Introduction

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.

Publications

  1. A high-density 3D localization algorithm for stochastic optical reconstruction microscopy.
    Cite this
    Babcock H, Sigal YM, Zhuang X, 2012-01-01 - Optical nanoscopy

Credits

  1. Hazen Babcock
    Developer

  2. Yaron M Sigal
    Developer

  3. Xiaowei Zhuang
    Investigator

    Center for Brain Science, Harvard University Department of Chemistry and Chemical Biology

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Summary
AccessionBT006475
Tool TypeApplication
Category
PlatformsWindows
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByXiaowei Zhuang