Non-bias-limited tracking of spherical particles, enabling nanometer resolution at low magnification

We present a three-dimensional tracking routine for nondiffraction-limited particles, which significantly reduces pixel bias. Our technique allows for increased resolution compared to that of previous methods, especially at low magnification or at high signal/noise ratio. This enables tracking with nanometer accuracy in a wide field of view and tracking of many particles. To reduce bias induced by pixelation, the tracking algorithm uses interpolation of the image on a circular grid to determine the x-, y-, and z-positions. We evaluate the proposed algorithm by tracking simulated images and compare it to well-known center-of-mass and cross-correlation methods. The final resolution of the described method improves up to an order of magnitude in three dimensions compared to conventional tracking methods. We show that errors in x,y-tracking can seriously affect z-tracking if interpolation is not used. We validate our results with experimental data obtained for conditions matching those used in the simulations. Finally, we show that the increased performance of the proposed algorithm uniquely enables it to extract accurate data for the persistence length and end-to-end distance of 107 DNA tethers in a single experiment.     

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.     

Tracking Image Correlation: Combining Single-Particle Tracking and Image Correlation

The interactions and coordination of biomolecules are crucial for most cellular functions. The observation of protein interactions in live cells may provide a better understanding of the underlying mechanisms. After fluorescent labeling of the interacting partners and live-cell microscopy, the colocalization is generally analyzed by quantitative global methods. Recent studies have addressed questions regarding the individual colocalization of moving biomolecules, usually by using single-particle tracking (SPT) and comparing the fluorescent intensities in both color channels. Here, we introduce a new method that combines SPT and correlation methods to obtain a dynamical 3D colocalization analysis along single trajectories of dual-colored particles. After 3D tracking, the colocalization is computed at each particle’s position via the local 3D image cross correlation of the two detection channels. For every particle analyzed, the output consists of the 3D trajectory, the time-resolved 3D colocalization information, and the fluorescence intensity in both channels. In addition, the cross-correlation analysis shows the 3D relative movement of the two fluorescent labels with an accuracy of 30 nm. We apply this method to the tracking of viral fusion events in live cells and demonstrate its capacity to obtain the time-resolved colocalization status of single particles in dense and noisy environments.     

Tendon strain measurements with dynamic ultrasound images: evaluation of digital image correlation

Strain is an essential metric in tissue mechanics. Strains and strain distributions during functional loads can help identify damaged and pathologic regions as well as quantify functional compromise. Noninvasive strain measurement in vivo is difficult to perform. The goal of this in vitro study is to determine the efficacy of digital image correlation (DIC) methods to measure strain in B-mode ultrasound images. The Achilles tendons of eight male Wistar rats were removed and mechanically cycled between 0 and 1% strain. Three cine video images were captured for each specimen: (1) optical video for manual tracking of optical markers; (2) optical video for DIC tracking of optical surface markers; and (3) ultrasound video for DIC tracking of image texture within the tissue. All three imaging modalities were similarly able to measure tendon strain during cyclic testing. Manual/ImageJ-based strain values linearly correlated with DIC (optical marker)-based strain values for all eight tendons with a slope of 0.970. DIC (optical marker)-based strain values linearly correlated with DIC (ultrasound texture)-based strain values for all eight tendons with a slope of 1.003. Strain measurement using DIC was as accurate as manual image tracking methods, and DIC tracking was equally accurate when tracking ultrasound texture as when tracking optical markers. This study supports the use of DIC to calculate strains directly from the texture present in standard B-mode ultrasound images and supports the use of DIC for in vivo strain measurement using ultrasound images without additional markers, either artificially placed (for optical tracking) or anatomically in view (i.e., bony landmarks and/or muscle-tendon junctions).     

电荷耦合器件荧光成象及生物学应用

冷却慢扫描电荷耦合器件照相机附在荧光显微镜上组成了一个系统,可把一系列成象进行贮存,通过标记颗粒的位置、强度以及强度分布的差别,在每幅象上可区分500个荧光点.荧光点的运动可以跟踪,通过分析可区分不同的运动方式,在活细胞表面,已观察到受体有直接扩散,随机扩散和局部随机扩散三种运动方式.电荷耦合器件的高灵敏度使观察过程中只产生少量的漂白.现介绍这系统的测量原理和它在生物学上的应用.     

Automatic control of a robot camera for broadcasting based on cameramen's techniques and subjective evaluation and analysis of reproduced images

With the goal of achieving an intelligent robot camera system that can take dynamic images automatically through humanlike, natural camera work, we analyzed how images were shot, subjectively evaluated reproduced images, and examined effects of camerawork, using camera control technique as a parameter. It was found that (1) A high evaluation is obtained when human-based data are used for the position adjusting velocity curve of the target; (2) Evaluation scores are relatively high for images taken with feedback-feedforward camera control method for target movement in one direction; (3) Keeping the target within the image area using the control method that imitates human camera handling becomes increasingly difficult when the target changes both direction and velocity and becomes bigger and faster, and (4) The mechanical feedback method can cope with rapid changes in the target's direction and velocity, constantly keeping the target within the image area, though the viewer finds the image rather mechanical as opposed to humanlike.     

Measurement and Correction of Microscopic Head Motion during Magnetic Resonance Imaging of the Brain

Magnetic resonance imaging (MRI) is a widely used method for non-invasive study of the structure and function of the human brain. Increasing magnetic field strengths enable higher resolution imaging; however, long scan times and high motion sensitivity mean that image quality is often limited by the involuntary motion of the subject. Prospective motion correction is a technique that addresses this problem by tracking head motion and continuously updating the imaging pulse sequence, locking the imaging volume position and orientation relative to the moving brain. The accuracy and precision of current MR-compatible tracking systems and navigator methods allows the quantification and correction of large-scale motion, but not the correction of very small involuntary movements in six degrees of freedom. In this work, we present an MR-compatible tracking system comprising a single camera and a single 15 mm marker that provides tracking precision in the order of 10 m and 0.01 degrees. We show preliminary results, which indicate that when used for prospective motion correction, the system enables improvement in image quality at both 3 T and 7 T, even in experienced and cooperative subjects trained to remain motionless during imaging. We also report direct observation and quantification of the mechanical ballistocardiogram (BCG) during simultaneous MR imaging. This is particularly apparent in the head-feet direction, with a peak-to-peak displacement of 140 m.     

Efficient Illumination for Microsecond Tracking Microscopy

The possibility to observe microsecond dynamics at the sub-micron scale, opened by recent technological advances in fast camera sensors, will affect many biophysical studies based on particle tracking in optical microscopy. A main limiting factor for further development of fast video microscopy remains the illumination of the sample, which must deliver sufficient light to the camera to allow microsecond exposure times. Here we systematically compare the main illumination systems employed in holographic tracking microscopy, and we show that a superluminescent diode and a modulated laser diode perform the best in terms of image quality and acquisition speed, respectively. In particular, we show that the simple and inexpensive laser illumination enables less than s camera exposure time at high magnification on a large field of view without coherence image artifacts, together with a good hologram quality that allows nm-tracking of microscopic beads to be performed. This comparison of sources can guide in choosing the most efficient illumination system with respect to the specific application.     

Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules.

We have developed a technique to detect, recognize, and track each individual low density lipoprotein receptor (LDL-R) molecule and small receptor clusters on the surface of human skin fibroblasts. Molecular recognition and high precision (30 nm) simultaneous automatic tracking of all of the individual receptors in the cell surface population utilize quantitative time-lapse low light level digital video fluorescence microscopy analyzed by purpose-designed algorithms executed on an image processing work station. The LDL-Rs are labeled with the biologically active, fluorescent LDL derivative dil-LDL. Individual LDL-Rs and unresolved small clusters are identified by measuring the fluorescence power radiated by the sub-resolution fluorescent spots in the image; identification of single particles is ascertained by four independent techniques. An automated tracking routine was developed to track simultaneously, and without user intervention, a multitude of fluorescent particles through a sequence of hundreds of time-lapse image frames. The limitations on tracking precision were found to depend on the signal-to-noise ratio of the tracked particle image and mechanical drift of the microscope system. We describe the methods involved in (i) time-lapse acquisition of the low-light level images, (ii) simultaneous automated tracking of the fluorescent diffraction limited punctate images, (iii) localizing particles with high precision and limitations, and (iv) detecting and identifying single and clustered LDL-Rs. These methods are generally applicable and provide a powerful tool to visualize and measure dynamics and interactions of individual integral membrane proteins on living cell surfaces.     

Human vs. machine: Detecting wildlife in camera trap images

As the capacity to collect and store large amounts of data expands, identifying and evaluating strategies to efficiently convert raw data into meaningful information is increasingly necessary. Across disciplines, this data processing task has become a significant challenge, delaying progress and actionable insights. In ecology, the growing use of camera traps (i.e., remotely triggered cameras) to collect information on wildlife has led to an enormous volume of raw data (i.e., images) in need of review and annotation. To expedite camera trap image processing, many have turned to the field of artificial intelligence (AI) and use machine learning models to automate tasks such as detecting and classifying wildlife in images. To contribute understanding of the utility of AI tools for processing wildlife camera trap images, we evaluated the performance of a state-of-the-art computer vision model developed by Microsoft AI for Earth named MegaDetector using data from an ongoing camera trap study in Arctic Alaska, USA. Compared to image labels determined by manual human review, we found MegaDetector reliably determined the presence or absence of wildlife in images generated by motion detection camera settings (≥94.6% accuracy), however, performance was substantially poorer for images collected with time-lapse camera settings (≤61.6% accuracy). By examining time-lapse images where MegaDetector failed to detect wildlife, we gained practical insights into animal size and distance detection limits and discuss how those may impact the performance of MegaDetector in other systems. We anticipate our findings will stimulate critical thinking about the tradeoffs of using automated AI tools or manual human review to process camera trap images and help to inform effective implementation of study designs.     

Practical considerations for using K3 cameras in CDS mode for high-resolution and high-throughput single particle cryo-EM

Detector technology plays a pivotal role in high-resolution and high-throughput cryo-EM structure determination. Compared with the first-generation, single-electron counting direct detection camera (Gatan K2), the latest K3 camera is faster, larger, and now offers a correlated-double sampling mode (CDS). Importantly this results in a higher DQE and improved throughput compared to its predecessor. In this study, we focused on optimizing camera data collection parameters for daily use within a cryo-EM facility and explored the balance between throughput and resolution. In total, eight data sets of murine heavy-chain apoferritin were collected at different dose rates and magnifications, using 9-hole image shift data collection strategies. The performance of the camera was characterized by the quality of the resultant 3D reconstructions. Our results demonstrated that the Gatan K3 operating in CDS mode outperformed standard (nonCDS) mode in terms of reconstruction resolution in all tested conditions with 8 electrons per pixel per second being the optimal dose rate. At low magnification (64kx) we were able to achieve reconstruction resolutions of 149% of the physical Nyquist limit (1.8 Å with a 1.346 Å physical pixel size). Low magnification allows more particles to be collected per image, aiding analysis of heterogeneous samples requiring large data sets. At moderate magnification (105kx, 0.834 Å physical pixel size) we achieved a resolution of 1.65 Å within 8-h of data collection, a condition optimal for achieving high-resolution on well behaved samples. Our results also show that for an optimal sample like apoferritin, one can achieve better than 2.5 Å resolution with 5 min of data collection. Together, our studies validate the most efficient ways of imaging protein complexes using the K3 direct detector and will greatly benefit the cryo-EM community.     

High accuracy 3D quantum dot tracking with multifocal plane microscopy for the study of fast intracellular dynamics in live cells

Single particle tracking in three dimensions in a live cell environment holds the promise of revealing important new biological insights. However, conventional microscopy-based imaging techniques are not well suited for fast three-dimensional (3D) tracking of single particles in cells. Previously we developed an imaging modality multifocal plane microscopy (MUM) to image fast intracellular dynamics in three dimensions in live cells. Here, we introduce an algorithm, the MUM localization algorithm (MUMLA), to determine the 3D position of a point source that is imaged using MUM. We validate MUMLA through simulated and experimental data and show that the 3D position of quantum dots can be determined over a wide spatial range. We demonstrate that MUMLA indeed provides the best possible accuracy with which the 3D position can be determined. Our analysis shows that MUM overcomes the poor depth discrimination of the conventional microscope, and thereby paves the way for high accuracy tracking of nanoparticles in a live cell environment. Here, using MUM and MUMLA we report for the first time the full 3D trajectories of QD-labeled antibody molecules undergoing endocytosis in live cells from the plasma membrane to the sorting endosome deep inside the cell.     

Bayesian inference for improved single molecule fluorescence tracking

Single molecule tracking is widely used to monitor the change in position of lipids and proteins in living cells. In many experiments in which molecules are tagged with a single or small number of fluorophores, the signal/noise ratio may be limiting, the number of molecules is not known, and fluorophore blinking and photobleaching can occur. All these factors make accurate tracking over long trajectories difficult and hence there is still a pressing need to develop better algorithms to extract the maximum information from a sequence of fluorescence images. We describe here a Bayesian-based inference approach, based on a trans-dimensional sequential Monte Carlo method that utilizes both the spatial and temporal information present in the image sequences. We show, using model data, where the real trajectory of the molecule is known, that our method allows accurate tracking of molecules over long trajectories even with low signal/noise ratio and in the presence of fluorescence blinking and photobleaching. The method is then applied to real experimental data.     

Assessing the capabilities of a 4kx4k CCD camera for electron cryo-microscopy at 300kV

CCD cameras have numerous advantages over photographic film for detecting electrons; however the point spread function of these cameras has not been sufficient for single particle data collection to subnanometer resolution with 300kV microscopes. We have adopted spectral signal to noise ratio (SNR) as a parameter for assessing detector quality for single particle imaging. The robustness of this parameter is confirmed under a variety of experimental conditions. Using this parameter, we demonstrate that the SNR of images of either amorphous carbon film or ice embedded virus particles collected on a new commercially available 4kx4k CCD camera are slightly better than photographic film at low spatial frequency (<1/5 Nyquist frequency), and as good as photographic film out to half of the Nyquist frequency. In addition it is slightly easier to visualize ice embedded particles on this CCD camera than on photographic film. Based on this analysis it is realistic to collect images containing subnanometer resolution data (6-9A) using this CCD camera at an effective magnification of approximately 112000x on a 300kV electron microscope.     

Extended-resolution imaging of the interaction of lipid droplets and mitochondria

Physical contacts between organelles play a pivotal role in intracellular trafficking of metabolites. Monitoring organelle interactions in living cells using fluorescence microscopy is a powerful approach to functionally assess these cellular processes. However, detailed target acquisition is typically limited due to light diffraction. Furthermore, subcellular compartments such as lipid droplets and mitochondria are highly dynamic and show significant subcellular movement. Thus, high-speed acquisition of these organelles with extended-resolution is appreciated. Here, we present an imaging informatics pipeline enabling spatial and time-resolved analysis of the dynamics and interactions of fluorescently labeled lipid droplets and mitochondria in a fibroblast cell line. The imaging concept is based on multispectral confocal laser scanning microscopy and includes high-speed resonant scanning for fast spatial acquisition of organelles. Extended-resolution is achieved by the recording of images at minimized pinhole size and by post-processing of generated data using a computational image restoration method. Computation of inter-organelle contacts is performed on basis of segmented spatial image data. We show limitations of the image restoration and segmentation part of the imaging informatics pipeline. Since both image processing methods are implemented in other related methodologies, our findings will help to identify artifacts and the false-interpretation of obtained morphometric data. As a proof-of-principle, we studied how lipid load and overexpression of PLIN5, considered to be involved in the tethering of LDs and mitochondria, affects organelle association.     

Feature point tracking and trajectory analysis for video imaging in cell biology

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.     

Signal to noise analysis of multiple color fluorescence imaging microscopy

BACKGROUND: Various approaches that were recently developed demonstrate the ability to simultaneously detect all human (or other species) chromosomes by using combinatorial labeling and fluorescence in situ hybridization (FISH). With the growing interest in this field, it is important to develop tools for optimizing and estimating the accuracy of different experimental methods. METHODS: We have analyzed the principles of multiple color fluorescence imaging microscopy. First, formalism based on the physical principles of fluorescence microscopy and noise analysis is introduced. Next, a signal to noise (S/N) analysis is performed and summarized in a simple accuracy criterion. The analysis assumes shot noise to be the dominant source of noise. RESULTS: The accuracy criterion was used to calculate the S/N of multicolor FISH (M-FISH), spectral karyotyping, ratio imaging, and a method based on using a set of broad band filters. Spectral karyotyping is tested on various types of samples and shows accurate classifications. We have also tested classification accuracy as a function of total measurement time. CONCLUSIONS: The accuracy criterion that we have developed can be used for optimizing and analyzing different multiple color fluorescence microscopy methods. The assumption that shot noise is dominant in these measurements is supported by our measurements.     

Precise nanometer localization analysis for individual fluorescent probes

Calculation of the centroid of the images of individual fluorescent particles and molecules allows localization and tracking in light microscopes to a precision about an order of magnitude greater than the microscope resolution. The factors that limit the precision of these techniques are examined and a simple equation derived that describes the precision of localization over a wide range of conditions. In addition, a localization algorithm motivated from least-squares fitting theory is constructed and tested both on image stacks of 30-nm fluorescent beads and on computer-generated images (Monte Carlo simulations). Results from the algorithm show good agreement with the derived precision equation for both the simulations and actual images. The availability of a simple equation to describe localization precision helps investigators both in assessing the quality of an experimental apparatus and in directing attention to the factors that limit further improvement. The precision of localization scales as the inverse square root of the number of photons in the spot for the shot noise limited case and as the inverse of the number of photons for the background noise limited case. The optimal image magnification depends on the expected number of photons and background noise, but, for most cases of interest, the pixel size should be about equal to the standard deviation of the point spread function.     

Single particle tracking. Analysis of diffusion and flow in two-dimensional systems.

Analysis of the trajectories of small particles at high spatial and temporal resolution using video enhanced contrast microscopy provides a powerful approach to characterizing the mechanisms of particle motion in living cells and in other systems. We present here the theoretical basis for the analysis of these trajectories for particles undergoing random diffusion and/or systematic transport at uniform velocity in two-dimensional systems. The single particle tracking method, based on observations of the trajectories of individual particles, is compared with methods that characterize the motions of a large collection of particles such as fluorescence photobleaching recovery. Determination of diffusion coefficients or transport velocities either from correlation of positions or of velocities of the particles is discussed. A result of practical importance is an analysis of the dependence of the expected statistical uncertainty of these determinations on the number of position measurements. This provides a way of judging the accuracy of the diffusion coefficients and transport velocities obtained using this approach.