Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.

Abstract

Subcellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with unprecedented precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the subcellular environment is labor intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework integrated in an analysis software, to interpret the diffusional two- or three-dimensional temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying endosomal organelles, clathrin-coated pits and vesicles among others with F1 scores of 81%, 82% and 95%, respectively, and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.