Segmentation, Modeling and Quantification of Electron Cryotomographic Datasets

Electron microscopy (EM) has long been a powerful tool for delineating the ultrastructure of cells and tissues beyond what can be seen by light microscopy, and the advent of cryo-EM has provided the opportunity to update our understanding of cellular/cytoplasmic structure with unprecedented preservation and clarity. Electron cryotomography (ECT), in particular, has become a method-of-choice for studying the three-dimensional (3-D) spatial configuration of molecules within their near-native cellular context [1], and in some cases is able to dissect the molecular architecture of individual macromolecular complexes in vivo. With the automation of tomographic data collection [2, 3] and advances in microscope stability [4], the throughput of this once “slow” imaging method has increased dramatically, making analysis of the large amounts of data generated a serious issue.