A high-density 3D localization algorithm for stochastic optical reconstruction microscopy.
A high-density 3D localization algorithm for stochastic optical reconstruction microscopy.
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DOI：
10.1186/2192-2853-1-6
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Zhuang X
中科院分区：
文献类型：
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作者：
Babcock H;Sigal YM;Zhuang X
随机光学重建显微镜（STORM）和相关方法通过单个荧光团的顺序激活和定位来实现亚衍射极限图像分辨率。来自这些方法的图像数据的分析通常局限于稀疏激活状态，其中激活的荧光团的密度足够低，使得相邻发射器的图像之间的重叠最小。最近报道了几种用于分析更高密度数据的方法，允许相邻发射器之间部分重叠。然而，到目前为止，这些方法仅限于二维成像，其中假设每个发射器的点扩散函数（PSF）相同。在这项工作中，我们提出了一种分析三维高密度超分辨率数据的方法，其中各个荧光团的图像不仅重叠，而且还具有取决于荧光团 z 位置的不同 PSF。这种方法可以准确地分析数据集，其发射器密度比以前使用稀疏发射器分析算法高出五倍。我们将该算法应用于分析取自膜标记视网膜和脑组织的数据集，这些数据集包含高密度的标签，并获得了显着改善的超分辨率图像质量。
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. 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. This approach can accurately analyze 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.