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

ABSTRACT: Sub-cellular 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 an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D 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 distinct types of endosomal organelles, and clathrin-coat

SUBMITTER: Kæstel-Hansen J 

PROVIDER: S-EPMC10680793 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature