Advanced microscopy techniques have opened new opportunities for biomedical research. Fluorescence microscopy enables researchers to observe subcellular structures with specific labeling. Quantitatively measuring the dynamics of intracellular objects is essential to understand the underlying regulatory mechanism. Protein-containing vesicles in cell are involved in various biological processes, such as material transportation, organelle interaction and hormonal regulation, whose dynamic characteristics are significant to disease diagnosis and drug screening. Although there have been some algorithms developed for vesicle tracking, most of them have limited performance when dealing with images with low resolution, poor signal-to-noise ratio (SNR) and complicated motion. In this article, we proposed a deep learning-based method for intracellular vesicle tracking. We trained the U-Net for vesicle localization and motion classification on the simulated datasets, which demonstrated high accuracy. We profoundly improved the performance of particle tracking using motion classification, and quantified the dynamic characteristics of intracellular vesicles according to the tracking results with satisfying outcomes. We anticipate that this novel method would have vast applications in analyzing the dynamics in living cell.
KEYWORDS: Particles, Signal to noise ratio, Detection and tracking algorithms, Image processing, Microscopy, Algorithm development, Motion models, Point spread functions
With the development of super-resolution fluorescence microscopy, complex dynamic processes in living cells can be observed and recorded with unprecedented temporal and spatial resolution. Single particle tracking is the most important step to explore the relationship between the spatio-temporal dynamics of subcellular molecules and their functions. Although previous studies have developed single particle tracking algorithms to quantitatively analyze particle dynamics in cell, traditional tracking methods have poor performance when dealing with intersecting trajectories. This can be attributed to two main reasons: 1) They do not have point compensation process for overlapping points; 2) They use inefficient motion prediction models. In this paper, we presented a novel Fan-shaped Tracker (FsT) algorithm to reconstruct the trajectories of subcellular molecules in living cells. We proposed a customized point compensation method for overlapping points based on the fan-shape motion trend of the particles to solve the merging trajectory problem. Furthermore, we compared the performance of our Fan-shaped Tracker with five state-of-the-art tracking algorithms in simulated time-lapse movies with variable imaging quality. Our results showed that the Fan-shaped Tracker achieves better performance than other reported methods as we systematically evaluated using a set of standard evaluation parameters. We anticipate that our FsT method will have vast applications in tracking of moving objects in cell.
Total internal reflection fluorescence microscopy (TIRFM) has been widely used in biomedical research to visualize cellular processes near the cell surface. In this study, a novel multi-angle ring-illuminated TIRFM system, equipped with two galvo mirrors that are on conjugate plan of a 4f optical system was developed. Multi-angle TIRFM generates images with different penetration depths through the controlled variation of the incident angle of illuminating laser. We presented a method to perform three-dimensional (3-D) reconstruction of microtubules from multi-angle TIRFM images. The performance of our method was validated in simulated microtubules with variable signal-to-noise ratios (SNR) and the axial resolution and accuracy of reconstruction were evaluated in selecting different numbers of illumination angles or in different SNR conditions. In U373 cells, we reconstructed the 3-D localization of microtubules near the cell surface with high resolution using over a hundred different illumination angles. Theoretically, the presented TIRFM setup and 3-D reconstruction method can achieve ∼40 nm axial resolution in experimental conditions where SNR is as low as 2, with ∼35 different illumination angles. Moreover, our system and reconstruction method have the potential to be used in live cells to track membrane dynamics in 3-D.
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