drift correction
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2021 ◽  
Author(s):  
Leonid Andronov ◽  
Rachel Genthial ◽  
Didier Hentsch ◽  
Bruno P Klaholz

Single molecule localization microscopy (SMLM) with a dichroic image splitter can provide invaluable multi-color information regarding colocalization of individual molecules, but it often suffers from technical limitations. So far, demixing algorithms give suboptimal results in terms of localization precision and correction of chromatic aberrations. Here we present an image splitter based multi-color SMLM method (splitSMLM) that offers much improved localization precision & drift correction, compensation of chromatic aberrations, and optimized performance of fluorophores in a specific buffer to equalize their reactivation rates for simultaneous imaging. A novel spectral demixing algorithm, SplitViSu, fully preserves localization precision with essentially no data loss and corrects chromatic aberrations at the nanometer scale. Multi-color performance is further improved by using optimized fluorophore and filter combinations. Applied to three-color imaging of the nuclear pore complex (NPC), this method provides a refined positioning of the individual NPC proteins and reveals that Pom121 clusters act as NPC deposition loci, hence illustrating strength and general applicability of the method.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael J. Wester ◽  
David J. Schodt ◽  
Hanieh Mazloom-Farsibaf ◽  
Mohamadreza Fazel ◽  
Sandeep Pallikkuth ◽  
...  

AbstractWe describe a robust, fiducial-free method of drift correction for use in single molecule localization-based super-resolution methods. The method combines periodic 3D registration of the sample using brightfield images with a fast post-processing algorithm that corrects residual registration errors and drift between registration events. The method is robust to low numbers of collected localizations, requires no specialized hardware, and provides stability and drift correction for an indefinite time period.


Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jielei Ni ◽  
Bo Cao ◽  
Gang Niu ◽  
Danni Chen ◽  
Guotao Liang ◽  
...  

Abstract Single-molecule localization microscopy (SMLM) plays an irreplaceable role in biological studies, in which nanometer-sized biomolecules are hardly to be resolved due to diffraction limit unless being stochastically activated and accurately located by SMLM. For biological samples preimmobilized for SMLM, most biomolecules are cross-linked and constrained at their immobilizing sites but still expected to undergo confined stochastic motion in regard to their nanometer sizes. However, few lines of direct evidence have been reported about the detectability and influence of confined biomolecule stochastic motion on localization precision in SMLM. Here, we access the potential stochastic motion for each immobilized single biomolecule by calculating the displacements between any two of its localizations at different frames during sequential imaging of Alexa Fluor-647-conjugated oligonucleotides. For most molecules, localization displacements are remarkably larger at random frame intervals than at shortest intervals even after sample drift correction, increase with interval times and then saturate, showing that biomolecule stochastic motion is detected and confined around the immobilizing sizes in SMLM. Moreover, localization precision is inversely proportional to confined biomolecule stochastic motion, whereas it can be deteriorated or improved by enlarging the biomolecules or adding a post-crosslinking step, respectively. Consistently, post-crosslinking of cell samples sparsely stained for tubulin proteins results in a better localization precision. Overall, this study reveals that confined stochastic motion of immobilized biomolecules worsens localization precision in SMLM, and improved localization precision can be achieved via restricting such a motion.


2021 ◽  
Author(s):  
Dhrubajit Chowdhury ◽  
Alexander Melin ◽  
Kris Villez
Keyword(s):  

2021 ◽  
Vol 29 (21) ◽  
pp. 34641
Author(s):  
Mingtao Shang ◽  
Zhiwei Zhou ◽  
Weibing Kuang ◽  
Yujie Wang ◽  
Bo Xin ◽  
...  

2021 ◽  
Author(s):  
Mengnan Liu ◽  
Yu Han ◽  
Xiaoqi Xi ◽  
Mingwan Zhu ◽  
Linlin Zhu ◽  
...  
Keyword(s):  

2021 ◽  
Vol 32 (12) ◽  
pp. 125403
Author(s):  
Mitsutoshi Kobayashi ◽  
Atsushi Miyamoto ◽  
Maki Tanaka ◽  
Yoshinobu Hoshino

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