2D Gaussian Fitting Research Articles

An automated approach applying 2D Gaussian fitting to quantitatively analyze the dynamic feature of immunoreceptor microclusters

An automated approach applying 2D Gaussian fitting to quantitatively analyze the dynamic feature of immunoreceptor microclusters

Application of microscopical techniques in the study of autolysosome dynamics in PC12 neurites

Autophagy is a principal degradation pathway for the turnover of intracellular proteins or cytoplasmic organelles in response to starvation. During autophagic activation, autophagosomes fuse with lysosomes to form autolysosomes where incorporated materials are degraded. However, the dynamics of autolysosomes in neurites of live cells was still poorly known. In this study, various subsets of microscope were applied to analyse the autophagy induction and highly dynamic transport of autolysosomes in rat PC12 neurites. Beading formation was found in degenerating PC12 neurites under phase contrast light microscope after serum deprivation. The monomeric red fluorescence protein-green fluorescence protein-light chain 3-labelled autolysosomes accumulated throughout PC12 neurites after 18 h of serum deprivation as revealed by fluorescence microscope analysis. The single-membrane autolysosomes were also visualized in PC12 cells under transmission electron microscope. Moreover, fluorescence recovery after photobleaching experiment, which was conducted by confocal laser scanning microscope, demonstrated that autolysosomes were motile vesicles and moved along PC12 neurites during starvation. The directional transport of monomeric red fluorescence protein -labelled autolysosomes in neurites was further monitored by a motorized video microscope. Both anterograde and retrograde transport of autolysosomes were observed. In addition, the autolysosomes were precisely mapped by using 2D Gaussian fitting and then their highly dynamic movement was robustly tracked by using multidimensional assignment. Collectively, by using different microscopical techniques, our results confirmed the dynamic transport of autolysosomes in starved PC12 neurites and may provide valuable insight into understanding the biophysical characteristics of autolysosomes in neurites under physiological and pathological conditions.

Analyzing Aβ Aggregates with High Resolution Microscopy

In a world where more people grow older aging-related neurodegeneration like Alzheimer's disease (AD) affects more and more people.Today, AD can be diagnosed with certainty only post mortem, detecting insoluble β-amyloid peptide (Aβ) aggregates and neurofibrillary tangles in the patient's brain tissue. Aggregates consisting of Aβ are a fundamental pathologic feature of AD. Today in many studies, concentrations of monomeric Aβ in body fluids are investigated, especially for diagnostic purposes. Nevertheless, for the detection, quantitation and qualification of aggregated pathologic Aβ forms, also in the course of aging, a highly sensitive detection assay system for aggregated Aβ species is necessary.We developed an ultra-sensitive assay for the detection of aggregated protein species out of body fluids. This highly specific and sensitive assay uses confocal fluorescence spectroscopy methods and is sensitive enough to detect single aggregates. For the procedure, pathologic aggregates out of body fluids are immobilized on a glass chip, subsequently fluorescence labeled and detected via confocal spectroscopy.Actually, we are optimizing the assay in concerns of instrumentation (imaging) and microscopy high-resolution and even super-resolution methods. We are developing methods to analyze aggregates via super-resolution microscopy. Setups like PAINT (Point Accumulation for Imaging in Nanoscale Topography) or STORM (Stochastic Optical Reconstruction Microscopy) allow resolutions in nanometer-range. PAINT is based on replacing the point-spread-function (PSF) of a fluorophore by a point in the middle of a 2D gaussian fit. First measurements show resolutions of 30 nm. STORM is based on high-accuracy localization of photoswitchable fluorophores. During one imaging cycle, only a small part of the fluorophores is turned on. This allows a high accuracy in determining the fluorophore position by replacing the PSF. The fluorophore positions obtained from a series of imaging cycles can be used to reconstruct the whole image.

Automated image analysis for tracking cargo transport in axons

The dynamics of cargo movement in axons encodes crucial information about the underlying regulatory mechanisms of the axonal transport process in neurons, a central problem in understanding many neurodegenerative diseases. Quantitative analysis of cargo dynamics in axons usually includes three steps: (1) acquiring time-lapse image series, (2) localizing individual cargos at each time step, and (3) constructing dynamic trajectories for kinetic analysis. Currently, the later two steps are usually carried out with substantial human intervention. This article presents a method of automatic image analysis aiming for constructing cargo trajectories with higher data processing throughput, better spatial resolution, and minimal human intervention. The method is based on novel applications of several algorithms including 2D kymograph construction, seed points detection, trajectory curve tracing, back-projection to extract spatial information, and position refining using a 2D Gaussian fitting. This method is sufficiently robust for usage on images with low signal-to-noise ratio, such as those from single molecule experiments. The method was experimentally validated by tracking the axonal transport of quantum dot and DiI fluorophore-labeled vesicles in dorsal root ganglia neurons.

Intrinsic Properties of immunoglobulin IgG1 Isotype-Switched B Cell Receptors Promote Microclustering and the Initiation of Signaling

Memory B cells express high-affinity, immunoglobulin GB cell receptors (IgG BCRs) that enhance B cell responses, giving rise to the rapid production of high-affinity, IgG antibodies. Despite the central role of IgG BCRs in memory responses, the mechanisms by which the IgG BCRs function to enhance B cell responses are not fully understood. Using high-resolution live-cell imaging, we showed that IgG1 BCRs dramatically enhanced the earliest BCR-intrinsic events that followed within seconds of B cells' encounter with membrane bound antigen, including BCR oligomerization and BCR microcluster growth, leading to Syk kinase recruitment and calcium responses. The enhancement of these early events was dependent on a membrane proximal region of the IgG1 cytoplasmic tail not previously appreciated to play a role in IgG1 BCR signaling. Thus, intrinsic properties of the IgG1 BCR enhance early antigen-driven events that ultimately translate into heightened signaling.

Optimizing Super Resolution Microscopy

Super-resolution microscopy is often done by imaging isolated fluorescent probes as diffraction-limited spots with objective-type total internal fluorescence (TIRF) microscopy. The centers of these spots are commonly, sub-optimally located by least-squares fitting a 2D Gaussian to each spot's intensity distribution. Here we give the optimal localization procedure based on the true point spread function (PSF) known from wave optics. From a single focused image of a fluorophore molecule with fixed or time-resolved spatial orientation, we estimate the fluorophore's position and orientation using maximum likelihood estimation. We achieve the highest possible precision, given by Fisher's information limit. In the same manner, optimal localization is demonstrated for isotropic distributions of dipoles, e.g. fluorescent beads, excited by the evanescent wave produced in TIRF. Using this as a baseline, we compare a number of estimators and demonstrate that (i) for a 2D Gaussian, the unweighted least-squares fitting squanders one third of the available information, and weighted least-squares fitting is unreliable; (ii) a popular formula for the localization error of a 2D Gaussian fit exaggerates its precision beyond Fisher's information limit; (iii) maximum likelihood fitting of a 2D Gaussian is, on the other hand, practically optimal. We also present new, reliable formulae for the precisions of the various localization methods.

Experimental validation of Monte Carlo (MANTIS) simulated x‐ray response of columnar CsI scintillator screens

MANTIS is a Monte Carlo code developed for the detailed simulation of columnar CsI scintillator screens in x-ray imaging systems. Validation of this code is needed to provide a reliable and valuable tool for system optimization and accurate reconstructions for a variety of x-ray applications. Whereas previous validation efforts have focused on matching of summary statistics, in this work the authors examine the complete point response function (PRF) of the detector system in addition to relative light output values. Relative light output values and high-resolution PRFs have been experimentally measured with a custom setup. A corresponding set of simulated light output values and PRFs have also been produced, where detailed knowledge of the experimental setup and CsI:Tl screen structures are accounted for in the simulations. Four different screens were investigated with different thicknesses, column tilt angles, and substrate types. A quantitative comparison between the experimental and simulated PRFs was performed for four different incidence angles (0 degrees, 15 degrees, 30 degrees, and 45 degrees) and two different x-ray spectra (40 and 70 kVp). The figure of merit (FOM) used measures the normalized differences between the simulated and experimental data averaged over a region of interest. Experimental relative light output values ranged from 1.456 to 1.650 and were in approximate agreement for aluminum substrates, but poor agreement for graphite substrates. The FOMs for all screen types, incidence angles, and energies ranged from 0.1929 to 0.4775. To put these FOMs in context, the same FOM was computed for 2D symmetric Gaussians fit to the same experimental data. These FOMs ranged from 0.2068 to 0.8029. Our analysis demonstrates that MANTIS reproduces experimental PRFs with higher accuracy than a symmetric 2D Gaussian fit to the experimental data in the majority of cases. Examination of the spatial distribution of differences between the PRFs shows that the main reason for errors between MANTIS and the experimental data is that MANTIS-generated PRFs are sharper than the experimental PRFs. The experimental validation of MANTIS performed in this study demonstrates that MANTIS is able to reliably predict experimental PRFs, especially for thinner screens, and can reproduce the highly asymmetric shape seen in the experimental data. As a result, optimizations and reconstructions carried out using MANTIS should yield results indicative of actual detector performance. Better characterization of screen properties is necessary to reconcile the simulated light output values with experimental data.

Image Analysis with Rapid and Accurate Two-Dimensional Gaussian Fitting

A computationally rapid image analysis method, weighted overdetermined regression, is presented for two-dimensional (2D) Gaussian fitting of particle location with subpixel resolution from a pixelized image of light intensity. Compared to least-squares Gaussian iterative fitting, which is most exact but prohibitively slow for large data sets, the precision of this new method is equivalent when the signal-to-noise ratio is high and approaches it when the signal-to-noise ratio is low, while enjoying a more than 100-fold improvement in computational time. Compared to another widely used approximation method, nine-point regression, we show that precision and speed are both improved. Additionally, weighted regression runs nearly as fast and with greatly improved precision compared to the simplest method, the moment method, which, despite its limited precision, is frequently employed because of its speed. Quantitative comparisons are presented for both circular and elliptical Gaussian intensity distributions. This new image analysis method may be useful when dealing with large data sets such as those frequently met in astronomy or in single-particle and single-molecule tracking using microscopy and may facilitate advances such as real-time quantification of microscopy images.

Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences

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.

Optimising the precision for localising fluorescent proteins in living cells by 2D Gaussian fitting of digital images: application to COPII-coated endoplasmic reticulum exit sites

An insight into the operation of molecular motors has already been obtained under in vitro conditions from single-molecule tracking of proteins. It remains to analyse the effects of these motors on the position and secretion of specific organelles in the environment of the cell. For this purpose, we have investigated the accuracy of a standard algorithm to enable the tracking of particles in live-cell microscopy. The results have been applied to an example study into the role of the microtubule-motor kinesin on the function of COPII-coated secretory-cargo exit sites forming part of the mammalian endoplasmic reticulum. These exit sites are marked with multiple EYFP-tagged proteins to produce bright fluorescent particles, and a demonstration of the motility of vesicles, under different conditions in the cell, is described here. It is essential to use a low-level expression of fluorescent protein-tagged cellular components to ensure faithful replication for the behaviour of endogenous protein. However, this leads to a lower ratio for the signal-to-noise than is desired for the sub-pixel tracking of objects in digital images. This has driven the present effort to develop a computational model of the experiment in order to estimate the precision for localization of a fluorescent particle. Our work gives a greater insight, than has been managed in the past, into the accuracy and precision of particle tracking from live-cell imaging under a variety of different conditions, and it takes into consideration the current standards in digital technology for optical microscopy.

Dissecting Multiple Steps of GLUT4 Trafficking and Identifying the Sites of Insulin Action

Insulin-stimulated GLUT4 translocation is central to glucose homeostasis. Functional assays to distinguish individual steps in the GLUT4 translocation process are lacking, thus limiting progress toward elucidation of the underlying molecular mechanism. Here we have developed a robust method, which relies on dynamic tracking of single GLUT4 storage vesicles (GSVs) in real time, for dissecting and systematically analyzing the docking, priming, and fusion steps of GSVs with the cell surface in vivo. Using this method, we have shown that the preparation of GSVs for fusion competence after docking at the surface is a key step regulated by insulin, whereas the docking step is regulated by PI3K and its downstream effector, the Rab GAP AS160. These data show that Akt-dependent phosphorylation of AS160 is not the major regulated step in GLUT4 trafficking, implicating alternative Akt substrates or alternative signaling pathways downstream of GSV docking at the cell surface as the major regulatory node.