使用定量成像技术鉴定和表征异源细胞群体
Summary
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。
Abstract
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。
Introduction
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。
Protocol
Representative Results
Discussion
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.
Divulgations
The authors have nothing to disclose.
Acknowledgements
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。
Materials
| RPMI-1640 | Corning | 10-040-CV | cell culture medium |
| FBS | Gemini | 100-106 | medium supplement |
| Penicillin/streptomycin | Gibco | 15-140-122 | medium supplement |
| TC20 | Biorad | 1450102 | cell counter |
| TC20 slides with trypan blue | Biorad | 1450003 | cell counter slides |
| 96-well plates | Corning | 3904 | clear bottom black plates |
| Erlotinib | LC Laboratories | E-4007 | |
| DMSO | VWR | 317275-100ML | solution to resuspend drug |
| Hoechst | Invitrogen | H21491 | nuclear dye |
| Propidium Iodide | Invitrogen | P1304MP | dead cell stain |
| Cell Tracker Orange CMRA | Life Technologies | C34551 | whole cell stain |
| Operetta high content imaging system | Perkin Elmer | ||
| CellProfiler | Broad Institue | version 2.2.0 (rev 9969f42) | http://cellprofiler.org/releases/ |
| Cell culture incubator | Any cell culture incubator will be suitable – cells were cultured under 37 ºC at 5% CO2. | ||
| 15 mL Falcon conical tubes | Falcon | 14-959-53A | |
| 10 cm2 cell culture plates | TPP | 93040 |
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