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.

The response of living systems to nanoparticles is thought to depend on the protein corona, which forms shortly after exposure to physiological fluids and which is linked to a wide array of pathophysiologies. A mechanistic understanding of the dynamic interaction between proteins and nanoparticles and thus the biological fate of nanoparticles and associated proteins is, however, often missing mainly due to the inadequacies in current ensemble experimental approaches. Through the application of a variety of single molecule and single particle spectroscopic techniques in combination with ensemble level characterization tools, we identified different interaction pathways between gold nanorods and bovine serum albumin depending on the protein concentration. Overall, we found that local changes in protein concentration influence everything from cancer cell uptake to nanoparticle stability and even protein secondary structure. We envision that our findings and methods will lead to strategies to control the associated pathophysiology of nanoparticle exposure in vivo.

Chromatographic protein separations, immunoassays, and biosensing all typically involve the adsorption of proteins to surfaces decorated with charged, hydrophobic, or affinity ligands. Despite increasingly widespread use throughout the pharmaceutical industry, mechanistic detail about the interactions of proteins with individual chromatographic adsorbent sites is available only via inference from ensemble measurements such as binding isotherms, calorimetry, and chromatography. In this work, we present the direct superresolution mapping and kinetic characterization of functional sites on ion-exchange ligands based on agarose, a support matrix routinely used in protein chromatography. By quantifying the interactions of single proteins with individual charged ligands, we demonstrate that clusters of charges are necessary to create detectable adsorption sites and that even chemically identical ligands create adsorption sites of varying kinetic properties that depend on steric availability at the interface. Additionally, we relate experimental results to the stochastic theory of chromatography. Simulated elution profiles calculated from the molecular-scale data suggest that, if it were possible to engineer uniform optimal interactions into ion-exchange systems, separation efficiencies could be improved by as much as a factor of five by deliberately exploiting clustered interactions that currently dominate the ion-exchange process only accidentally.ion-exchange chromatography | single-molecule kinetics | bioseparations | optical nanoscopy T he hundred-billion-dollar global pharmaceutical industry relies increasingly on the painstaking purification of therapeutic biomolecules such as proteins and nucleic acids (1). Separation of biologics is often performed using ion-exchange chromatography on stationary phases supporting singly charged ligands (2, 3) and constitutes an expensive, bottlenecking step in production. Improving bioseparations is thus highly desirable (4, 5); yet, a molecular-scale, mechanistic understanding is lacking, for ionexchange chromatography in particular (6). Mechanistic detail is lost in ensemble analyses, reflecting the inherent heterogeneity of both the adsorbed biomolecules and the porous stationary phase supports (7). Ensemble adsorption isotherms, however, suggest the likelihood that protein and nucleic acid separations in ion-exchange columns may involve random ligand clustering (8-10). Additional support for such an assertion lies in the implementation of stationary phases of very high charge density by polymerization of charged monomers or layer-by-layer deposition (11-13), and in the demonstration that patches of high charge density on proteins often play a disproportionate role in their adsorption (4,6,(14)(15)(16)(17). In this work, we provide direct evidence of the importance of charge clustering in ion-exchange systems by direct observation of individual adsorption sites.Although the role of multivalency is broadly accepted and exploited in a wide range of associative and adsorption ...

To fabricate robust metallic nanostructures with top-down patterning methods such as electron-beam lithography, an initial nanometer-scale layer of a second metal is deposited to promote adhesion of the metal of interest. However, how this nanoscale layer affects the mechanical properties of the nanostructure and how adhesion layer thickness controls the binding strength to the substrate are still open questions. Here we use ultrafast laser pulses to impulsively launch acoustic phonons in single gold nanodisks with variable titanium layer thicknesses, and observe an increase in phonon frequencies as a thicker adhesion layer facilitates stronger binding to the glass substrate. In addition to an all-optical interrogation of nanoscale mechanical properties, our results show that the adhesion layer can be used to controllably modify the acoustic phonon modes of a gold nanodisk. This direct coupling between optically excited plasmon modes and phonon modes can be exploited for a variety of emerging optomechanical applications.

We introduce a step transition and state identification (STaSI) method for piecewise constant single-molecule data with a newly derived minimum description length equation as the objective function. We detect the step transitions using the Student’s t test and group the segments into states by hierarchical clustering. The optimum number of states is determined based on the minimum description length equation. This method provides comprehensive, objective analysis of multiple traces requiring few user inputs about the underlying physical models and is faster and more precise in determining the number of states than established and cutting-edge methods for single-molecule data analysis. Perhaps most importantly, the method does not require either time-tagged photon counting or photon counting in general and thus can be applied to a broad range of experimental setups and analytes.