A practical workflow for analyzing organelle spatial proximity with super-resolution microscopy.

Abstract

Organelles establish dynamic contacts to facilitate inter-organelle communication. Although their structures and dynamics are commonly observed by light microscopy, the spatial precision of organelle proximity is restricted by optical diffraction limit. While super-resolution microscopy has significantly advanced the visualization of subcellular ultrastructure, optimizing imaging parameters and robust downstream analysis remains a key challenge. Here we present a practical workflow for live-cell super-resolution imaging and quantitative analysis of organelle spatial proximity. By combining super-resolution microscopy and machine learning-driven batch image processing, this method enables accurate estimation of organelle morphology and juxtapositions. Notably, this workflow is broadly adaptable to different subcellular structures, labeling strategies and imaging conditions. It is designed to be accessible to most cell biology laboratories without requiring large-scale training datasets or extensive computational resources.