Imaging the behavior of molecules in biological systems: breaking the 3D speed barrier with 3D multi-resolution microscopy

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.

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.

Progress and perspectives in single-molecule optical spectroscopy

We review some of the progress of single-molecule optical experiments in the past 20 years and propose some perspectives for the coming years. We particularly focus on methodological advances in fluorescence, super-resolution, photothermal contrast, and interferometric scattering and briefly discuss a few of the applications. These advances have enabled the exploration of new emitters and quantum optics; the chemistry and biology of complex heterogeneous systems, nanoparticles, and plasmonics; and the detection and study of non-fluorescing and non-absorbing nano-objects. We conclude by proposing some ideas for future experiments. The field will move toward more and better signals of a broader variety of objects and toward a sharper view of the surprising complexity of the nanoscale world of single (bio-)molecules, nanoparticles, and their nano-environments.

Sub-millisecond Translational and Orientational Dynamics of a Freely Moving Single Nanoprobe

This paper presents a new experiment with which we are able to measure the 3D translational motion of a single particle at 10 μs time resolution and with ∼10 nm spatial resolution while at the same time determining the 3D orientation of the same single particle with 250 μs time resolution. These high time resolutions are ∼40 times greater than previous simultaneous measurements of 3D position and 3D orientation. Detailed numerical simulations and experiments are used to demonstrate that the technique can measure 3D orientation at the shot-noise limit. The microscope is also able to simultaneously measure the length or width (with the other assumed) of the plasmonic nanorods used here in situ and nondestructively, which should yield a greater understanding of the underlying dynamics. This technique should be applicable to a broad range of problems where environments which change in space and time may perturb physical and chemical dynamics.

Leveraging lifetime information to perform real-time 3D single-particle tracking in noisy environments

A microscopy platform that leverages the arrival time of individual photons to enable 3D single-particle tracking of fast-moving (translational diffusion coefficient of ≃3.3 µm²/s) particles in high-background environments is reported here. It combines a hardware-based time-gating module, which enables the rate of photon processing to be as high as 100 MHz, with a two-photon-excited 3D single-particle tracking confocal microscope to enable high sample penetration depth. Proof-of-principle experiments where single quantum dots are tracked in solutions containing dye-stained cellulose, are shown with tracking performance markedly improved using the hardware-based time-gating module. Such a microscope design is anticipated to be of use to a variety of communities who wish to track single particles in cellular environments, which commonly have high fluorescence and scattering background.

Inferring effective forces for Langevin dynamics using Gaussian processes

Effective forces derived from experimental or in silico molecular dynamics time traces are critical in developing reduced and computationally efficient descriptions of otherwise complex dynamical problems. This helps motivate why it is important to develop methods to efficiently learn effective forces from time series data. A number of methods already exist to do this when data are plentiful but otherwise fail for sparse datasets or datasets where some regions of phase space are undersampled. In addition, any method developed to learn effective forces from time series data should be minimally a priori committal as to the shape of the effective force profile, exploit every data point without reducing data quality through any form of binning or pre-processing, and provide full credible intervals (error bars) about the prediction for the entirety of the effective force curve. Here, we propose a generalization of the Gaussian process, a key tool in Bayesian nonparametric inference and machine learning, which meets all of the above criteria in learning effective forces for the first time.

An Adaptive Real-Time 3D Single Particle Tracking Method for Monitoring Viral First Contacts

Here, an adaptive real-time 3D single particle tracking method is proposed, which is capable of capturing heterogeneous dynamics. Using a real-time measurement of a rapidly diffusing particle's positional variance, the 3D precision adaptive real-time tracking (3D-PART) microscope adjusts active-feedback parameters to trade tracking speed for precision on demand. This technique is demonstrated first on immobilized fluorescent nanoparticles, with a greater than twofold increase in the lateral localization precision (≈25 to ≈11 nm at 1 ms sampling) as well as a smaller increase in the axial localization precision (≈ 68 to ≈45 nm). 3D-PART also shows a marked increase in the precision when tracking freely diffusing particles, with lateral precision increasing from ≈100 to ≈70 nm for particles diffusing at 4 µm2 s-1 , although with a sacrifice in the axial precision (≈250 to ≈350 nm). This adaptive microscope is then applied to monitoring the viral first contacts of virus-like particles to the surface of live cells, allowing direct and continuous measurement of the viral particle at initial contact with the cell surface.

Single-Particle Dynamic Light Scattering: Shapes of Individual Nanoparticles

Metallic nanoparticles (MNPs) are prevalent in modern nanotechnologies due to their unique optical properties, chemical and photostability, and ease of manipulation. In particular, many recent advances have highlighted the importance of fundamentally understanding dynamic reconfiguration in MNP morphologies and compositions. Techniques to measure the shape of a single particle are lacking, however, often requiring immobilization, extensive numerical simulations, and irreversible alterations of the particle or its environment. In this work, we introduce "single-particle dynamic light scattering" (SP-DLS) as a far-field technique capable of analyzing the shape of individual, freely diffusing MNPs. Assuming symmetric-top rotors for MNPs and passively confining them to the focal volume of a dark-field microscope for long-term observation, we directly relate polarization dynamic fluctuations in the scattered light to the relative difference between the nondegenerate axes of individual particles. Our results show remarkable agreement with transmission electron microscopy analyses of the same population and allow for unprecedented measurements of the extent of prolate or oblate asphericity of nominally spherical MNPs in solution where the current implementation affords an asphericity detection limit of ∼2.5% assuming a 10% relative error. SP-DLS should serve as a powerful, nondestructive technique for characterizing the shapes of individual MNPs and other nanostructures.

A Protocol for Real-time 3D Single Particle Tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 µm²/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.

Single Particle Tracking: From Theory to Biophysical Applications

After three decades of developments, single particle tracking (SPT) has become a powerful tool to interrogate dynamics in a range of materials including live cells and novel catalytic supports because of its ability to reveal dynamics in the structure-function relationships underlying the heterogeneous nature of such systems. In this review, we summarize the algorithms behind, and practical applications of, SPT. We first cover the theoretical background including particle identification, localization, and trajectory reconstruction. General instrumentation and recent developments to achieve two- and three-dimensional subdiffraction localization and SPT are discussed. We then highlight some applications of SPT to study various biological and synthetic materials systems. Finally, we provide our perspective regarding several directions for future advancements in the theory and application of SPT.

Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions

Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.