该项目由加拿大卫生研究所和艾伯塔省创新保健解决方案/艾伯塔省癌症基金会资助。作者希望认识到神经生物学核心设施中的再生单元使用他们的设备, 宝拉 Gedraitis 在建立 WHCAS 上的滑动扫描的基础上的工作, 以及产品的创造者本文提到的分子器件。

根据当地机构审查委员会和道德委员会批准的议定书获得肿瘤标本, 并按照国家条例进行。本文中使用的 WHCAS 及其相关软件列在材料表中。
1. 导入日志
<ol><li>打开关联的软件。</li>
	<li>下载材料表中指示的日志套件。</li>
	<li>单击主菜单标题日记本, 选择导入日记套件., 然后单击导入, 将日志套件导入适当的目录。</li>
</ol>2. 创建预览扫描采集设置
<ol><li>在车牌采集设置对话框中, 转到目标和照相机选项卡. 选择4X 作为放大倍数, 1 作为照相机 binning, 并且2作为增益。</li>
	<li>在板-Slidescanning下的图 1A中输入设置。 注意: 已创建这些设置以允许用户查看和浏览多达3张幻灯片。每张幻灯片都分为3个相邻的井, 方便导航和 ' 活 ' 可视化的组织切片或细胞。</li>
	<li>在要访问的站点选项卡中, 将井与站点统一填充。</li>
	<li>单击编辑板底设置...并输入图 1B中显示的值。</li>
	<li>在获取循环选项卡下, 为预览扫描输入所需的波长 (本例中的任何核污点)。为了得到最佳的对比度和基于图像的聚焦, 请使用最亮的荧光信号 (例如, 通常是核污点)。 注意: 建议用明亮的污渍 (如核污点) 染色组织, 因为这提供了与背景相比的高对比度。这不仅有助于在样本位置, 但当成像多个荧光在下面的高放大率采集, 最明亮的颜色将用于基础上的其他颜色的成像偏移。</li>
	<li>在 "板捕获" 模式下启用底纹校正, 将图像拼接在一起。</li>
	<li>将这些设置保存为设置 A。</li>
</ol>3. 在高放大率下创建幻灯片采集设置
<ol><li>在车牌采集设置对话框中, 转到目标和照相机选项卡. 选择40X 作为放大倍数, 1 作为照相机 binning (为最高的数字分辨率) 和2作为增益 (增加图像的信号和亮度)。 注意: 可以使用较低的目标放大设置, 但作者发现40X 是使用多波长细胞评分模块分析脑肿瘤组织中人小胶质细胞和巨噬细胞的最佳设置。</li>
	<li>确保板-Slidescanning和编辑板底设置的所有设置...选项卡与设置 A (请参见步骤 2) 相同。</li>
	<li>如果获得的图像不脆, 请更改井深度、板高和板底部设置以调整焦点。如果焦点仍存在问题, 请在自动对焦选项部分中选择具有图像恢复的激光。</li>
	<li>在获取循环选项卡下, 输入示例中存在的波长数。检查启用基于激光的聚焦和启用基于映像的聚焦 (用于获取或激光恢复)选项。</li>
	<li>在自动对焦选项卡下, 选择将焦点放在 "板" 和 "底端"。</li>
	<li>在日志选项卡中, 选择图 2中所示的选项, 确保防止异步硬件移动也被选中。</li>
	<li>将这些设置保存为设置 B。</li>
</ol>4. 将幻灯片放在 Widefield 高内容分析系统中
<ol><li>按F4以获取主菜单。选择幻灯片扫描。单击打开门–弹出幻灯片并插入幻灯片和幻灯片适配器 (可容纳多达三张幻灯片) 到 WHCAS (图 3A 和 3B)。将幻灯片定向到向下的盖玻片和幻灯片适配器中凹槽一侧的标签(图 3A)。 注: 本实验中使用的幻灯片是标准的 25 x 75 x 1 mm 幻灯片。</li>
	<li>单击关闭门–加载幻灯片。</li>
</ol>5. 获取预览扫描
<ol><li>在筛选标题下, 选择车牌获取和控制...和车牌采集设置. 和加载设置 A。</li>
	<li>在要访问的水井选项卡下, 使用鼠标上的右键单击按钮, 移动到包含该示例的井中。在要访问的站点选项卡下, 在单元格找到之前, 移动到具有Live图像捕获的不同站点。手动将焦点放在示例上或使用自动对焦。使用快照捕获图像。 注意: 该示例更容易找到更多的站点, 并将站点调整到井 (参见步骤 2.3)。</li>
	<li>在主菜单上, 选择预览幻灯片。</li>
	<li>在自动弹出的对话框中, 选择反映要扫描多少张幻灯片的选项。一次扫描一张幻灯片。按确定。选择4X 作为放大倍数。按确定。选择盖玻片 (0.17毫米) 作为幻灯片方向 (如果使用不同的盖玻片粗细, 请使用更正衣领)。按确定。单击继续。 注意: 如果用户希望获取2或更多幻灯片的预览扫描, 则必须在获取相应的高放大图像之前分别打开每个预览扫描。</li>
	<li>在加载扫描幻灯片设置下, 确保选中购置设置和校准框。按取消。当扫描幻灯片设置框重新出现时, 选择继续。</li>
	<li>出现扫描幻灯片对话框时, 根据图 4A-4C中的建议调整设置。完成后选择关闭。 注: 增加相机的 binning 和减少相机曝光时间将导致更快的扫描, 虽然在较低的数字分辨率和动态范围, 从而降低图像质量。底纹校正将确保整个单一视野的均匀强度。打开硬件和图像自动对焦将确保扫描处于焦点, 但会减慢扫描速度。为了节省时间, 建议在获取低放大率扫描时有阴影校正和硬件和图像自动对焦, 但在高放大率成像期间为步骤6打开这些选项。低放大率扫描的分辨率不会影响高放大扫描的分辨率。</li>
	<li>为幻灯片扫描数据选择一个目录。单击确定。输入文件的名称。单击确定。</li>
	<li>绘图区域对话框将自动显示。使用矩形区域工具 (图 5A) 选择整个预览扫描区域, 如图 5B所示。单击继续。</li>
	<li>将出现安装完成对话框。选择继续。 注意: 预览幻灯片扫描现在将开始。</li>
	<li>预览完成后, 将显示扫描完成对话框。选择继续。不要关闭预览扫描窗口 (在此示例中为DAPI 扫描窗口; 请参见图 6)。 注: 安装和低放大扫描采集过程将需要大约20分钟。随后的高放大图像采集时间将取决于所选站点的数量以及每个通道的曝光时间。</li>
</ol>6. 高放大率扫描
<ol><li>负载设置 B。</li>
	<li>确定要成像的井。在要访问的水井选项卡下, 使用鼠标上的右键单击按钮, 移动到包含该示例的井中。 注: 这项技术是在这里展示的成像井1。</li>
	<li>在要访问的站点选项卡下, 使用Live功能移动到不同的站点, 直到单元格或组织位于该位置。手动将焦点放在示例上或使用自动对焦。使用快照捕获图像。 注意: 为了帮助在高放大率下定位样本, 请使用 "车牌采集和控制" 对话框, 从5到100µm 调整步长大小, 以便在自动对焦不能的情况下查找样本。</li>
	<li>在W1 DAPI选项卡下, 单击自动公开。确保车牌捕获快照 DAPI图像是脆的。</li>
	<li>在主菜单下, 单击日记本以显示下拉。选择运行日志...。</li>
	<li>双击幻灯片区域购置设置日志以启动它。当出现安装程序幻灯片区域捕获对话框时, 选择继续。</li>
	<li>指示时, 正确选择DAPI 扫描以选择低放大率幻灯片图像。按确定。同样地, 当被要求进行车牌采集时, 请突出显示 "DAPI" 单元。单击确定。</li>
	<li>当出现创建或加载区域框时, 使用矩形区域工具在 DAPI 扫描 (图 6) 上选择 (a) 感兴趣的区域。按继续。 注意: 可在40X 中选择的最大区域为45列, 35 行。如果选择了多个感兴趣区域, 则区域将被调整为具有最大区域的框。可以在调整大小后重新定位这些框。</li>
	<li>在加载区域对话框下选择否。 注意: 选择是将以前保存的区域加载到与感兴趣位置相同区域的多个幻灯片上。</li>
	<li>使用定位器工具, 突出显示感兴趣的最大区域, 然后在选择区域对话框下按继续。当出现确认区域框时, 选择继续。确定在出现保存区域对话框时是否需要保存区域。</li>
	<li>下一个对话框站点间距允许用户选择平铺, 这会导致图像不重叠, 或者10% 重叠。选择一个选项, 然后单击确定。作者选择平铺。</li>
	<li>三对话框将立即出现。安装完成框可以通过按继续来解除。保留 "40X" 的 "采集站点设置" 框 (图 7A) 现在打开, 因为它明确了需要在车牌采集设置框 (图 7B) 中输入多少列和行。保留显示在图像持续时间左下角的WellPositions框 (图 7C)。</li>
	<li>在车牌采集设置框中, 在水井访问下, 必须突出显示以前选定的感兴趣区域的相同数量的水井 (图 7D)。 注: 作者为每张幻灯片最多开发了九个感兴趣的区域。如果有更多, 只需增加 "板-SlideScanning " 选项卡中的列或行数, 以反映感兴趣区域的数目。这些井不以任何方式在空间上代表感兴趣区域的位置, 但必须突出显示适当的数字 (左键单击)。</li>
	<li>在车牌采集设置框中, 转到要访问的站点选项卡. 输入建议的列和行 (图 7B)。请记住单击平铺站点。</li>
	<li>要消除查找包含该示例的站点的猜测, 请在摘要选项卡下按获取板, 将舞台放置在所选的感兴趣区域中。一旦摄像机在包含单元格的站点上平移, 请按下右下角的取消以停止图像获取。确保舞台现在位于样品上。</li>
	<li>优化波长设置, 以确保获得聚焦和高动态范围图像。满意时, 单击摘要选项卡和获取板。</li>
</ol>7. 图像分析
<ol><li>选择所需的自定义模块。 注: 作者选择了多波长细胞评分模块来演示脑肿瘤切片的自动分割。</li>
	<li>对所有站点运行分析。如果许多幻灯片的分析参数相同, 则可以将分析包括在 "后购置" 选项卡下的 "板购置设置" 框中。</li>
	<li>完成分析后, 排除任何图像质量较差的站点, 以免对结果产生偏差。由于这一分析的目的是量化小胶质细胞和巨噬菌在肿瘤实质, 选择感兴趣的区域内的肿瘤标本的边缘。此外, 排除不聚焦的部位和/或包含组织褶皱或气泡和眼泪的组织。有关演示, 请参见代表结果。或者, 使用为每个站点生成的激光聚焦评分, 视激光反射的振幅为阈值, 以下是要排除的站点 (阈值是可自定义的, 取决于参数, 如曝光时间和使用的媒体类型)。 注: 高放大图像可以明确排除结构, 如大血管和坏死区, 如有必要。也有可能只包括感兴趣的领域, 如那些 hypercellular。这种方法可以提高分析的特异性和准确性。</li>
</ol>

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作者对本手稿所描述的产品没有任何财务利益, 也没有其他东西可以透露。

一个普遍的问题仍然阻碍生物科学研究的效率是发展的协议为无偏, 准确, 精确量化的荧光标记组织及其结构内。大量的时间和精力用于寻找方法来分析组织的幻灯片, 一旦他们已经成像。许多现有方法为用户提供了在程序12、13、14中重新创建的算法。这些方法是可以接受的, 但此报告的意义在于, 如果用户能够访问 WHCAS, 则可以轻松快速地建立完整的图像获取和分析协议。例如, 在相关软件中已经有了区分细胞类型和量化多个结构和过程、细胞周期分析和核易位的化验。
安装程序涉及几个关键步骤。首先, 幻灯片的空间参数被定义为, 如果它是一个板块。其次, 创建了低放大率概览扫描, 从中选择感兴趣的区域进行高放大率成像。最后, 排除了影响后续分析的准确性和精确性的站点。此技术的最大限制是它的适用性取决于用户是否可以访问 WHCAS。然而, 随着对高内容分析系统的需求日益增加, 许多机构正在向其研究人员提供这些信息, 以保持竞争力15。当组织切片的厚度不相同时, 通常需要进行故障排除。如果选择了多个感兴趣的区域, 则某些地区将处于焦点, 而其他部分则不会。理想情况下, 在切片过程中, 用户将注意创建均匀的样本。但是, 如果样本不一致, 则重点放在核着色波长 (或用户最亮的荧光) 上, 这是用来聚焦的, 可以对每个焦点以外的区域进行重新调整, 甚至对每个视场进行调整。由于其他波长只是从核着色波长抵消, 只有这种波长需要调整。
在本报告中, 我们详细介绍了如何使用 WHCAS 和相关软件扫描和分析幻灯片。多波长细胞评分模块允许用户自动计数任何荧光标记的核或细胞质标记。在调整焦点设置并定义细胞特征 (如宽度和区域) 以自定义模块到组织成像时, 不再需要用户干预来获取图像幻灯片和量化数据。一次可以拍摄多达三张幻灯片, 可以定义多个感兴趣的区域。此协议允许需要分析幻灯片的 WHCAS 用户利用可自定义的、多用途的自动化工作流, 无需进行优化, 并且可以应用于将来涉及组织组织学分析的任何项目中。

在许多科学领域中, 荧光标记组织的准确精确定量是一种非常抢手的技术。然而, 研究人员经常手工计数标本或花费大量的时间开发深奥的自动化技术来实现这一点。在此, 我们提供了一个协议, 以自动幻灯片扫描和定量的细胞使用 WHCAS 及其相关软件, 与先天免疫细胞在冷冻人脑肿瘤切片作为一个例子。关联的软件提供了广泛的内置可自定义模块从突起的产生计算到分化的细胞类型1,2,3,4,5,6. 此方法的目的是为研究人员提供一个开始到完成的、易于重现的协议, 以便在任何幻灯片安装的组织中获取和定量荧光标记的实体的图像。
在本协议中, WHCAS 主要用于对相关软件进行后续分析的成像板, 即使幻灯片适配器和幻灯片扫描7的基本知识可用。这是禁止图像幻灯片, 因为仔细的空间校准的采集区域, 选择适当的期刊, 创造量身定做的 loadouts, 并与产品代表的联系是必需的。在更广泛的文学作品中, 代替购买专门的幻灯片成像和分析仪器8, 以前的技术报告可以访问该软件, 绕过了 WHCAS 共9上的幻灯片的图像获取。在不同的平台上进行图像采集或图像分析, 需要额外的工作, 以确保它们彼此兼容。
使用 WHCAS 及其软件进行图像捕获的能力将避免搜索或开发与这些工具不相关的工作流的不必要的复杂性。在本文中, 创建低放大率概述扫描和相应的高放大图像的步骤需要将幻灯片作为一个板块处理, 随后使用多波长单元评分细分模块进行的分析允许重新调整 WHCAS 的用途。这种易于使用的协议比替代技术提供了优势, 因为在 WHCAS 上获取图像后, 无需开发算法或多步骤计数协议10,11 。此协议可减少优化定量技术所需的时间, 更精确的12和效率比手动计数和最大化使用 WHCAS。该协议可以广泛且易于使用, 因为它能够对幻灯片上任何荧光标记的组织进行成像和分析。

<table><tbody><tr><td>ImageXpress MicroXLS</td><td>Molecular Devices</td><td>NA</td><td>Apparatus for image acquisition</td></tr><tr><td>MetaXpress 5.1</td><td>Molecular Devices</td><td>NA</td><td>Associated software for ImageXpress MicroXL (runs on a PC with the Windows operating system).</td></tr><tr><td>Slide adapter</td><td>Molecular Devices</td><td>NA</td><td>Metal slide holder that fits into ImageXpress MicroXL</td></tr><tr><td>Slide_Region_Acquisition_revA.jzp</td><td>Molecular Devices</td><td>NA</td><td>The journal can be obtained from metamorph.moleculardevices.com/forum/showthread.php?tid=218&amp;amp;highlight=slide or from contacting a Molecular Devices representative</td></tr><tr><td>Slide_Region_Acquisition&lt;br /&gt;_Setup.JNL</td><td>Molecular Devices</td><td>NA</td><td>Select this journal in Step 6.6.</td></tr></tbody></table>

automated slide scanning and segmentation in fluorescently-labeled tissues using a widefield high-content analysis system

荧光标记组织的自动滑动扫描和分割是分析整张幻灯片或大型组织切片的最有效方法。不幸的是, 许多研究人员花费大量的时间和资源来开发和优化只与他们自己的实验相关的工作流。在本文中, 我们描述了一个协议, 可供那些访问 widefield 高内容分析系统 (WHCAS) 的人用来映像任何幻灯片安装的组织, 并在相关软件中找到预先构建的模块中的自定义选项。本篇文章中详细介绍的步骤并没有用于幻灯片扫描, 因此可以在 WHCAS 中获取可导入到相关软件中的幻灯片扫描图像。在这个例子中, 脑肿瘤切片的自动分割被证明, 但任何荧光标记的核或细胞质标记的自动分割是可能的。此外, 还有多种其他定量的软件模块, 包括蛋白质定位/易位、细胞增殖/生存能力/细胞凋亡和可运行的血管生成的化验。这项技术将节省研究人员的时间和精力, 并创建一个自动的幻灯片分析协议。

这些图像可以在 WHCAS 软件中查看。图 8显示了所定义区域中所有站点的高放大率缩略图。检查每个站点以确定应从分析中排除的那些 (示例在图 8和图 9中分别显示为低和高放大倍数)。例如, 站点149不集中 (图 9A), 站点219有气泡 (图 9B), 站点54包含折叠 (图 9C), 应该排除。这是典型的排除 10-15% 的所有网站映像。在提供的例子中, 15.3% 的站点被排除了 (25/300 有气泡, 17/300 是不集中, 3/300 被折叠, 并且1/300 在边缘)。图 10A是为分析选择的代表性图像 (其相应的低放大率缩略图显示在图 8中)。在这里, 由多波长单元评分模块生成的相应叠加显示了在站点59上进行的自动分割的结果, 根据作者的规格 (原子核最小宽度 = 2.5 µm, 最大宽度 = 7.5 µm,和强度以上的地方背景 = 35 graylevels;CD11b-positive 细胞的最小宽度 = 4 µm, 最大宽度 = 18 µm, 最小染色面积 = 15 µm2, 强度高于局部背景 = 310 graylevels;和 CD45-positive 细胞的最小宽度 = 4 µm, 最大宽度 = 18 µm, 最小染色面积 = 15 µm2, 强度高于本地背景 = 50 graylevels)。分割后, 每个标记的细胞比例的定量数据 (分别为6.2 到5.1% 和 3.8 CD11b+和 CD45+单元格), 两个标记 (3.5 @ 2.1% CD11b+CD45+单元格), 没有标记 (86.6 @ 9.0% CD11b-CD45-单元格;图 10B), 平均染色面积 (40.0 到9.2 µm 和 36.7 CD11b+和 CD45+单元格分别为7.6 µm;图 10C) 和平均值荧光强度 (分别为 CD11b+和 CD45+单元格的 408.9 40.3 相对荧光单元和 373.9 * 38.1 相对荧光单元;图 10D)可以获得。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57440/57440fig1.jpg"> 图 1: 低放大率下的幻灯片获取设置。一个. 此图像显示板块设置。B. 在这里, 显示板底设置。<a href="/files/ftp_upload/57440/57440fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57440/57440fig2.jpg"> 图 2: 高放大率下的幻灯片获取设置.此图显示相应日志的选择。<a href="/files/ftp_upload/57440/57440fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57440/57440fig3.jpg"> 图 3: 演示如何使用幻灯片适配器加载幻灯片.A. 将幻灯片置于幻灯片适配器切口一侧的标签盖玻片下。B. 幻灯片适配器加载到 widefield 高内容分析系统中。<a href="/files/ftp_upload/57440/57440fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57440/57440fig4.jpg"> 图 4: 预览幻灯片设置.A. 此图显示获取的参数。B. 这里, 显示 Hoescht/DAPI 波长设置的值。C. 此图像显示日志设置。<a href="/files/ftp_upload/57440/57440fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57440/57440fig5.jpg"> 图 5: 绘制幻灯片区域.A. 矩形区域工具 (无法使用其他绘图工具) 用于选择预览扫描区域, 并在 DAPI 扫描中创建感兴趣的区域。B. 此图中的蓝绿色轮廓显示应如何选择幻灯片的整个区域 (包括标签)。<a href="/files/ftp_upload/57440/57440fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57440/57440fig6.jpg"> 图 6: 在预览扫描中创建感兴趣的区域.预览或 DAPI 扫描表示整个幻灯片区域。在这个例子中, 有三个连续的脑组织部分 (实心白色长方形轮廓)。这些节上方的区域表示幻灯片上的圆形标签。不同的分区安排不会限制随后选择感兴趣的区域。此特定示例中的感兴趣区域显示为一个虚线白色矩形轮廓。比例条 = 1 毫米<a href="/files/ftp_upload/57440/57440fig6large.jpg" target="_blank">请单击此处查看此图的较大版本</a>。
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/57440/57440fig7.jpg"> 图 7: 用于高放大图像获取的基本窗口.A. "购置站点设置" 窗口将显示有多少列和行位于感兴趣的区域内。B. 确保将图 7A中指示的列和行数输入到 "车牌采集设置" 框中, 并平铺这些站点。C. 即使是空白的, 也必须保持 WellPositions 窗口在图像获取的持续时间内打开。D. 必须强调适当的利益区域数目。<a href="/files/ftp_upload/57440/57440fig7large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/57440/57440fig8.jpg"> 图 8: 感兴趣区域的代表性图像.每个站点都可以显示在感兴趣区域的复合缩略图中。已排除的代表站点 (标记为红色框) 和包含站点 (标记为绿色框) 的示例以更高的放大倍数显示。建议检查每个站点, 以确保它具有足够的分析质量。比例条 = 1 毫米<a href="/files/ftp_upload/57440/57440fig8large.jpg" target="_blank">请单击此处查看此图的较大版本</a>。
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/57440/57440fig9.jpg"> 图 9: 应排除在分析之外的站点的示例.A. 人脑肿瘤切片染色的 CD11b (绿色) 和 CD45 (红色), 小胶质细胞和巨噬菌的标记。此图像已不集中, 已被排除在分析之外。B. 此图像中存在气泡, 但最终分析中已排除。C。由于组织中的褶皱, 本网站被排除在分析之外。刻度条为20µm.<a href="/files/ftp_upload/57440/57440fig9large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/57440/57440fig10.jpg"> 图 10: 代表性图像和分析.A. 每个图像都显示在表示自动分割结果的叠加的旁边。在叠加中, 细胞核显示在白色, 正面染色的细胞呈绿色显示为 CD11b 和红色, 用于 CD45。在从分析中排除质量不足的站点后, 将所有剩余站点的结果合并在一起, B。每个站点中为每个标记着色为正数并为两个标记 ( C) 共同标记的单元格总数的平均比例。平均染色区域和D。平均细胞强度以图形方式表示。RFU = 相对荧光单位。数据被表示为平均值, 即标准差。刻度条为20µm.<a href="/files/ftp_upload/57440/57440fig10large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System at JoVE.com

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

在这里, 我们描述了一个协议, 在幻灯片上使用 widefield 高含量分析系统 (WHCAS) 自动分割荧光标记的组织。该协议在任何领域都有广泛的应用, 包括生物科学、医学工程和健康科学等生物组织中荧光标记的定量。

用 Widefield 高含量分析系统实现荧光标记组织的自动滑动扫描和分割

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. ...

automated-slide-scanning-segmentation-fluorescently-labeled-tissues

Research

JoVE Journal

Bioengineering

8.1K 次观看.  University of Calgary. 在这里, 我们描述了一个协议, 在幻灯片上使用 widefield 高含量分析系统 (WHCAS) 自动分割荧光标记的组织。该协议在任何领域都有广泛的应用, 包括生物科学、医学工程和健康科学等生物组织中荧光标记的定量。

视频: Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Immune cells are important components of the tumor microenvironment and influence tumor growth and evolution at all stages of carcinogenesis. Notably, it is now well established that the immune infiltrate in human tumors can correlate with prognosis and response to therapy. The analysis of the immune infiltrate in the tumor microenvironment has become a major challenge for the classification of patients and the response to treatment.
The co-expression of inhibitory receptors such as Program Cell Death Protein 1 (PD1; also known as CD279), Cytotoxic T Lymphocyte Associated Protein 4 (CTLA-4), T-Cell Immunoglobulin and Mucin Containing Protein-3 (Tim-3; also known as CD366), and Lymphocyte Activation Gene 3 (Lag-3; also known as CD223), is a hallmark of T cell exhaustion. We developed a multiparametric in situ immunofluorescence staining to identify and quantify at the cellular level the co-expression of these inhibitory receptors. On a retrospective series of frozen tissue of renal cell carcinomas (RCC), using a fluorescence multispectral imaging technology coupled with an image analysis software, it was found that co-expression of PD-1 and Tim-3 on tumor infiltrating CD8+ T cells is correlated with a poor prognosis in RCC. To our knowledge, this represents the first study demonstrating that this automated multiplex in situ technology may have some clinical relevance.

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Studying tumor microenvironment may identify prognostic or predictive biomarkers of clinical response to immunotherapy. Presented here, is an innovative method based on in situ fluorescence multispectral imaging to analyze and count automatically various subpopulations of CD8+ T cells. This reproducible and reliable technique is suitable for large cohort analyses.

肿瘤 PD-1 的多路免疫荧光分析及量化研究+ Tim-3+ CD8+ T 单元格

Immune cells are important components of the tumor microenvironment and influence tumor growth and evolution at all stages of carcinogenesis. Notably, it ...

科研

癌症研究

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 ...

医学

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 ...

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the tumor immune landscape. Although standard immunohistochemistry is suitable for studying tissue architecture, it is limited to the analysis of a small number of markers. Conversely, techniques such as flow cytometry can evaluate multiple markers simultaneously, although information about tissue morphology is lost. In recent years, multiplexed strategies that integrate phenotypic and spatial analysis have emerged as comprehensive approaches to the characterization of the tumor immune landscape. Herein, we discuss an innovative technology combining metal-labeled antibodies and secondary ion mass spectrometry focusing on the technical steps in assay development and optimization, tissue preparation, and image acquisition and processing. Before staining, a metal-labeled antibody panel must be developed and optimized. This hi-plex image system supports up to 40 metal-tagged antibodies in a single tissue section. Of note, the risk of signal interference increases with the number of markers included in the panel. After panel design, particular attention should be given to the metal isotope assignment to the antibody to minimize this interference. Preliminary panel testing is performed using a small subset of antibodies and subsequent testing of the entire panel in control tissues. Formalin-fixed, paraffin-embedded tissue sections are obtained and mounted on gold-coated slides and further stained. The staining takes 2 days and closely resembles standard immunohistochemical staining. Once samples are stained, they are placed in the image acquisition instrument. Fields of view are selected, and images are acquired, uploaded, and stored. The final stage is image preparation for the filtering and removal of interference using the system&#39;s image processing software. A disadvantage of this platform is the lack of analytical software. However, the images generated are supported by different computational pathology software.

In the era of cancer immunotherapy, interest in elucidating tumor microenvironment dynamics has increased strikingly. This protocol details a mass spectrometry imaging technique with respect to its staining and imaging steps, which allow for highly multiplexed spatial analysis.

使用福尔马林固定和石蜡包埋组织样品的质谱成像扩展肿瘤微环境的理解

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the ...

癌症组织中免疫细胞标志物的原位免疫组织化学检测

Multiplex Cyclic Fluorescent Immunohistochemistry

The tumor microenvironment involves interactions between host cells, tumor cells, immune cells, stromal cells, and vasculature. Characterizing and spatially organizing immune cell subsets and target proteins are crucial for prognostic and therapeutic purposes. This has led to the development of multiplexed immunohistochemistry staining methods. Multiplex fluorescence immunohistochemistry allows the simultaneous detection of multiple markers, facilitating a comprehensive understanding of cell function and intercellular interactions. In this paper, we describe a workflow for the multiplex cyclic fluorescent immunohistochemistry assay and its application in the quantification analysis of lymphocyte subsets. The multiplex cyclic fluorescent immunohistochemistry staining follows similar steps and reagents as standard immunohistochemistry, involving antigen retrieval, cyclic antibody incubation, and staining on a formalin-fixed paraffin-embedded (FFPE) tissue slide. During the antigen-antibody reaction, a mixture of antibodies from different species is prepared. Conditions, such as antigen retrieval time and antibody concentration, are optimized and validated to increase the signal-to-noise ratio. This technique is reproducible and serves as a valuable tool for immunotherapy research and clinical applications.

作者聚焦：使用荧光免疫组织化学高效检测癌症组织中的免疫细胞浸润

Multiplex cyclic immunohistochemistry allows in situ detection of multiple markers simultaneously using repeated antigen-antibody incubation, image scanning, and image alignment and integration. Here, we present the operating protocol for identifying immune cell substrates with this technology in lung cancer and paired brain metastasis samples.

多重环状荧光免疫组化

The tumor microenvironment involves interactions between host cells, tumor cells, immune cells, stromal cells, and vasculature. Characterizing and ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

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

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

采用先进的3D荧光显微镜定量分析自噬

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

生物学

A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

Multispectral fluorescence imaging on formalin-fixed paraffin-embedded (FFPE) tissues enables the detection of multiple markers in a single tissue sample that can provide information about antigen coexpression and spatial distribution of the markers. However, a lack of suitable antibodies for formalin-fixed tissues may restrict the nature of markers that can be detected. In addition, the staining method is time-consuming. Here we describe a rapid method to perform multispectral fluorescence imaging on frozen tissues. The method includes the fluorophore combinations used, detailed steps for the staining of mouse and human frozen tissues, and the scanning, acquisition, and analysis procedures. For staining analysis, a commercially available semiautomated multispectral fluorescence imaging system is used. Through this method, up to six different markers were stained and detected in a single frozen tissue section. The machine learning analysis software can phenotype cells that can be used for quantitative analysis. The method described here for frozen tissues is useful for the detection of markers that cannot be detected in FFPE tissues or for which antibodies are not available for FFPE tissues.

We describe a rapid staining method to perform multispectral imaging on frozen tissues.

冷冻组织部分多光谱荧光成像的快速方法

Multispectral fluorescence imaging on formalin-fixed paraffin-embedded (FFPE) tissues enables the detection of multiple markers in a single tissue sample ...

免疫与感染

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

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.
Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software1&#160;to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.

工作流的高含量，荧光标记的万能工具显微镜数据，支持开源软件的单个细胞定量

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

用 Widefield 高含量分析系统实现荧光标记组织的自动滑动扫描和分割

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此视频中的章节

肿瘤 PD-1 的多路免疫荧光分析及量化研究⁺ Tim-3⁺ CD8⁺ T 单元格

在使用定量免疫肿瘤的异质性蛋白表达的映射

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

使用福尔马林固定和石蜡包埋组织样品的质谱成像扩展肿瘤微环境的理解

多重环状荧光免疫组化

多重环状荧光免疫组化

多重环状荧光免疫组化

采用先进的3D荧光显微镜定量分析自噬

冷冻组织部分多光谱荧光成像的快速方法

工作流的高含量，荧光标记的万能工具显微镜数据，支持开源软件的单个细胞定量