Tracking molecular particles in live cells using fuzzy rule-based system

Single-Virus Tracking: From Imaging Methodologies to Virological Applications

Uncovering the mechanisms of virus infection and assembly is crucial for preventing the spread of viruses and treating viral disease. The technique of single-virus tracking (SVT), also known as single-virus tracing, allows one to follow individual viruses at different parts of their life cycle and thereby provides dynamic insights into fundamental processes of viruses occurring in live cells. SVT is typically based on fluorescence imaging and reveals insights into previously unreported infection mechanisms. In this review article, we provide the readers a broad overview of the SVT technique. We first summarize recent advances in SVT, from the choice of fluorescent labels and labeling strategies to imaging implementation and analytical methodologies. We then describe representative applications in detail to elucidate how SVT serves as a valuable tool in virological research. Finally, we present our perspectives regarding the future possibilities and challenges of SVT.

Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy

Biological studies of intracellular dynamic processes commonly require motion analysis of large numbers of particles in live-cell time-lapse fluorescence microscopy imaging data. Many particle tracking methods have been developed in the past years as a first step toward fully automating this task and enabling high-throughput data processing. Two crucial aspects of any particle tracking method are the detection of relevant particles in the image frames and their linking or association from frame to frame to reconstruct the trajectories. The performance of detection techniques as well as specific combinations of detection and linking techniques for particle tracking have been extensively evaluated in recent studies. Comprehensive evaluations of linking techniques per se, on the other hand, are lacking in the literature. Here we present the results of a quantitative comparison of data association techniques for solving the linking problem in biological particle tracking applications. Nine multiframe and two more traditional two-frame techniques are evaluated as a function of the level of missing and spurious detections in various scenarios. The results indicate that linking techniques are generally more negatively affected by missing detections than by spurious detections. If misdetections can be avoided, there appears to be no need to use sophisticated multiframe linking techniques. However, in the practically likely case of imperfect detections, the latter are a safer choice. Our study provides users and developers with novel information to select the right linking technique for their applications, given a detection technique of known quality.

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify time-lapse imaging data with single-cell precision, notably throughout multiple cell cycles. We developed CAST (Cell Automated Segmentation and Tracking platform) to automatically and robustly detect the position and size of cells or nuclei, quantify the corresponding light signals, while taking into account both cell divisions (lineage tracking) and migration events. We present here how CAST analyzes bioluminescence data from a short-lived transcriptional luciferase reporter. However, our flexible and modular implementation makes it easily adaptable to a wide variety of time-lapse recordings. We exemplify how CAST efficiently quantifies single-cell gene expression over multiple cell cycles using mouse NIH3T3 culture cells with a luminescence expression driven by the Bmal1 promoter, a central gene of the circadian oscillator. We further illustrate how such data can be used to quantify transcriptional bursting in conditions of lengthened circadian period, revealing thereby remarkably similar bursting signature compared to the endogenous circadian condition despite marked period lengthening. In summary, we establish CAST as novel tool for the efficient segmentation, signal quantification, and tracking of time-lapse images from mammalian cell culture.

A PAR-1-dependent orientation gradient of dynamic microtubules directs posterior cargo transport in the Drosophila oocyte

A PAR-1–mediated bias in microtubule organization in the Drosophila oocyte underlies posterior-directed mRNA transport.

Cytoskeletal organization is central to establishing cell polarity in various cellular contexts, including during messenger ribonucleic acid sorting in Drosophila melanogaster oocytes by microtubule (MT)-dependent molecular motors. However, MT organization and dynamics remain controversial in the oocyte. In this paper, we use rapid multichannel live-cell imaging with novel image analysis, tracking, and visualization tools to characterize MT polarity and dynamics while imaging posterior cargo transport. We found that all MTs in the oocyte were highly dynamic and were organized with a biased random polarity that increased toward the posterior. This organization originated through MT nucleation at the oocyte nucleus and cortex, except at the posterior end of the oocyte, where PAR-1 suppressed nucleation. Our findings explain the biased random posterior cargo movements in the oocyte that establish the germline and posterior.

Quantitative comparison of spot detection methods in fluorescence microscopy

Quantitative analysis of biological image data generally involves the detection of many subresolution spots. Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods. We perform experiments on synthetic images of three different types, for which the ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations to compare with. The results from both types of experiments suggest that for very low SNRs ( approximately 2), the supervised (machine learning) methods perform best overall. Of the unsupervised methods, the detectors based on the so-called h -dome transform from mathematical morphology or the multiscale variance-stabilizing transform perform comparably, and have the advantage that they do not require a cumbersome learning stage. At high SNRs ( > 5), the difference in performance of all considered detectors becomes negligible.

Robust single-particle tracking in live-cell time-lapse sequences

Single particle tracking (SPT) is often the rate-limiting step in live cell imaging studies of sub-cellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout the movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long and short-lived endocytic structures, and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.