5,096 research outputs found

    Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

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    This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

    Protein Tracking by CNN-Based Candidate Pruning and Two-Step Linking with Bayesian Network

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    Protein trafficking plays a vital role in understanding many biological processes and disease. Automated tracking of protein vesicles is challenging due to their erratic behaviour, changing appearance, and visual clutter. In this paper we present a novel tracking approach which utilizes a two-step linking process that exploits a probabilistic graphical model to predict tracklet linkage. The vesicles are initially detected with help of a candidate selection process, where the candidates are identified by a multi-scale spot enhancing filter. Subsequently, these candidates are pruned and selected by a light weight convolutional neural network. At the linking stage, the tracklets are formed based on the distance and the detection assignment which is implemented via combinatorial optimization algorithm. Each tracklet is described by a number of parameters used to evaluate the probability of tracklets connection by the inference over the Bayesian network. The tracking results are presented for confocal fluorescence microscopy data of protein trafficking in epithelial cells. The proposed method achieves a root mean square error (RMSE) of 1.39 for the vesicle localisation and of 0.7 representing the degree of track matching with ground truth. The presented method is also evaluated against the state-of-the-art “Trackmate“ framework

    Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

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    Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods

    Plasmonic artificial virus nano-particles for probing virus-host cell interactions

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    Targeting of key events in viral infection pathways creates opportunities for virus disease prevention and therapy. Nanoparticles with well-defined surfaces are promising tools for the direct visualization of biological processes and for interrogating virus behavior that is usually determined by the synergistic interplay of multiple factors and involves various transient signaling steps. Smart nanoparticles mimicking enveloped viral particles are thus developed and tested in this work with the aim to de-couple key steps in human immune-deficiency virus HIV-1 trans-infection with an engineerable viral model system. Uni-lamellar liposomes resemble biological lipid bilayer membrane structures with tunable particle size, surface charge, and composition. Pretreatment with ganglioside-GM3-containing liposomes inhibited the binding of HIV-1 by dendritic cells, indicating an essential role for GM3 in virus binding. To equip the liposome based model systems with strong non bleaching optical properties, the membranes were in the next step assembled around noble metal nanoparticle core. Noble metal nanoparticles with a size of 20nm-100nm have extraordinarily large scattering cross-sections and enable prolonged tracking of even individual particles with high temporal and spatial resolutions. The plasmon resonance peak of near-field coupled gold nanoparticles red-shifts within decreasing interparticle separation. The distance dependent optical properties of noble metal nanoparticles were utilized for characterizing clustering levels of breast cancer cell marker protein CD24 and CD44 on immortalized cancer cell lines. These encouraging results supported the choice of gold nanoparticles as core for multi-modal artificial virus nanoparticles. Artificial virus nanoparticles combine the biological versatility of a self-assembled membrane with the unique optical properties of a nanoparticle core. We developed these hybrid materials specifically for the purpose of elucidating key steps of the glycoprotein independent binding and uptake of HIV-1 during trans-infection. Systematic validation experiments revealed that GM3 containing artificial virus nanoparticles (AVNs) recapitulate the initial capture and uptake of viruses by sialoadhesin CD169 presenting cells. The AVNs also reproduced the tendency of the virus to re-distribute into confined cluster spots in cell peripheral areas. Upon contact formation between T cell and DC, the AVNs developed a polarized distribution in which they enriched at the interface between DC and CD4+ T cells. The multimodality of the AVNs was instrumental in determining the detailed location and kinetics of the nanoparticles during the trans-infection process, proving the AVN system to be a unique model system to address key mechanistic questions in the infection pathway of enveloped virus particles

    Digital detection of exosomes by interferometric imaging

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    Exosomes, which are membranous nanovesicles, are actively released by cells and have been attributed to roles in cell-cell communication, cancer metastasis, and early disease diagnostics. The small size (30–100 nm) along with low refractive index contrast of exosomes makes direct characterization and phenotypical classification very difficult. In this work we present a method based on Single Particle Interferometric Reflectance Imaging Sensor (SP-IRIS) that allows multiplexed phenotyping and digital counting of various populations of individual exosomes (>50 nm) captured on a microarray-based solid phase chip. We demonstrate these characterization concepts using purified exosomes from a HEK 293 cell culture. As a demonstration of clinical utility, we characterize exosomes directly from human cerebrospinal fluid (hCSF). Our interferometric imaging method could capture, from a very small hCSF volume (20 uL), nanoparticles that have a size compatible with exosomes, using antibodies directed against tetraspanins. With this unprecedented capability, we foresee revolutionary implications in the clinical field with improvements in diagnosis and stratification of patients affected by different disorders.This work was supported by Regione Lombardia and Fondazione Cariplo through POR-FESR, project MINER (ID 46875467); Italian Ministry of Health, Ricerca Corrente. This work was partially supported by The Scientific and Technological Research Council of Turkey (grant #113E643). (Regione Lombardia; 46875467 - Fondazione Cariplo through POR-FESR, project MINER; Italian Ministry of Health, Ricerca Corrente; 113E643 - Scientific and Technological Research Council of Turkey)Published versio

    Deep learning for intracellular particle tracking and motion analysis

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    Deep learning for intracellular particle tracking and motion analysis

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    Marker-Less Stage Drift Correction in Super-Resolution Microscopy Using the Single-Cluster PHD Filter

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    Fluorescence microscopy is a technique which allows the imaging of cellular and intracellular dynamics through the activation of fluorescent molecules attached to them. It is a very important technique because it can be used to analyze the behavior of intracellular processes in vivo in contrast to methods like electron microscopy. There are several challenges related to the extraction of meaningful information from images acquired from optical microscopes due to the low contrast between objects and background and the fact that point-like objects are observed as blurred spots due to the diffraction limit of the optical system. Another consideration is that for the study of intracellular dynamics, multiple particles must be tracked at the same time, which is a challenging task due to problems such as the presence of false positives and missed detections in the acquired data. Additionally, the objective of the microscope is not completely static with respect to the cover slip due to mechanical vibrations or thermal expansions which introduces bias in the measurements. In this paper, a Bayesian approach is used to simultaneously track the locations of objects with different motion behaviors and the stage drift using image data obtained from fluorescence microscopy experiments. Namely, detections are extracted from the acquired frames using image processing techniques, and then these detections are used to accurately estimate the particle positions and simultaneously correct the drift introduced by the motion of the sample stage. A single cluster Probability Hypothesis Density (PHD) filter with object classification is used for the estimation of the multiple target state assuming different motion behaviors. The detection and tracking methods are tested and their performance is evaluated on both simulated and real data

    Quantitative Live-Cell Imaging of Human Immunodeficiency Virus (HIV-1) Assembly

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    Advances in fluorescence methodologies make it possible to investigate biological systems in unprecedented detail. Over the last few years, quantitative live-cell imaging has increasingly been used to study the dynamic interactions of viruses with cells and is expected to become even more indispensable in the future. Here, we describe different fluorescence labeling strategies that have been used to label HIV-1 for live cell imaging and the fluorescence based methods used to visualize individual aspects of virus-cell interactions. This review presents an overview of experimental methods and recent experiments that have employed quantitative microscopy in order to elucidate the dynamics of late stages in the HIV-1 replication cycle. This includes cytosolic interactions of the main structural protein, Gag, with itself and the viral RNA genome, the recruitment of Gag and RNA to the plasma membrane, virion assembly at the membrane and the recruitment of cellular proteins involved in HIV-1 release to the nascent budding site
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