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Obtaining 3D Super-resolution Information from 2D Super-resolution Images through a 2D-to-3D Transformation Algorithm

Andrew Ruba, Wangxi Luo, Joseph Kelich, Weidong Yang
doi: https://doi.org/10.1101/188060
Andrew Ruba
Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Wangxi Luo
Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Joseph Kelich
Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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Weidong Yang
Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
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  • For correspondence: weidong.yang@temple.edu
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Abstract

Currently, it is highly desirable but still challenging to obtain three-dimensional (3D) superresolution information of structures in fixed specimens as well as dynamic processes in live cells with a high spatiotemporal resolution. Here we introduce an approach, without using 3D superresolution microscopy or real-time 3D particle tracking, to achieve 3D sub-diffraction-limited information with a spatial resolution of ≤ 1 nm. This is a post-localization analysis that transforms 2D super-resolution images or 2D single-molecule localization distributions into their corresponding 3D spatial probability information. The method has been successfully applied to obtain structural and functional information for 25-300 nm sub-cellular organelles that have rotational symmetry. In this article, we will provide a comprehensive analysis of this method by using experimental data and computational simulations.

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Posted October 11, 2017.
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Obtaining 3D Super-resolution Information from 2D Super-resolution Images through a 2D-to-3D Transformation Algorithm
Andrew Ruba, Wangxi Luo, Joseph Kelich, Weidong Yang
bioRxiv 188060; doi: https://doi.org/10.1101/188060
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Obtaining 3D Super-resolution Information from 2D Super-resolution Images through a 2D-to-3D Transformation Algorithm
Andrew Ruba, Wangxi Luo, Joseph Kelich, Weidong Yang
bioRxiv 188060; doi: https://doi.org/10.1101/188060

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