Quantifying Food Web Flows Using Linear Inverse Models

The quantitative mapping of food web flows based on empirical data is a crucial yet difficult task in ecology. The difficulty arises from the under-sampling of food webs, because most data sets are incomplete and uncertain. In this article, we review methods to quantify food web flows based on empirical data using linear inverse models (LIM). The food web in a LIM is described as a linear function of its flows, which are estimated from empirical data by inverse modeling. The under-sampling of food webs implies that infinitely many different solutions exist that are consistent with a given data set. The existing approaches to food web LIM select a single solution from this infinite set by invoking additional assumptions: either a specific selection criterion that has no solid ecological basis is used or the data set is artificially upgraded by assigning fixed values to, for example, physiological parameters. Here, we advance a likelihood approach (LA) that follows a different solution philosophy. Rather than singling out one particular solution, the LA generates a large set of possible solutions from which the marginal probability density function (mPDF) of each flow and correlations between flows can be derived. The LA is exemplified with an example model of a soil food web and is made available in the open-source R-software. Moreover, we show how stoichiometric data, stable isotope signatures, and fatty acid compositions can be included in the LIM to alleviate the under-sampling problem. Overall, LIM prove to be a powerful tool in food web research, which can bridge the gap between empirical data and the analysis of food web structures.     

Multi-Color Single Particle Tracking with Quantum Dots

Quantum dots (QDs) have long promised to revolutionize fluorescence detection to include even applications requiring simultaneous multi-species detection at single molecule sensitivity. Despite the early promise, the unique optical properties of QDs have not yet been fully exploited in e. g. multiplex single molecule sensitivity applications such as single particle tracking (SPT). In order to fully optimize single molecule multiplex application with QDs, we have in this work performed a comprehensive quantitative investigation of the fluorescence intensities, fluorescence intensity fluctuations, and hydrodynamic radii of eight types of commercially available water soluble QDs. In this study, we show that the fluorescence intensity of CdSe core QDs increases as the emission of the QDs shifts towards the red but that hybrid CdSe/CdTe core QDs are less bright than the furthest red-shifted CdSe QDs. We further show that there is only a small size advantage in using blue-shifted QDs in biological applications because of the additional size of the water-stabilizing surface coat. Extending previous work, we finally also show that parallel four color multicolor (MC)-SPT with QDs is possible at an image acquisition rate of at least 25 Hz. We demonstrate the technique by measuring the lateral dynamics of a lipid, biotin-cap-DPPE, in the cellular plasma membrane of live cells using four different colors of QDs; QD565, QD605, QD655, and QD705 as labels.     

多目标视觉追踪中的分组效应及其可加性

如何减少注意资源的消耗、提升人类在动态视觉持续性注意任务中的表现,是持续性注意研究关注的重点问题,具有理论和实践的重要意义。多目标追踪任务是研究个体持续性注意的常用实验室方法。多目标追踪任务中,观察者可以利用基于物体特征的分组效应将多个运动目标知觉为一个更大的运动单元,从而减少注意资源的消耗、提高追踪任务表现。为了进一步节省注意资源、提升注意追踪的表现,研究者提出了注意追踪中分组效应的可加性问题。分组效应的可加性表现为基于两个及以上特征的分组对追踪表现的提高优于基于一个特征的分组。可加性的研究对理解不同分组效应的认知机制,个体动态视觉追踪中的注意机制和注意资源分配等具有重要意义。本文对以往的行为以及神经影像学研究进行了汇总,讨论了不同类型分组效应的知觉加工机制及其可加性,系统阐述了基于不同表面特征的不可加性,和基于表面特征与特定时空特征可加性的认知及其神经基础。未来可以从行为学实验角度探究更多基于不同特征分组效应的可加性,或者从注意追踪中基于不同分组效应的神经机制入手,探讨分组效应的可加性问题,为分组效应的分类及可加性研究提供更多认知和神经层面的依据。     

An Efficient Object Tracking Method on Quad-/Oc-Trees

We introduce a fast error-free tracking method applicable to sequences of two and three dimensional images. The core idea is to use Quadtree (resp. Octree) data structures for representing the spatial discretization of an image in two (resp. three) spatial dimensions. This representation enables one to merge into large computational cells the regions that can be faithfully described with such a coarse representation, thus significantly reducing the total number of degrees of freedom that are processed, without compromising accuracy. This encoding is particularly effective in the case of algorithms based on moving fronts, since the adaptive refinement provides a natural means to focus the processing resources on information near the moving front. In this paper, we use an existing contour based tracker and reformulate it to the case of Quad-/Oc-tree data structures. Relevant mathematical assumptions and derivations are presented for this purpose. We then demonstrate that, on standard bio-medical image sequences, a speed up of 5X is easily achieved in 2D and about 10X in 3D.     

Efficient Multiple Object Tracking Using Mutually Repulsive Active Membranes

Studies of social and group behavior in interacting organisms require high-throughput analysis of the motion of a large number of individual subjects. Computer vision techniques offer solutions to specific tracking problems, and allow automated and efficient tracking with minimal human intervention. In this work, we adopt the open active contour model to track the trajectories of moving objects at high density. We add repulsive interactions between open contours to the original model, treat the trajectories as an extrusion in the temporal dimension, and show applications to two tracking problems. The walking behavior of Drosophila is studied at different population density and gender composition. We demonstrate that individual male flies have distinct walking signatures, and that the social interaction between flies in a mixed gender arena is gender specific. We also apply our model to studies of trajectories of gliding Myxococcus xanthus bacteria at high density. We examine the individual gliding behavioral statistics in terms of the gliding speed distribution. Using these two examples at very distinctive spatial scales, we illustrate the use of our algorithm on tracking both short rigid bodies (Drosophila) and long flexible objects (Myxococcus xanthus). Our repulsive active membrane model reaches error rates better than per fly per second for Drosophila tracking and comparable results for Myxococcus xanthus.     

Relationship of Lipid Rafts to Transient Confinement Zones Detected by Single Particle Tracking

We examined the physical and chemical characteristics of transient confinement zones (TCZs) that are detected in single particle trajectories of molecules moving within the membrane of C3H 10T1/2 murine fibroblasts and their relationship to “rafts.” We studied the lateral movement of different membrane molecules thought to partition to varying degrees into or out of the putative lipid domains known as rafts. We found that lipid analogs spend significantly less time in TCZs compared with Thy-1, a glycosylphosphatidylinositol-anchored protein, and GM1, a glycosphingolipid. For Thy-1, we found that zone abundance was markedly reduced by cholesterol extraction, suggesting that a major source of the observed temporary confinement is related to the presence of raft domains. More detailed analysis of particle trajectories reveals that zones can be revisited even tens of seconds after the original escape and that diffusion within the zones is reduced by a factor of ∼2, consistent with the zone being a cholesterol-rich liquid-ordered phase. Surprisingly, transient confinement was not strongly temperature dependent. Overall, our data demonstrate that there are raft-related domains present in certain regions of the plasma membrane of C3H cells, which can persist for tens of seconds.     

A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.     

Immobilization of Pseudorabies Virus in Porcine Tracheal Respiratory Mucus Revealed by Single Particle Tracking

Pseudorabies virus (PRV) initially replicates in the porcine upper respiratory tract. It easily invades the mucosae and submucosae for subsequent spread throughout the body via blood vessels and nervous system. In this context, PRV developed ingenious processes to overcome different barriers such as epithelial cells and the basement membrane. Another important but often overlooked barrier is the substantial mucus layer which coats the mucosae. However, little is known about how PRV particles interact with porcine respiratory mucus. We therefore measured the barrier properties of porcine tracheal respiratory mucus, and investigated the mobility of nanoparticles including PRV in this mucus. We developed an in vitro model utilizing single particle tracking microscopy. Firstly, the mucus pore size was evaluated with polyethylene glycol coupled (PEGylated) nanoparticles and atomic force microscope. Secondly, the mobility of PRV in porcine tracheal respiratory mucus was examined and compared with that of negative, positive and PEGylated nanoparticles. The pore size of porcine tracheal respiratory mucus ranged from 80 to 1500 nm, with an average diameter of 455±240 nm. PRV (zeta potential: −31.8±1.5 mV) experienced a severe obstruction in porcine tracheal respiratory mucus, diffusing 59-fold more slowly than in water. Similarly, the highly negatively (−49.8±0.6 mV) and positively (36.7±1.1 mV) charged nanoparticles were significantly trapped. In contrast, the nearly neutral, hydrophilic PEGylated nanoparticles (−9.6±0.8 mV) diffused rapidly, with the majority of particles moving 50-fold faster than PRV. The mobility of the particles measured was found to be related but not correlated to their surface charge. Furthermore, PEGylated PRV (-13.8±0.9 mV) was observed to diffuse 13-fold faster than native PRV. These findings clearly show that the mobility of PRV was significantly hindered in porcine tracheal respiratory mucus, and that the obstruction of PRV was due to complex mucoadhesive interactions including charge interactions rather than size exclusion.     

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here protocols are presented for preparing embryos and adult C. elegans animals for short- and long-term time-lapse microscopy and methods for tracking and quantification of developmental processes. The methods presented are all based on C. elegans strains available from the Caenorhabditis Genetics Center and on open-source software that can be easily implemented in any laboratory independently of the microscopy system used. A reconstruction of a 3D cell-shape model using the modelling software IMOD, manual tracking of fluorescently-labeled subcellular structures using the multi-purpose image analysis program Endrov, and an analysis of cortical contractile flow using PIVlab (Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB) are shown. It is discussed how these methods can also be deployed to quantitatively capture other developmental processes in different models, e.g., cell tracking and lineage tracing, tracking of vesicle flow.     

On the Stereological Estimation of Numerical Density of Particle Systems by an Object Counting Method

The determination of numerical density Nv of particles is one of the most complex problems in stereology. This paper presents a method for solving it, consisting only of counting of intersections between particles and thick slices of some different thicknesses. The essential stereological parameters Nv and mean caliper diameter D of the particles are determined by means of a regression line. The basic formula is a simple consequence of general principles for deriving stereological formulae which is described in this paper. The object counting method is applicable if the particles are convex and if the whole particle system can be described by a stationary isotropic ergodic marked point process. A practical example (liver cell nuclei of rats) demonstrates the application of the method.     

3D Orbital Tracking in a Modified Two-photon Microscope: An Application to the Tracking of Intracellular Vesicles

The objective of this video protocol is to discuss how to perform and analyze a three-dimensional fluorescent orbital particle tracking experiment using a modified two-photon microscope¹. As opposed to conventional approaches (raster scan or wide field based on a stack of frames), the 3D orbital tracking allows to localize and follow with a high spatial (10 nm accuracy) and temporal resolution (50 Hz frequency response) the 3D displacement of a moving fluorescent particle on length-scales of hundreds of microns². The method is based on a feedback algorithm that controls the hardware of a two-photon laser scanning microscope in order to perform a circular orbit around the object to be tracked: the feedback mechanism will maintain the fluorescent object in the center by controlling the displacement of the scanning beam^3-5. To demonstrate the advantages of this technique, we followed a fast moving organelle, the lysosome, within a living cell^6,7. Cells were plated according to standard protocols, and stained using a commercially lysosome dye. We discuss briefly the hardware configuration and in more detail the control software, to perform a 3D orbital tracking experiment inside living cells. We discuss in detail the parameters required in order to control the scanning microscope and enable the motion of the beam in a closed orbit around the particle. We conclude by demonstrating how this method can be effectively used to track the fast motion of a labeled lysosome along microtubules in 3D within a live cell. Lysosomes can move with speeds in the range of 0.4-0.5 µm/sec, typically displaying a directed motion along the microtubule network⁸.     

Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.     

Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis

Single-particle tracking is an important technique in the life sciences to understand the kinetics of biomolecules. The analysis of apparent diffusion coefficients in vivo, for example, enables researchers to determine whether biomolecules are moving alone, as part of a larger complex, or are bound to large cellular components such as the membrane or chromosomal DNA. A remaining challenge has been to retrieve quantitative kinetic models, especially for molecules that rapidly switch between different diffusional states. Here, we present analytical diffusion distribution analysis (anaDDA), a framework that allows for extracting transition rates from distributions of apparent diffusion coefficients calculated from short trajectories that feature less than 10 localizations per track. Under the assumption that the system is Markovian and diffusion is purely Brownian, we show that theoretically predicted distributions accurately match simulated distributions and that anaDDA outperforms existing methods to retrieve kinetics, especially in the fast regime of 0.1–10 transitions per imaging frame. AnaDDA does account for the effects of confinement and tracking window boundaries. Furthermore, we added the option to perform global fitting of data acquired at different frame times to allow complex models with multiple states to be fitted confidently. Previously, we have started to develop anaDDA to investigate the target search of CRISPR-Cas complexes. In this work, we have optimized the algorithms and reanalyzed experimental data of DNA polymerase I diffusing in live Escherichia coli. We found that long-lived DNA interaction by DNA polymerase are more abundant upon DNA damage, suggesting roles in DNA repair. We further revealed and quantified fast DNA probing interactions that last shorter than 10 ms. AnaDDA pushes the boundaries of the timescale of interactions that can be probed with single-particle tracking and is a mathematically rigorous framework that can be further expanded to extract detailed information about the behavior of biomolecules in living cells.     

Heterogeneous Intracellular Trafficking Dynamics of Brain-Derived Neurotrophic Factor Complexes in the Neuronal Soma Revealed by Single Quantum Dot Tracking

Accumulating evidence underscores the importance of ligand-receptor dynamics in shaping cellular signaling. In the nervous system, growth factor-activated Trk receptor trafficking serves to convey biochemical signaling that underlies fundamental neural functions. Focus has been placed on axonal trafficking but little is known about growth factor-activated Trk dynamics in the neuronal soma, particularly at the molecular scale, due in large part to technical hurdles in observing individual growth factor-Trk complexes for long periods of time inside live cells. Quantum dots (QDs) are intensely fluorescent nanoparticles that have been used to study the dynamics of ligand-receptor complexes at the plasma membrane but the value of QDs for investigating ligand-receptor intracellular dynamics has not been well exploited. The current study establishes that QD conjugated brain-derived neurotrophic factor (QD-BDNF) binds to TrkB receptors with high specificity, activates TrkB downstream signaling, and allows single QD tracking capability for long recording durations deep within the soma of live neurons. QD-BDNF complexes undergo internalization, recycling, and intracellular trafficking in the neuronal soma. These trafficking events exhibit little time-synchrony and diverse heterogeneity in underlying dynamics that include phases of sustained rapid motor transport without pause as well as immobility of surprisingly long-lasting duration (several minutes). Moreover, the trajectories formed by dynamic individual BDNF complexes show no apparent end destination; BDNF complexes can be found meandering over long distances of several microns throughout the expanse of the neuronal soma in a circuitous fashion. The complex, heterogeneous nature of neuronal soma trafficking dynamics contrasts the reported linear nature of axonal transport data and calls for models that surpass our generally limited notions of nuclear-directed transport in the soma. QD-ligand probes are poised to provide understanding of how the molecular mechanisms underlying intracellular ligand-receptor trafficking shape cell signaling under conditions of both healthy and dysfunctional neurological disease models.     

Intracellular survival by Chlamydia

Chlamydiae are obligate intracellular bacterial pathogens whose entry into mucosal epithelial cells is required for intracellular survival and subsequent growth. After a seemingly stealthy entry, chlamydiae quickly modify their vacuole (i) for exit from the endosomal pathway to the exocytic pathway and (ii) to permit fusion with intercepted endoplasmic reticulum- and Golgi-derived vesicles carrying glycerophospholipids and sphingolipids for chlamydiae-containing vacuole membrane expansion. Chlamydiae possess novel hollow proteinaceous structures, termed projections, which they use to pierce the inclusion membrane, possibly to acquire from the epithelial cytoplasm nutrients they cannot synthesize; whether or not these truncated flagellar-like structures serve a dual exchange function for secretion of molecules to programme host cell signalling is unknown. Despite the accumulation of some 500–1000 progeny in the enormously enlarged inclusion, host cell function is surprisingly little disrupted, and progeny escape can be unobtrusive. This elegant adaptive pathogen strategy, which leads to silent, chronic human infection, is fascinating from a cellular microbiology perspective.     

Quantifying Subpixel Accuracy: An Experimental Method for Measuring Accuracy in Image-Correlation-Based, Single-Particle Tracking

Single-particle tracking (SPT) is a range of powerful analysis techniques that measure particle motion from video microscopy image sequences. SPT is used to study the behavior of motor proteins and associated organelle transport within a cell. Many SPT algorithms deliver subpixel accurate measurements with noisy data corresponding to sub-10-nm resolution. Image-correlation techniques have been shown to be the most accurate method of tracking extended objects. However, to date, it has not been possible to determine the level of error when measuring the motion of an arbitrary particle with this method. In this article we derive a method for experimentally determining the accuracy of image-correlation-based SPT. We then apply this technique to a series of confocal fluorescence microscope image sequences of mitochondria, demonstrating the possibility of making measurements accurate to 5 nm when working with extended objects within live cells. In doing so we show that for particles with a low signal/noise ratio, the accuracy can vary by a factor of 2, corresponding to different particle shapes for a given signal/noise ratio. Use of the presented technique will allow researchers to quantify the accuracy of their measurements on a per-particle basis. This in turn will allow the selection of the most accurately tracked particles, helping to push the accuracy of spatial measurements well below the diffraction limit. This is particularly important for the study of molecular motors whose step size is a similar scale to these limits.     

Collective Bacterial Dynamics Revealed Using a Three-Dimensional Population-Scale Defocused Particle Tracking Technique

An ability to monitor bacterial locomotion and collective dynamics is crucial to our understanding of a number of well-characterized phenotypes including biofilm formation, chemotaxis, and virulence. Here, we report the tracking of multiple swimming Escherichia coli cells in three spatial dimensions and at single-cell resolution using a novel three-dimensional (3D) defocused particle tracking (DPT) method. The 3D trajectories were generated for wild-type Escherichia coli strain RP437 as well as for isogenic derivatives that display smooth swimming due to a cheA deletion (strain RP9535) or incessant tumbling behavior due to a cheZ deletion (strain RP1616). The 3D DPT method successfully differentiated these three modes of locomotion and allowed direct calculation of the diffusion coefficient for each strain. As expected, we found that the smooth swimmer diffused more readily than the wild type, and both the smooth swimmer and the wild-type cells exhibited diffusion coefficients that were at least two orders of magnitude larger than that of the tumbler. Finally, we found that the diffusion coefficient increased with increasing cell density, a phenomenon that can be attributed to the hydrodynamic disturbances caused by neighboring bacteria.     

RNA Interference and Single Particle Tracking Analysis of Hepatitis C Virus Endocytosis

Hepatitis C virus (HCV) enters hepatocytes following a complex set of receptor interactions, culminating in internalization via clathrin-mediated endocytosis. However, aside from receptors, little is known about the cellular molecular requirements for infectious HCV entry. Therefore, we analyzed a siRNA library that targets 140 cellular membrane trafficking genes to identify host genes required for infectious HCV production and HCV pseudoparticle entry. This approach identified 16 host cofactors of HCV entry that function primarily in clathrin-mediated endocytosis, including components of the clathrin endocytosis machinery, actin polymerization, receptor internalization and sorting, and endosomal acidification. We next developed single particle tracking analysis of highly infectious fluorescent HCV particles to examine the co-trafficking of HCV virions with cellular cofactors of endocytosis. We observe multiple, sequential interactions of HCV virions with the actin cytoskeleton, including retraction along filopodia, actin nucleation during internalization, and migration of internalized particles along actin stress fibers. HCV co-localizes with clathrin and the ubiquitin ligase c-Cbl prior to internalization. Entering HCV particles are associated with the receptor molecules CD81 and the tight junction protein, claudin-1; however, HCV-claudin-1 interactions were not restricted to Huh-7.5 cell-cell junctions. Surprisingly, HCV internalization generally occurred outside of Huh-7.5 cell-cell junctions, which may reflect the poorly polarized nature of current HCV cell culture models. Following internalization, HCV particles transport with GFP-Rab5a positive endosomes, which is consistent with trafficking to the early endosome. This study presents technical advances for imaging HCV entry, in addition to identifying new host cofactors of HCV infection, some of which may be antiviral targets.