Single Particle Tracking: From Theory to Biophysical Applications

Benchmarking Thiolate-Driven Photoswitching of Cyanine Dyes

Carbocyanines are among the best performing dyes in single-molecule localization microscopy (SMLM), but their performance critically relies on optimized photoswitching buffers. Here, we study the versatile role of thiols in cyanine photoswitching at varying intensities generated in a single acquisition by a microelectromechanical systems (MEMS) mirror placed in the excitation path. The key metrics we have analyzed as a function of the thiolate concentration are photon budget, on-state and off-state lifetimes and the corresponding impact on image resolution. We show that thiolate acts as a concentration bandpass filter for the maximum achievable resolution and determine a minimum of ∼1 mM is necessary to facilitate SMLM measurements. We also identify a concentration bandwidth of 1-16 mM in which the photoswitching performance can be balanced between high molecular brightness and high off-time to on-time ratios. Furthermore, we monitor the performance of the popular oxygen scavenger system based on glucose and glucose oxidase over time and show simple measures to avoid acidification during prolonged measurements. Finally, the impact of buffer settings is quantitatively tested on the distribution of the glucose transporter protein 4 within the plasma membrane of adipocytes. Our work provides a general strategy for achieving optimal resolution in SMLM with relevance for the development of novel buffers and dyes.

Spatial Stochastic Model of the Pre-B Cell Receptor

Survival and proliferation of immature B lymphocytes requires expression and tonic signaling of the pre-B cell receptor (pre-BCR). This low level, ligand-independent signaling is likely achieved through frequent, but short-lived, homo interactions. Tonic signaling is also central in the pathology of precursor B acute lymphoblastic leukemia (B-ALL). In order to understand how repeated, transient events can lead to sustained signaling and to assess the impact of receptor accumulation induced by the membrane landscape, we developed a spatial stochastic model of receptor aggregation and downstream signaling events. Our rule- and agent-based model builds on previous mature BCR signaling models and incorporates novel parameters derived from single particle tracking of pre-BCR on surfaces of two different B-ALL cell lines, 697 and Nalm6. Live cell tracking of receptors on the two cell lines revealed characteristic differences in their dimer dissociation rates and diffusion coefficients. We report here that these differences affect pre-BCR aggregation and consequent signal initiation events. Receptors on Nalm6 cells, which have a lower off-rate and lower diffusion coefficient, more frequently form higher order oligomers than pre-BCR on 697 cells, resulting in higher levels of downstream phosphorylation in the Nalm6 cell line.

Characterizing Properties of Biomolecular Condensates Below the Diffraction Limit In Vivo

Fluorescence microscopy assays enable the investigation of endogenous biomolecular condensates directly in their cellular context. With appropriate experimental designs, these assays yield quantitative information on condensate material properties and inform on biophysical mechanisms of condensate formation. Single-molecule super-resolution and tracking experiments grant access to the smallest condensates and early condensation stages not resolved by conventional imaging approaches. Here, we discuss considerations for using single-molecule assays to extract quantitative information about biomolecular condensates directly in their cellular context.

Single-Particle Tracking of Virus Entry in Live Cells

Novel imaging technologies such as single-particle tracking provide tools to study the intricate process of virus infection in host cells. In this chapter, we provide an overview of studies in which single-particle tracking technologies were applied for the analysis of the viral entry pathways in the context of the live host cell. Single-particle tracking techniques have been dependent on advances in the fluorescent labeling microscopy method and image analysis. The mechanistic and kinetic insights offered by this technique will provide a better understanding of virus entry and may lead to a rational design of antiviral interventions.

Effects of morphology and size of nanoscale drug carriers on cellular uptake and internalization process: a review

In the field of targeted drug delivery, the effects of size and morphology of drug nanocarriers are of great importance and need to be discussed in depth. To be concise, among all the various shapes of nanocarriers, rods and tubes with a narrow cross-section are the most preferred shapes for the penetration of a cell membrane. In this regard, several studies have focused on methods to produce nanorods and nanotubes with controlled optimized size and aspect ratio (AR). Additionally, a non-spherical orientation could affect the cellular uptake process while a tangent angle of less than 45° is better at penetrating the membrane, and Ω = 90° is beneficial. Moreover, these nanocarriers show different behaviors when confronting diverse cells whose fields should be investigated in future studies. In this survey, a comprehensive classification based on carrier shape is first submitted. Then, the most commonly used methods for control over the size and shape of the carriers are reviewed. Finally, influential factors on the cellular uptake and internalization processes and related analytical methods for evaluating this process are discussed.

Coexistence of ergodicity and nonergodicity in the aging two-state random walks

The two-state stochastic phenomenon is observed in various systems and is attracting more interest, and it can be described by the two-state random walk (TSRW) model. The TSRW model is a typical two-state renewal process alternating between the continuous-time random walk state and the Lévy walk state, in both of which the sojourn time distributions follow a power law. In this paper, by discussing the statistical properties and calculating the ensemble averaged and time averaged mean squared displacement, the ergodic property and the ultimate diffusive behavior of the aging TSRW is studied. Results reveal that because of the two-state intermittent feature, ergodicity and nonergodicity can coexist in the aging TSRW, which behave as the time scalings of the time averages and ensemble averages not being identically equal. In addition, we find that the unique state occupation mechanism caused by the diverging mean of the sojourn times of one state, determines the aging TSRW's ultimate diffusive behavior at extremely large timescales, i.e., instead of the term with the larger diffusion exponent, the diffusion is surprisingly characterized by the term with the smaller one, which is distinctly different from previous conclusions and known results. At last, we note that the Lévy walk with rests model which also displays aging and ergodicity breaking, can be generalized by the TSRW model.

Calcite-Assisted Localization and Kinetics (CLocK) Microscopy

Localization-based super-resolution imaging techniques have improved the spatial resolution of optical microscopy well below the diffraction limit, yet encoding additional information into super-resolved images, such as anisotropy and orientation, remains a challenge. Here we introduce calcite-assisted localization and kinetics (CLocK) microscopy, a multiparameter super-resolution imaging technique easily integrated into any existing optical microscope setup at low cost and with straightforward analysis. By placing a rotating calcite crystal in the infinity space of an optical microscope, CLocK microscopy provides immediate polarization and orientation information while maintaining the ability to localize an emitter/scatterer with <10 nm resolution. Further, kinetic information an order of magnitude shorter than the integration time of the camera is encoded in the unique point spread function of a CLocK image, allowing for new mechanistic insight into dynamic processes such as single-nanoparticle dissolution and single-molecule surface-enhanced Raman scattering.

Enabling Spectrally Resolved Single-Molecule Localization Microscopy at High Emitter Densities

Single-molecule localization microscopy (SMLM) is a powerful super-resolution technique for elucidating structure and dynamics in the life- and material sciences. Simultaneously acquiring spectral information (spectrally resolved SMLM, sSMLM) has been hampered by several challenges: an increased complexity of the optical detection pathway, lower accessible emitter densities, and compromised spatio-spectral resolution. Here we present a single-component, low-cost implementation of sSMLM that addresses these challenges. Using a low-dispersion transmission grating positioned close to the image plane, the +1^stdiffraction order is minimally elongated and is analyzed using existing single-molecule localization algorithms. The distance between the 0^th and 1^st order provides accurate information on the spectral properties of individual emitters. This method enables a 5-fold higher emitter density while discriminating between fluorophores whose peak emissions are less than 15 nm apart. Our approach can find widespread use in single-molecule applications that rely on distinguishing spectrally different fluorophores under low photon conditions.

Time to reach the maximum for a stationary stochastic process

We consider a one-dimensional stationary time series of fixed duration T. We investigate the time t_{m} at which the process reaches the global maximum within the time interval [0,T]. By using a path-decomposition technique, we compute the probability density function P(t_{m}|T) of t_{m} for several processes, that are either at equilibrium (such as the Ornstein-Uhlenbeck process) or out of equilibrium (such as Brownian motion with stochastic resetting). We show that for equilibrium processes the distribution of P(t_{m}|T) is always symmetric around the midpoint t_{m}=T/2, as a consequence of the time-reversal symmetry. This property can be used to detect nonequilibrium fluctuations in stationary time series. Moreover, for a diffusive particle in a confining potential, we show that the scaled distribution P(t_{m}|T) becomes universal, i.e., independent of the details of the potential, at late times. This distribution P(t_{m}|T) becomes uniform in the "bulk" 1≪t_{m}≪T and has a nontrivial universal shape in the "edge regimes" t_{m}→0 and t_{m}→T. Some of these results have been announced in a recent letter [Europhys. Lett. 135, 30003 (2021)0295-507510.1209/0295-5075/ac19ee].

Synthetic Stochastic Motion Platform for Testing Single Particle Tracking Microscopes

We describe the design and implementation of a control system for testing the performance of single particle tracking microscopes with the method of synthetic motion. Single particle tracking (SPT) has become a common and powerful tool in the study of biomolecular transport in cellular biology, providing the ability to track individual biological macromolecules in their native environment. Existing methods for testing SPT techniques rely on physical simulations and there is a clear need for experimental-based schemes for both comparing different approaches and for characterizing the accuracy and precision of techniques on particular experimental setups. Synthetic motion, that is, using an actuator such as a nanopositioning stage to drive a particle along a known ground-truth trajectory, is a means for achieving these ends. However, the resolution, accuracy, and flexibility of this method is limited by the actuator static and dynamic characteristics. In this work we apply system identification and model inverse feedforward control to increase actuator bandwidth and address some common actuator nonlinearities, develop a set of dimensionless numbers that describe system limitations, and provide a set of guidelines for the practical use of synthetic motion in SPT.

Extracting Mural and Volumetric Growth Patterns of Platelet Aggregates on Engineered Surfaces by Use of an Entity Tracking Algorithm

Thrombosis is a major complication that can occur in both blood-contacting devices and regions and in regions of vascular damage. Microfluidic devices are popular templates to model various thrombogenic settings and to assess conditions that lead to bulk channel occlusion. However, area-averaged measurements miss the opportunity to extract real-time information on thrombus evolution and early dynamics of thrombus formation and propagation, which result in late-stage bulk channel occlusion. To clarify these dynamics, we have developed a standalone tracking algorithm that uses consecutive image connectivity and minimal centroid distance mappings to uniquely index all appearing thrombi in fluorescence time-lapse videos http://links.lww.com/ASAIO/A887 , and http://links.lww.com/ASAIO/A888 . This leads to measurements of all individual aggregates that can in turn be studied as ensembles. We applied tracking to fluorescence time-lapse videos http://links.lww.com/ASAIO/A887 , and http://links.lww.com/ASAIO/A888 of thrombosis across both collagen-functionalized substrate and across the surface of a roughened titanium alloy (Ti6Al4V) at a shear rate of 4000 s -1 . When comparing ensemble-averaged measurements to area-averaged metrics, we unveil immediate, steady thrombus growth at early phases on collagen surfaces and unstable thrombus attachment to roughened Ti6Al4V surfaces on Ti6Al4V surfaces. Additionally, we introduce tracked thrombus eccentricity and fluorescence intensity as additional volumetric measures of thrombus growth that relate back to the primary thrombosis mechanism at play. This work advocates for the complementation of surface macrostate metrics with characteristic thrombus microstate growth patterns to accurately predict critical thrombosis events.

Methods to analyze extracellular vesicles at single particle level

Extracellular vesicles (EVs) are nano-sized vesicles derived from cells that transport biomaterials between cells through biofluids. Due to their biological role and components, they are considered as potential drug carriers and for diagnostic applications. Today's advanced nanotechnology enables single-particle-level analysis that was difficult in the past due to its small size below the diffraction limit. Single EV analysis reveals the heterogeneity of EVs, which could not be discovered by various ensemble analysis methods. Understanding the characteristics of single EVs enables more advanced pathological and biological researches. This review focuses on the advanced techniques employed for EV analysis at the single particle level and describes the principles of each technique.

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

Single-molecule microscopy is advantageous in characterizing heterogeneous dynamics at the molecular level. However, there are several challenges that currently hinder the wide application of single molecule imaging in bio-chemical studies, including how to perform single-molecule measurements efficiently with minimal run-to-run variations, how to analyze weak single-molecule signals efficiently and accurately without the influence of human bias, and how to extract complete information about dynamics of interest from single-molecule data. As a new class of computer algorithms that simulate the human brain to extract data features, deep learning networks excel in task parallelism and model generalization, and are well-suited for handling nonlinear functions and extracting weak features, which provide a promising approach for single-molecule experiment automation and data processing. In this perspective, we will highlight recent advances in the application of deep learning to single-molecule studies, discuss how deep learning has been used to address the challenges in the field as well as the pitfalls of existing applications, and outline the directions for future development.

Transport in the Brain Extracellular Space: Diffusion, but Which Kind?

The mechanisms of transport of substances in the brain parenchyma have been a hot topic in scientific discussion in the past decade. This discussion was triggered by the proposed glymphatic hypothesis, which assumes a directed flow of cerebral fluid within the parenchyma, in contrast to the previous notion that diffusion is the main mechanism. However, when discussing the issue of “diffusion or non-diffusion”, much less attention was given to the question that diffusion itself can have a different character. In our opinion, some of the recently published results do not fit into the traditional understanding of diffusion. In this regard, we outline the relevant new theoretical approaches on transport processes in complex random media such as concepts of diffusive diffusivity and time-dependent homogenization, which expands the understanding of the forms of transport of substances based on diffusion.

Protein Desorption Kinetics Depends on the Timescale of Observation

The presence of so-called reversible and irreversible protein adsorption on solid surfaces is well documented in the literature and represents the basis for the development of nanoparticles and implant materials to control interactions in physiological environments. Here, using a series of complementary single-molecule tracking approaches appropriate for different timescales, we show that protein desorption kinetics is much more complex than the traditional reversible-irreversible binary picture. Instead, we find that the surface residence time distribution of adsorbed proteins transitions from power law to exponential behavior when measured over a large range of timescales (10^-2-10⁶ s). A comparison with macroscopic results obtained using a quartz crystal microbalance suggested that macroscopic measurements have generally failed to observe such nonequilibrium phenomena because they are obscured by ensemble-averaging effects. These findings provide new insights into the complex phenomena associated with protein adsorption and desorption.

A method for single particle tracking through a multimode fiber

Multimode optical fiber (MMF) endoscopes have recently gained widespread attention as a novel tool for imaging deep within tissue using light microscopy. We here present a method for particle tracking through the MMF, which overcomes the lack of a fast enough wide-field fluorescence imaging modality for this type of endoscope, namely a discrete implementation of orbital particle tracking. We achieve biologically relevant tracking speeds (up to 1.2 μm/s) despite using a slow SLM for the wavefront shaping. We demonstrate a tracking accuracy of λ/50 for a 0.3 NA fiber and show tracking of a pinhole moving to mimic Brownian motion with diffusion rates of up to 0.3 μm²/s.

Viscosity Measurement in Biocondensates Using Deep-Learning-Assisted Single-Particle Rotational Analysis

Viscoelastic characterization is of great importance for the investigation of biomolecular condensates. Single-particle-tracking-based rotational diffusion analysis of single nanorods is an effective approach for quantitative viscosity measurement. However, in the case of high background and noise with high-speed image acquisition, accurate extraction of diffusivity from the data is a challenging task. Here, we develop a novel frequency-domain-based deep learning (DL) method for single nanorod rotational tracking analysis. We synthesized Brownian rotational time-series data for training, designed a data preprocessing module to reduce the effect of noise, and extracted rotational diffusion coefficient using recurrent neural networks in the frequency domain. Compared with the traditional curve-fitting-based methods, our method shows higher accuracy and a wider detection range for viscosity measurement. We verified our method using experimental data from plasmonic imaging of single gold nanorods (AuNRs) in glycerol solution and PGL droplets. Our method can be potentially applied to the viscosity measurement of different biomolecular condensates in vitro and in vivo.

Single-shot optical surface profiling using extended depth of field 3D microscopy

The measurement of three-dimensional samples at high speed is essential for many applications, either due to the requirement for measuring samples that change fast over time, or due to the requirement of reducing the scanning time, and therefore inspection cost, in industrial environments. Conventionally, the measurement of surface topographies at high resolution typically requires an axial scanning of the sample. We report the implementation of a technique able to reconstruct surface topographies at high resolution, only from the acquisition of a single camera shot, dropping the need to perform an axial scan. A system prototype is reported and assessed as an ultra-fast optical surface profiler. We propose robust calibration and operation methods and algorithms to reconstruct surface topographies of optically-rough samples, and compare the experimental results with a reference confocal optical profiler.

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.

Cholesterol-Dependent Dynamics of the Serotonin1A Receptor Utilizing Single Particle Tracking: Analysis of Diffusion Modes

G protein-coupled receptors (GPCRs) are signaling hubs in cell membranes that regulate a wide range of physiological processes and are popular drug targets. Serotonin_1A receptors are important members of the GPCR family and are implicated in neuropsychiatric disorders. Cholesterol is a key constituent of higher eukaryotic membranes and is believed to contribute to the segregated distribution of membrane constituents into domains. To explore the role of cholesterol in lateral dynamics of GPCRs, we utilized single particle tracking (SPT) to monitor diffusion of serotonin_1A receptors under acute and chronic cholesterol-depleted conditions. Our results show that the short-term diffusion coefficient of the receptor decreases upon cholesterol depletion, irrespective of the method of cholesterol depletion. Analysis of SPT trajectories revealed that relative populations of receptors undergoing various modes of diffusion change upon cholesterol depletion. Notably, in cholesterol-depleted cells, we observed an increase in the confined population of the receptor accompanied by a reduction in diffusion coefficient for chronic cholesterol depletion. These results are supported by our recent work and present observations that show polymerization of G-actin in response to chronic cholesterol depletion. Taken together, our results bring out the interdependence of cholesterol and actin cytoskeleton in regulating diffusion of GPCRs in membranes.

Phase image correlation spectroscopy for detecting microfluidic dynamics

It is essential to quantify the physical properties and the dynamics of flowing particles in many fields, especially in microfluidic-related applications. We propose phase image correlation spectroscopy (PICS) as a versatile tool to quantify the concentration, hydro-diameter, and flow velocity of unlabeled particles by correlating the pixels of the phase images taken on flowing particles in a microfluidic device. Compared with conventional image correlation spectroscopy, PICS is minimally invasive, relatively simple, and more efficient, since it utilizes the intrinsic phase of the particles to provide a contrast instead of fluorescent labeling. We demonstrate the feasibility of PICS by measuring flowing polymethylmethacrylate (PMMA) microspheres and yeast in a microfluidic device. We can envisage that PICS will become an essential inspection tool in biomedicine and industry.

Real-Time Feedback-Driven Single-Particle Tracking: A Survey and Perspective

Real-time feedback-driven single-particle tracking (RT-FD-SPT) is a class of techniques in the field of single-particle tracking that uses feedback control to keep a particle of interest in a detection volume. These methods provide high spatiotemporal resolution on particle dynamics and allow for concurrent spectroscopic measurements. This review article begins with a survey of existing techniques and of applications where RT-FD-SPT has played an important role. Each of the core components of RT-FD-SPT are systematically discussed in order to develop an understanding of the trade-offs that must be made in algorithm design and to create a clear picture of the important differences, advantages, and drawbacks of existing approaches. These components are feedback tracking and control, ranging from simple proportional-integral-derivative control to advanced nonlinear techniques, estimation to determine particle location from the measured data, including both online and offline algorithms, and techniques for calibrating and characterizing different RT-FD-SPT methods. Then a collection of metrics for RT-FD-SPT is introduced to help guide experimentalists in selecting a method for their particular application and to help reveal where there are gaps in the techniques that represent opportunities for further development. Finally, this review is concluded with a discussion on future perspectives in the field.

Nanoparticle transport within non-Newtonian fluid flow in porous media

Control over dispersion of nanoparticles in polymer solutions through porous media is important for subsurface applications such as soil remediation and enhanced oil recovery. Dispersion is affected by the spatial heterogeneity of porous media, the non-Newtonian behavior of polymer solutions, and the Brownian motion of nanoparticles. Here, we use the Euler-Lagrangian method to simulate the flow of nanoparticles and inelastic non-Newtonian fluids (described by Meter model) in a range of porous media samples and injection rates. In one case, we use a fine mesh of more than 3 million mesh points to model nanoparticles transport in a sandstone sample. The results show that the velocity distribution of nanoparticles in the porous medium is non-Gaussian, which leads to the non-Fickian behavior of nanoparticles dispersion. Due to pore-space confinement, the long-time mean-square displacement of nanoparticles depends nonlinearly on time. Additionally, the gradient of shear stress in the pore space of the porous medium dictates the transport behavior of nanoparticles in the porous medium. Furthermore, the Brownian motion of nanoparticles increases the dispersion of nanoparticles along the longitudinal and transverse direction.

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neuronal traces. In particular, means to characterize the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, measuring for each point of a curve the characteristic length where it is fully 3D as opposed to being embedded in a 2D plane or 1D line. The larger this metric is and the more complex the local 3D loops and turns of the curve are. Available through the GeNePy3D open-source Python quantitative geometry library (https://genepy3d.gitlab.io), this approach termed nAdder offers new means of describing and comparing axonal and dendritic arbors. We validate this metric on simulated and real traces. By reanalysing a published zebrafish larva whole brain dataset, we show its ability to characterize different population of commissural axons, distinguish afferent connections to a target region and differentiate portions of axons and dendrites according to their behavior, shedding new light on the stereotypical nature of neurites' local geometry.

Fluorescence Resonance Energy Transfer Measurements in Polymer Science: A Review

Fluorescence resonance energy transfer (FRET) is a non-invasive characterization method for studying molecular structures and dynamics, providing high spatial resolution at nanometer scale. Over the past decades, FRET-based measurements are developed and widely implemented in synthetic polymer systems for understanding and detecting a variety of nanoscale phenomena, enabling significant advances in polymer science. In this review, the basic principles of fluorescence and FRET are briefly discussed. Several representative research areas are highlighted, where FRET spectroscopy and imaging can be employed to reveal polymer morphology and kinetics. These examples include understanding polymer micelle formation and stability, detecting guest molecule release from polymer host, characterizing supramolecular assembly, imaging composite interfaces, and determining polymer chain conformations and their diffusion kinetics. Finally, a perspective on the opportunities of FRET-based measurements is provided for further allowing their greater contributions in this exciting area.

Classification-based motion analysis of single-molecule trajectories using DiffusionLab

Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available.

LFA1 Activation: Insights from a Single-Molecule Approach

Integrin LFA1 is a cell adhesion receptor expressed exclusively in leukocytes, and plays crucial roles in lymphocyte trafficking, antigen recognition, and effector functions. Since the discovery that the adhesiveness of LFA1 can be dynamically changed upon stimulation, one challenge has been understanding how integrins are regulated by inside-out signaling coupled with macromolecular conformational changes, as well as ligand bindings that transduce signals from the extracellular domain to the cytoplasm in outside-in signaling. The small GTPase Rap1 and integrin adaptor proteins talin1 and kindlin-3 have been recognized as critical molecules for integrin activation. However, their cooperative regulation of integrin adhesiveness in lymphocytes requires further research. Recent advances in single-molecule imaging techniques have revealed dynamic molecular processes in real-time and provided insight into integrin activation in cellular environments. This review summarizes integrin regulation and discusses new findings regarding the bidirectionality of LFA1 activation and signaling processes in lymphocytes.

Quantum Dots Tracking Endocytosis and Transport of Proteins Displayed by Mammalian Cells

Mammalian cell display technology uses eukaryotic protein expression system to display proteins on cell surfaces and has become an important method in biological research. Although mammalian cell display technology has many advantages and development potential, certain attributes of the displayed protein remain uncharacterized, such as whether the displayed proteins re-enter the cell and how displayed proteins move into the cell. Here, we present the endocytosis mechanism, motility behavior, and transport kinetics of displayed proteins determined using HaloTag as the displayed protein and quantum dot-based single-particle tracking. The displayed protein enters the cell through clathrin-mediated endocytosis and is transported through the cell in three stages, which is dependent on microfilaments and microtubules. The dynamic information obtained in this study provides answers to questions about endocytosis and postendocytosis transport of displayed proteins and, therefore, is expected to facilitate the development of surface display technology.

Spatiotemporal three-dimensional transport dynamics of endocytic cargos and their physical regulations in cells

Intracellular transport, regulated by complex cytoarchitectures and active driving forces, is crucial for biomolecule translocations and relates to many cellular functions. Despite extensive knowledge obtained from two-dimensional (2D) experiments, the real three-dimensional (3D) spatiotemporal characteristics of intracellular transport is still unclear. With 3D single-particle tracking, we comprehensively studied the transport dynamics of endocytic cargos. With varying timescale, the intracellular transport changes from thermal-dominated 3D-constrained motion to active-dominated quasi-2D motion. Spatially, the lateral motion is heterogeneous with peripheral regions being faster than perinuclear regions, while the axial motion is homogeneous across the cells. We further confirmed that such anisotropy and heterogeneity of vesicle transport result from actively directed motion on microtubules. Strikingly, inside the vesicles, we observed endocytic nanoparticles make diffusive motions on their inner membranes when microtubules are absent, suggesting endocytic cargos are normally localized at the inner vesicle membranes through a physical connection to the microtubules outside during transport.

•

Endocytic transport changes from 3D-constrained to quasi-2D motions with timescales

•

Lateral motion is heterogeneous while axial motion is homogeneous across the cells

•

Microtubules cause the anisotropy and heterogeneity of vesicle transport

•

Endocytic particles make diffusive motion on the inner membrane of vesicles

Direct Observation of Endocytosis Dynamics of Anti-ErbB Modified Single Nanocargoes

The ErbB receptor family, including the epidermal growth factor receptor (EGFR) and ErbB2/3/4, regulate cell proliferation, differentiation, apoptosis, motility, etc., and their abnormalities can cause cancer and other diseases. Ligand-induced endocytosis of ErbB receptors is the key to various cancer treatment strategies, and different techniques have been developed to study this important process. Among them, single particle tracking (SPT) can reveal the spatiotemporal heterogeneity of ErbB receptors on the live cell membrane and has been used to characterize the EGFR dimerization process. Herein, we studied the endocytosis dynamics of two different ErbB receptors using dark-field microscopy. With anti-ErbB modified plasmonic gold nanorods (AuNRs) as probes, we compared the trajectories of individual anti-EGFR AuNRs (cAuNRs) and anti-ErbB AuNRs (tAuNRs) interacting with MCF-7 cells in situ in real time. The results revealed that the internalization rate of cAuNRs was faster than that of tAuNRs. Detailed SPT analysis suggests that cAuNRs enter cells through EGFR endocytosis pathway, and multiple intracellular transport modes sort the cAuNRs away from the transmembrane site. In contrast, the endocytosis resistance of ErbB2 slows down the cellular uptake rate of tAuNRs and causes some tAuNRs-ErbB2 complexes to be confined on the membrane with "circular" and "rolling circle" motions for a much longer time. Our results provide insights into the endocytosis process of the ErbB receptor family at the nanometer scale and could be potentially useful to develop cancer treatment strategies.

When Super-Resolution Localization Microscopy Meets Carbon Nanotubes

We recently assisted in a revolution in the realm of fluorescence microscopy triggered by the advent of super-resolution techniques that surpass the classic diffraction limit barrier. By providing optical images with nanometer resolution in the far field, super-resolution microscopy (SRM) is currently accelerating our understanding of the molecular organization of bio-specimens, bridging the gap between cellular observations and molecular structural knowledge, which was previously only accessible using electron microscopy. SRM mainly finds its roots in progress made in the control and manipulation of the optical properties of (single) fluorescent molecules. The flourishing development of novel fluorescent nanostructures has recently opened the possibility of associating super-resolution imaging strategies with nanomaterials’ design and applications. In this review article, we discuss some of the recent developments in the field of super-resolution imaging explicitly based on the use of nanomaterials. As an archetypal class of fluorescent nanomaterial, we mainly focus on single-walled carbon nanotubes (SWCNTs), which are photoluminescent emitters at near-infrared (NIR) wavelengths bearing great interest for biological imaging and for information optical transmission. Whether for fundamental applications in nanomaterial science or in biology, we show how super-resolution techniques can be applied to create nanoscale images “in”, “of” and “with” SWCNTs.

Liquid-liquid phase separation as an organizing principle of intracellular space: overview of the evolution of the cell compartmentalization concept

At the turn of the twenty-first century, fundamental changes took place in the understanding of the structure and function of proteins and then in the appreciation of the intracellular space organization. A rather mechanistic model of the organization of living matter, where the function of proteins is determined by their rigid globular structure, and the intracellular processes occur in rigidly determined compartments, was replaced by an idea that highly dynamic and multifunctional "soft matter" lies at the heart of all living things. According this "new view", the most important role in the spatio-temporal organization of the intracellular space is played by liquid-liquid phase transitions of biopolymers. These self-organizing cellular compartments are open dynamic systems existing at the edge of chaos. They are characterized by the exceptional structural and compositional dynamics, and their multicomponent nature and polyfunctionality provide means for the finely tuned regulation of various intracellular processes. Changes in the external conditions can cause a disruption of the biogenesis of these cellular bodies leading to the irreversible aggregation of their constituent proteins, followed by the transition to a gel-like state and the emergence of amyloid fibrils. This work represents a historical overview of changes in our understanding of the intracellular space compartmentalization. It also reflects methodological breakthroughs that led to a change in paradigms in this area of science and discusses modern ideas about the organization of the intracellular space. It is emphasized here that the membrane-less organelles have to combine a certain resistance to the changes in their environment and, at the same time, show high sensitivity to the external signals, which ensures the normal functioning of the cell.

Nanodelivery of nucleic acids

There is growing need for a safe, efficient, specific and non-pathogenic means for delivery of gene therapy materials. Nanomaterials for nucleic acid delivery offer an unprecedented opportunity to overcome these drawbacks; owing to their tunability with diverse physico-chemical properties, they can readily be functionalized with any type of biomolecules/moieties for selective targeting. Nucleic acid therapeutics such as antisense DNA, mRNA, small interfering RNA (siRNA) or microRNA (miRNA) have been widely explored to modulate DNA or RNA expression Strikingly, gene therapies combined with nanoscale delivery systems have broadened the therapeutic and biomedical applications of these molecules, such as bioanalysis, gene silencing, protein replacement and vaccines. Here, we overview how to design smart nucleic acid delivery methods, which provide functionality and efficacy in the layout of molecular diagnostics and therapeutic systems. It is crucial to outline some of the general design considerations of nucleic acid delivery nanoparticles, their extraordinary properties and the structure-function relationships of these nanomaterials with biological systems and diseased cells and tissues.

Uncovering the F-Actin-Based Nuclear Egress Mechanism of Newly Synthesized Influenza A Virus Ribonucleoprotein Complexes by Single-Particle Tracking

Nuclear trafficking of viral genome is an essential cellular process in the life cycles of viruses. Despite substantial progress in uncovering a wide variety of complicated mechanisms of virus entry, intracellular transport of viral components, virus assembly, and egress, the temporal and spatial dynamics of viral genes trafficking within the nucleus remains poorly understood. Herein, using single-particle tracking, we explored the real-time dynamic nuclear trafficking of influenza A virus (IAV) genes packaged as the viral ribonucleoprotein complexes (vRNPs) by combining a four-plasmid DNA transfection system for the reconstruction of green fluorescent protein (GFP)-labeled vRNPs and a spinning disk super-resolution fluorescence microscope. We found that IAV infection significantly induced the formation of actin microfilaments (F-actin) in the nucleus. In combination with the fluorescent protein-tagged nuclear F-actin probe, we visualized the directed movement of GFP-labeled vRNPs foci along the nuclear F-actin with a speed of 0.18 μm/s, which is similar to the microfilaments-dependent slow directed motion of IAVs in the cytoplasm. The disruption of nuclear F-actin after treatment with microfilament inhibitors caused a considerable decrease in vRNPs motility and suppressed the nuclear export of newly produced vRNPs, indicating that the slow, directed movement plays a crucial role in facilitating the nuclear egress of vRNPs. Our findings identified a nuclear F-actin-dependent pathway for IAV vRNPs transporting from the nucleus into the cytoplasm, which may in turn uncover a novel target for antiviral treatment.

Motion of an active particle with dynamical disorder

We propose a model for investigating the motion of a single active particle in a heterogeneous environment where the heterogeneity may arise due to crowding, conformational fluctuations and/or slow rearrangement of the surroundings. Describing the active particle in terms of the Ornstein-Uhlenbeck process (OUP) and incorporating heterogeneity in a thermal bath using two separate models, namely "diffusing diffusivity" and "switching diffusion", we explore the essential dynamical properties of the particle for its one-dimensional motion. In addition, we show how the dynamical behavior is controlled by dynamical variables associated with active noise such as strength and persistence time. Our model is relevant in the context of single particle dynamics in a crowded environment, driven by activity.

Displacement Statistics of Unhindered Single Molecules Show no Enhanced Diffusion in Enzymatic Reactions

Recent studies have sparked debate over whether catalytic reactions enhance the diffusion coefficients D of enzymes. Through high statistics of the transient (600 μs) displacements of unhindered single molecules freely diffusing in common buffers, we here quantify D for four enzymes under catalytic turnovers. We thus formulate how ∼ ±1% precisions may be achieved for D, and show no changes in diffusivity for catalase, urease, aldolase, and alkaline phosphatase under the application of wide concentration ranges of substrates. Our single-molecule approach thus overcomes potential limitations and artifacts underscored by recent studies to show no enhanced diffusion in enzymatic reactions.

The competing influence of surface roughness, hydrophobicity, and electrostatics on protein dynamics on a self-assembled monolayer

Surface morphology, in addition to hydrophobic and electrostatic effects, can alter how proteins interact with solid surfaces. Understanding the heterogeneous dynamics of protein adsorption on surfaces with varying roughness is experimentally challenging. In this work, we use single-molecule fluorescence microscopy to study the adsorption of α-lactalbumin protein on the glass substrate covered with a self-assembled monolayer (SAM) with varying surface concentrations. Two distinct interaction mechanisms are observed: localized adsorption/desorption and continuous-time random walk (CTRW). We investigate the origin of these two populations by simultaneous single-molecule imaging of substrates with both bare glass and SAM-covered regions. SAM-covered areas of substrates are found to promote CTRW, whereas glass surfaces promote localized motion. Contact angle measurements and atomic force microscopy imaging show that increasing SAM concentration results in both increasing hydrophobicity and surface roughness. These properties lead to two opposing effects: increasing hydrophobicity promotes longer protein flights, but increasing surface roughness suppresses protein dynamics resulting in shorter residence times. Our studies suggest that controlling hydrophobicity and roughness, in addition to electrostatics, as independent parameters could provide a means to tune desirable or undesirable protein interactions with surfaces.

Receptor tyrosine kinase MET ligand-interaction classified via machine learning from single-particle tracking data

Internalin B–mediated activation of the membrane-bound receptor tyrosine kinase MET is accompanied by a change in receptor mobility. Conversely, it should be possible to infer from receptor mobility whether a cell has been treated with internalin B. Here, we propose a method based on hidden Markov modeling and explainable artificial intelligence that machine-learns the key differences in MET mobility between internalin B–treated and –untreated cells from single-particle tracking data. Our method assigns receptor mobility to three diffusion modes (immobile, slow, and fast). It discriminates between internalin B–treated and –untreated cells with a balanced accuracy of >99% and identifies three parameters that are most affected by internalin B treatment: a decrease in the mobility of slow molecules (1) and a depopulation of the fast mode (2) caused by an increased transition of fast molecules to the slow mode (3). Our approach is based entirely on free software and is readily applicable to the analysis of other membrane receptors.

Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies

In situ transmission electron microscopy (TEM) studies of dynamic events produce large quantities of data especially under the form of images. In the important case of heterogeneous catalysis, environmental TEM (ETEM) under gas and temperature allows to follow a large population of supported nanoparticles (NPs) evolving under reactive conditions. Interpreting properly large image sequences gives precious information on the catalytic properties of the active phase by identifying causes for its deactivation. To perform a quantitative, objective and robust treatment, we propose an automatic procedure to track nanoparticles observed in Scanning ETEM (STEM in ETEM). Our approach involves deep learning and computer vision developments in multiple object tracking. At first, a registration step corrects the image displacements and misalignment inherent to the in situ acquisition. Then, a deep learning approach detects the nanoparticles on all frames of video sequences. Finally, an iterative tracking algorithm reconstructs their trajectories. This treatment allows to deduce quantitative and statistical features about their evolution or motion, such as a Brownian behavior and merging or crossing events. We treat the case of in situ calcination of palladium (oxide) / delta-alumina, where the present approach allows a discussion of operating processes such as Ostwald ripening or NP aggregative coalescence.

Single-Micelle and Single-Zinc-Particle Imaging Provides Insights into the Physical Processes Underpinning Organozinc Reactions in Water

Micelles on the surfaces of individual metallic zinc particles are imaged by fluorescence microscopy with sensitivity up to single micelles. These micelles are made fluorescent to enable imaging, through the incorporation of boron dipyrromethene fluorophores as representative organic molecular "cargo". Highlighting an advantage of this in situ and sensitive fluorescence technique, the same micelles are not visible by ex situ scanning electron microscopy/energy dispersive X-ray spectroscopy analysis. Examination of micellar solutions with zinc reveals an aging process: micelles do not immediately adhere to the zinc surfaces upon mixing but rather build up over time. Furthermore, at longer times, smaller zinc particles become fully encased in micelle "shells". Once adhered, micelles remain in the local regions of the zinc surface for the duration of the imaging experiments (>2 h). Single micelles are imaged in solution, and their molecular contents are characterized. Two-color fluorescence crossover experiments show that micelles adhered to the surface of the zinc exchange molecular contents with micelles in solution, achieving molecular exchange equilibrium in ∼2.5 h. Unique (non-ensemble averaged) exchange kinetics are displayed by micelles at different locations on the zinc surface, consistent with exchange kinetics of single micelles or small local clusters of micelles. The aging of the micellar solutions and the rate of exchange while on the surface of the zinc suggest that micelle mass transport processes may contribute to overall reaction barriers in sustainable organozinc cross-coupling reactions in micellar water. The observed aging of the system suggests routes for improvement of preparative, bench-scale synthetic reactions involving micellar preparations of organozinc compounds.

Optimization of Spot Efficiency of Double-Helix Point Spread Function and Its Application in Intracellular Imaging

The nano-scale spatial positioning of nanoparticles in tumor cells can be achieved through the double-helix point spread functions (DH-PSF). Nevertheless, certain issues such as low light intensity concentration of the main lobes, the influence of the side lobes, and the aberrations of the imaging system result in poor image quality and reduce the positioning accuracy of the fluorescent nanoparticles. In this paper, an iterative optimization algorithm that combines Laguerre–Gaussian modes and Zernike polynomials is proposed. The double-helix point spread function, constructed by the linear superposition of the Laguerre–Gaussian mode and Zernike polynomials, is used to express aberrations in the imaging system. The simulation results indicated that the light intensity concentration of the main lobes is increased by 45.51% upon the use of the optimization process. Based on the simulation results, the phase modulation plate was designed and processed while a 4f positioning imaging system was built. Human osteosarcoma cells, labeled by CdTe/CdS/ZnS quantum dots, were used as samples, and the position imaging experiment was carried out. The image information entropy was used as the clarity evaluation index. The experimental results showed that the image information entropy of the DH-PSF position imaging was reduced from 4.22 before optimization to 2.65 after optimization, and the image clarity was significantly improved. This result verified the effectiveness of the optimization method that was proposed in this work.

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.

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (∼5-40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a "black box" for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.

Shedding light on membrane rafts structure and dynamics in living cells

Cellular membranes are fundamental building blocks regulating an extensive repertoire of biological functions. These structures contain lipids and membrane proteins that are known to laterally self-aggregate in the plane of the membrane, forming defined membrane nanoscale domains essential for protein activity. Membrane rafts are described as heterogeneous, dynamic, and short-lived cholesterol- and sphingolipid-enriched membrane nanodomains (10-200 nm) induced by lipid-protein and lipid-lipid interactions. Those membrane nanodomains have been extensively characterized using model membranes and in silico methods. However, despite the development of advanced fluorescence microscopy techniques, undoubted nanoscale visualization by imaging techniques of membrane rafts in the membrane of unperturbed living cells is still uncompleted, increasing the skepticism about their existence. Here, we broadly review recent biochemical and microscopy techniques used to investigate membrane rafts in living cells and we enumerate persistent open questions to answer before unlocking the mystery of membrane rafts in living cells.

Valence-controlled protein conjugation on nanoparticles via re-arrangeable multivalent interactions of tandem repeat protein chains

Tandem repeat protein chains were wrapped around nanoparticles via re-arrangeable multivalent interactions for valence controlled protein conjugation.