Three-Dimensional Single Particle Tracking and Its Applications in Confined Environments

Single molecule analysis of CENP-A chromatin by high-speed atomic force microscopy

Chromatin accessibility is modulated in a variety of ways to create open and closed chromatin states, both of which are critical for eukaryotic gene regulation. At the single molecule level, how accessibility is regulated of the chromatin fiber composed of canonical or variant nucleosomes is a fundamental question in the field. Here, we developed a single-molecule tracking method where we could analyze thousands of canonical H3 and centromeric variant nucleosomes imaged by high-speed atomic force microscopy. This approach allowed us to investigate how changes in nucleosome dynamics in vitro inform us about transcriptional potential in vivo. By high-speed atomic force microscopy, we tracked chromatin dynamics in real time and determined the mean square displacement and diffusion constant for the variant centromeric CENP-A nucleosome. Furthermore, we found that an essential kinetochore protein CENP-C reduces the diffusion constant and mobility of centromeric nucleosomes along the chromatin fiber. We subsequently interrogated how CENP-C modulates CENP-A chromatin dynamics in vivo. Overexpressing CENP-C resulted in reduced centromeric transcription and impaired loading of new CENP-A molecules. From these data, we speculate that factors altering nucleosome mobility in vitro, also correspondingly alter transcription in vivo. Subsequently, we propose a model in which variant nucleosomes encode their own diffusion kinetics and mobility, and where binding partners can suppress or enhance nucleosome mobility.

Single-Molecule Fluorescence Investigations of Solute Transport Dynamics in Nanostructured Membrane Separation Materials

Many materials used for membrane separations are composed of nanoscale structures such as pores and domains. Such nanostructures often control the solute permeability and selectivity of the separation membranes. Thus, for future development of highly efficient separation membranes, it is important to understand the structural and chemical properties of these nanostructures and also their influences on solute transport dynamics. For the last two decades, single-molecule fluorescence techniques have been used to measure the detailed dynamics of solute molecules diffusing in various nanostructured materials, giving valuable insights into molecular transport mechanisms influenced by nanoscale material heterogeneity. This Perspective discusses recent single-molecule fluorescence studies on solute diffusion in materials relevant to membrane separations, including dense polymer films and nanoporous materials. These studies have revealed the formation and properties of nanostructures and unique transport dynamics of solute molecules manipulated by their confinement and partitioning to the nanostructures, which play key roles in membrane separations. This Perspective will also point out scientific challenges toward a thorough understanding of molecular-level mechanisms in membrane separations.

Information optimization of laser scanning microscopes for real-time feedback-driven single particle tracking

Real-time feedback-driven single particle tracking (RT-FD-SPT) is a class of microscopy techniques that uses measurements of finite excitation/detection volume in a feedback control loop to actuate that volume and track with high spatio-temporal resolution a single particle moving in three dimensions. A variety of methods have been developed, each defined by a set of user-defined choices. Selection of those values is typically done through ad hoc, off-line tuning for the best perceived performance. Here we present a mathematical framework, based on optimization of the Fisher information, to select those parameters such that the best information is acquired for estimating parameters of interest, such as the location of the particle, specifics of the excitation beam such as its dimensions or peak intensity, or the background noise. For concreteness, we focus on tracking of a fluorescently-labeled particle and apply this framework to determine the optimal parameters for three existing fluorescence-based RT-FD-SPT techniques with respect to particle localization.

Trap-and-Track for Characterizing Surfactants at Interfaces

Understanding the behavior of surfactants at interfaces is crucial for many applications in materials science and chemistry. Optical tweezers combined with trajectory analysis can become a powerful tool for investigating surfactant characteristics. In this study, we perform trap-and-track analysis to compare the behavior of cetyltrimethylammonium bromide (CTAB) and cetyltrimethylammonium chloride (CTAC) at water-glass interfaces. We use optical tweezers to trap a gold nanoparticle and statistically analyze the particle's movement in response to various surfactant concentrations, evidencing the rearrangement of surfactants adsorbed on glass surfaces. Our results show that counterions have a significant effect on surfactant behavior at the interface. The greater binding affinity of bromide ions to CTA+ micelle surfaces reduces the repulsion among surfactant head groups and enhances the mobility of micelles adsorbed on the interface. Our study provides valuable insights into the behavior of surfactants at interfaces and highlights the potential of optical tweezers for surfactant research. The development of this trap-and-track approach can have important implications for various applications, including drug delivery and nanomaterials.

Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes

The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle-substrate interactions. Thus, the detection of a modified particle-substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a "hopping"-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle-substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.

Imaging Dynamic Processes in Multiple Dimensions and Length Scales

Optical microscopy has become an invaluable tool for investigating complex samples. Over the years, many advances to optical microscopes have been made that have allowed us to uncover new insights into the samples studied. Dynamic changes in biological and chemical systems are of utmost importance to study. To probe these samples, multidimensional approaches have been developed to acquire a fuller understanding of the system of interest. These dimensions include the spatial information, such as the three-dimensional coordinates and orientation of the optical probes, and additional chemical and physical properties through combining microscopy with various spectroscopic techniques. In this review, we survey the field of multidimensional microscopy and provide an outlook on the field and challenges that may arise. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A guide for single-particle chromatin tracking in live cell nuclei

The emergence of labeling strategies and live cell imaging methods enables the imaging of chromatin in living cells at single digit nanometer resolution as well as milliseconds temporal resolution. These technical breakthroughs revolutionize our understanding of chromatin structure, dynamics and functions. Single molecule tracking algorithms are usually preferred to quantify the movement of these intranucleus elements to interpret the spatiotemporal evolution of the chromatin. In this review, we will first summarize the fluorescent labeling strategy of chromatin in live cells which will be followed by a sys-tematic comparison of live cell imaging instrumentation. With the proper microscope, we will discuss the image analysis pipelines to extract the biophysical properties of the chromatin. Finally, we expect to give practical suggestions to broad biologists on how to select methods and link to the model properly according to different investigation pur-poses. This article is protected by copyright. All rights reserved.

Simulating stochastic adsorption of diluted solute molecules at interfaces

This report uses Monte Carlo simulations to connect stochastic single-molecule and ensemble surface adsorption of molecules from dilute solutions. Monte Carlo simulations often use a fundamental time resolution to simulate each discrete step for each molecule. The adsorption rate obtained from such a simulation surprisingly contains an error compared to the results obtained from the traditional method. Simulating adsorption kinetics is interesting in many processes, such as mass transportation within cells, the kinetics of drug-receptor interactions, membrane filtration, and other general reaction kinetics in diluted solutions. Thus, it is important to understand the origin of the disagreement and find a way to correct the results. This report reviews the traditional model, explains the single-molecule simulations, and introduces a method to correct the results of adsorption rate. For example, one can bin finer time steps into time steps of interest to simulate the fractal diffusion or simply introduce a correction factor for the simulations. Then two model systems, self-assembled monolayer (SAM) and biosensing on the patterned surface, are simulated to check the accuracy of the equations. It is found that the adsorption rate of SAM is highly dependent on the conditions and the uncertainty is large. However, the biosensing system is relatively accurate. This is because the concentration gradient near the interface varies significantly with reaction conditions for SAMs while relatively stable for the biosensing system.

Untying the Gordian KNOT: Unbiased Single Particle Tracking Using Point Clouds and Adaptive Motion Analysis

Achieving mechanistic understanding of transport in complex environments such as inside cells or at polymer interfaces is challenging. We need better ways to image transport in 3-D and better single particle tracking algorithms to determine transport that are not systemically biased toward any classical motion model. Here we present an unbiased single particle tracking algorithm: Knowing Nothing Outside Tracking (KNOT). KNOT uses point clouds provided by iterative deconvolution to educate individual particle localizations and link particle positions between frames to achieve 2-D and 3-D tracking. Information from prior point clouds fuels an independent adaptive motion model for each particle to avoid global models that could introduce biases. KNOT competes with or surpasses other 2-D methods from the 2012 particle tracking challenge while accurately tracking adsorption dynamics of proteins on polymer surfaces and early endosome transport in live cells in 3-D. We apply KNOT to study 3-D endosome transport to reveal new physical insight into locally directed and diffusive transport in live cells. Our analysis demonstrates better accuracy in classifying local motion and its direction compared to previous methods, revealing intricate intracellular transport heterogeneities.

Super-resolution fluorescence imaging of extracellular environments

The extracellular matrix (ECM) is an important biophysical environment that plays a role in a number of physiological processes. The ECM is highly dynamic, with changes occurring as local, nanoscale, physicochemical variations in physical confinement and chemistry from the perspective of biological molecules. The length and time scale of ECM dynamics are challenging to measure with current spectroscopic techniques. Super-resolution fluorescence microscopy has the potential to probe local, nanoscale, physicochemical variations in the ECM. Here, we review super-resolution imaging and analysis methods and their application to study model nanoparticles and biomolecules within synthetic ECM hydrogels and the brain extracellular space (ECS). We provide a perspective of future directions for the field that can move super-resolution imaging of the ECM towards more biomedically-relevant samples. Overall, super-resolution imaging is a powerful tool that can increase our understanding of extracellular environments at new spatiotemporal scales to reveal ECM processes at the molecular-level.

Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images

Single Particle Tracking (SPT) is a well known class of tools for studying the dynamics of biological macromolecules moving inside living cells. In this paper, we focus on the problem of localization and parameter estimation given a sequence of segmented images. In the standard paradigm, the location of the emitter inside each frame of a sequence of camera images is estimated using, for example, Gaussian fitting (GF), and these locations are linked to provide an estimate of the trajectory. Trajectories are then analyzed by using Mean Square Displacement (MSD) or Maximum Likelihood Estimation (MLE) techniques to determine motion parameters such as diffusion coefficients. However, the problems of localization and parameter estimation are clearly coupled. Motivated by this, we have created an Expectation Maximization (EM) based framework for simultaneous localization and parameter estimation. We demonstrate this framework through two representative methods, namely, Sequential Monte Carlo combined with Expectation Maximization (SMC-EM) and Unscented Kalman Filter combined with Expectation Maximization (U-EM). Using diffusion in two-dimensions as a prototypical example, we conduct quantitative investigations on localization and parameter estimation performance across a wide range of signal to background ratios and diffusion coefficients and compare our methods to the standard techniques based on GF-MSD/MLE. To demonstrate the flexibility of the EM based framework, we do comparisons using two different camera models, an ideal camera with Poisson distributed shot noise but no readout noise, and a camera with both shot noise and the pixel-dependent readout noise that is common to scientific complementary metal-oxide semiconductor (sCMOS) camera. Our results indicate our EM based methods outperform the standard techniques, especially at low signal levels. While U-EM and SMC-EM have similar accuracy, U-EM is significantly more computationally efficient, though the use of the Unscented Kalman Filter limits U-EM to lower diffusion rates.

An Estimation Algorithm for General Linear Single Particle Tracking Models with Time-Varying Parameters

Single Particle Tracking (SPT) is a powerful class of methods for studying the dynamics of biomolecules inside living cells. The techniques reveal the trajectories of individual particles, with a resolution well below the diffraction limit of light, and from them the parameters defining the motion model, such as diffusion coefficients and confinement lengths. Most existing algorithms assume these parameters are constant throughout an experiment. However, it has been demonstrated that they often vary with time as the tracked particles move through different regions in the cell or as conditions inside the cell change in response to stimuli. In this work, we propose an estimation algorithm to determine time-varying parameters of systems that discretely switch between different linear models of motion with Gaussian noise statistics, covering dynamics such as diffusion, directed motion, and Ornstein-Uhlenbeck dynamics. Our algorithm consists of three stages. In the first stage, we use a sliding window approach, combined with Expectation Maximization (EM) to determine maximum likelihood estimates of the parameters as a function of time. These results are only used to roughly estimate the number of model switches that occur in the data to guide the selection of algorithm parameters in the second stage. In the second stage, we use Change Detection (CD) techniques to identify where the models switch, taking advantage of the off-line nature of the analysis of SPT data to create non-causal algorithms with better precision than a purely causal approach. Finally, we apply EM to each set of data between the change points to determine final parameter estimates. We demonstrate our approach using experimental data generated in the lab under controlled conditions.

Fluorescence Microscopic Investigations of Molecular Dynamics in Self-Assembled Nanostructures

Many analytical methods employ self-assembled nanostructured materials as chemical recognition media. Molecular permeation through these materials exhibits unique selectivity owing to nanoconfinement-induced enhancement of permeant-nanostructure interactions. This Personal Account introduces our efforts to investigate the detailed dynamics of single or a small number of molecules in nanostructured materials. We developed new experimental and analysis approaches built upon laser-based fluorescence microscopy to measure the detailed translational and orientational dynamics of molecules diffusing in horizontally-oriented, cylindrical nanostructures, including surfactant micelles, silica mesopores, block copolymer microdomains, and bolaamphiphile-based organic nanotubes. Our studies clarified nanoscale details on the structural/chemical heterogeneity of the nanostructures, and their impacts on molecular mass transport dynamics.

Transforming Separation Science with Single-Molecule Methods

Empirical optimization of the multiscale parameters underlying chromatographic and membrane separations leads to enormous resource waste and production costs. A bottom-up approach to understand the physical phenomena underlying challenges in separations is possible with single-molecule observations of solute-stationary phase interactions. We outline single-molecule fluorescence techniques that can identify key interactions under ambient conditions. Next, we describe how studying increasingly complex samples heightens the relevance of single-molecule results to industrial applications. Finally, we illustrate how separation methods that have not been studied at the single-molecule scale can be advanced, using chiral chromatography as an example case. We hope new research directions based on a molecular approach to separations will emerge based on the ideas, technologies, and open scientific questions presented in this Perspective.

Tracking Interfacial Dynamics of a Single Nanoparticle Using Plasmonic Scattering Interferometry

The ability to track interfacial dynamics of a single nanoparticle at the solution-solid interface is crucial for understanding physical, chemical, and biological processes, but it remains a challenge. Here, we demonstrated a plasmonic imaging technique that can track unlabeled nanoparticles at the solution-solid interface with high spatial and temporal resolutions. This technique is based on particle-induced interferometric scattering of a surface plasmonic wave, which results in a high vertical sensitivity. Using this ability, we tracked the trajectories of a single nanoparticle interacting with a surface, measured the hydrodynamically hindered diffusion of nanoparticles, and revealed the surface chemistry-dependent behavior of nanoparticles at the interface. The application for tracking formation of membranes from a lipid vesicle was demonstrated, indicating the potential for investigating a broad range of nano-objects at interfaces in a complex environment.