No abstract

Summary Many lines of evidence suggest that memory in the mammalian brain is stored with distinct spatiotemporal patterns1,2. Despite recent progresses in identifying neuronal populations involved in memory coding3–5, the synapse-level mechanism is still poorly understood. Computational models and electrophysiological data have shown that functional clustering of synapses along dendritic branches leads to nonlinear summation of synaptic inputs and greatly expands the computing power of a neural network6–10. However, whether neighboring synapses are involved in encoding similar memory and how task-specific cortical networks develop during learning remain elusive. Using transcranial two-photon microscopy11, we followed apical dendrites of layer 5 (L5) pyramidal neurons in the motor cortex while mice practiced novel forelimb skills. Here we show that a third of new dendritic spines (postsynaptic structures of most excitatory synapses) formed during the acquisition phase of learning emerge in clusters, and the majority of such clusters are neighboring spine pairs. These clustered new spines are more likely to persist throughout prolonged learning sessions and even long after training stops, compared to non-clustered counterparts. Moreover, formation of new spine clusters requires repetition of the same motor task, and the emergence of succedent new spine(s) accompanies the strengthening of the first new spine in the cluster. We also show that under control conditions new spines appear to avoid existing stable spines, rather than being uniformly added along dendrites. However, succedent new spines in clusters overcome such a spatial constraint and form in close vicinity to neighboring stable spines. Our findings suggest that clustering of new synapses along dendrites is induced by repetitive activation of the cortical circuitry during learning, providing a structural basis for spatial coding of motor memory in the mammalian brain.

Synapses are the fundamental units of neuronal circuits. Synaptic plasticity can occur through changes in synaptic strength, as well as through the addition/removal of synapses. Two-photon microscopy, in combination with fluorescence labeling, offers a powerful tool to peek into the living brain and follow structural reorganization at individual synapses. Time-lapse imaging depicts a dynamic picture, in which experience-dependent plasticity of synaptic structures varies between different cortical regions and layers, as well as between neuronal subtypes. Recent studies have demonstrated that the formation and elimination of synaptic structures happens rapidly in a subpopulation of cortical neurons during various sensorimotor learning experiences, and that stabilized synaptic structures are associated with long-lasting memories for the task. Thus, circuit plasticity, mediated by structural remodeling, provides an underlying mechanism for learning and memory.

The Gp78 E3 ubiquitin ligase is shown to target the mitofusin mitochondrial fusion proteins for degradation, inducing mitochondrial fission and mitofusin 1–dependent mitophagy of uncoupled mitochondria. Mitophagy induced by endoplasmic reticulum–associated gp78 defines a distinct cellular pathway to eliminate damaged mitochondria.

SUMMARY We developed a technology (Capturing Activated Neural Ensembles, or CANE) to label, manipulate, and trans-synaptically trace neural circuits that are transiently activated in behavioral contexts with high efficiency and temporal precision. CANE consists of a knock-in mouse and engineered viruses designed to specifically infect activated neurons. Using CANE, we selectively labeled neurons that were activated by either fearful or aggressive social encounters in a hypothalamic subnucleus previously known as a locus for aggression, and discovered that social fear and aggression neurons are intermixed but largely distinct. Optogenetic stimulation of CANE-captured social fear neurons (SFNs) is sufficient to evoke fear-like behaviors in normal social contexts, whereas silencing SFNs resulted in reduced social avoidance. CANE-based mapping of axonal projections and presynaptic inputs to SFNs further revealed a highly distributed and recurrent neural network. CANE is a broadly applicable technology for dissecting causality and connectivity of spatially intermingled but functionally distinct ensembles.

This study examined the contributions of collagen and elastin to the tensile elastic properties of the vocal fold lamina propria. Uniaxial stress-strain responses of vocal fold cover and vocal ligament specimens from 20 human larynges (12 males, 8 females) were quantified with sinusoidal stretch-release deformation in vitro. Mid-coronal sections of 12 specimens were examined histologically with Masson's trichrome and elastin van Gieson stain to quantify the relative densities of collagen and elastin fibers. Results showed that significantly higher levels of collagen were found in the male vocal fold than female, for both the cover and the ligament. For male there was a significantly higher level of elastin in the cover than in the ligament. On average, the elastic modulus of the male cover was about twice that of the female at high-tensile strain (35-40%), whereas the male ligament was 3-5 times stiffer than the female in the same range. The ligament was stiffer than the cover for male, but the opposite was observed for female. These findings suggested that collagen and elastin could contribute differentially to elasticity of the cover and the ligament. The data may provide guidance for surgical reconstruction and tissue engineering of different lamina propria layers.

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.

Gp78 (also known as AMFR), an endoplasmic-reticulum (ER)-associated protein degradation (ERAD) E3 ubiquitin ligase, localizes to mitochondria-associated ER and targets the mitofusin (Mfn1 and Mfn2) mitochondrial fusion proteins for degradation. Gp78 is also the cell surface receptor for autocrine motility factor (AMF), which prevents Gp78-dependent mitofusin degradation. Gp78 ubiquitin ligase activity promotes ER-mitochondria association and ER-mitochondria Ca 2+ coupling, processes that are reversed by AMF. Electron microscopy of HT-1080 fibrosarcoma cancer cells identified both smooth ER (SER; ∼8 nm) and wider (∼50-60 nm) rough ER (RER)-mitochondria contacts. Both short hairpin RNA (shRNA)-mediated knockdown of Gp78 (shGp78) and AMF treatment selectively reduced the extent of RER-mitochondria contacts without impacting on SER-mitochondria contacts. Concomitant small interfering RNA (siRNA)-mediated knockdown of Mfn1 increased SER-mitochondria contacts in both control and shGp78 cells, whereas knockdown of Mfn2 increased RER-mitochondria contacts selectively in shGp78 HT-1080 cells. The mitofusins therefore inhibit ER-mitochondria interaction. Regulation of close SER-mitochondria contacts by Mfn1 and of RER-mitochondria contacts by AMFsensitive Gp78-mediated degradation of Mfn2 define new mechanisms that regulate ER-mitochondria interactions.