We present a new software package called Focus that interfaces cryo-transmission electron microscopy (cryo-EM) data collection with computer image processing. Focus creates a user-friendly environment to import and manage data recorded by direct electron detectors and perform elemental image processing tasks in a high-throughput manner while new data is being acquired at the microscope. It provides the functionality required to remotely monitor the progress of data collection and data processing, which is essential now that automation in cryo-EM allows a steady flow of images of single particles, two-dimensional crystals, or electron tomography data to be recorded in overnight sessions. The rapid detection of any errors that may occur greatly increases the productivity of recording sessions at the electron microscope.

Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (∼3.2 MDa), Rubisco (∼560 kDa soluble complex), and photosystem II (∼550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semi-automated analysis of a wide range of molecular targets in cellular tomograms.

The cilium is an antenna-like organelle that performs numerous cellular functions, including motility, sensing, and signaling. The base of the cilium contains a selective barrier that regulates the entry of large intraflagellar transport (IFT) trains, which carry cargo proteins required for ciliary assembly and maintenance. However, the native architecture of the ciliary base and the process of IFT train assembly remain unresolved. In this work, we used in situ cryo–electron tomography to reveal native structures of the transition zone region and assembling IFT trains at the ciliary base in Chlamydomonas . We combined this direct cellular visualization with ultrastructure expansion microscopy to describe the front-to-back stepwise assembly of IFT trains: IFT-B forms the backbone, onto which bind IFT-A, dynein-1b, and finally kinesin-2 before entry into the cilium.

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