Smoothness correction for better SOFI imaging

Siewert Hugelier,Tomáš Lukeš,Peter Dedecker,Wim Vandenberg,Paul H C Eilers,Cyril Ruckebusch,Kristin S Grußmayer

doi:10.1038/s41598-021-87164-4

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.

Highlights

Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale
We focus on super-resolution optical fluctuation imaging (SOFI)[17]
In SOFI, hundreds to thousands of fluorescence images are acquired from a sample labeled with ‘blinking’ fluorophores, and the resulting fluorescence dynamics are analyzed statistically to obtain an image with an improved spatial resolution

Summary

Introduction

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 SOFI, hundreds to thousands of fluorescence images are acquired from a sample labeled with ‘blinking’ fluorophores, and the resulting fluorescence dynamics are analyzed statistically to obtain an image with an improved spatial resolution It can be used directly or combined with other super-resolution techniques such as image scanning microscopy (SOFISM)[18]. The SOFI principle can be described by an analytical model[17,21] which provides a sound mathematical basis that supports the robustness of SOFI imaging, and we developed an approach to obtain unbiased estimates of the per-pixel reliability of a SOFI image[22]

Methods

Results

Conclusion