Quantitative Comparison of Algorithms for Tracking Single Fluorescent Particles

Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908, USA.
Biophysical Journal (Impact Factor: 3.97). 11/2001; 81(4):2378-88. DOI: 10.1016/S0006-3495(01)75884-5
Source: PubMed


Single particle tracking has seen numerous applications in biophysics, ranging from the diffusion of proteins in cell membranes to the movement of molecular motors. A plethora of computer algorithms have been developed to monitor the sub-pixel displacement of fluorescent objects between successive video frames, and some have been claimed to have "nanometer" resolution. To date, there has been no rigorous comparison of these algorithms under realistic conditions. In this paper, we quantitatively compare specific implementations of four commonly used tracking algorithms: cross-correlation, sum-absolute difference, centroid, and direct Gaussian fit. Images of fluorescent objects ranging in size from point sources to 5 microm were computer generated with known sub-pixel displacements. Realistic noise was added and the above four algorithms were compared for accuracy and precision. We found that cross-correlation is the most accurate algorithm for large particles. However, for point sources, direct Gaussian fit to the intensity distribution is the superior algorithm in terms of both accuracy and precision, and is the most robust at low signal-to-noise. Most significantly, all four algorithms fail as the signal-to-noise ratio approaches 4. We judge direct Gaussian fit to be the best algorithm when tracking single fluorophores, where the signal-to-noise is frequently near 4.

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    • ", y 0 ) are the fluorescence intensity and the position of the fluorescing center, respectively, σ is the radial standard deviation of the Gaussian function, and C is the background fluorescence. This analysis can be used to measure the center position of the image (Kubitscheck et al., 2000; Cheezum et al., 2001; Thompson et al., 2002; Small and Stahlheber, 2014). Though there are other common methods for determining the center, including cross-correlation, sum-absolute difference, and simple centroid, Gaussian fitting has the highest robustness at low signal-to-noise ratios, which is common in biological studies (Thompson et al., 2002). "
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    ABSTRACT: Over the past decade, great developments in optical microscopy have made this technology increasingly compatible with biological studies. Fluorescence microscopy has especially contributed to investigating the dynamic behaviors of live specimens and can now resolve objects with nanometer precision and resolution due to super-resolution imaging. Additionally, single particle tracking provides information on the dynamics of individual proteins at the nanometer scale both in vitro and in cells. Complementing advances in microscopy technologies has been the development of fluorescent probes. The quantum dot, a semi-conductor fluorescent nanoparticle, is particularly suitable for single particle tracking and super-resolution imaging. This article overviews the principles of single particle tracking and super resolution along with describing their application to the nanometer measurement/observation of biological systems when combined with quantum dot technologies.
    Frontiers in Physiology 07/2014; 5:273. DOI:10.3389/fphys.2014.00273 · 3.53 Impact Factor
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    • "The shape of this intensity profile is known as the point spread function (PSF) and can, in many cases, be well approximated by a Gaussian function [26]. This allows the exact position of an isolated molecule to be determined with much higher precision than the size of the PSF by fitting the image to a Gaussian mask (Fig. 1A I) [27]. The uncertainty of the fitted position depends mainly on the number of photons collected [26,28]; for typical single-molecule experiments using fluorescent proteins, the uncertainty is between 10 and 50 nm. "
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    ABSTRACT: In vivo single-molecule experiments offer new perspectives on the behaviour of DNA binding proteins, from the molecular level to the length scale of whole bacterial cells. With technological advances in instrumentation and data analysis, fluorescence microscopy can detect single-molecules in live cells, opening the doors to directly follow individual proteins binding to DNA in real time. In this review, we describe key technical considerations for implementing in vivo single-molecule fluorescence microscopy. We discuss how single-molecule tracking and quantitative super-resolution microscopy can be adapted to extract DNA binding kinetics, spatial distributions, and copy numbers of proteins, as well as stoichiometries of protein complexes. We highlight experiments which have exploited these techniques to answer important questions in the field of bacterial gene regulation and transcription, as well as chromosome replication, organisation and repair. Together, these studies demonstrate how single-molecule imaging is transforming our understanding of DNA-binding proteins in cells.
    FEBS Letters 05/2014; 588(19). DOI:10.1016/j.febslet.2014.05.026 · 3.17 Impact Factor
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    • "A common and established approach for extracting this type of information consists in individually tracking particles in fluorescence video-microscopy. The most widespread tracking concept is the correspondence approach [1], [2] which consists in detecting particles independently in each frame and then associating the detected objects over time. The object association is particularly difficult when the density of particles is high, their appearance is similar and their trajectories interact. "
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    ABSTRACT: Automatic analysis of the dynamic content in fluorescence video-microscopy is crucial for understanding molecular mechanisms involved in cell functions. In this paper, we propose an original approach for analyzing particle trafficking in these sequences. Instead of individually tracking every particle, we estimate the particle flows between predefined regions. This approach allows us to process image sequences with a high number of particles and a low frame rate. We investigate several ways to estimate the particle flow at the cellular level and evaluate their performance in synthetic and real image sequences.
    2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014); 04/2014
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