AI-driven single-particle tracking for cancer cell characterization

Abstract

Prostate cancer exhibits altered intracellular phenotypes that require novel biomarker approaches for accurate diagnosis and treatment stratification. This thesis presents an integrated computational framework to reconstruct and analyze the intracellular dynamics of vesicle-encapsulated gold nanoparticles (AuNPs) from large-scale scat tering microscopy videos, with the goal of identifying dynamic indices that can serve as potential biomarkers for distinguishing between prostate cancer cell lines. The proposed framework combines two major components: trajectory recon struction using deep learning models, specifically the MAGIK graph neural network, and inference of diffusion properties from the reconstructed trajectories. To address the challenges caused by the large scale of the experimental videos, the study intro duces a segmentation-based pipeline that processes smaller video sequences, inte grates predicted graphs, and builds accurate nanoparticle trajectories. Importantly, the MAGIK model is trained on simulated trajectories that simulate biologically relevant motion types, reducing the need for labor-intensive manual annotations. Subsequently, the study applies a modified version of the MAGIK model to pre dict point-wise diffusion exponent values for each trajectory, allowing classification of distinct motion types such as directed motion and subdiffusive motion. Among the extracted dynamic indices, nanoparticle velocities during directed motion emerge as promising biomarkers, showing different distributions between the LNCaP and PC3 prostate cancer cell lines. Overall, this work demonstrates the potential of applying deep learning methods to uncover novel dynamic biomarkers in cancer research. Future directions include extending the analysis to additional diffusion properties and expanding the reper toire of dynamic indices with biological significance to further enhance biomarker discovery and improve our understanding of intracellular transport in cancer cells.