Accuracy of automated structural RNA alignment is improved by using models that consider not only primary sequence but also secondary structure information. However, current RNA structural alignment approaches tend to perform poorly on incomplete sequence fragments, such as single reads from metagenomic environmental surveys, because nucleotides that are expected to be base paired are missing.

We present a simple, yet effective, auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of Local Shape Descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinitybased segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient—a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.Competing Interest StatementThe authors have declared no competing interest.

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

Serotonin plays different roles across networks within the same sensory modality. Previously, we used whole-cell electrophysiology in Drosophila to show that serotonergic neurons innervating the first olfactory relay are inhibited by odorants (Zhang and Gaudry, 2016). Here we show that network-spanning serotonergic neurons segregate information about stimulus features, odor intensity and identity, by using opposing coding schemes in different olfactory neuropil. A pair of serotonergic neurons (the CSDns) innervate the antennal lobe and lateral horn, which are first and second order neuropils. CSDn processes in the antennal lobe are inhibited by odors in an identity independent manner. In the lateral horn, CSDn processes are excited in an odor identity dependent manner. Using functional imaging, modeling, and EM reconstruction, we demonstrate that antennal lobe derived inhibition arises from local GABAergic inputs and acts as a means of gain control on branch specific inputs that the CSDns receive within the lateral horn.

Single molecule localization microscopy relies on the precise quantification of the position of single dye emitters in a sample. This precision is improved by the number of photons that can be detected from each molecule. Particularly recording at cryogenic temperatures dramatically reduces photobleaching and would, hence, in principle, allow the user to massively increase the illumination time to several seconds. The downside of long illuminations, however, would be image blur due to inevitable jitter or drift occurring during the illuminations, which deteriorates the localization precision. In this paper, we theoretically demonstrate that a parallel recording of the fiducial marker beads together with a fitting approach accounting for the full drift trajectory allows for largely eliminating drift effects for drift magnitudes of several hundred nanometers per frame. We showcase the method for linear and diffusional drift as well as oscillations, assuming fixed dipole orientations during each illumination.

The Drosophila gene sevenless encodes a putative trans-membrane receptor required for the formation of one particular cell, the R7 photoreceptor, in each ommatidium of the compound eye. Mutations in this gene result in the cell normally destined to form the R7 cell forming a non-neuronal cell type instead. These observations have led to the proposal that the sevenless protein receives at least part of the positional information required for the R7 developmental pathway. We have generated antibodies specific for sevenless and have examined expression of the protein by light and electron microscopy. sevenless protein is present transiently at high levels in at least 9 cells in each developing ommatidium and is detectable several hours before any overt differentiation of R7. The protein is mostly localized at the apices of the cells, in microvilli, but is also found deeper in the tissue where certain cells contact the R8 cell. This finding suggests that R8 expresses a ligand for the sevenless protein.

Cell invasion across basement membrane barriers is important in both normal development and cancer metastasis. In this issue of Developmental Cell, Naegeli et al. (2017) identify a mechanism for breaching basement membranes. Localized lysosome exocytosis fuels generation of large, invasive cellular protrusions that expand tiny basement membrane openings.

Long-term potentiation (LTP) of synaptic transmission underlies aspects of learning and memory. LTP is input-specific at the level of individual synapses, but neural network models predict interactions between plasticity at nearby synapses. Here we show in mouse hippocampal pyramidal cells that LTP at individual synapses reduces the threshold for potentiation at neighbouring synapses. After input-specific LTP induction by two-photon glutamate uncaging or by synaptic stimulation, subthreshold stimuli, which by themselves were too weak to trigger LTP, caused robust LTP and spine enlargement at neighbouring spines. Furthermore, LTP induction broadened the presynaptic-postsynaptic spike interval for spike-timing-dependent LTP within a dendritic neighbourhood. The reduction in the threshold for LTP induction lasted approximately 10 min and spread over approximately 10 microm of dendrite. These local interactions between neighbouring synapses support clustered plasticity models of memory storage and could allow for the binding of behaviourally linked information on the same dendritic branch.

For a more complete understanding of molecular mechanisms, it is important to study macromolecules and their assemblies in the broader context of the cell. This context can be visualized at nanometer resolution in three dimensions (3D) using electron cryo-tomography, which requires tilt series to be recorded and computationally aligned, currently limiting throughput. Additionally, the high-resolution signal preserved in the raw tomograms is currently limited by a number of technical difficulties, leading to an increased false-positive detection rate when using 3D template matching to find molecular complexes in tomograms. We have recently described a 2D template matching approach that addresses these issues by including high-resolution signal preserved in single-tilt images. A current limitation of this approach is the high computational cost that limits throughput. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software TEM that allows for easy scaling and improves the accessibility of this approach. We apply 2D template matching to identify ribosomes in images of frozen-hydrated cells with high precision and sensitivity, demonstrating that this is a versatile tool for visual proteomics and structure determination. We benchmark the results with 3D template matching of tomograms acquired on identical sample locations and identify strengths and weaknesses of both techniques, which offer complementary information about target localization and identity.

Double-stranded RNA (dsRNA) viruses transcribe and replicate RNA within an assembled, inner capsid particle; only plus-sense mRNA emerges into the intracellular milieu. During infectious entry of a rotavirus particle, the outer layer of its three-layer structure dissociates, delivering the inner double-layered particle (DLP) into the cytosol. DLP structures determined by X-ray crystallography and electron cryomicroscopy (cryoEM) show that the RNA coils uniformly into the particle interior, avoiding a "fivefold hub" of more structured density projecting inward from the VP2 shell of the DLP along each of the twelve 5-fold axes. Analysis of the X-ray crystallographic electron density map suggested that principal contributors to the hub are the N-terminal arms of VP2, but reexamination of the cryoEM map has shown that many features come from a molecule of VP1, randomly occupying five equivalent and partly overlapping positions. We confirm here that the electron density in the X-ray map leads to the same conclusion, and we describe the functional implications of the orientation and position of the polymerase. The exit channel for the nascent transcript directs the nascent transcript toward an opening along the 5-fold axis. The template strand enters from within the particle, and the dsRNA product of the initial replication step exits in a direction tangential to the inner surface of the VP2 shell, allowing it to coil optimally within the DLP. The polymerases of reoviruses appear to have similar positions and functional orientations.