A high-density 3D localization algorithm for stochastic optical reconstruction microscopy

被引：109
作者：
Babcock, Hazen [1 ,2 ]
Sigal, Yaron M. [1 ,2 ]
Zhuang, Xiaowei [1 ,2 ,3 ,4 ]
机构：
[1] Center for Brain Science, Harvard University, Cambridge, MA
[2] Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
[3] Department of Physics, Harvard University, Cambridge, MA
[4] Howard Hughes Medical Institute, Cambridge, MA
来源：
Optical Nanoscopy | 2012年 / 1卷 / 01期
基金：
美国国家卫生研究院;
关键词：
D O I：
10.1186/2192-2853-1-6
中图分类号：
学科分类号：
摘要：
Background: Stochastic optical reconstruction microscopy (STORM) and related methods achieves sub-diffraction-limit image resolution through sequential activation and localization of individual fluorophores. The analysis of image data from these methods has typically been confined to the sparse activation regime where the density of activated fluorophores is sufficiently low such that there is minimal overlap between the images of adjacent emitters. Recently several methods have been reported for analyzing higher density data, allowing partial overlap between adjacent emitters. However, these methods have so far been limited to two-dimensional imaging, in which the point spread function (PSF) of each emitter is assumed to be identical. Methods: In this work, we present a method to analyze high-density super-resolution data in three dimensions, where the images of individual fluorophores not only overlap, but also have varying PSFs that depend on the z positions of the fluorophores. Results and conclusion: This approach accurately analyzed data sets with an emitter density five times higher than previously possible with sparse emitter analysis algorithms. We applied this algorithm to the analysis of data sets taken from membrane-labeled retina and brain tissues which contain a high-density of labels, and obtained substantially improved super-resolution image quality. © 2012 Babcock et al.
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页码：1 / 10
页数：9
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