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

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

Single-Molecule Dynamics Reflect IgG Conformational Changes Associated with Ion-Exchange Chromatography.

Conformational changes of antibodies and other biologics can decrease the effectiveness of pharmaceutical separations. Hence, a detailed mechanistic picture of antibody-stationary phase interactions that occur during ion-exchange chromatography (IEX) can provide critical insights. This work examines antibody conformational changes and how they perturb antibody motion and affect ensemble elution profiles. We combine IEX, three-dimensional single-protein tracking, and circular dichroism spectroscopy to investigate conformational changes of a model antibody, immunoglobulin G (IgG), as it interacts with the stationary phase as a function of salt conditions. The results indicate that the absence of salt enhances electrostatic attraction between IgG and the stationary phase, promotes surface-induced unfolding, slows IgG motion, and decreases elution from the column. Our results reveal previously unreported details of antibody structural changes and their influence on macroscale elution profiles.

A new metric for relating macroscopic chromatograms to microscopic surface dynamics: the distribution function ratio (DFR).

Heterogeneous stationary phase chemistry causes chromatographic tailing that lowers separation efficiency and complicates optimizing mobile phase conditions. Model-free metrics are attractive for assessing optimal separation conditions due to the low quantity of information required, but often do not reveal underlying mechanisms that cause tailing, for example, heterogeneous retention modes. We report a new metric, which we call the Distribution Function Ratio (DFR), based on a graphical comparison between the chromatogram and Gaussian cumulative distribution functions, achieving correspondence to ground truth surface dynamics with a single chromatogram. Using a Monte Carlo framework, we show that the DFR can predict the prevalence of heterogeneous retention modes with high precision when the relative desorption rate between modes is known, as in during surface dynamics experiments. Ground truth comparisons reveal that the DFR outperforms both the asymmetry factor and skewness by yielding a one-to-one correspondence with heterogeneous retention mode prevalence over a broad range of experimentally realistic values. Perhaps of more value, we illustrate that the DFR, when combined with the asymmetry factor and skewness, can estimate microscopic surface dynamics, providing valuable insights into surface chemistry using existing chromatographic instrumentation. Connecting ensemble results to microscopic quantities through the lens of simulation establishes a new chemistry-driven route to measuring and advancing separations.

Transforming Separation Science with Single-Molecule Methods.

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

Unraveling peak asymmetry in chromatography through stochastic theory powered Monte Carlo simulations.

An overarching theory of chromatography capable of modeling all analyte-stationary phase interactions would enable predictive design of pharmaceutically relevant separations. The stochastic theory of chromatography has been postulated as a suitable basis to achieve this goal. Here, we implement Dondi and Cavazzini's Monte Carlo framework that utilizes experimentally accessible single molecule kinetics and use it to correlate heterogenous adsorption statistics at the stationary phase to shifts in asymmetry. The contributions cannot be captured or modeled through ensemble chemometrics. Simulations reveal that peak asymmetry scales non-linearly with longer analyte-stationary phase interactions and migrates towards symmetry across the column length, even without column overloading.

Imaging Switchable Protein Interactions with an Active Porous Polymer Support.

Mechanistic details about how local physicochemistry of porous interfaces drives protein transport mechanisms are necessary to optimize biomaterial applications. Cross-linked hydrogels made of stimuli-responsive polymers have potential for active protein capture and release through tunable steric and chemical transformations. Simultaneous monitoring of dynamic changes in both protein transport and interfacial polymer structure is an experimental challenge. We use single-particle tracking (SPT) and fluorescence correlation spectroscopy Super-resolution Optical Fluctuation Imaging (fcsSOFI) to relate the switchable changes in size and structure of a pH-responsive hydrogel to the interfacial transport properties of a model protein, lysozyme. SPT analysis reveals the reversible switching of protein transport dynamics in and at the hydrogel polymer in response to pH changes. fcsSOFI allows us to relate tunable heterogeneity of the hydrogels and pores to reversible changes in the distribution of confined diffusion and adsorption/desorption. We find that physicochemical heterogeneity of the hydrogels dictates protein confinement and desorption dynamics, particularly at pH conditions in which the hydrogels are swollen.

A mechanistic examination of salting out in protein-polymer membrane interactions.

Developing a mechanistic understanding of protein dynamics and conformational changes at polymer interfaces is critical for a range of processes including industrial protein separations. Salting out is one example of a procedure that is ubiquitous in protein separations yet is optimized empirically because there is no mechanistic description of the underlying interactions that would allow predictive modeling. Here, we investigate peak narrowing in a model transferrin-nylon system under salting out conditions using a combination of single-molecule tracking and ensemble separations. Distinct surface transport modes and protein conformational changes at the negatively charged nylon interface are quantified as a function of salt concentration. Single-molecule kinetics relate macroscale improvements in chromatographic peak broadening with microscale distributions of surface interaction mechanisms such as continuous-time random walks and simple adsorption-desorption. Monte Carlo simulations underpinned by the stochastic theory of chromatography are performed using kinetic data extracted from single-molecule observations. Simulations agree with experiment, revealing a decrease in peak broadening as the salt concentration increases. The results suggest that chemical modifications to membranes that decrease the probability of surface random walks could reduce peak broadening in full-scale protein separations. More broadly, this work represents a proof of concept for combining single-molecule experiments and a mechanistic theory to improve costly and time-consuming empirical methods of optimization.

From a Protein's Perspective: Elution at the Single-Molecule Level.

Column chromatography is a widely used analytical technique capable of identifying and isolating a desired chemical species from a more complicated mixture. Despite the method's prevalence, theoretical descriptions have not advanced to accommodate today's common analyte, proteins. Proteins are increasingly used as biologics, a term that refers to biological pharmaceuticals, and present new complexities for chromatographic separation. Large variations in surface charge, chemistry, and structure among protein analytes expose the limits in the current theoretical framework's ability to predict the efficiency of a column without empirical data. The bottleneck created by empirical optimization is a strong motivation for a renewed effort to achieve an in-depth understanding of the range of interactions that occur between a protein analyte and the stationary phase that together enable its selective separation from other constituents of a mixture. The physical and chemical processes that dictate the amount of time an analyte spends in the column are often abstracted by theory and treated as statistical distributions. Until recently, these distributions could not be mapped experimentally as traditional experimental techniques could not reveal underlying heterogeneity in structure, charge, and dynamics. Aligning the latest experimental and theoretical advances is thus a hurdle to be overcome so that significant progress can be made toward a predictive chromatographic theory. In this Account, we detail the work of the Landes Lab in developing single-molecule techniques that refine the stochastic theory of chromatography as a first step toward predictive chromatographic column design. We provide a brief review of the development of stochastic theory and establish a mathematical framework to put the discussed physical chemistry in context. We describe our investigations of three pertinent phenomena: mobile/stationary phase exchange, adsorption/desorption kinetics, and hindered diffusion. We highlight experimental evidence that points to nonuniform behavior. Then, we describe our work in developing single-molecule techniques that can evaluate these effects on a protein-by-protein basis. We highlight two developments: fast imaging via super temporal-resolved microscopy (STReM) and visualizing diffusion within pores via a combination of fluorescence correlation spectroscopy and super-resolution optical fluctuation imaging (fcsSOFI). Both methods offer new ways to study chromatographic elution at the single-protein level. Such methods can identify the rare heterogeneities that prevent efficient separations and advance the field closer to predictively optimized protein separations.

Enhancing Analytical Separations Using Super-Resolution Microscopy.

Super-resolution microscopy is becoming an invaluable tool to investigate structure and dynamics driving protein interactions at interfaces. In this review, we highlight the applications of super-resolution microscopy for quantifying the physics and chemistry that occur between target proteins and stationary-phase supports during chromatographic separations. Our discussion concentrates on the newfound ability of super-resolved single-protein spectroscopy to inform theoretical parameters via quantification of adsorption-desorption dynamics, protein unfolding, and nanoconfined transport.

The structure-energy landscape of NMDA receptor gating.

N-Methyl-D-aspartate (NMDA) receptors are the main calcium-permeable excitatory receptors in the mammalian central nervous system. The NMDA receptor gating is complex, exhibiting multiple closed, open, and desensitized states; however, central questions regarding the conformations and energetics of the transmembrane domains as they relate to the gating states are still unanswered. Here, using single-molecule Förster resonance energy transfer (smFRET), we map the energy landscape of the first transmembrane segment of the Rattus norvegicus NMDA receptor under resting and various liganded conditions. These results show kinetically and structurally distinct changes associated with apo, agonist-bound, and inhibited receptors linked by a linear mechanism of gating at this site. Furthermore, the smFRET data suggest that allosteric inhibition by zinc occurs by an uncoupling of the agonist-induced changes at the extracellular domains from the gating motions leading to an apo-like state, while dizocilpine, a pore blocker, stabilizes multiple closely packed transmembrane states.

Variable Lysozyme Transport Dynamics on Oxidatively Functionalized Polystyrene Films.

Tuning protein adsorption dynamics at polymeric interfaces is of great interest to many biomedical and material applications. Functionalization of polymer surfaces is a common method to introduce application-specific surface chemistries to a polymer interface. In this work, single-molecule fluorescence microscopy is utilized to determine the adsorption dynamics of lysozyme, a well-studied antibacterial protein, at the interface of polystyrene oxidized via UV exposure and oxygen plasma and functionalized by ligand grafting to produce varying degrees of surface hydrophilicity, surface roughness, and induced oxygen content. Single-molecule tracking indicates lysozyme loading capacities, and surface mobility at the polymer interface is hindered as a result of all functionalization techniques. Adsorption dynamics of lysozyme depend on the extent and the specificity of the oxygen functionalities introduced to the polystyrene surface. Hindered adsorption and mobility are dominated by hydrophobic effects attributed to water hydration layer formation at the functionalized polystyrene surfaces.

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.

Effects of Solute-Solvent Hydrogen Bonding on Nonaqueous Electrolyte Structure.

We investigate the source of Raman background signal commonly misidentified as fluorescence in nonaqueous electrolytes via a variety of spectroscopies (Raman, fluorescence, NMR) and find evidence of hydrogen-bonding interactions. This hydrogen bonding gives rise to broadband anharmonic vibrational modes and suggests that anions play an important and underappreciated role in the structure of nonaqueous electrolytes. Controlling electrolyte structure has important applications in advancing in operando spectroscopy measurements as well as understanding the stability of high concentration electrolytes for next-generation electrochemical energy storage devices.