Rollins Geoffrey C, Shin Jae Yen, Bustamante Carlos, Pressé Steve
Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158;
Howard Hughes Medical Institute and.
Proc Natl Acad Sci U S A. 2015 Jan 13;112(2):E110-8. doi: 10.1073/pnas.1408071112. Epub 2014 Dec 22.
Superresolution imaging methods--now widely used to characterize biological structures below the diffraction limit--are poised to reveal in quantitative detail the stoichiometry of protein complexes in living cells. In practice, the photophysical properties of the fluorophores used as tags in superresolution methods have posed a severe theoretical challenge toward achieving this goal. Here we develop a stochastic approach to enumerate fluorophores in a diffraction-limited area measured by superresolution microscopy. The method is a generalization of aggregated Markov methods developed in the ion channel literature for studying gating dynamics. We show that the method accurately and precisely enumerates fluorophores in simulated data while simultaneously determining the kinetic rates that govern the stochastic photophysics of the fluorophores to improve the prediction's accuracy. This stochastic method overcomes several critical limitations of temporal thresholding methods.
超分辨率成像方法——如今广泛用于表征低于衍射极限的生物结构——有望定量详细揭示活细胞中蛋白质复合物的化学计量。实际上，在超分辨率方法中用作标记的荧光团的光物理性质对实现这一目标构成了严峻的理论挑战。在此，我们开发了一种随机方法，用于在超分辨率显微镜测量的衍射极限区域内对荧光团进行计数。该方法是离子通道文献中为研究门控动力学而开发的聚合马尔可夫方法的推广。我们表明，该方法能准确精确地对模拟数据中的荧光团进行计数，同时确定控制荧光团随机光物理过程的动力学速率，以提高预测的准确性。这种随机方法克服了时间阈值方法的几个关键局限性。
