A mean shift algorithm for drift correction in localization microscopy

Fazekas, F.; Kim, Sumin; Bogucki, Ryan A.; Veatch, Sarah L.

doi:10.1016/j.bpr.2021.100008

Cited by 17 publications

(10 citation statements)

References 39 publications

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“…Alternatively, the positions of the emitters at different time points can be compared with each other. The mean shift of the localizations over time is a measure for the drift, similar to the shift of the maximum position of the cross-correlation images, ( Cnossen et al, 2021 ; Fazekas et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

2022

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Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (∼5–40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a “black box” for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.

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Section: Resultsmentioning

confidence: 99%

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

2022

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“…Sample drift can also introduce a combination of diffusion and directed motion in single-molecule tracking. Indeed, the thermal expansion of instrument components like microscope stages can induce steady motion that masks the biologically relevant diffusive motion (38). We first tested that our method could correctly differentiate directed motion from diffusion (Fig.…”

Section: Characterizing Directed Motionmentioning

confidence: 99%

Detecting directed motion and confinement in single-particle trajectories using hidden variables

Simon,

Ramadier,

Fonquernie

et al. 2024

Preprint

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Single-particle tracking is a powerful tool for understanding protein dynamics and characterizing microenvironments. As the motion of unconstrained nanoscale particles is governed by Brownian diffusion, deviations from this behavior are biophysically insightful. The stochastic nature of particle movement and the presence of localization error, however, pose a challenge for robust classification of non-Brownian motion. Here, we present aTrack, a versatile tool for classifying and extracting key parameters of tracks in Brownian, confined or directed motion. Our tool quickly and accurately determines estimates motion parameters from individual tracks and categorizes its motion state. Further, our tool can analyze populations of tracks and determine the most likely number of motion states. We demonstrate the working range of our approach on simulated tracks and show the application of our tool for characterizing particle motion in cells and biosensing applications. Our tool is implemented as a stand-alone software package, making it simple to analyze track data.

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“…MS was conceptualized to iteratively climb through data points until reaching local density modes (stationary points for the iterative procedure) in the data space (Comaniciu and Meer, 2002;Rao, Martins and Príncipe, 2009). Most MS applications are distinguished by two main features: the search type for local density modes and the application of iterative procedures to find these modes (Comaniciu and Meer, 2002;Barash and Comaniciu, 2004;Hu, Juan and Wang, 2008;Fazekas et al, 2021). These two features define the classical iterative procedure of MS.…”

Section: Mean-shift Super Resolution Microscopymentioning

confidence: 99%

“… 75 , 76 Most MS applications are distinguished by two main features: the search type for local density modes and the application of iterative procedures to find these modes. 76 , 77 , 78 , 79 These two features define the classical iterative procedure of MS. However, unlike the classical iterative procedure of MS, MSSR does not search modes along the data space and computes only the first MS value; this means that MSSR does not require MS iterations on its calculations.…”

Section: Mean‐shift Super‐resolution (Mssr) Microscopymentioning

confidence: 99%

Fluorescence fluctuation‐based super‐resolution microscopy: Basic concepts for an easy start

Alva

Brito‐Alarcón

Linares

et al. 2022

Journal of Microscopy

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Due to the wave nature of light, optical microscopy has a lower-bound lateral resolution limit of about half of the wavelength of the detected light, i.e., within the range of 200 to 300 nm. The Fluorescence Fluctuation based Super Resolution Microscopy (FF-SRM) encompases a collection of image analysis techniques which rely on the statistical processing of temporal variations of fluorescence to reduce the uncertainty about the fluorophore positions within a sample, hence, bringing spatial resolution down to several tens of nm. The FF-SRM is known to be suitable for live-cell imaging due to its compatibility with most fluorescent probes and lower instrumental and experimental requirements, which are mostly camera-based epifluorescence instruments. Each FF-SRM approach has strengths and weaknesses, which depend directly on the underlying statistical principles through which enhanced spatial resolution is achieved. In this review, the basic concepts and principles behind a range of FF-SRM methods published to date are revisited. Their operational parameters are explained and guidance for its selection is provided.

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A mean shift algorithm for drift correction in localization microscopy

Cited by 17 publications

References 39 publications

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Detecting directed motion and confinement in single-particle trajectories using hidden variables

Fluorescence fluctuation‐based super‐resolution microscopy: Basic concepts for an easy start

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