作者希望感谢 Gráinne 蒂尔尼的编辑协助。

该议定书遵循红外人类研究伦理委员会的准则。
注: 本程序专门用于检测人类新鲜冰冻组织中的少量 cDNA 分子。组织切片已被切割在干冰上, 同时仍被冰冻, 从先前经证实的病人衍生的胃肿瘤或正常组织标本。
1. RNA 的分离纯化
<ol><li>
RNA 提取 注意: 使用特定产品在引擎盖下执行 RNA 隔离 (请参见材料表)。但是, 市面上有各种隔离套件。<ol>
<li>质 50-100 毫克的细切新鲜冷冻组织样品在1.5 毫升管与1毫升的 RNA 隔离试剂和漩涡大力十五年代. 在-80 ° c 的夜间孵育管。</li>
		<li>在室温下孵育含有样品的试管5分钟, 并由涡流在十五年代混合。</li>
		<li>保持在冰上的管, 增加200µL 的氯仿, 和漩涡大力十五年代。</li>
		<li>孵育管在室温下为六十年代和离心机在 1.2万 x g 为15分钟在4° c。</li>
		<li>在1.5 毫升管中转移水相, 并加入20µg 的糖原。 注意: 重要的是要小心避免转移任何的界面或有机层, 以减少污染。</li>
		<li>加入500µL 的异丙醇, 倒置管混合, 在冰上孵育10分钟。</li>
		<li>离心管在 1.2万 x g 为15分钟在4° c 沉淀 RNA。 注: RNA 会出现在试管的侧面和底部的凝胶状颗粒中, 通常在离心后是隐形的。</li>
		<li>除去上清液而不干扰沉淀, 用移1毫升75% 乙醇洗涤。</li>
		<li>离心管在 7500 x g 为5分钟在4° c 和去除上清, 不用干扰药丸。</li>
		<li>让颗粒干燥, 直到沉淀变得透明, 并溶解在50µL 的核糖核酸无水。</li>
		<li>在定量之前, 至少在一夜之间将样品冷冻在-80 ° c。</li>
	</ol>
</li>
	<li>
基因组 DNA 清除与 RNA 纯化 注: dnasei 消化步骤, 其次是基于柱的 RNA 纯化, 建议用于分析低丰度目标, 以消化污染的 DNA。<ol>
<li>溶解冻干 dnasei I (1500 Kunitz 单位) 在550µL 的核糖核酸无水使用注射器, 混合轻轻地倒置小瓶, 并将重组的库存解决方案分为等分。</li>
		<li>在一个1.5 毫升的管传输的样本量计算与15µg 的 RNA, 10 µL dnasei 消化缓冲剂 (列于材料表), 2.5 µL dnasei I 库存解决方案, 核糖核酸-免费水到100µL. 室温孵育10分钟。</li>
		<li>添加350µL 的组织裂解缓冲液 (从工具箱中, 请参阅材料表) 并由移混合。</li>
		<li>添加250µL 100% 乙醇和混合移。</li>
		<li>将整个卷 (700 µL) 转换为新的自旋柱, 并在 1.2万 x g 上离心十五年代。</li>
		<li>将过滤器转换为新的收集管, 将500µL 80% 乙醇添加到柱上, 然后在 1.2万 x 克上离心2分钟。</li>
		<li>打开旋转柱的盖子, 在 1.2万 x 克上离心5分钟, 用新的收集管将过滤器烘干;然后将过滤器转到1.5 毫升管。</li>
		<li>将14µL 的无核糖核酸水直接添加到过滤器柱上, 并在 1.2万 x g 离心1分钟。</li>
		<li>保持样品在冰上, 并进行量化使用2µL 的样品在一个台式分光光度计或存储在-80 ° c 的 RNA, 直到使用。</li>
	</ol>
</li>
</ol>2. cDNA 合成
<ol><li>放置 1000 ng RNA, 4 µL 的主混合, 1 µL 的逆转录酶, 和核糖核酸无菌的水到最后的体积20µL 在一个不育的 PCR 管。轻轻地混合, 向下旋转。</li>
	<li>在热循环中孵育反应混合物, 应用以下条件: 5 分钟在25° c, 30 分钟在42° c, 5 min 在85° c。</li>
	<li>离心管的简要, 并进行 dPCR 分析或存储在-20 ° c, 直到需要。</li>
</ol>3. 数字 PCR 反应的建立
<ol><li>
dPCR 反应混合和样品制备
	<ol>
<li>在室温下解冻主混合和化验至少20分钟。</li>
		<li>在6µL 的水中, 将 cDNA 样本稀释到 300 ng 的浓度。</li>
		<li>轻轻地漩涡主混合和准备混合在一个无菌管与8.7 µL 的主混合, 0.87 µL 的CDH1a定制设计的化验底漆, 和1.83 µL 核酸无水的最后量11.4 µL。 注: 对于CDH1a自定义的检测底漆详细信息, 请参阅材料表。</li>
		<li>将准备好的混合物的11.4 µL 转移到稀释的 cDNA 样本, 轻轻地混合, 并简要地离心。 注: 卷包括20% 的过剩, 以弥补移的体积损失。准备所有样品和无模板控制 (NTC) 的混合。</li>
	</ol>
</li>
	<li>
芯片准备 注: 为了获得最佳效果, 请尽快加载芯片。<ol>
<li>插入芯片装载器, 等待指示灯变绿。</li>
		<li>通过轻轻地拉回柱塞 1-2 mm, 卸下浸入式液体注射器的盖子, 并松开它以方便这一步, 并将其替换为提示。</li>
		<li>拿一个新的芯片, 并记下在盖子上写的代码, 将它与样品相关联。</li>
		<li>小心地握住盖子的侧面, 剥去保护膜, 并将盖子与粘脸贴在正确的方向上。</li>
		<li>小心地拿起一块芯片, 小心不要触摸内部部分, 并把它加载到芯片窝在正确的位置, 按下杠杆打开钳。</li>
		<li>加载一个新的加载刀片到加载程序, 并轻轻地推动它, 以确保它是牢固到位。</li>
		<li>将 dPCR 反应混合物的14.5 µL 转移到加载刀片上, 而不制造气泡或偏转刀片, 然后按黑色加载按钮将卷放在芯片上。</li>
		<li>使用浸入式液体注射器转移大约20滴到芯片表面注意不要接触表面与提示。 注意: 重要的是要覆盖整个表面而不溢出液体的边缘。</li>
		<li>旋转装载机臂, 使盖子与芯片接触, 并按下十五年代。</li>
		<li>按下盖子按钮, 释放芯片, 并返回其位置的手臂。</li>
		<li>将组装好的芯片放在45°的角度, 并小心地将浸入式液体与注射器通过填充口, 稍微旋转芯片以确保没有气泡, 并用无菌擦拭去除多余的液体。</li>
		<li>通过轻轻剥离芯片盖顶部的标签, 然后按下填充端口至少5秒来密封芯片机箱。</li>
		<li>将芯片存储在黑暗中, 直到准备好装入热循环。 注: 准备好的芯片应在2小时内使用。</li>
	</ol>
</li>
	<li>
dPCR 反应
	<ol>
<li>打开盖子, 将适配器安装到两个块上, 即使使用了单个块。</li>
		<li>将芯片放在样品块的正确位置。 注: 填充端口必须朝向热循环的前部, 使其处于升高的位置, 以允许任何气泡浮到顶部, 而不会干扰芯片的窗口。用空筹码来平衡两个街区。</li>
		<li>将热垫放在样品块上, 以完全覆盖芯片。</li>
		<li>关闭盖子, 开始 PCR 运行, 应用以下条件: 保持在96° c 10 分钟;45周期60° c 为 2 min 和98° c 为三十年代;保持在60° c 为2分钟;保持在4° c。关闭热循环, 在室温下解冻芯片至少10分钟。 注: 芯片分析必须在一小时内完成。</li>
	</ol>
</li>
	<li>
芯片分析
	<ol>
<li>打开仪表盖, 卸下散热片, 然后从适配器中取出芯片。 注意: 将芯片存放在一个黑暗、干净的位置, 直到分析。</li>
		<li>用异丙醇和无菌擦拭清洗芯片表面。 注意: 检查每个芯片是否有泄漏或潜在的问题。</li>
		<li>将 USB 插入检测器系统以保存数据。</li>
		<li>打开检测系统的芯片托盘, 将芯片正面装入正确位置, 然后关闭托盘。</li>
		<li>等待三十年代进行处理, 然后取出芯片并插入下一条。</li>
		<li>等待分析完成所有处理过的芯片然后插入 USB 到计算机转移文件。 注: 分析的总时间大约是2到3分钟/芯片。</li>
	</ol>
</li>
</ol>4. 数据分析和解释
<ol><li>连接到执行所有下游分析所需的基于云的软件平台。</li>
	<li>创建项目并导入感兴趣的芯片的所有数据文件。</li>
	<li>输入示例名称;选择 "定义芯片" 标签中使用的染料和化验。</li>
	<li>通过在 "审阅数据" 选项卡中进行可视化来确定芯片是否可以接受, 检查该样本在芯片上的加载程度以及评价的数据点。 注: 拒绝少于1.3万评价数据点的芯片。</li>
	<li>移动到屏幕右侧所选芯片的散射图;应用阈值为6000的荧光素 amidite (家) 报告染料信号 (Y 轴) 的所有芯片。 注: 设定的阈值可能根据所使用的化验方法而有所不同。</li>
	<li>通过使用套索工具在散射图上选择相对点, 并按 "待定" 键, 消除任何可疑的正信号以防止误报结果。所有剩余的阳性斑点均表明, 所分析的稀有靶点存在 cDNA 拷贝。</li>
</ol>

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作者报告这项工作没有利益冲突。

dPCR 最初开发了为脱氧核糖核酸分子测量10,11,12,13 , 并且在时间这技术是适应的定量的 rna 并且 RNA 抄本5, 14,15。在本协议中, 我们扩展了应用程序列表, 包括检测来自新鲜冰冻组织样本的稀有记录。为此, 我们使用了相当高的 cDNA (300 ng)。虽然这一数额不是可用于 dPCR 的最大值, 但它足以建立标准化的条件来比较我们所有的样品。通过将花期信号阈值设置为 6000, 并在芯片和散射图上兼顾正向放大信号的位置, 成功地检测到了胃肿瘤组织标本中的这一罕见靶点。
通过 dPCR, 我们不能绝对地量化CDH1a, 因为目标的数量非常少, 但我们可以可靠地评估此罕见记录的存在/缺席。实际上, 这种技术的数量性受到分析样本7中的初始目标数量的限制。的相关性, 该技术本身仍然是相当昂贵和时间密集型。因此, 当需要分析许多示例和/或多个目标时, 应将替代技术视为7,8。
这样说, dPCR 无疑提供了对核酸敏感测量和量化的终极平台, 因为它提高了对 qPCR78的精确度和重现性。此外, 它可以被认为是唯一的方法, 以分析基因表达的低丰富的核酸, 接近极限的 qPCR 敏感性6。
这里所描述的协议可以适应任何组织的 rna, 从而能够检测到稀有的目标, 如我们的研究。此外, 该化验可以扩展到绝对量化的成绩单。在这种情况下, 仪器所报告的样品的拷贝/µL 的数量必须简单地乘以载入的反应量, 并除以初始的 cDNA 数量。为了克服由反向转录产生的变异, 应在可能的情况下包括基因特异的校准仪6。展望未来, dPCR 在成为黄金标准平台方面具有相当大的潜力, 不仅适用于稀有的序列检测, 还有稀有的突变检测和精确的拷贝数量化7。这是特别相关的基因嵌, 以及在癌症的研究, 肿瘤异质性可以影响病人的临床结果和反应的治疗。
从实用的角度来看, 在设置 dPCR 协议时, 必须突出显示多个关键步骤。首先, 反应混合含有适当数量的 cDNA 的基础上的目标表达水平的预期, 应转移非常小心在加载刀片, 以不创造气泡, 这可能会干扰样品的均匀性。此外, 刀片本身应首先适当定位;否则会导致样本分布不均匀。此外, 充分的涂层与浸泡液和准确的密封盖应始终执行。对于芯片的加工和分析, 重要的是要知道在芯片上积聚冷凝的可能性;在这种情况下, 应该用异丙醇重新擦拭芯片, 再重新分析。此外, 由于 dPCR 基于泊松统计, 因此芯片上必须有最小数量的评价数据点 (> 1万), 否则样本应该再次运行。最后, 对于低目标拷贝检测实验, 一个关键的混淆因素可能是信噪比, 但一个可靠的阈值定位可以帮助识别真正的正信号。

CDH1基因对 e-钙黏蛋白进行编码, 这是通过调节细胞黏附、存活、增殖和迁移来维持正常胃上皮的关键因素,1。由于CDH1的有害系或体细胞的改变而导致 e-钙黏附蛋白的丧失, 已与 GC23的开发相关。来自基因内含子2的非规范转录也被假设在胃癌癌变中起到一定作用4,5。特别是, 一个这样的成绩单, CDH1a, 已被证明是用 GC 细胞系表达, 但没有从正常胃4。最近, 我们在 gc 患者使用基于芯片的 dPCR5中检测到气相色谱组织样本中的CDH1a 。dPCR 用于第一次评估在肠道 GC 和正常组织中存在的 CDH1a基因转录。
测定基因表达的金标准方法是实时定量 PCR (qPCR)。但是, 结果数据有时可能是可变的, 质量较差, 尤其是当样本中的目标级别较低时。这种变化可能是由污染物引起的, 它抑制聚合酶活性和底漆退火, 导致非特异的放大和竞争性的副反应6。
虽然 dPCR 的基本生物化学原理与 qPCR 相似, 但 dPCR 显示出一些优势, 允许对基因组 dna (gDNA)/互补 dna (cDNA) 分子进行非常精确的测量。事实上, dPCR 是一个最终点的反应, 它依赖于将一个样本的标定分成数千个井, 因此每个井都包含零个或一个目标分子。放大然后发生在包含目标的拷贝的井, 并且由一个萤光信号指示。然后, 通过用二项式泊松统计值确定正向总分区的比值来计算原始样本中的目标分子的绝对数量7。
此外, dPCR 技术消除了运行标准曲线的需要, 从而避免了相关的偏差和可变性, 从而可以直接量化目标8,9;由于其对抑制剂的高耐受性, 它可以独立于污染物和效率产生更精确和可重现的结果10;它比 qPCR 更灵敏、更特异, 因此是检测稀有靶点的可靠方法。最后, 将样本分成多个反应, 减少了与背景分子的竞争, 提高了目标检测的极限, 使放大成为可能, 并促进了单分子 gDNA/cDNA 的检测6.以芯片为基础的核酸的检测和定量 dPCR 已越来越多地应用于拷贝数变异, DNA 片段的量化, 和突变分析11,12,13, 给出精度和低材料输入要求的方法。另外, dPCR 最近被集成了入分析 rna14和基因抄本5,15。

<table border="0" cellpadding="0" cellspacing="0" width="1127">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:285px;">TRIazol Reagent</td>
			<td style="width:159px;">Thermo Fisher Scientific</td>
			<td style="width:344px;">15596018</td>
			<td style="width:339px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Glycogen 20 mg/ml</td>
			<td>ROCHE</td>
			<td>10901393001</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">RNeasy MinElute Cleanup kit</td>
			<td>QIAGEN</td>
			<td>74204</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">iScript cDNA Synthesis kit</td>
			<td>BioRad</td>
			<td>1708891</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">QuantStudio 3D Digital PCR Master Mix v2</td>
			<td>Thermo Fisher Scientific</td>
			<td>A26358</td>
			<td></td>
		</tr>
		<tr height="126">
			<td height="126" style="height:126px;">CDH1a IDT custom designed assay</td>
			<td style="width:159px;">Integrated DNA Technologies (IDT)</td>
			<td>NA</td>
			<td style="width:339px;">F) GCTGCAGTTTCACTTTTAGTG 
			(R) ACTTTGAATCGGGTGTCGAG 
			(P)/FAM/CGGTCGACAAAGGACAGCCTATT/TAMRA/ 
			[dPCR optimized assay concentrations: 900 nM (F),&#160;&#160;&#160;&#160;&#160;&#160;&#160; 900 nM (R), 250 nM (P)]</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">QuantStudio 3D Digital PCR 20K Chip Kit v2</td>
			<td>Thermo Fisher Scientific</td>
			<td>A26316</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Heraeus Biofuge Fresco</td>
			<td>Thermo&#160; Scientific</td>
			<td>75002402</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Thermocycler (Labcycler)</td>
			<td>Sensoquest</td>
			<td>011-103</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">GeneAmp PCR System 9700</td>
			<td>Thermo Fisher Scientific</td>
			<td>N805-0200</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Dual Flat Block Sample Module</td>
			<td>Thermo Fisher Scientific</td>
			<td>4425757</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:285px;">QuantStudio 3D Tilt Base for Dual Flat Block GeneAmp PCR System 9700</td>
			<td>Thermo Fisher Scientific</td>
			<td>4486414</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:285px;">QuantStudio 3D Digital PCR Chip Adapter Kit for Flat Block Thermal Cycler</td>
			<td>Thermo Fisher Scientific</td>
			<td style="width:344px;">4485513</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">QuantStudio 3D Digital PCR Chip Loader</td>
			<td>Thermo Fisher Scientific</td>
			<td>4482592</td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:285px;">QuantStudio 3D Digital PCR Instrument with power cord</td>
			<td>Thermo Fisher Scientific</td>
			<td>4489084</td>
			<td></td>
		</tr>
	</tbody>
</table>

detection of a cdh1 rare transcript variant in fresh-frozen gastric cancer tissues by chip-based digital pcr

CDH1a是CDH1基因的非规范化转录, 它被发现在一些胃癌 (GC) 细胞系中表达, 而在正常的胃粘膜中没有。最近, 我们在 GC 患者的新鲜冰冻肿瘤组织中检测到了CDH1a成绩单变体。本文采用基于芯片的数字 PCR (dPCR) 方法, 研究了该变异体在组织样品中的表达。dPCR 提供了一个准确的, 强健的, 高度敏感的核酸测量的潜力, 并越来越多地用于不同领域的应用。dPCR 能探测稀有目标;此外, dPCR 提供了绝对和精确的核酸定量的可能性, 不需要校准仪和标准曲线。事实上, 反应分区从背景中丰富了目标, 提高了放大效率和对抑制剂的耐受性。这样的特性使 dPCR 成为检测CDH1a稀有脚本的最佳工具。

使用这里介绍的过程, 我们检查了在胃新鲜冷冻组织中罕见的转录变体CDH1a的表达。dPCR 的分析是在21配对正常和癌症组织样本和11额外的肿瘤样本中进行的。CDH1a在 32 (47%) 个肿瘤中可检测到 15, 而没有正常的组织样本显示这一罕见的转录的存在5。在我们的分析中, 不到1.3万数据点的芯片被拒绝, 与非均质加载的芯片一样。图 1显示了评价 (图 1A) 和非评价 (图 1B、C) 示例的芯片视图。对于后者, 必须丢弃芯片, 因为存在多个气泡 (图 1B)、单个大气泡 (图 1C), 或者由于阈值以上的数据点数量不足 (图 1B)。这些示例必须再次运行才能获得解释结果。关于软件提供的散点图 (图 2), 它通常描述来自 "来自于" 的来自于来自于受害者 (制造商专有的) 报告染料 (X 轴) 的信号 (Y 轴)。在散布图上显示的数据点是彩色编码的, 在这里, 我们只用蓝色 (图 2A) 来直观地显示出了该对象的染料信号, 表明了目标被放大的井。这些信号更接近 Y 轴, 并进一步从情节的起源。在图中看到的第二种颜色是黄色的, 它指示了没有放大的井。对正信号的可靠阈值进行了设置, 并应用于所有的芯片, 以防止呼叫偏置。在这里, 选定的阈值是6000的基础上的各种芯片在这些实验中观察到的信号范围。在许多肿瘤样本中发现了CDH1a cDNA 拷贝 (蓝色信号), 显示了从负信号 (图 2A) 的振幅明显不同。相反, 在CDH1a成绩单的阴性样本、即、所有正常的组织样本和一些肿瘤样本 (图 2B) 中, 没有找到高于所选阈值的信号。同样, 在 NTC 中没有检测到水被添加而不是 cDNA 的阳性信号, 因此作为 dPCR 反应的负控制 (图 2C)。
必须采用精细的芯片分析方法, 对低质量数据点进行过滤, 消除模糊结果的风险。这是通过查看芯片来确定相应的正信号的确切位置: 如果蓝色信号位于芯片的边界 (图 3A) 或气泡周围 (图 1B), 则从分析中排除信号.为了进一步防止误报的风险, 在6000以上的家族通道和情节的最右边的信号被认为可能是具体, 因此被过滤掉了, 即使它们在芯片内很好地被本地化了 (图 3B)。这样, 只要该信号符合上述条件, 就会被认为是对罕见的成绩单 ( CDH1a) 的表达式为正值, 即使它显示的是单个放大信号。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57066/57066fig1.jpg"> 图 1.具有代表性的评价和非评价芯片.蓝色的点是井在其中的CDH1a成绩单被放大;黄色的圆点是没有放大的井;白色的圆点是空的井。每个芯片下面都显示了阈值以上的数据点。(A) 带有蓝色信号的评价芯片在芯片边界内很好地定位。(B) 一个非评价芯片, 在阈值以上少于1.3万数据点。(C) 一个带有大气泡的非评价芯片。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57066/57066fig1large.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/57066/57066fig2.jpg"> 图 2.代表性数字 PCR 散射图CDH1a表达式。图中描绘的是来自于来自维也纳国际中心记者染料 (X 轴) 的信号 (Y 轴)。蓝色点是CDH1a正向表达式信号, 而黄色点是 "无放大" 信号。(A) CDH1a-阳性样本。(B) CDH1a-负值示例。(C) 没有显示零CDH1a表达式的模板控件 (NTC)。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57066/57066fig2large.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/57066/57066fig3.jpg"> 图 3.数字 PCR 散射图形的典型精细分析。左边是芯片视图与感兴趣信号放大-在一个黑正方形, 而在右边是对应的分散剧情。(a) 在图块的正确分数上定位的蓝色信号, 但在芯片的边界上。(B) 三蓝色信号以灰色局部化显示在图的最右边, 但在芯片内。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57066/57066fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR at JoVE.com

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

基于芯片的数字 PCR 检测新鲜冷冻胃癌组织中的一个CDH1罕见转录变体

该手稿描述了一种基于芯片的数字 PCR 方法, 用于检测从胃癌患者中获得的新鲜冰冻正常和肿瘤组织中罕见的CDH1转录变体 (CDH1a)。

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal ...

detection-cdh1-rare-transcript-variant-fresh-frozen-gastric-cancer

Nanyang Technological University

Research

JoVE Journal

Cancer Research

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

6.1K 次观看.  Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS. 该手稿描述了一种基于芯片的数字 PCR 方法, 用于检测从胃癌患者中获得的新鲜冰冻正常和肿瘤组织中罕见的CDH1转录变体 (CDH1a)。

视频: Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

Development of a Human Preclinical Model of Osteoclastogenesis from Peripheral Blood Monocytes Co-cultured with Breast Cancer Cell Lines

The crosstalk between tumor cells and bone cells in the bone microenvironment is crucial to understanding the mechanism of bone metastasis formation. We developed an in vitro fully human preclinical model of a co-culture of breast cancer cells and monocytes undergoing differentiation towards osteoclasts. We optimized a model of osteoclastogenesis starting from a sample of peripheral blood collected from healthy donors. Peripheral blood mononuclear cells (PBMCs) were first separated by density gradient centrifugation, seeded at a high density and induced to differentiate by adding two growth factors (GFs): receptor activator of nuclear factor-&#954;B ligand (RANKL) and macrophage colony-stimulating factor (MCSF). The cells were left in culture for 14 days and then fixed and analyzed by downstream analysis. In osteolytic bone metastases, one of the effects of cancer cell arrival in bone is the induction of osteoclastogenesis. We thus challenged our model with co-cultures of breast cancer cells to study the differentiation power of cancer cells with respect to GFs. A straightforward way of studying cancer cell-osteoclast interaction is to perform indirect co-cultures based on the use of conditioned medium collected from breast cancer cell cultures and mixed with fresh medium. This mixture is then used to induce osteoclast differentiation. We also optimized a method of direct co-culture in which cancer cells and monocytes undergoing differentiation share the medium and exchange secreted factors. This is a significant improvement over the original indirect co-culture method as researchers can observe the reciprocal interactions of the two cell types and perform downstream analyses for both cancer cells and osteoclasts. This method enables us to study the effect of drugs on the metastatic bone microenvironment and to seed cell lines other than those derived from breast cancer. The model can also be used to study other diseases such as osteoporosis or other bone conditions.

This protocol describes development of an in vitro human preclinical model of osteoclastogenesis from peripheral blood monocytes cultured with breast cancer cell lines to mimic the cancer cell-osteoclast interaction. The model could be used to further our understanding of bone metastasis formation and improve therapeutic options.

乳腺癌细胞系外周血单核细胞破的人前临床模型的建立

The crosstalk between tumor cells and bone cells in the bone microenvironment is crucial to understanding the mechanism of bone metastasis formation. We ...

科研

癌症研究

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. However, the application of this method is challenging due to its low sensitivity in analyzing the trace amount of ctDNA in the blood. Furthermore, the method may generate false positive and negative results from this sequencing and subsequent analysis. To improve the feasibility and reliability of ctDNA detection in the clinic, here we present a technique which enriches rare mutations for sequencing, Enrich Rare Mutation Sequencing (ER-Seq). ER-Seq can distinguish a single mutation out of 1 x 107 wild-type nucleotides, which makes it a promising tool to detect extremely low frequency genetic alterations and thus will be very useful in studying disease heterogenicity. By virtue of the unique sequencing adapter&#39;s ligation, this method enables an efficient recovery of ctDNA molecules, while at the same time correcting for errors bidirectionally (sense and antisense). Our selection of 1021 kb probes enriches the measurement of target regions that cover over 95% of the tumor-related driver mutations in 12 tumors. This cost-effective and universal method enables a uniquely successful accumulation of genetic data. After efficiently filtering out background error, ER-seq can precisely detect rare mutations. Using a case study, we present a detailed protocol demonstrating probe design, library construction, and target DNA capture methodologies, while also including the data analysis workflow. The process to carry out this method typically takes 1-2 days.

This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement.

利用下一代测序检测 CtDNA 中的稀有突变

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. ...

Serum and Plasma Copy Number Detection Using Real-time PCR

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, and prediction of treatment resistance. Starting from the hypothesis that androgen receptor (AR) gene copy number (CN) gain is a frequent event in metastatic castration resistance prostate cancer (mCRPC), we propose to analyze this event in cfDNA as a potential predictive biomarker.
We evaluated AR CN in cfDNA using 2 different real-time PCR assays and 2 reference genes (RNaseP and AGO1). DNA amount of 60 ng was used for each assay combination. AR CN gain was confirmed using Digital PCR as a more accurate method. CN variation analysis has already been demonstrated to be informative for the prediction of treatment resistance in the setting of mCRPC, but it could be useful also for other purposes in different patient settings. CN analysis on cfDNA has several advantages: it is non-invasive, rapid and easy to perform, and it starts from a small volume of serum or plasma material.

This manuscript describes the copy number variation analysis performed in serum or plasma DNA using real-time PCR approach. This method is suitable for the prediction of drug resistance in castration resistant prostate cancer patients, but it could be informative also for other diseases.

应用实时 PCR 技术检测血清和血浆拷贝数

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, ...

Elastic Staining on Paraffin-embedded Slides of pT3N0M0 Gastric Cancer Tissue

The elastic lamina, which is usually located in the sub-mesothelial layer, adjacent to the peritoneum mesothelial cells, is amphiphilic on hematoxylin and eosin (H&amp;E) staining but can be visualized through elastic staining. One of the important benefits of elastic staining is that it makes it easy to determine whether tumor cells have invaded beyond the elastic lamina. This helps in examining the extent of peritoneal surface invasion, thereby distinguishing the pT3 and pT4 stages in gastrointestinal cancer. In this study, we present a protocol to identify elastic fibers in the formalin-fixed, paraffin-embedded pT3N0M0 gastric cancer tissue sections. We prepare 5-&#181;m paraffin sections fixed on slides, and then all the slides are deparaffinated and rehydrated during the staining procedure. Subsequently, all the sections are oxidized by potassium permanganate, bleached by oxalic acid, stained by elastic staining, and counterstained by Van Gieson's. Finally, we examine the effect of staining; elastic lamina is stained blue-black and collagen fibers are stained red, while the cancer cells are stained in varying shades of yellow. We also describe in detail the methods used for determining the positional relationship between cancer cells and elastic lamina. This method is simple, low-cost, and widely applicable in the identification of peritoneal surface invasion in gastrointestinal cancer because of its outstanding selectivity for elastic fibers and authentic results.

Here, we present a protocol of elastic staining to identify elastic fibers in pT3N0M0 gastric cancer tissues on formalin-fixed, paraffin-embedded sections. Subsequently, we describe a method to determine whether tumor cells invade beyond the elastic lamina.

pT3N0M0 胃癌组织石蜡嵌入幻灯片上的弹性染色

The elastic lamina, which is usually located in the sub-mesothelial layer, adjacent to the peritoneum mesothelial cells, is amphiphilic on hematoxylin and ...

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

The purpose of this study is to introduce a methodology of the immunostaining of human lung tissues, followed by whole-slide digital scanning and image analysis. Digital scanning is a fast way to scan a stack of slides and produce digital images with high quality. It can produce concordant results with conventional light microscopy (CLM) by pathologists. Furthermore, the availability of digital images makes it possible that the same slide can be concurrently observed by multiple people. Moreover, digital images of slides can be stored in a database, which means the long-term deterioration of glass slides is avoided. The limitations of this technique are as follows. First, it needs high-quality prepared tissue and the original immunohistochemistry (IHC) slides without any damage or excess sealant residue. Second, tumor or nontumor areas should be specified by experienced pathologists before the analysis using software, in order to avoid any confusion about the tumor or nontumor areas during scoring. Third, the operator needs to control the color reproduction throughout the digitization process in whole-slide imaging.

ZW10 interacting protein (ZWINT) participates in the mitotic spindle checkpoint and the pathogenesis of carcinoma. Here, we introduce a methodology of the immunostaining of ZWINT in human lung cancer tissues, followed by the digital scanning of whole slides and image analysis. This methodology can provide high-quality digital images and reliable results.

人肺组织中ZW10相互作用蛋白免疫染色的数字化分析

The purpose of this study is to introduce a methodology of the immunostaining of human lung tissues, followed by whole-slide digital scanning and image ...

Detection of Protease Activity by Fluorescent Peptide Zymography

The purpose of this method is to measure the proteolytic activity of complex biological samples. The samples are separated by molecular weight using electrophoresis through a resolving gel embedded with a degradable substrate. This method differs from traditional gel zymography in that a quenched fluorogenic peptide is covalently incorporated into the resolving gel instead of full length proteins, such as gelatin or casein. Use of the fluorogenic peptides enables direct detection of proteolytic activity without additional staining steps. Enzymes within the biological samples cleave the quenched fluorogenic peptide, resulting in an increase in fluorescence. The fluorescent signal in the gels is then imaged with a standard fluorescent gel scanner and quantified using densitometry. The use of peptides as the degradable substrate greatly expands the possible proteases detectable with zymographic techniques.

Here, we present a detailed protocol for a modified zymographic technique in which fluorescent peptides are used as the degradable substrate in place of native proteins. Electrophoresis of biological samples in fluorescent peptide zymograms enables detection of a wider range of proteases than previous zymographic techniques.

荧光肽酶谱法检测蛋白酶活性

The purpose of this method is to measure the proteolytic activity of complex biological samples. The samples are separated by molecular weight using ...

Establishment of Gastric Cancer Patient-derived Xenograft Models and Primary Cell Lines

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. Although there are many established gastric cancer cell lines and many conventional transgenic mouse models for preclinical research, the disadvantages of these in vitro and in vivo models limit their applications. Because the characteristics of these models have changed in culture, they no longer model tumor heterogeneity, and their responses have not been able to predict responses in humans. Thus, alternative models that better represent tumor heterogeneity are being developed. Patient-derived xenograft (PDX) models preserve the histologic appearance of cancer cells, retain intratumoral heterogeneity, and better reflect the relevant human components of the tumor microenvironment. However, it usually takes 4-8 months to develop a PDX model, which is longer than the expected survival of many gastric patients. For this reason, establishing primary cancer cell lines may be an effective complementary method for drug response studies. The current protocol describes methods to establish PDX models and primary cancer cell lines from surgical gastric cancer samples. These methods provide a useful tool for drug development and cancer biology research.

The current protocol describes methods to establish patient-derived xenograft (PDX) models and primary cancer cell lines from surgical gastric cancer samples. The methods provide a useful tool for drug development and cancer biology research.

建立胃癌患者衍生异种移植模型和原细胞系

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. ...

Detection of Retrotransposition Activity of Hot LINE-1s by Long-Distance Inverse PCR

Long interspersed nuclear elements 1 (LINE-1s) are the only family of mobile genetic elements in the human genome that can move autonomously. They do so by a process called retrotransposition wherein they transcribe to form an mRNA intermediate which is then consequently inserted into the genome by reverse transcription. Despite being silent in normal cells, LINE-1s are highly active in different epithelial tumors. De novo LINE-1 insertions can potentially drive tumorigenesis, and hence it is important to systematically study LINE-1 retrotransposition in cancer. Out of ~150 retrotransposition-competent LINE-1s present in the human genome, only a handful of LINE-1 loci, also referred to as &#34;hot&#34; LINE-1s, account for the majority of de novo LINE-1 insertion in different cancer types. We have developed a simple polymerase chain reaction (PCR)-based method to monitor retrotransposition activity of these hot LINE-1s. This method, based on long-distance inverse (LDI)-PCR, takes advantage of 3&#180; transduction, a mechanism by which a LINE-1 mobilizes its flanking non-repetitive region, which can subsequently be used to identify de novo LINE-1 3&#180; transduction events stemming from a particular hot LINE-1.

This article outlines a simple PCR-based assay to monitor the activity of an active LINE-1 retrotransposon and to map de novo retrotranspositions in a given genome. Using the MCF7 cell line, we demonstrate herein how this method can be applied to detect activity of a LINE-1 located at 22q12.1.

长距离反向PCR对热线1的逆变活性的检测

Long interspersed nuclear elements 1 (LINE-1s) are the only family of mobile genetic elements in the human genome that can move autonomously. They do so ...

Detection of Lung Tumor Progression in Mice by Ultrasound Imaging

With ~1.6 million victims per year, lung cancer contributes tremendously to the worldwide burden of cancer. Lung cancer is partly driven by genetic alterations in oncogenes such as the KRAS oncogene, which constitutes ~25% of lung cancer cases. The difficulty in therapeutically targeting KRAS-driven lung cancer partly stems from having poor models that can mimic the progression of the disease in the lab. We describe a method that permits the relative quantification of primary KRAS lung tumors in a Cre-inducible LSL-KRAS G12D mouse model via ultrasound imaging. This method relies on brightness (B)-mode acquisition of the lung parenchyma. Tumors that are initially formed in this model are visualized as B-lines and can be quantified by counting the number of B-lines present in the acquired images. These would represent the relative tumor number formed on the surface of the mouse lung. As the formed tumors develop with time, they are perceived as deep clefts within the lung parenchyma. Since the circumference of the formed tumor is well-defined, calculating the relative tumor volume is achieved by measuring the length and width of the tumor and applying them in the formula used for tumor caliper measurements. Ultrasound imaging is a non-invasive, fast and user-friendly technique that is often used for tumor quantifications in mice. Although artifacts may appear when obtaining ultrasound images, it has been shown that this imaging technique is more advantageous for tumor quantifications in mice compared to other imaging techniques such as computed tomography (CT) imaging and bioluminescence imaging (BLI). Researchers can investigate novel therapeutic targets using this technique by comparing lung tumor initiation and progression between different groups of mice.

This protocol describes the steps taken to induce KRAS lung tumors in mice as well as the quantification of formed tumors by ultrasound imaging. Small tumors are visualized in early timepoints as B-lines. At later timepoints, relative tumor volume measurements are achieved by the measurement tool in the ultrasound software.

超声成像对小鼠肺肿瘤进展的检测

With ~1.6 million victims per year, lung cancer contributes tremendously to the worldwide burden of cancer. Lung cancer is partly driven by genetic ...

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous metastasis models are available. Thus, the experimental metastasis model involving tail-vein injection of suitable cell lines is a mainstay of metastasis research. When cancer cells are injected into the lateral tail-vein, the lung is their preferred site of colonization. A potential limitation of this technique is the accurate quantification of the metastatic lung tumor burden. While some investigators count macrometastases of a pre-defined size and/or include micrometastases following sectioning of tissue, others determine the area of metastatic lesions relative to normal tissue area. Both of these quantification methods can be exceedingly difficult when the metastatic burden is high. Herein, we demonstrate an intravenous injection model of lung metastasis followed by an advanced method for quantifying metastatic tumor burden using image analysis software. This process allows for investigation of multiple end-point parameters, including average metastasis size, total number of metastases, and total metastasis area, to provide a comprehensive analysis. Furthermore, this method has been reviewed by a veterinary pathologist board-certified by the American College of Veterinary Pathologists (SEK) to ensure accuracy.

Intravenous injection of cancer cells is often used in metastasis research, but the metastatic tumor burden can be difficult to analyze. Herein, we demonstrate a tail-vein injection model of metastasis and include a novel approach to analyze the resulting metastatic lung tumor burden.

肿瘤细胞外侧尾静脉注射后肺转移的病理分析

Metastasis, the primary cause of morbidity and mortality for most cancer patients, can be challenging to model preclinically in mice. Few spontaneous ...