An image-based computational method for characterizing whole-cell scale spatiotemporal dynamics of intracellular transport

Intracellular transport is the process of distributing and collecting materials, often packaged in different forms of cargoes, to fulfill changing structural and functional needs within the cell. Although the molecular machinery for intracellular transport has largely been identified, how this process is regulated globally in space and time remains unclear. In a step towards answering this question, we developed an image-based computational method for characterizing spatiotemporal dynamics of intracellular transport at the whole-cell scale to separate signals of moving and stationary cargoes. Specifically, we extended an algorithm that we developed previously for removing background fluorescence of static images. This separation allows us to extract from time-lapse images the global pattern of cargo movement without single particle tracking of individual cargoes. Based on this separation, we characterized the spatiotemporal dynamics of intracellular transport in terms of the spatial localization of stationary cargoes and the spatiotemporal patterns of cargo movement, respectively. We first tested and validated our methods using synthetic images. We then used our methods to analyze time-lapse images of Lamp1 transport and found different global patterns of transport behavior.