Computational image analysis of cellular dynamics: a case study based on particle tracking

Findaureus: An open-source application for locating Staphylococcus aureus in fluorescence-labelled infected bone tissue slices

Staphylococcus aureus (S. aureus) is a facultative pathogenic bacterium that can cause infections in various tissue types in humans. Fluorescence imaging techniques have been employed to visualize the bacteria in ex-vivo samples mostly in research, aiding in the understanding of the etiology of the pathogen. However, the multispectral images generated from fluorescence microscopes are complex, making it difficult to locate bacteria across image files, especially in consecutive planes with different imaging depths. To address this issue, we present Findaureus, an open-source application specifically designed to locate and extract critical information about bacteria, especially S. aureus. Due to the limited availability of datasets, we tested the application on a dataset comprising fluorescence-labelled infected mouse bone tissue images, achieving an accuracy of 95%. We compared Findaureus with other state-of-the-art image analysis tools and found that it performed better, given its specificity toward bacteria localization. The proposed approach has the potential to aid in medical research of bone infections and can be extended to other tissue and bacteria types in the future.

Protocol and Software for Automated Detection of Lysosome Active "Runs" and "Flights" with Wavelet Transform Approach

Lysosomes are highly dynamic degradation/recycling organelles that harbor sophisticated molecular sensors and signal transduction machinery through which they control cell adaptation to environmental cues and nutrients. The movements of these signaling hubs comprise persistent, directional runs-active, ATP-dependent transport along the microtubule tracks-interspersed by short, passive movements and pauses imposed by cytoplasmic constraints. The trajectories of individual lysosomes are usually obtained by time-lapse imaging of the acidic organelles labeled with LysoTracker dyes or fluorescently-tagged lysosomal-associated membrane proteins LAMP1 and LAMP2. Subsequent particle tracking generates large data sets comprising thousands of lysosome trajectories and hundreds of thousands of data points. Analyzing such data sets requires unbiased, automated methods to handle large data sets while capturing the temporal heterogeneity of lysosome trajectory data. This chapter describes integrated and largely automated workflow from live cell imaging to lysosome trajectories to computing the parameters of lysosome dynamics. We describe an open-source code for implementing the continuous wavelet transform (CWT) to distinguish trajectory segments corresponding to active transport (i.e., "runs" and "flights") versus passive lysosome movements. Complementary cumulative distribution functions (CDFs) of the "runs/flights" are generated, and Akaike weight comparisons with several competing models (lognormal, power law, truncated power law, stretched exponential, exponential) are performed automatically. Such high-throughput analyses yield useful aggregate/ensemble metrics for lysosome active transport.

Nanotechnology-Assisted Cell Tracking

The usefulness of nanoparticles (NPs) in the diagnostic and/or therapeutic sector is derived from their aptitude for navigating intra- and extracellular barriers successfully and to be spatiotemporally targeted. In this context, the optimization of NP delivery platforms is technologically related to the exploitation of the mechanisms involved in the NP-cell interaction. This review provides a detailed overview of the available technologies focusing on cell-NP interaction/detection by describing their applications in the fields of cancer and regenerative medicine. Specifically, a literature survey has been performed to analyze the key nanocarrier-impacting elements, such as NP typology and functionalization, the ability to tune cell interaction mechanisms under in vitro and in vivo conditions by framing, and at the same time, the imaging devices supporting NP delivery assessment, and consideration of their specificity and sensitivity. Although the large amount of literature information on the designs and applications of cell membrane-coated NPs has reached the extent at which it could be considered a mature branch of nanomedicine ready to be translated to the clinic, the technology applied to the biomimetic functionalization strategy of the design of NPs for directing cell labelling and intracellular retention appears less advanced. These approaches, if properly scaled up, will present diverse biomedical applications and make a positive impact on human health.

Bioimaging approaches for quantification of individual cell behavior during cell fate decisions

Tracking individual cells has allowed a new understanding of cellular behavior in human health and disease by adding a dynamic component to the already complex heterogeneity of single cells. Technically, despite countless advances, numerous experimental variables can affect data collection and interpretation and need to be considered. In this review, we discuss the main technical aspects and biological findings in the analysis of the behavior of individual cells. We discuss the most relevant contributions provided by these approaches in clinically relevant human conditions like embryo development, stem cells biology, inflammation, cancer and microbiology, along with the cellular mechanisms and molecular pathways underlying these conditions. We also discuss the key technical aspects to be considered when planning and performing experiments involving the analysis of individual cells over long periods. Despite the challenges in automatic detection, features extraction and long-term tracking that need to be tackled, the potential impact of single-cell bioimaging is enormous in understanding the pathogenesis and development of new therapies in human pathophysiology.

Single molecule tracking and analysis framework including theory-predicted parameter settings

Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

A High-Content Screen Identifies TPP1 and Aurora B as Regulators of Axonal Mitochondrial Transport

Dysregulated axonal trafficking of mitochondria is linked to neurodegenerative disorders. We report a high-content screen for small-molecule regulators of the axonal transport of mitochondria. Six compounds enhanced mitochondrial transport in the sub-micromolar range, acting via three cellular targets: F-actin, Tripeptidyl peptidase 1 (TPP1), or Aurora Kinase B (AurKB). Pharmacological inhibition or small hairpin RNA (shRNA) knockdown of each target promotes mitochondrial axonal transport in rat hippocampal neurons and induced pluripotent stem cell (iPSC)-derived human cortical neurons and enhances mitochondrial transport in iPSC-derived motor neurons from an amyotrophic lateral sclerosis (ALS) patient bearing one copy of SOD1^A4V mutation. Our work identifies druggable regulators of axonal transport of mitochondria, provides broadly applicable methods for similar image-based screens, and suggests that restoration of proper axonal trafficking of mitochondria can be achieved in human ALS neurons.

Shlevkov et al. establish a high-content screen for enhancers of axonal mitochondrial trafficking. Identified compounds act through three cellular targets: F-Actin, Tripeptidyl peptidase 1, and Aurora Kinase B. Motor neurons derived from a SOD1^+/A4VALS patient have decreased mitochondrial motility, which can be reversed by inhibitors of these targets.

Analysis and refinement of 2D single-particle tracking experiments

In recent decades, single particle tracking (SPT) has been developed into a sophisticated analytical approach involving complex instruments and data analysis schemes to extract information from time-resolved particle trajectories. Very often, mobility-related properties are extracted from these particle trajectories, as they often contain information about local interactions experienced by the particles while moving through the sample. This tutorial aims to provide a comprehensive overview about the accuracies that can be achieved when extracting mobility-related properties from 2D particle trajectories and how these accuracies depend on experimental parameters. Proper interpretation of SPT data requires an assessment of whether the obtained accuracies are sufficient to resolve the effect under investigation. This is demonstrated by calculating mean square displacement curves that show an apparent super- or subdiffusive behavior due to poor measurement statistics instead of the presence of true anomalous diffusion. Furthermore, the refinement of parameters involved in the design or analysis of SPT experiments is discussed and an approach is proposed in which square displacement distributions are inspected to evaluate the quality of SPT data and to extract information about the maximum distance over which particles should be tracked during the linking process.

Practical Considerations in Particle and Object Tracking and Analysis

The rapid advancement of live-cell imaging technologies has enabled biologists to generate high-dimensional data to follow biological movement at the microscopic level. Yet, the "perceived" ease of use of modern microscopes has led to challenges whereby sub-optimal data are commonly generated that cannot support quantitative tracking and analysis as a result of various ill-advised decisions made during image acquisition. Even optimally acquired images often require further optimization through digital processing before they can be analyzed. In writing this article, we presume our target audience to be biologists with a foundational understanding of digital image acquisition and processing, who are seeking to understand the essential steps for particle/object tracking experiments. It is with this targeted readership in mind that we review the basic principles of image-processing techniques as well as analysis strategies commonly used for tracking experiments. We conclude this technical survey with a discussion of how movement behavior can be mathematically modeled and described. © 2019 by John Wiley & Sons, Inc.

Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking

Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three-dimensionally and densely packed in a time-dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real-world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data-independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow - cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations.

Image Restoration and Analysis of Influenza Virions Binding to Membrane Receptors Reveal Adhesion-Strengthening Kinetics

With the development of single-particle tracking (SPT) microscopy and host membrane mimics called supported lipid bilayers (SLBs), stochastic virus-membrane binding interactions can be studied in depth while maintaining control over host receptor type and concentration. However, several experimental design challenges and quantitative image analysis limitations prevent the widespread use of this approach. One main challenge of SPT studies is the low signal-to-noise ratio of SPT videos, which is sometimes inevitable due to small particle sizes, low quantum yield of fluorescent dyes, and photobleaching. These situations could render current particle tracking software to yield biased binding kinetic data caused by intermittent tracking error. Hence, we developed an effective image restoration algorithm for SPT applications called STAWASP that reveals particles with a signal-to-noise ratio of 2.2 while preserving particle features. We tested our improvements to the SPT binding assay experiment and imaging procedures by monitoring X31 influenza virus binding to α2,3 sialic acid glycolipids. Our interests lie in how slight changes to the peripheral oligosaccharide structures can affect the binding rate and residence times of viruses. We were able to detect viruses binding weakly to a glycolipid called GM3, which was undetected via assays such as surface plasmon resonance. The binding rate was around 28 folds higher when the virus bound to a different glycolipid called GD1a, which has a sialic acid group extending further away from the bilayer surface than GM3. The improved imaging allowed us to obtain binding residence time distributions that reflect an adhesion-strengthening mechanism via multivalent bonds. We empirically fitted these distributions using a time-dependent unbinding rate parameter, koff, which diverges from standard treatment of koff as a constant. We further explain how to convert these models to fit ensemble-averaged binding data obtained by assays such as surface plasmon resonance.

α-SNAP regulates dynamic, on-site assembly and calcium selectivity of Orai1 channels

Ion channel subunits typically assemble in the endoplasmic reticulum. α-SNAP orchestrates a unique assembly and calcium selectivity of Orai1 subunits into functional multimers. Dynamic assembly of Orai1 and its dependence on α-SNAP could enable localization of calcium signals and regulation of rate and amount of calcium influx.

Orai1 forms a highly calcium-selective pore of the calcium release activated channel, and α-SNAP is necessary for its function. Here we show that α-SNAP regulates on-site assembly of Orai1 dimers into calcium-selective multimers. We find that Orai1 is a dimer in resting primary mouse embryonic fibroblasts but displays variable stoichiometry in the plasma membrane of store-depleted cells. Remarkably, α-SNAP depletion induces formation of higher-order Orai1 oligomers, which permeate significant levels of sodium via Orai1 channels. Sodium permeation in α-SNAP–deficient cells cannot be corrected by tethering multiple Stim1 domains to Orai1 C-terminal tail, demonstrating that α-SNAP regulates functional assembly and calcium selectivity of Orai1 multimers independently of Stim1 levels. Fluorescence nanoscopy reveals sustained coassociation of α-SNAP with Stim1 and Orai1, and α-SNAP–depleted cells show faster and less constrained mobility of Orai1 within ER-PM junctions, suggesting Orai1 and Stim1 coentrapment without stable contacts. Furthermore, α-SNAP depletion significantly reduces fluorescence resonance energy transfer between Stim1 and Orai1 N-terminus but not C-terminus. Taken together, these data reveal a unique role of α-SNAP in the on-site functional assembly of Orai1 subunits and suggest that this process may, in part, involve enabling crucial low-affinity interactions between Orai1 N-terminus and Stim1.

CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data

Background

Time-lapse analysis of cellular images is an important and growing need in biology. Algorithms for cell tracking are widely available; what researchers have been missing is a single open-source software package to visualize standard tracking output (from software like CellProfiler) in a way that allows convenient assessment of track quality, especially for researchers tuning tracking parameters for high-content time-lapse experiments. This makes quality assessment and algorithm adjustment a substantial challenge, particularly when dealing with hundreds of time-lapse movies collected in a high-throughput manner.

Results

We present CellProfiler Tracer, a free and open-source tool that complements the object tracking functionality of the CellProfiler biological image analysis package. Tracer allows multi-parametric morphological data to be visualized on object tracks, providing visualizations that have already been validated within the scientific community for time-lapse experiments, and combining them with simple graph-based measures for highlighting possible tracking artifacts.

Conclusions

CellProfiler Tracer is a useful, free tool for inspection and quality control of object tracking data, available from http://www.cellprofiler.org/tracer/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0759-x) contains supplementary material, which is available to authorized users.

Imaging live cells at the nanometer-scale with single-molecule microscopy: obstacles and achievements in experiment optimization for microbiology

Single-molecule fluorescence microscopy enables biological investigations inside living cells to achieve millisecond- and nanometer-scale resolution. Although single-molecule-based methods are becoming increasingly accessible to non-experts, optimizing new single-molecule experiments can be challenging, in particular when super-resolution imaging and tracking are applied to live cells. In this review, we summarize common obstacles to live-cell single-molecule microscopy and describe the methods we have developed and applied to overcome these challenges in live bacteria. We examine the choice of fluorophore and labeling scheme, approaches to achieving single-molecule levels of fluorescence, considerations for maintaining cell viability, and strategies for detecting single-molecule signals in the presence of noise and sample drift. We also discuss methods for analyzing single-molecule trajectories and the challenges presented by the finite size of a bacterial cell and the curvature of the bacterial membrane.

Objective comparison of particle tracking methods

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.

A quantitative comparison of single-dye tracking analysis tools using Monte Carlo simulations

Single-particle tracking (SPT) is widely used to study processes from membrane receptor organization to the dynamics of RNAs in living cells. While single-dye labeling strategies have the benefit of being minimally invasive, this comes at the expense of data quality; typically a data set of short trajectories is obtained and analyzed by means of the mean square displacements (MSD) or the distribution of the particles' displacements in a set time interval (jump distance, JD). To evaluate the applicability of both approaches, a quantitative comparison of both methods under typically encountered experimental conditions is necessary. Here we use Monte Carlo simulations to systematically compare the accuracy of diffusion coefficients (D-values) obtained for three cases: one population of diffusing species, two populations with different D-values, and a population switching between two D-values. For the first case we find that the MSD gives more or equally accurate results than the JD analysis (relative errors of D-values <6%). If two diffusing species are present or a particle undergoes a motion change, the JD analysis successfully distinguishes both species (relative error <5%). Finally we apply the JD analysis to investigate the motion of endogenous LPS receptors in live macrophages before and after treatment with methyl-β-cyclodextrin and latrunculin B.

Image analysis in fluorescence microscopy: bacterial dynamics as a case study

Fluorescence microscopy is the primary tool for studying complex processes inside individual living cells. Technical advances in both molecular biology and microscopy have made it possible to image cells from many genetic and environmental backgrounds. These images contain a vast amount of information, which is often hidden behind various sources of noise, convoluted with other information and stochastic in nature. Accessing the desired biological information therefore requires new tools of computational image analysis and modeling. Here, we review some of the recent advances in computational analysis of images obtained from fluorescence microscopy, focusing on bacterial systems. We emphasize techniques that are readily available to molecular and cell biologists but also point out examples where problem-specific image analyses are necessary. Thus, image analysis is not only a toolkit to be applied to new images but also an integral part of the design and implementation of a microscopy experiment.

Interactive, computer-assisted tracking of speckle trajectories in fluorescence microscopy: application to actin polymerization and membrane fusion

Analysis of particle trajectories in images obtained by fluorescence microscopy reveals biophysical properties such as diffusion coefficient or rates of association and dissociation. Particle tracking and lifetime measurement is often limited by noise, large mobilities, image inhomogeneities, and path crossings. We present Speckle TrackerJ, a tool that addresses some of these challenges using computer-assisted techniques for finding positions and tracking particles in different situations. A dynamic user interface assists in the creation, editing, and refining of particle tracks. The following are results from application of this program: 1), Tracking single molecule diffusion in simulated images. The shape of the diffusing marker on the image changes from speckle to cloud, depending on the relationship of the diffusion coefficient to the camera exposure time. We use these images to illustrate the range of diffusion coefficients that can be measured. 2), We used the program to measure the diffusion coefficient of capping proteins in the lamellipodium. We found values ∼0.5 μm(2)/s, suggesting capping protein association with protein complexes or the membrane. 3), We demonstrate efficient measuring of appearance and disappearance of EGFP-actin speckles within the lamellipodium of motile cells that indicate actin monomer incorporation into the actin filament network. 4), We marked appearance and disappearance events of fluorescently labeled vesicles to supported lipid bilayers and tracked single lipids from the fused vesicle on the bilayer. This is the first time, to our knowledge, that vesicle fusion has been detected with single molecule sensitivity and the program allowed us to perform a quantitative analysis. 5), By discriminating between undocking and fusion events, dwell times for vesicle fusion after vesicle docking to membranes can be measured.

Object tracking with particle filtering in fluorescence microscopy images: application to the motion of neurofilaments in axons

Neurofilaments are long flexible cytoplasmic protein polymers that are transported rapidly but intermittently along the axonal processes of nerve cells. Current methods for studying this movement involve manual tracking of fluorescently tagged neurofilament polymers in videos acquired by time-lapse fluorescence microscopy. Here, we describe an automated tracking method that uses particle filtering to implement a recursive Bayesian estimation of the filament location in successive frames of video sequences. To increase the efficiency of this approach, we take advantage of the fact that neurofilament movement is confined within the boundaries of the axon. We use piecewise cubic spline interpolation to model the path of the axon and then we use this model to limit both the orientation and location of the neurofilament in the particle tracking algorithm. Based on these two spatial constraints, we develop a prior dynamic state model that generates significantly fewer particles than generic particle filtering, and we select an adequate observation model to produce a robust tracking method. We demonstrate the efficacy and efficiency of our method by performing tracking experiments on real time-lapse image sequences of neurofilament movement, and we show that the method performs well compared to manual tracking by an experienced user. This spatially constrained particle filtering approach should also be applicable to the movement of other axonally transported cargoes.

plusTipTracker: Quantitative image analysis software for the measurement of microtubule dynamics

Here we introduce plusTipTracker, a Matlab-based open source software package that combines automated tracking, data analysis, and visualization tools for movies of fluorescently-labeled microtubule (MT) plus end binding proteins (+TIPs). Although +TIPs mark only phases of MT growth, the plusTipTracker software allows inference of additional MT dynamics, including phases of pause and shrinkage, by linking collinear, sequential growth tracks. The algorithm underlying the reconstruction of full MT trajectories relies on the spatially and temporally global tracking framework described in Jaqaman et al. (2008). Post-processing of track populations yields a wealth of quantitative phenotypic information about MT network architecture that can be explored using several visualization modalities and bioinformatics tools included in plusTipTracker. Graphical user interfaces enable novice Matlab users to track thousands of MTs in minutes. In this paper, we describe the algorithms used by plusTipTracker and show how the package can be used to study regional differences in the relative proportion of MT subpopulations within a single cell. The strategy of grouping +TIP growth tracks for the analysis of MT dynamics has been introduced before (Matov et al., 2010). The numerical methods and analytical functionality incorporated in plusTipTracker substantially advance this previous work in terms of flexibility and robustness. To illustrate the enhanced performance of the new software we thus compare computer-assembled +TIP-marked trajectories to manually-traced MT trajectories from the same movie used in Matov et al. (2010).

Analysis of video-based microscopic particle trajectories using Kalman filtering

The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes.

Tracking and quantitative analysis of dynamic movements of cells and particles

Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.