Reconstruction Of Neuronal Morphology Research Articles

RESPAN: an accurate, unbiased and automated pipeline for analysis of dendritic morphology and dendritic spine mapping.

Accurate and unbiased reconstructions of neuronal morphology, including quantification of dendritic spine morphology and distribution, are widely used in neuroscience but remain a major roadblock for large-scale analysis. Traditionally, spine analysis has required labor-intensive manual annotation, which is prone to human error and impractical for large 3D datasets. Previous automated tools for reconstructing neuronal morphology and quantitative dendritic spine analysis face challenges in generating accurate results and, following close inspection, often require extensive manual correction. While recent tools leveraging deep learning approaches have substantially increased accuracy, they lack functionality and useful outputs, necessitating additional tools to perform a complete analysis and limiting their utility. In this paper, we describe Restoration Enhanced SPine And Neuron (RESPAN) analysis, a new comprehensive pipeline developed as an open-source, easily deployable solution that harnesses recent advances in deep learning and GPU processing. Our approach demonstrates high accuracy and robustness, validated extensively across a range of imaging modalities for automated dendrite and spine mapping. It also offers extensive visual and tabulated data outputs, including detailed morphological and spatial metrics, dendritic spine classification, and 3D renderings. Additionally, RESPAN includes tools for validating results, ensuring scientific rigor and reproducibility.

An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology

Research advancements in neuroscience entail the production of a substantial amount of data requiring interpretation, analysis, and integration. The complexity and diversity of neuroscience data necessitate the development of specialized databases and associated standards and protocols. NeuroMorpho.Org is an online repository of over one hundred thousand digitally reconstructed neurons and glia shared by hundreds of laboratories worldwide. Every entry of this public resource is associated with essential metadata describing animal species, anatomical region, cell type, experimental condition, and additional information relevant to contextualize the morphological content. Until recently, the lack of a user-friendly, structured metadata annotation system relying on standardized terminologies constituted a major hindrance in this effort, limiting the data release pace. Over the past 2 years, we have transitioned the original spreadsheet-based metadata annotation system of NeuroMorpho.Org to a custom-developed, robust, web-based framework for extracting, structuring, and managing neuroscience information. Here we release the metadata portal publicly and explain its functionality to enable usage by data contributors. This framework facilitates metadata annotation, improves terminology management, and accelerates data sharing. Moreover, its open-source development provides the opportunity of adapting and extending the code base to other related research projects with similar requirements. This metadata portal is a beneficial web companion to NeuroMorpho.Org which saves time, reduces errors, and aims to minimize the barrier for direct knowledge sharing by domain experts. The underlying framework can be progressively augmented with the integration of increasingly autonomous machine intelligence components.

TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain

Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We develop TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we have produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction.

Optimization of Traced Neuron Skeleton Using Lasso-Based Model.

Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology.

PaperBot: open-source web-based search and metadata organization of scientific literature

BackgroundThe biomedical literature is expanding at ever-increasing rates, and it has become extremely challenging for researchers to keep abreast of new data and discoveries even in their own domains of expertise. We introduce PaperBot, a configurable, modular, open-source crawler to automatically find and efficiently index peer-reviewed publications based on periodic full-text searches across publisher web portals.ResultsPaperBot may operate stand-alone or it can be easily integrated with other software platforms and knowledge bases. Without user interactions, PaperBot retrieves and stores the bibliographic information (full reference, corresponding email contact, and full-text keyword hits) based on pre-set search logic from a wide range of sources including Elsevier, Wiley, Springer, PubMed/PubMedCentral, Nature, and Google Scholar. Although different publishing sites require different search configurations, the common interface of PaperBot unifies the process from the user perspective. Once saved, all information becomes web accessible allowing efficient triage of articles based on their actual relevance and seamless annotation of suitable metadata content. The platform allows the agile reconfiguration of all key details, such as the selection of search portals, keywords, and metadata dimensions. The tool also provides a one-click option for adding articles manually via digital object identifier or PubMed ID. The microservice architecture of PaperBot implements these capabilities as a loosely coupled collection of distinct modules devised to work separately, as a whole, or to be integrated with or replaced by additional software. All metadata is stored in a schema-less NoSQL database designed to scale efficiently in clusters by minimizing the impedance mismatch between relational model and in-memory data structures.ConclusionsAs a testbed, we deployed PaperBot to help identify and manage peer-reviewed articles pertaining to digital reconstructions of neuronal morphology in support of the NeuroMorpho.Org data repository. PaperBot enabled the custom definition of both general and neuroscience-specific metadata dimensions, such as animal species, brain region, neuron type, and digital tracing system. Since deployment, PaperBot helped NeuroMorpho.Org more than quintuple the yearly volume of processed information while maintaining a stable personnel workforce.

SmartScope2: Simultaneous Imaging and Reconstruction of Neuronal Morphology

Quantitative analysis of neuronal morphology is critical in cell type classification and for deciphering how structure gives rise to function in the brain. Most current approaches to imaging and tracing neuronal 3D morphology are data intensive. We introduce SmartScope2, the first open source, automated neuron reconstruction machine integrating online image analysis with automated multiphoton imaging. SmartScope2 takes advantage of a neuron’s sparse morphology to improve imaging speed and reduce image data stored, transferred and analyzed. We show that SmartScope2 is able to produce the complex 3D morphology of human and mouse cortical neurons with six-fold reduction in image data requirements and three times the imaging speed compared to conventional methods.

An ontology-based search engine for digital reconstructions of neuronal morphology

Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.

Automated Neuron Tracing Methods: An Updated Account.

The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used.

The importance of metadata to assess information content in digital reconstructions of neuronal morphology.

Digital reconstructions of axonal and dendritic arbors provide a powerful representation of neuronal morphology in formats amenable to quantitative analysis, computational modeling, and data mining. Reconstructed files, however, require adequate metadata to identify the appropriate animal species, developmental stage, brain region, and neuron type. Moreover, experimental details about tissue processing, neurite visualization and microscopic imaging are essential to assess the information content of digital morphologies. Typical morphological reconstructions only partially capture the underlying biological reality. Tracings are often limited to certain domains (e.g., dendrites and not axons), may be incomplete due to tissue sectioning, imperfect staining, and limited imaging resolution, or can disregard aspects irrelevant to their specific scientific focus (such as branch thickness or depth). Gauging these factors is critical in subsequent data reuse and comparison. NeuroMorpho.Org is a central repository of reconstructions from many laboratories and experimental conditions. Here, we introduce substantial additions to the existing metadata annotation aimed to describe the completeness of the reconstructed neurons in NeuroMorpho.Org. These expanded metadata form a suitable basis for effective description of neuromorphological data.

Dendritic Volume Mesh Reconstruction for STEPS: How Does Mesh Quality Affect Stochastic Reaction-Diffusion Simulation?

Volume mesh generation is one of the vital procedures in spatial, Gillespie SSA-based [1], reaction-diffusion simulations of dendritic models. Yet over the years few protocols have been established for this procedure due to the highly varied requirements of different research projects. In the attempt of establishing a mesh generation protocol for our spatial SSA simulator, STEPS [2], we investigate the procedures of tetrahedral mesh generation for STEPS and analyze how mesh quality affects simulation results. STEPS (STochastic Engine for Pathway Simulation) is a GNU-licensed simulation platform that uses an extension of Gillespie's SSA [1] to deal with reactions and diffusion of molecules in 3D tetrahedral mesh reconstructions of neuronal morphology and tissue [2]. In STEPS, the diffusion of molecules is simulated as diffusive fluxes between tetrahedral elements in the mesh, represented by a series of first-order reactions. Tetrahedral meshes used in STEPS simulations are generated in third party mesh generators such as CUBIT [3] and TetGen [4], and then imported via STEPS mesh importing utilities. On one hand, this feature provides maximum flexibility for mesh generation. On the other hand, it requires sufficient understanding and experience of mesh generation and quality control from the user. The case studies presented here can help modelers to avoid various pitfalls during the mesh generation process, which in turn helps to improve the quality of simulation results. Existing approaches of tetrahedral, dendritic mesh reconstruction fall into two different schemes according to their initial geometry representations. The common approach starts from a computational Constructive Solid Geometry (CSG) representation of the modeling subject, formed by Boolean operations on standard geometry primitives such as cubes, cylinders and spheres. The second approach begins with a contour tracing representation from experimental Electron Microscopic images. Meshing algorithms such as Delaunay triangulation and tetrahedralization [5] can then be applied to both representations to create tetrahedral meshes for the simulation. Although the first approach is widely used in present neuroscience research due to its simplicity, the lack of morphological accuracy significantly limits its application. The second approach is more advanced, producing meshes with high morphological accuracy, whilst its quality control can be a great challenge since experimental data acquisition can often not be repeated. In our investigation, we apply meshes that are generated from both approaches to STEPS simulations. Different mesh quality is achieved by varying the parameter settings of the mesh generator. Multiple instances of the simulation are executed for each mesh and the results are averaged. For simple morphologies where analytical solutions exist, we compare the simulation results with the analytical solutions, or else results of different quality meshes for the same morphology are compared. Based on these comparisons, we aim to reveal how SSA simulation is affected by mesh quality, and to draw guidelines of mesh generation for STEPS.

Potential connectomics complements the endeavour of ‘no synapse left behind’ in the cortex

Potential connectomics complements the endeavour of ‘no synapse left behind’ in the cortex

Digital Reconstructions of Neuronal Morphology: Three Decades of Research Trends

The importance of neuronal morphology has been recognized from the early days of neuroscience. Elucidating the functional roles of axonal and dendritic arbors in synaptic integration, signal transmission, network connectivity, and circuit dynamics requires quantitative analyses of digital three-dimensional reconstructions. We extensively searched the scientific literature for all original reports describing reconstructions of neuronal morphology since the advent of this technique three decades ago. From almost 50,000 titles, 30,000 abstracts, and more than 10,000 full-text articles, we identified 902 publications describing ∼44,000 digital reconstructions. Reviewing the growth of this field exposed general research trends on specific animal species, brain regions, neuron types, and experimental approaches. The entire bibliography, annotated with relevant metadata and (wherever available) direct links to the underlying digital data, is accessible at NeuroMorpho.Org.

Improving performance of the STochastic Engine for Pathway Simulation (STEPS)

STEPS (http://steps.sourceforge.net) is a GNU-licensed simulation platform that uses an extension of Gillespie's SSA [1] to deal with reactions and diffusion of molecules in 3D reconstructions of neuronal morphology and tissue [2]. In STEPS, the diffusion of molecules is simulated as diffusive fluxes between tetrahedral elements in the mesh, represented by a series of first-order reactions. STEPS has been used in various research projects where its overall performance was considered adequate. However, as more complex models are being developed and investigated, total model simulation times became an issue leading to requests for a faster reaction-diffusion simulator. Here we discuss a number of strategies we have followed to improve STEPS performance at different levels. The previous implementation of STEPS adapted Gibson and Bruck’s enhancement of the Direct method [3] with a k-ary tree structure. The overall complexity of searching and updating of this method is O(logkN), giving N as the total number of kinetic processes in the system and k as the branch width of the tree. Although this is a great improvement comparing to the O(N) complexity of Gillespie’s original method, the logarithmic dependency of N becomes a critical limitation of performance, particularly in simulations with high amount of tetrahedral elements and consequently large N. Recently a constant-time version of the Direct method with a slightly more complex data structure has been introduced [4], providing an attractive searching and updating complexity of O(1), independent of N. This method is implemented in the new version of STEPS. In the poster, I will present the validation of the new simulator by comparing the stochastic simulation results with analytical solutions. I will also present the improvement made by adapting the new method. Several approaches to parallelizing STEPS have been investigated. In a realistic simulation, the state of the system, including molecule distribution, reaction/diffusion rates, may be frequently read and/or modified during the simulation. Some of these operations could be greatly parallelized in shared memory architecture, efficiently reducing the cost of data accessing. Previous studies [5] also suggested that the Direct SSA could be parallelized as a Parallel Discrete-Event Simulation (PDES). However, the performance of such a parallelization significantly depends on the model itself as well as the approach of simulation decomposition. Some examples of above methods will be present and discussed in the poster.

Anisotropic path searching for automatic neuron reconstruction

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila's projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.

The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron

Digital reconstructions of neuronal morphology are used to study neuron function, development, and responses to various conditions. Although many measures exist to analyze differences between neurons, none is particularly suitable to compare the same arborizing structure over time (morphological change) or reconstructed by different people and/or software (morphological error). The metric introduced for the DIADEM (DIgital reconstruction of Axonal and DEndritic Morphology) Challenge quantifies the similarity between two reconstructions of the same neuron by matching the locations of bifurcations and terminations as well as their topology between the two reconstructed arbors. The DIADEM metric was specifically designed to capture the most critical aspects in automating neuronal reconstructions, and can function in feedback loops during algorithm development. During the Challenge, the metric scored the automated reconstructions of best-performing algorithms against manually traced gold standards over a representative data set collection. The metric was compared with direct quality assessments by neuronal reconstruction experts and with clocked human tracing time saved by automation. The results indicate that relevant morphological features were properly quantified in spite of subjectivity in the underlying image data and varying research goals. The DIADEM metric is freely released open source ( http://diademchallenge.org ) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.

Web-based collaborative neuronal reconstruction with CATMAID

Event Abstract Back to Event Web-based collaborative neuronal reconstruction with CATMAID Stephan Gerhard1*, Mark Longair1, Stephan Saalfeld2, Pavel Tomancak2 and Albert Cardona1 1 UZH / ETHZ, Institute of Neuroinformatics, Switzerland 2 Max Planck Institute of Molecular Cell Biology and Genetics, Germany Reconstructing neuronal circuits at such high resolutions that synaptic connections are clearly visible can currently only be done from image data acquired via electron microscopy (EM). These stacks of images enable precise 3D reconstructions of neuronal morphology. While automatic methods for segmenting such images are certainly improving, much annotation and segmentation still needs to be done by human operators carefully examining the images. In addition, the EM data sets that must be dealt with are often many terabytes in size. The requirement for hundreds of annotators to each have a local copy would be prohibitively expensive. To address these requirements, we have extended CATMAID, the Collaborative Annotation Toolkit for Massive Amounts of Image Data,¹ to allow many researchers to trace neurons collaboratively in the same data set. CATMAID is a web-based system, so each annotator only needs a web browser and minimal local storage space requirements. The system already provides an elegant Google Maps-style interface for browsing huge image stacks, collaborative text annotation and a simple interface for asynchronous server-side jobs. We have added two further types of annotation primitive: skeletons and connectors. Skeletons are tree structures suitable for representing the midlines of neuronal arborizations. Connectors link the nodes of skeletons in a many-to-many relationship through a central point, and are suitable for representing polyadic synapses. These annotations are stored in a logical hierarchy, which can be arranged by the researchers in the web interface in order to best represent the structure and biological understanding of the tissue under examination. Every new or changed annotation is immediately reflected in the remote centralized database, so that each researcher always sees up-to-date annotations. Hundreds of annotators can thus concurrently reconstruct the many neurons that make up a circuit. The centralized storage of annotations also allows automatic incremental backups and making the data available via web services. We also have added in-browser 3D visualization of the skeletons, text-tagging of skeleton nodes and connectors, and statistics showing the progress of the tracing. Skeletons can be exported in the standard SWC format for further analysis. While we have tailored the user interface of CATMAID for tracing the midlines of neurons and adding synapses, the annotation primitives of skeletons and connectors are stored in the database as elements of tissue-agnostic subject-predicate-object relations, which means both that the system can be easily adapted to different annotation tasks, and that the data can be made accessible via semantic web technologies. We present, as an example, our progress in tracing a terabyte serial section TEM (Transmission Electron Microscopy) data set from one abdominal segment of the ventral nerve cord of a first instar Drosophila larva. ¹ Saalfeld S, Cardona A, Hartenstein V, Tomancák P (2009) CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25: 1984–1986 Figure 1 Keywords: General neuroinformatics, Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Gerhard S, Longair M, Saalfeld S, Tomancak P and Cardona A (2011). Web-based collaborative neuronal reconstruction with CATMAID. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00093 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Stephan Gerhard, UZH / ETHZ, Institute of Neuroinformatics, Zurich, Switzerland, connectome@unidesign.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona Google Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona Google Scholar Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona PubMed Stephan Gerhard Mark Longair Stephan Saalfeld Pavel Tomancak Albert Cardona Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Tetrahedral mesh generation and visualization for stochastic reaction-diffusion simulation

The generation of high quality three-dimensional meshes is important in voxel-based computational modeling and simulation of stochastic reaction-diffusion systems. Compared with cubical meshes used in some reaction-diffusion simulators such as MesoRD [1], tetrahedral meshes are more flexible in representing complex geometry boundaries, for instance, the spherical head of a dendritic spine. We describe the interaction between tetrahedral mesh generators (CUBIT [2], TetGen [3]) and our STochastic Engine for Pathway Simulation (STEPS) [4], including the generation and importing of meshes, as well as the visualization of the simulations in a 3D graphical environment. STEPS is a GNU-licensed simulation platform that uses an extension of Gillespie's SSA [5] to deal with reactions and diffusion of molecules in 3D reconstructions of neuronal morphology and tissue [4]. In order to support a range of research goals, it provides a flexible Python frontend together with an efficient C++ backend. In STEPS, the diffusion of molecules is simulated as diffusive fluxes between tetrahedral elements in the mesh, represented by a series of first-order reactions. Since many mesh generating methods and software packages are available in public, instead of implementing our own mesh generator, STEPS provides a generic, extendable Python-based import interface for different tetrahedral mesh formats. Thus the first step is to create a suitable mesh, which can be challenging. In our research [6] it involves the reconstruction of anatomical/morphological structures from combinations of simple geometry primitives like cylinders and spheres. Once the mesh file is generated, it can be imported to the simulations using either pre-implemented import functions (currently supporting Abaqus and TetGen mesh formats) or user-developed functions by adapting the STEPS element proxy interface. The element proxy interface performs automatic mapping between mesh elements in external files and the ones used by the STEPS simulation. Monitoring mesh quality is possible via mesh generators or the STEPS internal quality metric. This metric calculates the Radius-Edge Ratio (RER) for each tetrahedron, given by dividing the radius of the tetrahedron's circumsphere with the length of the shortest edge. The smaller this value, the more regular the tetrahedron. The minimum value of this metric is given by computing the RER for a fully regular tetrahedron, that is, sqrt(6)/4. Although the visualization of stochastic reaction-diffusion simulations with 3D meshes is optional due to the high number of reactions/diffusions that take place during a simulation, it is sometimes required for demonstration and education. Animations of such a simulation can be made by a script-controlled 3D graphical environment, for example the python frontend of CUBIT, with user-specified data extracted from the STEPS simulation. Alternatively, STEPS provides a preliminary Python-based visual toolkit for general purposes of displaying meshes and simple reaction-diffusion simulations. Overall, the collaborative workflow between STEPS and external mesh generators discussed here has shown its flexibility and will facilitate a broad range of applications in computational pathway modeling and simulation.

Sensory Neurons in the Drosophila Genital Tract Regulate Female Reproductive Behavior

Females of many animal species behave very differently before and after mating. In Drosophila melanogaster, changes in female behavior upon mating are triggered by the sex peptide (SP), a small peptide present in the male's seminal fluid. SP activates a specific receptor, the sex peptide receptor (SPR), which is broadly expressed in the female reproductive tract and nervous system. Here, we pinpoint the action of SPR to a small subset of internal sensory neurons that innervate the female uterus and oviduct. These neurons express both fruitless (fru), a marker for neurons likely to have sex-specific functions, and pickpocket (ppk), a marker for proprioceptive neurons. We show that SPR expression in these fru+ ppk+ neurons is both necessary and sufficient for behavioral changes induced by mating. These neurons project to regions of the central nervous system that have been implicated in the control of reproductive behaviors in Drosophila and other insects.

Successes and Rewards in Sharing Digital Reconstructions of Neuronal Morphology

The computer-assisted three-dimensional reconstruction of neuronal morphology is becoming an increasingly popular technique to quantify the arborization patterns of dendrites and axons. The resulting digital files are suitable for comprehensive morphometric analyses as well as for building anatomically realistic compartmental models of membrane biophysics and neuronal electrophysiology. The digital tracings acquired in a lab for a specific purpose can be often re-used by a different research group to address a completely unrelated scientific question, if the original investigators are willing to share the data. Since reconstructing neuronal morphology is a labor-intensive process, data sharing and re-analysis is particularly advantageous for the neuroscience and biomedical communities. Here we present numerous cases of "success stories" in which digital reconstructions of neuronal morphology were shared and re-used, leading to additional, independent discoveries and publications, and thus amplifying the impact of the "source" study for which the data set was first collected. In particular, we overview four main applications of this kind of data: comparative morphometric analyses, statistical estimation of potential synaptic connectivity, morphologically accurate electrophysiological simulations, and computational models of neuronal shape and development.