Correlation analysis framework for localization-based superresolution microscopy release_gvpdlevoa5dhtnt2ouhhllnbiu

by Joerg Schnitzbauer, Yina Wang, Shijie Zhao, Matthew Bakalar, Tulip Nuwal, Baohui Chen, Bo Huang

Published in Proceedings of the National Academy of Sciences of the United States of America by Proceedings of the National Academy of Sciences.

2018   Volume 115, Issue 13, p3219-3224

Abstract

Superresolution images reconstructed from single-molecule localizations can reveal cellular structures close to the macromolecular scale and are now being used routinely in many biomedical research applications. However, because of their coordinate-based representation, a widely applicable and unified analysis platform that can extract a quantitative description and biophysical parameters from these images is yet to be established. Here, we propose a conceptual framework for correlation analysis of coordinate-based superresolution images using distance histograms. We demonstrate the application of this concept in multiple scenarios, including image alignment, tracking of diffusing molecules, as well as for quantification of colocalization, showing its superior performance over existing approaches.
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