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.

Formerly regarded as small 'bags' of nucleic acids with randomly diffusing enzymes, bacteria are organized by a sophisticated and tightly regulated molecular machinery. Here, we review qualitative and quantitative data on the intracellular organization of bacteria and provide a detailed inventory of macromolecular structures such as the divisome, the degradosome and the bacterial 'nucleolus'. We discuss how these metabolically active structures manage the spatial organization of the cell and how macromolecular crowding influences them. We present for the first time a visualization program, lifeexplorer, that can be used to study the interplay between metabolism and spatial organization of a prokaryotic cell.

Cryo-electron tomography (cryo-ET) allows one to visualize and study the 3D spatial distribution of macromolecules, in their native states and at nanometer resolution in single cells. While this label-free cryogenic imaging technology produces data containing rich structural information, automatic localization and identification of macromolecules are prone to noise and reconstruction artifacts, and to the presence of many molecular species in small areas. Hence, we present a computational procedure that uses artificial neural networks to accurately localize several macromolecular species in cellular cryo-electron tomograms. The DeepFinder algorithm leverages deep learning and outperforms the commonly-used template matching method on synthetic datasets. Meanwhile, DeepFinder is very fast when compared to template matching, and is better capable of localizing and identifying small macromolecules than other competitive deep learning methods. On experimental cryo-ET data depicting ribosomes, the localization and structure resolution (determined through subtomogram averaging) results obtained with DeepFinder are consistent with those obtained by experts. The DeepFinder algorithm is able to imitate the analysis performed by experts, and is therefore a very promising algorithm to investigate efficiently the contents of cellular tomograms. Furthermore, we show that DeepFinder can be combined with a template matching procedure to localize the missing macromolecules not found by one or the other method. Application of this collaborative strategy allowed us to find additional 20.5% membrane-bound ribosomes that had been missed or discarded during manual template matching-assisted annotation.

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The GraphiteLifeExplorer tool enables biologists to reconstruct 3D cellular complexes built from proteins and DNA molecules. Models of DNA molecules can be drawn in an intuitive way and assembled to proteins or others globular structures. Real time navigation and immersion offer a unique view to the reconstructed biological machinery.