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Data Fusion and Smoothing for Probabilistic Tracking of Viral Structures in Fluorescence Microscopy Images
Abstract
Automatic tracking of viral structures displayed as small spots in fluorescence microscopy images is an important task to determine quantitative information about cellular processes. We introduce a novel probabilistic approach for tracking multiple particles based on multi-sensor data fusion and Bayesian smoothing methods. The approach exploits multiple measurements as in a particle filter, both detection-based measurements and prediction-based measurements from a Kalman filter using probabilistic data association with elliptical sampling. Compared to previous probabilistic tracking methods, our approach exploits separate uncertainties for the detection-based and prediction-based measurements, and integrates them by a sequential multi-sensor data fusion method. In addition, information from both past and future time points is taken into account by a Bayesian smoothing method in conjunction with the covariance intersection algorithm for data fusion. Also, motion information based on displacements is used to improve correspondence finding. Our approach has been evaluated on data of the Particle Tracking Challenge and yielded state-of-the-art results or outperformed previous approaches. We also applied our approach to challenging time-lapse fluorescence microscopy data of human immunodeficiency virus type 1 and hepatitis C virus proteins acquired with different types of microscopes and spatial-temporal resolutions. It turned out, that our approach outperforms existing methods.
... However, tracking these movements was laborious and challenging due to the large number of small vesicles, their low signal to noise ratio compared to the much brighter Golgi and their dispersed origins from Golgi mini-stacks throughout the cytoplasm (Fig 3A). Many different tracking algorithms have been developed in recent years that use deep learning (DL) methods to enhance target detection and object tracking [34][35][36][37][38][39][40][41][42][43][44][45][46][47]. However, these machine learning methods usually require an extensive training dataset that must be updated for each new experimental system, which can be impractical and time consuming, some were developed for particular imaging techniques, while others perform poorly in a noisy environment. ...
Understanding how specific secretory cargoes are targeted to distinct domains of the plasma membrane in epithelial cells requires analyzing the trafficking of post-Golgi vesicles to their sites of secretion. We used the RUSH (retention using selective hooks) system to synchronously release an apical cargo, Cadherin 99C (Cad99C), and a basolateral cargo, the ECM protein Nidogen, from the endoplasmic reticulum and followed their movements to the plasma membrane. We also developed an interactive vesicle tracking framework, MSP-tracker and viewer, that exploits developments in computer vision and deep learning to determine vesicle trajectories in a noisy environment without the need for extensive training data. MSP-tracker outperformed other tracking software in detecting and tracking post-Golgi vesicles, revealing that Cad99c vesicles predominantly move apically with a mean speed of 1.1µm/sec. This is reduced to 0.85 µm/sec by a dominant slow dynein mutant, demonstrating that dynein transports Cad99C vesicles to the apical cortex. Furthermore, both the dynein mutant and microtubule depolymerization cause lateral Cad99C secretion. Thus, microtubule organization plays a central role in targeting apical secretion, suggesting that Drosophila does not have distinct apical versus basolateral vesicle fusion machinery. Nidogen vesicles undergo planar-polarized transport to the leading edge of follicle cells as they migrate over the ECM, whereas most Collagen is secreted at trailing edges. The follicle cells therefore bias secretion of different ECM components to opposite sides of the cell, revealing that the secretory pathway is more spatially organized than previously thought.
... The 3D chromatin structures within cell nuclei were tracked in the registered 3D live-cell fluorescence microscopy image sequences using a probabilistic particle tracking method (34). The method combines Kalman filtering with particle filtering and uses sequential multi-sensor data fusion and separate sensor models to integrate multiple measurements. ...
Chromatin dynamics play a crucial role in cellular differentiation, yet tools for studying global chromatin mobility in living cells remain limited. Here, we developed a novel probe for the metabolic labeling of chromatin and tracking its mobility during neural differentiation. The labeling system utilizes a newly developed silicon rhodamine-conjugated deoxycytidine triphosphate (dCSiRTP). We show that this dCTP is efficiently delivered into living human induced pluripotent stem cells (iPSCs) and neural stem cells (NSCs) via a synthetic transporter (SNTT1). Using correlative confocal microscopy and stimulated emission depletion (STED) super-resolution microscopy, we quantified the sizes of labeled chromatin domains. Time lapse super-resolution microscopy combined with single particle tracking revealed that chromatin mobility decreases during the transition from iPSCs (pluripotent state) to NSCs and neurons (differentiated state). This reduction in mobility correlates with the differentiation state, suggesting a role for chromatin dynamics in cellular plasticity. Concomitant mechanistic insights obtained from MNase digestion assays, chromatin compaction and histone modification analyses revealed a decrease in chromatin accessibility during neuronal differentiation, indicating that chromatin adopts a more constrained and compacted structure. These findings provide new insights into chromatin regulation during neurogenesis.
... In this paper, we developed an approximated Gaussian integral-based smoothing algorithm for the nonlinear stochastic state space models. The Smoothing algorithms find application in various areas like target tracking [1], navigation [2], robotics [3], image analysis [4], statistical modelling [5], and finances [6]. Here, we consider a discrete-time stochastic state space model in the following form [7] x k =f (x k−1 ) + µ k−1 , ...
This work introduces the Gaussian integration to address a smoothing problem of a nonlinear stochastic state space model. The probability densities of states at each time instant are assumed to be Gaussian, and their means and covariances are evaluated by utilizing the odd-even properties of Gaussian integral, which are further utilized to realize Rauch-Tung-Striebel (RTS) smoothing expressions. Given that the Gaussian integration provides an exact solution for the integral of a polynomial function over a Gaussian probability density function, it is anticipated to provide more accurate results than other existing Gaussian approximation-based smoothers such as extended Kalman, cubature Kalman, and unscented Kalman smoothers, especially when polynomial types of nonlinearity are present in the state space models. The developed smoothing algorithm is applied to the Van der Pol oscillator, where the nonlinearity associated with their dynamics is represented using polynomial functions. Simulation results are provided to demonstrate the superiority of the proposed algorithm.
... Three-dimensional chromatin structures were tracked in 3D (x, y, z) within single cell nuclei in 3D live-cell fluorescence microscopy images to determine the motility. A probabilistic particle tracking method was used to determine the movement of multiple fluorescently labeled 3D chromatin structures (Ritter et al. 2021). The method is based on Bayesian filtering and multi-sensor data fusion and combines Kalman filtering with particle filtering. ...
The dynamics of DNA in the cell nucleus plays a role in cellular processes and fates but the interplay of DNA mobility with the hierarchical levels of DNA organization is still underexplored. Here, we made use of DNA replication to directly label genomic DNA in an unbiased genome-wide manner. This was followed by live-cell time-lapse microscopy of the labeled DNA combining imaging at different resolutions levels simultaneously and allowing one to trace DNA motion across organization levels within the same cells. Quantification of the labeled DNA segments at different microscopic resolution levels revealed sizes comparable to the ones reported for DNA loops using 3D super-resolution microscopy, topologically associated domains (TAD) using 3D widefield microscopy, and also entire chromosomes. By employing advanced chromatin tracking and image registration, we discovered that DNA exhibited higher mobility at the individual loop level compared to the TAD level and even less at the chromosome level. Additionally, our findings indicate that chromatin movement, regardless of the resolution, slowed down during the S phase of the cell cycle compared to the G1/G2 phases. Furthermore, we found that a fraction of DNA loops and TADs exhibited directed movement with the majority depicting constrained movement. Our data also indicated spatial mobility differences with DNA loops and TADs at the nuclear periphery and the nuclear interior exhibiting lower velocity and radius of gyration than the intermediate locations. On the basis of these insights, we propose that there is a link between DNA mobility and its organizational structure including spatial distribution, which impacts cellular processes.
... The background image intensity was adjusted for each image sequence to the computed mean intensity value over all time points within a manually selected region of interest (ROI) of the background. Automatic tracking of multiple fluorescently labeled chromatin structures was performed using a probabilistic particle tracking approach, which is based on Bayesian filtering and multi-sensor data fusion (Ritter et al., 2021). This approach combines Kalman filtering with particle filtering and integrates multiple measurements by separate sensor models and sequential multi-sensor data fusion. ...
Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged PCNA, a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.– Direct and sequence unbiased labeling of DNA genome-wide– DNA labeled chromatin is more mobile in G1/G2 relative to the S-phase– Restriction of chromatin motion occurs proximal to sites of DNA replication– Loading of replisomes, even in the absence of processive DNA synthesis, restricts chromatin motion
... This can be performed for the whole FoV as showcased so far, but can also be performed in a sub-FoV manner to spatially assess the distribution of multiple or mixed populations with ∼250 nm resolution or lower (Supplementary Note 10). TARDIS can be used on its own, or alternatively, the output of TARDIS can be used as accurate a priori information in tracking software, especially Bayesian SPT software (for example, swift, MHT 25 or others 49,50 ). Via this combination (Figs. ...
In single-particle tracking, individual particles are localized and tracked over time to probe their diffusion and molecular interactions. Temporal crossing of trajectories, blinking particles, and false-positive localizations present computational challenges that have remained difficult to overcome. Here we introduce a robust, parameter-free alternative to single-particle tracking: temporal analysis of relative distances (TARDIS). In TARDIS, an all-to-all distance analysis between localizations is performed with increasing temporal shifts. These pairwise distances represent either intraparticle distances originating from the same particle, or interparticle distances originating from unrelated particles, and are fitted analytically to obtain quantitative measures on particle dynamics. We showcase that TARDIS outperforms tracking algorithms, benchmarked on simulated and experimental data of varying complexity. We further show that TARDIS performs accurately in complex conditions characterized by high particle density, strong emitter blinking or false-positive localizations, and is in fact limited by the capabilities of localization algorithms. TARDIS’ robustness enables fivefold shorter measurements without loss of information.
... Features based on[11] use the keypoint movements to identify temporal patterns like directed or undirected motion. ...
We present a human-in-the-loop system for efficient rodent behavior analysis in drug development. Addressing the time-consuming and labor-intensive nature of manual behavior categorization, this UX-optimized platform integrates AI for complex behavior prediction and active learning to identify rare events. The proposed solution leverages a cloud-native data processing pipeline, AI-based novelty behavior recognition and multi-class classification, demonstrating significant improvements in behavior labeling and discovery.
... The background image intensity was adjusted for each image sequence to the computed mean intensity value over all time points within a manually selected region of interest (ROI) of the background. Automatic tracking of multiple fluorescently labeled chromatin structures was performed using a probabilistic particle tracking approach, which is based on Bayesian filtering and multi-sensor data fusion (Ritter et al., 2021). This approach combines Kalman filtering with particle filtering and integrates multiple measurements by separate sensor models and sequential multi-sensor data fusion. ...
Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged proliferating cell nuclear antigen (PCNA), a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases in tumor as well as normal diploid cells using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.
... The background image intensity was adjusted for each image sequence to the computed mean intensity value over all time points within a manually selected region of interest (ROI) of the background. Automatic tracking of multiple fluorescently labeled chromatin structures was performed using a probabilistic particle tracking approach, which is based on Bayesian filtering and multi-sensor data fusion (Ritter et al., 2021 ). This approach combines Kalman filtering with particle filtering and integrates multiple measurements by separate sensor models and sequential multi-sensor data fusion. ...
Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged PCNA, a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases in tumor as well as normal diploid cells using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.
Direct and sequence unbiased labeling of DNA genome-wide
DNA labeled chromatin is more mobile in G1/G2 relative to the S-phase
Restriction of chromatin motion occurs proximal to sites of DNA replication
Loading of replisomes, even in the absence of processive DNA synthesis, restricts chromatin motion
... Three-dimensional chromatin structures were tracked in 3D (x, y, z) within single cell nuclei in 3D live-cell fluorescence microscopy images to determine the motility. A probabilistic particle tracking method was used to determine the movement of multiple fluorescently labeled 3D chromatin structures (Ritter et al. 2021). The method is based on Bayesian filtering and multi-sensor data fusion and combines Kalman filtering with particle filtering. ...
Chromatin has been shown to undergo diffusional motion, which is affected during gene transcription by RNA polymerase activity. However, the relationship between chromatin mobility and other genomic processes remains unclear. Hence, we set out to label the DNA directly in a sequence unbiased manner and followed labeled chromatin dynamics in interphase human cells expressing GFP-tagged PCNA, a cell cycle marker and core component of the DNA replication machinery. We detected decreased chromatin mobility during the S-phase compared to G1 and G2 phases in tumor as well as normal diploid cells using automated particle tracking. To gain insight into the dynamical organization of the genome during DNA replication, we determined labeled chromatin domain sizes and analyzed their motion in replicating cells. By correlating chromatin mobility proximal to the active sites of DNA synthesis, we showed that chromatin motion was locally constrained at the sites of DNA replication. Furthermore, inhibiting DNA synthesis led to increased loading of DNA polymerases. This was accompanied by accumulation of the single-stranded DNA binding protein on the chromatin and activation of DNA helicases further restricting local chromatin motion. We, therefore, propose that it is the loading of replisomes but not their catalytic activity that reduces the dynamics of replicating chromatin segments in the S-phase as well as their accessibility and probability of interactions with other genomic regions.
Direct and sequence unbiased labeling of DNA genome-wide
DNA labeled chromatin is more mobile in G1/G2 relative to the S-phase
Restriction of chromatin motion occurs proximal to sites of DNA replication
Loading of replisomes, even in the absence of processive DNA synthesis, restricts chromatin motion
... Automated particle tracking in fluorescence microscopy images. Particle tracking in fluorescence microscopy images was performed by using a probabilistic particle tracking approach that is based on Bayesian filtering and multi-sensor data fusion [117]. This approach combines Kalman filtering and particle filtering and integrates multiple measurements by separate sensor models as well as sequential multi-sensor data fusion. ...
Liver-generated plasma Apolipoprotein E (ApoE)-containing lipoproteins (LPs) (ApoE-LPs) play central roles in lipid transport and metabolism. Perturbations of ApoE can result in several metabolic disorders and ApoE genotypes have been associated with multiple diseases. ApoE is synthesized at the endoplasmic reticulum and transported to the Golgi apparatus for LP assembly; however, the ApoE-LPs transport pathway from there to the plasma membrane is largely unknown. Here, we established an integrative imaging approach based on a fully functional fluorescently tagged ApoE. We found that newly synthesized ApoE-LPs accumulate in CD63-positive endosomes of hepatocytes. In addition, we observed the co-egress of ApoE-LPs and CD63-positive intraluminal vesicles (ILVs), which are precursors of extracellular vesicles (EVs), along the late endosomal trafficking route in a microtubule-dependent manner. A fraction of ApoE-LPs associated with CD63-positive EVs appears to be co-transmitted from cell to cell. Given the important role of ApoE in viral infections, we employed as well-studied model the hepatitis C virus (HCV) and found that the viral replicase component nonstructural protein 5A (NS5A) is enriched in ApoE-containing ILVs. Interaction between NS5A and ApoE is required for the efficient release of ILVs containing HCV RNA. These vesicles are transported along the endosomal ApoE egress pathway. Taken together, our data argue for endosomal egress and transmission of hepatic ApoE-LPs, a pathway that is hijacked by HCV. Given the more general role of EV-mediated cell-to-cell communication, these insights provide new starting points for research into the pathophysiology of ApoE-related metabolic and infection-related disorders.
Influenza A viruses (IAV) display a broad variety of morphologies ranging from spherical to long filamentous virus particles. These diverse phenotypes are believed to allow the virus to overcome various immunological and pulmonary barriers during entry into the airway epithelium and influence the viral entry pathway. Remarkably, lab-adapted IAV strains lost this morphological variance and exhibit preferred spherical morphology. However, it remains unclear which factors lead to this lab-adapted preference and which pulmonary defense factors are responsible for the preferred filamentous morphology in physiological settings. In this study, we established fluorescent reporter viruses with spherical or filamentous morphology but with the same surface glycoproteins. We developed a correlative fluorescence and scanning electron microscopy workflow to analyze the impact of viral morphology on cell-to-cell spread and identify conditions under which IAV with either spherical or filamentous morphology confer an advantage. Our findings demonstrate that filamentous IAV cell-to-cell spread is significantly slower in various cell lines, which can explain the predominant spherical morphology in lab-adapted strains. This observation is consistent with delayed entry kinetics of filamentous viruses structurally analyzed by cellular cryo-electron tomography. We found that cellular junction integrity and mucin do not exert morphology-dependent inhibition of IAV cell-to-cell spread. On the other hand, filamentous virions confer an advantage under the pressure exerted by neutralizing antibodies against hemagglutinin.
We describe u-track3D, a software package that extends the versatile u-track framework established in 2D to address the specific challenges of 3D particle tracking. First, we present the performance of the new package in quantifying a variety of intracellular dynamics imaged by multiple 3D microcopy platforms and on the standard 3D test dataset of the particle tracking challenge. These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability that automatically defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track3D provides a complete framework for unbiased studies of molecular processes in complex volumetric sequences.
Liver-generated plasma Apolipoprotein E (ApoE)-containing lipoproteins (LPs) (ApoE-LPs) play central roles in lipid transport and metabolism. Perturbations of ApoE can result in several metabolic disorders and ApoE genotypes have been associated with multiple diseases. ApoE is synthesized at the endoplasmic reticulum and transported to the Golgi apparatus for LP assembly; however, ApoE-LPs transport from there to the plasma membrane is largely unknown. Here, we established an integrative imaging approach based on a fully functional fluorescently tagged ApoE. We found that ApoE-LPs accumulate in CD63-positive endosomes of hepatocytes. In addition, we observed the co-egress of ApoE-LPs and extracellular vesicles (EVs) along the late endosomal trafficking route. Moreover, complexes of ApoE-LPs and CD63-positive EVs were found to be transmitted from cell to cell. Given the important role of ApoE in viral infections, we studied the hepatitis C virus (HCV) and found that the viral replicase protein NS5A is enriched in ApoE-containing intraluminal vesicles. Interaction between NS5A and ApoE is required for the efficient release of EVs containing viral RNA. These vesicles are transported along the endosomal ApoE egress pathway. Taken together, our data argue for endosomal egress and transmission of hepatic ApoE-LPs, a pathway that is hijacked by HCV. Given the more general role of EV-mediated cell-to-cell communication, these insights provide new starting points for research into the pathophysiology of ApoE-related metabolic and infection-related disorders.
Author Summary
The post-Golgi egress pathway of hepatocyte-derived ApoE-containing lipoproteins (ApoE-LPs) is largely unknown. By using integrative imaging analyses, we show that ApoE-LPs are enriched in CD63-positive endosomes suggesting that these endosomes might be a central hub for the storage of ApoE-LPs from which they are released into the circulation. In addition, we provide evidence for the co-egress of ApoE-LPs with extracellular vesicles (EVs) along the late endosomal route and their transfer from cell to cell. This pathway is hijacked by the hepatitis C virus that induces the production of ApoE-associated EVs containing viral RNA. Given the important role of ApoE in multiple metabolic, degenerative and infectious diseases, and the role of EVs in cell-to-cell communication, these results provide important information how perturbations of ApoE might contribute to various pathophysiologies.
Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy (R2=0.92) the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein.
Statement of significance
We present a physics-informed machine learning architecture that can predict macroscopic mechanical properties of arterial tissue with high accuracy (R2=0.92) from the tissue microstructure (characterized by imaging data). For the first time, this architecture enables also a fully automatic and largely unbiased quantification of the relevance of different microstructural features (such as collagen volume fraction and fiber straightness) for the macroscopic mechanical properties. This approach opens up unprecedented ways to predictive mechanical modeling of soft biological tissues. Moreover, it provides quantitative insights into the relation between tissue microstructure and its macroscopic properties that promise to play an important role in future tissue engineering.
The hepatitis C virus (HCV) is a major cause of chronic liver disease, affecting around 71 million people worldwide. Viral RNA replication occurs in a membranous compartment composed of double-membrane vesicles (DMVs), whereas virus particles are thought to form by budding into the endoplasmic reticulum (ER). It is unknown how these steps are orchestrated in space and time. Here, we established an imaging system to visualize HCV structural and replicase proteins in live cells and with high resolution. We determined the conditions for the recruitment of viral proteins to putative assembly sites and studied the dynamics of this event and the underlying ultrastructure. Most notable was the selective recruitment of ER membranes around lipid droplets where structural proteins and the viral replicase colocalize. Moreover, ER membranes wrapping lipid droplets were decorated with double membrane vesicles, providing a topological map of how HCV might coordinate the steps of viral replication and virion assembly.
We present an overview of diffusion models commonly used for quantifying the dynamics of intracellular particles (e.g. biomolecules) inside eukaryotic living cells. It is established that inference on the modes of mobility of molecules is central in cell biology since it reflects interactions between structures and determines functions of biomolecules in the cell. In that context, Brownian motion is a key component in short distance transportation (e.g. connectivity for signal transduction). Another dynamical process that has been heavily studied in the past decade is the motor-mediated transport (e.g. dynein, kinesin and myosin) of molecules. Primarily supported by actin filament and microtubule network, it ensures spatial organization and temporal synchronization in the intracellular mechanisms and structures. Nevertheless, the complexity of internal structures and molecular processes in the living cell influence the molecular dynamics and prevent the systematic application of pure Brownian or directed motion modeling. On the one hand, cytoskeleton density will hinder the free displacement of the particle, a phenomenon called subdiffusion. On the other hand, the cytoskeleton elasticity combined with thermal bending can contribute a phenomenon called superdiffusion. This paper discusses the basics of diffusion modes observed in eukariotic cells, by introducing the essential properties of these processes. Applications of diffusion models include protein trafficking and transport and membrane diffusion.
Pathogens face varying microenvironments in vivo, but suitable experimental systems and analysis tools to dissect how three-dimensional (3D) tissue environments impact pathogen spread are lacking. Here we develop an Integrative method to Study Pathogen spread by Experiment and Computation within Tissue-like 3D cultures (INSPECT-3D), combining quantification of pathogen replication with imaging to study single-cell and cell population dynamics. We apply INSPECT-3D to analyze HIV-1 spread between primary human CD4 T-lymphocytes using collagen as tissue-like 3D-scaffold. Measurements of virus replication, infectivity, diffusion, cellular motility and interactions are combined by mathematical analyses into an integrated spatial infection model to estimate parameters governing HIV-1 spread. This reveals that environmental restrictions limit infection by cell-free virions but promote cell-associated HIV-1 transmission. Experimental validation identifies cell motility and density as essential determinants of efficacy and mode of HIV-1 spread in 3D. INSPECT-3D represents an adaptable method for quantitative time-resolved analyses of 3D pathogen spread.
We present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools.
One of the key challenges in distributed linear estimation is the systematic fusion of estimates. While the fusion gains that minimize the mean squared error of the fused estimate for known correlations have been established, no analogous statement could be obtained so far for unknown correlations. In this contribution, we derive the gains that minimize the bound on the true covariance of the fused estimate and prove that Covariance Intersection (CI) is the optimal bounding algorithm for two estimates under completely unknown correlations. When combining three or more variables, the CI equations are not necessarily optimal, as shown by a counterexample.
Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to re-calculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2D and 3D images as well as to real 2D and 3D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2D and 3D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.
Data fusion is an important process in a variety of tasks in the intelligent transportation systems field. Most existing data fusion methods rely on the assumption of conditional independence or known statistics of data correlation. In contrast, the split covariance intersection filter (split CIF) was heuristically presented in literature, which aims at providing a mechanism to reasonably handle both known independent information and unknown correlated information in source data. In this paper, we provide a theoretical foundation for the split CIF. First, we clearly specify the consistency definition (coined as split consistency) for estimates in split form. Second, we provide a theoretical proof for the fusion consistency of the split CIF. Finally, we provide a theoretical derivation of the split CIF for the partial observation case. We also present a general architecture of decentralized vehicle localization, which serves as a concrete application example of the split CIF to demonstrate the advantages of the split CIF and how it can potentially benefit vehicle localization (noncooperative and cooperative). In general, this paper aims at providing a baseline for researchers who might intend to incorporate the split CIF (a useful tool for general data fusion) into their prospective research works.
Addresses the problem of obtaining a consistent estimate (or upper bound) of the covariance matrix when combining two quantities with unknown correlation. The combination is defined linearly with two gains. When the gains are chosen a priori, a family of consistent estimates is presented in the note. The member in this family having minimal trace is said to be "family-optimal." When the gains are to be optimized in order to achieve minimal trace of the family-optimal estimate of the covariance matrix, it is proved that the global optimal solution is actually given by the covariance intersection algorithm, which conducts the search only along a one-dimensional curve in the n-squared-dimensional space of combination gains.
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence, and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach which enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the Multiple Hypothesis Tracking algorithm to the proposed framework, resulting in a method with a favorable trade-off between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.
Current research in biology uses evermore complex computational and imaging tools. Here we describe Icy, a collaborative bioimage informatics platform that combines a community website for contributing and sharing tools and material, and software with a high-end visual programming framework for seamless development of sophisticated imaging workflows. Icy extends the reproducible research principles, by encouraging and facilitating the reusability, modularity, standardization and management of algorithms and protocols. Icy is free, open-source and available at http://icy.bioimageanalysis.org/.
For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the
analysis of scientific images. We discuss the origins, challenges and solutions of these two programs,
and how their history can serve to advise and inform other software projects.
Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular 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 a 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.
The lateral mobility of individual murine polyoma virus–like particles (VLPs) bound to live cells and artificial lipid bilayers was studied by single fluorescent particle tracking using total internal reflection fluorescence microscopy. The particle trajectories were analyzed in terms of diffusion rates and modes of motion as described by the moment scaling spectrum. Although VLPs bound to their ganglioside receptor in lipid bilayers exhibited only free diffusion, analysis of trajectories on live 3T6 mouse fibroblasts revealed three distinct modes of mobility: rapid random motion, confined movement in small zones (30–60 nm in diameter), and confined movement in zones with a slow drift. After binding to the cell surface, particles typically underwent free diffusion for 5–10 s, and then they were confined in an actin filament-dependent manner without involvement of clathrin-coated pits or caveolae. Depletion of cholesterol dramatically reduced mobility of VLPs independently of actin, whereas inhibition of tyrosine kinases had no effect on confinement. The results suggested that clustering of ganglioside molecules by the multivalent VLPs induced transmembrane coupling that led to confinement of the virus/receptor complex by cortical actin filaments.
• gangliosides
• lipid raft
• total internal reflection fluorescence microscopy
• virus entry
• confinement zone
We propose a method to detect and track multiple moving biological spot-like particles showing different kinds of dynamics in image sequences acquired through multidimensional fluorescence microscopy. It enables the extraction and analysis of information such as number, position, speed, movement, and diffusion phases of, e.g., endosomal particles. The method consists of several stages. After a detection stage performed by a three-dimensional (3-D) undecimated wavelet transform, we compute, for each detected spot, several predictions of its future state in the next frame. This is accomplished thanks to an interacting multiple model (IMM) algorithm which includes several models corresponding to different biologically realistic movement types. Tracks are constructed, thereafter, by a data association algorithm based on the maximization of the likelihood of each IMM. The last stage consists of updating the IMM filters in order to compute final estimations for the present image and to improve predictions for the next image. The performances of the method are validated on synthetic image data and used to characterize the 3-D movement of endocytic vesicles containing quantum dots.
Emerging real-time techniques for imaging viral infections provide powerful tools for understanding the dynamics of virus-host cell interactions. Here we labeled human immunodeficiency virus-1 (HIV-1) integrase with a small tetracysteine tag, which preserved the virus' infectivity while allowing it to be labeled with the bis-arsenical fluorescein derivative FlAsH. This labeling allowed us to image both intracytoplasmic and intranuclear HIV-1 complexes in three dimensions over time (4D) in human cells and enabled us to analyze HIV-1 kinetics by automated 4D quantitative particle tracking. In the cytoplasm, HIV-1 complexes underwent directed movements toward the nuclear compartment, kinetically characteristic of both microtubule- and actin-dependent transport. The complexes then adopted smaller movements in a very confined volume once associated with the nuclear membrane and more diffuse movements once inside the nucleus. This work contributes new insight into the various movements of HIV-1 complexes within infected cells and provides a useful tool for the study of virus-host cell interactions during infection.
Multisensor fusion and integration is a rapidly evolving research
area and requires interdisciplinary knowledge in control theory, signal
processing, artificial intelligence, probability and statistics, etc.
The advantages gained through the use of redundant, complementary, or
more timely information in a system can provide more reliable and
accurate information. This paper provides an overview of current sensor
technologies and describes the paradigm of multisensor fusion and
integration as well as fusion techniques at different fusion levels.
Applications of multisensor fusion in robotics, biomedical system,
equipment monitoring, remote sensing, and transportation system are also
discussed. Finally, future research directions of multisensor fusion
technology including microsensors, smart sensors, and adaptive fusion
techniques are presented
Semiconductor quantum dots (QDs) are new fluorescent probes with great promise for ultrasensitive biological imaging. When detected at the single-molecule level, QD-tagged molecules can be observed and tracked in the membrane of live cells over unprecedented durations. The motion of these individual molecules, recorded in sequences of fluorescence images, can reveal aspects of the dynamics of cellular processes that remain hidden in conventional ensemble imaging. Due to QD complex optical properties, such as fluorescence intermittency, the quantitative analysis of these sequences is, however, challenging and requires advanced algorithms. We present here a novel approach, which, instead of a frame by frame analysis, is based on perceptual grouping in a spatiotemporal volume. By applying a detection process based on an image fluorescence model, we first obtain an unstructured set of points. Individual molecular trajectories are then considered as minimal paths in a Riemannian metric derived from the fluorescence image stack. These paths are computed with a variant of the fast marching method and few parameters are required. We demonstrate the ability of our algorithm to track intermittent objects both in sequences of synthetic data and in experimental measurements obtained with individual QD-tagged receptors in the membrane of live neurons. While developed for tracking QDs, this method can, however, be used with any fluorescent probes.
Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework. It better exploits spatiotemporal information and prior knowledge than common approaches, yielding more robust tracking also in cases of photobleaching and object interaction. The tracking method was evaluated using simulated but realistic image sequences, for which ground truth was available. The results of these experiments show that the method is more accurate and robust than popular tracking methods. In addition, validation experiments were conducted with real fluorescence microscopy image data acquired for microtubule growth analysis. These demonstrate that the method yields results that are in good agreement with manual tracking performed by expert cell biologists. Our findings suggest that the method may replace laborious manual procedures.
Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods.
Tracking subcellular structures displayed as small spots in fluorescence microscopy images is important to determine quantitative information of biological processes. We have developed an approach for tracking multiple fluorescent particles based on two-filter smoothing and probabilistic data association. Compared to previous work, our approach exploits information from past and future time points, integrates multiple measurements, and combines Kalman filtering and particle filtering. We evaluated our approach based on data from the ISBI Particle Tracking Challenge and found that it yields state-of-the-art results for low signal-to-noise ratios. We also applied our method to live cell fluorescence microscopy image sequences of HIV-1 particles and HCV proteins. It turned out that the new approach generally outperforms existing methods.
One of the major challenges in multiple particle tracking is the capture of extremely heterogeneous movements of objects in crowded scenes. The presence of numerous assignment candidates in the expected range of particle motion makes the tracking ambiguous and induces false positives. Lowering the ambiguity by reducing the search range, on the other hand, is not an option, as this would increase the rate of false negatives. We propose here a piecewise-stationary motion model (PMM) for the particle transport along an iterative smoother that exploits recursive tracking in multiple rounds in forward and backward temporal directions. By fusing past and future information, our method, termed PMMS, can recover fast transitions from freely or confined diffusive to directed motions with linear time complexity. To avoid false positives we complemented recursive tracking with a robust inline estimator of the search radius for assignment (a.k.a. gating), where past and future information are exploited using only two frames at each optimization step. We demonstrate the improvement of our technique on simulated data – especially the impact of density, variation in frame to frame displacements, and motion switching probability. We evaluated our technique on the 2D particle tracking challenge dataset published by Chenouard et al in 2014. Using high SNR to focus on motion modeling challenges, we show superior performance at high particle density. On biological applications, our algorithm allows us to quantify the extremely small percentage of motor-driven movements of fluorescent particles along microtubules in a dense field of unbound, diffusing particles. We also show with virus imaging that our algorithm can cope with a strong reduction in recording frame rate while keeping the same performance relative to methods relying on fast sampling.
For an airborne Doppler radar, the carrying platform is moving and has time-varying attitudes. The filtering algorithms applicable to local coordinate system are not suitable anymore. In this paper, we propose a sequential converted measurement Kalman filter (SCMKF) with Doppler, which is based on the earth-centered earth-fixed (ECEF) coordinate system. First, to effectively utilize Doppler measurement with probable correlation with slant range, the correlated Doppler and range are decorrelated by the Cholesky factorization. The range component remains unchanged while the Doppler component is changed. Second, by a series of coordinate transformations with unchanged range component and other observations, the converted position measurement is obtained. Meanwhile, the corresponding error covariance is derived by using Taylor series expansion. With the resulting converted measurement and covariance, the target state is filtered by the converted measurement Kalman filter (CMKF), which is extended to the ECEF system. Finally, the filtered state is updated sequentially with the changed Doppler. Compared with the posterior Cramer-Rao lower bound as well as the CMKF, the proposed SCMKF is shown to be an efficient solution, and is superior to the CMKF. Although the gain brought from low-accuracy Doppler is limited, the introduction of high-accuracy Doppler can improve tracking performance significantly.
Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multiframe association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm which is based on a temporally semi-global formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.
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.
Copyright © 2015 Elsevier B.V. All rights reserved.
While multiple hypothesis tracking (MHT) is widely acknowl-edged as an effective methodology for multi-target surveillance, there is a challenge to manage effectively a potentially large number of track hypotheses. Advanced single-stage track-while-fuse does not always offer the best processing scheme. We study two instances where multi-stage MHT processing is beneficial–dense target sce-narios and complementary-sensor surveillance–and propose two processing schemes for these challenges: track-break-fuse and track-before-fuse, respectively. We provide simulation results demonstrat-ing the advantages of these schemes over track-while-fuse. More gen-erally, we argue that multi-stage MHT offers a powerful and flexible paradigm to circumvent limitations in conventional MHT process-ing.
In vitro studies in primary or immortalized cells continue to be used to elucidate the essential principles that govern the interactions between HIV-1 and isolated target cells. However, until recently, substantial technical barriers prevented this information from being efficiently translated to the more complex scenario of HIV-1 spread in the host in vivo, which has limited our understanding of the impact of host physiological parameters on the spread of HIV-1. In this Review, we discuss the recent development of imaging approaches to visualize HIV-1 spread and the adaptation of these approaches to organotypic ex vivo models and animal models. We focus on new concepts, including the mechanisms and in vivo relevance of cell-cell transmission for HIV-1 spread and the function of the HIV-1 pathogenesis factor Nef, which have emerged from the application of these integrative approaches in complex cell systems.
The classical filtering and prediction problem is re-examined using the Bode-Sliannon representation of random processes and the “state-transition” method of analysis of dynamic systems. New results are: (1) The formulation and methods of solution of the problem apply without modification to stationary and nonstationary statistics and to growing-memory and infinitememory filters. (2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the coefficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations. (3) The filtering problem is shown to be the dual of the noise-free regulator problem. The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.
Automated tracking of fluorescent particles in living cells is vital for subcellular stoichoimetry analysis. Here, a new automatic tracking algorithm is described to track multiple particles, based on minimal path optimization. After linking feature points frame-by-frame, spatio-temporal data from time-lapse microscopy are combined together to construct a transformed 3D volume. The trajectories are then generated from the minimal energy path as defined by the solution of the time-dependent partial differential equation using a gray weighted distance transform dynamic programming method. Results from simulated and experimental data demonstrate that our novel automatic method gives sub-pixel accuracy even for very noisy images.
Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the track-splitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasing- ly more complicated optimization. Real-time constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed.
Image fusion of multi-spectral images and panchromatic images has been widely applied to imaging sensors. Multi-spectral images are rich in spectral information whereas panchromatic images have relatively higher spatial resolution. In this paper, we consider the image fusion as an estimation problem, that is to estimate the ideal scene of multi-spectral images at the resolution of panchromatic images. We propose a method of combining the covariance intersection (CI) principle with the expectation maximization (EM) algorithm to develop a novel image fusion approach. In contrast to other fusion methods, the proposed scheme takes cross-correlation among data sources into account, and thus provides consistent and accurate estimates through convex combinations. Since the covariance information is usually unknown in practice, the EM method is employed to provide a maximum likelihood estimate (MLE) of the covariance matrix. Real multi-spectral and panchromatic images are used to evaluate the effectiveness of the proposed EM–CI method. The proposed algorithm is found to preserve both the spectral information of the multi-spectral image and the high spatial resolution information of the panchromatic image more effectively than the conventional image fusion techniques.
Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy images. The deterministic approaches follow a traditional two-step paradigm comprising particle localization based on either the spot-enhancing filter or 2D Gaussian fitting, as well as motion correspondence based on a global nearest neighbor scheme. Our probabilistic approaches are based on particle filters. We describe approaches based on a mixture of particle filters and based on independent particle filters. For the latter, we have developed a penalization strategy that prevents the problem of filter coalescence (merging) in cases where objects lie in close proximity. A quantitative comparison based on synthetic image sequences is carried out to evaluate the performance of our approaches. In total, eight different tracking approaches have been evaluated. We have also applied these approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approaches based on independent particle filters are superior to the deterministic schemes as well as to the approaches based on a mixture of particle filters.
This paper presents a computationally efficient, two-dimensional, feature point tracking algorithm for the automated detection and quantitative analysis of particle trajectories as recorded by video imaging in cell biology. The tracking process requires no a priori mathematical modeling of the motion, it is self-initializing, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. The efficiency of the algorithm is validated on synthetic video data where it is compared to existing methods and its accuracy and precision are assessed for a wide range of signal-to-noise ratios. The algorithm is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. Its applicability is demonstrated in three case studies involving transport of low-density lipoproteins in endosomes, motion of fluorescently labeled Adenovirus-2 particles along microtubules, and tracking of quantum dots on the plasma membrane of live cells. The present automated tracking process enables the quantification of dispersive processes in cell biology using techniques such as moment scaling spectra.
Human immunodeficiency virus (HIV) delivers its genome to a host cell through fusion of the viral envelope with a cellular membrane. While the viral and cellular proteins involved in entry have been analyzed in detail, the dynamics of virus-cell fusion are largely unknown. Single virus tracing (SVT) provides the unique opportunity to visualize viral particles in real time allowing direct observation of the dynamics of this stochastic process. For this purpose, we developed a double-coloured HIV derivative carrying a green fluorescent label attached to the viral matrix protein combined with a red label fused to the viral Vpr protein designed to distinguish between complete virions and subviral particles lacking MA after membrane fusion. We present here a detailed characterization of this novel tool together with exemplary live cell imaging studies, demonstrating its suitability for real-time analyses of HIV-cell interaction.
An algorithm, the bootstrap filter, is proposed for implementing
recursive Bayesian filters. The required density of the state vector is
represented as a set of random samples, which are updated and propagated
by the algorithm. The method is not restricted by assumptions of
linearity or Gaussian noise: it may be applied to any state transition
or measurement model. A simulation example of the bearings only tracking
problem is presented. This simulation includes schemes for improving the
efficiency of the basic algorithm. For this example, the performance of
the bootstrap filter is greatly superior to the standard extended Kalman
filter
The testing of the hypothesis whether two tracks represent the same target is considered. Previous works in the literature assumed that the estimates of the same target's state from two track files are uncorrelated. A test that includes their correlation is presented.
A solution to the optimum linear smoothing problem is presented in which the smoother is interpreted as a combination of two optimum linear filters. This result is obtained from the well-known equation for the maximum likelihood combination of two independent estimates and equivalence to previous formulations is demonstrated. Forms of the solution which are convenient for practical computation are developed.
This note deals with the effect of the common process noise on the fusion (combination) of the state estimates of a target based on measurements obtained by two different sensors. This problem arises in a multisensor environment where each sensor has its information processing (tracking) subsystem. In the case of an ¿-à tracking filter the effect of the process noise is that, over a wide range of its variance, the uncertainty area corresponding to the fused estimates is about 70 percent of the single-sensor uncertainty area as opposed to 50 percent obtained if the dependence is ignored.
This paper addresses the problem of estimation when the cross-correlation in the errors between different random variables are unknown. A new data fusion algorithm, the Covariance Intersection Algorithm (CI), is presented. It is proved that this algorithm yields consistent estimates irrespective of the actual correlations. This property is illustrated in an application of decentralised estimation where it is impossible to consistently use a Kalman filter.
Sequential covariance intersection fusion Kalman filter
- Deng
Deep particle tracker: automatic tracking of particles in fluorescence microscopy images using deep learning
- Spilger