Julian C. Shillcock

Reconstruction and Simulation of Neocortical Microcircuitry

UNLABELLED We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies. PAPERCLIP VIDEO ABSTRACT.

The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex.

We have established a multi-constraint, data-driven process to digitally reconstruct, and simulate prototypical neocortical microcircuitry, using sparse experimental data. We applied this process to reconstruct the microcircuitry of the somatosensory cortex in juvenile rat at the cellular and synaptic levels. The resulting reconstruction is broadly consistent with current knowledge about the neocortical microcircuit and provides an array of predictions on its structure and function. To engage the community in exploring, challenging, and refining the reconstruction, we have developed a collaborative, internet-accessible facility-the Neocortical Microcircuit Collaboration portal (NMC portal; https://bbp.epfl.ch/nmc-portal). The NMC portal allows users to access the experimental data used in the reconstruction process, download cellular and synaptic models, and analyze the predicted properties of the microcircuit: six layers, similar to 31,000 neurons, 55 morphological types, 11 electrical types, 207 morpho-electrical types, 1941 unique synaptic connection types between neurons of specific morphological types, predicted properties for the anatomy and physiology of similar to 40 million intrinsic synapses. It also provides data supporting comparison of the anatomy and physiology of the reconstructed microcircuit against results in the literature. The portal aims to catalyzee consensus on the cellular and synaptic organization of neocortical microcircuitry (ion channel, neuron and synapse types and distributions, connectivity, etc.). Community feedback will contribute to refined versions of the reconstruction to be released periodically. We consider that the reconstructions and the simulations they enable represent a major step in the development of in silica neuroscience.

Mechanism of Shiga Toxin Clustering on Membranes

The bacterial Shiga toxin interacts with its cellular receptor, the glycosphingolipid globotriaosylceramide (Gb3 or CD77), as a first step to entering target cells. Previous studies have shown that toxin molecules cluster on the plasma membrane, despite the apparent lack of direct interactions between them. The precise mechanism by which this clustering occurs remains poorly defined. Here, we used vesicle and cell systems and computer simulations to show that line tension due to curvature, height, or compositional mismatch, and lipid or solvent depletion cannot drive the clustering of Shiga toxin molecules. By contrast, in coarse-grained computer simulations, a correlation was found between clustering and toxin nanoparticle-driven suppression of membrane fluctuations, and experimentally we observed that clustering required the toxin molecules to be tightly bound to the membrane surface. The most likely interpretation of these findings is that a membrane fluctuation-induced force generates an effective attraction between toxin molecules. Such force would be of similar strength to the electrostatic force at separations around 1 nm, remain strong at distances up to the size of toxin molecules (several nanometers), and persist even beyond. This force is predicted to operate between manufactured nanoparticles providing they are sufficiently rigid and tightly bound to the plasma membrane, thereby suggesting a route for the targeting of nanoparticles to cells for biomedical applications.

The effects of globotriaosylceramide tail saturation level on bilayer phases

Globotriaosylceramide (Gb3) is a glycosphingolipid present in the plasma membrane that is the natural receptor of the bacterial Shiga toxin. The unsaturation level of Gb3 acyl chains has a drastic impact on lipid bilayer properties and phase behaviour, and on many Gb3-related cellular processes. For example: the Shiga toxin B subunit forms tubular invaginations in the presence of Gb3 with an unsaturated acyl chain (U-Gb3), while in the presence of Gb3 with a saturated acyl chain (S-Gb3) such invagination does not occur. We have used all-atom molecular dynamics simulations to investigate the effects of the Gb3 concentration and its acyl chain saturation on the phase behaviour of a mixed bilayer of dioleoylphosphatidylcholine and Gb3. The simulation results show that: (1) the Gb3 acyl chains (longer tails) from one leaflet interdigitate into the opposing leaflet and lead to significant bilayer rigidification and immobilisation of the lipid tails. S-Gb3 can form a highly ordered, relatively immobile phase which is resistant to bending while these changes for U-Gb3 are not significant. (2) At low concentrations of Gb3, U-Gb3 and S-Gb3 have a similar impact on the bilayer reminiscent of the effect of sphingomyelin lipids and (3) At higher Gb3 concentrations, U-Gb3 mixes better with dioleoylphosphatidylcholine than S-Gb3. Our simulations also provide the first molecular level structural model of Gb3 in membranes.

Vesicles and vesicle fusion: coarse-grained simulations

Biological cells are highly dynamic, and continually move material around their own volume and between their interior and exterior. Much of this transport encapsulates the material inside phospholipid vesicles that shuttle to and from, fusing with, and budding from, other membranes. A feature of vesicles that is crucial for this transport is their ability to fuse to target membranes and release their contents to the distal side. In industry, some personal care products contain vesicles to help transport reagents across the skin, and research on drug formulation shows that packaging active compounds inside vesicles delays their clearance from the blood stream. In this chapter, we survey the biological role and physicochemical properties of phospholipids, and describe progress in coarse-grained simulations of vesicles and vesicle fusion. Because coarse-grained simulations retain only those molecular details that are thought to influence the large-scale processes of interest, they act as a model embodying our current understanding. Comparing the predictions of these models with experiments reveals the importance of the retained microscopic details and also the deficiencies that can suggest missing details, thereby furthering our understanding of the complex dynamic world of vesicles.

Clustering on Membranes: Fluctuations and More

Clustering of extracellular ligands and proteins on the plasma membrane is required to perform specific cellular functions, such as signaling and endocytosis. Attractive forces that originate in perturbations of the membranes physical properties contribute to this clustering, in addition to direct protein-protein interactions. However, these membrane-mediated forces have not all been equally considered, despite their importance. In this review, we describe how line tension, lipid depletion, and membrane curvature contribute to membrane-mediated clustering. Additional attractive forces that arise from protein-induced perturbation of a membranes fluctuations are also described. This review aims to provide a survey of the current understanding of membrane-mediated clustering and how this supports precise biological functions.

Reconstructing the brain: from image stacks to neuron synthesis

Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. The BigNeuron initiative is assembling community-generated, open-source, automated reconstruction algorithms into an open platform, and is beginning to generate an increasing flow of high-quality reconstructed neurons. We propose a novel extension of this workflow to use this data stream to generate an unlimited number of statistically equivalent, yet distinct, digital morphologies. This will bring automated processing of reconstructed cells into digital neurons to the wider neuroscience community, and enable a range of morphologically accurate computational models.

Bio-physically plausible visualization of highly scattering fluorescent neocortical models for in silico experimentation

BackgroundWe present a visualization pipeline capable of accurate rendering of highly scattering fluorescent neocortical neuronal models. The pipeline is mainly developed to serve the computational neurobiology community. It allows the scientists to visualize the results of their virtual experiments that are performed in computer simulations, or in silico. The impact of the presented pipeline opens novel avenues for assisting the neuroscientists to build biologically accurate models of the brain. These models result from computer simulations of physical experiments that use fluorescence imaging to understand the structural and functional aspects of the brain. Due to the limited capabilities of the current visualization workflows to handle fluorescent volumetric datasets, we propose a physically-based optical model that can accurately simulate light interaction with fluorescent-tagged scattering media based on the basic principles of geometric optics and Monte Carlo path tracing. We also develop an automated and efficient framework for generating dense fluorescent tissue blocks from a neocortical column model that is composed of approximately 31000 neurons.ResultsOur pipeline is used to visualize a virtual fluorescent tissue block of 50 μm3 that is reconstructed from the somatosensory cortex of juvenile rat. The fluorescence optical model is qualitatively analyzed and validated against experimental emission spectra of different fluorescent dyes from the Alexa Fluor family.ConclusionWe discussed a scientific visualization pipeline for creating images of synthetic neocortical neuronal models that are tagged virtually with fluorescent labels on a physically-plausible basis. The pipeline is applied to analyze and validate simulation data generated from neuroscientific in silico experiments.

A Topological Representation of Branching Neuronal Morphologies

Many biological systems consist of branching structures that exhibit a wide variety of shapes. Our understanding of their systematic roles is hampered from the start by the lack of a fundamental means of standardizing the description of complex branching patterns, such as those of neuronal trees. To solve this problem, we have invented the Topological Morphology Descriptor (TMD), a method for encoding the spatial structure of any tree as a “barcode”, a unique topological signature. As opposed to traditional morphometrics, the TMD couples the topology of the branches with their spatial extents by tracking their topological evolution in 3-dimensional space. We prove that neuronal trees, as well as stochastically generated trees, can be accurately categorized based on their TMD profiles. The TMD retains sufficient global and local information to create an unbiased benchmark test for their categorization and is able to quantify and characterize the structural differences between distinct morphological groups. The use of this mathematically rigorous method will advance our understanding of the anatomy and diversity of branching morphologies.

Framework for efficient synthesis of spatially embedded morphologies

Many problems in science and engineering require the ability to grow tubular or polymeric structures up to large volume fractions within a bounded region of three-dimensional space. Examples range from the construction of fibrous materials and biological cells such as neurons, to the creation of initial configurations for molecular simulations. A common feature of these problems is the need for the growing structures to wind throughout space without intersecting. At any time, the growth of a morphology depends on the current state of all the others, as well as the environment it is growing in, which makes the problem computationally intensive. Neuron synthesis has the additional constraint that the morphologies should reliably resemble biological cells, which possess nonlocal structural correlations, exhibit high packing fractions, and whose growth responds to anatomical boundaries in the synthesis volume. We present a spatial framework for simultaneous growth of an arbitrary number of nonintersecting morphologies that presents the growing structures with information on anisotropic and inhomogeneous properties of the space. The framework is computationally efficient because intersection detection is linear in the mass of growing elements up to high volume fractions and versatile because it provides functionality for environmental growth cues to be accessed by the growing morphologies. We demonstrate the framework by growing morphologies of various complexity.