Effect of probe diffusion on the SOFI imaging accuracy

Vandenberg, Wim; Dedecker, Peter

doi:10.1038/srep44665

Cited by 17 publications

(23 citation statements)

References 33 publications

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“…In general, the resolution is compromised when the frame acquisition time and the feature velocity lead to a displacement on the order of the attainable resolution 29 . Diffusion of fluorophores for an overall stationary structure has only minor effects on the SOFI signal while substantially improving the spatial sampling 30 . In general, SOFI tolerates higher labeling densities, on-time ratios, lower signal-to-noise ratio and needs less frames to reconstruct an image than SMLM.…”

Section: Resultsmentioning

confidence: 99%

Spectral cross-cumulants for multicolor super-resolved SOFI imaging

et al. 2020

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Super-resolution optical fluctuation imaging provides a resolution beyond the diffraction limit by analysing stochastic fluorescence fluctuations with higher-order statistics. Using n th order spatio-temporal cross-cumulants the spatial resolution and the sampling can be increased up to n-fold in all spatial dimensions. In this study, we extend the cumulant analysis into the spectral domain and propose a multicolor super-resolution scheme. The simultaneous acquisition of two spectral channels followed by spectral cross-cumulant analysis and unmixing increases the spectral sampling. The number of discriminable fluorophore species is thus not limited to the number of physical detection channels. Using two color channels, we demonstrate spectral unmixing of three fluorophore species in simulations and experiments in fixed and live cells. Based on an eigenvalue/vector analysis, we propose a scheme for an optimized spectral filter choice. Overall, our methodology provides a route for easy-toimplement multicolor sub-diffraction imaging using standard microscopes while conserving the spatial super-resolution property.

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Section: Resultsmentioning

confidence: 99%

Spectral cross-cumulants for multicolor super-resolved SOFI imaging

et al. 2020

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“…Cells were then monitored for the recovery of the fluorescence intensity. To estimate the diffusion coefficients, our previous published finite element mathematical model and algorithm were used (Lu et al, 2008; Vandenberg and Dedecker, 2017). This method is flexible to handle different imaging and photobleach protocols, as well as variable cell geometry.…”

Section: Star Methodsmentioning

confidence: 99%

Directed Evolution to Engineer Monobody for FRET Biosensor Assembly and Imaging at Live-Cell Surface

Limsakul

Qin

et al. 2018

Cell Chemical Biology

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Monitoring enzymatic activities at the cell surface is challenging due to the poor efficiency of transport and membrane integration of fluorescence resonance energy transfer (FRET)-based biosensors. Therefore, we developed a hybrid biosensor with separate donor and acceptor that assemble in situ. The directed evolution and sequence-function analysis technologies were integrated to engineer a monobody variant (PEbody) that binds to R-phycoerythrin (R-PE) dye. PEbody was used for visualizing the dynamic formation/separation of intercellular junctions. We further fused PEbody with the enhanced CFP and an enzyme-specific peptide at the extracellular surface to create a hybrid FRET biosensor upon R-PE capture for monitoring membrane-type-1 matrix metalloproteinase (MT1-MMP) activities. This biosensor revealed asymmetric distribution of MT1-MMP activities, which were high and low at loose and stable cell-cell contacts, respectively. Therefore, directed evolution and rational design are promising tools to engineer molecular binders and hybrid FRET biosensors for monitoring molecular regulations at the surface of living cells.

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“…In addition to the improved spatial resolution, SOFI also reduces emission not originating from the labels (background signal) and improves contrast 16 , 18 . Compared to other super-resolution techniques, SOFI does not require special imaging equipment, requires minimal sample preparation steps 19 , 20 , and can work well under a wide range of imaging conditions, such as a low signal-to-noise ratio (SNR) 21 or the presence of steady state diffusion 22 . Another advantage is that the entire image formation process is described by a simple analytical model 16 , 23 , 24 .…”

Section: Introductionmentioning

confidence: 99%

Correcting for photodestruction in super-resolution optical fluctuation imaging

Peeters

Vandenberg

Duwé

et al. 2017

Sci Rep

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Super-resolution optical fluctuation imaging overcomes the diffraction limit by analyzing fluctuations in the fluorophore emission. A key assumption of the imaging is that the fluorophores are independent, though this is invalidated in the presence of photodestruction. In this work, we evaluate the effect of photodestruction on SOFI imaging using theoretical considerations and computer simulations. We find that photodestruction gives rise to an additional signal that does not present an easily interpretable view of the sample structure. This additional signal is strong and the resulting images typically exhibit less noise. Accordingly, these images may be mis-interpreted as being more visually pleasing or more informative. To address this uncertainty, we develop a procedure that can robustly estimate to what extent any particular experiment is affected by photodestruction. We also develop a detailed assessment methodology and use it to evaluate the performance of several correction algorithms. We identify two approaches that can correct for the presence of even strong photodestruction, one of which can be implemented directly in the SOFI calculation software.

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Effect of probe diffusion on the SOFI imaging accuracy

Cited by 17 publications

References 33 publications

Spectral cross-cumulants for multicolor super-resolved SOFI imaging

Spectral cross-cumulants for multicolor super-resolved SOFI imaging

Directed Evolution to Engineer Monobody for FRET Biosensor Assembly and Imaging at Live-Cell Surface

Correcting for photodestruction in super-resolution optical fluctuation imaging

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