Phasor based single-molecule localization microscopy in 3D (pSMLM-3D): an algorithm for MHz localization rates using standard CPUs

AbstractWe present a fast and model-free 2D and 3D single-molecule localization algorithm that allows more than 3 million localizations per second on a standard multi-core CPU with localization accuracies in line with the most accurate algorithms currently available. Our algorithm converts the region of interest around a point spread function (PSF) to two phase vectors (phasors) by calculating the first Fourier coefficients in both x- and y-direction. The angles of these phasors are used to localize the center of the single fluorescent emitter, and the ratio of the magnitudes of the two phasors is a measure for astigmatism, which can be used to obtain depth information (z-direction). Our approach can be used both as a stand-alone algorithm for maximizing localization speed and as a first estimator for more time consuming iterative algorithms.

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Accurate 4Pi single-molecule localization using an experimental PSF model

10.1101/2020.03.18.997163 ◽

2020 ◽

Author(s):

Yiming Li ◽

Elena Buglakova ◽

Yongdeng Zhang ◽

Jervis Vermal Thevathasan ◽

Joerg Bewersdorf ◽

...

Keyword(s):

Single Molecule ◽

Simulated Data ◽

Current Data ◽

Localization Algorithm ◽

Resolution Limit ◽

Isotropic Resolution ◽

Spread Function ◽

Phase Images ◽

Analysis Workflow ◽

Single Molecule Localization Microscopy

Interferometric single-molecule localization microscopy (iPALM, 4Pi-SMS) uses multiphase interferometry to localize single fluorophores and achieves nanometer isotropic resolution in 3D. The current data analysis workflow, however, fails to reach the theoretical resolution limit due to the suboptimal localization algorithm. Here, we develop a method to fit an experimentally derived point spread function (PSF) model to the interference 4Pi-PSF. As the interference phase is not fixed with respect to the shape of the PSF, we decoupled the phase term in the model from the 3D position of the PSF. The fitter can reliably infer the interference period even without introducing astigmatism, reducing the complexity of the microscope. Using a spline-interpolated experimental PSF model and by fitting all phase images globally, we show on simulated data that we can achieve the theoretical limit of 3D resolution, the Cramer-Rao lower bound (CRLB), also for the 4Pi microscope.

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Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software

Nature Methods ◽

10.1038/s41592-019-0364-4 ◽

2019 ◽

Vol 16 (5) ◽

pp. 387-395 ◽

Cited By ~ 70

Author(s):

Daniel Sage ◽

Thanh-An Pham ◽

Hazen Babcock ◽

Tomas Lukes ◽

Thomas Pengo ◽

...

Keyword(s):

Single Molecule ◽

Super Resolution ◽

Localization Microscopy ◽

Fight Club ◽

2D And 3D ◽

Single Molecule Localization Microscopy

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Adaptive holographic region of interest illumination with oblique angles for use in single molecule localization microscopy

Advances in Microscopic Imaging II ◽

10.1117/12.2525253 ◽

2019 ◽

Author(s):

Alexander Jügler ◽

Jan Becker ◽

Ronny Förster ◽

Rainer Heintzmann

Keyword(s):

Single Molecule ◽

Region Of Interest ◽

Localization Microscopy ◽

Single Molecule Localization Microscopy

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Super-resolution fight club: A broad assessment of 2D & 3D single-molecule localization microscopy software

10.1101/362517 ◽

2018 ◽

Cited By ~ 5

Author(s):

Daniel Sage ◽

Thanh-An Pham ◽

Hazen Babcock ◽

Tomas Lukes ◽

Thomas Pengo ◽

...

Keyword(s):

Single Molecule ◽

Double Helix ◽

Super Resolution ◽

Large Set ◽

Holistic View ◽

Localization Microscopy ◽

Fight Club ◽

Community Effort ◽

2D And 3D ◽

Single Molecule Localization Microscopy

ABSTRACTWith the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. To guide researchers on the optimal analytical software for their experiments, we have designed, in a large community effort, a competition to extensively characterise and rank these options. We generated realistic simulated datasets for popular imaging modalities – 2D, astigmatic 3D, biplane 3D, and double helix 3D – and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software, provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions, and ultimately provides insight into the current limits of the field.

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Photon count estimation in single-molecule localization microscopy

10.1101/396424 ◽

2018 ◽

Cited By ~ 2

Author(s):

Rasmus Ø. Thorsen ◽

Christiaan N. Hulleman ◽

Mathias Hammer ◽

David Grünwald ◽

Sjoerd Stallinga ◽

...

Keyword(s):

Single Molecule ◽

Axial Position ◽

Optical Aberrations ◽

Intensity Estimation ◽

Localization Microscopy ◽

Photon Count ◽

Point Spread ◽

Spread Function ◽

Gaussian Point ◽

Single Molecule Localization Microscopy

Recently, Franke, Sauer and van de Linde1 introduced a way to estimate the axial position of single-molecules (TRABI). To this end, they compared the detected photon count from a temporal radial-aperture-based intensity estimation to the estimated count from Gaussian point-spread function (PSF) fitting to the data. Empirically they found this photometric ratio to be around 0.7-0.8 close to focus and decreasing away from it. Here, we explain this reported but unexplained discrepancy and furthermore show that the photometric ratio as indicator for axial position is susceptible even to typical optical aberrations.

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A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

10.31219/osf.io/y62rq ◽

2020 ◽

Author(s):

Anish Mukherjee

Keyword(s):

Deep Learning ◽

Single Molecule ◽

Super Resolution ◽

Point Sources ◽

Learning Approaches ◽

Localization Algorithm ◽

Localization Microscopy ◽

Trade Offs ◽

Emitter Localization ◽

Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

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Analyzing Single Molecule Localization Microscopy Data Using Cubic Splines

10.1101/083402 ◽

2016 ◽

Cited By ~ 2

Author(s):

Hazen P. Babcock ◽

Xiaowei Zhuang

Keyword(s):

Single Molecule ◽

Super Resolution ◽

Computation Time ◽

Cubic Splines ◽

Localization Algorithm ◽

Localization Microscopy ◽

Analytical Functions ◽

Open Source Software Package ◽

Microscopy Data ◽

Single Molecule Localization Microscopy

AbstractThe resolution of super-resolution microscopy based on single molecule localization is in part determined by the accuracy of the localization algorithm. In most published approaches to date this localization is done by fitting an analytical function that approximates the point spread function (PSF) of the microscope. However, particularly for localization in 3D, analytical functions such as a Gaussian, which are computationally inexpensive, may not accurately capture the PSF shape leading to reduced fitting accuracy. On the other hand, analytical functions that can accurately capture the PSF shape, such as those based on pupil functions, can be computationally expensive. Here we investigate the use of cubic splines as an alternative fitting approach. We demonstrate that cubic splines can capture the shape of any PSF with high accuracy and that they can be used for fitting the PSF with only a 2-3x increase in computation time as compared to Gaussian fitting. We provide an open-source software package that measures the PSF of any microscope and uses the measured PSF to perform 3D single molecule localization microscopy analysis with reasonable accuracy and speed.

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Minimizing Structural Bias in Single-Molecule Super-Resolution Microscopy

10.1101/293670 ◽

2018 ◽

Author(s):

Hesam Mazidi ◽

Jin Lu ◽

Arye Nehorai ◽

Matthew D. Lew

Keyword(s):

Single Molecule ◽

Super Resolution ◽

Estimation Algorithm ◽

Image Resolution ◽

Spread Function ◽

Localization Errors ◽

Imaging Artifacts ◽

Detection And Localization ◽

Super Resolution Microscopy ◽

Single Molecule Localization Microscopy

ABSTRACTSingle-molecule localization microscopy (SMLM) depends on sequential detection and localization of individual molecular blinking events. Due to the stochasticity of single-molecule blinking and the desire to improve SMLM’s temporal resolution, algorithms capable of analyzing frames with a high density (HD) of active molecules, or molecules whose images overlap, are a prerequisite for accurate location measurements. Thus far, HD algorithms are evaluated using scalar metrics, such as root-mean-square error, that fail to quantify the structure of errors caused by the structure of the sample. Here, we show that the spatial distribution of localization errors within super-resolved images of biological structures are vectorial in nature, leading to systematic structural biases that severely degrade image resolution. We further demonstrate that the shape of the microscope’s point-spread function (PSF) fundamentally affects the characteristics of imaging artifacts. We built a Robust Statistical Estimation algorithm (RoSE) to minimize these biases for arbitrary structures and PSFs. RoSE accomplishes this minimization by estimating the likelihood of blinking events to localize molecules more accurately and eliminate false localizations. Using RoSE, we measure the distance between crossing microtubules, quantify the morphology of and separation between vesicles, and obtain robust recovery using diverse 3D PSFs with unmatched accuracy compared to state-of-the-art algorithms.

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