Quantitative analysis of microscopy

Particle tracking is an essential tool for the study of dynamics of biological processes. The dynamics of these processes happens in three-dimensional (3D) space as the biological structures themselves are 3D. The focus of this thesis is on the development of single particle tracking methods for analysis of the dynamics of biological processes through the use of image processing techniques. Firstly, introduced is a novel particle tracking method that works with two-dimensional (2D) image data. This method uses the theory of Haar-like features for particle detection and trajectory linking is achieved using a combination of three Kalman filters within an interacting multiple models framework. The trajectory linking process utilises an extended state space variable which better describe the morphology and intensity profiles of the particles under investigation at their current position. This tracking method is validated using both 2D synthetically generated images as well as 2D experimentally collected images. It is shown that this method outperforms 14 other stateof-the-art methods. Next this method is used to analyse the dynamics of fluorescently labelled particles using a live-cell fluorescence microscopy technique, specifically a variant of the super-resolution (SR) method PALM, spt-PALM. From this application, conclusions about the organisation of the proteins under investigation at the cell membrane are drawn. Introduced next is a second particle tracking method which is highly efficient and capable of working with both 2D and 3D image data. This method uses a novel Haar-inspired feature for particle detection, drawing inspiration from the type of particles to be detected which are typically circular in 2D space and spherical in 3D image space. Trajectory linking in this method utilises a global nearest neighbour methodology incorporating both motion models to describe the motion of the particles under investigation and a further extended state space variable describing many more aspects of the particles to be linked. This method is validated using a variety of both 2D and 3D synthetic image data. The methods performance is compared with 14 other state-of-the-art methods showing it to be one of the best overall performing methods. Finally, analysis tools to study a SR image restoration method developed by our research group, referred to as Translation Microscopy (TRAM) are investigated [1]. TRAM can be implemented on any standardised microscope and deliver an improvement in resolution of up to 7-fold. However, the results from TRAM and other SR imaging methods require specialised tools to validate and analyse them. Tools have been developed to validate that TRAM performs correctly using a specially designed ground truth. Furthermore, through analysis of results on a biological sample corroborate other published results based on the size of biological structures, showing again that TRAM performs as expected.EPSC

Translation Microscopy (TRAM) for super-resolution imaging

Super-resolution microscopy is transforming our understanding of biology but accessibility is limited by its technical complexity, high costs and the requirement for bespoke sample preparation. We present a novel, simple and multi-color super-resolution microscopy technique, called translation microscopy (TRAM), in which a super-resolution image is restored from multiple diffraction-limited resolution observations using a conventional microscope whilst translating the sample in the image plane. TRAM can be implemented using any microscope, delivering up to 7-fold resolution improvement. We compare TRAM with other super-resolution imaging modalities, including gated stimulated emission deletion (gSTED) microscopy and atomic force microscopy (AFM). We further developed novel ‘ground-truth’ DNA origami nano-structures to characterize TRAM, as well as applying it to a multi-color dye-stained cellular sample to demonstrate its fidelity, ease of use and utility for cell biology

Data from: Automated single particle detection and tracking for large microscopy datasets

Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates

Navigation through the plasma membrane molecular landscape shapes random organelle movement

Eukaryotic plasma membrane organization theory has long been controversial, in part due to a dearth of suitably high-resolution techniques to probe molecular architecture in situ and integrate information from diverse data streams [1]. Notably, clustered patterning of membrane proteins is a commonly conserved feature across diverse protein families (reviewed in [2]), including the SNAREs [3], SM proteins [4, 5], ion channels [6, 7], and receptors (e.g., [8]). Much effort has gone into analyzing the behavior of secretory organelles [9–13], and understanding the relationship between the membrane and proximal organelles [4, 5, 12, 14] is an essential goal for cell biology as broad concepts or rules may be established. Here we explore the generally accepted model that vesicles at the plasmalemma are guided by cytoskeletal tracks to specific sites on the membrane that have clustered molecular machinery for secretion [15], organized in part by the local lipid composition [16]. To increase our understanding of these fundamental processes, we integrated nanoscopy and spectroscopy of the secretory machinery with organelle tracking data in a mathematical model, iterating with knockdown cell models. We find that repeated routes followed by successive vesicles, the re-use of similar fusion sites, and the apparently distinct vesicle “pools” are all fashioned by the Brownian behavior of organelles overlaid on navigation between non-reactive secretory protein molecular depots patterned at the plasma membrane

Limiting arms, enforcing limits: International inspections and the challenges of compellance in Germany post-1919, Iraq post-1991

This article compares efforts to curb German military power after 1919 with attempts to limit that of Iraq after 1991. It argues that incomplete defeat in each case, compounded by disputes among the victors (exploited by the Germans and Iraqis) undermined a long-term maintenance of each settlement.UNSCOM’s problems in Iraq in the 1990s replicated much of what had hamstrung the IMCC in Germany in the 1920s. Crucial was the lack of autonomous intelligence and verification capabilities, enabling the targeted regimes to defy inspections, whilst challenging the impartiality and legitimacy of the enforcers. Facing devious and unrepentant adversaries, both inspection regimes survived barely seven years. In both cases a second war would ensue against the non-compliers – Germany in 1939, Iraq in 2003