Current advances in imaging technologies now allow us to capture biological processes with unprecedented spatial and temporal resolution, opening new windows into host-pathogen interactions, vaccine delivery mechanisms, and protein aggregation dynamics. Yet, the multidimensional multi-terabyte data that 4D microscopy data generate demand equally powerful analytical solutions. Here, I will present machine learning analysis pipelines accelerating data processing speeds by up to 6 orders of magnitude analysis allowing the extraction of quantitative mechanistic information directly from high-dimensional biological recordings. I will initially discuss SEMORE, our framework for the automated analysis of super-resolution data, which integrates segmentation and classification to dissect the nanoscale organization of biomolecules. I will then introduce DeepSPT, a deep learning framework integrated in an analysis software that rapidly and precisely resolves 2D and 3D diffusion dynamics, identifying molecular or particulate species based exclusive on their motion patterns. Together, these approaches illustrate how machine learning driven microscopy pipelines can allow academia and industry to move beyond visualization to rapid mechanistic understanding, providing powerful tools to decode and ultimately control aberrant biological functions
Nikos S Hatzakis (Sun,) studied this question.