Local translation in neurons is partly mediated by the reactivation of stalled polysomes. Stalled polysomes may be enriched within the granule fraction, defined as the pellet of sucrose gradients used to separate polysomes from monosomes. The mechanism of how elongating ribosomes are reversibly stalled and unstalled on mRNAs is still unclear. In the present study, we characterize the ribosomes in the granule fraction using immunoblotting, cryogenic electron microscopy (cryo-EM), and ribosome profiling. We find that this fraction, isolated from 5-d-old rat brains of both sexes, is enriched in proteins implicated in stalled polysome function, such as the fragile X mental retardation protein (FMRP) and Up-frameshift mutation 1 homologue. Cryo-EM analysis of ribosomes in this fraction indicates they are stalled, mainly in the hybrid state. Ribosome profiling of this fraction reveals (1) an enrichment for footprint reads of mRNAs that interact with FMRPs and are associated with stalled polysomes, (2) an abundance of footprint reads derived from mRNAs of cytoskeletal proteins implicated in neuronal development, and (3) increased ribosome occupancy on mRNAs encoding RNA binding proteins. Compared with those usually found in ribosome profiling studies, the footprint reads were longer and were mapped to reproducible peaks in the mRNAs. These peaks were enriched in motifs previously associated with mRNAs cross-linked to FMRP in vivo, independently linking the ribosomes in the granule fraction to the ribosomes associated with FMRP in the cell. The data supports a model in which specific sequences in mRNAs act to stall ribosomes during translation elongation in neurons.SIGNIFICANCE STATEMENT Neurons send mRNAs to synapses in RNA granules, where they are not translated until an appropriate stimulus is given. Here, we characterize a granule fraction obtained from sucrose gradients and show that polysomes in this fraction are stalled on consensus sequences in a specific state of translational arrest with extended ribosome-protected fragments. This finding greatly increases our understanding of how neurons use specialized mechanisms to regulate translation and suggests that many studies on neuronal translation may need to be re-evaluated to include the large fraction of neuronal polysomes found in the pellet of sucrose gradients used to isolate polysomes.

Electron cryomicroscopy (cryo-EM) has emerged as a powerful structural biology instrument to solve near-atomic three-dimensional structures. Despite the fast growth in the number of density maps generated from cryo-EM data, comparison tools among these reconstructions are still lacking. Current proposals to compare cryo-EM data derived volumes perform map subtraction based on adjustment of each volume grey level to the same scale. We present here a more sophisticated way of adjusting the volumes before comparing, which implies adjustment of grey level scale and spectrum energy, but keeping phases intact inside a mask and imposing the results to be strictly positive. The adjustment that we propose leaves the volumes in the same numeric frame, allowing to perform operations among the adjusted volumes in a more reliable way. This adjustment can be a preliminary step for several applications such as comparison through subtraction, map sharpening, or combination of volumes through a consensus that selects the best resolved parts of each input map. Our development might also be used as a sharpening method using an atomic model as a reference. We illustrate the applicability of this algorithm with the reconstructions derived of several experimental examples. This algorithm is implemented in Xmipp software package and its applications are user-friendly accessible through the cryo-EM image processing framework Scipion.

Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different \'cleaning steps\' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%.