Template-free 2D particle fusion in localization microscopy
- 17 September 2018
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Methods
- Vol. 15 (10), 781-784
- https://doi.org/10.1038/s41592-018-0136-6
Abstract
Methods that fuse multiple localization microscopy images of a single structure can improve signal-to-noise ratio and resolution, but they generally suffer from template bias or sensitivity to registration errors. We present a template-free particle-fusion approach based on an all-to-all registration that provides robustness against individual misregistrations and underlabeling. We achieved 3.3-nm Fourier ring correlation (FRC) image resolution by fusing 383 DNA origami nanostructures with 80% labeling density, and 5.0-nm resolution for structures with 30% labeling density.This publication has 23 references indexed in Scilit:
- Avoiding the pitfalls of single particle cryo-electron microscopy: Einstein from noiseProceedings of the National Academy of Sciences of the United States of America, 2013
- Nuclear Pore Scaffold Structure Analyzed by Super-Resolution Microscopy and Particle AveragingScience, 2013
- Measuring image resolution in optical nanoscopyNature Methods, 2013
- LIMITING FACTORS IN SINGLE PARTICLE CRYO ELECTRON TOMOGRAPHYComputational and Structural Biotechnology Journal, 2012
- Super-resolution imaging visualizes the eightfold symmetry of gp210 proteins around the nuclear pore complex and resolves the central channel with nanometer resolutionJournal of Cell Science, 2012
- Simultaneous multiple-emitter fitting for single molecule super-resolution imagingBiomedical Optics Express, 2011
- The effect of photoswitching kinetics and labeling densities on super-resolution fluorescence imagingJournal of Biotechnology, 2010
- Fast, single-molecule localization that achieves theoretically minimum uncertaintyNature Methods, 2010
- EMAN2: An extensible image processing suite for electron microscopyJournal of Structural Biology, 2006
- Robustness in Motion AveragingLecture Notes in Computer Science, 2006