A nested sampling algorithm for quantifying protein copy number in super-resolution microscopyKOŠUTA, TINA (Avtor) Perman, Mihael (Mentor) Manzo, Carlo (Komentor) super-resolution microscopyprotein quantificationSTORMnestedsamplingBayesian statisticevidenceThis master thesis addresses the issue of protein cluster quantification in superresolution microscopy. Methods in super-resolution microscopy, typically referred to as single molecule localization microscopy, have greatly improved the optical resolution by sequentially exciting only the part of fluorescent molecules and image their signals through time. The signal is processed and stored as a localization in space. The improved resolution has further demonstrated molecular clustering as a relevant biological feature for cellular function. However, each cluster can be composed of several molecules and each of them is subjected to stochasticity of molecule labeling and complex photophysics of the fluorescent probes. This leads to a broad distribution of number of localizations for each cluster size, which impacts exact quantification of cluster stoichiometry. The aim of this master thesis was to investigate the performance of nested sampling compared to the previously developed method in Zanacchi et al. (2017). In the said article, the authors developed a method based on numerical approximation which estimated the total protein count as the mixture model of several oligomeric states. The proportions of each oligomeric state were estimated based on the number of localizations in each cluster. In this master thesis, we implemented the nested sampling based on the mixture model described above and compared both approaches on simulated and real data. The goal of both approaches was firstly to estimate the number of different oligomeric states in the model and secondly their corresponding proportions. The methods were evaluated in a simulation study and on the data generated from STORM imaging. The simulation study showed better performance of nested sampling especially in smaller samples, while in larger samples the methods performed similarly. In the STORM image analysis, where all the samples were large (n > 1000), all fitted distributions were almost identical.20212021-08-10 14:33:01Magistrsko delo/naloga128877sl