浦久保 秀俊

Abstract Familial neurohypophysial diabetes insipidus (FNDI) is an autosomal dominant disorder caused by mutations in the arginine vasopressin (AVP) gene. In AVP neurons in a mouse model of FNDI, aggregates of mutant AVP precursors accumulate within a specific compartment of the endoplasmic reticulum (ER). However, as FNDI mice aged, or were exposed to repeated water deprivation, the ER lumen dilated and mutant aggregates dispersed throughout the ER. Meanwhile, autophagic isolation membranes, known as phagophores, emerged to envelop ER containing these aggregates, indicating induction of ER-phagy. Previous in vitro studies showed that phagophores originate from ER membranes, but the structural relationship between phagophores and the ER membrane in vivo remains unknown. In this study, we used serial block-face scanning electron microscopy to investigate the structural relationship between phagophores, ER membranes, and protein aggregates within dilated ER of AVP neurons from FNDI mice subjected to intermittent water deprivation for 4 weeks. Three-dimensional analysis revealed that phagophores enveloped aggregates located within the dilated ER. Serial imaging further demonstrated a physical connection between these phagophores and intact ER membranes. This study provides the first in vivo evidence of the structural continuity between phagophores and the ER membrane in AVP neurons in a mouse model of FNDI.

This study developed a three-dimensional ultrastructural analysis application using serial block-face scanning electron microscopy (SBF-SEM) to investigate surgically acquired human skin tissues containing the arrector pili muscle. We utilized the en bloc staining, including reduced osmium, thiocarbohydrazide, and lead aspartate, as well as the embedding using a carbon-based conductive resin. Next, we obtained serial images with SBF-SEM. The results revealed dense nerve fiber networks branching from nearby nerve fiber bundles outside the muscle and running among muscle fibers. Additionally, the dense nerve network running through and along arrector pili muscle fibers rarely penetrates the connective tissues between smooth muscle fibers and epithelial cells. Furthermore, in the observation area, no individual smooth muscle fibers formed adhesion structures with the epithelial cells of the hair follicle, ending in the dermal extracellular matrix near the epithelial cells. These results indicate the usefulness of this approach for three-dimensional ultrastructural analyses of human skin tissues comprising follicular units and revealing structural changes in skin tissues, especially the arrector pili muscle and nerve fibers with hair follicular epithelium, in aging and diseased conditions.

Precise information on synapse organization in a dendrite is crucial to understanding the mechanisms underlying voltage integration and the variability in the strength of synaptic inputs across dendrites of different complex morphologies. Here, we used focused ion beam/scanning electron microscope (FIB/SEM) to image the dendritic spines of mice in the hippocampal CA1 region, CA3 region, somatosensory cortex, striatum, and cerebellum (CB). Our results show that the spine geometry and dimensions differ across neuronal cell types. Despite this difference, dendritic spines were organized in an orchestrated manner such that the postsynaptic density (PSD) area per unit length of dendrite scaled positively with the dendritic diameter in CA1 proximal stratum radiatum (PSR), cortex and CB. The ratio of the PSD area to neck length was kept relatively uniform across dendrites of different diameters in CA1 PSR. Computer simulation suggests that a similar level of synaptic strength across different dendrites in CA1 PSR enables the effective transfer of synaptic inputs from the dendrites towards soma. Excitatory postsynaptic potentials (EPSPs), evoked at single spines by glutamate uncaging and recorded at the soma, show that the neck length is more influential than head width in regulating the EPSP magnitude at the soma. Our study describes thorough morphological features and the organizational principles of dendritic spines in different brain regions.Significance statement Little is known about the characteristic anatomical features underlying the organization of spine synapses in a dendrite. This study used volume electron microscopy to make an extensive characterization of dendritic spine synapses in multiple regions of the mouse brain to uncover the principles underlying their placement along a dendritic shaft. By using a combination of approaches such as two-photon imaging, glutamate uncaging, electrophysiology, and computer simulation, we reveal the functional importance of regulated spine placement along a dendritic trunk. Our research presents a crucial step in understanding the synaptic computational principle in dendrites by highlighting the generalizable features of dendritic spine organization in a neuron.

Animals remember temporal links between their actions and subsequent rewards. We previously discovered a synaptic mechanism underlying such reward learning in D1 receptor (D1R)-expressing spiny projection neurons (D1 SPN) of the striatum. Dopamine (DA) bursts promote dendritic spine enlargement in a time window of only a few seconds after paired pre- and post-synaptic spiking (pre-post pairing), which is termed as reinforcement plasticity (RP). The previous study has also identified underlying signaling pathways; however, it still remains unclear how the signaling dynamics results in RP. In the present study, we first developed a computational model of signaling dynamics of D1 SPNs. The D1 RP model successfully reproduced experimentally observed protein kinase A (PKA) activity, including its critical time window. In this model, adenylate cyclase type 1 (AC1) in the spines/thin dendrites played a pivotal role as a coincidence detector against pre-post pairing and DA burst. In particular, pre-post pairing (Ca2+ signal) stimulated AC1 with a delay, and the Ca2+-stimulated AC1 was activated by the DA burst for the asymmetric time window. Moreover, the smallness of the spines/thin dendrites is crucial to the short time window for the PKA activity. We then developed a RP model for D2 SPNs, which also predicted the critical time window for RP that depended on the timing of pre-post pairing and phasic DA dip. AC1 worked for the coincidence detector in the D2 RP model as well. We further simulated the signaling pathway leading to Ca2+/calmodulin-dependent protein kinase II (CaMKII) activation and clarified the role of the downstream molecules of AC1 as the integrators that turn transient input signals into persistent spine enlargement. Finally, we discuss how such timing windows guide animals' reward learning.

Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.

See also: https://youtu.be/GWVtrtwNovw