Rapid extraction of time-dependent behaviors of endo-lysosomal structures and their cargo aided by deep learning.

Abstract

The diffusional properties of subcellular structures correlate with functional properties, such as assembly of growing structures, characteristics of enclosing membranes, transient interactions with cytoskeletal elements or organelles, or capture by molecular motors. However, due to the complex, heterogeneous diffusional behavior displayed by subcellular structures, such as endo-lysosomes, it is a daunting task and major bottleneck to extract their time-dependent diffusional patterns and thus functional properties. A first step developed recently by Pinholt et al. 2021 (PNAS) to resolve this bottleneck uses a machine-learning toolbox to extract key diffusional descriptors from two-dimensional coordinates of the objects. Here we extend this work, by developing a novel deep learning modelling framework able to automatically deconvolute complex time-dependent behavior in 2D and 3D. Using this model, we correlate endosomal trafficking pathways with the diffusional properties of cargo contained within. To develop the analytical pipeline, we used 3D time-series acquired using fast live-cell lattice light sheet fluorescence microscopy of cells expressing gene-edited endo-lysosomal markers that had been infected with VSV-SARS-CoV-2 viruses tagged with other fluorescent labels. The current version of our single particle tracking end-to-end neural network model successfully ranks the key descriptive features of each trajectory point by point along the trace and takes seconds to analyze thousands of traces. It succeeded in agnostically identifying early steps of virus infection for all viral particles, including endocytosis and genome delivery into the cytosol upon virus-endosome membrane fusion.