@phdthesis{Meiser2023,
  author    = {Meiser, Elisabeth},
  title     = {Single-molecule dynamics at a bottleneck: a systematic study of the narrow escape problem in a disc},
  doi       = {10.25972/OPUS-31965},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319650},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Diffusion facilitates numerous reactions within the biological context of a cell. It is remarkable how the cost-efficient random process of Brownian motion promotes fast reactions. From the narrow escape theory, it is possible to determine the mean first passage time of such processes based on their reaction space and diffusion coefficient. The narrow escape theory of Brownian particles is characterized by a confining domain with reflective boundaries and a small reaction site. In this thesis, the mean first passage time was systematically tested in a disc as a function of the escape opening size in vitro and in silico. For the in vitro experiments, a model system of patterned supported-lipid bilayers (SLB) was established. Such a model is prepared by a combined colloid metalization approach, where a gold scaffold on glass facilitates assembly of SLB patches of distinct sizes through vesicle fusion. The model setup was evaluated and found to match all necessary requirements to test the nar- row escape problem in vitro. In particular, the reflectivity of the boundaries, the unhindered, free diffusion of the tracer lipids, and the distinct area were assessed. Observed results of the mean first passage time agreed with the theory of the narrow escape problem. There was excellent agreement in both absolute values and across a range of small escape opening sizes. Additionally, I developed a straightforward method, a correction factor, to calculate the mean first passage time from incomplete experimental traces. By re-scaling the mean first passage time to the fraction of particles that escaped, I was able to overcome the lifetime limitations of fluorescent probes. Previously inaccessible measurements of the mean first passage time relying on fluorescent probes will be made possible through this approach. The in vitro experiments were complemented with various in silico experiments. The latter were based on random walk simulations in discs, mimicking the in vitro situation with its uncertainties. The lifetime of single particles was either set sufficiently long to allow all particles to escape, or was adjusted to meet the lifetime limitations observed in the in vitro experiments. A comparison of the mean first passage time from lifetime-unlimited particles to the corrected, lifetime-limited particles did support the use of the correction factor. In agreement with the narrow escape theory, it was experimentally found that the mean first passage time is independent of the start point of the particle within the domain. This is when the particle adheres to a minimum distance to the escape site. In general, the presented random walk simulations do accurately represent the in vitro experiments in this study. The required hardware for the establishment of an astigmatism-based 3D system was installed in the existing microscope. The first attempts to analyze the obtained 3D imaging data gave insight into the potential of the method to investigate molecule dynamics in living trypanosome cells. The full functionality will be realized with the ongoing improvement of image analysis outside of this thesis.},
  subject      = {Freies Molek{\"u}l},
  language  = {en}
}
@phdthesis{Reinhard2023,
  author    = {Reinhard, Sebastian},
  title     = {Improving Super-Resolution Microscopy Data Reconstruction and Evaluation by Developing Advanced Processing Algorithms and Artifcial Neuronal Networks},
  doi       = {10.25972/OPUS-31695},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-316959},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {The fusion of methods from several disciplines is a crucial component of scientific development. Artificial Neural Networks, based on the principle of biological neuronal networks, demonstrate how nature provides the best templates for technological advancement. These innovations can then be employed to solve the remaining mysteries of biology, including, in particular, processes that take place on microscopic scales and can only be studied with sophisticated techniques. For instance, direct Stochastic Optical Reconstruction Microscopy combines tools from chemistry, physics, and computer science to visualize biological processes at the molecular level. One of the key components is the computer-aided reconstruction of super-resolved images. Improving the corresponding algorithms increases the quality of the generated data, providing further insights into our biology. It is important, however, to ensure that the heavily processed images are still a reflection of reality and do not originate in random artefacts. Expansion microscopy is expanding the sample by embedding it in a swellable hydrogel. The method can be combined with other super-resolution techniques to gain additional resolution. We tested this approach on microtubules, a well-known filamentous reference structure, to evaluate the performance of different protocols and labelling techniques. We developed LineProfiler an objective tool for data collection. Instead of collecting perpendicular profiles in small areas, the software gathers line profiles from filamentous structures of the entire image. This improves data quantity, quality and prevents a biased choice of the evaluated regions. On the basis of the collected data, we deployed theoretical models of the expected intensity distribution across the filaments. This led to the conclusion that post-expansion labelling significantly reduces the labelling error and thus, improves the data quality. The software was further used to determine the expansion factor and arrangement of synaptonemal complex data. Automated Simple Elastix uses state-of-the-art image alignment to compare pre- and post-expansion images. It corrects linear distortions occurring under isotropic expansion, calculates a structural expansion factor and highlights structural mismatches in a distortion map. We used the software to evaluate expanded fungi and NK cells. We found that the expansion factor differs for the two structures and is lower than the overall expansion of the hydrogel. Assessing the fluorescence lifetime of emitters used for direct Stochastic Optical Reconstruction Microscopy can reveal additional information about the molecular environment or distinguish dyes emitting with a similar wavelength. The corresponding measurements require a confocal scanning of the sample in combination with the fluorescent switching of the underlying emitters. This leads to non-linear, interrupted Point Spread Functions. The software ReCSAI targets this problem by combining the classical algorithm of compressed sensing with modern methods of artificial intelligence. We evaluated several different approaches to combine these components and found, that unrolling compressed sensing into the network architecture yields the best performance in terms of reconstruction speed and accuracy. In addition to a deep insight into the functioning and learning of artificial intelligence in combination with classical algorithms, we were able to reconstruct the described non-linearities with significantly improved resolution, in comparison to other state-of-the-art architectures.},
  subject      = {Mikroskopie},
  language  = {en}
}