Gold nanomaterials and their potential use as cryo-electron tomography labels

Rapid advances in cryo-electron tomography (cryo-ET) are driving a revolution in cellular structural biology. However, unambiguous identification of specific biomolecules within cellular tomograms remains challenging. Overcoming this obstacle and reliably identifying targets in the crowded cellular environment is of major importance for the understanding of cellular function and is a pre-requisite for high-resolution structural analysis. The use of highly-specific, readily visualised and adjustable labels would help mitigate this issue, improving both data quality and sample throughput. While progress has been made in cryo-CLEM and in the development of cloneable high-density tags, technical issues persist and a robust 'cryo-GFP' remains elusive. Readily-synthesized gold nanomaterials conjugated to small 'affinity modules' may represent a solution. The synthesis of materials including gold nanoclusters (AuNCs) and gold nanoparticles (AuNPs) is increasingly well understood and is now within the capabilities of non-specialist laboratories. The remarkable chemical and photophysical properties of <3nm diameter nanomaterials and their emergence as tools with widespread biomedical application presents significant opportunities to the cryo-microscopy community. In this review, we will outline developments in the synthesis, functionalisation and labelling uses of both AuNPs and AuNCs in cryo-ET, while discussing their potential as multi-modal probes for cryo-CLEM

From single-molecule spectroscopy to super-resolution imaging of the neuron: a review.

For more than 20 years, single-molecule spectroscopy has been providing invaluable insights into nature at the molecular level. The field has received a powerful boost with the development of the technique into super-resolution imaging methods, ca. 10 years ago, which overcome the limitations imposed by optical diffraction. Today, single molecule super-resolution imaging is routinely used in the study of macromolecular function and structure in the cell. Concomitantly, computational methods have been developed that provide information on numbers and positions of molecules at the nanometer-scale. In this overview, we outline the technical developments that have led to the emergence of localization microscopy techniques from single-molecule spectroscopy. We then provide a comprehensive review on the application of the technique in the field of neuroscience research.This work was supported by grants from the UK Engineering and Physical Sciences Research Council (EPSRC), The Wellcome Trust, Alzheimer’s Research UK, the Medical Research Council (MRC), and the Biotechnology and Biological Sciences Resesarch Council (BBSRC)

Single-molecule techniques in biophysics : a review of the progress in methods and applications

Single-molecule biophysics has transformed our understanding of the fundamental molecular processes involved in living biological systems, but also of the fascinating physics of life. Far more exotic than a collection of exemplars of soft matter behaviour, active biological matter lives far from thermal equilibrium, and typically covers multiple length scales from the nanometre level of single molecules up several orders of magnitude to longer length scales in emergent structures of cells, tissues and organisms. Biological molecules are often characterized by an underlying instability, in that multiple metastable free energy states exist which are separated by energy levels of typically just a few multiples of the thermal energy scale of kBT, where kB is the Boltzmann constant and T the absolute temperature, implying complex, dynamic inter-conversion kinetics across this bumpy free energy landscape in the relatively hot, wet environment of real, living biological matter. The key utility of single-molecule biophysics lies in its ability to probe the underlying heterogeneity of free energy states across a population of molecules, which in general is too challenging for conventional ensemble level approaches which measure mean average properties. Parallel developments in both experimental and theoretical techniques have been key to the latest insights and are enabling the development of highly-multiplexed, correlative techniques to tackle previously intractable biological problems. Experimentally, technological developments in the sensitivity and speed of biomolecular detectors, the stability and efficiency of light sources, probes and microfluidics, have enabled and driven the study of heterogeneous behaviours both in vitro and in vivo that were previously undetectable by ensemble methods..

Approaching deep learning based object detection in microscopy images to non-expert users

In this project, we have first carried out a study of the state of the art in object detection with Deep Learning, and then we have designed and implemented an approach that is oriented to be run in a cloud service by non-expert users. More specifically, due to its possible applications in microscopy image analysis, a web-based solution that uses the state-of-the-art RetinaNet model has been developed in the open-source ZeroCostDL4Mic environment. Moreover, our implementation uses the TensorFlow 2 object detection API, that allows different backbone networks, and it has been accepted as part of the official ZeroCostDL4Mic platform. Finally, the evaluation of the proposed solution has been performed in a public dataset and compares positively with alternative state-of-the-art approaches

Investigating bacteroidetes gliding motility

Bacteroidetes gliding motility is a type of surface motility in which rod-shaped bacteria move up to 2 µm s in a corkscrewing motion. Flavobacterium johnsoniae is the primary model organism for the study of Bacteroidetes gliding. SprB is the main adhesin in this organism and moves in a helix along the cell surface. This movement is guided by an underlying track that is anchored to the inner leaflet of the outer membrane. The essential gliding lipoprotein GldJ, which is helically arranged when visualised in fixed cells, is suggested to form this track. However, direct in vivo imaging of GldJ is yet to be achieved. Two currently outstanding questions about Bacteroidetes gliding motility are 1) how adhesion of SprB to the substratum is controlled so that binding only occurs when moving from the leading to the lagging cell pole and 2) how/if the cell discriminate between the poles. In this thesis, a fusion of the HaloTag domain to SprB enabled labelling of SprB with stable and bright dyes. The movement of SprB could then be visualised using single-particle tracking to reveal the underlying track topology. These tracking data suggest that the underlying track is not a single closed loop currently proposed, but rather a complex and potentially dynamic structure that can form multiple loops and cover most of the cell surface. SprB is encoded by the sprB operon that further encodes RemFG, Fjoh_0982, and SprCDF. In this thesis I show that all these components, except fjoh_0982, are required for gliding motility but only sprF are required for SprB helical movement. All the sprB operon components required for gliding are also required for SprB-mediated attachment to glass, indicating that they regulate adhesion of SprB. RemG and SprCD move in a helix reminiscent of the SprB movement pattern. The helical movement does not depend on SprF or SprB, but rather on the SprFhomologous N-terminal domain of SprD. Observations of gliding cells with fluorescently labelled SprC revealed accumulation of SprC near the leading cell pole. This polar accumulation correlated with the direction of movement and was not observed in cells that did not move. Furthermore, a mutant lacking the C-terminal 50 residues of SprD was unable to accumulate SprC at the leading pole. SprB did not show a similar asymmetric distribution in gliding cells. Fluorescence microscopy shows that helically moving sprB operon proteins accumulate at midcell in dividing cells in a GldJ dependent manner. Cross-linking mass spectrometry indicates that GldJ interacts with the sprB operon proteins as well as GldKNO, essential outer membrane components of the type 9 secretion system which is a pre-requisite for Bacteroidetes gliding motility

Super-resolution methods for fluorescence microscopy

Fluorescence microscopy is an important tool for biological research. However, the resolution of a standard fluorescence microscope is limited by diffraction, which makes it difficult to observe small details of a specimen’s structure. We have developed two fluorescence microscopy methods that achieve resolution beyond the classical diffraction limit. The first method represents an extension of localisation microscopy. We used nonnegative matrix factorisation (NMF) to model a noisy dataset of highly overlapping fluorophores with intermittent intensities. We can recover images of individual sources from the optimised model, despite their high mutual overlap in the original dataset. This allows us to consider blinking quantum dots as bright and stable fluorophores for localisation microscopy. Moreover, NMF allows recovery of sources each having a unique shape. Such a situation can arise, for example, when the sources are located in different focal planes, and NMF can potentially be used for three dimensional superresolution imaging. We discuss the practical aspects of applying NMF to real datasets, and show super-resolution images of biological samples labelled with quantum dots. It should be noted that this technique can be performed on any wide-field epifluorescence microscope equipped with a camera, which makes this super-resolution method very accessible to a wide scientific community. The second optical microscopy method we discuss in this thesis is a member of the growing family of structured illumination techniques. Our main goal is to apply structured illumination to thick fluorescent samples generating a large out-of-focus background. The out-of-focus fluorescence background degrades the illumination pattern, and the reconstructed images suffer from the influence of noise. We present a combination of structured illumination microscopy and line scanning. This technique reduces the out-of-focus fluorescence background, which improves the quality of the illumination pattern and therefore facilitates reconstruction. We present super-resolution, optically sectioned images of a thick fluorescent sample, revealing details of the specimen’s inner structure. In addition, in this thesis we also discuss a theoretical resolution limit for noisy and pixelated data. We correct a previously published expression for the so-called fundamental resolution measure (FREM) and derive FREM for two fluorophores with intermittent intensity. We show that the intensity intermittency of the sources (observed for quantum dots, for example) can increase the “resolution” defined in terms of FREM

The spatial dynamics of insulin-regulated GLUT4 dispersal

Insulin regulates glucose homeostasis by stimulation of glucose transport into adipose and muscle tissues through the regulated trafficking of glucose transporter 4 (GLUT4). In response to insulin GLUT4 rapidly translocates from intracellular storage sites to the plasma membrane where it facilitates glucose uptake. Significant impairments in glucose transport and GLUT4 trafficking are a major hallmark of diabetes mellitus type II. Recent advances in light microscopy techniques enabled the study of GLUT4 dynamics in the plasma membrane and it was reported that the transporter was clustered in the basal state and insulin stimulation resulted in GLUT4 dispersal. The main aim of this study was to develop a microscopy-based assay to study and quantify insulin-stimulated GLUT4 dispersal dynamics in the plasma membrane. Insulin-stimulated GLUT4 dispersal has only been observed in adipocytes and therefore we have chosen this model as a starting point to investigate the molecular mechanisms behind GLUT4 clustering and dispersal. We explored a range of cluster analysis methods to find the most suitable way to quantify GLUT4 clustering dynamics. Furthermore, this project aimed to optimise super resolution imaging in a variety of cell culture models to determine whether insulin-stimulated GLUT4 dispersal operates in skeletal and cardiac muscle and whether this process is affected by disease. Using a range of approaches we showed that insulin results in GLUT4 translocation and dispersal within the plasma membrane of 3T3-L1 adipocytes. We found that AMPK activation attenuated insulin-stimulated glucose uptake in 3T3-L1 adipocytes and also GLUT4 dispersal. It was observed that cholesterol depletion resulted in increased glucose uptake rates and GLUT4 clustering. Knock down of the membrane-localised protein EFR3 that has previously been shown to be involved in glucose uptake resulted in disruption of GLUT4 dispersal in adipocytes. We also found that HeLa cells show similar insulin-stimulated GLUT4 dispersal as adipocytes and suggest that HeLa cells are a suitable experimental model for initial studies of GLUT4 trafficking and dispersal. Chronic insulin treatment was observed to induce a state of cellular insulin resistance in 3T3-L1 adipocytes and resulted in reduced GLUT4 translocation and a more clustered GLUT4 configuration for both basal and insulin-stimulated cells. This indicates that insulin resistance affects intracellular GLUT4 trafficking pathways as well as the organization of the transporter within the plasma membrane in adipocytes. Moreover, we found a negative correlation between adipocyte cell area and insulin-stimulated GLUT4 translocation. We also report that insulin did not stimulate the reorganisation of the transferrin receptor in the plasma membrane of HeLa cells suggesting that insulin-stimulated GLUT4 dispersal did not originate from endosomal compartments in HeLa cells and that this observed effect may be specific for GLUT4. Finally, we observed that insulin did not affect GLUT4 distribution in the membrane of a commercially available model of skeletal muscle from healthy and diabetic donors. Sortilin is a sorting receptor involved in the formation of GLUT4 containing vesicles and levels of this protein were found to be reduced in skeletal muscle myotubes derived from a diabetic donor. Finally, we discovered that insulin stimulated GLUT4 dispersal also operates in stem cell-derived cardiomyocytes and have investigated GLUT4 dispersal in a variety of in vitro models of cardiac muscle tissue. Taken together, this thesis has detailed several novel findings regarding the regulation of GLUT4 clustering in adipose and muscle tissues. A robust assay to measure GLUT4 dispersal has been established and molecular mechanisms behind the observed GLUT4 clustering dynamics have been described in adipocytes. Furthermore GLUT4 clustering was characterised in several cell culture models of skeletal and cardiac muscle for the first time

Monte Carlo Methods in Practice and Efficiency Enhancements via Parallel Computation

Monte Carlo methods are crucial when dealing with advanced problems in Bayesian inference. Indeed, common approaches such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) can be endlessly adapted to tackle the most complex problems. What is important then is to construct efficient algorithms, and significant attention in the literature is devoted to developing algorithms that mix well, have low computational complexity and can scale up to large datasets. One of the most commonly used and straightforward approaches is to speed up Monte Carlo algorithms by running them in parallel computing environments. The compute time of Monte Carlo algorithms is random and can vary depending on the current state of the Markov chain. Other computing-infrastructure related factors, such as competing jobs on the same processor, or memory bandwidth, which are prevalent in shared computing architectures such as cloud computing, can also affect this compute time. However, many algorithms running in parallel require the processors to communicate every so often, and for that we must ensure that they are simultaneously ready and any idle wait time is minimised. This can be done by employing a framework known as Anytime Monte Carlo, which imposes a real-time deadline on parallel computations. The contributions in this thesis include novel applications of the Anytime framework to construct efficient Anytime MCMC and SMC algorithms which make use of parallel computing in order to perform inference for advanced problems. Examples of such problems investigated include models in which the likelihood cannot be evaluated analytically, and changepoint models, which are often used to model the heterogeneity of sequential data, but tricky to infer upon due to the unknown number and locations of the changepoints. This thesis also focuses on the difficult task of performing parameter inference in single-molecule microscopy, a category of models in which the arrival rate of observations is not uniformly distributed and measurement models have complex forms. These issues are exacerbated when molecules have trajectories described by stochastic differential equations. The original contributions of this thesis are organised in Chapters 4-6. Chapter 4 shows the development of a novel Anytime parallel tempering algorithm and demonstrates the performance enhancements the Anytime framework brings to parallel tempering, an algorithm, which runs multiple interacting MCMC chains in order to more efficiently explore the state space. In Chapter 5, a general Anytime SMC sampler is developed for performing changepoint inference using reversible jump MCMC (RJ-MCMC), an algorithm that takes into account the unknown number of changepoints by including transdimensional MCMC updates. The workings of the algorithm are illustrated on a particularly complex changepoint model, and once again the improvements in performance brought by employing the Anytime framework are demonstrated. Chapter 6 moves away from the Anytime framework, and presents a novel and general SMC approach to performing parameter inference for molecules with stochastic trajectories