Recent advances in optical microscopic methods for single-particle tracking in biological samples

Ma, Yuanyuan; Wang, Xiao; Liu, Hua; Wei, Lin; Xiao, Lehui

doi:10.1007/s00216-019-01638-z

Cited by 37 publications

(32 citation statements)

References 143 publications

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“…As advanced microscopes nowadays enable fully automated acquisition of time-lapse images of living cells and intracellular particles, this calls for robust computational methods capable of not only detecting and segmenting objects, but also tracking them over time in these images. Object tracking is generally considered to be “one of the most challenging computer vision problems” [231] and is a common task also in bioimage analysis [22] , [232] , [233] , [234] , [235] . The first international competition of particle tracking methods was held before deep learning broke through, but already led researchers to suggest the development of learning-based tracking methods [21] .…”

Section: Deep Learning For Bioimage Analysismentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

115

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Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

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Section: Deep Learning For Bioimage Analysismentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

115

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“…After running CD, the original data can be segmented into windows of maximal length, each of which has a fixed model. In Stage 3, the constant parameter EM algorithm in Equations (2) and 3is then run on each segment independently to determine the final estimates.…”

Section: Stage 3: Final Estimation and The Complete Algorithmmentioning

confidence: 99%

“…Single Particle Tracking (SPT) is a class of experimental techniques and mathematical algorithms for following sub diffraction-limit sized particles moving inside living cells, including viruses, proteins, and strands of RNA [ 1 , 2 , 3 ]. Particles of this size cannot be resolved with standard optical microscopy, irrespective of the magnification.…”

Section: Introductionmentioning

confidence: 99%

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

2021

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Single Particle Tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein–Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use Change Detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

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“…Optical microscopy is probably the simplest and oldest among the microscopy techniques [88]. It is a two-dimensional imaging technique and since its time of inception, there have been incessant research upgrades to suit modern needs [89][90][91][92]. A simple optical microscope comprises of two converging lenses, an objective, and an eyepiece and utilizes the optical theory of lenses to operate, wherein light emerging from the sample will be collected by the objective and directed towards the eyepiece [93].…”

Section: Optical Microscopymentioning

confidence: 99%

Microscopic Techniques for the Analysis of Micro and Nanostructures of Biopolymers and Their Derivatives

et al. 2020

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Natural biopolymers, a class of materials extracted from renewable sources, is garnering interest due to growing concerns over environmental safety; biopolymers have the advantage of biocompatibility and biodegradability, an imperative requirement. The synthesis of nanoparticles and nanofibers from biopolymers provides a green platform relative to the conventional methods that use hazardous chemicals. However, it is challenging to characterize these nanoparticles and fibers due to the variation in size, shape, and morphology. In order to evaluate these properties, microscopic techniques such as optical microscopy, atomic force microscopy (AFM), and transmission electron microscopy (TEM) are essential. With the advent of new biopolymer systems, it is necessary to obtain insights into the fundamental structures of these systems to determine their structural, physical, and morphological properties, which play a vital role in defining their performance and applications. Microscopic techniques perform a decisive role in revealing intricate details, which assists in the appraisal of microstructure, surface morphology, chemical composition, and interfacial properties. This review highlights the significance of various microscopic techniques incorporating the literature details that help characterize biopolymers and their derivatives.

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Recent advances in optical microscopic methods for single-particle tracking in biological samples

Cited by 37 publications

References 143 publications

A bird’s-eye view of deep learning in bioimage analysis

A bird’s-eye view of deep learning in bioimage analysis

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

Microscopic Techniques for the Analysis of Micro and Nanostructures of Biopolymers and Their Derivatives

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