1. Statistical Deconvolution for Superresolution Fluorescence Microscopy
Eran A. Mukamel, Hazen Babcock, Xiaowei Zhuang 
Biophysical Journal 
Volume 102, Issue 10, Pages (May 2012)
DOI: /j.bpj
Copyright © 2012 Biophysical Society Terms and Conditions

2. Figure 1
Schematic illustration of the deconSTORM algorithm for analyzing multiframe fluorescence movie data of intermittently activated fluorophores. (a) Simulation of a small field of view containing two nearby fluorophores. In this sequence of six frames, open and solid circles denote the off (dark) and activated (fluorescent) states, respectively. (b) Simulated fluorescence data for each frame; σ is the width parameter of the microscope's PSF, which is equal to the pixel spacing of the simulated fluorescence data. (c) The deconSTORM compression parameter, γkt(x), at pixel x and time frame k at iteration t = 500. (d) Estimated image for each movie frame after 500 iterations of deconSTORM deconvolution.
Biophysical Journal  , DOI: ( /j.bpj )
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3. Figure 2
Comparison of the performance of the three deconvolution methods (RL, RL with constant prior, and deconSTORM) and two emitter localization methods (single-emitter localization with maximum likelihood fitting and multiemitter localization with DAOSTORM) for simulated fluorescence data. (a) The true locations of the simulated emitters, arranged in the shape of an arrow. (b) Sum of fluorescence data from all movie frames representing the diffraction-limited image. (c and d) Results of localization-based analysis using single-emitter maximum-likelihood fitting (c) and DAOSTORM multiemitter fitting (d). (e–g) Deconvolution-based sample estimates using the classic RL algorithm (e), RL with a constant prior (f), and deconSTORM (g). We divided the simulated movie into 16 subsets of 400 frames each and ran 2000 iterations of deconvolution for each subset. The final reconstructed image is the sum of the estimated images for each movie frame. σ is the width of the Gaussian PSF. Simulated data sets included 6400 frames, and the activation parameters, α = 0.5 and β = , were set such that the average density of activated emitters was ρ=1/32σ2 (average emitter separation d ∼ 5.7 σ, or 2.4 times the PSF full width at half-maximum).
Biophysical Journal  , DOI: ( /j.bpj )
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4. Figure 3
Quantitative comparison of the five analysis algorithms using simulated fluorescence movie data. (a1 and a2) Relative mean-squared error of the 2D Fourier transform of the estimated image as a function of spatial frequency, k. The activation parameter, β, was varied to produce a large average separation between simultaneously activated emitters (a1, d = 14σ) or a relatively small separation (a2, d = 4.3σ). For deconvolution results, n = 2000 iterations were used. (b1 and b2) Mean-squared error at a representative spatial frequency (k = 10/σ) as a function of the number of deconvolution iterations applied. The errors for localization-based sample estimates at this spatial frequency are shown as horizontal lines for comparison. (c) Phase diagram showing the range over which each algorithm achieves relative error, ε ≤ 0.5. (d) Expected increase in the image acquisition speed (speedup) relative to single-emitter fitting for each spatial frequency. Here, the relative acquisition speed of a superresolution image is defined as the maximum emitter density that can be used with error ε ≤ 0.5 for each method relative to that for the single-emitter localization method based on maximum-likelihood fitting. Since the time required for image acquisition is inversely proportional to the density of activated emitters/frame, the maximum density provides a measure of the gain in imaging speed.
Biophysical Journal  , DOI: ( /j.bpj )
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5. Figure 4
Examples of individual fluorescence data frames and corresponding sample estimates from immunohistochemically labeled microtubules. (a) STORM analysis (red circles) localizes one emitter; DAOSTORM (cyan crosses) and deconSTORM (brown pattern) identify two. RL and RL with constant prior (brown pattern) provide blurred estimates of emitter location. (b) DeconSTORM estimates a sample with three bright spots; the other methods localize one or two. (c) Three nearby peaks identified by deconvolution are not captured by either of the localization approaches alone. (d) A crowded sample containing several putative emitters. Whereas analysis by localization identifies two or three emitters, the deconvolution methods provide a more complex sample estimate.
Biophysical Journal  , DOI: ( /j.bpj )
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6. Figure 5
Analysis of imaging data from immunohistochemically labeled microtubules with the five methods. Images in the upper row represent data from 625 frames and those in the lower row data from 5000 frames. (a) Summed fluorescence data showing diffraction-limited resolution. (b–e) Results of analysis using single-emitter localization (23) (b) and DAOSTORM multiemitter localization (c), as well as three statistical deconvolution algorithms: RL (d), RL with constant prior (e), and deconSTORM (f). All deconvolution analyses were performed with n = 1000 iterations. (g) Magnified view of a region of interest indicated by dashed box 1 in f. (h) Magnification of dashed box 2 in f, showing a microtubule that is detected by deconSTORM but not clearly visible in the other sample estimates. (i) Cross section through each sample estimate at the location indicated by the solid box in f.
Biophysical Journal  , DOI: ( /j.bpj )
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