Objective Approaches to Single-Molecule Time Series Analysis

Single-molecule spectroscopy has provided a means to uncover pathways and heterogeneities that were previously hidden beneath the ensemble average. Such heterogeneity, however, is often obscured by the artifacts of experimental noise and the occurrence of undesired processes within the experimental medium. This has subsequently caused in the need for new analytical methodologies. It is particularly important that objectivity be maintained in the development of new analytical methodology so that bias is not introduced and the results improperly characterized. The research presented herein identifies two such sources of experimental uncertainty, and constructs objective approaches to reduce their effects in the experimental results. The first, photoblinking, arises from the occupation of dark electronic states within the probe molecule, resulting in experimental data that is distorted by its contribution. A method based in Bayesian inference is developed, and is found to nearly eliminate photoblinks from the experimental data while minimally affecting the remaining data and maintaining objectivity. The second source of uncertainty is electronic shot-noise, which arises as a result of Poissonian photon collection. A method based in wavelet decomposition is constructed and applied to simulated and experimental data. It is iii found that, while making only one assumption, that photon collection is indeed a Poisson process, up to 75% of the shot-noise contribution may be removed from the experimental signal by the wavelet-based procedure. Lastly, in an effort to connect model-based approaches such as molecular dynamics simulation to model-free approaches that rely solely on the experimental data, a coarse-grained molecular model of a molecular ionic fluorophore diffusing within an electrostatically charged polymer brush is constructed and characterized. It is found that, while the characteristics of the coarse-grained simulation compare well with atomistic simulations, the model is lacking in its representation of the electrostatically-driven behavior of the experimental system

MODULATION OF THE RECEPTOR GATING MECHANISM AND ALLOSTERIC COMMUNICATION IN IONOTROPIC GLUTAMATE RECEPTORS

Ionotropic glutamate receptors (iGluRs) found in mammalian brain are primarily known to mediate excitatory synaptic transmission crucial for learning and memory formation. The family of iGluRs consists of AMPA receptors, NMDA receptors and kainate receptors with each member having distinct physiological role. In the recent years, significant progress has been made in understanding the biophysical, and functional properties of iGluRs. The development of Cryo-EM and X-Ray crystallography techniques have further facilitated in the structural understanding of these receptors. However, the multidomain nature, large size of the protein, complex gating mechanism and inadequate knowledge regarding the conformational dynamics of the receptors during channel gating mechanism have been some of the limiting factors in elucidating the structure-function relation of iGluRs. Thus, to understand the conformational dynamics of iGluR family and correlate to its functional behavior, I have utilized single molecule Forster Resonance Energy Transfer (smFRET) and molecular dynamics simulation and specifically investigated the factors influencing gating mechanism and allosteric communication in heteromeric kainate receptor GluK2/K5 and NMDA receptor GluN1/N2A. Some of the major finding in this dissertation includes subunit arrangement of GluK2/K5 and its dynamics involved in resting and desensitized conditions. For the first time we have identified the conformational changes induced at GluK2 and GluK5 subunits in a heteromer GluK2/K5 when bound to different agonists. Utilizing MD simulations in GluN1/N2A NMDA receptors we have identified the structural pathway regarding the mechanism underlying negative cooperativity and how mutation in the receptor leads to abnormal functional behavior. These findings will allow us to understand the conformational control regarding modulation of receptor function and will serve as a basis for developing subunit and conformation-specific therapeutic drugs that can potentially control the abnormal activity of the receptors linked to several neurological diseases

Smoothing dynamic positron emission tomography time courses using functional principal components

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error

Single atom imaging with time-resolved electron microscopy

Developments in scanning transmission electron microscopy (STEM) have opened up new possibilities for time-resolved imaging at the atomic scale. However, rapid imaging of single atom dynamics brings with it a new set of challenges, particularly regarding noise and the interaction between the electron beam and the specimen. This thesis develops a set of analytical tools for capturing atomic motion and analyzing the dynamic behaviour of materials at the atomic scale. Machine learning is increasingly playing an important role in the analysis of electron microscopy data. In this light, new unsupervised learning tools are developed here for noise removal under low-dose imaging conditions and for identifying the motion of surface atoms. The scope for real-time processing and analysis is also explored, which is of rising importance as electron microscopy datasets grow in size and complexity. These advances in image processing and analysis are combined with computational modelling to uncover new chemical and physical insights into the motion of atoms adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis, where the catalytic activity can depend intimately on the atomic environment. The study of Cu atoms on a graphene oxide support reveals that the atoms undergo anomalous diffusion as a result of spatial and energetic disorder present in the substrate. The investigation is extended to examine the structure and stability of small Cu clusters on graphene oxide, with atomistic modelling used to understand the significant role played by the substrate. Finally, the analytical methods are used to study the surface reconstruction of silicon alongside the electron beam-induced motion of adatoms on the surface. Taken together, these studies demonstrate the materials insights that can be obtained with time-resolved STEM imaging, and highlight the importance of combining state-ofthe- art imaging with computational analysis and atomistic modelling to quantitatively characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)

Optimising the quantitative analysis in functional pet brain imaging

Patlak analysis techniques based on linear regression are often applied to positron emission tomography (PET) images to estimate a number of physiological parameters. The Patlak equation forms the basis for most extension works regarding graphical analysis of many tracers in quantitative PET measurements. Patlak analysis is primarily used to obtain the rate constant Ki, which represents the tracer transfer rate from plasma to the targeted tissue. One of the most common issues associated with Patlak analysis is the introduction of statistical noise, adopted originally from the images, that affects the slope of the graphical plot, leading to bias, and causes errors in the calculation of the rate constant Ki i. In this thesis, several statistical and noise reduction methods for 2 and 3 dimensional data are proposed and applied to simulated 18F-FDOPA brain images generated from a PET imaging simulator. The methods were applied to investigate whether their utilisation could reduce the bias and error caused by noisy images and improve the accuracy of quantitative measurements. Then, validation step extended to 18F-FDOPA PET images obtained from a clinical trial for Parkinson’s disease. The minimum averaged SE, SSE and the highest averaged reduction of noisy Ki values were found with the feasible generalised least squares (FGLS) model. Battle-Lemarie wavelet (BLW) showed significant change in data for the 3D PET images. Savitzky-Golay filtering (SGF) demonstrated significant change for most of the noise levels applied to 2D data. In clinical 18F-FDOPA images, the mean and standard deviation of standard error (SE) and sum-squared error (SSE) were significantly reduced in both baseline and after therapy groups. This work has the potential to be extended to other graphical analysis in quantitative PET data measurements

Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography

This thesis will focus on two important aspects of dynamic Positron Emission Tomography (PET): (i) Motion-compensation , and (ii) Pharmacokinetic analysis (also called parametric imaging) of dynamic PET images. Both are required to enable fully quantitative PET imaging which is increasingly finding applications in the clinic. Motion-compensation in Dynamic Brain PET Imaging: Dynamic PET images are degraded by inter-frame and intra-frame motion artifacts that can a ffect the quantitative and qualitative analysis of acquired PET data. We propose a Generalized Inter-frame and Intra-frame Motion Correction (GIIMC) algorithm that uni fies in one framework the inter-frame motion correction capability of Multiple Acquisition Frames and the intra-frame motion correction feature of (MLEM)-type deconvolution methods. GIIMC employs a fairly simple but new approach of using time-weighted average of attenuation sinograms to reconstruct dynamic frames. Extensive validation studies show that GIIMC algorithm outperforms conventional techniques producing images with superior quality and quantitative accuracy. Parametric Myocardial Perfusion PET Imaging: We propose a novel framework of robust kinetic parameter estimation applied to absolute flow quantification in dynamic PET imaging. Kinetic parameter estimation is formulated as nonlinear least squares with spatial constraints problem where the spatial constraints are computed from a physiologically driven clustering of dynamic images, and used to reduce noise contamination. The proposed framework is shown to improve the quantitative accuracy of Myocardial Perfusion (MP) PET imaging, and in turn, has the long-term potential to enhance capabilities of MP PET in the detection, staging and management of coronary artery disease

Agonist-Induced Conformational Changes in the NMDA Receptor

NMDA receptors are ligand-gated ion channels that mediate a number of physiological and pathological phenomena within the mammalian central nervous system. Under the typical course of activation, these receptors bind to glycine and glutamate molecules and undergo a series of conformational changes that results in the opening of a cation-permeable pore in the neuronal plasma membrane. Various aspects of NMDA receptor function are not fully understood, including the phenomenon of negative cooperativity between the glycine- and glutamate-binding sites of the receptor and the mechanism controlling partial agonism. Past studies utilizing static structural snapshots of the receptor or isolated domains of the receptor have provided insufficient insights to fully understand these issues. Herein, I have conducted Förster Resonance Energy Transfer measurements on individual NMDA receptor molecules to observe their conformational landscape under various conditions. These studies have revealed changes in conformation of the receptor that underlie negative cooperativity and partial agonism, thereby affording new insights into the mechanisms controlling these processes