Unusual nuclear structures in male meiocytes of wild-type rye as revealed by volume microscopy.

BACKGROUND AND AIMS: During the analysis of plant male meiocytes coming from destroyed meiocyte columns (united multicellular structures formed by male meiocytes in each anther locule), a considerable amount of information becomes unavailable. Therefore, in this study intact meiocyte columns were studied by volume microscopy in wild-type rye for the most relevant presentation of 3-D structure of rye meiocytes throughout meiosis. METHODS: We used two types of volume light microscopy: confocal laser scanning microscopy and non-confocal bright-field scanning microscopy combined with alcohol and aldehyde fixation, as well as serial block-face scanning electron microscopy. KEY RESULTS: Unusual structures, called nuclear protuberances, were detected. At certain meiotic stages, nuclei formed protuberances that crossed the cell wall through intercellular channels and extended into the cytoplasm of neighbouring cells, while all other aspects of cell structure appeared to be normal. This phenomenon of intercellular nuclear migration (INM) was detected in most meiocytes at leptotene/zygotene. No cases of micronucleus formation or appearance of binucleated meiocytes were noticed. There were instances of direct contact between two nuclei during INM. No influence of fixation or of mechanical impact on the induction of INM was detected. CONCLUSIONS: Intercellular nuclear migration in rye may be a programmed process (a normal part of rye male meiosis) or a tricky artefact that cannot be avoided in any way no matter which approach to meiocyte imaging is used. In both cases, INM seems to be an obligatory phenomenon that has previously been hidden by limitations of common microscopic techniques and by 2-D perception of plant male meiocytes. Intercellular nuclear migration cannot be ignored in any studies involving manipulations of rye anthers.

Ultrastructure of the lamprey head mesoderm reveals evolution of the vertebrate head.

The cranial muscle is a critical component in the vertebrate head for a predatory lifestyle. However, its evolutionary origin and possible segmental nature during embryogenesis have been controversial. In jawed vertebrates, the presence of pre-otic segments similar to trunk somites has been claimed based on developmental observations. However, evaluating such arguments has been hampered by the paucity of research on jawless vertebrates. Here, we discovered different cellular arrangements in the head mesoderm in lamprey embryos (Lethenteron camtschaticum) using serial block-face scanning electron and laser scanning microscopies. These cell populations were morphologically and molecularly different from somites. Furthermore, genetic comparison among deuterostomes revealed that mesodermal gene expression domains were segregated antero-posteriorly in vertebrates, whereas such segregation was not recognized in invertebrate deuterostome embryos. These findings indicate that the vertebrate head mesoderm evolved from the anteroposterior repatterning of an ancient mesoderm and developmentally diversified before the split of jawless and jawed vertebrates.

Tri-view two-photon microscopic image registration and deblurring with convolutional neural networks.

Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed neural structures in the x-y image space, the image quality along the depth direction is lower because of lens blur, which often makes it difficult to identify the neural connectivity. To address this problem, we propose a novel approach for restoring the isotropic image volume by estimating and fusing the intersection regions of the images captured from three orthogonal viewpoints using convolutional neural networks (CNNs). Because convolution on 3D images is computationally complex, the proposed method takes the form of cascaded CNN models consisting of rigid transformation, dense registration, and deblurring networks for more efficient processing. In addition, to enable self-supervised learning, we trained the CNN models with simulated synthetic images by considering the distortions of the microscopic imaging process. Through extensive experiments, the proposed method achieved substantial image quality improvements.

The critical balance between dopamine D2 receptor and RGS for the sensitive detection of a transient decay in dopamine signal.

In behavioral learning, reward-related events are encoded into phasic dopamine (DA) signals in the brain. In particular, unexpected reward omission leads to a phasic decrease in DA (DA dip) in the striatum, which triggers long-term potentiation (LTP) in DA D2 receptor (D2R)-expressing spiny-projection neurons (D2 SPNs). While this LTP is required for reward discrimination, it is unclear how such a short DA-dip signal (0.5-2 s) is transferred through intracellular signaling to the coincidence detector, adenylate cyclase (AC). In the present study, we built a computational model of D2 signaling to determine conditions for the DA-dip detection. The DA dip can be detected only if the basal DA signal sufficiently inhibits AC, and the DA-dip signal sufficiently disinhibits AC. We found that those two requirements were simultaneously satisfied only if two key molecules, D2R and regulators of G protein signaling (RGS) were balanced within a certain range; this balance has indeed been observed in experimental studies. We also found that high level of RGS was required for the detection of a 0.5-s short DA dip, and the analytical solutions for these requirements confirmed their universality. The imbalance between D2R and RGS is associated with schizophrenia and DYT1 dystonia, both of which are accompanied by abnormal striatal LTP. Our simulations suggest that D2 SPNs in patients with schizophrenia and DYT1 dystonia cannot detect short DA dips. We finally discussed that such psychiatric and movement disorders can be understood in terms of the imbalance between D2R and RGS.

Geometry and the Organizational Principle of Spine Synapses along a Dendrite.

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 toward 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 morphologic features and the organizational principles of dendritic spines in different brain regions.

Signaling models for dopamine-dependent temporal contiguity in striatal synaptic plasticity.

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.

Generative and discriminative model-based approaches to microscopic image restoration and segmentation.

Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.

Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration.

Advances in two two-photon microscopy (2PM) have made three-dimensional (3D) neural imaging of deep cortical regions possible. However, 2PM often suffers from poor image quality because of various noise factors, including blur, white noise, and photo bleaching. In addition, the effectiveness of the existing image processing methods is limited because of the special features of 2PM images such as deeper tissue penetration but higher image noises owing to rapid laser scanning. To address the denoising problems in 2PM 3D images, we present a new algorithm based on deep convolutional neural networks (CNNs). The proposed model consists of multiple U-nets in which an individual U-net removes noises at different scales and then yields a performance improvement based on a coarse-to-fine strategy. Moreover, the constituent CNNs employ fully 3D convolution operations. Such an architecture enables the proposed model to facilitate end-to-end learning without any pre/post processing. Based on the experiments on 2PM image denoising, we observed that our new algorithm demonstrates substantial performance improvements over other baseline methods.

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images.

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.

GABAergic inhibition reduces the impact of synaptic excitation on somatic excitation.

The effect of excitatory synaptic input on the excitation of the cell body is believed to vary depending on where and when the synaptic activation occurs in dendritic trees and the spatiotemporal modulation by inhibitory synaptic input. However, few studies have examined how individual synaptic inputs influence the excitability of the cell body in spontaneously active neuronal networks mainly because of the lack of an appropriate method. We developed a calcium imaging technique that monitors synaptic inputs to hundreds of spines from a single neuron with millisecond resolution in combination with whole-cell patch-clamp recordings of somatic excitation. In rat hippocampal CA3 pyramidal neurons ex vivo, a fraction of the excitatory synaptic inputs were not detectable in the cell body against background noise. These synaptic inputs partially restored their somatic impact when a GABAA receptor blocker was intracellularly perfused. Thus, GABAergic inhibition reduces the influence of some excitatory synaptic inputs on the somatic excitability. Numerical simulation using a single neuron model demonstrates that the timing and locus of a dendritic GABAergic input are critical to exert this effect. Moreover, logistic regression analyses suggest that the GABAergic inputs sectionalize spine activity; that is, only some subsets of synchronous synaptic activity seemed to be preferably passed to the cell body. Thus, dendrites actively sift inputs from specific presynaptic cell assemblies.

A computational model of afterimage rotation in the peripheral drift illusion based on retinal ON/OFF responses.

Human observers perceive illusory rotations after the disappearance of circularly repeating patches containing dark-to-light luminance. This afterimage rotation is a very powerful phenomenon, but little is known about the mechanisms underlying it. Here, we use a computational model to show that the afterimage rotation can be explained by a combination of fast light adaptation and the physiological architecture of the early visual system, consisting of ON- and OFF-type visual pathways. In this retinal ON/OFF model, the afterimage rotation appeared as a rotation of focus lines of retinal ON/OFF responses. Focus lines rotated clockwise on a light background, but counterclockwise on a dark background. These findings were consistent with the results of psychophysical experiments, which were also performed by us. Additionally, the velocity of the afterimage rotation was comparable with that observed in our psychophysical experiments. These results suggest that the early visual system (including the retina) is responsible for the generation of the afterimage rotation, and that this illusory rotation may be systematically misinterpreted by our high-level visual system.

A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.

A critical time window for dopamine actions on the structural plasticity of dendritic spines.

Animal behaviors are reinforced by subsequent rewards following within a narrow time window. Such reward signals are primarily coded by dopamine, which modulates the synaptic connections of medium spiny neurons in the striatum. The mechanisms of the narrow timing detection, however, remain unknown. Here, we optically stimulated dopaminergic and glutamatergic inputs separately and found that dopamine promoted spine enlargement only during a narrow time window (0.3 to 2 seconds) after the glutamatergic inputs. The temporal contingency was detected by rapid regulation of adenosine 3',5'-cyclic monophosphate in thin distal dendrites, in which protein-kinase A was activated only within the time window because of a high phosphodiesterase activity. Thus, we describe a molecular basis of reinforcement plasticity at the level of single dendritic spines.

Stochasticity in Ca2+ increase in spines enables robust and sensitive information coding.

A dendritic spine is a very small structure (∼0.1 µm3) of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca2+ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca2+ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca2+ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca2+ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca2+ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.

In vitro reconstitution of a CaMKII memory switch by an NMDA receptor-derived peptide.

Ca(2+)/Calmodulin-dependent protein kinase II (CaMKII) has been shown to play a major role in establishing memories through complex molecular interactions including phosphorylation of multiple synaptic targets. However, it is still controversial whether CaMKII itself serves as a molecular memory because of a lack of direct evidence. Here, we show that a single holoenzyme of CaMKII per se serves as an erasable molecular memory switch. We reconstituted Ca(2+)/Calmodulin-dependent CaMKII autophosphorylation in the presence of protein phosphatase 1 in vitro, and found that CaMKII phosphorylation shows a switch-like response with history dependence (hysteresis) only in the presence of an N-methyl-D-aspartate receptor-derived peptide. This hysteresis is Ca(2+) and protein phosphatase 1 concentration-dependent, indicating that the CaMKII memory switch is not simply caused by an N-methyl-D-aspartate receptor-derived peptide lock of CaMKII in an active conformation. Mutation of a phosphorylation site of the peptide shifted the Ca(2+) range of hysteresis. These functions may be crucial for induction and maintenance of long-term synaptic plasticity at hippocampal synapses.

A common framework of signal processing in the induction of cerebellar LTD and cortical STDP.

Cerebellar long-term depression (LTD) and cortical spike-timing-dependent synaptic plasticity (STDP) are two well-known and well-characterized types of synaptic plasticity. Induction of both types of synaptic plasticity depends on the spike timing, pairing frequency, and pairing numbers of two different sources of spiking. This implies that the induction of synaptic plasticity may share common frameworks in terms of signal processing regardless of the different signaling pathways involved in the two types of synaptic plasticity. Here we propose that both types share common frameworks of signal processing for spike-timing, pairing-frequency, and pairing-numbers detection. We developed system models of both types of synaptic plasticity and analyzed signal processing in the induction of synaptic plasticity. We found that both systems have upstream subsystems for spike-timing detection and downstream subsystems for pairing-frequency and pairing-numbers detection. The upstream systems used multiplication of signals from the feedback filters and nonlinear functions for spike-timing detection. The downstream subsystems used temporal filters with longer time constants for pairing-frequency detection and nonlinear switch-like functions for pairing-numbers detection, indicating that the downstream subsystems serve as a leaky integrate-and-fire system. Thus, our findings suggest that a common conceptual framework for the induction of synaptic plasticity exists despite the differences in molecular species and pathways.

Analysis of development of direction selectivity in retinotectum by a neural circuit model with spike timing-dependent plasticity.

The development of direction selectivity in the visual system depends on visual experience. In the developing Xenopus retinotectal system, tectal neurons (TNs) become direction selective through spike timing-dependent plasticity (STDP) after repetitive retinal exposure to a moving bar in a specific direction. We investigated the mechanism responsible for the development of direction selectivity in the Xenopus retinotectal system using a neural circuit model with STDP. In this retinotectal circuit model, a moving bar stimulated the retinal ganglion cells (RGCs), which provided feedforward excitation to the TNs and interneurons (INs). The INs provided delayed feedforward inhibition to the TNs. The TNs also received feedback excitation from neighboring TNs. As a synaptic learning rule, a molecular STDP model was used for synapses between the RGCs and TNs. The retinotectal circuit model reproduced experimentally observed features of the development of direction selectivity, such as increase in input to the TN. The peak of feedforward excitation from RGCs to TNs shifted earlier as a result of STDP. Together with the delayed feedforward inhibition, a stronger earlier transient feedforward signal was generated, which exceeded the threshold of the feedback excitation from the neighboring TNs and resulted in amplification of input to the TN. The suppression of the delayed feedforward inhibition resulted in the development of orientation selectivity rather than direction selectivity, indicating the pivotal role of the delayed feedforward inhibition in direction selectivity. We propose a mechanism for the development of direction selectivity involving a delayed feedforward inhibition with STDP and the amplification of feedback excitation.

Experimental and computational aspects of signaling mechanisms of spike-timing-dependent plasticity.

STDP (spike-timing-dependent synaptic plasticity) is thought to be a synaptic learning rule that embeds spike-timing information into a specific pattern of synaptic strengths in neuronal circuits, resulting in a memory. STDP consists of bidirectional long-term changes in synaptic strengths. This process includes long-term potentiation and long-term depression, which are dependent on the timing of presynaptic and postsynaptic spikings. In this review, we focus on computational aspects of signaling mechanisms that induce and maintain STDP as a key step toward the definition of a general synaptic learning rule. In addition, we discuss the temporal and spatial aspects of STDP, and the requirement of a homeostatic mechanism of STDP in vivo.

Requirement of an allosteric kinetics of NMDA receptors for spike timing-dependent plasticity.

Spike timing-dependent synaptic plasticity (STDP) plays an important role in neural development and information processing in the brain; however, the mechanism by which spike timing information is encoded into STDP remains unclear. Here, we show that a novel allosteric kinetics of NMDA receptors (NMDARs) is required for STDP. We developed a detailed biophysical model of STDP and found that the model required spike timing-dependent distinct suppression of NMDARs by Ca(2+)-calmodulin. This led us to predict an allosteric kinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca(2+)-calmodulin with prespiking --> postspiking and postspiking --> prespiking, respectively. We found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of amplitudes and peak time of NMDAR-mediated EPSPs in experiments. We found that the allosteric kinetics of NMDARs was also valid for synaptic plasticity induced by more complex spike trains in layer II/III of visual cortex. We extracted an essential synaptic learning rule by reduction of the allosteric STDP model and found that spike timing-dependent bidirectional role of postspiking in synaptic modification, which depends on the allosteric kinetics, is the essential principle in STDP. Thus, we propose a simple hypothesis of the allosteric kinetics of NMDARs that can coherently explain critical features of spike timing-dependent NMDAR-mediated EPSPs and synaptic plasticity.

Ca2+ -independent phospholipase A2-dependent sustained Rho-kinase activation exhibits all-or-none response.

Sustained contraction of cells depends on sustained Rho-associated kinase (Rho-kinase) activation. We developed a computational model of the Rho-kinase pathway to understand the systems characteristics. Thrombin-dependent in vivo transient responses of Rho activation and Ca2+ increase could be reproduced in silico. Low and high thrombin stimulation induced transient and sustained phosphorylation, respectively, of myosin light chain (MLC) and myosin phosphatase targeting subunit 1 (MYPT1) in vivo. The transient phosphorylation of MLC and MYPT1 could be reproduced in silico, but their sustained phosphorylation could not. This discrepancy between in vivo and in silico in the sustained responses downstream of Rho-kinase indicates that a missing pathway(s) may be responsible for the sustained Rho-kinase activation. We found, experimentally, that the sustained phosphorylation of MLC and MYPT1 exhibit all-or-none responses. Bromoenol lactone, a specific inhibitor of Ca2+ -independent phospholipase A2 (iPLA2), inhibited sustained phosphorylation of MLC and MYPT1, which indicates that sustained Rho-kinase activation requires iPLA2 activity. Thus, the systems analysis of the Rho-kinase pathway identified a novel iPLA2-dependent mechanism of the sustained Rho-kinase activation, which exhibits an all-or-none response.