Spatio-temporal microscopy analysis of ER-shaping proteins impact on the endoplasmic reticulum architecture

Fluorescence microscopy enables us to examine cellular structures with exceptional detail, significantly advancing our understanding of subcellular components. Among these components, the endoplasmic reticulum (ER) stands out due to its pivotal influence on various cellular processes. The ER is crucial for protein synthesis, folding, lipid metabolism, and stress response. It regulates calcium storage and signaling, which are essential for muscle contraction and neurotransmitter release. The ER comprises smooth tubules, ribosome-studded sheets, and peripheral sheets that can present as tubular matrices. ER morphology is determined by ER-shaping proteins; however, their role in tubular matrix formation necessitates reconstructing the dynamic, convoluted ER network. Existing reconstruction methods are sensitive to parameters or require extensive annotation and training for deep learning. In this thesis, we introduce nERdy, an image processing-based approach, and nERdy+, a D4-equivariant neural network, for accurate extraction and representation of ER networks and junction dynamics, outperforming previous methods. nERdy builds on a set of morphological operations, whereas nERdy+ provides an efficient approach to learning equivariant representations from the data for ER segmentation. We also present a method-agnostic framework based on graph processing algorithms to understand the ER network dynamics. Comparison of stable and dynamic representations of the extracted ER structure reports on tripartite junction movement and distinguishes tubular matrices from peripheral ER networks. Our analysis of live cell confocal microscopy and super-resolution microscopy time series data shows that two ER-shaping proteins, Atlastin and Reticulon4, promote dynamic tubular matrix formation and enhance junction dynamics, identifying novel roles for these ER-shaping proteins in regulating ER structure and dynamics. Our findings can guide the development of novel therapeutic strategies targeting ER dynamics and function, potentially improving treatments and outcomes for a range of diseases.