Design of experiments for the optimization of SOFI super-resolution microscopy imaging.

Dario Cevoli,Robin Van den Eynde,Raffaele Vitale,Wim Vandenberg,Cyril Ruckebusch,Peter Dedecker,Siewert Hugelier

doi:10.1364/boe.421168

Dario Cevoli, Robin Van den Eynde + Show 5 more

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https://doi.org/10.1364/boe.421168

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Abstract

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time t on, off-time t off, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest t on and t off), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of t off can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of t on and t off have been found to be highly dependent from each other and from the measurement duration.

Highlights

Super-resolution optical fluctuation imaging (SOFI) is a data post-processing technique for fluorescence microscopy which takes advantage of fluctuations in the fluorophore emission to achieve a sub-diffraction resolution [1]
A set of Matlab routines to produce a D-Optimal design and to reproduce the analysis presented in this work are available at the GitHub repository: https://github.com/dcevoli/doe4sofi
In this work we exploited the principles of design of experiments (DOE) to construct an empirical model linking five fluorescence image acquisition parameters to the quality of the SOFI images they yield, as evaluated through the use of the Signal to Noise Ratio (SNR)

Summary

Introduction

Super-resolution optical fluctuation imaging (SOFI) is a data post-processing technique for fluorescence microscopy which takes advantage of fluctuations in the fluorophore emission to achieve a sub-diffraction resolution [1]. Because each fluorescent molecule behaves independently, its contribution can be distinguished from that of other molecules by correlating the observed emission traces, which can be achieved by an analysis based on statistical cumulants. Overall, these relatively mild requirements render SOFI capable of coping with data recorded on any sufficiently sensitive microscope. The method can be extended to 3D applications [6], a variety of fluorophores [7,8], and its effectiveness at short acquisition times makes it well suited for achieving the temporal resolution necessary for live cell imaging [4]

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