Parabolic Potential Surfaces Localize Charge Carriers in Nonblinking Long-Lifetime "Giant" Colloidal Quantum Dots.

Materials for studying biological interactions and for alternative energy applications are continuously under development. Semiconductor quantum dots are a major part of this landscape due to their tunable optoelectronic properties. Size-dependent quantum confinement effects have been utilized to create materials with tunable bandgaps and Auger recombination rates. Other mechanisms of electronic structural control are under investigation as not all of a material's characteristics are affected by quantum confinement. Demonstrated here is a new structure-property concept that imparts the ability to spatially localize electrons or holes within a core/shell heterostructure by tuning the charge carrier's kinetic energy on a parabolic potential energy surface. This charge carrier separation results in extended radiative lifetimes and in continuous emission at the single-nanoparticle level. These properties enable new applications for optics, facilitate novel approaches such as time-gated single-particle imaging, and create inroads for the development of other new advanced materials.

Amplifying Intermolecular Events by Streptavidin-Induced Proximity.

Weak interactions between biomolecules play important roles in many cellular functions. Structural and kinetic analyses of these interactions, however, have been hindered by the transient nature of such events. Here, we pointed out a general approach to overcome this obstacle─anchoring the molecular partners to streptavidin hosts─and achieved constrained proximity and stoichiometry for the sought-after molecular coupling. We elaborated this idea through a series of DNA hybridization reactions and quantitatively characterized them using single-molecule experiments. Compared to a nominally 1 µM solution, for example, the streptavidin-induced proximity (SIP) amounted to an effective molarity of ∼10-30 µM for the binding partners. There was also a significantly increased proportion of molecular association, manifested in both ensemble population and single-molecule residence time. As an application example, we showed how SIP enabled the observation and quantitative characterization of an unstable complex between Cas9-RNA and noncognate DNA substrates, interactions that had been challenging to characterize previously. Conceptually simple and implementationally robust, SIP was shown to considerably enhance the efficacy in capturing weak interactions and, as demonstrated here, could empower scientists to see the otherwise unseeable.

Sorting Nanoparticles by Valency with DNA Barcoding.

Self-assembly of DNA-labeled nanoparticles is an effective strategy to fabricate new nanocomposite materials and nanoscale devices from the bottom-up. To tailor the properties of the resulting material or device, one requires access to a wide range of nanoparticle sizes and shapes, as well as control over the number (valency) of DNA molecules on the nanoparticle surface. Currently, nanoparticles with a defined DNA valency can only be obtained in a narrow range of sizes, and in small quantities, limiting the properties of the resulting composite structures and their applications. Here, we leverage the digital information encoded in the number and sequence of short DNA barcodes to generate preparatory amounts of nanoparticles bearing a specific number of DNA molecules, irrespective of the identity of the nanocomponent. We show that this DNA valency sorting chromatography, which is driven by the selective affinity of Watson-Crick base pairs, is applicable to arbitrary DNA sequences and a broad range of nanoparticle sizes, shapes, and material compositions. To further demonstrate this fact, we use valency-sorted large gold nanospheres directly in self-assembly schemes to create, in one synthesis step, large amounts of several previously inaccessible molecule-like dimer and trimer nanostructures with unique optical properties. We anticipate that the expanded scope of DNA valency-defined nanoparticle reagents, and the increased scale at which they can be produced, will open new avenues for the molecularly precise manipulation of nanoscale matter.

Reproducibly Measuring Plasmon-Enhanced Fluorescence in Bulk Solution Across a 20-Fold Range of Optical Densities.

It is well-known that plasmonic nanoparticles can modify the spectroscopic properties of nearby optical probes, for example, enhanced emission of a fluorescent dye. Yet, the detection and quantification of this effect in bulk solution remain challenging even while size- and shape-controlled nanoparticles have become readily available. We investigated this problem and identified two main difficulties which we were able to overcome through systematic studies. For the detection of fluorescence emanating from optically dense nanoparticle solutions, we describe an analytical model that provides guidelines for experimentalists to maximize the fluorescence intensity by optimizing the concentration, light paths, and excitation-detection volume of the sample. For the quantification of enhancement, which critically hinges upon the comparison to an accurate reference sample, we exploit the tools of DNA nanotechnology to remove the fluorophore from plasmonic coupling on-demand, forming an in situ reference. Using a model system of fluorophore Cy3 and 80 nm gold nanoparticles, we show that these strategies enable the quantitative measurement of plasmonic enhancement across a 20-fold range of optical densities. We anticipate that the presented experimental framework will allow for routine, quantitative measurements for the research and development of plasmon-enhanced phenomena.

Information bounds in determining the 3D orientation of a single emitter or scatterer using point-detector-based division-of-amplitude polarimetry.

Determining the 3D orientation of a single molecule or particle, encoded in its polar and azimuthal angles, is of interest for a variety of fields, being relevant to a range of questions in elementary chemical reactivity, biomolecular motors, and nanorheology. A popular experimental method, known as division-of-amplitude polarimetry, for determining the real-time orientation of a single particle is to split the emitted/scattered light into multiple polarizations and to measure the light intensity using point detectors at these polarizations during a time interval Δt. Here, we derive the Cramér-Rao lower bounds for this method from the perspective of information theory in the cases of utilizing a chromophore or a scattering particle as a 3D orientation probe. Such Cramér-Rao lower bounds are new for using this experimental method to measure the full 3D orientation in both the scattering case and the fluorescence case. These results show that, for a scatterer, the information content of one photon is 1.16 deg-2 in the polar and 58.71 deg-2 in the azimuthal angles, respectively. For a chromophore, the information content of one photon is 2.54 deg-2 in the polar and 80.29 deg-2 in the azimuthal angles. In addition, the Cramér-Rao lower bound scales with the square root of the total signal photons. To determine orientation to an uncertainty of one degree requires 7.40 × 104 and 2.34 × 103 photons for the polar and the azimuthal angles, respectively, for fluorescence, whereas it takes 1.62 × 105 and 3.20 × 103 photons for scattering. This work provides experimentalists new guidelines by which future experiments can be designed and interpreted.

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.

Prior-Apprised Unsupervised Learning of Subpixel Curvilinear Features in Low Signal/Noise Images.

Many biophysical problems involve molecular and nanoscale targets moving next to a curvilinear track, e.g., a cytosolic cargo transported by motor proteins moving along a microtubule. For this type of problem, fluorescence imaging is usually the primary tool of choice. There is, however, an ∼20-fold mismatch between target-localization precision and track-imaging resolution such that questions requiring high-fidelity definition of the target's track remain inaccessible. On the other hand, if the contextual image of the tracks can be refined to a level comparable to that of the target, many intuitive yet mechanistically important issues can begin to be addressed. This work demonstrates that it is possible to statistically infer, to subpixel precision, curvilinear features in a low signal/noise image. This is achieved by a framework that consists of three stages: the Hessian-based feature enhancement, the subimage feature sampling and registration, and the statistical learning of the underlying curvilinear structure using a new, to our knowledge, method developed here for inferring the principal curves. In each stage, the descriptive prior information that the features come from curvilinear elements is explicitly taken into account. It is fully automated without user supervision, which is distinctly different from approaches that require user seeding or well-defined training data sets. Computer simulations of realistic images are used to investigate the performance of the framework and its implementation. The characterization results suggest that curvilinear features are refined to the same order of precision as that of the target and that the bootstrap confidence intervals from the analysis allow an estimate for the statistical bounds of the simulated "true" curve. Also shown are analyses of experimental images from three different microscopy modalities: two-photon laser-scanning microscopy, epifluorescence microscopy, and total internal reflection fluorescence microscopy. The practical application of this prior-apprised unsupervised learning framework as well as its potential outlook are discussed.

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.

FRET monitoring of a nonribosomal peptide synthetase.

Nonribosomal peptide synthetases (NRPSs) are multidomain enzyme templates for the synthesis of bioactive peptides. Large-scale conformational changes during peptide assembly are obvious from crystal structures, yet their dynamics and coupling to catalysis are poorly understood. We have designed an NRPS FRET sensor to monitor, in solution and in real time, the adoption of the productive transfer conformation between phenylalanine-binding adenylation (A) and peptidyl-carrier-protein domains of gramicidin synthetase I from Aneurinibacillus migulanus. The presence of ligands, substrates or intermediates induced a distinct fluorescence resonance energy transfer (FRET) readout, which was pinpointed to the population of specific conformations or, in two cases, mixtures of conformations. A pyrophosphate switch and lysine charge sensors control the domain alternation of the A domain. The phenylalanine-thioester and phenylalanine-AMP products constitute a mechanism of product inhibition and release that is involved in ordered assembly-line peptide biosynthesis. Our results represent insights from solution measurements into the conformational dynamics of the catalytic cycle of NRPSs.

Photobleaching statistics in single-molecule on-/off-time distributions.

The on- and and off-time distributions from fluorescence single-molecule experiments are widely used to extract kinetics parameters with the goal to provide a quantitative description for the molecule's behavior on the ensemble level. Such experiments are inevitably influenced by photobleaching, where the fluorescent probe transitions to a nonemissive state. Yet, it appears that few reports went beyond acknowledging this unavoidable complication; in fact, it has so far been ignored when evaluating off-time distributions. Here, we present a theoretical framework that allows the derivation of analytical equations in which photobleaching kinetics are rigorously incorporated. Unexpectedly, our results indicate that the off-time distribution should be nonexponential even when all the rate processes are single exponential. With the analytical theory understood and demonstrated as easy to implement, such ubiquitous photochemical processes can now be readily included in routine experimental analyses.

Detection of Velocity and Diffusion Coefficient Change Points in Single-Particle Trajectories.

The position-time trajectory of a biological subject moving in a complex environment contains rich information about how it interacts with the local setting. Whether the subject be an animal or an intracellular endosomal vesicle, the two primary modes of biological locomotion are directional movement and random walk, respectively characterized by velocity and diffusion coefficient. This contribution introduces a method to quantitatively divide a single-particle trajectory into segments that exhibit changes in the diffusion coefficient, velocity, or both. With the determination of these two physical parameters given by the maximum likelihood estimators, the relative precisions are given as explicit functions of the number of data points and total trajectory time. The method is based on rigorous statistical tests and does not require any presumed kinetics scheme. Results of extensive characterizations, extensions to 2D and 3D trajectories, and applications to common scenarios are also discussed.

Compound Molecular Logic in Accessing the Active Site of Mycobacterium tuberculosis Protein Tyrosine Phosphatase B.

Protein tyrosine phosphatase B (PtpB) from Mycobacterium tuberculosis (Mtb) extends the bacteria's survival in hosts and hence is a potential target for Mtb-specific drugs. To study how Mtb-specific sequence insertions in PtpB may regulate access to its active site through large-amplitude conformational changes, we performed free-energy calculations using an all-atom explicit solvent model. Corroborated by biochemical assays, the results show that PtpB's active site is controlled via an "either/or" compound conformational gating mechanism, an unexpected discovery that Mtb has evolved to bestow a single enzyme with such intricate logical operations. In addition to providing unprecedented insights for its active-site surroundings, the findings also suggest new ways of inactivating PtpB.

Theory for controlling individual self-propelled micro-swimmers by photon nudging I: directed transport.

Photon nudging is a new experimental method which enables the force-free manipulation and localization of individual self-propelled artificial micro-swimmers in fluidic environments. It uses a weak laser to stochastically and adaptively turn on and off the swimmer's propulsion when the swimmer, through rotational diffusion, points towards or away from its target, respectively. This contribution presents a theoretical framework for the statistics of both 2D and 3D controls. The main results are: the on- and off-time distributions for the controlling laser, the arrival time statistics for the swimmer to reach a remote target, and how the experimentally accessible control parameters influence the control, e.g., the optimal acceptance angle for directed transport. The results are general in that they are independent of the propulsion or the actuation mechanisms. They provide a concrete physical picture for how a single artificial micro-swimmer could be navigated under thermal fluctuations-insights that could also be useful for understanding biological micro-swimmers.

Theory for controlling individual self-propelled micro-swimmers by photon nudging II: confinement.

Photon nudging allows the manipulation and confinement of individual self-propelled micro-swimmers in 2D and 3D environments using feedback controls. Presented in this second part of a two-part contribution are theoretical models that afford the characterization for the positioning distribution associated with active localization. A derivation for the optimal nudging speed and acceptance angle is given for minimal placement uncertainty. The analytical solutions allow for a discussion on the physical underpinning that underlies controllability and optimality.

Statistical Learning of Discrete States in Time Series.

Time series obtained from time-dependent experiments contain rich information of kinetics and dynamics of the system under investigation. This work describes an unsupervised learning framework, along with the derivation of the necessary analytical expressions, for the analysis of Gaussian-distributed time series that exhibit discrete states. After the time series has been partitioned into segments in a model-free manner using the previously developed change-point (CP) method, this protocol starts with an agglomerative hierarchical clustering algorithm to classify the detected segments into possible states. The initial state clustering is further refined using an expectation-maximization (EM) procedure, and the number of states is determined by a Bayesian information criterion (BIC). Also introduced here is an achievement scalarization function, usually seen in artificial intelligence literature, for quantitatively assessing the performance of state determination. The statistical learning framework, which is comprised of three stages---detection of signal change, clustering, and number-of-state determination---was thoroughly characterized using simulated trajectories with random intensity segments that have no underlying kinetics, and its performance critically evaluated. The application to experimental data is also demonstrated. The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.

A versatile optical microscope for time-dependent single-molecule and single-particle spectroscopy.

This work reports the design and implementation of a multi-function optical microscope for time-dependent spectroscopy on single molecules and single nanoparticles. It integrates the now-routine single-object measurements into one standalone platform so that no reconfiguration is needed when switching between different types of sample or spectroscopy modes. The illumination modes include evanescent field through total internal reflection, dark-field illumination, and epi-excitation onto a diffraction-limited spot suitable for confocal detection. The detection modes include spectrally resolved line imaging, wide-field imaging with dual-color capability, and two-color single-element photon-counting detection. The switch between different spectroscopy and data acquisition modes is fully automated and executed through computer programming. The capability of this microscope is demonstrated through selected proof-of-principle experiments.

Parallelization of Change Point Detection.

The change point detection method ( Watkins , L. P. ; Yang , H. J. Phys. Chem. B 2005 , 109 , 617 ) allows the objective identification and isolation of abrupt changes along a data series. Because this method is grounded in statistical tests, it is particularly powerful for probing complex and noisy signals without artificially imposing a kinetics model. The original algorithm, however, has a time complexity of [Formula: see text], where N is the size of the data and is, therefore, limited in its scalability. This paper puts forth a parallelization of change point detection to address these time and memory constraints. This parallelization method was evaluated by applying it to changes in the mean of Gaussian-distributed data and found that time decreases superlinearly with respect to the number of processes (i.e., parallelization with two processes takes less than half of the time of one process). Moreover, there was minimal reduction in detection power. These results suggest that our parallelization algorithm is a viable scheme that can be implemented for other change point detection methods.

Discovery of protein- and DNA-imperceptible nanoparticle hard coating using gel-based reaction tuning.

The seemingly inevitable protein corona appears to be an insurmountable obstacle to wider application of functional nanomaterials in biotechnology. The accumulation of serum proteins can block targeting functionalities and alter the in vivo fate of these nanomaterials. Here we demonstrate a method to generate non-stick, robustly passivated functional nanoparticles (NPs) using a tailored silica coating. We apply agarose gel electrophoresis to sensitively evaluate protein binding to NPs with different surface chemistry. Using gel banding and retardation as a read-out for protein adsorption, we optimize the surface chemistry to yield a mixed charge surface which displays remarkable binding resistance to a wide range of serum proteins and nucleic acids. The hard silica shell also protects the functional NP core in harsh environments (down to pH 1) while still showing the ability to be targeted for cellular uptake with little or no non-specific binding.

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

The overwhelming effort in the development of new microscopy methods has been focused on increasing the spatial and temporal resolution in all three dimensions to enable the measurement of the molecular scale phenomena at the heart of biological processes. However, there exists a significant speed barrier to existing 3D imaging methods, which is associated with the overhead required to image large volumes. This overhead can be overcome to provide nearly unlimited temporal precision by simply focusing on a single molecule or particle via real-time 3D single-particle tracking and the newly developed 3D Multi-resolution Microscopy (3D-MM). Here, we investigate the optical and mechanical limits of real-time 3D single-particle tracking in the context of other methods. In particular, we investigate the use of an optical cantilever for position sensitive detection, finding that this method yields system magnifications of over 3000×. We also investigate the ideal PID control parameters and their effect on the power spectrum of simulated trajectories. Taken together, these data suggest that the speed limit in real-time 3D single particle-tracking is a result of slow piezoelectric stage response as opposed to optical sensitivity or PID control.

Kinetics profiling of gramicidin S synthetase A, a member of nonribosomal peptide synthetases.

Nonribosomal peptide synthetases (NRPS) incorporate assorted amino acid substrates into complex natural products. The substrate is activated via the formation of a reactive aminoacyl adenylate and is subsequently attached to the protein template via a thioester bond. The reactive nature of such intermediates, however, leads to side reactions that also break down the high-energy anhydride bond. The off-pathway kinetics or their relative weights compared to that of the on-pathway counterpart remains generally elusive. Here, we introduce multiplatform kinetics profiling to quantify the relative weights of on- and off-pathway reactions. Using the well-defined stoichiometry of thioester formation, we integrate a mass spectrometry (MS) kinetics assay, a high-performance liquid chromatography (HPLC) assay, and an ATP-pyrophosphate (PPi) exchange assay to map out a highly efficient on-pathway kinetics profile of the substrate activation and intermediate uploading (>98% relative weight) for wide-type gramicidin S synthetase A (GrsA) and a 87% rate profile for a cysteine-free GrsA mutant. Our kinetics profiling approach complements the existing enzyme-coupled byproduct-release assays, unraveling new mechanistic insights of substrate activation/channeling in NRPS enzymes.