Name: quantitation of protein expression and co-localization using multiplexed immuno-histochemical staining and multispectral imaging
Uploaded: 2016-04-08T15:00:00.000+00:00
Duration: 8 min 40 s
Description: Watch this Scientific Journal Video about Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging at JoVE.com

作者感谢威斯康星转化研究倡议的大学病理实验室，由病理及实验室医学系UW支持一部分，UWCCC授予P30 CA014520，使用其设施和服务。

注:此协议描述了染色和组织芯片（TMA）的分析，先前12,17,18描述。从使用标准切片石蜡块获得4微米厚的TMA部分。 注:对于4色原和染液谱库应进行图像定量创建。为了做到这一点，对于每个单独的抗体的优化的协议，应每滑动一种抗体，减去最终染液运行。第五幻灯片应该用苏木染色生成创建谱库所需要的5张图像。 1.多重免疫组化<ol><li>烘烤载玻片在60℃的烘箱中20分钟。使用化学通风橱，浸泡在100％二甲苯滑动。重复二甲苯2的变化。浸泡在100％的乙醇滑动5分钟。用新鲜的100％乙醇重复。 </li><li>沉浸在95％乙醇的幻灯片3分钟。用新鲜的重复95％的乙醇。沉浸在70％乙醇幻灯片3分钟。浸入蒸馏水幻灯片3分钟。用新鲜蒸馏水重复。点击幻灯片垂直于从幻灯片沥干水分。应用200微升准备使用的内源性过氧化物酶阻断剂5分钟。 </li><li>从滑漏过氧化物阻断剂和在45ml浸入现成使用检索缓冲液（pH值小于7.0）。在高压锅地方滑动容器并开始程序设定为124℃进行4分钟。允许压力锅冷却20分钟。开幕前。 </li><li>除去检索缓冲器的容器，并允许冷却另外10分钟。在冲洗蒸馏水滑10分钟。从幻灯片沥干水分，仔细勾画具有疏水屏障笔幻灯片组织。 </li><li>加入200μl的磷酸缓冲盐水（PBS）.Transfer滑动的与盖（孵育室）含有湿润纱布扁平容器。 </li><li>通过稀释50米1升的1x Tris缓冲盐水与吐温20（TBST）20倍的Tris l的缓冲含有1％吐温20和950毫升蒸馏水盐水。 </li><li>从幻灯片排水PBS并应用200微升1X TBST的。孵育2分钟。从幻灯片排水TBST并返回到幻灯片孵化室。应用200微升准备使用的通用蛋白块，接近室孵育7分钟。 </li><li>排水蛋白块，并返回到幻灯片孵化室。通过加入0.6微升抗体与239.4微升抗体稀释剂的400:稀释抗ERα抗体1。应用200微升稀释的抗体滑动并关闭室。 </li><li>在室温下孵育幻灯片2小时。从滑漏抗体并用TBST随后浸渍在TBST 5分钟冲洗。 </li><li>从幻灯片排水TBST并返回到幻灯片孵化室。应用200微升准备使用的山羊抗兔辣根过氧化物酶聚合物和关闭腔室。温育30分钟。在室温下。用TBST和地点滑动5分钟冲洗幻灯片在TBST的容器中。&lt;/ LI&gt; <li>准备棕色辣根加入8微升DAB显色的250微升DAB底物缓冲液和混合以及色原过氧化物酶3,3&#39;-二氨基联苯胺（HRP-DAB）。 </li><li>从幻灯片，返回滑动到孵育室漏TBST和应用200μl的棕色HRP-DAB色原体的6分钟。彻底冲洗用蒸馏水和地方滑动的滑动中的蒸馏水的容器中。 </li><li>通过组合50μl的用150微升第二缓冲试剂的第一次洗脱试剂的混合的变性溶液。排水关闭滑动并应用稀释变性溶液在滑动3分钟。在室温下。倒出和冲洗变性试剂与TBST。沉浸在TBST滑动5分钟。 </li><li>通过稀释抗AR抗体的4.2微升和2.8微升抗CD147抗体与203微升抗体稀释剂准备AR / CD147抗体的鸡尾酒。从幻灯片排水TBST，返回幻灯片孵化室，并应用200微升AR / CD147的cocktail的幻灯片。靠近腔和孵育在室温下1小时。用TBST漂洗滑动并在TBST浸泡5分钟。 </li><li>漏TBST，返回滑动到孵育室和应用200微升准备使用的山羊抗兔碱性磷酸酶聚合物。关闭室和30分钟孵蛋幻灯片。在室温下。用TBST漂洗滑动并在TBST浸泡5分钟。 </li><li>准备加入3.2微升红色发色的250微升发色红缓冲色原红碱性磷酸酶拌匀。 </li><li>从幻灯片排水TBST并返回到幻灯片孵化室。应用200微升稀释红色碱性磷酸酶色原到滑动，关闭腔室孵育7分钟。 </li><li>排水显色进行彻底的冲洗用蒸馏水幻灯片。放置幻灯片中的蒸馏水的容器中5分钟。从滑动沥干水分，用TBST冲洗，并返回到孵育室。应用200微升山羊抗小鼠-HRP聚合物在滑动和闭Ë室。温育30分钟。在室温下。 </li><li>从滑动漏极聚合物并用TBST冲洗。在TBST地方滑动5分钟。加入3.2微升稳定剂250微升缓冲的准备200微升紫色HRP联发色拌匀。加入3.2微升紫色显色拌匀，然后3.2微升过氧化氢试剂拌匀。 </li><li>漏TBST关滑和应用200微升制备的紫色原的滑动并孵育7分钟。在室温下。用蒸馏水进行5分钟的容器蒸馏水和地方滑动漂洗幻灯片。 </li><li>通过混合50微升用150微升第二缓冲试剂的第一次洗脱试剂的混合的变性溶液。排水关闭滑动并应用稀释变性溶液在滑动3分钟。在室温下。 </li><li>变性冲洗掉试剂用TBST。放置在TBST的容器滑动5分钟。稀释抗E-cadherin抗体到1:200，加入1.1微升抗体218.9微升抗体稀释剂。从幻灯片，幻灯片回报孵化室沥干TBST并应用200微升E-cadherin的的幻灯片。关闭室和孵化30分钟。在室温下。 </li><li>在TBST集装箱TBST和地点滑动5分钟冲洗幻灯片。从滑漏TBST，返回滑动到孵育室和应用200μl的山羊抗小鼠HRP聚合物。在室温下孵育30分钟。在TBST集装箱TBST和地点滑动5分钟冲洗幻灯片。 </li><li>准备黑HRP联通过添加8微升显色的250微升缓冲色原。应用200微升色原滑动并开发2分钟。彻底冲洗用蒸馏水滑梯和蒸馏水浸泡的容器。 </li><li>加入40微升苏木200微升蒸馏水稀释苏木。从幻灯片沥干水分，并应用200微升稀释苏木以30秒滑动。幻灯片冲洗彻底自来水进行2分钟，然后在蒸馏水中漂洗30秒。在60℃的烘箱中15-30分钟处滑动。 </li><li>浸入新鲜配制的100％二甲苯幻灯片30秒，擦去多余的二甲苯起飞和永久性补充安装介质一滴。用1.5号盖玻片盖玻片。 </li></ol> 2.自动图像采集与分析<ol><li>加载彩色幻灯片到自动幻灯片扫描仪。基于直径，分离和数目TMA核，创建根据制造商的说明书的自动扫描协议。 </li><li>开放式多光谱成像软件构建同个别发色团和苏木精滑染色对照切片谱库。打开从控制幻灯片获取的图像立方体，并选择四到五个阳性染色领域，光学定义显色。与其他控制幻灯片图像立方体重复，直到一个完整的光谱库representi纳克所有的色原被创建，然后保存光谱库。 </li><li>开始多光谱成像软件中的一个新项目。选择"多光谱（.im3）"为样本格式图像格式选项和"明场"。 "段组织"，"查找功能"，"表现型"，"分数"和"出口":第一，选择需要的选项配置项目。在这一点上，图像分辨率可以改变，如果需要的，以加快分析时间。 </li></ol> 3.组织分割<ol><li>导入以前创建的光谱库，然后选择要包含在分析所有的色原。 </li><li>要包含在训练数据通过选择"打开图像立方"选项设置打开的图像立方体。以保证训练精度，选择图像的总数的至少18％进行分析。选择一个代表所有疾病状态，以提高分割精度的图像。图像的这一部分我叫做图像的训练集。 </li><li>白平衡图像通过选择吸管工具，并选择在一个图像是白色的区域设置了培训。 </li><li>选择"高级"按钮移动到组织分割。用"组织类别"面板以选择的组织类型进行分析（ 即 "组织"和"非组织"）。为了更准确的蛋白质组织定位，多个组织类可以使用（ 即 ，"上皮"，"基质"和"非组织"）。 </li><li>开始创建算法和训练图像中绘制细胞周围的群体界定组织类别。当一个组织类别完成后，重复其他组织类别。一定要属于该组织类别类型的特征图像中选择的细胞群体。 </li><li>重复此过程训练组图像内的所有图像。 </li><li>选择组件（发色团）被列入TRAI宁为"组织分段"。当执行明场免疫图像的分析中，所有部件通常包括在训练。包括设置，以避免在此步骤偏见培训丰富负染图像。 </li><li>选择一个合适的"图案比例"用于训练组织分段。在20X的放大倍率，大格局规模通常是合适的。当组织更高的放大倍率下工作有精美的建筑，更小的图案比例是比较合适的。 </li><li>选择"列车组织分段器"按钮，开始训练的组织分段。观察一个弹出框，显示精度反映训练区域内的像素被正确分类的比例。 注:该软件不断尝试提高算法的准确度，直到手动停止。之前我们已经证实的在97％训练精度12图像效果18％的培训。 </li&gt; <li>组织分割器被训练后，选择相应的分段分辨率。段分辨率对应所需图像分割的时间，与需要较少的时间和精细的分辨率需要更多的时间粗分辨率。 </li><li>段通过点击"图像分割"整个训练集图像。让软件充分的时间算法适用于所有训练图像。完成后，查看训练集找到与当前的训练算法错误分类组织。 </li><li>要微调组织分割过程中，添加，编辑或删除组织培训的区域。如果像素在一个组织类别的边缘到另一个延长使用"修剪边"选项。如果小的像素组合会被误判，调整最小段大小的阈值。 </li><li>当编辑完成后，选择"图像分割"，这将重新训练组织分段创建组织分割的新算法。以前的算法是自动保存，并且可以返回到如果需要的话。 </li><li>当有信心组织分割算法的结果，通过选择"高级"按钮，进入到细胞分割。 </li></ol> 4.细胞分割<ol><li>被包括在小区分段选择亚细胞区室。确保"核"已被选中，选择"细胞质"或"膜"。 注意:当特定的膜蛋白标志物包括在IHC，如E-钙粘蛋白只应选择"膜"分割。 </li><li>内的"核"选项卡中，有几个选项。通过选择核分割相应的设置开始（见步骤4.3）。选择单个或所有组织类别是否会被纳入分割。 </li><li>选择核分割的方法<ol><li>选择染液"基于像素（阈值）"的方式为获得良好的简化方法结果不知道太多关于图像处理的设置。 注:这往往使一个良好的第一选择。当选择的方法，"基于像素（阈值）"更合适时，有一个可靠的核反击污点，而如果是缺乏一致的核染液的"基于对象"应使用。 </li><li>选择"基于像素（阈值）"的方法时，有一个可靠的核染液。 注:此方法是纯粹基于像素的。这种方法也可用于其它图像分析需要，可以用一个简单的阈值来满足，如组织类别，对于一个IHC染色染色阳性内检测所有像素。 </li><li>选择基于对象的方法，如果核染液不提供核对象的一致性和特异性染色，并且需要更高级的基于形态的办法来检测核。 </li><li>选择"组件核分割"，"Nucl果穗大小"和"清理"，如果用户想进一步确定核分段设置。 </li></ol></li><li>选择细胞质形状参数<ol><li>选择"高级"按钮移动到细胞质分割。 注:有胞浆内分割几个选项可供选择。被称为"内距离核心"的选择是基于像素。它反映核的外侧和细胞质的内侧之间的距离。增加该值越小，核信号跨越到细胞质车厢的概率。 </li><li>接着选择"外距离核心"。 注:这也是基于像素。到核外距离反映核的外侧和电池的外侧之间的距离。这个值可以被设置为大或小到足以包括或者如果需要排除膜的信号。 </li><li>接着选择"最小尺寸"。 注:最小尺寸，PIXE基于升，反映了要包括在分析的最小细胞质样本大小。如果细胞质大小是在特定细胞较小，如当细胞被紧密地挤在一起，这种细胞质分割排除分析。 </li><li>选择下一个选项，"分量"与"小"，"中等"，然后选择"第三"作为次要选项。 注:在这里，人们可以选择一个特定的细胞质标记。类似于核分割，如果特定胞质IHC标记已被使用，该组件可以用于通过寻找最小阈值来定义细胞质。这有助于限定细胞质但没有必要为可接受的准确度。 </li><li>移动到最后的三个标签选项是"膜"。这里，用户可以定义与蛋白标记在膜隔室。选择"主要"，"次要"和/或"三级"为每个使用的膜特异性标记物。调整"满量程ØD"找到一个最低门槛，或积极为细胞膜上的每个标记。 </li><li>在"膜"标签下继续并选择选项"最大单元格大小"。 注意:这是对膜的值的距离，以像素为单位，指定了细胞的最大尺寸。 12的值通常适合于在20X放大倍率拍摄的图像。 </li><li>选择所有选项后，选择"图像分割"或"段全部"。应用设置图像和"单独"或"画廊"模式观察他们。 注:如有必要，调整设置和阈值，并重新细分，直至满足于训练集图像的细胞分割结果。 </li></ol></li></ol> 5.表型细胞注:精确细胞分割中需要为了获得准确的细胞表型，和表型特性是可训练。 <ol><li>使用"添加＆＃34;按钮，表型添加到"表型"名单。用户可以选择用于表型的颜色。 </li><li>点击"编辑表型"，以表型分配给小区。点击一个特定的细胞，弹出一个下拉菜单，允许用户选择的表型，然后继续前进。确定，以便继续在各表型至少五（5）细胞。选择每个表型的25个或更多的细胞，需要以达到最佳效果。 </li></ol> 6.得分IHC和共定位<ol><li>选择"高级"移动到"分数IHC或IF"的一步。选择一个"组织类别"得分。 注意:只有一个组织类别可在特定的分析期间进行评分，但是，如果在另一组织类别的得分结果需要，该分析可以在以后使用不同的设置重复。 </li><li>选择所需的"得分王"的类型。 注意:正性对于感兴趣的组件创建两个仓（正和负），而双阳性用于共定位分析。其他选项包括4槽，10槽和50槽分析了选择的标记。 </li><li>选择细胞"室"，在得分分析中使用。 </li><li>寻找的主要核部件的最低门槛类似，选择"查看组件数据"移动光标在训练图像找到染色的适当的光学密度最低标准，为感兴趣的成分（S）阳性细胞。 </li><li>如果强烈染色的细胞是被排除在外，将阈值降低最大到适当的水平。否则，使用自动按钮选择一个阈值最大的进行分析。 </li><li>选择训练图像，以便验证阈值的得分。 注:细胞的特定仓中的百分比将被返回，并且可以通过目测或人工计数进行比较。完成后，选择"高级"，进入"导出"的一步。 </li></ol> &lt;p系列="jove_title"&gt; 7。运用算法和批次分析<ol><li>通过导出数据对于训练集测试穿过组织分割，细胞分割，和得分值创建的算法。按照提示的出口目录中创建一个新的文件夹。要创建并包含在分析中选择的图像和表格。通过选择"导出为人人"进行分析。 </li><li>表导出在制表符分隔文本文件，可以在大多数数据分析程序打开。 </li><li>当分析完成后，查看电池分段和得分数据与高温和低温染色，以评估设置精度的图像。 </li><li>当设置满意，从申请通过​​批量分析训练集图像，以整组图像创建的算法。点击"批量分析"选项卡，打开算法从活动项目中复制。 </li><li>选择一个新的出口目录并选择图像和TABLES将被包括在分析中。在输入文件选项，选择"添加图片"，然后选择要包含在批次分析中的所有图像。 </li><li>通过选择"运行"选项进行分析。与训练组的分析，这一步骤将要求可变的时间量，这取决于特定的设置和包括在分析中的图像的数目。 </li><li>完成后，前进到"查看/合并"选项卡。批处理目录默认为从批次分析导出目录和如果需要，可以改变。选择"包含所有"，然后选择"合并"来创建用于分析数据的汇总数据表。 </li></ol> 8.导出数据的分析<ol><li>打开出口表中的数据分析软件，并删除或隐藏数据列是无关的分析，如最大和最小值为每个组件。在分析中使用的主要数据是在平均光密度Çolumn为每个组件。 </li><li>回顾远销的分割地图和原始图像，以确定哪些样本或TMA核心是要从分析中排除。组织百分比值帮助确定样品是否应当包括或不与最常用于排除标准的&lt;5％的任意截止。 </li><li>删除不相关的数据或包​​含样本行从数据分析中排除。 </li><li>使用蛋白质定量数据来研究疾病状态的变化或与疾病12,18-21的临床病理特征的关系。 </li></ol>

<ol>
	<li>Valdman, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expression+of+redox+pathway+enzymes+in+human+prostatic+tissue.">Expression of redox pathway enzymes in human prostatic tissue.</a> Anal Quant Cytol Histol. 31 (6), 367-374 (2009).</li><li>Rimm, D. L., Camp, R. L., Charette, L. A., Olsen, D. A., Provost, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amplification+of+tissue+by+construction+of+tissue+microarrays.">Amplification of tissue by construction of tissue microarrays.</a> Exp Mol Pathol. 70 (3), 255-264 (2001).</li><li>Jonmarker, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expression+of+PDX-1+in+prostate+cancer,+prostatic+intraepithelial+neoplasia+and+benign+prostatic+tissue.">Expression of PDX-1 in prostate cancer, prostatic intraepithelial neoplasia and benign prostatic tissue.</a> APMIS. 116 (6), 491-498 (2008).</li><li>McCarty, K. S., Miller, L. S., Cox, E. B., Konrath, J., McCarty, K. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estrogen+receptor+analyses.+Correlation+of+biochemical+and+immunohistochemical+methods+using+monoclonal+antireceptor+antibodies.">Estrogen receptor analyses. Correlation of biochemical and immunohistochemical methods using monoclonal antireceptor antibodies.</a> Arch Pathol Lab Med. 109 (8), 716-721 (1985).</li><li>Volante, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Somatostatin+receptor+type+2A+immunohistochemistry+in+neuroendocrine+tumors:+a+proposal+of+scoring+system+correlated+with+somatostatin+receptor+scintigraphy.">Somatostatin receptor type 2A immunohistochemistry in neuroendocrine tumors: a proposal of scoring system correlated with somatostatin receptor scintigraphy.</a> Mod Pathol. 20 (11), 1172-1182 (2007).</li><li>Muris, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunohistochemical+profiling+of+caspase+signaling+pathways+predicts+clinical+response+to+chemotherapy+in+primary+nodal+diffuse+large+B-cell+lymphomas.">Immunohistochemical profiling of caspase signaling pathways predicts clinical response to chemotherapy in primary nodal diffuse large B-cell lymphomas.</a> Blood. 105 (7), 2916-2923 (2005).</li><li>Jaraj, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intra-+and+interobserver+reproducibility+of+interpretation+of+immunohistochemical+stains+of+prostate+cancer.">Intra- and interobserver reproducibility of interpretation of immunohistochemical stains of prostate cancer.</a> Virchows Arch. 455 (4), 375-381 (2009).</li><li>Nakane, P. K., Pierce, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enzyme-labeled+antibodies:+preparation+and+application+for+the+localization+of+antigens.">Enzyme-labeled antibodies: preparation and application for the localization of antigens.</a> J Histochem Cytochem. 14 (12), 929-931 (1966).</li><li>Peters, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Investigation+of+tissue+organelles+by+a+combination+of+analytical+subcellular+fractionation+and+enzymic+microanalysis:+a+new+approach+to+pathology.">Investigation of tissue organelles by a combination of analytical subcellular fractionation and enzymic microanalysis: a new approach to pathology.</a> J Clin Pathol. 34 (1), 1-12 (1981).</li><li>Emmert-Buck, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laser+Capture+Microdissection.">Laser Capture Microdissection.</a> Science. 274 (5289), 998-1001 (1996).</li><li>Waters, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accuracy+and+precision+in+quantitative+fluorescence+microscopy.">Accuracy and precision in quantitative fluorescence microscopy.</a> J Cell Biol. 185 (7), 1135-1148 (2009).</li><li>Huang, W., Hennrick, K., Drew, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+colorful+future+of+quantitative+pathology:+validation+of+Vectra+technology+using+chromogenic+multiplexed+immunohistochemistry+and+prostate+tissue+microarrays.">A colorful future of quantitative pathology: validation of Vectra technology using chromogenic multiplexed immunohistochemistry and prostate tissue microarrays.</a> Hum Pathol. 44 (1), 29-38 (2013).</li><li>Rimm, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=C-Path:+A+Watson-Like+Visit+to+the+Pathology+Lab.">C-Path: A Watson-Like Visit to the Pathology Lab.</a> Science Translational Medicine. 3 (108), (2011).</li><li>Fiore, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Utility+of+multispectral+imaging+in+automated+quantitative+scoring+of+immunohistochemistry.">Utility of multispectral imaging in automated quantitative scoring of immunohistochemistry.</a> J Clin Pathol. 65 (6), 496-502 (2012).</li><li>Stack, E. C., Wang, C., Roman, K. A., Hoyt, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplexed+immunohistochemistry,+imaging,+and+quantitation:+A+review,+with+an+assessment+of+Tyramide+signal+amplification,+multispectral+imaging+and+multiplex+analysis.">Multiplexed immunohistochemistry, imaging, and quantitation: A review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis.</a> Methods. 70 (1), 46-58 (2014).</li><li>Rizzardi, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+comparison+of+immunohistochemical+staining+measured+by+digital+image+analysis+versus+pathologist+visual+scoring.">Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring.</a> Diagn Pathol. 7, 42 (2012).</li><li>Bauman, T. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+fibrillar+collagens+and+extracellular+matrix+of+glandular+benign+prostatic+hyperplasia+nodules.">Characterization of fibrillar collagens and extracellular matrix of glandular benign prostatic hyperplasia nodules.</a> PLoS One. 9 (10), e109102 (2014).</li><li>Bauman, T. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beta-catenin+is+elevated+in+human+benign+prostatic+hyperplasia+specimens+compared+to+histologically+normal+prostate+tissue.">Beta-catenin is elevated in human benign prostatic hyperplasia specimens compared to histologically normal prostate tissue.</a> Am J Clin Exp Urol. 2 (4), 313-322 (2014).</li><li>Bauman, T. M., Ewald, J. A., Huang, W., Ricke, W. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CD147+expression+predicts+biochemical+recurrence+after+prostatectomy+independent+of+histologic+and+pathologic+features.">CD147 expression predicts biochemical recurrence after prostatectomy independent of histologic and pathologic features.</a> BMC Cancer. 15 (1), 549 (2015).</li><li>Bauman, T. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finasteride+treatment+alters+tissue+specific+androgen+receptor+expression+in+prostate+tissues.">Finasteride treatment alters tissue specific androgen receptor expression in prostate tissues.</a> Prostate. 74 (9), 923-932 (2014).</li><li>Nicholson, T. M., Sehgal, P. D., Drew, S. A., Huang, W., Ricke, W. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sex+steroid+receptor+expression+and+localization+in+benign+prostatic+hyperplasia+varies+with+tissue+compartment.">Sex steroid receptor expression and localization in benign prostatic hyperplasia varies with tissue compartment.</a> Differentiation. 85 (4-5), 140-149 (2013).</li><li>Taylor, C. R., Levenson, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+immunohistochemistry--issues+concerning+methods,+utility+and+semiquantitative+assessment+II.">Quantification of immunohistochemistry--issues concerning methods, utility and semiquantitative assessment II.</a> Histopathology. 49 (4), 411-424 (2006).</li><li>Matos, L. L., Trufelli, D. C., de Matos, M. G., da Silva Pinhal, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immunohistochemistry+as+an+important+tool+in+biomarkers+detection+and+clinical+practice.">Immunohistochemistry as an important tool in biomarkers detection and clinical practice.</a> Biomark Insights. 5, 9-20 (2010).</li><li>Kononen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tissue+microarrays+for+high-throughput+molecular+profiling+of+tumor+specimens.">Tissue microarrays for high-throughput molecular profiling of tumor specimens.</a> Nat Med. 4 (7), 844-847 (1998).</li><li>Ong, C. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer-assisted+pathological+immunohistochemistry+scoring+is+more+time-effective+than+conventional+scoring,+but+provides+no+analytical+advantage.">Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage.</a> Histopathology. 56 (4), 523-529 (2010).</li></ol>

作者宣称，他们没有竞争的经济利益。

使用用于评估蛋白表达的传统免疫组织化学被分析22,23的主观的，半定量法的限制。提前平台已经为生物标志物的表达和定位的高通量分析创建。两个组织和亚细胞区室的详细分割允许用户来研究生物标志物表达，定位，和共定位与其他感兴趣的标记。在以前的研究中，我们已经证明IHC和多光谱成像，用于研究定位于相同的细胞隔室18,20,21的蛋白质尤其是当的效用。当组织芯片结?...

<html>免疫组织化学（IHC）是组织中检测蛋白质的标准实验室技术，IHC仍广泛用于研究和诊断病理学。 IHC染色的评价往往是半定量，引入潜在的偏见到结果的解释。许多半定量方法已经开发了包含两个染色强度和染色程度成最终诊断1-4。其它系统包括，以更好地定位表达5得分强度和亚细胞定位。从多个观看者平均分数的掺入通常被用于以最小化单个观察者偏倚6的影响。尽管有这些努力，主观性分析仍然存在，评估染色7的程度时尤为如此。协议的标准化和减少主观性的人力投入是极为重要的创造准确，重现IHC结果。 内容"&gt;还有除了IHC其他选项来确定组织中的表达。在研究设置，免疫组化历来被视为检验蛋白定位8的一种手段，而其他技术，如免疫印迹被视为黄金标准调查的表达。确定组织或细胞特异性车厢表达很难不采用先进的技术，如细 ​​胞分馏或激光捕获显微切割9,10。在组织切片中使用荧光抗体提供了一个合理的妥协，但背景自发荧光由于NADPH，lipofuscins，网状纤维，胶原和弹性蛋白可以作出准确的定量困难11。 自动计算病理平台是病理染色12-15的更客观的定量一个有前途的方向。结合组织芯片多光谱成像便于在大样本蛋白表达的高通量分析。用这些技术，蛋白的共定位，染色不均匀性，以及组织和亚细胞定位的分析是可能的，同时基本上降低了两个固有偏差和必要的分析时间，同时在连续返回数据，而不是绝对的格式16。因此，本研究的目的是为了展示与分析进行复用免疫组化，采用多光谱成像软件工具和方法。 该协议具有四个优化单克隆抗体单一组织切片上的手册，复用免疫组化染色写的。作为一个代表性实验，核抗兔雌激素受体α（ERα）​​和雄激素受体（AR）被复用膜结合的抗小鼠CD147和膜结合的抗小鼠E-钙粘蛋白。选择的任何抗体可substitut编为本文所列的抗体，但是抗体的每一个组合都需要单独的最优化。前处理的所有抗体必须相同。 AR和CD147抗体应单独再优化为鸡尾酒。每种抗体是使用无生物素的聚合物系统和4个独特的色原一个检测到。

<table><tbody><tr><td>Xylene</td><td>Fisher Chemical</td><td>X3F1GAL</td><td>NFPA rating: Health &amp;ndash; 2, Fire &amp;ndash; 3 , Reactivity &amp;ndash; 0</td></tr><tr><td>Ethyl Alcohol-200 proof</td><td>Fisher Scientific</td><td>4355223</td><td>NFPA rating: Health &amp;ndash; 0, Fire &amp;ndash; 3 , Reactivity &amp;ndash; 0</td></tr><tr><td>Tris Base</td><td>Fisher Scientific</td><td>BP152-500</td><td>NFPA rating: Health &amp;ndash; 2, Fire &amp;ndash; 0 , Reactivity &amp;ndash; 0</td></tr><tr><td>Tris Hydroxymethyl aminomethane HCl</td><td>Fisher Scientific</td><td>BP153-1</td><td>NFPA rating: Health &amp;ndash; 2, Fire &amp;ndash; 0 , Reactivity &amp;ndash; 0</td></tr><tr><td>Tween 20</td><td>Chem-Impex</td><td>1512</td><td>NFPA rating: Health &amp;ndash; 0, Fire &amp;ndash; 1 , Reactivity &amp;ndash; 0</td></tr><tr><td>Phosphate-buffered saline</td><td>Fisher Scientific</td><td>BP2944-100</td><td>NFPA rating: Health &amp;ndash; 1, Fire &amp;ndash; 0 , Reactivity &amp;ndash; 0</td></tr><tr><td>Peroxidazed</td><td>Biocare Medical</td><td>PX968</td><td>Avoid contact with skin and eyes. May cause skin irritation and eye damage.</td></tr><tr><td>Estrogen Receptor alpha</td><td>Thermo Fisher Scientific-Labvision</td><td>RM9101</td><td>Not classified as hazardous</td></tr><tr><td>Androgen Receptor</td><td>SCBT</td><td>sc-816</td><td>Not classified as hazardous</td></tr><tr><td>CD147</td><td>Biodesign</td><td>P87535M</td><td>Not classified as hazardous</td></tr><tr><td>E-cadherin</td><td>Dako</td><td>M3612</td><td>Not classified as hazardous</td></tr><tr><td>Renoir Red Andibody Diluent</td><td>Biocare Medical</td><td>PD904</td><td>It is specially designed to work with Tris-based antibodies</td></tr><tr><td>DeCloaking Chamber&amp;nbsp;</td><td>Biocare Medical</td><td>Model DC2002</td><td>Take normal precautions for using a pressure cooker</td></tr><tr><td>Barrier pen-Immuno Edge&amp;nbsp;</td><td>Vector Labs</td><td>H-4000</td><td></td></tr><tr><td>Denaturing Kit-Elution step</td><td>Biocare Medical</td><td>DNS001H</td><td>Not classified as hazardous</td></tr><tr><td>Mach 2 Goat anti-Rabbit HRP Polymer</td><td>Biocare Medical</td><td>RHRP520</td><td>Not classified as hazardous</td></tr><tr><td>Mach 2 Goat anti-Rabbit AP Polymer</td><td>Biocare Medical</td><td>RALP525</td><td>Not classified as hazardous</td></tr><tr><td>Mach 2 Goat anti-Mouse HRP Polymer</td><td>Biocare Medical</td><td>M3M530</td><td>Not classified as hazardous</td></tr><tr><td>Betazoid DAB Chromogen Kit</td><td>Biocare Medical</td><td>BDB2004</td><td>1. DAB is known to be a suspected carcinogen.&lt;br/&gt; 2. Do not expose DAB components to strong light or direct sunlight.&lt;br/&gt; 3. Wear appropriate personal protective equipment and clothing.&lt;br/&gt; 4. DAB may cause sensitization of skin. Avoid contact with skin and eyes.&lt;br/&gt; 5. Observe all federal, state and local environmental regarding disposal</td></tr><tr><td>Warp Red Chromogen Kit</td><td>Biocare Medical</td><td>WR806</td><td>Corrosive. Acid that may cause skin irritation or eye damage.&amp;nbsp;</td></tr><tr><td>Vina Green Chromogen Kit</td><td>Biocare Medical</td><td>BRR807</td><td>Harmful if swallowed</td></tr><tr><td>Bajoran Purple Chromogen Kit</td><td>Biocare Medical</td><td>BJP807</td><td>Flammable liquid. Keep away from heat, flames and sparks. Harmful by ingestion or absorption. Avoid contact with skin or eyes, and avoid inhalation.</td></tr><tr><td>Cat Hematoxylin</td><td>Biocare Medical</td><td>CATHE</td><td>Purple &amp;nbsp;solution &amp;nbsp;with &amp;nbsp;a &amp;nbsp;mild &amp;nbsp;acetic &amp;nbsp;acid &amp;nbsp;(vinegar) &amp;nbsp;scent. &amp;nbsp;May &amp;nbsp;be&lt;br/&gt; &amp;nbsp;irritating &amp;nbsp;to &amp;nbsp;skin &amp;nbsp;or &amp;nbsp;eyes. &amp;nbsp;Avoid &amp;nbsp;contact &amp;nbsp;with &amp;nbsp;skin &amp;nbsp;and &amp;nbsp;eyes. &amp;nbsp;Avoid &amp;nbsp;ingestion.</td></tr><tr><td>XYL Mounting Media</td><td>Richard Allen</td><td>8312-4</td><td>NFPA rating: Health &amp;ndash; 2, Fire &amp;ndash; 3 , Reactivity &amp;ndash; 0</td></tr><tr><td>1.5 Coverslips</td><td>Fisher Brand</td><td>22266858</td><td>Sharp edges</td></tr><tr><td>Incubation (Humidity)Chamber</td><td>obsolete</td><td>obsolete</td><td>Multiple vendors available</td></tr><tr><td>Convection Oven</td><td>Stabil- Therm</td><td>C-4008-Q</td><td></td></tr><tr><td>Background Punisher Blocking Reagent</td><td>Biocare Medical</td><td>BP974</td><td>This product is not classified as hazardous.&amp;nbsp;</td></tr><tr><td>inForm software</td><td>PerkinElmer</td><td>CLS135781</td><td>Primary multispectral imaging software used in manuscript</td></tr><tr><td>Nuance software</td><td>PerkinElmer</td><td>Nuance EX</td><td>Software used for making spectral libraries within manuscript</td></tr><tr><td>Vectra microscope and slide scanner</td><td>PerkinElmer</td><td>VECTRA</td><td>Automated slide scanner and microscope for obtaining IM3 image cubes</td></tr></tbody></table>

quantitation of protein expression and co-localization using multiplexed immuno-histochemical staining and multispectral imaging

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.

在图1中 ，训练是在前列腺组织进行图像分割成上皮细胞和基质部，与非组织隔室沿。通过使用上皮细胞膜标记物E-钙粘蛋白，进行小区分割至细胞核，细胞质和膜的部分，在图2所示分开。 在一项实验中，我们使用多路复用IHC研究AR，ERα，E-钙粘蛋白，和CD147的表达和定位， 如图3。使用这...

Watch this Scientific Journal Video about Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging at JoVE.com

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.

蛋白表达和共定位使用复用免疫组织化学染色和多光谱成像定量

Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within ...

quantitation-protein-expression-co-localization-using-multiplexed

Research

JoVE Journal

Biology

12.7K 次观看.  Washington University in St. Louis School of Medicine. Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.

视频: Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of areas of distinct differentiation into recognized histological subtypes, or different pathological grade, often there are more subtle differences in phenotype which defy accurate classification (Figure 1). Ultimately, since morphology is dictated by the underlying molecular phenotype, areas with visible differences are likely to be accompanied by differences in the expression of proteins which orchestrate cellular function and behavior, and therefore, appearance. The significance of visible and invisible (molecular) heterogeneity for prognosis is unknown, but recent evidence suggests that, at least at the genetic level, heterogeneity exists in the primary tumor1,2, and some of these sub-clones give rise to metastatic (and therefore lethal) disease. 
Moreover, some proteins are measured as biomarkers because they are the targets of therapy (for instance ER and HER2 for tamoxifen and trastuzumab (Herceptin), respectively). If these proteins show variable expression within a tumor then therapeutic responses may also be variable. The widely used histopathologic scoring schemes for immunohistochemistry either ignore, or numerically homogenize the quantification of protein expression. Similarly, in destructive techniques, where the tumor samples are homogenized (such as gene expression profiling), quantitative information can be elucidated, but spatial information is lost. Genetic heterogeneity mapping approaches in pancreatic cancer have relied either on generation of a single cell suspension3, or on macrodissection4. A recent study has used quantum dots in order to map morphologic and molecular heterogeneity in prostate cancer tissue5, providing proof of principle that morphology and molecular mapping is feasible, but falling short of quantifying the heterogeneity. Since immunohistochemistry is, at best, only semi-quantitative and subject to intra- and inter-observer bias, more sensitive and quantitative methodologies are required in order to accurately map and quantify tissue heterogeneity in situ.
We have developed and applied an experimental and statistical methodology in order to systematically quantify the heterogeneity of protein expression in whole tissue sections of tumors, based on the Automated QUantitative Analysis (AQUA) system6. Tissue sections are labeled with specific antibodies directed against cytokeratins and targets of interest, coupled to fluorophore-labeled secondary antibodies. Slides are imaged using a whole-slide fluorescence scanner. Images are subdivided into hundreds to thousands of tiles, and each tile is then assigned an AQUA score which is a measure of protein concentration within the epithelial (tumor) component of the tissue. Heatmaps are generated to represent tissue expression of the proteins and a heterogeneity score assigned, using a statistical measure of heterogeneity originally used in ecology, based on the Simpson's biodiversity index7.
To date there have been no attempts to systematically map and quantify this variability in tandem with protein expression, in histological preparations. Here, we illustrate the first use of the method applied to ER and HER2 biomarker expression in ovarian cancer. Using this method paves the way for analyzing heterogeneity as an independent variable in studies of biomarker expression in translational studies, in order to establish the significance of heterogeneity in prognosis and prediction of responses to therapy.

Here we describe a method to quantify molecular heterogeneity in histological sections of tumor material using quantitative immunofluorescence, image analysis, and a statistical measure of heterogeneity. The method is intended for use in clinical biomarker development and analysis.

在使用定量免疫肿瘤的异质性蛋白表达的映射

Morphologic heterogeneity within an individual tumor is well-recognized by histopathologists in surgical practice. While this often takes the form of ...

科研

医学

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of tissue samples from formalin-fixed, paraffin-embedded (FFPE) biopsies has become a key tool for understanding the complexity of tumor immunology and discovering novel predictive biomarkers for cancer immunotherapy. Immunoprofiling analysis of tissues requires the evaluation of combined markers, including inflammatory cell subpopulations and immune checkpoints, in the tumor microenvironment. The advent of novel multiplex immunohistochemical methods allows for a more efficient multiparametric analysis of single tissue sections than does standard monoplex immunohistochemistry (IHC). One commercially available multiplex immunofluorescence (IF) method is based on tyramide-signal amplification and, combined with multispectral microscopic analysis, allows for a better signal separation of diverse markers in tissue. This methodology is compatible with the use of unconjugated primary antibodies that have been optimized for standard IHC on FFPE tissue samples. Herein we describe in detail an automated protocol that allows multiplex IF labeling of carcinoma tissue samples with a six-marker multiplex antibody panel comprising PD-L1, PD-1, CD68, CD8, Ki-67, and AE1/AE3 cytokeratins with 4&#8242;,6-diamidino-2-phenylindole as a nuclear cell counterstain. The multiplex panel protocol is optimized in an automated IHC stainer for a staining time that is shorter than that of the manual protocol and can be directly applied and adapted by any laboratory investigator for immuno-oncology studies on human FFPE tissue samples. Also described are several controls and tools, including a drop-control method for fine quality control of a new multiplex IF panel, that are useful for the optimization and validation of the technique.

A detailed protocol for a six-marker multiplex immunofluorescence panel is optimized and performed, using an automated stainer for more consistent results and a shorter procedure time. This approach can be directly adapted by any laboratory for immuno-oncology studies.

福尔马林固定癌组织标本免疫肿瘤学自动复合免疫荧光小组研究

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of ...

癌症研究

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that participate in regenerating tissue, tumor formation, or vasculogenesis, we devised a multi-colored cellular transplant model of tumor development in which cell populations originate from different fluorescently colored reporter gene mice and are transplanted, engrafted or injected in and around a developing tumor. These colored cells are then recruited and incorporated into the tumor stroma. In order to quantitatively assess bone marrow derived tumor stromal cells, we transplanted GFP expressing transgenic whole bone marrow into lethally irradiated RFP expressing mice as approved by IACUC. 0ovarian tumors that were orthotopically injected into the transplanted mice were excised 6-8 weeks post engraftment and analyzed for bone marrow marker of origin (GFP) as well as antibody markers to detect tumor associated stroma using multispectral imaging techniques. We then adapted a methodology we call MIMicc- Multispectral Interrogation of Multiplexed cellular compositions, using multispectral unmixing of fluoroprobes to quantitatively assess which labeled cell came from which starting populations (based on original reporter gene labels), and as our ability to unmix 4, 5, 6 or more spectra per slide increases, we&#39;ve added additional immunohistochemistry associated with cell lineages or differentiation to increase precision. Utilizing software to detect co-localized multiplexed-fluorescent signals, tumor stromal populations can be traced, enumerated and characterized based on marker staining.1

Complex tissue masses, from organs to tumors, are composed of various cellular elements. We elucidated the contribution of cellular phenotypes within a tissue utilizing multi-labeled fluorescent transgenic mice in combination with multiparameter immunofluorescent staining followed by spectral unmixing to decipher cell origin as well as cell characteristics based on protein expression.

多光谱定量分析继荧光组织移植的细胞起源，类型和相互作用的可视化

With the desire to understand the contributions of multiple cellular elements to the development of a complex tissue; such as the numerous cell types that ...

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

多重条形码图像分析，用于石蜡组织样品单细胞分析中的免疫分析和空间映射表征

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

肿瘤微环境的多重免疫荧光和空间图像分析

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending on its composition and the interactions between cancer cells and peri-tumoral cells, the TME may affect cancer progression. The characterization of tumors and their complex microenvironment could improve the understanding of cancer diseases and may help scientists and clinicians to discover new biomarkers.
We recently developed several multiplex immunofluorescence (mIF) panels based on tyramide signal amplification (TSA) for the characterization of the TME in colorectal cancer, head and neck squamous cell carcinoma, melanoma, and lung cancer. Once the staining and scanning of the corresponding panels are completed, the samples are analyzed on an image analysis software. The spatial position and the staining of each cell are then exported from this quantification software into R. We developed R scripts that allow us not only to analyze the density of each cell type in several tumor compartments (e.g. the center of the tumor, the margin of the tumor, and the stroma) but also to perform distance-based analyses between different cell types.
This particular workflow adds a spatial dimension to the classical density analysis already routinely performed for several markers. mIF analysis could allow scientists to have a better understanding of the complex interaction between cancer cells and the TME and to discover new predictive biomarkers of response to treatments, such as immune checkpoint inhibitors, and targeted therapies.

作者聚焦：多重免疫荧光结合空间图像分析用于肿瘤微环境的临床和生物学评估  

In this article, a protocol for manual tyramide signal amplification (TSA) multiplex immunofluorescence (mIF) combined with image analysis and spatial analysis is described. This protocol can be used with formalin-fixed paraffin-embedded (FFPE) sections for the staining of two to six antigens per slide depending on the slide scanner available in the laboratory.

多重免疫荧光结合空间图像分析，用于肿瘤微环境的临床和生物学评估

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending ...

绘制肿瘤免疫景观图谱：深入的多重 IHC 分析方案

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

作者聚焦：解锁对肿瘤免疫细胞景观的见解

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

肿瘤微环境空间免疫细胞景观的多重免疫组织化学分析

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. ...

蛋白表达和共定位使用复用免疫组织化学染色和多光谱成像定量

摘要

探索更多视频

此视频中的章节

在使用定量免疫肿瘤的异质性蛋白表达的映射

福尔马林固定癌组织标本免疫肿瘤学自动复合免疫荧光小组研究

多光谱定量分析继荧光组织移植的细胞起源，类型和相互作用的可视化

多重条形码图像分析，用于石蜡组织样品单细胞分析中的免疫分析和空间映射表征

多重免疫荧光结合空间图像分析，用于肿瘤微环境的临床和生物学评估

多重免疫荧光结合空间图像分析，用于肿瘤微环境的临床和生物学评估

多重免疫荧光结合空间图像分析，用于肿瘤微环境的临床和生物学评估

肿瘤微环境空间免疫细胞景观的多重免疫组织化学分析

肿瘤微环境空间免疫细胞景观的多重免疫组织化学分析

肿瘤微环境空间免疫细胞景观的多重免疫组织化学分析