SOFI Simulation Tool: A Software Package for Simulating and Testing Super-Resolution Optical Fluctuation Imaging

Girsault, Arik; Lozano-Pérez, Tomás; Sharipov, Azat; Geissbuehler, Stefan; Leutenegger, Marcel; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Lasser, Theo

doi:10.1371/journal.pone.0161602

Cited by 54 publications

(45 citation statements)

References 20 publications

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“…In previous work we and other groups demonstrated that super-resolution optical fluctuation imaging (SOFI) is well suited to live-cell super-resolution imaging5678, due to its ability to image in challenging conditions such as low signal to noise and high background9. The technique operates by analyzing spontaneous fluctuations in fluorophore emission that arise through blinking, made observable by analyzing multiple (100 or more) fluorescence images rapidly acquired from the same sample.…”

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Effect of probe diffusion on the SOFI imaging accuracy

Vandenberg

Dedecker

2017

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Live-cell super-resolution fluorescence imaging is becoming commonplace for exploring biological systems, though sample dynamics can affect the imaging quality. In this work we evaluate the effect of probe diffusion on super-resolution optical fluctuation imaging (SOFI), using a theoretical model and numerical simulations based on the imaging of live cells labelled with photochromic fluorescent proteins. We find that, over a range of physiological conditions, fluorophore diffusion results in a change in the amplitude of the SOFI signal. The magnitude of this change is approximately proportional to the on-time ratio of the fluorophores. However, for photochromic fluorescent proteins this effect is unlikely to present a significant distortion in practical experiments in biological systems. Due to this lack of distortions, probe diffusion strongly enhances the SOFI imaging by avoiding spatial undersampling caused by the limited labeling density.

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confidence: 99%

Effect of probe diffusion on the SOFI imaging accuracy

Vandenberg

Dedecker

2017

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“…We simulated the acquisition of K = 10 images of size N = 64 over 0.1 sec using the molecule distribution of the microtubules dataset (MT0) from the SMLM challenge 2016, 3 containing 8 731 molecules. The temporal blinking profiles were generated using the SOFI simulation tool [16], as described in Section 3.3. The ground truth and observed image (with a 5× zoom) of Fig.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…1, where five stacks of 20 molecules uniformly distributed over the border of a square with varying side length are processed. The temporal profiles were generated with the SOFI simulation tool [16] and are discretized realizations of a continuous-time Markovian process with emitting (average lifetime τ on = 20 ms), dark (τ off = 40 ms) and bleached (average emitting time until bleaching τ bl = 80 s) states. The number of photons per frame of an emitting molecule is a Poisson variable with mean 400.…”

Section: P ) As the Matrix Between Parentheses In This Expression Eqmentioning

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A Study on Tensor and Matrix Models for Super-Resolution Fluorescence Microscopy

Goulart

Blanc‐Féraud

Debreuve

et al. 2019

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Super-resolution techniques for fluorescence microscopy are invaluable tools for studying phenomena that take place at sub-cellular scales, thanks to their capability of overcoming light diffraction. Yet, achieving sufficient temporal resolution for imaging live-cell processes remains a challenging problem. Exploiting the temporal fluctuations (blinking) of fluorophores is a promising approach that allows employing standard equipment and harmless excitation levels. In this work, we study a novel constrained tensor modeling approach that takes this temporal diversity into account to estimate the spatial distribution of fluorophores and their overall intensities. We compare this approach with an also novel matrix-based formulation which promotes structured sparsity via a continuous approximation of the cardinality function, as well as with other state-of-the-art methods.

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“…SOFI analysis was carried out as reported previously [ 49 ]. We used a custom written algorithm (Matlab, The Mathworks) based on the code of our SOFI simulation tool [ 51 ] and the bSOFI algorithm [ 50 ]. The sequence of camera frames was divided into subsequences of 500 frames each.…”

Section: Methodsmentioning

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Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

et al. 2018

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BackgroundSuper-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared with organic dyes, which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms.FindingsFour complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented, including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM datasets using a different method: super-resolution optical fluctuation imaging (SOFI). The 2 modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes.ConclusionsThis dataset has potential for extensive reuse. Complete raw data from SMLM experiments have typically not been published. The YFP data exhibit low signal-to-noise ratios, making data analysis a challenge. These datasets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.

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SOFI Simulation Tool: A Software Package for Simulating and Testing Super-Resolution Optical Fluctuation Imaging

Cited by 54 publications

References 20 publications

Effect of probe diffusion on the SOFI imaging accuracy

Effect of probe diffusion on the SOFI imaging accuracy

A Study on Tensor and Matrix Models for Super-Resolution Fluorescence Microscopy

Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

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