Name: confocal microscopy reveals cell surface receptor aggregation through image correlation spectroscopy
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Description: Watch this Scientific Journal Video about Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy at JoVE.com

作者承认来自澳洲的资金支持 (奖学金1084178和赠款 1087850, 1030469, 1075898 (AMS)), 癌症澳大利亚, Ludwig 癌症研究, 约翰·里德信托, 治疗脑癌基金会, La 筹伯大学和维多利亚癌症机构。澳大利亚维多利亚政府提供的运营基础设施支持计划的资金也得到了承认。

1. 贴壁细胞株在成像实验中的播种
<ol><li>种子 1万 A431 表皮细胞癌细胞每井 (O/N) 在300µL Dulbecco 的改良鹰培养基/营养混合物 F-12 (DMEM/F-12) 培养基中含有10% 胎牛血清, 1% 青霉素-链霉素和 1% GlutaMAX 在8腔内成像适用于高分辨率油浸泡物镜的滑动 (例如, NuncTM 实验室 TekTM 腔 coverglass)。</li>
	<li>在室温下 (RT), 在培养基中添加100毫升的 EGF 配体, 刺激受体在 A431 细胞中的聚集, 10 分钟。</li>
</ol>2. 原发性抗体孵化
<ol><li>用300µL 室温 (RT) 磷酸盐缓冲盐水 (1x PBS) 两次去除介质并冲洗细胞。</li>
	<li>修复细胞与200µL 4% 多聚甲醛在 PBS 为10分钟的 RT。 注意: 为了探索目标聚集的时间变化, 必须省略此固定步骤。</li>
	<li>用300µL 室温 PBS 两次去除粉煤灰和洗涤细胞。</li>
	<li>孵化细胞与200µL 的 PBS 含有主要抗体在10µg/毫升浓度至少2小时4摄氏度。 注意: 为使 ICS 计算有效, 所有在细胞表面存在的受体必须用原抗体标记。这需要初步实验的每一个主要抗体稀释系列 (1:10, 1:50, 1:100, 1:200, 1:500 1:1, 000,等) 将执行。通过荧光强度与簇密度的分析, 确定了最佳主抗体浓度。选定的主要浓度发生时, 簇密度高原随着主要抗体浓度的增加之前的簇密度进一步增加, 由于非特定的抗体结合。在进行时间-过程成像实验时, 血清游离培养基中的原发抗体可以稀释。</li>
	<li>完成所需的潜伏期后, 冲洗细胞与300µL 的 RT PBS 两次。</li>
	<li>在 PBS 中用5% 牛血清白蛋白 (BSA) 阻断样品, 至少在4摄氏度时为2小时。 注意: 固定的样品在一夜之间经常被阻拦 (O/N) 在4°c。</li>
</ol>3. 次生抗体孵化
<ol><li>在 PBS 中准备10µg/毫升荧光标记的二级抗体 (如Alexa 氟 488 IgG)。2µg/毫升的赫斯特33342可能被包括在这个稀释, 以允许改进的样品发现在显微镜上。 注意: 对于时间推移实验, PBS 可以交换为血清自由介质。</li>
	<li>从细胞中取出 5% BSA/PBS, 用二次抗体溶液孵育2小时4摄氏度, 同时保护样品不受环境光线的侵害。</li>
	<li>在二次抗体潜伏期完成后, 用300µL 的 PBS (两次) 冲洗细胞, 并贮存在4摄氏度, 保护环境光线。</li>
	<li>为了防止蒸发, 用 parafilm 将燃烧室的边缘包裹起来。</li>
</ol>4. 样品的共焦显微术
注意: 用于共焦分析的设置将因个别实验中使用的设备和显影而异。现代共焦仪器提供的机制, 以保存在实验中使用的所有成像参数的本机图像文件格式的仪器。必须记录这些设置, 以便在不同的数据收集期间适当地复制实验条件。
<ol><li>在荧光信号的成像过程中, 避免过度饱和。使用图像查找表, 提供像素饱和的假颜色表示, 并减少探测器增益和激光功率相应。在采集软件中, 大多数共焦仪器包括 "范围指示器" 设置或类似选项。</li>
	<li>尽量减少对激光功率荧光的曝光。使用 DNA 标记染色, 如赫斯特33342提供了一个简单的机制, 以检测和定位样品在视野中收集。</li>
	<li>以低分辨率执行示例的初始扫描, 如果软件允许快速扫描速度。这将限制示例的可能漂白。</li>
	<li>在准备收集图像数据时, 增加捕获分辨率并减慢扫描速度。</li>
	<li>如果共焦仪器允许, 使用光子计数模式收集数据。 注: 如果仪器上没有光子计数, 请确保图像采集过程中的灰度是线性的。</li>
	<li>设置每个激光线的针孔, 用于1通风单元 (AU)。</li>
	<li>在图像采集 (x4) 中使用直线或帧平均来减少检测器相关的噪声。 注意: 在这个变型的 ICS 中, 只有一个时间点被捕获的采集时间不是实验限制因素。</li>
	<li>为图像收集选择高放大率, 高数值孔径目标, 如计划复消色差目标, 提供最大分辨率和校正的球面和色差 (例如计划-复消色差100X 或 63X/1.40石油目标)。</li>
	<li>细分采样图像中包含的捕获像素的大小。建议每个像素捕获一个 50 x 50 nm 区域。对于有效的 ICS, 像素大小必须小于 0.1 x 0.1 µm 每像素2 。这是通过放大扫描一个相对较小的区域并选择一个相对较大的图像格式 (例如1024 x 1024 或 2048 x 2048 像素) 来实现的。</li>
	<li>把注意力集中在细胞的顶端或基底表面, 尽可能平坦的区域。在 ICS 分析过程中单独处理的一个图像和感兴趣的区域可以收集多个单元格。</li>
	<li>使用相同的仪器设置来捕获一个无单元格背景区域, 用于示例规范化。</li>
</ol>5. 图像相关谱的计算
注: 在 ImageJ 程序的分发中, 斐济是开源成像处理平台, 需要执行对 ICS 至关重要的自相关和数学功能。ImageJ 的这种分布是推荐的, 因为它包括许多预安装的插件和一个可以跨多个成像实验执行操作的更简单的更新体系结构。
<ol><li>安装斐济程序 (http://fiji.sc/Downloads)。</li>
	<li>加载获取的数据集。</li>
	<li>识别感兴趣的顶端或基底细胞表面膜区, 并复制到像素大小为 2n (256 x 256, 128 x 128, 64 x 64)。"菜单: 图像 > 复制..."</li>
	<li>计算所裁剪区域的平均强度 "菜单: 分析 > 测量"。</li>
	<li>识别背景区域并复制到捕获细胞表面膜的相同像素大小 (2n: 256x256、128x128、64x64)。"菜单: 图像 > 复制..."</li>
	<li>计算感兴趣的背景裁剪区域的平均强度。"菜单: 分析 > 测量。</li>
	<li>从包含感兴趣区域的图像中减去背景的平均强度。"过程 > 图像计算器 > 减法"。</li>
	<li>
执行自相关计算。此计算包含在菜单结构中: "过程 > FTT > FD 数学"。
	<ol>
<li>在 FD 数学对话框中选择: 图像 1: 裁剪图像名称, 操作为相关, 图像 2: 裁剪图像名称, 反向转换。</li>
	</ol>
</li>
	<li>通过除以总像素数来规范化生成的图像。"菜单: 过程 > 数学 > 鸿沟..."(即: 64x64 像素 = 4096)</li>
	<li>通过按规范化裁剪区域平方的平均强度除以, 重新正常化。"菜单: 过程 > 数学 > 鸿沟..." 注意: 这可以通过将图像除以平均强度值两次来实现。</li>
	<li>通过点传播函数绘制一条直线。</li>
	<li>绘制此行的剖面线以计算峰值值。"菜单: 分析 > 剧情简介"。</li>
	<li>通过将峰值值 (结果 5.12) 传输到电子表格并执行以下计算 (Eq 6), 计算每个波束区域的簇数: <img alt="Equation 7" src="/files/ftp_upload/57164/57164eq7v2.jpg">
</li>
</ol>6. 集成电路数据集的批处理
注: 建议建立斐济/ImageJ 宏, 以复制上述协议中描述的程序。这使得在图像相关谱分析中对多个图像文件进行一致和快速的分析。
<ol><li>通过记录 ics 协议中的每个菜单命令建立宏 ics 工作流 "菜单: 插件 > 宏 > 记录..."。</li>
	<li>选择 "创建" 以生成宏。</li>
	<li>选择 "语言 > IJ1 宏"。</li>
	<li>保存宏。</li>
	<li>
将对话框添加到宏的各个步骤中, 为每个函数的运算符提供提醒。对话框还用作暂停分析过程的方法, 以允许在进一步处理之前 (即区域裁剪) 之前进行适当的选择。
	<ol>
<li>对话框宏代码 标题 = "对话框标题"; 味精 = "对话框/协议步骤消息"; waitForUser (标题, 味精); 注意: 可以通过订阅斐济更新程序 (指令 https://imagej.net/BAR) 中的 "条码" 站点来建立额外的效率。这为斐济提供了 "一组广泛适用的例程" 14。一个这样的惯例就是找到顶峰。菜单结构: "条形 > 数据分析 > 查找峰值"。此脚本提供了一种快速计算点扩展函数配置文件中峰值值的方法。</li>
	</ol>
</li>
</ol>7. 协议扩展: 显微镜光束面积的计算
注意: 显微镜的光学传递功能确保即使是分子尺寸的物体在 x-y 平面中的半径约为 200-300 nm 的图像也会出现。ICS 协议允许在亚分辨率荧光珠上执行步骤4-5 来确定点传播函数。特别:
<ol><li>安装亚分辨率 (即< 100 nm 直径) 荧光珠到8室成像幻灯片。</li>
	<li>图像珠, 根据描述的协议, 使用共聚焦显微镜, 用于集成电路实验。</li>
	<li>根据协议步骤 5.1-5.13, 从生成的图像中计算 ICS 函数。</li>
	<li>从绘制的剖面, 计算光束半径 (r) 为全宽的最大值的 ICS 函数的一半。</li>
	<li>计算光束面积 (BA) 为 (Eq 7):<img alt="Equation 8" src="/files/ftp_upload/57164/57164eq8v2.jpg">
</li>
</ol>8. 协议扩展: 计算每单位面积的簇数
<ol><li>计算每个区域单位的簇密度 (CD), 如 (Eq 8): <img alt="Equation 9" src="/files/ftp_upload/57164/57164eq9v2.jpg"> <img alt="Equation 10" src="/files/ftp_upload/57164/57164eq10v2.jpg">
</li>
	<li>除以光束区域的簇 (结果为 5.13) 由梁的面积 (结果为 7.5)。</li>
</ol>

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安德鲁 M 斯科特有 AbbVie 药剂, EMD 雪兰诺和第一惠州市三协 Co 的研究资金支持, 并有顾问和股票所有权的生命科学药物。作者没有其他相关的关联或财务参与任何组织或实体的财务利益或财务冲突的主题或材料讨论的手稿, 除了那些披露。

我们在本协议中描述的图像相关光谱学 (ICS) 技术使用标准共焦显微镜, 无需专门的探测器。ICS 技术描述利用已建立好的免疫细胞化学方法, 提供快速抽样的多种治疗条件, 以增加统计分析。这种方法这样做的绝对精度略有降低, 与替代单分子技术相比, 基于荧光波动的移动分子扩散通过共焦体积, 如荧光相关光谱学 (FCS)15。更专业的 FCS 方法还需要高灵敏度的光子计数探测器, 如雪?...

治疗性抗体的发展在治疗多种肿瘤类型1中表现出显著的成功。最近的进展, 抗体-药物共轭 (ADC) 作为细胞毒性化合物的传递机制, 扩大了了解抗体动态的要求: 细胞表面的受体相互作用2。在成功地靶向细胞表面受体的抗体后, 这些配合物可以诱导相似的聚集模式对在配体中观察到的那些: 抗体相互作用3。受体聚集的改变会诱发细胞膜的改变, 并导致受体的内化和细胞表面的去除。在抗体-药物共轭的背景下, 这一过程随后将细胞毒性有效载荷释放到内内涵体内, 随后形成细胞质, 从而有效地杀死细胞。
共焦显微镜提供了一种有效的方法来可视化这些重要的相互作用的抗体和他们的目标受体4。探讨靶分子在细胞表面聚集的变化本协议利用空间图像相关光谱学 (ICS) 技术5、6、7 对共焦显微图像进行后处理..
图像相关光谱学的基础是观察到空间荧光强度波动与标记结构的密度和聚集状态的关系。这种关系是在计算被捕获的图像5的空间自相关函数之后建立的。
图像相关光谱学的所有变体都需要对图像自相关进行计算。接着将此函数拟合为二维高斯曲线, 用于提取图像中包含的定量聚合状态参数。简单地说, 图像自相关的计算涉及将图像中包含的所有可能的像素对进行比较, 并计算两者之间相等的亮度。这是可视化为一个函数的距离和方向的像素分离8。
建立了图像相关谱的理论框架, 并由彼得森和智者等来定义。5,6. 在本议定书中, 在斐济/ImageJ 和电子表格应用程序中执行自相关计算, 可将强度波动空间自相关函数的基础描述为(Eq 1):
<img alt="Equation 1" src="/files/ftp_upload/57164/57164eq1v2.jpg">     
其中F代表傅立叶变换;F−1 逆傅里叶变换;F *其复杂共轭;和η的空间滞后变量。在空间集成电路中, 如本协议所述, 可使用2D 快速傅立叶变换算法7、9计算自相关函数。在零空间分离的自相关, 即零滞后, g11(00), 提供了显微镜下每束区域呈现的粒子的逆平均数。它可以通过将空间自相关函数拟合为二维高斯函数(Eq 2)来获得:
<img alt="Equation 3" src="/files/ftp_upload/57164/57164eq3v2.jpg">     
由于图像中捕获的像素包含在集合区域内, 而这些度量值不扩展到无穷大, 因此术语 g∞用作偏移量, 用于计算图像中包含的远距离空间相关性。对于分子大小的聚集体, ω是显微镜的点扩散函数, 由全宽半最大空间自相关函数描述。该仪器的点传播功能范围内, 可通过使用亚分辨率荧光珠校准。
对于本文所描述的图像相关光谱学协议, 使用开放源图像处理平台 (斐济10) 进行 ImageJ 的分布, 完成 ICS 所需的自相关和数学功能。节目11,12。斐济/ImageJ 在 FFT 数学函数中利用预安装的快速傅立叶变换。此函数通过在每个维度13中减小数据的范围, 从而减少了计算所需的计算时间。当2D 自相关函数在 x、y 轴上近似对称时, 通过自相关图像的单行剖面图可以用来测量原始自相关作为空间滞后的函数。在进一步计算之前, 任何零滞后噪声都将被移除, 由此产生的自相关振幅 (峰值、 g(0)T) 在表达式(Eq 3)的背景下得到修正:
<img alt="Equation 4" src="/files/ftp_upload/57164/57164eq4v2.jpg">     
其中 Ib是来自背景区域的平均强度 (不包括单元格)。簇密度或荧光物体的密度是由(Eq 4)定义的:
<img alt="Equation 5" src="/files/ftp_upload/57164/57164eq5v2.jpg">    
在本文所描述的协议中, 我们进一步简单地计算了聚类密度 (CD), 假设是在观测的基础上, 一个归一化的自相关函数将衰减到一个值接近1.0 的空间滞后。与最大空间滞后, 不再有任何相关性的荧光强度值, 因而没有相关的计算在这个区域计算的强度乘以这个强度, 随后除以那强度的正方形, 按定义是相等的到1.0。因此, 从归一化自相关函数的聚类密度可以计算在取其倒数(Eq 5)之前, 从规范化自相关函数中减去 1.0:
<img alt="Equation 6" src="/files/ftp_upload/57164/57164eq6v2.jpg">     
可以对光束区域进行进一步的标定, 以定量在该仪器的点传播函数区域内的簇数。这种校准必须使用相同的光学条件在图像相关光谱学分析中使用。

<table><tbody><tr><td>Nunc Lab-Tek II Chambered Coverglass - 8 well</td><td>ThermoFisher Scientific</td><td>155409</td><td></td></tr><tr><td>DPBS, no calcium, no magnesium</td><td>ThermoFisher Scientific</td><td>14190144</td><td></td></tr><tr><td>Fetal Bovine Serum</td><td>ThermoFisher Scientific</td><td>10099141</td><td></td></tr><tr><td>TrypLE Express Enzyme (1x), no phenol red</td><td>ThermoFisher Scientific</td><td>12604021</td><td></td></tr><tr><td>Penicillin-Streptomycin (10,000 U/mL)</td><td>ThermoFisher Scientific</td><td>15140122</td><td></td></tr><tr><td>GlutaMAX Supplement</td><td>ThermoFisher Scientific</td><td>35050061</td><td></td></tr><tr><td>Goat anti-Human IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 488</td><td>ThermoFisher Scientific</td><td>A11013</td><td></td></tr><tr><td>TetraSpeck Fluorescent Microsphere Standards 0.1&amp;micro;m</td><td>ThermoFisher Scientific</td><td>T7279</td><td></td></tr><tr><td>Cetuximab</td><td>Merck Serono</td><td>3023715501</td><td></td></tr><tr><td>Parafilm M 38mx100mm</td><td>Merck Millipore</td><td>BRND701605</td><td></td></tr><tr><td>16% Paraformaldehyde (formaldehyde) aqueous solution</td><td>ProSciTech</td><td>C004</td><td></td></tr><tr><td>Recombinant Human EGF</td><td>R&amp;amp;D System</td><td>236-EG</td><td></td></tr></tbody></table>

confocal microscopy reveals cell surface receptor aggregation through image correlation spectroscopy

共焦显微术提供了一个可访问的方法, 以捕获的亚细胞相互作用关键的表征和进一步发展的临床前药物标记荧光探针。随着最近在抗体基础上的细胞毒性药物传递系统的进展, 了解这些药物诱导在受体聚集和内化领域的变化是至关重要的。该协议利用了 ImageJ 的荧光免疫细胞化学和开放源斐济分布的良好方法, 其内置自相关和图像数学功能, 以执行空间图像相关性光谱学 (ICS)。该协议 quantitates 了标记受体的荧光强度作为共焦显微镜光束区域的函数。这为细胞表面的靶分子聚集状态提供了定量测量。这种方法的重点是静态细胞的特征, 有潜力扩展到时间调查的受体聚集。该协议提出了一种可访问的方法, 用于提供在细胞表面发生的聚类事件的量化, 利用已建立好的技术和非专业的成像设备。

成功的图像相关光谱学实验依赖于治疗控制的正确应用。这些治疗组包括检查无原发抗体和无二级抗体治疗组的荧光。一旦为实验建立了最佳共焦激光设置, 就必须捕捉这些控制组的图像, 以确认样本中缺乏非特异荧光。对于许多贴壁癌细胞系, 适用于 ICS 的区域的识别受到缺乏平坦表面的限制。这是补偿的能力, 捕捉一个大视野的多癌细胞在一个单一的共焦图像。这提供了...

Watch this Scientific Journal Video about Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy at JoVE.com

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

共聚焦显微镜通过图像相关谱揭示细胞表面受体聚集

与细胞表面上的靶受体结合的抗体可以赋予构象和聚类改变。这些动态变化对表征靶细胞内药物的发展有影响。该协议利用共焦显微和图像相关光谱学通过 ImageJ/斐济, 以量化的程度, 受体聚类在细胞表面。

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of ...

confocal-microscopy-reveals-cell-surface-receptor-aggregation-through

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

7.0K 次观看.  Olivia Newton-John Cancer Research Institute. 与细胞表面上的靶受体结合的抗体可以赋予构象和聚类改变。这些动态变化对表征靶细胞内药物的发展有影响。该协议利用共焦显微和图像相关光谱学通过 ImageJ/斐济, 以量化的程度, 受体聚类在细胞表面。

视频: Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Visualisation and Quantification of Intracellular Interactions of Neisseria meningitidis and Human &alpha;-actinin by Confocal Imaging

The Opc protein of Neisseria meningitidis (Nm, meningococcus) is a surface-expressed integral outer membrane protein, which can act as an adhesin and an effective invasin for human epithelial and endothelial cells. We have identified endothelial surface-located integrins as major receptors for Opc, a process which requires Opc to first bind to integrin ligands such as vitronectin and via these to the cell-expressed receptors1. This process leads to bacterial invasion of endothelial cells2. More recently, we observed an interaction of Opc with a 100kDa protein found in whole cell lysates of human cells3. We initially observed this interaction when host cell proteins separated by electrophoresis and blotted on to nitrocellulose were overlaid with Opc-expressing Nm. The interaction was direct and did not involve intermediate molecules. By mass spectrometry, we established the identity of the protein as &alpha;-actinin. As no surface expressed &alpha;-actinin was found on any of the eight cell lines examined, and as Opc interactions with endothelial cells in the presence of serum lead to bacterial entry into the target cells, we examined the possibility of the two proteins interacting intracellularly. For this, cultured human brain microvascular endothelial cells (HBMECs) were infected with Opc-expressing Nm for extended periods and the locations of internalised bacteria and &alpha;-actinin were examined by confocal microscopy. We observed time-dependent increase in colocalisation of Nm with the cytoskeletal protein, which was considerable after an eight hour period of bacterial internalisation. In addition, the use of quantitative imaging software enabled us to obtain a relative measure of the colocalisation of Nm with &alpha;-actinin and other cytoskeletal proteins. Here we present a protocol for visualisation and quantification of the colocalisation of the bacterium with intracellular proteins after bacterial entry into human endothelial cells, although the procedure is also applicable to human epithelial cells.

Visualisation and Quantification of Intracellular Interactions of Neisseria meningitidis and Human &#945;-actinin by Confocal Imaging

Neisseria meningitidis (Nm), a gram negative human-specific respiratory pathogen, can bind to human &alpha;-actinin. Here we present a protocol for visualisation of colocalisation of the bacterium with intracellular &alpha;-actinin after bacterial entry into human brain microvascular endothelial cells (HBMECs).

胞内相互作用的可视化和量化脑膜炎奈瑟菌和人类α-辅肌动蛋白共聚焦成像

The Opc protein of Neisseria meningitidis (Nm, meningococcus) is a surface-expressed integral outer membrane protein, which can act as an adhesin and an ...

科研

免疫与感染

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 ...

生物化学

Real-time Live Imaging of T-cell Signaling Complex Formation

Protection against infectious diseases is mediated by the immune system 1,2. T lymphocytes are the master coordinators of the immune system, regulating the activation and responses of multiple immune cells 3,4. T-cell activation is dependent on the recognition of specific antigens displayed by antigen presenting cells (APCs). The T-cell antigen receptor (TCR) is specific to each T-cell clone and determines antigen specificity 5. The binding of the TCR to the antigen induces the phosphorylation of components of the TCR complex. In order to promote T-cell activation, this signal must be transduced from the membrane to the cytoplasm and the nucleus, initiating various crucial responses such as recruitment of signaling proteins to the TCR;APC site (the immune synapse), their molecular activation, cytoskeletal rearrangement, elevation of intracellular calcium concentration, and changes in gene expression 6,7. The correct initiation and termination of activating signals is crucial for appropriate T-cell responses. The activity of signaling proteins is dependent on the formation and termination of protein-protein interactions, post translational modifications such as protein phosphorylation, formation of protein complexes, protein ubiquitylation and the recruitment of proteins to various cellular sites 8. Understanding the inner workings of the T-cell activation process is crucial for both immunological research and clinical applications.
Various assays have been developed in order to investigate protein-protein interactions; however, biochemical assays, such as the widely used co-immunoprecipitation method, do not allow protein location to be discerned, thus precluding the observation of valuable insights into the dynamics of cellular mechanisms. Additionally, these bulk assays usually combine proteins from many different cells that might be at different stages of the investigated cellular process. This can have a detrimental effect on temporal resolution. The use of real-time imaging of live cells allows both the spatial tracking of proteins and the ability to temporally distinguish between signaling events, thus shedding light on the dynamics of the process 9,10. We present a method of real-time imaging of signaling-complex formation during T-cell activation. Primary T-cells or T-cell lines, such as Jurkat, are transfected with plasmids encoding for proteins of interest fused to monomeric fluorescent proteins, preventing non-physiological oligomerization 11. Live T cells are dropped over a coverslip pre-coated with T-cell activating antibody 8,9, which binds to the CD3/TCR complex, inducing T-cell activation while overcoming the need for specific activating antigens. Activated cells are constantly imaged with the use of confocal microscopy. Imaging data are analyzed to yield quantitative results, such as the colocalization coefficient of the signaling proteins.

We describe a live-cell imaging method that provides insight into protein dynamics during the T-cell activation process. We demonstrate the combined usage of the T-cell spreading assay, confocal microscopy and imaging analysis to yield quantitative results to follow signaling complex formation throughout T-cell activation.

T细胞实时成像实时信号复合物的形成

Protection against infectious diseases is mediated by the immune system 1,2. T lymphocytes are the master coordinators of the immune system, regulating ...

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

Studying the Stoichiometry of Epidermal Growth Factor Receptor in Intact Cells using Correlative Microscopy

This protocol describes the labeling of epidermal growth factor receptor (EGFR) on COS7 fibroblast cells, and subsequent correlative fluorescence microscopy and environmental scanning electron microscopy (ESEM) of whole cells in hydrated state. Fluorescent quantum dots (QDs) were coupled to EGFR via a two-step labeling protocol, providing an efficient and specific protein labeling, while avoiding label-induced clustering of the receptor. Fluorescence microscopy provided overview images of the cellular locations of the EGFR. The scanning transmission electron microscopy (STEM) detector was used to detect the QD labels with nanoscale resolution. The resulting correlative images provide data of the cellular EGFR distribution, and the stoichiometry at the single molecular level in the natural context of the hydrated intact cell. ESEM-STEM images revealed the receptor to be present as monomer, as homodimer, and in small clusters. Labeling with two different QDs, i.e.,&#160;one emitting at 655 nm and at 800 revealed similar characteristic results.

This protocol describes the labeling of epidermal growth factor receptor (EGFR) on COS7 fibroblast cells, and subsequent correlative light- and electron microscopy of whole cells in hydrated state. The label contained fluorescent quantum dots. The protocol can be used to study the stoichiometry of EGFR at the single molecule level.

研究表皮生长因子受体的完整细胞使用相关显微镜的化学计量

This protocol describes the labeling of epidermal growth factor receptor (EGFR) on COS7 fibroblast cells, and subsequent correlative fluorescence ...

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. Lipid rafts are membrane microdomains enriched in cholesterol and sphingolipids. They play a role in cellular physiological processes such as signalling, and trafficking1,2 but are also thought to be key players in several diseases including viral or bacterial infections and neurodegenerative diseases3.
Yet their existence is still a matter of controversy4,5. Indeed, lipid raft size has been estimated to be around 20 nm6, far under the resolution limit of conventional microscopy (around 200 nm), thus precluding their direct imaging. Up to now, the main techniques used to assess the partition of proteins of interest inside lipid rafts were Detergent Resistant Membranes (DRMs) isolation and co-patching with antibodies. Though widely used because of their rather easy implementation, these techniques were prone to artefacts and thus criticized7,8. Technical improvements were therefore necessary to overcome these artefacts and to be able to probe lipid rafts partition in living cells.
Here we present a method for the sensitive analysis of lipid rafts partition of fluorescently-tagged proteins or lipids in the plasma membrane of living cells. This method, termed Fluorescence Correlation Spectroscopy (FCS), relies on the disparity in diffusion times of fluorescent probes located inside or outside of lipid rafts. In fact, as evidenced in both artificial membranes and cell cultures, probes would diffuse much faster outside than inside dense lipid rafts9,10. To determine diffusion times, minute fluorescence fluctuations are measured as a function of time in a focal volume (approximately 1 femtoliter), located at the plasma membrane of cells with a confocal microscope (Fig. 1). The auto-correlation curves can then be drawn from these fluctuations and fitted with appropriate mathematical diffusion models11.
FCS can be used to determine the lipid raft partitioning of various probes, as long as they are fluorescently tagged. Fluorescent tagging can be achieved by expression of fluorescent fusion proteins or by binding of fluorescent ligands. Moreover, FCS can be used not only in artificial membranes and cell lines but also in primary cultures, as described recently12. It can also be used to follow the dynamics of lipid raft partitioning after drug addition or membrane lipid composition change12.

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS

A technique to probe the lipid raft partitioning of fluorescent proteins at the plasma membrane of living cells is described. It takes advantage of the disparity in diffusion times of proteins located inside or outside of lipid rafts. Acquisition can be performed dynamically in control conditions or after drug addition.

脂筏的测定分区在活细胞中的荧光标记探针的荧光相关光谱（FCS）

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. ...

生物学

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 ...

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 ...

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and so on. To detect and analyze soluble or diffuse protein oligomers or aggregates, fluorescence correlation spectroscopy (FCS), which can detect the diffusion speed and brightness of a single particle with a single molecule sensitivity, has been used. However, the proper procedure and know-how for protein aggregation detection have not been widely shared. Here, we show a standard procedure of FCS measurement for diffusion properties of aggregation-prone proteins in cell lysate and live cells: ALS-associated 25 kDa carboxyl-terminal fragment of TAR DNA/RNA-binding protein 43 kDa (TDP25) and superoxide dismutase 1 (SOD1). The representative results show that a part of aggregates of green fluorescent protein (GFP)-tagged TDP25 was slightly included in the soluble fraction of murine neuroblastoma Neuro2a cell lysate. Moreover, GFP-tagged SOD1 carrying ALS-associated mutation shows a slower diffusion in live cells. Accordingly, we here introduce the procedure to detect the protein aggregation via its diffusion property using FCS.

We here introduce a procedure to measure protein oligomers and aggregation in cell lysate and live cells using fluorescence correlation spectroscopy.

使用荧光相关光谱检测蛋白质聚合

Protein aggregation is a hallmark of neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's ...

Cell Aggregation Assay to Detect Trans Interactions between Cell Adhesion Molecules

Source: Restrepo, S., et al., <a href="https://appjove.unicartagenaproxy.elogim.com/v/61237">Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System.</a> J. Vis. Exp. (2020)
In this video, we demonstrate the technique to measure transcellular interactions between synaptic cell adhesion molecules through the HEK cell aggregation assay. In this assay, an equal number of HEK cells expressing a desired presynaptic cell adhesion molecule and a fluorescent protein are mixed with HEK cells expressing a post-synaptic cell adhesion molecule and another fluorescent protein. The interaction between the cell adhesion molecules forces the cells to aggregate, which can be measured by fluorescence microscopy.

检测细胞粘附分子之间反式相互作用的细胞聚集检测

Source: Restrepo, S., et al., Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System. J. Vis. Exp. (2020)
In this ...