Quantitative, multiplexed and high-throughput: macroarrays of lysate microarrays

定量、多重和高通量：裂解物微阵列的宏阵列

• 批准号: 7614543
• 负责人: GAVIN MACBEATH
• 金额: $ 16.78万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7614543
• 关键词: Adopted; Affect; Antibodies; Automation; Bayesian Method; Biochemical Pathway; Biological; Cancer Model; Cell Culture Techniques; Cells; Collaborations; Color; Cytolysis; Data; Epidermal Growth Factor; Etiology; Flow Cytometry; Fluorescence Microscopy; Glass; Homeostasis; Human; Immunoblotting; Individual; Investigation; Learning; Letters; Malignant Neoplasms; Measures; Methods; Microarray Analysis; Output; Pan Genus; Pathway interactions; Phospho-Specific Antibodies; Post-Translational Protein Processing; Principal Investigator; Protein Microchips; Proteins; Pyroxylin; Sampling; Serum; Signal Transduction; Statistical Models; Stimulus; System; Techniques; Technology; Time; Universities; base; biological systems; density; fluorophore; insight; interest; miniaturize; predictive modeling; programs; technology development

DESCRIPTION (provided by applicant): Mammalian signaling networks comprise biochemical pathways with shared components, common inputs, and overlapping outputs. Understanding how information flows through these pathways requires information on signaling networks as a whole, rather than on one or two components. To study signaling at a systems level, we need ways to measure the abundance and post-translational modification of many proteins in a parallel, quantitative, and reliable manner. In addition, since an understanding of signaling requires the frequent temporal sampling of many proteins under multiple conditions, these methods must be high-throughput. Here, we describe technology that mimics an immunoblot, but in a multiplexed and extremely miniaturized format. Cells are cultured in 96-well plates and subjected to a variety of perturbations (stimulation with epidermal growth factor in the presence of selected shRNA's or cDNA's). The cells are then lysed and the lysates arrayed at high spatial density onto glass-supported nitrocellulose pads, also arranged in a 96-well format. By probing each pad with a different antibody, the `state' of the signaling network is assessed. Currently, high- throughput multidimensional readouts can be obtained either by automated fluorescence microscopy or by multiplexed flow cytometry. Although both techniques provide the ability to track more than one protein simultaneously, they rely on the use of different colored fluorophores and hence can only follow about a dozen proteins. In contrast, the technology described here enables a single sample to be replicated thousands of times on separate microarrays and is thus easily scaled. This application details efforts to make lysate microarray technology rigorously quantitative and outlines automation strategies that render it high-throughput and reproducible. In addition, since one of the biggest challenges in analyzing cancer at a systems level is to go beyond a mere description of the data, a strategy is also presented to build predictive models of cell signaling using Bayesian methods. As proof-of-concept, we will focus on epidermal growth factor signaling in A431 cells. Although this system is relatively well-understood, our approach should capture higher-order interdependencies between proteins that are not evident from traditional studies. More importantly, our strategy should provide a general way to uncover causal relationships in less well-studied networks using data derived from our high-throughput microarrays.

描述(申请人提供)：哺乳动物信号网络包括具有共享组件、公共输入和重叠输出的生化通路。要了解信息如何通过这些路径流动，需要关于整个信令网络的信息，而不是关于一两个组件的信息。为了在系统水平上研究信号，我们需要以并行、定量和可靠的方式测量许多蛋白质的丰度和翻译后修饰。此外，由于理解信号需要在多种条件下对许多蛋白质进行频繁的时间采样，因此这些方法必须是高通量的。在这里，我们描述一种模仿免疫印迹的技术，但以一种多路和极其微型化的形式。细胞在96孔板中培养，并受到各种干扰(在选定的shRNA或cDNAs存在的情况下，用表皮生长因子刺激)。然后细胞被裂解，裂解物以高空间密度排列在玻璃支撑的硝酸纤维素垫上，也以96孔的形式排列。通过用不同的抗体探测每个PAD，可以评估信令网络的“状态”。目前，高通量的多维读数可以通过自动荧光显微镜或多路传输的流式细胞术获得。虽然这两种技术都提供了同时跟踪多个蛋白质的能力，但它们依赖于不同颜色的荧光团的使用，因此只能跟踪大约12种蛋白质。相比之下，这里描述的技术使单个样本能够在单独的微阵列上复制数千次，因此很容易扩展。本申请详细说明了使裂解微阵列技术严格定量的努力，并概述了使其高通量和可重复性的自动化策略。此外，由于在系统层面分析癌症的最大挑战之一是超越单纯的数据描述，因此还提出了使用贝叶斯方法建立细胞信号预测模型的策略。作为概念验证，我们将重点研究A431细胞中的表皮生长因子信号。虽然这个系统相对容易理解，但我们的方法应该捕捉到传统研究中不明显的蛋白质之间的高阶相互依赖关系。更重要的是，我们的战略应该提供一种普遍的方法，利用我们的高通量微阵列获得的数据，在研究较少的网络中揭示因果关系。