可选功能的说明，以及所有关键步骤的故障排除指南。最后，我们还包括d的比较带有图像分析软件的其他程序可用于丝状伪迹定量。在一起，所提出的方案提供了使用图像分析软件准确分析丝状体突起中的蛋白质动力学的框架。

使用用丝状肌动蛋白（f-tractin 18 ，红色）标记和细胞溶质参考（绿色）转染的COS细胞，我们发现肌动蛋白丰富的丝状体突起（ 图3A ，上图 ）。时间序列表明，丝状伪足迅速伸缩（ 图3A ，中间图 ）。使用图像分析软件，我们跟踪单个的虚拟眼镜。通过手测量的光?...

Watch this Scientific Journal Video about A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions at JoVE.com

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

我们提供了一个软件解决方案，用于半自动跟踪动态细胞突起长度的相对蛋白质浓度。

软件辅助跟踪动态细胞突变中蛋白质浓度的图形用户界面

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex ...

a-graphical-user-interface-for-software-assisted-tracking-protein

Research

JoVE Journal

Biology

7.3K 次观看.  University of Muenster. 我们提供了一个软件解决方案，用于半自动跟踪动态细胞突起长度的相对蛋白质浓度。 

视频: A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring cells. Of interest, only few assays are capable of specifically probing such interactions directly in living cells. Here, we present an assay to measure the binding of proteins expressed at the surfaces of neighboring cells, at cell-cell contacts. This assay consists of two steps: mixing of cells expressing the proteins of interest fused to different fluorescent proteins, followed by fluorescence fluctuation spectroscopy measurements at cell-cell contacts using a confocal laser scanning microscope. We demonstrate the feasibility of this assay in a biologically relevant context by measuring the interactions of the amyloid precursor-like protein 1 (APLP1) across cell-cell junctions. We provide detailed protocols on the data acquisition using fluorescence-based techniques (scanning fluorescence cross-correlation spectroscopy, cross-correlation number and brightness analysis) and the required instrument calibrations. Further, we discuss critical steps in the data analysis and how to identify and correct external, spurious signal variations, such as those due to photobleaching or cell movement.
In general, the presented assay is applicable to any homo- or heterotypic protein-protein interaction at cell-cell contacts, between cells of the same or different types and can be implemented on a commercial confocal laser scanning microscope. An important requirement is the stability of the system, which needs to be sufficient to probe diffusive dynamics of the proteins of interest over several minutes.

This protocol describes a fluorescence fluctuation spectroscopy-based approach to investigate interactions among proteins mediating cell-cell interactions, i.e. proteins localized in cell junctions, directly in living cells. We provide detailed guidelines on instrument calibration, data acquisition and analysis, including corrections to possible artefact sources.

细胞细胞接触物蛋白质-蛋白质相互作用的荧光波动光谱测定

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring ...

科研

生物化学

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and millisecond temporal resolution. These capabilities make single-molecule localization microscopy ideally suited to study molecular level biological functions in physiologically relevant environments. Here, we demonstrate an integrated protocol for both acquisition and processing/analysis of single-molecule tracking data to extract the different diffusive states a protein of interest may exhibit. This information can be used to quantify molecular complex formation in living cells. We provide a detailed description of a camera-based 3D single-molecule localization experiment, as well as the subsequent data processing steps that yield the trajectories of individual molecules. These trajectories are then analyzed using a numerical analysis framework to extract the prevalent diffusive states of the fluorescently labeled molecules and the relative abundance of these states. The analysis framework is based on stochastic simulations of intracellular Brownian diffusion trajectories that are spatially confined by an arbitrary cell geometry. Based on the simulated trajectories, raw single-molecule images are generated and analyzed in the same way as experimental images. In this way, experimental precision and accuracy limitations, which are difficult to calibrate experimentally, are explicitly incorporated into the analysis workflow. The diffusion coefficient and relative population fractions of the prevalent diffusive states are determined by fitting the distributions of experimental values using linear combinations of simulated distributions. We demonstrate the utility of our protocol by resolving the diffusive states of a protein that exhibits different diffusive states upon forming homo- and hetero-oligomeric complexes in the cytosol of a bacterial pathogen.

3D single-molecule localization microscopy is utilized to probe the spatial positions and motion trajectories of fluorescently labeled proteins in living bacterial cells. The experimental and data analysis protocol described herein determines the prevalent diffusive behaviors of cytosolic proteins based on pooled single-molecule trajectories.

单分子跟踪显微镜 - 确定细胞分子扩散状态的工具

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and ...

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

受体动力学通过单分子荧光显微镜的高分辨率时空分析

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

生物工程

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome organization are governed by DNA-binding proteins that recognize specific DNA structures or sequences. In vitro experiments have helped to generate detailed models for the function of many types of DNA-binding proteins, yet, the exact mechanisms of these processes and their organization in the complex environment of the living cell remain far less understood. We recently introduced a method for quantifying DNA-repair activities in live Escherichia coli cells using Photoactivated Localization Microscopy (PALM) combined with single-molecule tracking. Our general approach identifies individual DNA-binding events by the change in the mobility of a single protein upon association with the chromosome. The fraction of bound molecules provides a direct quantitative measure for the protein activity and abundance of substrates or binding sites at the single-cell level. Here, we describe the concept of the method and demonstrate sample preparation, data acquisition, and data analysis procedures.

Photoactivated localization microscopy (PALM) combined with single-molecule tracking allows direct observation and quantification of protein-DNA interactions in live Escherichia coli cells.

可视化的活细菌细胞蛋白-DNA相互作用使用光活化的单分子跟踪

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome ...

免疫与感染

Label-Free Imaging of Single Proteins Secreted from Living Cells via iSCAT Microscopy

We demonstrate interferometric scattering (iSCAT) microscopy, a method capable of detecting single unlabeled proteins secreted from individual living cells in real time. In this protocol, we cover the fundamental steps to realize an iSCAT microscope and complement it with additional imaging channels to monitor the viability of a cell under study. Following this, we use the method for real-time detection of single proteins as they are secreted from a living cell which we demonstrate with an immortalized B-cell line (Laz388). Necessary steps concerning the preparation of microscope and sample as well as the analysis of the recorded data are discussed. The video protocol demonstrates that iSCAT microscopy offers a straightforward method to study secretion at the single-molecule level.

We present a protocol for the real-time optical detection of single unlabeled proteins as they are secreted from living cells. This is based on interferometric scattering (iSCAT) microscopy, which can be applied to a variety of different biological systems and configurations.

通过 iscat 显微镜对活细胞分泌的单一蛋白质进行无标签成像

We demonstrate interferometric scattering (iSCAT) microscopy, a method capable of detecting single unlabeled proteins secreted from individual living ...

工程学

Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics

Short-lived or transient interactions of macromolecules at and with lipid membranes, an interface where a multitude of essential biological reactions take place, are inherently difficult to assess with standard biophysical methods. The introduction of mass-sensitive particle tracking (MSPT) constitutes an important step toward a thorough quantitative characterization of such processes. Technically, this was made possible through the advent of interferometric scattering microscopy (iSCAT)-based mass photometry (MP). When the background removal strategy is optimized to reveal the two-dimensional motion of membrane-associated particles, this technique allows the real-time analysis of both diffusion and molecular mass of unlabeled macromolecules on biological membranes. Here, a detailed protocol to perform and analyze mass-sensitive particle tracking of membrane-associated systems is described. Measurements performed on a commercial mass photometer achieve time resolution in the millisecond regime and, depending on the MP system, a mass detection limit down to 50 kDa. To showcase the potential of MSPT for the in-depth analysis of membrane-catalyzed macromolecule dynamics in general, results obtained for exemplary protein systems such as the native membrane interactor annexin V are presented.

This protocol describes an iSCAT-based image processing and single-particle tracking approach that enables the simultaneous investigation of the molecular mass and the diffusive behavior of macromolecules interacting with lipid membranes. Step-by-step instructions for sample preparation, mass-to-contrast conversion, movie acquisition, and post-processing are provided alongside directions to prevent potential pitfalls.

质量敏感颗粒跟踪，用于表征膜相关的高分子动力学

Short-lived or transient interactions of macromolecules at and with lipid membranes, an interface where a multitude of essential biological reactions take ...

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

Here, we present a protocol for single particle tracking image analysis that allows quantitative evaluation of diffusion coefficients, types of motion and cluster sizes of single particles detected by fluorescence microscopy.

用荧光显微镜分析膜受体扩散和簇簇的图像处理协议

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using ...

Micromanipulation Techniques Allowing Analysis of Morphogenetic Dynamics and Turnover of Cytoskeletal Regulators

Examining the spatiotemporal dynamics of proteins can reveal their functional importance in various contexts. In this article, it is discussed how fluorescent recovery after photobleaching (FRAP) and photoactivation techniques can be used to study the spatiotemporal dynamics of proteins in subcellular locations. We also show how these techniques enable straightforward determination of various parameters linked to actin cytoskeletal regulation and cell motility. Moreover, the microinjection of cells is additionally described as an alternative treatment (potentially preceding or complementing the aforementioned photomanipulation techniques) to trigger instantaneous effects of translocated proteins on cell morphology and function. Micromanipulation such as protein injection or local application of plasma membrane-permeable drugs or cytoskeletal inhibitors can serve as powerful tool to record immediate consequences of a given treatment on cell behavior at the single cell and subcellular level. This is exemplified here by immediate induction of lamellipodial cell edge protrusion by the injection of recombinant Rac1 protein, as established a quarter-century ago. In addition, we provide a protocol for determining the turnover of enhanced green fluorescent protein (EGFP)-VASP, an actin filament polymerase prominently accumulating at lamellipodial tips of B16-F1 cells, employing FRAP and including associated data analysis and curve fitting. We also present guidelines for estimating the rates of lamellipodial actin network polymerization, as exemplified by cells expressing EGFP-tagged &#946;-actin. Finally, instructions are given for how to investigate the rates of actin monomer mobility within the cell cytoplasm, followed by actin incorporation at sites of rapid filament assembly, such as the tips of protruding lamellipodia, using photoactivation approaches. None of these protocols is&#160;restricted to components or regulators of the actin cytoskeleton, but can easily be extended to explore in analogous fashion the spatiotemporal dynamics and function of proteins in various different subcellular structures or functional contexts.

We describe how micro- and photomanipulation techniques such as FRAP and photoactivation enable the determination of motility parameters and the spatiotemporal dynamics of proteins within migrating cells. Experimental readouts include subcellular dynamics and turnover of motility regulators or of the underlying actin cytoskeleton.

微操作系统技术允许分析骨架调节器的形态发生动力学和营业额

Examining the spatiotemporal dynamics of proteins can reveal their functional importance in various contexts. In this article, it is discussed how ...

生物学

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles involved in the endo/exocytotic trafficking of solutes and biomolecules. iMSD relies on standard time-lapse imaging, is compatible with any optical setup, and does not need to dwell on single objects to extract trajectories. From each iMSD trace, a unique triplet of average structural and dynamic parameters (i.e., size, local diffusivity, anomalous coefficient) is calculated and combined to build the "iMSD signature" of the nanostructure under study. 
The potency of this approach is proved here with the exemplary case of macropinosomes. These vesicles evolve in time, changing their average size, number, and dynamic properties passing from early to late stages of intracellular trafficking. As a control, insulin secretory granules (ISGs) are used as a reference for subcellular structures that live in a stationary state in which the average structural and dynamic properties of the whole population of objects are invariant in time. The iMSD analysis highlights these peculiar features quantitatively and paves the way to similar applications at the sub-cellular level, both in the physiological and pathological states.

Imaging-derived mean square displacement (iMSD) analysis is applied to macropinosomes to highlight their intrinsic time-evolving nature in terms of structural and dynamic properties. Macropinosomes are then compared to insulin secretory granules (ISGs) as a reference for subcellular structures with time-invariant average structural/dynamic properties.

基于时空波动光谱的运输亚细胞纳米结构的结构和动态性质

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles ...

Measuring Cell-Edge Protrusion Dynamics during Spreading using Live-Cell Microscopy

The development and homeostasis of multicellular organisms rely on coordinated regulation of cell migration. Cell migration is an essential event in the construction and regeneration of tissues, and is critical in embryonic development, immunological responses, and wound healing. Dysregulation of cell motility contributes to pathological disorders, such as chronic inflammation and cancer metastasis. Cell migration, tissue invasion, axon, and dendrite outgrowth all initiate with actin polymerization-mediated cell-edge protrusions. Here, we describe a simple, efficient, time-saving method for the imaging and quantitative analysis of cell-edge protrusion dynamics during spreading. This method measures discrete features of cell-edge membrane dynamics, such as protrusions, retractions, and ruffles, and can be used to assess how manipulations of key actin regulators impact cell-edge protrusions in diverse contexts.

This protocol aims to measure the dynamic parameters (protrusions, retractions, ruffles) of protrusions at the edge of spreading cells.

使用活细胞显微镜测量扩散过程中的细胞边缘突起动力学

The development and homeostasis of multicellular organisms rely on coordinated regulation of cell migration. Cell migration is an essential event in the ...