Development of a Software Framework for Correlating Atomic Force and Fluorescence Microscopy Data

Abstract

This thesis presents the development of a C++ software framework for multimodal nanoscale analysis, designed to correlate data from Atomic Force Microscopy (AFM) and Single molecule Fluorescence Microscopy (SMFM). The tool enables the integration of high-resolution structural and molecular data, allowing the investigation of any artificial or biological nanoparticle carrying both a fluorescent signature and a measurable surface profile. Although the system is generically applicable for any SMFM and AFM datasets, extracellular vesicles (EVs) were used as a representative biological test case. The software supports the loading of AFM and SMFM images or point-based datasets, detection of features in AFM using intensity-based clustering, and extraction of quantitative descriptors such as center of mass, height, area, and full width at half maximum (FWHM). It also enables user-guided or automated selection of a region of interest (ROI) in the SMFM channel, followed by preprocessing and structural alignment with the AFM pattern using geometry-based matching. This includes triangle-based vector shape comparison and affine transformations. After alignment, the system computes spatial correlation metrics between SMFM and AFM features to quantify overlap and pattern similarity and delivers a colocalization table. The result is a semi-automated, reproducible workflow for combining topographical and fluorescence data at the nanoscale. This provides a flexible foundation for correlative analysis of nanoparticle systems, with EVs serving as a case study for validation.

Date of Award	2025
Original language	English
Supervisor	Jaroslaw Jacak (Supervisor)