Neuron tracing in perspective

Design of three-dimensional engineered protein hydrogels for tailored control of neurite growth

The design of bioactive materials allows tailored studies probing cell-biomaterial interactions, however, relatively few studies have examined the effects of ligand density and material stiffness on neurite growth in three-dimensions. Elastin-like proteins (ELPs) have been designed with modular bioactive and structural regions to enable the systematic characterization of design parameters within three-dimensional (3-D) materials. To promote neurite out-growth and better understand the effects of common biomaterial design parameters on neuronal cultures we here focused on the cell-adhesive ligand density and hydrogel stiffness as design variables for ELP hydrogels. With the inherent design freedom of engineered proteins these 3-D ELP hydrogels enabled decoupled investigations into the effects of biomechanics and biochemistry on neurite out-growth from dorsal root ganglia. Increasing the cell-adhesive RGD ligand density from 0 to 1.9×10(7)ligands μm(-3) led to a significant increase in the rate, length, and density of neurite out-growth, as quantified by a high throughput algorithm developed for dense neurite analysis. An approximately two-fold improvement in total neurite out-growth was observed in materials with the higher ligand density at all time points up to 7 days. ELP hydrogels with initial elastic moduli of 0.5, 1.5, or 2.1kPa and identical RGD ligand densities revealed that the most compliant materials led to the greatest out-growth, with some neurites extending over 1800μm by day 7. Given the ability of ELP hydrogels to efficiently promote neurite out-growth within defined and tunable 3-D microenvironments these materials may be useful in developing therapeutic nerve guides and the further study of basic neuron-biomaterial interactions.

Neurient: an algorithm for automatic tracing of confluent neuronal images to determine alignment

A goal of neural tissue engineering is the development and evaluation of materials that guide neuronal growth and alignment. However, the methods available to quantitatively evaluate the response of neurons to guidance materials are limited and/or expensive, and may require manual tracing to be performed by the researcher. We have developed an open source, automated Matlab-based algorithm, building on previously published methods, to trace and quantify alignment of fluorescent images of neurons in culture. The algorithm is divided into three phases, including computation of a lookup table which contains directional information for each image, location of a set of seed points which may lie along neurite centerlines, and tracing neurites starting with each seed point and indexing into the lookup table. This method was used to obtain quantitative alignment data for complex images of densely cultured neurons. Complete automation of tracing allows for unsupervised processing of large numbers of images. Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle. The alignment information obtained from traced images can be used to compare the response of neurons to a range of conditions. This tracing algorithm is freely available to the scientific community under the name Neurient, and its implementation in Matlab allows a wide range of researchers to use a standardized, open source method to quantitatively evaluate the alignment of dense neuronal cultures.

Characterization of biaxial mechanical behavior of porcine aorta under gradual elastin degradation

Arteries are composed of multiple constituents that endow the wall with proper structure and function. Many vascular diseases are associated with prominent mechanical and biological alterations in the wall constituents. In this study, planar biaxial tensile test data of elastase-treated porcine aortic tissue (Chow et al. in Biomech Model Mechanobiol 2013) is re-examined to characterize the altered mechanical behavior at multiple stages of digestion through constitutive modeling. Exponential-based as well as recruitment-based strain energy functions are employed and the associated constitutive parameters for individual digestion stages are identified using nonlinear parameter estimation. It is shown that when the major portion of elastin is degraded from a cut-open artery in the load-free state, the embedded collagen fibers are recruited at lower stretch levels under biaxial loads, leading to a rapid stiffening behavior of the tissue. Multiphoton microscopy illustrates that the collagen waviness decreases significantly with the degradation time, resulting in a rapid recruitment when the tissue is loaded. It is concluded that even when residual stresses are released, there exists an intrinsic mechanical interaction between arterial elastin and collagen that determines the mechanics of arteries and carries important implications to vascular mechanobiology.

A stochastic model for automatic extraction of 3D neuronal morphology

Tubular structures are frequently encountered in bio-medical images. The center-lines of these tubules provide an accurate representation of the topology of the structures. We introduce a stochastic Marked Point Process framework for fully automatic extraction of tubular structures requiring no user interaction or seed points for initialization. Our Marked Point Process model enables unsupervised network extraction by fitting a configuration of objects with globally optimal associated energy to the centreline of the arbors. For this purpose we propose special configurations of marked objects and an energy function well adapted for detection of 3D tubular branches. The optimization of the energy function is achieved by a stochastic, discrete-time multiple birth and death dynamics. Our method finds the centreline, local width and orientation of neuronal arbors and identifies critical nodes like bifurcations and terminals. The proposed model is tested on 3D light microscopy images from the DIADEM data set with promising results.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.

Morphologic evidence for spatially clustered spines in apical dendrites of monkey neocortical pyramidal cells

The general organization of neocortical connectivity in rhesus monkey is relatively well understood. However, mounting evidence points to an organizing principle that involves clustered synapses at the level of individual dendrites. Several synaptic plasticity studies have reported cooperative interaction between neighboring synapses on a given dendritic branch, which may potentially induce synapse clusters. Additionally, theoretical models have predicted that such cooperativity is advantageous, in that it greatly enhances a neuron's computational repertoire. However, largely because of the lack of sufficient morphologic data, the existence of clustered synapses in neurons on a global scale has never been established. The majority of excitatory synapses are found within dendritic spines. In this study, we demonstrate that spine clusters do exist on pyramidal neurons by analyzing the three-dimensional locations of ∼40,000 spines on 280 apical dendritic branches in layer III of the rhesus monkey prefrontal cortex. By using clustering algorithms and Monte Carlo simulations, we quantify the probability that the observed extent of clustering does not occur randomly. This provides a measure that tests for spine clustering on a global scale, whenever high-resolution morphologic data are available. Here we demonstrate that spine clusters occur significantly more frequently than expected by pure chance and that spine clustering is concentrated in apical terminal branches. These findings indicate that spine clustering is driven by systematic biological processes. We also found that mushroom-shaped and stubby spines are predominant in clusters on dendritic segments that display prolific clustering, independently supporting a causal link between spine morphology and synaptic clustering.

Morphological assessment of neurite outgrowth in hippocampal neuron-astrocyte co-cultures

Neurite outgrowth is a fundamental event in brain development, as well as in regeneration of damaged neurons. Astrocytes play a major role in neuritogenesis, by expressing and releasing factors that facilitate neurite outgrowth, such as extracellular matrix proteins, and factors that can inhibit neuritogenesis, such as the chondroitin sulfate proteoglycan neurocan. In this unit we describe a noncontact co-culture system of hippocampal neurons and cortical (or hippocampal) astrocytes for measurement of neurite outgrowth. Hippocampal pyramidal neurons are plated on glass coverslips, which are inverted onto an astrocyte feeder layer, allowing exposure of neurons to astrocyte-derived factors without direct contact between these two cell types. After co-culture, neurons are stained and photographed, and processes are assessed morphologically using Metamorph software. This method allows exposing astrocytes to various agents before co-culture in order to assess how these exposures may influence the ability of astrocytes to foster neurite outgrowth.

Segmentation and tracing of single neurons from 3D confocal microscope images

In order to understand the brain, we need to first understand the morphology of neurons. In the neurobiology community, there have been recent pushes to analyze both neuron connectivity and the influence of structure on function. Currently, a technical road block that stands in the way of these studies is the inability to automatically trace neuronal structure from microscopy. On the image processing side, proposed tracing algorithms face difficulties in low contrast, indistinct boundaries, clutter, and complex branching structure. To tackle these difficulties, we develop Tree2Tree, a robust automatic neuron segmentation and morphology generation algorithm. Tree2Tree uses a local medial tree generation strategy in combination with a global tree linking to build a maximum likelihood global tree. Recasting the neuron tracing problem in a graph-theoretic context enables Tree2Tree to estimate bifurcations naturally, which is currently a challenge for current neuron tracing algorithms. Tests on cluttered confocal microscopy images of Drosophila neurons give results that correspond to ground truth within a margin of ±2.75% normalized mean absolute error.

Development of a method for the measurement of primary cilia length in 3D

Background

Primary cilia length is an important measure of cell and tissue function. While accurate length measurements can be calculated from cells in 2D culture, measurements in tissue or 3D culture are inherently difficult due to optical distortions. This study uses a novel combination of image processing techniques to rectify optical distortions and accurately measure cilia length from 3D images.

Methods

Point spread functions and experimental resolutions were calculated from subresolution microspheres embedded in 3D agarose gels for both wide-field fluorescence and confocal laser scanning microscopes. The degree of axial smearing and spherical aberration was calculated from xy:xz diameter ratios of 3D image data sets of 4 μm microspheres that had undergone deconvolution and/or Gaussian blurring. Custom-made 18 and 50 μm fluorescent microfibers were also used as calibration objects to test the suitability of processed image sets for 3D skeletonization. Microfiber length in 2D was first measured to establish an original population mean. Fibers were then embedded in 3D agarose gels to act as ciliary models. 3D image sets of microfibers underwent deconvolution and Gaussian blurring. Length measurements within 1 standard deviation of the original 2D population mean were deemed accurate. Finally, the combined method of deconvolution, Gaussian blurring and skeletonization was compared to previously published methods using images of immunofluorescently labeled renal and chondrocyte primary cilia.

Results

Deconvolution significantly improved contrast and resolution but did not restore the xy:xz diameter ratio (0.80). Only the additional step of Gaussian blurring equalized xy and xz resolutions and yielded a diameter ratio of 1.02. Following image processing, skeletonization successfully estimated microfiber boundaries and allowed reliable and repeatable measurement of fiber lengths in 3D. We also found that the previously published method of calculating length from 2D maximum projection images significantly underestimated ciliary length.

Conclusions

This study used commercial and public domain image processing software to rectify a long-standing problem of 3D microscopy. We have shown that a combination of deconvolution and Gaussian blurring rectifies optical distortions inherent in 3D images and allows accurate skeletonization and length measurement of microfibers and primary cilia that are bent or curved in 3D space.

Serial section registration of axonal confocal microscopy datasets for long-range neural circuit reconstruction

In the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships among the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information.

Automated tracing of neurites from light microscopy stacks of images

Automating the process of neural circuit reconstruction on a large-scale is one of the foremost challenges in the field of neuroscience. In this study we examine the methodology for circuit reconstruction from three-dimensional light microscopy (LM) stacks of images. We show how the minimal error-rate of an ideal reconstruction procedure depends on the density of labeled neurites, giving rise to the fundamental limitation of an LM based approach for neural circuit research. Circuit reconstruction procedures typically involve steps related to neuron labeling and imaging, and subsequent image pre-processing and tracing of neurites. In this study, we focus on the last step--detection of traces of neurites from already pre-processed stacks of images. Our automated tracing algorithm, implemented as part of the Neural Circuit Tracer software package, consists of the following main steps. First, image stack is filtered to enhance labeled neurites. Second, centerline of the neurites is detected and optimized. Finally, individual branches of the optimal trace are merged into trees based on a cost minimization approach. The cost function accounts for branch orientations, distances between their end-points, curvature of the merged structure, and its intensity. The algorithm is capable of connecting branches which appear broken due to imperfect labeling and can resolve situations where branches appear to be fused due the limited resolution of light microscopy. The Neural Circuit Tracer software is designed to automatically incorporate ImageJ plug-ins and functions written in MatLab and provides roughly a 10-fold increases in speed in comparison to manual tracing.

Neuronal tracing for connectomic studies

Reconstruction of the complete wiring diagram, or connectome, of a neural circuit provides an alternative approach to conventional circuit analysis. One major obstacle of connectomics lies in segmenting and tracing neuronal processes from the vast number of images obtained with optical or electron microscopy. Here I review recent progress in automated tracing algorithms for connectomic reconstruction with fluorescence and electron microscopy, and discuss the challenges to image analysis posed by novel optical imaging techniques.

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.

A broadly applicable 3-D neuron tracing method based on open-curve snake

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

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.

Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology

The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes

MOTIVATION

Advances in techniques to sparsely label neurons unlock the potential to reconstruct connectivity from 3D image stacks acquired by light microscopy. We present an application for semi-automated tracing of neurons to quickly annotate noisy datasets and construct complex neuronal topologies, which we call the Simple Neurite Tracer.

AVAILABILITY

Simple Neurite Tracer is open source software, licensed under the GNU General Public Licence (GPL) and based on the public domain image processing software ImageJ. The software and further documentation are available via http://fiji.sc/Simple_Neurite_Tracer as part of the package Fiji, and can be used on Windows, Mac OS and Linux. Documentation and introductory screencasts are available at the same URL.

CONTACT

longair@ini.phys.ethz.ch; longair@ini.phys.ethz.ch.

Localization of presynaptic inputs on dendrites of individually labeled neurons in three dimensional space using a center distance algorithm

The spatial distribution of synaptic inputs on the dendritic tree of a neuron can have significant influence on neuronal function. Consequently, accurate anatomical reconstructions of neuron morphology and synaptic localization are critical when modeling and predicting physiological responses of individual neurons. Historically, generation of three-dimensional (3D) neuronal reconstructions together with comprehensive mapping of synaptic inputs has been an extensive task requiring manual identification of putative synaptic contacts directly from tissue samples or digital images. Recent developments in neuronal tracing software applications have improved the speed and accuracy of 3D reconstructions, but localization of synaptic sites through the use of pre- and/or post-synaptic markers has remained largely a manual process. To address this problem, we have developed an algorithm, based on 3D distance measurements between putative pre-synaptic terminals and the post-synaptic dendrite, to automate synaptic contact detection on dendrites of individually labeled neurons from 3D immunofluorescence image sets. In this study, the algorithm is implemented with custom routines in Matlab, and its effectiveness is evaluated through analysis of primary sensory afferent terminals on motor neurons. Optimization of algorithm parameters enabled automated identification of synaptic contacts that matched those identified by manual inspection with low incidence of error. Substantial time savings and the elimination of variability in contact detection introduced by different users are significant advantages of this method.

NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery

Background

Automatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings. However, there exist very few freeware tools and methods which provide automatic neuronal morphology quantification for pharmacological discovery.

Results

This study proposes an effective quantification method, called NeurphologyJ, capable of automatically quantifying neuronal morphologies such as soma number and size, neurite length, and neurite branching complexity (which is highly related to the numbers of attachment points and ending points). NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image processing and analysis platform. The high performance of NeurphologyJ arises mainly from an elegant image enhancement method. Consequently, some morphology operations of image processing can be efficiently applied. We evaluated NeurphologyJ by comparing it with both the computer-aided manual tracing method NeuronJ and an existing ImageJ-based plugin method NeuriteTracer. Our results reveal that NeurphologyJ is comparable to NeuronJ, that the coefficient correlation between the estimated neurite lengths is as high as 0.992. NeurphologyJ can accurately measure neurite length, soma number, neurite attachment points, and neurite ending points from a single image. Furthermore, the quantification result of nocodazole perturbation is consistent with its known inhibitory effect on neurite outgrowth. We were also able to calculate the IC50 of nocodazole using NeurphologyJ. This reveals that NeurphologyJ is effective enough to be utilized in applications of pharmacological discoveries.

Conclusions

This study proposes an automatic and fast neuronal quantification method NeurphologyJ. The ImageJ plugin with supports of batch processing is easily customized for dealing with high-content screening applications. The source codes of NeurphologyJ (interactive and high-throughput versions) and the images used for testing are freely available (see Availability).

Stages of neuronal morphological development in vitro--an automated assay

Following plating in vitro, neurons pass through a series of morphological stages as they adhere and mature. These morphological stage transitions can be monitored as a function of time to evaluate the relative health and development of neuronal cultures under different conditions. While morphological development is usually quite obvious to the experienced eye, it can often be difficult to quantify in a meaningful way. Morphology quantification typically relies on manual image measurement and can therefore be tedious, time consuming and prone to human error. Here we report the successful development of an automated process using the commercially available image analysis program MetaMorph(®) to analyze the morphology and quantify the growth of embryonic spinal motor neurons in vitro. Our process relied on the Neurite Outgrowth and Cell Scoring modules included in MetaMorph(®) and on analyzing the exported data with an algorithm written in MATLAB(®). We first adopted a series of stages of motor neuron development in vitro. Neurons were classified into these stages directly from the available output of MetaMorph(®) using the algorithm written in MATLAB(®). We validated the results of the automated analysis against a manual analysis of the same images and found no statistically significant difference between the two methods. When properly configured, automated image analysis with MetaMorph(®) is a rapid and reliable alternative to manual measurement and has the potential to accelerate the research process.

Automated analysis of neuronal morphology, synapse number and synaptic recruitment

The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.

EXTRACTING BIOMEDICALLY IMPORTANT INFORMATION FROM LARGE, AUTOMATED IMAGING EXPERIMENTS

Major challenges remain in the extraction of rich information from high-throughput microscopy experiments. In this paper, I describe some of these challenges, particularly those that are the subject of ongoing research in my laboratory. The challenges include segmenting neurons, co-cultures of different cell types, and whole organisms; segmenting and tracking cells in time-lapse images; quantifying complex phenotypic changes; and discovering biologically relevant subpopulations of cells.

Unveiling the neuromorphological space

This article proposes the concept of neuromorphological space as the multidimensional space defined by a set of measurements of the morphology of a representative set of almost 6000 biological neurons available from the NeuroMorpho database. For the first time, we analyze such a large database in order to find the general distribution of the geometrical features. We resort to McGhee's biological shape space concept in order to formalize our analysis, allowing for comparison between the geometrically possible tree-like shapes, obtained by using a simple reference model, and real neuronal shapes. Two optimal types of projections, namely, principal component analysis and canonical analysis, are used in order to visualize the originally 20-D neuron distribution into 2-D morphological spaces. These projections allow the most important features to be identified. A data density analysis is also performed in the original 20-D feature space in order to corroborate the clustering structure. Several interesting results are reported, including the fact that real neurons occupy only a small region within the geometrically possible space and that two principal variables are enough to account for about half of the overall data variability. Most of the measurements have been found to be important in representing the morphological variability of the real neurons.

Fasciculation and defasciculation of neurite bundles on micropatterned substrates

We describe experiments of fasciculation, i.e., bundling, of chick sensory neurites on 2D striped substrates. By Fourier decomposition, we separate left-going and right-going neurite components from in vitro images, and we find first that neurite bundles orient toward preferred angles with respect to the stripe direction, and second that in vitro bundles travel in leftward and rightward directions nearly uninterrupted by crossings of bundles traveling in the opposing direction. We explore mechanisms that lead to these behaviors, and summarize implications for future models for neurite outgrowth and guidance.