这项工作得到了国家杜邦癌症 (印加) (et)、甲组织癌症 (et)、大学大学巴黎太阳 (et)、Selectimmunco (et)、Labex 免疫肿瘤学 (et)、SIRIC CARPEM (CG 等) 赠款的资助。电子是由一个基金会的研究金弧形。EV 和 CD 由 APHP (交易所 année 研究) 的奖学金资助。慢性乙肝由大学索邦巴黎太阳 (合同博士) 的奖学金资助。作者感谢布里斯托尔迈尔斯宝在这个项目上的资助。作者感谢医院 europ 乔治蓬皮杜和尼克 (Laurianne Chambolle, Elodie 米歇尔和 Gisèle Legall) 的病理学系。作者感谢 PARCC, 医院 europ 乔治蓬皮杜 (科琳 Lesaffre) 的组织学平台。

这项研究是按照《赫尔辛基宣言》进行的, 并得到当地道德委员会 (2012-05-04) 的批准。从参与人中获得了知情同意。
1. 组织材料
<ol><li>在手术当天收集碾压混凝土组织标本。在室温下处理手术标本 (病理科)。</li>
	<li>在干管中收集大约0.5 厘米 x 0.5 cm x 0.5 厘米大小的肿瘤样本。在液氮中冷冻, 贮存在-80 ° c。</li>
	<li>将样品涂在最佳切削温度复合物上。部分样品与低温在-20 ° c 入 4-6 µm 厚实的部分。让样品的空气干燥12小时, 并直接储存在-80 ° c, 以避免干燥。</li>
	<li>通过查看苏木精和曙红染色切片检查样品的质量。</li>
</ol>2.原位til 免疫荧光染色
<ol><li>
程序指南
	<ol>
<li>在室温下执行所有实验步骤。</li>
		<li>对于所有步骤, 执行稀释与三缓冲盐水 (TBS; 请参见材料表)。</li>
		<li>除主抗体孵育后, 在 TBS 中执行所有步骤的洗涤。对于后者, 使用三缓冲盐水 Tween20 (TBST, 请参阅材料表)。对于缓冲区组成和重构, 请参见补充表 1。</li>
		<li>使用湿度室的抗体孵化和染色罐洗涤。</li>
		<li>在手术过程中不要让部分干涸。</li>
	</ol>
</li>
	<li>
预处理
	<ol>
<li>将含有组织样品的幻灯片解冻, 并用纸巾小心地将试样周围的滑动干燥。用疏水屏障笔界定包含组织的反应区域 (请参见材料表)。干燥2分钟。</li>
		<li>将100% 丙酮中的样品固定5分钟, 干燥2分钟, 用 TBS 洗涤10分钟。</li>
	</ol>
</li>
	<li>
饱和和封锁
	<ol>
<li>预处理的幻灯片为10分钟, 3 滴素 0.1%, 点击和/或轻弹幻灯片, 以分发素和消除气泡, 然后治疗10分钟与3滴的生物素 0.01% (见材料表), 点击, 和/或轻弹。用 TBS 洗。</li>
		<li>执行 Fc 受体阻滞。应用100µL 5% 体积/体积的正常血清稀释在 TBS. 使用同一寄主物种的血清作为标记的二级或三级抗体 (见材料表);在这里, 驴血清被使用。孵育30分钟。</li>
		<li>在孵化期间, 简要地旋转 anti-CD8、PD-1 和 Tim-3 抗体 (材料目录)。准备在 TBS 的主要抗体混合, 如补充表 2中所述。</li>
	</ol>
</li>
	<li>
CD8、PD-1 和 Tim-3 的免疫染色
	<ol>
<li>准备主抗体混合物。有关所使用的抗体 (anti-CD8、PD-1、Tim-3 及其相应的二级抗体) 及其浓度, 请参阅材料表和补充表 2 。</li>
		<li>轻拍和/或挥出剩余的驴血清 (添加在步骤 2.3.2)。用100µL 的非标记主抗体混合在湿化室中孵育1小时的幻灯片。</li>
		<li>在孵化期间, 简要地旋转青菁5抗兔, AF488-anti-goat, 和化抗鼠抗体, 并准备在补充表 2中描述的二次抗体混合。</li>
		<li>在 TBST 中清洗幻灯片5分钟。</li>
		<li>在湿化室中孵育30分钟的二次抗体的100µL 的幻灯片。</li>
		<li>如补充表 2所述, 准备含有 Cy3-streptavidin 的三级抗体混合物。</li>
		<li>在 TBS 中冲洗幻灯片10分钟, 烘干幻灯片。</li>
		<li>孵育的幻灯片与100µL 的第三抗体混合物30分钟。</li>
		<li>在 TBS 中冲洗幻灯片10分钟, 烘干幻灯片。</li>
	</ol>
</li>
	<li>
单元安装
	<ol>
<li>安装在 4 ", 6-Diamidino-2-吲, 盐酸 (DAPI)-包含安装介质 (1.5 µg/毫升 DAPI) 与片兼容荧光显微镜 (见材料表) 的幻灯片。 注意: 作为对每项实验的阴性对照, 一张与同种匹配的抗体的滑动与相应的抗体浓度相同。对于这种染色, 我们使用了小鼠 IgG1, 兔 igg, 和山羊 igg 阴性控制抗体 (参见材料表)。作为一个积极的控制, 我们使用人类增生扁桃体, 这是已知的是积极的测试标记, 每一个实验。结果中描述了一个典型的染色 (图 1)。CD8、PD-1 和 Tim-3 的单染色应单独进行 (每张幻灯片染色一次), 并进行相同的前处理和无 DAPI。在同样的实验条件下, 没有任何抗体和只有 DAPI 安装介质的滑动也应进行。一张幻灯片应使用相同的实验条件, 没有任何抗体和没有 DAPI 成像。</li>
	</ol>
</li>
</ol>3. 荧光分析和自动细胞计数
<ol><li>图像采集与自动显微镜
	<ol>
<li>创建一个协议。<ol>
<li>打开自动显微镜软件和荧光照明灯。</li>
			<li>在菜单栏中, 单击 "文件"、"创建协议"、"选择 DAPI"、"FITC" 和 "TRITC"。</li>
			<li>单击 "下一步"、"组织部分", 然后单击 "下一步"、"协议名称"。选择一个名称, 例如, "CD8-PD-1-Tim-3", 单击 "下一步" 和 "保存"。</li>
			<li>手动将幻灯片放在舞台上。</li>
			<li>在菜单栏中, 单击 "安装"、"设置"。在新窗口中单击 "设置主页 Z"。</li>
			<li>使用正控制滑块调整曝光时间即CD8、PD-1 和 Tim-3 三重染色的 DAPI 样本。</li>
			<li>在控制栏中单击 "设置曝光"。</li>
			<li>调整每个滤镜的曝光时间, 直到获得足够但不饱和的信号。</li>
			<li>对于单色成像, 请执行以下操作:</li>
			<li>选择购置和在 ' 自动对焦 ' 选择 DAPI 过滤器。</li>
			<li>在 "扫描区域限制" 中, 将目标上移到幻灯片的左上角。单击 "标记"。在右下角做同样的事情。 注: 此步骤 delimitates 4X 低功耗成像 (LP 成像) 的区域;注意将标记放置好, 以便围绕系列的所有示例。</li>
			<li>对于大功率 (HP) 成像, 请执行以下操作:</li>
			<li>在 "自动对焦" 下, 选择 "DAPI"。</li>
			<li>在 "获取" 波段下, 选择 "DAPI"、"FITC"、"Cy3" 和 "Cy5"。单击 "确定"。在菜单栏上单击 "文件", "保存协议"。</li>
		</ol>
</li>
		<li>扫描幻灯片。<ol>
<li>通过单击菜单栏中的 "文件"、"加载协议" 来加载协议。单击 "开始"。</li>
			<li>输入 "实验室 ID" (用于图像存储的文件夹位置)。输入幻灯片 ID 以标识幻灯片。单击 "下一步"。</li>
			<li>单击 "单色成像" (在明亮的光场下扫描整个幻灯片, 并使用4x 目标获取序列图像)。 注意: 这将生成幻灯片的灰度概览。</li>
			<li>点击 "查找标本"。选择低分辨率扫描的组织区域 (4x): 按住 Ctrl 键并使用光标单击字段以选择或取消选中字段 (图 2A)。</li>
			<li>单击 "LP 成像" (4x 成像), 获取每个字段的荧光红蓝绿色图像。</li>
			<li>对于 HP 字段选择, 按住 "Ctrl" 键并使用光标单击字段以选择或取消选中与将在高分辨率 (20x) 扫描的组织区域相对应的字段。选择5字段 (图 2B)。</li>
			<li>单击 "HP 成像" (20x 成像), 获取每个字段的多光谱图像 (图 2C)。</li>
			<li>单击 "数据存储" 以将图像存储在 LabID 开始处选定的文件夹中。 注意: 该过程在图 2中进行了总结和说明。对于每个步骤 (组织检测, 字段选择), 一个算法可以增加和培训的用户对整个自动化扫描过程。</li>
		</ol>
</li>
	</ol></li>
	<li>用耦合分析软件进行荧光图像分析和自动计数
	<ol>
<li>构建频谱库。<ol>
<li>打开图像分析软件。</li>
			<li>在左侧面板中, 打开 "生成库"。在 "加载图像" 下, 单击 "浏览" 并选择一个单声道彩色图像 (例如, CD8/Cyanine 5)。</li>
			<li>选择荧光, (例如, 菁 5)。单击 "提取"。单击 "保存" 以存储在库中。单击 "保存"。</li>
			<li>重复相同的过程, PD-1, Tim-3, 和 DAPI 单染色幻灯片, 以整合的频谱, 每荧光的兴趣。 注意: 构建频谱库集成了用于特定组织 (这里, 肾脏) 的每个荧光的频谱, 但是已经存在的 "合成" 库可以使用。由于自体荧光光谱是严格的相对于组织, 它是重要的是执行一个自动萤光和光谱库的每个项目。</li>
		</ol>
</li>
		<li>创建新项目。<ol>
<li>从菜单栏中, 单击 "文件"、"新建项目"。在 "查找功能" 选项卡中, 选择 "单元格分割"。在 "分" 选项卡中, 选择 "分"。单击 "创建"。</li>
			<li>将代表图像集成到项目中。<ol>
<li>在 "文件" 选项卡上, 单击 "打开图像"。选择10到30代表性的整个系列图像。添加非着色幻灯片以删除荧光。在右侧面板: 选择库源。选择荧光并选择对应于以前构建的频谱库的。</li>
			</ol>
</li>
			<li>要去除组织的自发荧光, 请单击 "AF 按钮", 然后选择空白幻灯片上的自发荧光区域。</li>
			<li>图像处理和复合图像生成。<ol>
<li>单击 "准备全部" 以集成荧光库和自发荧光光谱来生成复合图像 (图 3)。单击 "眼睛" 图标打开 "视图编辑器" 面板, 然后选择显示的数据: 选择 CD8、PD-1、Tim-3 和 DAPI 的标记。去掉荧光。按照软件提供的步骤操作。</li>
			</ol>
</li>
			<li>分割单元格。<ol>
<li>要分割细胞, 在 ' 隔间 ', 选择 "细胞核" 和 "膜"。在 "细胞核" 标签中, 设置 DAPI 为核 counterstain。</li>
				<li>在 "最大和最小大小 (px)" 选项卡中分别输入 "40" 和 "176" 作为最小和最大大小。对于 "最小" 信号, 设置 "0.13"。在 "拆分" 选项卡中, 设置 "2.60"。在 "细胞核" 标签中, 设置 "0.81"。选择 "使用膜信号帮助分割"。</li>
				<li>单击屏幕底部的 "分段单元格"。检查细胞的细胞核和细胞膜的分割, 如果不符合细胞的 DAPI 和细胞膜荧光, 则重试不同的参数。 注: 该软件识别的细胞与核 DAPI 染色, 并根据细胞大小。根据项目调整单元格参数 (大小、拆分、像素)。</li>
			</ol>
</li>
			<li>细胞表型。<ol>
<li>在 "表型" 标签中, 单击 "添加"。创建细胞表型分类: CD8 (蓝点)/CD8-pd-1 (红点)/CD8-pd-1-Tim-3 (绿点)/其他 (黑点) (图 4)。</li>
				<li>选择每个类别中5多个单元格的示例。单击 "训练分类器"。 注意: 这个软件生成了一个统计分类算法。</li>
				<li>点击 "表型全部" 获得每个细胞的表型。 注: 该软件给出了一个具有置信区间 (CI) 精度的细胞表型。</li>
				<li>通过训练软件改进算法, 直到眼睛和自动计数之间的差异是一致的 (错误 < 5%)。选择对感兴趣的单元格可以接受的 CI。 注: 我们选择在 55% CI 的基础上进行培训, 可以接受。</li>
			</ol>
</li>
			<li>保存算法和项目。</li>
			<li>在 "文件" 下选择 "项目"。命名它并单击 "保存"。</li>
		</ol>
</li>
		<li>对系列进行批量分析。<ol>
<li>选择 "批次分析"。选择项目。添加要分析的图像。为数据选择一个存储文件夹。单击 "运行"。检查复合图像的质量。验证该系列的所有图像的分。 注: 耦合图像分析软件集成了所有的细胞隔间 (膜/细胞质/核) 信号。分步骤是至关重要的, 尽管算法的训练可以是一个耗时的步骤。每个图像的所有数据都在一个 .txt 文件中。一个细胞的所有数据 (特别是它的表型给与它的 CI) 在一条线。对于每张幻灯片, 图像获取和后续计数都在5字段上执行。</li>
		</ol>
</li>
	</ol></li>
	<li>原始数据的综合化和自动细胞计数的世代
	<ol>
<li>用统计程序计算数据。<ol>
<li>计算所有的数据从5图像对应的5字段分析为1患者。使用统计平台, 有经验丰富的统计学家、数据管理人员或数据科学家进行分析。建立一个脚本, 自动计数的细胞取决于他们的表型, 选择 CI > 55%。</li>
			<li>将该系列的所有 .txt 文件放在同一个文件夹中。</li>
		</ol>
</li>
		<li>提取数据。<ol>
<li>将所有 .txt 文件放在一个文件夹中。运行在步骤3.3.1 中构建的自动脚本。打开由 R 脚本生成的输出. csv 文件。另存为. xl 格式。输出为每个患者收集每个表型的单元格数 (即、单独的 CD8、CD8-PD-1、CD8-PD-1-Tim-3)。</li>
			<li>计算每个字段每个患者的平均单元格数: 按字段个数 (即, 5) 划分单元格数, 以规范化每个字段每个患者的单元格数。</li>
			<li>计算总 CD8+ t 单元格中的 PD-1+单元格的百分比, 在总 CD8 + t 单元格中 PD-1 + Tim-3+单元格。有关此步骤的摘要, 请参见图 5 。 注意: R 语言 (或其他编程软件的选择) 是需要正确的创建和执行命令, 所以有经验的用户或统计学家/数据科学家是必不可少的。R 程序可以计算同一病人的数据, 但一个病人的 5 .txt 文件的名称应该有一个相同的开始, 对应于一个独特的病人标识符。</li>
		</ol>
</li>
	</ol></li>
	<li>统计分析
	<ol>
<li>使用统计软件对结果进行统计分析。</li>
		<li>在统计学家的帮助下进行适当的统计分析。<ol>
<li>使用非参数魏氏的签名秩测试, 比较 PD-1 在两个细胞表型之间 (图 6)。协和组织学特征与皮尔逊的 chi 平方检验相关联。</li>
			<li>使用卡普兰-梅尔方法估计的进展自由生存, 和 Cox 回归模型, 以估计协影响的时间-事件的结果, 如整体生存和无病生存。</li>
			<li>请考虑p-值小于0.05。</li>
		</ol>
</li>
	</ol></li>
</ol>

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<li>Fourcade, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Upregulation+of+Tim-3+and+PD-1+expression+is+associated+with+tumor+antigen-specific+CD8%2B+T+cell+dysfunction+in+melanoma+patients%5BTitle%5D)+AND+%22J+Exp+Med%22%5BJournal%5D)">Upregulation of Tim-3 and PD-1 expression is associated with tumor antigen-specific CD8+ T cell dysfunction in melanoma patients.</a> J Exp Med. 207 (10), 2175-2186 (2010).</li>
<li>Koyama, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Adaptive+resistance+to+therapeutic+PD-1+blockade+is+associated+with+upregulation+of+alternative+immune+checkpoints%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints.</a> Nat Commun. 7, 10501(2016).</li>
<li>Wherry, E. J., Kurachi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+and+cellular+insights+into+T+cell+exhaustion%5BTitle%5D)+AND+%22Nat+Rev+Immunol%22%5BJournal%5D)">Molecular and cellular insights into T cell exhaustion.</a> Nat Rev Immunol. 15 (8), 486-499 (2015).</li>
<li>Cai, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Tim-3+expression+represents+dysfunctional+tumor+infiltrating+T+cells+in+renal+cell+carcinoma%5BTitle%5D)+AND+%22World+J+Urol%22%5BJournal%5D)">Tim-3 expression represents dysfunctional tumor infiltrating T cells in renal cell carcinoma.</a> World J Urol. 34 (4), 561-567 (2016).</li>
<li>Sakuishi, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Targeting+Tim-3+and+PD-1+pathways+to+reverse+T+cell+exhaustion+and+restore+anti-tumor+immunity%5BTitle%5D)+AND+%22J+Exp+Med%22%5BJournal%5D)">Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity.</a> J Exp Med. 207 (10), 2187-2194 (2010).</li>
<li>Stack, E. C., Wang, C., Roman, K. A., Hoyt, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multiplexed+immunohistochemistry%2C+imaging%2C+and+quantitation%3A+a+review%2C+with+an+assessment+of+Tyramide+signal+amplification%2C+multispectral+imaging+and+multiplex+analysis%5BTitle%5D)+AND+%22Methods%22%5BJournal%5D)">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>Bethmann, D., Feng, Z., Fox, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Immunoprofiling+as+a+predictor+of+patient%27s+response+to+cancer+therapy-promises+and+challenges%5BTitle%5D)+AND+%22Curr+Opin+Immunol%22%5BJournal%5D)">Immunoprofiling as a predictor of patient's response to cancer therapy-promises and challenges.</a> Curr Opin Immunol. 45, 60-72 (2017).</li>
<li>Badoual, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((PD-1-expressing+tumor-infiltrating+T+cells+are+a+favorable+prognostic+biomarker+in+HPV-associated+head+and+neck+cancer%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">PD-1-expressing tumor-infiltrating T cells are a favorable prognostic biomarker in HPV-associated head and neck cancer.</a> Cancer Res. 73 (1), 128-138 (2013).</li>
<li>Nizard, M., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pubmed/28537262">Induction of resident memory T cells enhances the efficacy of cancer vaccine.</a> Nat Commun. 8, 15221(2017).</li>
<li>Nghiem, P. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((PD-1+Blockade+with+Pembrolizumab+in+Advanced+Merkel-Cell+Carcinoma%5BTitle%5D)+AND+%22N+Engl+J+Med%22%5BJournal%5D)">PD-1 Blockade with Pembrolizumab in Advanced Merkel-Cell Carcinoma.</a> N Engl J Med. 374 (26), 2542-2552 (2016).</li>
<li>Roussel, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Composite+biomarkers+defined+by+multiparametric+immunofluorescence+analysis+identify+ALK-positive+adenocarcinoma+as+a+potential+target+for+immunotherapy%5BTitle%5D)+AND+%22Oncoimmunology%22%5BJournal%5D)">Composite biomarkers defined by multiparametric immunofluorescence analysis identify ALK-positive adenocarcinoma as a potential target for immunotherapy.</a> Oncoimmunology. (4), (2017).</li>
<li>Woods, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mismatch+in+epitope+specificities+between+IFNgamma+inflamed+and+uninflamed+conditions+leads+to+escape+from+T+lymphocyte+killing+in+melanoma%5BTitle%5D)+AND+%22J+Immunother+Cancer%22%5BJournal%5D)">Mismatch in epitope specificities between IFNgamma inflamed and uninflamed conditions leads to escape from T lymphocyte killing in melanoma.</a> J Immunother Cancer. 4, 10(2016).</li>
<li>Granier, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Tim-3+Expression+on+Tumor-Infiltrating+PD-1%2BCD8%2B+T+Cells+Correlates+with+Poor+Clinical+Outcome+in+Renal+Cell+Carcinoma%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Tim-3 Expression on Tumor-Infiltrating PD-1+CD8+ T Cells Correlates with Poor Clinical Outcome in Renal Cell Carcinoma.</a> Cancer Res. 77 (5), 1075-1082 (2017).</li>
<li>Feng, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multispectral+Imaging+of+T+and+B+Cells+in+Murine+Spleen+and+Tumor%5BTitle%5D)+AND+%22J+Immunol%22%5BJournal%5D)">Multispectral Imaging of T and B Cells in Murine Spleen and Tumor.</a> J Immunol. 196 (9), 3943-3950 (2016).</li>
<li>Feng, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multispectral+imaging+of+formalin-fixed+tissue+predicts+ability+to+generate+tumor-infiltrating+lymphocytes+from+melanoma%5BTitle%5D)+AND+%22J+Immunother+Cancer%22%5BJournal%5D)">Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma.</a> J Immunother Cancer. 3, 47(2015).</li>
<li>Fridman, W. H., Pages, F., Sautes-Fridman, C., Galon, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+immune+contexture+in+human+tumours%3A+impact+on+clinical+outcome%5BTitle%5D)+AND+%22Nat+Rev+Cancer%22%5BJournal%5D)">The immune contexture in human tumours: impact on clinical outcome.</a> Nat Rev Cancer. 12 (4), 298-306 (2012).</li>
<li>Nizard, M., Roussel, H., Tartour, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Resident+Memory+T+Cells+as+Surrogate+Markers+of+the+Efficacy+of+Cancer+Vaccines%5BTitle%5D)+AND+%22Clin+Cancer+Res%22%5BJournal%5D)">Resident Memory T Cells as Surrogate Markers of the Efficacy of Cancer Vaccines.</a> Clin Cancer Res. 22 (3), 530-532 (2016).</li>
<li>Sandoval, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mucosal+imprinting+of+vaccine-induced+CD8%28%2B%29+T+cells+is+crucial+to+inhibit+the+growth+of+mucosal+tumors%5BTitle%5D)+AND+%22Sci+Transl+Med%22%5BJournal%5D)">Mucosal imprinting of vaccine-induced CD8(+) T cells is crucial to inhibit the growth of mucosal tumors.</a> Sci Transl Med. 5 (172), 172ra120(2013).</li>
<li>Carstens, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Spatial+computation+of+intratumoral+T+cells+correlates+with+survival+of+patients+with+pancreatic+cancer%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer.</a> Nat Commun. 8, 15095(2017).</li>
</ol>

所有作者没有利益冲突声明。

修改和故障排除:
组织质量是一个重要的参数;它可以很容易地通过苏木精和曙红着色检查。
该技术的一个优点是可能的多路复用染色, 但为了避免荧光溢出, 它建议选择的发射波长的三角洲 10 nm 最小。在这里, 因为我们有4染色包括 DAPI, 我们选择展开波长 (DAPI: 460 nm, AF488: 519 nm, 菁 3: 570 nm, 菁 5: 670 nm)。通过使用软件计算混合发射信号中的荧光?...

在过去的几年中, 免疫治疗已经成为一种非常有前途的疗法, 许多类型的癌症, 包括 RCC。特别是, 基于抑制像 PD-1 和 CTLA-4 等抑制检查站的免疫疗法已被报告为临床有效的1,2,3,4,5,6. 抗 CTLA-4、PD-1 或程序死亡配体 1 (PD-L1) 的单克隆抗体已在几种癌症中获得批准, 并导致20% 以上患者的长期持续临床反应7。然而, 并非所有的病人都是应答者, 治疗的费用很高, 这些治疗是有毒的, 导致潜在的严重的自身免疫性副作用。因此, 当前的挑战是确定那些新的免疫的预测标记。据报道, 肿瘤的突变率, PD-L1 的表达, 或 CD8 细胞浸润的水平, 与临床反应相关.然而, 该协会仍然过于薄弱, 建议在临床实践中使用这些临床生物标志物, 除了在非小细胞肺癌 (NSCLC) 患者的 Pembrolizumab 前 PD-L1 的同伴试验,8 ,9,1011,12。结果表明, PD-1、Tim-3、Lag-3 和 CTLA-4 等多种抑制受体的共同表达, 诱导细胞衰竭表型和抗性治疗13,14,15。由于外周血不是肿瘤微环境的代表, 因此, 分析细胞的表型特征原位是很有意义的。PD-1 和 Tim-3 共表达 T 细胞已知是功能受损的细胞在几个上下文13,16,17。在这项研究中, 评估了两个抑制受体 PD-1 和 Tim-3 在 CD8 的 T 细胞上的共同表达对预后的影响.
到目前为止, 研究多标记物在肿瘤浸润淋巴细胞 (til) 中的协同表达主要是通过流式细胞仪分析进行的, 因此有必要对新鲜肿瘤进行研究, 从而排除回顾性分析。与常规的原位染色, 一次只能进行一次染色, 而与表达标记的细胞类型的表征是不可能的。例如, PD-L1 是由肿瘤微环境的许多细胞类型表达的, 使得传统的免疫组织化学分析难以定义 PD-L1 细胞的表达与相关研究的相关性更大。在这项工作中, 我们开发了一个创新的原位多免疫荧光法与计算机计数, 以关联 PD-1 和 Tim-3 的共同表达的肿瘤浸润 CD8+ T 细胞与临床结果在 RCC。这项技术有几个优点, 包括有可能在一个单一的细胞水平和多个标记同时使用多光谱相机, 可以捕获限制的时间间隔 > 10 nm 通过液晶滤镜18。此外, 该过程是自动的, 使互操作性和缩短分析相比, 手动技术19。在癌症领域, 几项研究报告有说服力的多染色免疫分子如 PD-1, PD-L1, 和 CD8 在默克尔-细胞癌, 肺癌, 头颈癌20,21,22,23. 通过用户的培训 (分步骤), 可以实现自动单元计数。荧光是在不同的细胞室 (细胞核, 细胞质, 和膜) 测量。
在大量的碾压混凝土中, 不同的肿瘤浸润 CD8+ T 细胞表达 PD-1 和/或 Tim-3, 结果与临床重力评分和生存参数相关。在细胞分辨率下, 也可以利用细胞内的平均荧光强度来分析细胞膜的荧光强度。据我们所知, 这是第一次使用这种基于多光谱成像的计数技术报告预后结果的研究。

<table><tbody><tr><td>Vectra 3 Automated Quantitative Pathology Imaging&amp;nbsp;</td><td>Perkin Elmer</td><td>CLS142338</td><td></td></tr><tr><td>inForm cell analysis 2.1.</td><td>Perkin Elmer</td><td>CLS135781</td><td></td></tr><tr><td>R software</td><td>https://www.r-project.org</td><td></td><td></td></tr><tr><td>Dakopen delimiting pen</td><td>Dako</td><td>S2002</td><td></td></tr><tr><td>Tris Buffer Salin TBS Tablets</td><td>Takara, Bio Inc.</td><td>TAKT9141Z</td><td>pH7.6 100 tablet</td></tr><tr><td>Tris Buffer Salin Tween 20 TBS(+Tween20)</td><td>Takara, Bio Inc.</td><td>TAKT9142Z</td><td>pH 7.6 100 tablets</td></tr><tr><td>Biotin blocking system</td><td>Dako</td><td>X0590</td><td>Avidin 0.1% and Biotin 0.01%</td></tr><tr><td>normal donkey serum</td><td>Jackson Immunoresearch</td><td>017-000-001</td><td>5% vol./vol. concentration</td></tr><tr><td>Fluoroshield with DAPI</td><td>Sigma-aldrich</td><td>F6057</td><td>1.5 &amp;micro;g/mL concentration</td></tr><tr><td>Knittel glass coverslip</td><td>Knittel Gl&amp;auml;ser,</td><td>100039</td><td>24x60 mm 100 cover slips</td></tr><tr><td>Rabbit anti-CD8 Clone P17-V</td><td>novus</td><td>NBP1-79055</td><td>use at 4&amp;micro;g/mL</td></tr><tr><td>Mouse anti-PD-1 Clone NAT</td><td>abcam</td><td>ab52587</td><td>use at 2 &amp;micro;g/mL</td></tr><tr><td>Goat anti-Tim-3</td><td>R&amp;amp;D</td><td>AF2365</td><td>use at 3 &amp;micro;g/mL</td></tr><tr><td>Rabbit anti-PD-L1 Clone SP142</td><td>Roche</td><td>7309457001</td><td>use at 1 &amp;micro;g/mL</td></tr><tr><td>Mouse AF647 labeled pan- Keratin Clone C11</td><td>Cell Signalling</td><td>4528</td><td>use at 0.5 &amp;micro;g/mL</td></tr><tr><td>Goat anti-human gal9</td><td>R&amp;amp;D</td><td>AF2045</td><td>use at 0.3 &amp;micro;g/mL</td></tr><tr><td>Cyan 5 conjugated donkey anti-rabbit</td><td>Jackson Immunoresearch</td><td>711-175-152</td><td>use at 5 &amp;micro;g/mL</td></tr><tr><td>Biotinylated F(ab&amp;rsquo;2) donkey anti-mouse IgG</td><td>Jackson Immunoresearch</td><td>715-066-150</td><td>use at 3 &amp;micro;g/mL</td></tr><tr><td>Alexa Fluor488 conjugated donkey anti-goat IgG</td><td>abcam</td><td>ab150133</td><td>use at 5 &amp;micro;g/mL</td></tr><tr><td>Cy3 labeled streptavidin</td><td>Amersham</td><td>PA43001</td><td>use at 3 &amp;micro;g/mL</td></tr><tr><td>negative control mouse IgG1</td><td>Dako</td><td>X0931</td><td>use at 2 &amp;micro;g/mL</td></tr><tr><td>IgG from goat serum</td><td>Sigma-aldrich</td><td>I5256</td><td>use at 3 &amp;micro;g/mL</td></tr><tr><td>IgG from rabbit serum</td><td>Sigma-aldrich</td><td>I5006</td><td>use at 4&amp;micro;g/mL</td></tr></tbody></table>

multiplexed immunofluorescence analysis and quantification of intratumoral pd-1+ tim-3+ cd8+ t cells

免疫细胞是肿瘤微环境的重要组成部分, 在癌变的各个阶段影响肿瘤的生长和进化。值得注意的是, 现在已经证实, 人体肿瘤的免疫渗透可以与预后和治疗反应相关联。对肿瘤微环境中免疫浸润的分析已成为患者分类和治疗反应的主要挑战。
抑制受体的共同表达, 如程序细胞死亡蛋白 1 (PD1; 也称为 CD279), 细胞毒 t 淋巴细胞相关蛋白 4 (CTLA-4), t 细胞免疫球蛋白和含 Protein-3 (Tim-3, 也称为 CD366), 淋巴细胞活化基因 3 (Lag-3, 也称为 CD223), 是 T 细胞衰竭的标志。我们开发了一个多的原位免疫荧光染色, 以确定和量化的细胞水平, 这些抑制受体的共同表达。在回顾性肾细胞癌 (RCC) 冰冻组织中, 应用荧光多光谱成像技术与图像分析软件结合, 发现 PD-1 和 Tim-3 对肿瘤浸润 CD8+ T 细胞的联合表达与碾压混凝土的预后差相关。根据我们的知识, 这是第一个研究表明, 这种自动复用的原位技术可能有一些临床相关性。

使用上述的一般协议, 我们的目的是量化肿瘤 CD8+ T 细胞联合表达的抑制受体 PD-1 和 Tim-3 在冰冻组织的患者与临床结果25。
CD8/PD-1/Tim-3 染色的优化:
为了避免不同抗体之间的交叉反应, 选择了不同种类的抗体 (小鼠、兔和山羊, 见材料表</st...

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

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

研究肿瘤微环境可以确定预后或预测的标志物的临床反应免疫治疗。本文介绍了一种基于原位荧光多光谱成像的创新方法, 对 CD8+ T 细胞的各种亚群进行自动分析和计数。这种可重现和可靠的技术适用于大型队列分析。

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

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

14.5K 次观看.  Université Paris Descartes. 研究肿瘤微环境可以确定预后或预测的标志物的临床反应免疫治疗。本文介绍了一种基于原位荧光多光谱成像的创新方法, 对 CD8+ T 细胞的各种亚群进行自动分析和计数。这种可重现和可靠的技术适用于大型队列分析。

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

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 ...

科研

癌症研究

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL of blood) warrant a poor prognosis and are correlated with shorter overall survival in several cancers (e.g., breast, prostate and colorectal). Currently, the anti-EpCAM-coated magnetic bead-based CTC capturing system is the gold standard test approved by the U.S. Food and Drug Administration (FDA) for enumerating CTCs in the bloodstream. This test is based on the use of magnetic beads coated with anti-EpCAM markers, which specifically target epithelial cancer cells. Many studies have illustrated that EpCAM is not the optimal marker for CTC detection. Indeed, CTCs are a heterogeneous subpopulation of cancer cells and are able to undergo an epithelial-to-mesenchymal transition (EMT) associated with metastatic proliferation and invasion. These CTCs are able to reduce the expression of cell surface epithelial marker EpCAM, while increasing mesenchymal markers such as vimentin. To address this technical hurdle, other isolation methods based on physical properties of CTCs have been developed. Microfluidic technologies enable a label-free approach to CTC enrichment from whole blood samples. The spiral microfluidic technology uses the inertial and Dean drag forces with continuous flow in curved channels generated within a spiral microfluidic chip. The cells are separated based on the differences in size and plasticity between normal blood cells and tumoral cells. This protocol details the different steps to characterize the programmed death-ligand 1 (PD-L1) expression of CTCs, combining a spiral microfluidic device with customizable immunofluorescence (IF) marker set.

The characterization of circulating tumor cells (CTCs) is a popular topic in translational research. This protocol describes a semi-automatic immunofluorescence (IF) assay for PD-L1 characterization and enumeration of CTCs in non-small cell lung cancer (NSCLC) patient samples.

免疫荧光对非小细胞肺癌患者循环肿瘤细胞的半自动PD-L1特征化及枚举

Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1&#8722;10 cells per mL ...

Optimization, Design and Avoiding Pitfalls in Manual Multiplex Fluorescent Immunohistochemistry

Microenvironment evaluation of intact tissue for analysis of cell infiltration and spatial organization are essential in understanding the complexity of disease processes. The principle techniques used in the past include immunohistochemistry (IHC) and immunofluorescence (IF) which enable visualization of cells as a snapshot in time using between 1 and 4 markers. Both techniques have shortcomings including difficulty staining poorly antigenic targets and limitations related to cross-species reactivity. IHC is reliable and reproducible, but the nature of the chemistry and reliance on the visible light spectrum allows for only a few markers to be used and makes co-localization challenging. Use of IF broadens potential markers but typically relies on frozen tissue due to the extensive tissue autofluorescence following formalin fixation. Flow cytometry, a technique that enables simultaneous labeling of multiple epitopes, abrogates many of the deficiencies of IF and IHC, however, the need to examine cells as a single cell suspension loses the spatial context of cells discarding important biologic relationships. Multiplex fluorescent immunohistochemistry (mfIHC) bridges these technologies allowing for multi-epitope cellular phenotyping in formalin fixed paraffin embedded (FFPE) tissue while preserving the overall microenvironment architecture and spatial relationship of cells within intact undisrupted tissue. High fluorescent intensity fluorophores that covalently bond to the tissue epitope enables multiple applications of primary antibodies without worry of species specific cross-reactivity by secondary antibodies. Although this technology has been proven to produce reliable and accurate images for the study of disease, the process of creating a useful mfIHC staining strategy can be time consuming and exacting due to extensive optimization and design. In order to make robust images that represent accurate cellular interactions in-situ and to mitigate the optimization period for manual analysis, presented here are methods for slide preparation, optimizing antibodies, multiplex design as well as errors commonly encountered during the staining process.

Multiplex fluorescent immunohistochemistry is an emerging technology that enables the visualization of multiple cell types within intact formalin-fixed, paraffin embedded (FFPE) tissue. Presented are guidelines for ensuring a successful 7-color multiplex with instructions for optimizing antibodies and reagents, preparing slides, design and tips for avoiding common problems.

手动复用荧光免疫学的优化、设计和避免陷阱

Microenvironment evaluation of intact tissue for analysis of cell infiltration and spatial organization are essential in understanding the complexity of ...

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. ...

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

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