这项工作由美国国家癌症研究所（NCI）拨款U54CA143798和U54CA143907资助，分别在达纳 - 法伯癌症研究所和南加州大学建立物理科学 - 肿瘤学中心（PS-OC）。 SM Mumenthaler获得了PS-OC跨网络奖，支持了这项工作。 衷心感谢我们的慈善支持者，特别是Stephenson家族，Emmet，Toni和Tessa，他们捐赠了Operetta HCS平台。我们还要感谢J. Foo指导，应用分子医学中心团队成员:D. Agus临床指导和指导，K. Patsch与实验设计进行有意义的讨论，R. Rawat为图像分析协议提供帮助，J. Katz与Operetta的技术协助，以及P. Macklin和D. Ruderman进行了有益的讨论和反馈。

细胞培养注意:在与肺成纤维细胞（CCD-19Lu GFP）共同培养的情况下，研究NSCLC细胞（H3255）对EGFR靶向药物埃罗替尼的表型反应。对于相同的数据集，进行了两个细胞群体（H3255和CCD-19Lu GFP）的基于荧光和基于形态的分类，以证明它们的一致性。然而，只需要使用一种分类方法，并应根据应用选择。 <ol><li>准备500毫升补充有10％热灭活FBS和1％青霉素/链霉素的RPMI-1640。 注意:生长培养基和补充剂可以根据其他细胞类型的要求进行更换。 </li><li>培养细胞在10cm 3平板中作为纯种群在5％CO 2中于37℃，并以适当的比例每3-4天通过。 </li></ol> 2.细胞的制备<ol><li>要制备细胞悬液，取出c并用5mL 1X磷酸盐缓冲盐水（PBS）洗涤细胞。 </li><li>从PBS中吸出，加入1 mL加入到37°C的0.05％胰蛋白酶，并在37℃下孵育5分钟（或直到细胞不再粘附于平板）。 </li><li>为了中和胰蛋白酶，向H3255和CCD-19Lu GFP板中加入4mL RPMI，并将细胞溶液移至15mL锥形管中。 </li><li>在室温下以150×g离心细胞5分钟。 </li><li>将上清液吸出并将细胞沉淀重悬于10mL RPMI培养基的锥形管中以准备播种。 </li></ol>电池电镀<ol><li> 计数细胞以使种子标准化。 <ol><li>反转管将溶液中的细胞混合。将10μL每个细胞悬浮液移至微量离心管中，并加入10μL台盼蓝。 </li><li>将10μL此溶液移至细胞计数载玻片并插入自动细胞计数器。 注意:其他细胞计数方法（ 即。血细胞计数器）。 </li><li>重复细胞计数至少三次，或直到获得的计数一致。 </li></ol></li><li> 将种子细胞置于多孔板上 注意:种子的数量取决于将被成像的时间点数。或者，如果细胞用组蛋白-2B-GFP（或提供足够的核分割的其他稳定的慢病毒核染色体）转导，则可以随时间重新成像一个平板。 <ol><li>在100μLRPMI中种子总共1500个细胞/孔。准备三种细胞悬浮液来平板三种细胞群体:H3255，CCD-19Lu GFP和50％H3255 + 50％CCD-19Lu GFP。对于每个时间点，使用相同的板设置执行一式三份。 注意:应针对每个细胞系和板尺寸优化初始播种密度。 </li><li>使用多通道移液器在3×96孔板中的种子细胞。在37℃，5％CO 2下孵育细胞O / N。 </li></ol> &lt;/ LI&gt; </ol>药物剂量<ol><li>将DMSO加入厄洛替尼粉末中以制备10mM浓度的最终埃罗替尼储备溶液。 注意:药物可以根据需要进行取代。 </li><li>用细胞培养基稀释药物至最终浓度的2倍，最终浓度为10μM，以1:10的比例连续稀释4次，共5种药物浓度和无药物对照。 注意:如果最高药物剂量的DMSO浓度等于或超过0.1％v / v，则无药物对照应含有等量的DMSO，以确保观察到的观察效果不是由于DMSO毒性所致。 </li><li>将100μL药物溶液移至相应的孔中，以最终药物浓度1×稀释于培养基中。 </li></ol>图像采集注意:在第0,2和3天获得图像。根据正在研究的细胞类型和细胞过程，o可以研究期望的时间点。 <ol><li> 染色细胞准备成像。 <ol><li>以下列终浓度补充染料溶液。 <ol><li>对于基于荧光的分类，在PBS中制备5μg/ mL核染色和5μM死细胞染色（ 参见表材料 ）。 </li><li>对于基于形态学的分类，在PBS中制备5μg/ mL核染色剂，5μM死细胞染色剂和5μM细胞染色剂（ 参见表材料 ）。 </li></ol></li><li>向每个孔中加入20μL染料溶液。在37°C孵育30分钟防光。 </li></ol></li><li> 优化和获取图像。 <ol><li>从培养箱中取出孔板，用70％EtOH擦拭板的底部，并将板放置在成像室中。 </li><li>在"设置"选项卡中，单击"通道选择"下的"+"按钮添加相应的通道图像（ 即明场，核染色，死细胞染色，GFP和RFP信号）。 </li><li>单击"布局选择"，并从0μm开始，以2μm开始，以2μm为单位进行z层叠的测试图像，并以20μm结束，以识别对焦平面。为"高度"下的每个通道输入此距离。 </li><li>点击每个通道下的"快照"来评估强度和优化曝光时间。根据需要在"时间"下输入更高或更低的值。 </li><li>在屏幕的右侧，在板原理图上突出显示适当的孔（待成像）。在下面的井示意图中，以类似的方式突出显示要成像的二十五个场。 </li><li>在"运行实验"下，在左侧选项卡上的"板名称"中标识板，然后单击"开始"按钮（下面），以10X物镜运行图像采集协议，以在上述通道中生成灰度TIFF图像。 注意:分步骤成像协议的拍卖在仪器之间可能不同，但参数值仍应进行优化。 </li><li>在第二天和第三天重复成像。 </li></ol></li></ol>图像分析注意:所有图像分析都是使用专有软件进行的。然而，由于这不是公开的，因此还在CellProfiler 2.2和CellProfiler Analyst 2.0上设计了可比较的分析，下面列出了一个简短的协议，并提供了详细的协议作为补充材料（测试图像已经被加载到管道中以测试工作流程）。来自该实验的图像在每个井基础上分组在一起，使得每个孔可以单独加载。下面的CellProfiler管道包含一个正则表达式，从每个图像文件名分析元数据信息，并允许图像按渠道进一步分组。 <ol><li>下载并安装开源软件:&#39;CellProfiler9;和"CellProfiler Analyst"。在安装期间接受所有默认选项和加载项。 </li><li> 将异源细胞培养物分类为亚群体。 <ol><li>打开"CellProfiler"软件，单击"文件"| "进口管道"| &#39;从文件&#39;，并选择相应的文件（"荧光分类cppipe"进行基于荧光的分类，或"Morphology_Classification.cppipe"用于基于形态的分类）。 注意:这些文件包含基本图像处理和核/细胞分割的协议。由于这些细胞上存在不均匀的Hoechst染色体，细胞核被分割和扩大，然后用于掩蔽图像以允许以更低的强度分割细胞核。 <ol><li>选择基于荧光的分类流程，根据eGFP强度将细胞分类为H3255或CCD-19Lu，并计算形态特征。 注意:CCD-19Lu细胞用GFP转导。 </li><li>选择基于形态的分类管线来细分核和细胞，并计算形态特征。 注意:需要进一步下游分析"CellProfiler Analyst"进行分类。死细胞通过基于荧光的分类进行分类。 </li></ol></li><li>点击屏幕左下角的"查看输出设置"。选择要分析的图像文件的位置（"默认输入文件夹"）和提取数据的目标位置（"默认输出文件夹"）。 </li><li>点击"分析图像"开始分析。分析完成后，点击"确定"，进入"默认输出文件夹"，查看计算的数据。 注: 补充文件1-CellProfilerProtocol.pdf中提供了示例参数值和图像。 </li><li>对于基于形态的分类（仅），请执行以下操作。 <ol><li>打开"CellProfiler Analyst"软件，选择CellProfiler中生成的数据库属性文件，然后单击"打开"。 </li><li>点击屏幕左上角的"分类器"选项卡。要从实验中调用随机单元格图像，请单击"提取";图像将显示在未分类的窗口中。 </li></ol></li><li>通过选择并将细胞拖动到适当的储存箱（见补充文件2-Pipeline.pdf : 图B ），将细胞手动分类为阳性（H3255）或阴性（CCD-19Lu）。每个子群体分类至少50个细胞后，单击"培训分类器"，然后单击"检查进度"。 注意:细胞通过用户监督的随机森林机器学习算法12进行分类 。如果准确度不高于批准阈值（&gt; 90％），可能需要返回并优化"CellProfiler"上的细胞分割，否则细胞可能不适合分类通过形态学。 </li><li>点击"全部评分"为每个子群体生成一个具有细胞计数的表格。 </li></ol></li></ol>

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作者没有什么可以披露的。

The protocol described above improves upon current cell biology assays by providing more comprehensive insights into phenotypic dynamics of multiple cell types in response to environmental perturbations while using reduced reagents and time. A major advantage of this experimental design is the ability to analyze multiple phenotypes with a single setup and generate quantitative data characterizing these phenotypes on a single cell level. One technical advantage to this platform is the ease of initial troubleshooting compared to other assays. Because this method is image-based, one is able to visualize the wells for apparent over/under seeding. It is advisable to have cells in the exponential growth phase for the duration of the experiment and not be limited by nutrient or spatial constraints or confounded by senescence due to scarce seeding. Otherwise, birth and death rates may not be reproducible between experiments. For reference, CellPD is a publicly available program for computation of birth and death rates14. Additionally, one can visualize whether adequate concentrations of dyes were added to each well. Pipetting issues in an individual well could result in missegmentation and skewed data, but can easily be detected with the aforementioned protocol.
Unfortunately, not all cell types may be amenable to this application. It is important to be able to accurately segment the nuclei and cells, therefore analysis of cells that organize in more sphere-like or clumped structures may not be suitable. For some cells, it also may be advantageous to use a cell strainer prior to seeding to ensure initial seeding of single cells. In addition, the linear classifier technique is only applicable to cell types that can be readily distinguished based on morphology features. 
For the success of the protocol, it is important to first optimize the imaging conditions, as the validity of downstream analyses is dependent on the quality of the images. While here we performed experiments using a high-content screening platform, image acquisition can also be performed using any fluorescent microscope (although an automated imaging platform is ideal for high-throughput approaches). Test images should be taken prior to each imaging time point to ensure that there are no problems with the microscope or protocol. The signal to noise ratio should be high, especially for the channels that will be used for segmentation (i.e. nuclei, cell stains). Additionally, it is important to image in the optimal plane of focus. If the images are out of focus, segmentation becomes much more difficult and the calculated morphological features will likely be inaccurate. Illumination differences between fields can cause problems with image segmentation as well. Large differences in brightness make the automated selection of threshold values difficult. Additionally, if there are heterogeneous fluorescence intensities between cells, a single threshold value may not sufficiently segment all the cells in an image. In this analysis, these problems were overcome by creating masks around brighter and dimmer cell populations and segmenting each population separately. 
While the phenotypes under investigation in this protocol are limited to live, dead, and morphological characterization, they can easily be expanded to investigate other features. For example, functional genetic studies can be added with RNAi, overexpression, or other chemical perturbations. 
In this paper, the capabilities of the protocol to measure the response of non-small cell lung cancer cells to erlotinib in the presence and absence of CAFs was demonstrated. However, this is merely one example of the many cell types and microenvironmental parameters that can be tested. We have extended this protocol to be used with other cell types and drug studies, including primary cells isolated from patient tumors13,15.

基于细胞的测定在基础研究和药物开发环境中已经成为一个主力。然而，这些标准测定的局限性随着体外和临床数据的不一致以及大多数药物未能获得FDA批准而变得越来越明显。在这里，我们提出了一种利用定量成像同时分析异源细胞表型以响应相关的共同环境刺激的新方法。 用于测量细胞活力的传统的基于细胞的测定法包括:台盼蓝排除测定，MTT / MTS和Annexin V-FITC流式细胞术染色。台盼蓝排除测定虽然简单而便宜，但需要大量的细胞，这是耗时的，并且经常受到用户偏倚的影响1 。 MTT和MTS测定通过测量线粒体代谢率间接测量细胞活力。然而，代谢活性细胞的数量可能受到不同培养条件（如培养基或氧浓度）的影响，导致不准确的结果，并阻止细胞类型和条件的标准化2,3 。这些技术的另一个主要缺点是它们无法区分多种细胞类型 - 大多数生物系统是异源细胞的。虽然流式细胞术方法具有区分多细胞群体的能力，但需要细胞标记，动态取样具有挑战性，而当使用贴壁细胞时，该应用程序变得耗时且容易出错。 其他重要的细胞表型，包括形态学变化，发生在响应于环境刺激，但不被传统的基于细胞的测定法捕获。通过形态表征和映射样本之间的相似性，分析细胞状态是一个强大的，无偏见的工具能够提供基础和翻译研究的许多方面的新见解，包括基础细胞生物学和药物发现4 。此外，已经显示肿瘤细胞形态与肿瘤亚型5和侵袭性6相关 。因此，研究这些细胞特征以及它们如何与特定的环境扰动有关是非常有意义的。此外，人们可以使用形态特征的差异来区分共培养系统中的亚群体。荧光标记细胞具有垮台（ 即改变固有的细胞特性，耗时），因此分类细胞类型的其它方法是有利的。 基于显微镜的成像是以多重，定量和鲁棒的方式分析细胞表型的替代方法。在这份手稿中，我们运用定量成像管道来突出演变肿瘤内异质细胞群的动态变化。我们专注于非小细胞肺癌（NSCLC）细胞和肿瘤相关成纤维细胞（CAF）之间的相互作用，这是肿瘤中最常见的基质细胞类型。 CAFs参与了肿瘤的起始，进展和治疗反应;因此，在不存在CAF的情况下对肿瘤细胞进行表型测定可能会误导7,8,9 。具体来说，我们评估了CAF对肿瘤细胞对厄洛替尼的影响，厄洛替尼是靶向经常用于NSCLC临床治疗的表皮生长因子受体（EGFR）的小分子。我们利用高分辨率的筛选平台及其附带的图像分析软件进行评估;然而，为了使其他研究人员可以使用这种方法，我们还使用开源软件开发了可比较的下游协议:CellProfiler 10和CellProfiler Analyst 11 。大多数基于图像的高含量筛选测定用特定于给定仪器模型的商业化软件进行分析。结果很难在其他实验室用不同的软件复制，因为底层算法通常是专有的。使用这种基于图像的管道，测量了使用基于荧光和形态的分类来响应药物治疗的异源细胞培养物的每个亚群的细胞增殖，死亡和形态。以下协议提供了一种鲁棒的方法来探测复杂的蜂窝过程。

<table><tbody><tr><td>RPMI-1640</td><td>Corning</td><td>10-040-CV</td><td>cell culture medium</td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>Gemini</td><td>100-106</td><td>medium supplement</td></tr><tr><td>Penicillin/streptomycin</td><td>Gibco</td><td>15-140-122</td><td>medium supplement</td></tr><tr><td>TC20</td><td>Biorad</td><td>1450102</td><td>cell counter</td></tr><tr><td>TC20 slides with trypan blue</td><td>Biorad</td><td>1450003</td><td>cell counter slides</td></tr><tr><td>96-well plates</td><td>Corning</td><td>3904</td><td>clear bottom black plates</td></tr><tr><td>Erlotinib</td><td>LC Laboratories</td><td>E-4007</td><td></td></tr><tr><td>DMSO</td><td>VWR</td><td>317275-100ML</td><td>solution to resuspend drug</td></tr><tr><td>Hoechst</td><td>Invitrogen</td><td>H21491</td><td>nuclear dye</td></tr><tr><td>Propidium Iodide (PI)</td><td>Invitrogen</td><td>P1304MP</td><td>dead cell stain</td></tr><tr><td>Cell Tracker Orange CMRA</td><td>Life Technologies</td><td>C34551</td><td>whole cell stain</td></tr><tr><td>Operetta high content imaging system</td><td>Perkin Elmer</td><td></td><td></td></tr><tr><td>CellProfiler</td><td>Broad Institue</td><td>version 2.2.0 (rev 9969f42)</td><td>http://cellprofiler.org/releases/</td></tr><tr><td>Cell culture incubator</td><td></td><td></td><td>Any cell culture incubator will be suitable - cells were cultured under 37 &amp;ordm;C at 5% CO&lt;sub&gt;2.&lt;/sub&gt;</td></tr><tr><td>15 mL Falcon conical tubes</td><td>Falcon</td><td>14-959-53A</td><td></td></tr><tr><td>10 cm&lt;sup&gt;2&lt;/sup&gt; cell culture plates</td><td>TPP</td><td>93040</td><td></td></tr></tbody></table>

discrimination and characterization of heterocellular populations using quantitative imaging techniques

细胞过程是复杂的，并且是由多种细胞类型与其环境之间的相互作用产生的。现有的细胞生物学技术往往不能准确地解释这种相互作用。使用基于定量成像的方法，我们提出了一个高含量的方案来表征异质细胞群体对环境刺激变化的动态表型反应（ 即形态学变化，增殖，凋亡）。我们强调我们根据应用来区分基于荧光强度或固有形态特征的细胞类型的能力。该平台允许更全面地表征亚群对扰动的反应，同时利用更短的时间，更少量的试剂，并且具有比传统细胞生物学测定更低的误差可能性。然而，在一些情况下，细胞群体可能难以基于复合纤维素进行鉴定和定量lar功能，需要额外的故障排除;我们强调协议中的某些情况。我们在癌症模型中使用对药物的反应来证明这一应用;然而，它可以更容易地应用于其他生理过程。该协议允许人们识别共培养系统内的亚群体，并表征每个人对外部刺激的特定反应。

我们生成了一个由25个字段/孔，54个孔/板（3个细胞群体×6个药物浓度×3个重复）组成的图像组，跨越三个平板，共有4,050个单独的图像。在实验过程中生成的图像集使用专有软件（参见材料表）进行分析，以提取细胞的各种定量性质（ 即形态学，荧光），然后可用于分类细胞亚群。然而，由于所使用的商业软件的访问有限，因此创建了CellProfiler和CellProfiler Analyst中可比较的下游管道。 异种细胞分类为亚群基于DNA染色（这里是Hoechst）鉴定和分割细胞核，并且基于荧光或莫尔将细胞群分类形态学（ 图1 ）。对于基于荧光的分类，成纤维细胞（CCD-19Lu）预先用GFP-慢病毒转导。测量每个核的GFP强度水平，并且计算高于允许阈值（基于背景信号）的那些被分类为CCD-19Lu，而下面被鉴定为肿瘤细胞（H3255）。对于基于形态学的分类，细胞先前用无毒细胞染色剂染色（参见材料表），并将其用于鉴定和分割细胞质。训练每个人口约50-100个细胞的机器学习算法。鉴定出种群间的形态特征差异显着，然后将其用于设计线性分类器以区分CCD-19Lu和H3255细胞。荧光和形态分类方案在区分两个细胞群体时一致为97.4％（n = 1403）在药物治疗条件（1μM埃罗替尼）中，92.5％（n = 916）与未治疗条件相同（ 图2 ）。 子群体表型分析除了区分细胞类型之外，我们旨在表征每个亚群的表型性质。多重测试可节省时间和试剂，增加一致性，并提供有关正在研究的系统的其他信息。有许多潜在的表型产出，应根据感兴趣的问题选择它们。在这里，研究了响应于厄洛替尼治疗的细胞形态和活力状态的变化。经过三天的药物治疗后，观察到H3255细胞的核面积减少和细胞面积的增加（ 图3A ）。核区域之间的平均差异通过双侧型-2（相等方差） t检验（ p = 7.92×10 -16 ），发现"无药物"和"药物"治疗的人群具有统计学显着性。我们假设这种观察是对药物治疗施加的压力的细胞反应。 研究药物是否具有细胞毒性（ 即 ，随着时间的推移死亡细胞数量的增加）或细胞抑制（ 即随着时间的推移而减少细胞出生数）对细胞的影响，因为这具有深刻的临床影响。例如，细胞抑制药物作用诱导生长停滞，但不能消除肿瘤中的细胞，因此一旦药物被除去，癌细胞就有可能重新启动细胞增殖。药物作用常常是上下文，浓度和细胞类型依赖性。我们以前观察到厄洛替尼在一种细胞类型中引发细胞毒性反应，同时显示ac在另一个13的 ytostatic反应。 传统的活力测定法输出相对细胞数，因此，不区分生长停滞和细胞死亡。在此，基于碘化丙啶染色鉴定死细胞（ 图3B ）。观察到厄洛替尼对H3255细胞的细胞毒性和细胞生长抑制作用，药物治疗后死亡人数增多，出生人数减少（ 图3C ）。值得注意的是，由于细胞清除，死亡细胞数量在第1天后下降。 CCD-19Lu细胞不受药物的影响。该平台的另外一个优点就是生成定量数据。例如，在我们的共培养实验中，在没有或与厄洛替丁组合的三天后，发现1,118（75.8％）H3255细胞的初始亚群为2,817（87.9％）或396（57.2％）ib处理（ 图4 ）。因为我们可以产生实际细胞计数而不是相对百分比（如流式细胞术方法），我们得出结论，药物治疗期间组成的变化是由于H3255细胞减少而不是CCD-19Lu的增加。由于细胞清除，死亡率可能被低估是值得的，这是难以通过实验评估，并且可能在细胞类型之间不同。 <img alt="图1" class="xfigimg" src="/files/ftp_upload/55844/55844fig1.jpg" /> 图1:图像分析协议概述。两个潜在的下游图像分析管线可以使用基于形态的分类或基于荧光的分类对异源细胞群进行分类。刻度棒=100μm。 <a href="http://ecsource.jove.com/files/ftp_upload/55844/55844fig1large.jpg" target="_blank">请点击她</a>e查看这个数字的较大版本。 <img alt="图2" class="xfigimg" src="/files/ftp_upload/55844/55844fig2.jpg" /> 图2:形态与基于荧光的分类之间的一致性。 （ A ）显示两个分类协议重叠的一致图。使用基于形态和荧光的分类将相同的细胞分类为H3255。两种方案是一致的，分类为未处理细胞的97.4％（n = 1403）和用厄洛替尼处理的细胞的92.5％（n = 916）（注意:白色区域太小而不可视化）。 （ B ）10X图像描绘了基于荧光和基于形态的分类之间良好和差的一致性的示例。白色箭头指出在平台之间不一致的细胞。输入图像:蓝 - 核（Hoechst）;绿色 - CCD19Lu（GFP）。 CLASSI化妆图像:红色 - H3255;绿色 - CCD-19Lu。刻度棒=100μm。 <a href="http://ecsource.jove.com/files/ftp_upload/55844/55844fig2large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <img alt="图3" class="xfigimg" src="/files/ftp_upload/55844/55844fig3.jpg" /> 图3:单个实验设置的复用表型测量。 （ A ）在存在和不存在药物时，在单细胞水平上计算细胞核和细胞面积等形态特征。注意:测量小于100μm2的细胞区域被认为是碎片，并排除在分析之外。箱体图描绘了具有第一和第三四分位数范围和95％置信区间误差条的中值。 （ B ）共同培养H3255（蓝色）和CCD-19Lu（绿色）细胞，基于丙酸的强度鉴定死细胞碘化镉染色（红色），并使用10X物镜成像。刻度棒= 1 mm（顶面）; 100μm（底部图像）。 （ C ）用或不用药物治疗三天计算活细胞死亡细胞数，活细胞数量明显减少，加上厄洛替尼死亡细胞数量增加。误差条代表基于三次重复的平均值的标准误差。 <a href="http://ecsource.jove.com/files/ftp_upload/55844/55844fig3large.jpg" target="_blank">请点击此处查看此图的较大版本。</a> <img alt="图4" class="xfigimg" src="/files/ftp_upload/55844/55844fig4.jpg" /> 图4:随时间推移的亚群动态。在含有和不含药物的第0天或第3天，含有H3255（蓝色）和CCD-19Lu（绿色）的孔的代表性10X图像。计数属于每个亚群的细胞，比例饼图显示a样本中人口组成的变化趋势。刻度棒= 1毫米（中间面板，闪烁;最后面板），1毫米（顶部图像），100μm（底部图像）。 <a href="http://ecsource.jove.com/files/ftp_upload/55844/55844fig4large.jpg" target="_blank">请点击此处查看此图的较大版本。</a>

Watch this Scientific Journal Video about Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques at JoVE.com

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

我们已经开发了一种用于研究异源细胞群体动力学以响应扰动的新方案。该手稿描述了基于成像的平台，其产生定量数据集，以稳健的方式同时表征异源细胞群体的多种细胞表型。

使用定量成像技术鉴定和表征异源细胞群体

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do ...

discrimination-characterization-heterocellular-populations-using

Research

JoVE Journal

Biology

7.4K 次观看.  University of Southern California (USC). 我们已经开发了一种用于研究异源细胞群体动力学以响应扰动的新方案。该手稿描述了基于成像的平台，其产生定量数据集，以稳健的方式同时表征异源细胞群体的多种细胞表型。 

视频: Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

造血干细胞的自动定量 - 在脱钙骨的组织学图像间质细胞的相互作用

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

科研

发育生物学

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

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

一种组合单细胞法表征人茎和祖种群的分子和免疫表型异质性

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

遗传学

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a combination of bright field and differential interference contrast imagery. Two primary approaches are presented: noninterferometric quantitative phase microscopy (NIQPM), to perform measurements of total cell mass and subcellular density distribution, and Hilbert transform differential interference contrast microscopy (HTDIC)&#160;to determine volume. NIQPM is based on a simplified model of wave propagation, termed the paraxial approximation, with three underlying assumptions: low numerical aperture (NA) illumination, weak scattering, and weak absorption of light by the specimen. Fortunately, unstained cellular specimens satisfy these assumptions and low NA illumination is easily achieved on commercial microscopes. HTDIC is used to obtain volumetric information from through-focus DIC imagery under high NA illumination conditions. High NA illumination enables enhanced sectioning of the specimen along the optical axis. Hilbert transform processing on the DIC image stacks greatly enhances edge detection algorithms for localization of the specimen borders in three dimensions by separating the gray values of the specimen intensity from those of the background. The primary advantages of NIQPM and HTDIC lay in their technological accessibility using &#8220;off-the-shelf&#8221; microscopes. There are two basic limitations of these methods: slow z-stack acquisition time on commercial scopes currently abrogates the investigation of phenomena faster than 1 frame/minute, and secondly, diffraction effects restrict the utility of NIQPM and HTDIC to objects from 0.2 up to 10 (NIQPM) and 20 (HTDIC) &#956;m in diameter, respectively. Hence, the specimen and its associated time dynamics of interest must meet certain size and temporal constraints to enable the use of these methods. Excitingly, most fixed cellular specimens are readily investigated with these methods.

We describe the use of a standard optical microscope to perform quantitative measurements of cellular mass, volume, and density through a combination of bright field and differential interference contrast imagery.

定量的光学显微镜:测量细胞生物物理特性与标准光学显微镜

We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a ...

生物工程

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro when stem cells are spatially confined onto micropatterns, thus setting up experimental models which offer unique opportunities to identify, in vitro, the fundamental principles of biological organisation.
Here we describe our own implementation of the methodology. We adapted a photo-patterning technique to reduce the need for specialized equipment to make it easier to establish the method in a standard cell biology laboratory. We also developed a free, open-source and easy to install image analysis framework in order to precisely measure the preferential positioning of sub-populations of cells within colonies of standard shapes and sizes. This method makes it possible to reveal the existence of patterning events even in seemingly disorganized populations of cells. The technique provides quantitative insights and can be used to decouple influences of the environment (e.g., physical cues or endogenous signaling), on a given patterning process.

The method presented here uses micropatterning together with quantitative imaging to reveal spatial organization within mammalian cultures. The technique is easy to establish in a standard cell biology laboratory and offers a tractable system to study patterning in vitro.

使用微模式和定量成像绘制哺乳动物细胞的新兴空间组织图

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro ...

High Throughput Characterization of Adult Stem Cells Engineered for Delivery of Therapeutic Factors for Neuroprotective Strategies

Mesenchymal stem cells (MSCs) derived from bone marrow are a powerful cellular resource and have been used in numerous studies as potential candidates to develop strategies for treating a variety of diseases. The purpose of this study was to develop and characterize MSCs as cellular vehicles engineered for delivery of therapeutic factors as part of a neuroprotective strategy for rescuing the damaged or diseased nervous system. In this study we used mouse MSCs that were genetically modified using lentiviral vectors, which encoded brain-derived neurotrophic factor (BDNF) or glial cell-derived neurotrophic factor (GDNF), together with green fluorescent protein (GFP). 
Before proceeding with in vivo transplant studies it was important to characterize the engineered cells to determine whether or not the genetic modification altered aspects of normal cell behavior. Different culture substrates were examined for their ability to support cell adhesion, proliferation, survival, and cell migration of the four subpopulations of engineered MSCs. High content screening (HCS) was conducted and image analysis performed. 
Substrates examined included: poly-L-lysine, fibronectin, collagen type I, laminin, entactin-collagen IV-laminin (ECL). Ki67 immunolabeling was used to investigate cell proliferation and Propidium Iodide staining was used to investigate cell viability. Time-lapse imaging was conducted using a transmitted light/environmental chamber system on the high content screening system.
Our results demonstrated that the different subpopulations of the genetically modified MSCs displayed similar behaviors that were in general comparable to that of the original, non-modified MSCs. The influence of different culture substrates on cell growth and cell migration was not dramatically different between groups comparing the different MSC subtypes, as well as culture substrates. 
This study provides an experimental strategy to rapidly characterize engineered stem cells and their behaviors before their application in long-term in vivo transplant studies for nervous system rescue and repair.

This study describes an experimental platform to rapidly characterize engineered stem cells and their behaviors before their application in long-term in vivo transplant studies for nervous system rescue and repair.

对专为递送治疗因素的神经保护策略成人干细胞高通量表征

Mesenchymal stem cells (MSCs) derived from bone marrow are a powerful cellular resource and have been used in numerous studies as potential candidates to ...

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are well orchestrated and are crucial in regulating cell functions in a living system. Although a number of researchers have attempted to investigate the correlation between environmental factors and desired cellular functions, much remains unknown. This is largely due to the lack of a proper methodology to mimic such environmental cues in vitro, and simultaneously test different environmental cues on cells. Here, we report an integrated platform of microfluidic channels and a nanofiber array, followed by high-content single-cell analysis, to examine stem cell phenotypes altered by distinct environmental factors. To demonstrate the application of this platform, this study focuses on the phenotypes of self-renewing human pluripotent stem cells (hPSCs). Here, we present the preparation procedures for a nanofiber array and the microfluidic structure in the fabrication of a Multiplexed Artificial Cellular MicroEnvironment (MACME) array. Moreover, overall steps of the single-cell profiling, cell staining with multiple fluorescent markers, multiple fluorescence imaging, and statistical analyses, are described.

This article describes the detailed methodology to prepare a Multiplexed Artificial Cellular MicroEnvironment (MACME) array for high-throughput manipulation of physical and chemical cues mimicking in vivo cellular microenvironments and to identify the optimal cellular environment for human pluripotent stem cells (hPSCs) with single-cell profiling.

多路人工细胞微环境阵列的制备

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

基于面积的图像分析算法，用于巨噬细胞-成纤维细胞共生群的定量

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

High Efficiency Differentiation of Human Pluripotent Stem Cells to Cardiomyocytes and Characterization by Flow Cytometry

There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the heart, and improving strategies to accurately model heart disease. The potential of exploiting human induced pluripotent stem cell (hiPSC) technology to generate cardiac muscle &#8220;in a dish&#8221; for these applications continues to generate high enthusiasm. In recent years, the ability to efficiently generate cardiomyogenic cells from human pluripotent stem cells (hPSCs) has greatly improved, offering us new opportunities to model very early stages of human cardiac development not otherwise accessible. In contrast to many previous methods, the cardiomyocyte differentiation protocol described here does not require cell aggregation or the addition of Activin A or BMP4 and robustly generates cultures of cells that are highly positive for cardiac troponin I and T (TNNI3, TNNT2), iroquois-class homeodomain protein IRX-4 (IRX4), myosin regulatory light chain 2, ventricular/cardiac muscle isoform (MLC2v) and myosin regulatory light chain 2, atrial isoform (MLC2a) by day 10 across all human embryonic stem cell (hESC) and hiPSC lines tested to date. Cells can be passaged and maintained for more than 90 days in culture. The strategy is technically simple to implement and cost-effective. Characterization of cardiomyocytes derived from pluripotent cells often includes the analysis of reference markers, both at the mRNA and protein level. For protein analysis, flow cytometry is a powerful analytical tool for assessing quality of cells in culture and determining subpopulation homogeneity. However, technical variation in sample preparation can significantly affect quality of flow cytometry data. Thus, standardization of staining protocols should facilitate comparisons among various differentiation strategies. Accordingly, optimized staining protocols for the analysis of IRX4, MLC2v, MLC2a, TNNI3, and TNNT2 by flow cytometry are described.

The article describes the detailed methodology to efficiently differentiate human pluripotent stem cells into cardiomyocytes by selectively modulating the Wnt pathway, followed by flow cytometry analysis of reference markers to assess homogeneity and identity of the population.

人类多能干细胞的高效分化为心肌细胞，并鉴定流式细胞仪

There is an urgent need to develop approaches for repairing the damaged heart, discovering new therapeutic drugs that do not have toxic effects on the ...

生物学

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

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

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