A high-density 3D localization algorithm for stochastic optical reconstruction microscopy

Babcock, Hazen P.; Sigal, Yaron M.; Zhuang, Xiaowei

doi:10.1186/2192-2853-1-6

Cited by 147 publications

(143 citation statements)

References 21 publications

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“…A recent trend in the super-resolution localization microscopy is the development of methods to detect multiple overlapping emitters in high density data sets 31,41,[52][53][54][55] . To benchmark these methods, our generated HD are particularly suitable.…”

Section: Competing Financial Interestsmentioning

confidence: 99%

Quantitative evaluation of software packages for single-molecule localization microscopy

et al. 2015

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the quality of super-resolution images obtained by singlemolecule localization microscopy (smlm) depends largely on the software used to detect and accurately localize point sources. in this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software. We prepared synthetic data that represent three-dimensional structures modeled after biological components, taking excitation parameters, noise sources, point-spread functions and pixelation into account. We then asked developers to run their software on our data; most responded favorably, allowing us to present a broad picture of the methods available. We evaluated their results using quantitative and user-interpretable criteria: detection rate, accuracy, quality of image reconstruction, resolution, software usability and computational resources. these metrics reflect the various tradeoffs of smlm software packages and help users to choose the software that fits their needs.We have conducted a large-scale comparative study of software packages developed in the context of SMLM, including recently developed algorithms. We designed realistic data that are generic and cover a broad range of experimental conditions and compared the software packages using a multiple-criterion quantitative assessment that is based on a known ground truth.Our study is based on the active participation of developers of SMLM software. More than 30 groups have participated so far, and the study is still under way. We provide participants access to our benchmark data as an ongoing public challenge. Participants run their own software on our data and report their list of localized particles for evaluation. The results of the challenge are accessible online and updated regularly.SMLM was demonstrated in 2006, independently by three research groups 1-3 , and has enabled subsequent breakthroughs in diverse fields 4,5 . SMLM can resolve biological structures at the nanometer scale (typically 20 nm lateral resolution), circumventing Abbe's diffraction limit. At the cost of a relatively simple setup 6,7 , it has opened exciting new opportunities in life science research 8,9 .The underlying principle of SMLM is the sequential imaging of sparse subsets of fluorophores distributed over thousands of frames, to populate a high-density map of fluorophore positions. Such large data sets require automated image-analysis algorithms to detect and precisely infer the position of individual fluorophore, taking advantage of their separation in space and time.The acquired data cannot be visualized directly; further computerized image-reconstruction methods are required. These typically comprise four steps: preprocessing, detection, localization and rendering. Preprocessing reduces the effects of the background and noise; detection identifies potential molecule candidates in each frame; localization performs a subpixel refine...

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Section: Competing Financial Interestsmentioning

confidence: 99%

Quantitative evaluation of software packages for single-molecule localization microscopy

et al. 2015

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“…For ratiometric two-color imaging, the fluorescence was split by a 624-nm longpass dichroic mirror into two channels and imaged on two halves of the same camera. The movies were analyzed by custom-written STORM analysis software as we previously described (31), or using the DAOSTORM (37) or a similar multiemitter fitting software (47). See SI Materials and Methods for details.…”

Section: Methodsmentioning

confidence: 99%

Super-resolution fluorescence imaging of organelles in live cells with photoswitchable membrane probes

Shim

Xia

Zhong

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Imaging membranes in live cells with nanometer-scale resolution promises to reveal ultrastructural dynamics of organelles that are essential for cellular functions. In this work, we identified photoswitchable membrane probes and obtained super-resolution fluorescence images of cellular membranes. We demonstrated the photoswitching capabilities of eight commonly used membrane probes, each specific to the plasma membrane, mitochondria, the endoplasmic recticulum (ER) or lysosomes. These small-molecule probes readily label live cells with high probe densities. Using these probes, we achieved dynamic imaging of specific membrane structures in living cells with 30-60 nm spatial resolution at temporal resolutions down to 1-2 s. Moreover, by using spectrally distinguishable probes, we obtained two-color super-resolution images of mitochondria and the ER. We observed previously obscured details of morphological dynamics of mitochondrial fusion/fission and ER remodeling, as well as heterogeneous membrane diffusivity on neuronal processes.nanoscopy | diffraction limit | photoswitchable dye | stochastic optical reconstruction microscopy | photoactivation localization microscopy

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“…Algorithm Comparison. To compare the quality of the reconstructions between different algorithms, we used a single-emitter fitting algorithm [24,25], the multi-emitter fitting algorithm 3d-DAOSTORM [13], L1H, and CVX to analyze 256 × 256 cameral pixel, five frame data simulated with the same parameters as above. We used the 2d option of 3d-DAOSTORM which produces similar results to the original DAOSTORM algorithm [9].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, several methods have been introduced to resolve partially overlapping emitter images [9][10][11][12][13][14][15]. Among these methods, one powerful approach [15] allows the highest density of emitters to be localized to date by adopting techniques from the rapidly advancing field of compressed sensing.…”

Section: Introductionmentioning

confidence: 99%

Fast compressed sensing analysis for super-resolution imaging using L1-homotopy

et al. 2013

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Abstract:In super-resolution imaging techniques based on single-molecule switching and localization, the time to acquire a super-resolution image is limited by the maximum density of fluorescent emitters that can be accurately localized per imaging frame. In order to increase the imaging rate, several methods have been recently developed to analyze images with higher emitter densities. One powerful approach uses methods based on compressed sensing to increase the analyzable emitter density per imaging frame by several-fold compared to other reported approaches. However, the computational cost of this approach, which uses interior point methods, is high, and analysis of a typical 40 µm x 40 µm field-of-view superresolution movie requires thousands of hours on a high-end desktop personal computer. Here, we demonstrate an alternative compressedsensing algorithm, L1-Homotopy (L1H), which can generate superresolution image reconstructions that are essentially identical to those derived using interior point methods in one to two orders of magnitude less time depending on the emitter density. Moreover, for an experimental data set with varying emitter density, L1H analysis is ~300-fold faster than interior point methods. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to superresolution image analysis.

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A high-density 3D localization algorithm for stochastic optical reconstruction microscopy

Cited by 147 publications

References 21 publications

Quantitative evaluation of software packages for single-molecule localization microscopy

Quantitative evaluation of software packages for single-molecule localization microscopy

Super-resolution fluorescence imaging of organelles in live cells with photoswitchable membrane probes

Fast compressed sensing analysis for super-resolution imaging using L1-homotopy

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