Rifkin Scott A
Division of Biological Sciences, Section of Ecology, Behavior and Evolution, University of California, San Diego, CA, USA.
Methods Mol Biol. 2011;772:329-48. doi: 10.1007/978-1-61779-228-1_20.
In the past several years, a host of new technologies have made it possible to visualize single molecules within cells and organisms (Raj et al., Nat Methods 5:877-879, 2008; Paré et al., Curr Biol 19:2037-2042, 2009; Lu and Tsourkas, Nucleic Acids Res 37:e100, 2009; Femino et al., Science 280:585-590, 1998; Rodriguez et al., Semin Cell Dev Biol 18:202-208, 2007; Betzig et al., Science 313:1642-1645, 2006; Rust et al., Nat Methods 3:793-796, 2006; Fusco et al., Curr Biol 13:161-167, 2003). Many of these are based on fluorescence, either fluorescent proteins or fluorescent dyes coupled to a molecule of interest. In many applications, the fluorescent signal is limited to a few pixels, which poses a classic signal processing problem: how can actual signal be distinguished from background noise? In this chapter, I present a MATLAB (MathWorks (2010) MATLAB. Retrieved from http://www.mathworks.com) software suite designed to work with these single-molecule visualization technologies (Rifkin (2010) spotFinding Suite. http://www.biology.ucsd.edu/labs/rifkin/software.html). It takes images or image stacks from a fluorescence microscope as input and outputs locations of the molecules. Although the software was developed for the specific application of identifying single mRNA transcripts in fixed specimens, it is more general than this and can be used and/or customized for other applications that produce localized signals embedded in a potentially noisy background. The analysis pipeline consists of the following steps: (a) create a gold-standard dataset, (b) train a machine-learning algorithm to classify image features as signal or noise depending upon user defined statistics, (c) run the machine-learning algorithm on a new dataset to identify mRNA locations, and (d) visually inspect and correct the results.
在过去几年中，一系列新技术使得在细胞和生物体中可视化单个分子成为可能（拉杰等人，《自然方法》5:877 - 879，2008年；帕雷等人，《当代生物学》19:2037 - 2042，2009年；卢和苏尔卡斯，《核酸研究》37:e100，2009年；费米诺等人，《科学》280:585 - 590，1998年；罗德里格斯等人，《细胞与发育生物学综述》18:202 - 208，2007年；贝齐格等人，《科学》313:1642 - 1645，2006年；拉斯特等人，《自然方法》3:793 - 796，2006年；富斯科等人，《当代生物学》13:161 - 167，2003年）。其中许多技术基于荧光，要么是荧光蛋白，要么是与感兴趣分子偶联的荧光染料。在许多应用中，荧光信号仅限于几个像素，这就带来了一个经典的信号处理问题：如何将实际信号与背景噪声区分开来？在本章中，我介绍一套MATLAB（MathWorks（2010）MATLAB。取自http://www.mathworks.com）软件套件，该套件旨在与这些单分子可视化技术配合使用（里夫金（2010）斑点查找套件。http://www.biology.ucsd.edu/labs/rifkin/software.html）。它以荧光显微镜的图像或图像堆栈作为输入，并输出分子的位置。尽管该软件是为在固定标本中识别单个mRNA转录本这一特定应用而开发的，但它的通用性更强，可用于和/或定制用于其他在潜在噪声背景中产生局部信号的应用。分析流程包括以下步骤：（a）创建一个金标准数据集，（b）训练机器学习算法，根据用户定义的统计数据将图像特征分类为信号或噪声，（c）在新数据集上运行机器学习算法以识别mRNA位置，以及（d）目视检查并校正结果。
