Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy (2024)
Super-resolution microscopy has transformed bioimaging by enabling nanoscale visualization of cellular structures. This thesis introduces PulseSTORM, a software application designed to streamline the quantitative analysis of Stochastic Optical Reconstruction Microscopy (STORM) and filament datasets. PulseSTORM integrates tools like ThunderSTORM, Ridge Detection, and SOAX into a modular platform for robust post-processing analysis. The research addresses the need for a comprehensive, user-friendly tool to analyze blinking statistics and filament structures, combining preprocessing, batch processing, and an analytics dashboard. Validation experiments with Cy5, C’dots, and aC’dots confirmed the accuracy of the software, with results aligning closely with literature values and offering actionable insights for sample preparation. PulseSTORM sets a foundation for advancing super-resolution microscopy, with future goals including direct integration with ThunderSTORM, optimization of computational performance, and exploration of recovery yield metrics. Beyond bioimaging, its potential extends to semiconductor defect detection, bridging biological and industrial applications.