DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging

Membrane contact site (MCS)-mediated organelle interactions play essential roles in the cell. Quantitative analysis of MCSs reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed DeepContact, a deep-learning protocol for optimizing organelle segmentation and contact analysis based on label-free EM. DeepContact presents high efficiency and flexibility in interactive visualizations, accommodating new morphologies of organelles and recognizing contacts in versatile width ranges, which enables statistical analysis of various types of MCSs in multiple systems. DeepContact profiled previously unidentified coordinative rearrangements of MCS types in cultured cells with combined nutritional conditions. DeepContact also unveiled a subtle wave of ER-mitochondrial entanglement in Sertoli cells during the seminiferous epithelial cycle, indicating its potential in bridging MCS dynamics to physiological and pathological processes.

Automated summary

In this study, a tool called DeepContact was developed for optimizing organelle segmentation and contact analysis using label-free electron microscopy (EM) through deep learning algorithms. DeepContact showed high efficiency and flexibility, capable of accommodating various organelle morphologies and identifying contacts of different widths. The study revealed previously unidentified coordinated rearrangements of organelles and a subtle wave of interaction between the endoplasmic reticulum (ER) and mitochondria in Sertoli cells during the seminiferous epithelial cycle, demonstrating the potential of DeepContact in bridging MCS dynamics to physiological and pathological processes.