Individual Dendrites Research Articles

Microbeam X-ray and Scanning Electron Microscopic Analyses on Sector-Banded Spherulites of Poly(p-dioxanone) Justified with Pixelated Iridescence.

Poly(p-dioxanone) (PPDO) is crystallized with amorphous poly(p-vinyl phenol) (PVPh) and tannic acid (TA) as co-diluents to regulate and induce dendritic-ringed PPDO spherulites, with spoke- or sector-bands, aiming for convenience of analyses on interior lamellar assembly. Morphologies and interior lamellar arrangement leading to the peculiar rings on individual dendrites are evaluated by using polarized-light microscopy (PLM) and scanning electron microscope (SEM). Combinatory microbeam small-/wide-angle X-ray scattering (SAXS/WAXS) analyses further confirm the unique assembly patterns in periodic cycles. Alternate gratings are packed with periodic ridges composed of feather-like branches and the valley is featured with some embossed textures. The periodic gratings in the ringed spokes resemble those in nature's structured coloration and are proven to display light-interference iridescence.

Tracing nerve fibers with volume electron microscopy to quantitatively analyze brain connectivity

The highly complex structure of the brain requires an approach that can unravel its connectivity. Using volume electron microscopy and a dedicated software we can trace and measure all nerve fibers present within different samples of brain tissue. With this software tool, individual dendrites and axons are traced, obtaining a simplified “skeleton” of each fiber, which is linked to its corresponding synaptic contacts. The result is an intricate meshwork of axons and dendrites interconnected by a cloud of synaptic junctions. To test this methodology, we apply it to the stratum radiatum of the hippocampus and layers 1 and 3 of the somatosensory cortex of the mouse. We find that nerve fibers are densely packed in the neuropil, reaching up to 9 kilometers per cubic mm. We obtain the number of synapses, the number and lengths of dendrites and axons, the linear densities of synapses established by dendrites and axons, and their location on dendritic spines and shafts. The quantitative data obtained through this method enable us to identify subtle traits and differences in the synaptic organization of the samples, which might have been overlooked in a qualitative analysis.

Modeling coupled growth and motion of solid-air dendrite induced by convection in liquid hydrogen using phase-field lattice Boltzmann method

The safety hazard arising from the growth, condensation and accumulation of trace air in liquid hydrogen deserves attention. In order to predict the microstructural growth evolution and oxygen solute segregation distribution characteristics during solid-air dendrite motion induced by solution convection, a partially saturated bounce-back lattice Boltzmann coupled non-isothermal phase field method (PSLBM-PF) is proposed in this study. The partially saturated bounce-back lattice Boltzmann method (PSLBM) is employed to calculate the flow field and the forces and moments between the fluid and solid phases, and the phase field method (PF) is utilized for the computation of the growth of solid-air dendrites and the distribution of solute segregation. The motion of solid-air dendrites is determined by the momentum equation. The accuracy of the calculated interaction forces between fluid and solid phases was validated through the sedimentation process of circular particles in the liquid, and the capability of the present model to preserve the shape during solid-air dendrite motion (translational and rotational) was confirmed. Subsequently, this model was employed to investigate the growth and sedimentation motion of solid-air dendrites with different growth orientations in the solution. During dendrite settlement, the growth of the lower dendrite arms is promoted, while the settlement velocity and the solid phase fraction are almost unaffected by the growth orientation. Then, the differences in morphology evolution and solute distribution under conditions of forced convection for both stationary and motion are comparatively investigated, utilizing the free growth of solid-air dendrites under convection-free conditions as a reference. Finally, the model was utilized to study the growth of individual solid-air dendrites as well as translational and rotational motion under the action of shear flow. The presence of convection significantly alters the solute and temperature distributions at the solid-liquid interface, thereby influencing the dendrite growth process. The simulation results indicate that this model can predict the solid-air dendrites microstructural growth evolution and oxygen solute segregation distribution accompanying the convection and motion.

STED microscopy reveals dendrite-specificity of spines in turtle cortex

Dendritic spines are key structures for neural communication, learning and memory. Spine size and shape probably reflect synaptic strength and learning. Imaging with superresolution STED microscopy the detailed shape of the majority of the spines of individual neurons in turtle cortex (Trachemys scripta elegans) revealed several distinguishable shape classes. Dendritic spines of a given class were not distributed randomly, but rather decorated significantly more often some dendrites than others. The individuality of dendrites was corroborated by significant inter-dendrite differences in other parameters such as spine density and length. In addition, many spines were branched or possessed spinules. These findings may have implications for the role of individual dendrites in this cortex.

Phase-Field Modeling of Dead Lithium Formation during Charge/Discharge Cycles

The formation of inactive or "dead" lithium is one of the main reasons for performance decay in lithium batteries, but recent studies find that modified charging protocols can revive this inactive portion of lithium. Thus, the ability to simulate events that lead to the disconnection and reconnection of lithium under experimental conditions can provide a fundamental understanding of the evolution of dead lithium during the charge/discharge cycle. Here, we apply a recently developed quantitative electrochemical phase-field model to study the morphological and topological evolution of lithium during battery operation. We simulate the charge/discharge behavior of lithium metal anodes from the scale of individual dendrites to anodes of relevant experimental size with realistic materials parameters to examine the pathways leading to disconnection and reconnection. Additionally, we quantify the amount of inactive lithium formed during cycling. We discuss the effect of various factors like electrolyte properties, discharge rate, and the assumed reaction kinetics (i.e., Butler-Volmer versus Marcus-Hush) on dead lithium formation. The presented simulation tool can be employed to explore battery design; thus, we will discuss possible ways to optimize the charge and discharge cycles to minimize the amount of dead lithium. Beyond the present application to lithium, the model can also be extended to other metal-anode batteries.

Growing dendrites enhance a neuron’s computational power and memory capacity

Neocortical pyramidal neurons have many dendrites, and such dendrites are capable of, in isolation of one-another, generating a neuronal spike. It is also now understood that there is a large amount of dendritic growth during the first years of a humans life, arguably a period of prodigious learning. These observations inspire the construction of a local, stochastic algorithm based on an earlier stochastic, homeostatic, Hebbian developmental theory. Here we investigate the neurocomputational advantages and limits on this novel algorithm that combines dendritogenesis with supervised adaptive synaptogenesis. Neurons created with this algorithm have enhanced memory capacity, can avoid catastrophic interference (forgetting), and have the ability to unmix mixture distributions. In particular, individual dendrites develop within each class, in an unsupervised manner, to become feature-clusters that correspond to the mixing elements of class-conditional mixture distribution. Error-free classification is demonstrated with input perturbations up to 40%. Although discriminative problems are used to understand the capabilities of the stochastic algorithm and the neuronal connectivity it produces, the algorithm is in the generative class, it thus seems ideal for decisions that require generalization, i.e., extrapolation beyond previous learning.

The use of L-dopa induces the resistance of Mauthner neurons to the neurotoxic action of beta-amyloid

BACKGROUND: Mauthner goldfish (Carassius auratus (L)) cells serve as model objects for studying brain pathologies at the level of identified neurons and their individual dendrites. L-dopa may be an agent that decelerates the destruction of neurons caused by the toxic effects of beta-amyloid.
 AIMS: To study the three-dimensional structure of Mauthner neurons in goldfish and the ultrastructure of their afferent synapses under the influence of L-dopa and the toxic 2535 fragment of beta-amyloid.
 MATERIAL AND METHODS: This study was performed on the Mauthner neurons of goldfish fry (n=12) by using light and electron microscopy. Serial sections 3 m thick were used to identify and integrally reconstruct the structure of Mauthner neurons; determine the volume of the soma and ventral and lateral dendrites; and study the structure of afferent synapses.
 RESULTS: The use of L-dopa stabilized the size of the soma and ventral dendrites. The reduction in the volume of lateral dendrites was accompanied either by an increase in the volume of their branches under the action of beta-amyloid followed by that of L-dopa or by an increase in the volume of medial dendrites under the action of L-dopa followed by that of beta-amyloid. Although pathological changes in the ultrastructure of neurons and afferent synapses were not found, signs of early amyloidosis were detected.
 CONCLUSION: The use of L-dopa decelerates the degeneration of Mauthner neurons. The resistance of whole neurons to the neurotoxic action of beta-amyloid has been suggested to be due to the mechanism of structural homeostasis aiming at the compensatory restoration of the morphological organization of neurons.

Microglial motility is modulated by neuronal activity and correlates with dendritic spine plasticity in the hippocampus of awake mice.

Microglia, the resident immune cells of the brain, play a complex role in health and disease. They actively survey the brain parenchyma by physically interacting with other cells and structurally shaping the brain. Yet, the mechanisms underlying microglial motility and significance for synapse stability, especially in the hippocampus during adulthood, remain widely unresolved. Here, we investigated the effect of neuronal activity on microglial motility and the implications for the formation and survival of dendritic spines on hippocampal CA1 neurons in vivo. We used repetitive two-photon in vivo imaging in the hippocampus of awake and anesthetized mice to simultaneously study the motility of microglia and their interaction with dendritic spines. We found that CA3 to CA1 input is sufficient to modulate microglial process motility. Simultaneously, more dendritic spines emerged in mice after awake compared to anesthetized imaging. Interestingly, the rate of microglial contacts with individual dendritic spines and dendrites was associated with the stability, removal, and emergence of dendritic spines. These results suggest that microglia might sense neuronal activity via neurotransmitter release and actively participate in synaptic rewiring of the hippocampal neural network during adulthood. Further, this study has profound relevance for hippocampal learning and memory processes.

Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their 'readout' of visual input.

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus's central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably 'read out' visually driven thalamic input.

Comparative analysis of working gas composition impact on diamond films microstructure

The paper report a diamond films with dendrite-like or nanofeathers structure. Such structures was synthesized both in CH4/H2 (12.2–19.3 vol.% CH4) and CH4/H2/N2 (7–18.5 vol.% N2) working gas mixture. The resulting diamond dendrites are composed of diamond crystallites of various sizes, as well as non-diamond carbon, with a height comparable to the film thickness and a lateral size does not exceed 200 nm (the minimum size of detected diamond crystallites is ∼ 5 nm). Such diamond structure seems cannot be classified to either conventionally nano- or ultrananocrystalline films. It is shown that nitrogen-rich diamond films have a larger size of both individual diamond crystallites and dendrites, as well as a higher level of residual stresses and roughness, compared to nitrogen-free diamond films. At the same time, nitrogen-free films have higher values of hardness and Young's modulus.

Neuron arbor geometry is sensitive to the limited-range fractal properties of their dendrites.

Fractal geometry is a well-known model for capturing the multi-scaled complexity of many natural objects. By analyzing three-dimensional images of pyramidal neurons in the rat hippocampus CA1 region, we examine how the individual dendrites within the neuron arbor relate to the fractal properties of the arbor as a whole. We find that the dendrites reveal unexpectedly mild fractal characteristics quantified by a low fractal dimension. This is confirmed by comparing two fractal methods-a traditional "coastline" method and a novel method that examines the dendrites' tortuosity across multiple scales. This comparison also allows the dendrites' fractal geometry to be related to more traditional measures of their complexity. In contrast, the arbor's fractal characteristics are quantified by a much higher fractal dimension. Employing distorted neuron models that modify the dendritic patterns, deviations from natural dendrite behavior are found to induce large systematic changes in the arbor's structure and its connectivity within a neural network. We discuss how this sensitivity to dendrite fractality impacts neuron functionality in terms of balancing neuron connectivity with its operating costs. We also consider implications for applications focusing on deviations from natural behavior, including pathological conditions and investigations of neuron interactions with artificial surfaces in human implants.

A Novel Two-Step Electrochemical Deposition Method for Sn-Pd Electrocatalyst Synthesis for a Potential Application in Direct Ethanol Fuel Cells

Sn-Pd electrocatalysts with a constant atomic ratio of 60 at.% Sn-40 at.% Pd suitable for potential application in direct ethanol fuel cells were synthesized using a novel two-step electrodeposition method. First, Sn was electrodeposited in various forms of dendrites, from spear-like and needle-like to individual fern-like dendrites to a network of intertwined fern-like dendrites, by varying the cathodic potential and then performing electrodeposition of Pd at a constant current density in the second step. A morphological and elemental analysis of Sn and Sn-Pd electrocatalysts was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) techniques, while the size of Sn dendrites was analyzed using the particle size distribution (PSD) method. Cyclic voltammetry (CV) and chronoamperometry were applied in order to study the catalytic behavior of Sn-Pd electrocatalysts in the ethanol oxidation reaction (EOR), while CO stripping was used to estimate the antipoisoning capability of the electrocatalysts. The Sn surface morphology of the sub-layer was highly correlated with the electrocatalytic activity of the examined Sn-Pd electrocatalysts. The high activity it presented towards the EOR showed the suitability of the Sn-Pd electrocatalyst constructed from individual fern-like Sn dendrites as a sub-layer. Compared to Pd alone, this Sn-Pd catalyst showed more than 3 times higher activity and improved EOR kinetics. This enhancement in the catalytic activity of the Sn-Pd electrocatalysts is attributed to both the morphological characteristics of Sn as a sub-layer and the bifunctional effect.

Spatiotemporal properties of glutamate input support direction selectivity in the dendrites of retinal starburst amacrine cells.

The asymmetric summation of kinetically distinct glutamate inputs across the dendrites of retinal 'starburst' amacrine cells is one of the several mechanisms that have been proposed to underlie their direction-selective properties, but experimentally verifying input kinetics has been a challenge. Here, we used two-photon glutamate sensor (iGluSnFR) imaging to directly measure the input kinetics across individual starburst dendrites. We found that signals measured from proximal dendrites were relatively sustained compared to those measured from distal dendrites. These differences were observed across a range of stimulus sizes and appeared to be shaped mainly by excitatory rather than inhibitory network interactions. Temporal deconvolution analysis suggests that the steady-state vesicle release rate was ~3 times larger at proximal sites compared to distal sites. Using a connectomics-inspired computational model, we demonstrate that input kinetics play an important role in shaping direction selectivity at low stimulus velocities. Taken together, these results provide direct support for the 'space-time wiring' model for direction selectivity.

Mechanisms of microglia-mediated synapse turnover and synaptogenesis.

Microglia shape synaptic connections among neurons of the central nervous system (CNS) during development and adulthood. In this review, the actions by which they facilitate pruning, refinement, and new synaptic development throughout the lifespan are considered, along with the molecular mechanisms by which neurons and microglia communicate to guide these actions. Microglia survey neuronal activity and selectively modify synaptic connections at the level of individual dendrites and synapses. This is important given that microglia are necessary for a healthy nervous system capable of learning and other neural phenomena based on synaptic modifications and can also cause pathological synaptic disfunctions in immunologically driven neurodegenerative diseases. Understanding how microglia directly shape synaptic connections between neurons yields a more complete understanding of normal neuroplasticity and provides new routes for understanding disease states.

Bending‐Insensitive Flexible SERS Sensor for Stable and Sensitive Detection on Curved Surfaces

AbstractSurface‐enhanced Raman scattering (SERS) based flexible sensing technology has been considered an essential candidate for future diagnostics in diverse fields. To obtain a reliable signal from the flexible SERS sensor, signal reproducibility even when the substrates are bent is highly desirable because there are various curvatures in the practical applications. However, this remains challenging because the “hot‐spots” were created between the individual nanostructures on the surface of the previous flexible SERS sensor. The distance between each nanostructure varied through the bending of the sensor, leading to an adverse effect on the unform intensity of the Raman signal. Herein, a bending‐insensitive flexible SERS substrate is reported for reliable and sensitive sensing on curved surfaces. It is based on silver nanodendrites on the surface of graphene‐modified PI film via electrodeposition. Numerous hot spots are created between the trunk‐branch and branch‐branch junctions inside individual silver dendrites. The inherent hot‐spots of silver nanodendrites exhibit a uniform and extremely enhanced Raman signal under the bending of the substrate, because the morphology of silver nanodendrites is insensitive to diverse degrees of bending. Thus, the flexible SERS sensor can detect 10−12 m (in buffer solution) and 10−8 m (serum‐containing condition) of 6‐thioguanine on arbitrary curved surfaces.

Multiscale simulation of powder-bed fusion processing of metallic alloys

We present a computational framework for the simulations of powder-bed fusion of metallic alloys, which combines: (1) CalPhaD calculations of temperature-dependent alloy properties and phase diagrams, (2) macroscale finite element (FE) thermal simulations of the material addition and fusion, and (3) microscopic phase-field (PF) simulations of solidification in the melt pool. The methodology is applied to simulate the selective laser melting (SLM) of an Inconel 718 alloy using realistic processing parameters. We discuss the effect of temperature-dependent properties and the importance of accounting for different properties between the powder bed and the dense material in the macroscale thermal simulations. Using a two-dimensional longitudinal slice of the thermal field calculated via FE simulations, we perform an appropriately-converged PF solidification simulation at the scale of the entire melt pool, resulting in a calculation with over one billion grid points, yet performed on a single cluster node with eight graphics processing units (GPUs). These microscale simulations provide new insight into the grain texture selection via polycrystalline growth competition under realistic SLM conditions, with a level of detail down to individual dendrites.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains.

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

Emergence of synaptic organization and computation in dendrites.

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons’ dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

A 3D Analysis of Dendritic Solidification and Mosaicity in Ni-Based Single Crystal Superalloys

Ni-based single crystal superalloys contain microstructural regions that are separated by low-angle grain boundaries. This gives rise to the phenomenon of mosaicity. In the literature, this type of defect has been associated with the deformation of dendrites during Bridgman solidification. The present study introduces a novel serial sectioning method that allows to rationalize mosaicity on the basis of spatial dendrite growth. Optical wide-field micrographs were taken from a series of cross sections and evaluated using quantitative image analysis. This allowed to explore the growth directions of close to 2500 dendrites in a large specimen volume of approximately 450 mm3. The application of tomography in combination with the rotation vector base-line electron back-scatter diffraction method allowed to analyze how small angular differences evolve in the early stages of solidification. It was found that the microstructure consists of dendrites with individual growth directions that deviate up to ≈4° from the average growth direction of all dendrites. Generally, individual dendrite growth directions coincide with crystallographic <001> directions. The quantitative evaluation of the rich data sets obtained with the present method aims at contributing to a better understanding of elementary processes that govern competitive dendrite growth and crystal mosaicity.

Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons

Dendrites shape inputs and integration of depolarization that controls neuronal activity in the nervous system. Neuron pathologies can damage dendrite architecture and cause abnormalities in morphologies after injury. Dendrite regeneration can be quantified by various parameters, including total dendrite length and number of dendrite branches using manual or automated image analysis approaches. However, manual quantification is tedious and time consuming and automated approaches are often trained using wildtype neurons, making them poorly suited for analysis of genetically manipulated or injured dendrite arbors. In this study, we tested how well automated image analysis software performed on class IV Drosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and two developed by independent research groups (DeTerm and Tireless Tracing Genie). Out of the three software tested, we determined that Imaris is the most efficient at reconstructing dendrite architecture, but does not accurately measure total dendrite length even after intensive manual editing. Imaris outperforms both DeTerm and Tireless Tracing Genie for counting dendrite branches, and is better able to recreate previous conclusions from this same dataset. This thorough comparison of strengths and weaknesses of each software demonstrates their utility for analyzing regenerated neuron phenotypes in future studies.