Calcium modeling of spine apparatus-containing human dendritic spines demonstrates an "all-or-nothing" communication switch between the spine head and dendrite.

Dendritic spines are highly dynamic neuronal compartments that control the synaptic transmission between neurons. Spines form ultrastructural units, coupling synaptic contact sites to the dendritic shaft and often harbor a spine apparatus organelle, composed of smooth endoplasmic reticulum, which is responsible for calcium sequestration and release into the spine head and neck. The spine apparatus has recently been linked to synaptic plasticity in adult human cortical neurons. While the morphological heterogeneity of spines and their intracellular organization has been extensively demonstrated in animal models, the influence of spine apparatus organelles on critical signaling pathways, such as calcium-mediated dynamics, is less well known in human dendritic spines. In this study we used serial transmission electron microscopy to anatomically reconstruct nine human cortical spines in detail as a basis for modeling and simulation of the calcium dynamics between spine and dendrite. The anatomical study of reconstructed human dendritic spines revealed that the size of the postsynaptic density correlates with spine head volume and that the spine apparatus volume is proportional to the spine volume. Using a newly developed simulation pipeline, we have linked these findings to spine-to-dendrite calcium communication. While the absence of a spine apparatus, or the presence of a purely passive spine apparatus did not enable any of the reconstructed spines to relay a calcium signal to the dendritic shaft, the calcium-induced calcium release from this intracellular organelle allowed for finely tuned "all-or-nothing" spine-to-dendrite calcium coupling; controlled by spine morphology, neck plasticity, and ryanodine receptors. Our results suggest that spine apparatus organelles are strategically positioned in the neck of human dendritic spines and demonstrate their potential relevance to the maintenance and regulation of spine-to-dendrite calcium communication.

Modeling calcium dynamics in neurons with endoplasmic reticulum: existence, uniqueness and an implicit-explicit finite element scheme.

Like many other biological processes, calcium dynamics in neurons containing an endoplasmic reticulum is governed by diffusion-reaction equations on interface-separated domains. Interface conditions are typically described by systems of ordinary differential equations that provide fluxes across the interfaces. Using the calcium model as an example of this class of ODE-flux boundary interface problems, we prove the existence, uniqueness and boundedness of the solution by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem. Then we propose and analyze an efficient implicit-explicit finite element scheme which is implicit for the parabolic operator and explicit for the nonlinear terms. We show that the stability does not depend on the spatial mesh size. Also the optimal convergence rate in H 1 norm is obtained. Numerical experiments illustrate the theoretical results.

Multi-scale modeling toolbox for single neuron and subcellular activity under Transcranial Magnetic Stimulation.

BACKGROUND: Transcranial Magnetic Stimulation (TMS) is a widely used non-invasive brain stimulation method. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research to study the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far. OBJECTIVE: We develop an open-source multi-scale toolbox Neuron Modeling for TMS (NeMo-TMS) to address this problem. METHODS: NeMo-TMS generates accurate neuron models from morphological reconstructions, couples them to the external electric fields induced by TMS, and simulates the cellular and subcellular responses of single-pulse and repetitive TMS. RESULTS: We provide examples showing some of the capabilities of the toolbox. CONCLUSION: NeMo-TMS toolbox allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS.

A Bead Movement Based Computational Framework for 3-Dimensional Analysis of Biofilm Material Heterogeneity.

Differences in the material properties of bacterial biofilms have been observed in biofilms of different bacterial species, within the same species under different growth conditions and after treatment with matrix modifying molecules. To better quantitate the material properties of 3D biofilms, an experimental and computational workflow was developed and applied to examine differences between Enterococcus faecalis, Salmonella enterica serotype Typhimurium and Escherichia coli biofilms as well as the role of the amyloid curli in confirming rigidity to Enterobacteriaceae biofilms. The spatio-temporal dynamics of 1 µm carboxylate beads in biofilms were tracked in 20 µm 3D biofilms over 20 minutes. The 4D image stacks were processed using the Mosaic plugin in ImageJ to produce 3D trajectory data of bead movement. This trajectory data was analyzed with a newly developed Bead Evaluator toolbox, where movement data, including trajectory lifespans, bead velocities, cell densities along trajectories, and bounding box information were computed and stored in csv-files. This paper presents the workflow from experimental setup and image recording to bead trajectory computation and analysis. The structure of curli-containing biofilms resulted in more stable bead interactions and less bead movement than in curli-mutant and Enterococcal biofilms. Bead movement did not appear strongly dependent on cell density when measuring the bead velocity and trajectory bounding box volume, supporting the hypothesis that other material properties of the biofilms control the bead dynamics. This technique is widely applicable to quantitating differences in biofilms of different matrix compositions as well as biofilms before and after matrix-modifying treatments.

Nitrate Is an Environmental Cue in the Gut for Salmonella enterica Serovar Typhimurium Biofilm Dispersal through Curli Repression and Flagellum Activation via Cyclic-di-GMP Signaling.

Curli, a major component of the bacterial biofilms in the intestinal tract, activates pattern recognition receptors and triggers joint inflammation after infection with Salmonella enterica serovar Typhimurium. The factors that allow S. Typhimurium to disperse from biofilms and invade the epithelium to establish a successful infection during acute inflammation remain unknown. Here, we studied S. Typhimurium biofilms in vitro and in vivo to understand how the inflammatory environment regulates the switch between multicellular and motile S. Typhimurium in the gut. We discovered that nitrate generated by the host is an environmental cue that induces S. Typhimurium to disperse from the biofilm. Nitrate represses production of an important biofilm component, curli, and activates flagella via the modulation of intracellular cyclic-di-GMP levels. We conclude that nitrate plays a central role in pathogen fitness by regulating the sessile-to-motile lifestyle switch during infection. IMPORTANCE Recent studies provided important insight into our understanding of the role of c-di-GMP signaling and the regulation of enteric biofilms. Despite an improved understanding of how c-di-GMP signaling regulates S. Typhimurium biofilms, the processes that affect the intracellular c-di-GMP levels and the formation of multicellular communities in vivo during infections remain unknown. Here, we show that nitrate generated in the intestinal lumen during infection with S. Typhimurium is an important regulator of biofilm formation in vivo.

Modeling Drug Absorption from the Dermis after an Injection.

A dermal absorption model for small and macromolecules was previously proposed by Ibrahim et al. This model estimated absorption of therapeutics from the dermal tissue based on their molecular size and protein binding through blood and lymphatics. Blood absorption followed a two-pore theory and the lymphatic absorption was limited by the constant lymphatic flow rate. Current work builds on this steady-state concept by modeling the absorption from the dermis immediately after an injection is given (unsteady state). An injection in the dermis creates a localized pressure gradient which resolves itself over time. This phenomenon is captured in the model to estimate the impact of injection volume on the absorption rate constant. Blood absorption follows the two-pore theory but is time-dependent and the lymphatic absorption is determined based on valve opening and pressure driven convective flow, returning to steady-state as the molecule is absorbed. A direct comparison of the steady-state analysis, experimental data and the current model is made. The results indicate that accounting for the localized time-varying pressure can better predict the experimental absorption rate constants. This work significantly improves the existing understanding of macromolecule uptake from the interstitial fluid following intradermal injection.

Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis.

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.

Spine-to-Dendrite Calcium Modeling Discloses Relevance for Precise Positioning of Ryanodine Receptor-Containing Spine Endoplasmic Reticulum.

The endoplasmic reticulum (ER) forms a complex endomembrane network that reaches into the cellular compartments of a neuron, including dendritic spines. Recent work discloses that the spine ER is a dynamic structure that enters and leaves spines. While evidence exists that ER Ca2+ release is involved in synaptic plasticity, the role of spine ER morphology remains unknown. Combining a new 3D spine generator with 3D Ca2+ modeling, we addressed the relevance of ER positioning on spine-to-dendrite Ca2+ signaling. Our simulations, which account for Ca2+ exchange on the plasma membrane and ER, show that spine ER needs to be present in distinct morphological conformations in order to overcome a barrier between the spine and dendritic shaft. We demonstrate that RyR-carrying spine ER promotes spine-to-dendrite Ca2+ signals in a position-dependent manner. Our simulations indicate that RyR-carrying ER can initiate time-delayed Ca2+ reverberation, depending on the precise position of the spine ER. Upon spine growth, structural reorganization of the ER restores spine-to-dendrite Ca2+ communication, while maintaining aspects of Ca2+ homeostasis in the spine head. Our work emphasizes the relevance of precise positioning of RyR-containing spine ER in regulating the strength and timing of spine Ca2+ signaling, which could play an important role in tuning spine-to-dendrite Ca2+ communication and homeostasis.

What Is Required for Neuronal Calcium Waves? A Numerical Parameter Study.

Neuronal calcium signals propagating by simple diffusion and reaction with mobile and stationary buffers are limited to cellular microdomains. The distance intracellular calcium signals can travel may be significantly increased by means of calcium-induced calcium release from internal calcium stores, notably the endoplasmic reticulum. The organelle, which can be thought of as a cell-within-a-cell, is able to sequester large amounts of cytosolic calcium ions via SERCA pumps and selectively release them into the cytosol through ryanodine receptor channels leading to the formation of calcium waves. In this study, we set out to investigate the basic properties of such dendritic calcium waves and how they depend on the three parameters dendrite radius, ER radius and ryanodine receptor density in the endoplasmic membrane. We demonstrate that there are stable and abortive regimes for calcium waves, depending on the above morphological and physiological parameters. In stable regimes, calcium waves can travel across long dendritic distances, similar to electrical action potentials. We further observe that abortive regimes exist, which could be relevant for spike-timing dependent plasticity, as travel distances and wave velocities vary with changing intracellular architecture. For some of these regimes, analytic functions could be derived that fit the simulation data. In parameter spaces, that are non-trivially influenced by the three-dimensional calcium concentration profile, we were not able to derive such a functional description, demonstrating the mathematical requirement to model and simulate biochemical signaling in three-dimensional space.

Generating Neuron Geometries for Detailed Three-Dimensional Simulations Using AnaMorph.

Generating realistic and complex computational domains for numerical simulations is often a challenging task. In neuroscientific research, more and more one-dimensional morphology data is becoming publicly available through databases. This data, however, only contains point and diameter information not suitable for detailed three-dimensional simulations. In this paper, we present a novel framework, AnaMorph, that automatically generates water-tight surface meshes from one-dimensional point-diameter files. These surface triangulations can be used to simulate the electrical and biochemical behavior of the underlying cell. In addition to morphology generation, AnaMorph also performs quality control of the semi-automatically reconstructed cells coming from anatomical reconstructions. This toolset allows an extension from the classical dimension-reduced modeling and simulation of cellular processes to a full three-dimensional and morphology-including method, leading to novel structure-function interplay studies in the medical field. The developed numerical methods can further be employed in other areas where complex geometries are an essential component of numerical simulations.

Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox.

The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic, or reconstruction) to the simulation platform UG 4 (which harbors a neuroscientific portfolio) and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g., new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.

Synaptic bouton properties are tuned to best fit the prevailing firing pattern.

The morphology of presynaptic specializations can vary greatly ranging from classical single-release-site boutons in the central nervous system to boutons of various sizes harboring multiple vesicle release sites. Multi-release-site boutons can be found in several neural contexts, for example at the neuromuscular junction (NMJ) of body wall muscles of Drosophila larvae. These NMJs are built by two motor neurons forming two types of glutamatergic multi-release-site boutons with two typical diameters. However, it is unknown why these distinct nerve terminal configurations are used on the same postsynaptic muscle fiber. To systematically dissect the biophysical properties of these boutons we developed a full three-dimensional model of such boutons, their release sites and transmitter-harboring vesicles and analyzed the local vesicle dynamics of various configurations during stimulation. Here we show that the rate of transmission of a bouton is primarily limited by diffusion-based vesicle movements and that the probability of vesicle release and the size of a bouton affect bouton-performance in distinct temporal domains allowing for an optimal transmission of the neural signals at different time scales. A comparison of our in silico simulations with in vivo recordings of the natural motor pattern of both neurons revealed that the bouton properties resemble a well-tuned cooperation of the parameters release probability and bouton size, enabling a reliable transmission of the prevailing firing-pattern at diffusion-limited boutons. Our findings indicate that the prevailing firing-pattern of a neuron may determine the physiological and morphological parameters required for its synaptic terminals.

1D-3D hybrid modeling-from multi-compartment models to full resolution models in space and time.

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator-which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics.

A method to investigate the diffusion properties of nuclear calcium.

Modeling biophysical processes in general requires knowledge about underlying biological parameters. The quality of simulation results is strongly influenced by the accuracy of these parameters, hence the identification of parameter values that the model includes is a major part of simulating biophysical processes. In many cases, secondary data can be gathered by experimental setups, which are exploitable by mathematical inverse modeling techniques. Here we describe a method for parameter identification of diffusion properties of calcium in the nuclei of rat hippocampal neurons. The method is based on a Gauss-Newton method for solving a least-squares minimization problem and was formulated in such a way that it is ideally implementable in the simulation platform uG. Making use of independently published space- and time-dependent calcium imaging data, generated from laser-assisted calcium uncaging experiments, here we could identify the diffusion properties of nuclear calcium and were able to validate a previously published model that describes nuclear calcium dynamics as a diffusion process.

Structural dynamics of the cell nucleus: basis for morphology modulation of nuclear calcium signaling and gene transcription.

Neuronal morphology plays an essential role in signal processing in the brain. Individual neurons can undergo use-dependent changes in their shape and connectivity, which affects how intracellular processes are regulated and how signals are transferred from one cell to another in a neuronal network. Calcium is one of the most important intracellular second messengers regulating cellular morphologies and functions. In neurons, intracellular calcium levels are controlled by ion channels in the plasma membrane such as NMDA receptors (NMDARs), voltage-gated calcium channels (VGCCs) and certain α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) as well as by calcium exchange pathways between the cytosol and internal calcium stores including the endoplasmic reticulum and mitochondria. Synaptic activity and the subsequent opening of ligand and/or voltage-gated calcium channels can initiate cytosolic calcium transients which propagate towards the cell soma and enter the nucleus via its nuclear pore complexes (NPCs) embedded in the nuclear envelope. We recently described the discovery that in hippocampal neurons the morphology of the nucleus affects the calcium dynamics within the nucleus. Here we propose that nuclear infoldings determine whether a nucleus functions as an integrator or detector of oscillating calcium signals. We outline possible ties between nuclear mophology and transcriptional activity and discuss the importance of extending the approach to whole cell calcium signal modeling in order to understand synapse-to-nucleus communication in healthy and dysfunctional neurons.

Synaptic activity induces dramatic changes in the geometry of the cell nucleus: interplay between nuclear structure, histone H3 phosphorylation, and nuclear calcium signaling.

Synaptic activity initiates many adaptive responses in neurons. Here we report a novel form of structural plasticity in dissociated hippocampal cultures and slice preparations. Using a recently developed algorithm for three-dimensional image reconstruction and quantitative measurements of cell organelles, we found that many nuclei from hippocampal neurons are highly infolded and form unequally sized nuclear compartments. Nuclear infoldings are dynamic structures, which can radically transform the geometry of the nucleus in response to neuronal activity. Action potential bursting causing synaptic NMDA receptor activation dramatically increases the number of infolded nuclei via a process that requires the ERK-MAP kinase pathway and new protein synthesis. In contrast, death-signaling pathways triggered by extrasynaptic NMDA receptors cause a rapid loss of nuclear infoldings. Compared with near-spherical nuclei, infolded nuclei have a larger surface and increased nuclear pore complex immunoreactivity. Nuclear calcium signals evoked by cytosolic calcium transients are larger in small nuclear compartments than in the large compartments of the same nucleus; moreover, small compartments are more efficient in temporally resolving calcium signals induced by trains of action potentials in the theta frequency range (5 Hz). Synaptic activity-induced phosphorylation of histone H3 on serine 10 was more robust in neurons with infolded nuclei compared with neurons with near-spherical nuclei, suggesting a functional link between nuclear geometry and transcriptional regulation. The translation of synaptic activity-induced signaling events into changes in nuclear geometry facilitates the relay of calcium signals to the nucleus, may lead to the formation of nuclear signaling microdomains, and could enhance signal-regulated transcription.

Filtering, reconstruction, and measurement of the geometry of nuclei from hippocampal neurons based on confocal microscopy data.

The cell nucleus is often considered a spherical structure. However, the visualization of proteins associated with the nuclear envelope in rat hippocampal neurons indicates that the geometry of nuclei is far more complex. The shape of cell nuclei is likely to influence the nucleo-cytoplasmic exchange of macromolecules and ions, in particular calcium, a key regulator of neuronal gene expression. We developed a tool to retrieve the 3-D view of cell nuclei from laser scanning confocal microscopy data. By applying an inertia-based filter, based on a special structure detection mechanism, the signal-to-noise ratio of the image is enhanced, the signal is smoothed, gaps in the membrane are closed, while at the same time the geometric properties, such as diameters of the membrane, are preserved. After segmentation of the image data, the microscopy data are sufficiently processed to extract surface information of the membrane by creating an isosurface with a marching tetrahedra algorithm combined with a modified Dijkstra graph-search algorithm. All methods are tested on artificial data, as well as on real data, which are recorded with a laser scanning confocal microscope. Significant advantages of the inertia-based filter can be observed when comparing it to other state of the art nonlinear diffusion filters. An additional program is written to calculate surface and volume of cell nuclei. These results represent the first step toward establishing a geometry-based model of the-dynamics of cytoplasmic and nuclear calcium.