Douglass Adam D, Vale Ronald D
Department of Cellular and Molecular Pharmacology, University of California, The Howard Hughes Medical Institute, San Francisco, California 94107, USA.
Methods Cell Biol. 2008;85:113-25. doi: 10.1016/S0091-679X(08)85006-6.
Single molecule imaging techniques overcome the averaging effects inherent in ensemble measurements and enable characterization of the enormous heterogeneity that exists in biomolecular systems. Though long the domain of a few highly specialized labs, optical imaging of single molecules in living cells is becoming a widely accessible technique. The development of commercially available microscopes, robust analysis tools, and sensitive, low-noise detectors has contributed to this dissemination, as has the ever-growing array of fluorescent proteins. The relative ease with which genetically-tagged proteins can be created and introduced into a cell has largely eliminated more cumbersome and less precise means of particle labeling. A number of special considerations apply when using genetically encoded fluorophores for single molecule experiments, however. We discuss the means by which fluorescent proteins can be transfected into living cells to obtain the low particle densities required for single molecule imaging, and consider the limitations that are placed on single molecule analysis by the fluorophore's photophysical properties. We also discuss the types of morphology and subcellular localization that make certain preparations more amenable to single particle imaging than others. Last, we discuss some common pitfalls involved in analyzing single molecule datasets, and consider some of the unique information that can be obtained using these techniques.
单分子成像技术克服了整体测量中固有的平均效应，并能够表征生物分子系统中存在的巨大异质性。尽管长期以来一直是少数高度专业化实验室的领域，但活细胞中单分子的光学成像正成为一种广泛可用的技术。商用显微镜、强大的分析工具以及灵敏、低噪声探测器的发展推动了这项技术的普及，荧光蛋白种类的不断增加也起到了同样的作用。通过基因标记创建并导入细胞中的蛋白质相对容易，这在很大程度上消除了更为繁琐且不太精确的粒子标记方法。然而，在单分子实验中使用基因编码荧光团时需要考虑一些特殊因素。我们将讨论如何将荧光蛋白转染到活细胞中以获得单分子成像所需的低粒子密度，并考虑荧光团的光物理性质对单分子分析造成的限制。我们还将讨论某些形态和亚细胞定位类型，这些类型使得某些样本比其他样本更适合单粒子成像。最后，我们将讨论分析单分子数据集时常见的一些陷阱，并考虑使用这些技术可以获得的一些独特信息。
