Improved Analysis for Determining Diffusion Coefficients from Short, Single-Molecule Trajectories with Photoblinking

Shuang, Bo; Byers, Chad P.; Kisley, Lydia; Wang, Lin-Yung; Zhao, Julia Xiaojun; Morimura, Hiroyuki; Link, Stephan; Landes, Christy F.

doi:10.1021/la304063j

Cited by 31 publications

(38 citation statements)

References 47 publications

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“…Overall, the two methods, MLE(1) and MLE(2), were shown to precisely estimate D from short trajectories (<100 time steps) exhibiting intermittency. 80 …”

Section: Methodsmentioning

confidence: 99%

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Troika of single particle tracking programing: SNR enhancement, particle identification, and mapping

Shuang

Chen

Kisley

et al. 2014

Phys. Chem. Chem. Phys.

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single particle tracking (SPT) techniques provide a microscopic approach to probe in vivo and in vitro structure and reactions. Automatic analysis of SPT data with high efficiency and accuracy spurs the development of SPT algorithms. In this perspective, we review a range of available techniques used in SPT analysis programs. In addition, we present an example SPT program step-by-step to provide a guide so that researchers can use, modify, and/or write a SPT program for their own purposes.

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“…Overall, the two methods, MLE(1) and MLE(2), were shown to precisely estimate D from short trajectories (<100 time steps) exhibiting intermittency. 80 …”

Section: Methodsmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Troika of single particle tracking programing: SNR enhancement, particle identification, and mapping

Shuang

Chen

Kisley

et al. 2014

Phys. Chem. Chem. Phys.

Self Cite

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“…Thus to correctly account for the motion-blur, the effective weighting of the dependence of observed position o i on x i and x i+1 changes, and our technique directly incorporates this effect. Trajectory intermittency has been accounted for in prior studies (19) and in those same studies extensions to dynamic errors were suggested, but a convenient computational framework for an estimator that seamlessly factors in both trajectory intermittencies and dynamic error had not been explicitly worked out until now.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the diffusion constant from intermittent trajectories with variable position uncertainties

et al. 2016

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The movement of a particle described by Brownian motion is quantified by a single parameter, D, the diffusion constant. The estimation of D from a discrete sequence of noisy observations is a fundamental problem in biological single-particle tracking experiments since it can provide information on the environment and/or the state of the particle itself via the hydrodynamic radius. Here, we present a method to estimate D that takes into account several effects that occur in practice, important for the correct estimation of D, and that have hitherto not been combined together for an estimation of D. These effects are motion blur from the finite integration time of the camera, intermittent trajectories, and time-dependent localization uncertainty. Our estimation procedure, a maximum-likelihood estimation with an information-based confidence interval, follows directly from the likelihood expression for a discretely observed Brownian trajectory that explicitly includes these effects. We begin with the formulation of the likelihood expression and then present three methods to find the exact solution. Each method has its own advantages in either computational robustness, theoretical insight, or the estimation of hidden variables. The Fisher information for this likelihood distribution is calculated and analyzed to show that localization uncertainties impose a lower bound on the estimation of D. Confidence intervals are established and then used to evaluate our estimator on simulated data with experimentally relevant camera effects to demonstrate the benefit of incorporating variable localization errors.

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“…Two such methods are Particle Image Correlation Spectroscopy (PICS) and Spatio-Temporal Image Correlation Spectroscopy (STICS) [133–135]. The former measures diffusion coefficients based on correlation of sequential particle localization maps, while the latter forgoes single-molecule localization and determines diffusion coefficients by correlating the raw data of the imaging frames themselves.…”

Section: Analysis Methodsmentioning

confidence: 99%

Imaging Live Cells at the Nanometer-Scale with Single-Molecule Microscopy: Obstacles and Achievements in Experiment Optimization for Microbiology

et al. 2014

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Single-molecule fluorescence microscopy enables biological investigations inside living cells to achieve millisecond- and nanometer-scale resolution. Although single-molecule-based methods are becoming increasingly accessible to non-experts, optimizing new single-molecule experiments can be challenging, in particular when super-resolution imaging and tracking are applied to live cells. In this review, we summarize common obstacles to live-cell single-molecule microscopy and describe the methods we have developed and applied to overcome these challenges in live bacteria. We examine the choice of fluorophore and labeling scheme, approaches to achieving single-molecule levels of fluorescence, considerations for maintaining cell viability, and strategies for detecting single-molecule signals in the presence of noise and sample drift. We also discuss methods for analyzing single-molecule trajectories and the challenges presented by the finite size of a bacterial cell and the curvature of the bacterial membrane.

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Improved Analysis for Determining Diffusion Coefficients from Short, Single-Molecule Trajectories with Photoblinking

Cited by 31 publications

References 47 publications

Troika of single particle tracking programing: SNR enhancement, particle identification, and mapping

Troika of single particle tracking programing: SNR enhancement, particle identification, and mapping

Estimation of the diffusion constant from intermittent trajectories with variable position uncertainties

Imaging Live Cells at the Nanometer-Scale with Single-Molecule Microscopy: Obstacles and Achievements in Experiment Optimization for Microbiology

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