Smoothness correction for better SOFI imaging
Abstract Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale. Statistical analysis methods such as super-resolution optical fluctuation imaging (SOFI) obtain an improved spatial resolution by analyzing fluorop...
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2021-04-01
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doaj-f9a2e049d318479aae5d45359e047cf92021-04-11T11:30:55ZengNature Publishing GroupScientific Reports2045-23222021-04-011111910.1038/s41598-021-87164-4Smoothness correction for better SOFI imagingSiewert Hugelier0Wim Vandenberg1Tomáš Lukeš2Kristin S. Grußmayer3Paul H. C. Eilers4Peter Dedecker5Cyril Ruckebusch6Laboratory for Nanobiology, KU LeuvenLaboratory for Nanobiology, KU LeuvenLaboratory of Nanoscale Biology, École Polytechnique Fédérale de LausanneLaboratory of Nanoscale Biology, École Polytechnique Fédérale de LausanneErasmus University Medical CentreLaboratory for Nanobiology, KU LeuvenUniversity of Lille, CNRS, UMR 8516, LASIREAbstract Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale. Statistical analysis methods such as super-resolution optical fluctuation imaging (SOFI) obtain an improved spatial resolution by analyzing fluorophore blinking but can be perturbed by the presence of non-stationary processes such as photodestruction or fluctuations in the illumination. In this work, we propose to use Whittaker smoothing to remove these smooth signal trends and retain only the information associated to independent blinking of the emitters, thus enhancing the SOFI signals. We find that our method works well to correct photodestruction, especially when it occurs quickly. The resulting images show a much higher contrast, strongly suppressed background and a more detailed visualization of cellular structures. Our method is parameter-free and computationally efficient, and can be readily applied on both two-dimensional and three-dimensional data.https://doi.org/10.1038/s41598-021-87164-4 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Siewert Hugelier Wim Vandenberg Tomáš Lukeš Kristin S. Grußmayer Paul H. C. Eilers Peter Dedecker Cyril Ruckebusch |
spellingShingle |
Siewert Hugelier Wim Vandenberg Tomáš Lukeš Kristin S. Grußmayer Paul H. C. Eilers Peter Dedecker Cyril Ruckebusch Smoothness correction for better SOFI imaging Scientific Reports |
author_facet |
Siewert Hugelier Wim Vandenberg Tomáš Lukeš Kristin S. Grußmayer Paul H. C. Eilers Peter Dedecker Cyril Ruckebusch |
author_sort |
Siewert Hugelier |
title |
Smoothness correction for better SOFI imaging |
title_short |
Smoothness correction for better SOFI imaging |
title_full |
Smoothness correction for better SOFI imaging |
title_fullStr |
Smoothness correction for better SOFI imaging |
title_full_unstemmed |
Smoothness correction for better SOFI imaging |
title_sort |
smoothness correction for better sofi imaging |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-04-01 |
description |
Abstract Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale. Statistical analysis methods such as super-resolution optical fluctuation imaging (SOFI) obtain an improved spatial resolution by analyzing fluorophore blinking but can be perturbed by the presence of non-stationary processes such as photodestruction or fluctuations in the illumination. In this work, we propose to use Whittaker smoothing to remove these smooth signal trends and retain only the information associated to independent blinking of the emitters, thus enhancing the SOFI signals. We find that our method works well to correct photodestruction, especially when it occurs quickly. The resulting images show a much higher contrast, strongly suppressed background and a more detailed visualization of cellular structures. Our method is parameter-free and computationally efficient, and can be readily applied on both two-dimensional and three-dimensional data. |
url |
https://doi.org/10.1038/s41598-021-87164-4 |
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