Neuron tracing in perspective

Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors

MOTIVATION

The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions.

RESULTS

Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni's L-measure. Coifman's harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances.

AVAILABILITY AND IMPLEMENTATION

Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors

CONTACT

broysam@central.uh.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fully automated, semiautomated, and manual morphometric analysis of corneal subbasal nerve plexus in individuals with and without diabetes

PURPOSE

The aim of the study was to determine the association, agreement, and detection capability of manual, semiautomated, and fully automated methods of corneal nerve fiber length (CNFL) quantification of the human corneal subbasal nerve plexus (SNP).

METHODS

Thirty-three participants with diabetes and 17 healthy controls underwent laser scanning corneal confocal microscopy. Eight central images of the SNP were selected for each participant and analyzed using manual (CCMetrics), semiautomated (NeuronJ), and fully automated (ACCMetrics) software to quantify the CNFL.

RESULTS

For the entire cohort, mean CNFL values quantified by CCMetrics, NeuronJ, and ACCMetrics were 17.4 ± 4.3 mm/mm, 16.0 ± 3.9 mm/mm, and 16.5 ± 3.6 mm/mm, respectively (P < 0.01). CNFL quantified using CCMetrics was significantly higher than those obtained by NeuronJ and ACCMetrics (P < 0.05). The 3 methods were highly correlated (correlation coefficients 0.87-0.98, P < 0.01). The intraclass correlation coefficients were 0.87 for ACCMetrics versus NeuronJ and 0.86 for ACCMetrics versus CCMetrics. Bland-Altman plots showed good agreement between the manual, semiautomated, and fully automated analyses of CNFL. A small underestimation of CNFL was observed using ACCMetrics with increasing the amount of nerve tissue. All 3 methods were able to detect CNFL depletion in diabetic participants (P < 0.05) and in those with peripheral neuropathy as defined by the Toronto criteria, compared with healthy controls (P < 0.05).

CONCLUSIONS

Automated quantification of CNFL provides comparable neuropathy detection ability to manual and semiautomated methods. Because of its speed, objectivity, and consistency, fully automated analysis of CNFL might be advantageous in studies of diabetic neuropathy.

neuTube 1.0: A New Design for Efficient Neuron Reconstruction Software Based on the SWC Format

Compared to other existing tools, the novel software we present has some unique features such as comprehensive editing functions and the combination of seed-based tracing and path searching algorithms, as well as their availability in parallel 2D and 3D visualization. These features allow the user to reconstruct neuronal morphology efficiently in a comfortable “What You See Is What You Get” (WYSIWYG) way.

Brain circuit mapping requires digital reconstruction of neuronal morphologies in complicated networks. Despite recent advances in automatic algorithms, reconstruction of neuronal structures is still a bottleneck in circuit mapping due to a lack of appropriate software for both efficient reconstruction and user-friendly editing. Here we present a new software design based on the SWC format, a standardized neuromorphometric format that has been widely used for analyzing neuronal morphologies or sharing neuron reconstructions via online archives such as NeuroMorpho.org. We have also implemented the design in our open-source software called neuTube 1.0. As specified by the design, the software is equipped with parallel 2D and 3D visualization and intuitive neuron tracing/editing functions, allowing the user to efficiently reconstruct neurons from fluorescence image data and edit standard neuron structure files produced by any other reconstruction software. We show the advantages of neuTube 1.0 by comparing it to two other software tools, namely Neuromantic and Neurostudio. The software is available for free at http://www.neutracing.com, which also hosts complete software documentation and video tutorials.

Bridging the gap in connectomic studies: A particle filtering framework for estimating structural connectivity at network scale

The ultimate goal of neuroscience is understanding the brain at a functional level. This requires the investigation of the structural connectivity at multiple scales: from the single-neuron micro-connectomics to the brain-region macro-connectomics. In this work, we address the study of connectomics at the intermediate mesoscale, introducing a probabilistic approach capable of reconstructing complex topologies of large neuronal networks. Suitable directional features are designed to model the local neuritic architecture and a feature-based particle filtering framework is proposed which allows the spatial tracking of neurites on microscopy images. The experimental results on cultures of increasing complexity, grown on High-Density Micro Electrode Arrays, show good stability and performance as compared to ground truth annotations drawn by domain experts. We also show how the method can be used to dissect the structural connectivity of inhibitory and excitatory subnetworks opening new perspectives towards the investigation of functional interactions among multiple cellular populations.

Regioselective biolistic targeting in organotypic brain slices using a modified gene gun

Transfection of DNA has been invaluable for biological sciences and with recent advances to organotypic brain slice preparations, the effect of various heterologous genes could thus be investigated easily while maintaining many aspects of in vivo biology. There has been increasing interest to transfect terminally differentiated neurons for which conventional transfection methods have been fraught with difficulties such as low yields and significant losses in viability. Biolistic transfection can circumvent many of these difficulties yet only recently has this technique been modified so that it is amenable for use in mammalian tissues.

New modifications to the accelerator chamber have enhanced the gene gun's firing accuracy and increased its depths of penetration while also allowing the use of lower gas pressure (50 psi) without loss of transfection efficiency as well as permitting a focused regioselective spread of the particles to within 3 mm. In addition, this technique is straight forward and faster to perform than tedious microinjections. Both transient and stable expression are possible with nanoparticle bombardment where episomal expression can be detected within 24 hr and the cell survival was shown to be better than, or at least equal to, conventional methods. This technique has however one crucial advantage: it permits the transfection to be localized within a single restrained radius thus enabling the user to anatomically isolate the heterologous gene's effects. Here we present an in-depth protocol to prepare viable adult organotypic slices and submit them to regioselective transfection using an improved gene gun.

Haptically assisted connection procedure for the reconstruction of dendritic spines

Dendritic spines are thin protrusions that cover the dendritic surface of numerous neurons in the brain and whose function seems to play a key role in neural circuits. The correct segmentation of those structures is difficult due to their small size and the resulting spines can appear incomplete. This paper presents a four-step procedure for the complete reconstruction of dendritic spines. The haptically driven procedure is intended to work as an image processing stage before the automatic segmentation step giving the final representation of the dendritic spines. The procedure is designed to allow both the navigation and the volume image editing to be carried out using a haptic device. A use case employing our procedure together with a commercial software package for the segmentation stage is illustrated. Finally, the haptic editing is evaluated in two experiments; the first experiment concerns the benefits of the force feedback and the second checks the suitability of the use of a haptic device as input. In both cases, the results shows that the procedure improves the editing accuracy.

Classic Cadherins Mediate Selective Intracortical Circuit Formation in the Mouse Neocortex

Understanding the molecular mechanisms underlying the formation of selective intracortical circuitry is one of the important questions in neuroscience research. "Barrel nets" are recently identified intracortical axonal trajectories derived from layer 2/3 neurons in layer 4 of the primary somatosensory (barrel) cortex. Axons of layer 2/3 neurons are preferentially distributed in the septal regions of layer 4 of the barrel cortex, where they show whisker-related patterns. Because cadherins have been viewed as potential candidates that mediate the formation of selective neuronal circuits, here we examined the role of cadherins in the formation of barrel nets. We disrupted the function of cadherins by expressing dominant-negative cadherin (dn-cadherin) using in utero electroporation and found that barrel nets were severely disrupted. Confocal microscopic analysis revealed that expression of dn-cadherin reduced the density of axons in septal regions in layer 4 of the barrel cortex. We also found that cadherins were important for the formation, rather than the maintenance, of barrel nets. Our results uncover an important role of cadherins in the formation of local intracortical circuitry in the neocortex.

img2net: automated network-based analysis of imaged phenotypes

SUMMARY

Automated analysis of imaged phenotypes enables fast and reproducible quantification of biologically relevant features. Despite recent developments, recordings of complex networked structures, such as leaf venation patterns, cytoskeletal structures or traffic networks, remain challenging to analyze. Here we illustrate the applicability of img2net to automatedly analyze such structures by reconstructing the underlying network, computing relevant network properties and statistically comparing networks of different types or under different conditions. The software can be readily used for analyzing image data of arbitrary 2D and 3D network-like structures.

AVAILABILITY AND IMPLEMENTATION

img2net is open-source software under the GPL and can be downloaded from http://mathbiol.mpimp-golm.mpg.de/img2net/, where supplementary information and datasets for testing are provided.

CONTACT

breuer@mpimp-golm.mpg.de.

A pipeline for neuron reconstruction based on spatial sliding volume filter seeding

Neuron's shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction.

Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology

The branching structures of neurons are a long-standing focus of neuroscience. Axonal and dendritic morphology affect synaptic signaling, integration, and connectivity, and their diversity reflects the computational specialization of neural circuits. Altered neuronal morphology accompanies functional changes during development, experience, aging, and disease. Technological improvements continuously accelerate high-throughput tissue processing, image acquisition, and morphological reconstruction. Digital reconstructions of neuronal morphologies allow for complex quantitative analyses that are unattainable from raw images or two-dimensional tracings. Furthermore, digitized morphologies enable computational modeling of biophysically realistic neuronal dynamics. Additionally, reconstructions generated to address specific scientific questions have the potential for continued investigations beyond the original reason for their acquisition. Facilitating multiple reuse are repositories like NeuroMorpho.Org, which ease the sharing of reconstructions. Here, we review selected scientific literature reporting the reconstruction of axonal or dendritic morphology with diverse goals including establishment of neuronal identity, examination of physiological properties, and quantification of developmental or pathological changes. These reconstructions, deposited in NeuroMorpho.Org, have since been used by other investigators in additional research, of which we highlight representative examples. This cycle of data generation, analysis, sharing, and reuse reveals the vast potential of digital reconstructions in quantitative investigations of neuronal morphology.

Active learning of neuron morphology for accurate automated tracing of neurites

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Cytometry in the brain: studying differentiation to diagnostic applications in brain disease and regeneration therapy

During brain development, a population of uniform embryonic cells migrates and differentiates into a large number of neural phenotypes - origin of the enormous complexity of the adult nervous system. Processes of cell proliferation, differentiation and programmed death of no longer required cells, do not occur only during embryogenesis, but are also maintained during adulthood and are affected in neurodegenerative and neuropsychiatric disease states. As neurogenesis is an endogenous response to brain injury, visible as proliferation (of to this moment silent stem or progenitor cells), its further stimulation can present a treatment strategy in addition to stem cell transfer for cell regeneration therapy. Concise techniques for studying such events in vitro and in vivo permit understanding of underlying mechanisms. Detection of subtle physiological alterations in brain cell proliferation and neurogenesis can be explored, that occur during environmental stimulation, exercise and ageing. Here, we have collected achievements in the field of basic research on applications of cytometry, including automated imaging for quantification of morphological or fluorescence-based parameters in cell cultures, towards imaging of three-dimensional brain architecture together with DNA content and proliferation data. Multi-parameter and more recently in vivo flow cytometry procedures, have been developed for quantification of phenotypic diversity and cell processes that occur during brain development as well as in adulthood, with importance for therapeutic approaches.

Past and present of corneal refractive surgery: a retrospective study of long-term results after photorefractive keratectomy and a prospective study of refractive lenticule extraction

Surgical correction of refractive errors is becoming increasingly popular. In the 1990s, the excimer laser revolutionized the field of corneal refractive surgery with PRK and LASIK, and lately refractive lenticule extraction (ReLEx) of intracorneal tissue, using only a femtosecond laser, has become possible. Two new procedures were developed, ReLEx flex (FLEX) and ReLEx smile (SMILE). Until this thesis, only a few long-term studies of PRK with a relatively limited number of patients had been published; therefore, this thesis intended to retrospectively evaluate long-term outcomes after PRK for all degrees of myopia for a large number of patients. Furthermore, a prospective contralateral eye study comparing FLEX and SMILE, when treating high to moderate degrees of myopia, had not been performed prior to this study. This was the second aim of this thesis. In the first study, results from 160 PRK patients (289 eyes) were presented. Preoperative spherical equivalent ranged from -1.25 to -20.25 D, with 78% having low myopia (<-6 D). Average follow-up time was 16 years (range 13-19 years), making this the longest published follow-up study on PRK patients. Outcomes from eyes with low myopia were generally superior to outcomes from eyes with high myopia, at final follow-up. Seventy-two percent were within ± 1.00 D of target refraction, as compared to 47% of eyes with high myopia. However, results from a subgroup of unilateral treated PRK patients indicated that refraction at final follow-up was affected by myopic progression. Fifty percent of eyes with low myopia had uncorrected 20/20 distance visual acuity or better, as compared to 22% of eyes with high myopia. Haze did not occur if attempted corrections were <-4 D, and only eyes with high myopia lost two lines or more of CDVA (corrected distance visual acuity). Eighty-one per cent were satisfied or very satisfied with their surgery.

CONCLUSION

The results support the continued use of the excimer laser for corneal surface ablation as a treatment option for correction of low degrees of myopia, and as the treatment of choice for subgroups of refractive patients (thin corneas, etc.). The results also highlight that treatment of higher degrees of myopia with standard PRK should only be done today under special circumstances, due to low refractive predictability, and high risk of corneal haze. Technological advances since then should be taken into account when comparing these results with contemporary techniques. In the second study, 35 patients were randomized to receive FLEX in one eye and SMILE in the other. Preoperative spherical equivalent refraction ranged from -6 to -10 D with low degrees of astigmatism. A total of 34 patients completed the 6 month follow-up period. Refractive and visual outcomes were very similar for the two methods, as well as tear film measurements and changes in corneal biomechanics. Ninety-seven percent were within ± 1.00 D of target refraction, no eyes lost two lines or more of CDVA, and contrast sensitivity was unaffected after both procedures. The changes in higher-order aberrations were also very similar. There were also no differences in tear film parameters 6 months after surgery, although less postoperative foreign body sensation was reported within the first week after surgery in SMILE eyes. Corneal sublayer pachymetry measurements demonstrated equally increased epithelial thickness 6 months after surgery. Contrary to expectations, it was not possible to measure the theoretical biomechanical advantages of a small corneal incision in SMILE as compared to a corneal flap in FLEX. The main differences between FLEX and SMILE were found when the corneal nerves and intraoperative complications were evaluated. Thus, corneal sensitivity was better preserved and corneal nerve morphology was less affected after SMILE, but intraoperative complications occurred more frequently, although without visual sequela. Finally, 97% were satisfied or very satisfied with both their surgeries.

CONCLUSION

The results support the continued use of both FLEX and SMILE for treatment of up to high degrees of myopia. Overall, refractive and visual results for both procedures were good and similar, but from a biological point of view, the less invasive SMILE technique is more attractive, as demonstrated in this study, despite being slightly more surgically demanding than FLEX.

AxonQuant: A Microfluidic Chamber Culture-Coupled Algorithm That Allows High-Throughput Quantification of Axonal Damage

Published methods for imaging and quantitatively analyzing morphological changes in neuronal axons have serious limitations because of their small sample sizes, and their time-consuming and nonobjective nature. Here we present an improved microfluidic chamber design suitable for fast and high-throughput imaging of neuronal axons. We developed the AxonQuant algorithm, which is suitable for automatic processing of axonal imaging data. This microfluidic chamber-coupled algorithm allows calculation of an 'axonal continuity index' that quantitatively measures axonal health status in a manner independent of neuronal or axonal density. This method allows quantitative analysis of axonal morphology in an automatic and nonbiased manner. Our method will facilitate large-scale high-throughput screening for genes or therapeutic compounds for neurodegenerative diseases involving axonal damage. When combined with imaging technologies utilizing different gene markers, this method will provide new insights into the mechanistic basis for axon degeneration. Our microfluidic chamber culture-coupled AxonQuant algorithm will be widely useful for studying axonal biology and neurodegenerative disorders.

Extraction of protein profiles from primary neurons using active contour models and wavelets

The function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means. We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites. We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses.

Non destructive characterization of cortical bone micro-damage by nonlinear resonant ultrasound spectroscopy

The objective of the study was to evaluate the ability of a nonlinear ultrasound technique, the so-called nonlinear resonant ultrasound spectroscopy (NRUS) technique, for detecting early microdamage accumulation in cortical bone induced by four-point bending fatigue. Small parallelepiped beam-shaped human cortical bone specimens were subjected to cyclic four-point bending fatigue in several steps. The specimens were prepared to control damage localization during four-point bending fatigue cycling and to unambiguously identify resonant modes for NRUS measurements. NRUS measurements were achieved to follow the evolution of the nonlinear hysteretic elastic behavior during fatigue-induced damage. After each fatigue step, a small number of specimens was removed from the protocol and set apart to quantitatively assess the microcrack number density and length using synchrotron radiation micro-computed tomography (SR-µCT). The results showed a significant effect of damage steps on the nonlinear hysteretic elastic behavior. No significant change in the overall length of microcracks was observed in damaged regions compared to the load-free control regions. Only an increased number of shortest microcracks, those in the lowest quartile, was noticed. This was suggestive of newly formed microcracks during the early phases of damage accumulation. The variation of nonlinear hysteretic elastic behavior was significantly correlated to the variation of the density of short microcracks. Our results suggest that the nonlinear hysteretic elastic behavior is sensitive to early bone microdamage. Therefore NRUS technique can be used to monitor fatigue microdamage progression in in vitro experiments.

Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling

Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.

MorphoNeuroNet: an automated method for dense neurite network analysis

High content cell-based screens are rapidly gaining popularity in the context of neuronal regeneration studies. To analyze neuronal morphology, automatic image analysis pipelines have been conceived, which accurately quantify the shape changes of neurons in cell cultures with non-dense neurite networks. However, most existing methods show poor performance for well-connected and differentiated neuronal networks, which may serve as valuable models for inter alia synaptogenesis. Here, we present a fully automated method for quantifying the morphology of neurons and the density of neurite networks, in dense neuronal cultures, which are grown for more than 10 days. MorphoNeuroNet, written as a script for ImageJ, Java based freeware, automatically determines various morphological parameters of the soma and the neurites (size, shape, starting points, and fractional occupation). The image analysis pipeline consists of a multi-tier approach in which the somas are segmented by adaptive region growing using nuclei as seeds, and the neurites are delineated by a combination of various intensity and edge detection algorithms. Quantitative comparison showed a superior performance of MorphoNeuroNet to existing analysis tools, especially for revealing subtle changes in thin neurites, which have weak fluorescence intensity compared to the rest of the network. The proposed method will help determining the effects of compounds on cultures with dense neurite networks, thereby boosting physiological relevance of cell-based assays in the context of neuronal diseases.

NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

A novel tracing method for the segmentation of cell wall networks

Cell wall networks are a common subject of research in biology, which are important for plant growth analysis, organ studies, etc. In order to automate the detection of individual cells in such cell wall networks, we propose a new segmentation algorithm. The proposed method is a network tracing algorithm, exploiting the prior knowledge of the network structure. The method is applicable on multiple microscopy modalities such as fluorescence, but also for images captured using non invasive microscopes such as differential interference contrast (DIC) microscopes.

Statistical analysis of dendritic spine distributions in rat hippocampal cultures

BACKGROUND

Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley's K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites.

RESULTS

Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type.

CONCLUSIONS

Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.

Morphological and physiological changes in mature in vitro neuronal networks towards exposure to short-, middle- or long-term simulated microgravity

One of the objectives of the current international space programmes is to investigate the possible effects of the space environment on the crew health. The aim of this work was to assess the particular effects of simulated microgravity on mature primary neuronal networks and specially their plasticity and connectivity. For this purpose, primary mouse neurons were first grown for 10 days as a dense network before being placed in the Random Positioning Machine (RPM), simulating microgravity. These cultures were then used to investigate the impact of short- (1 h), middle- (24 h) and long-term (10 days) exposure to microgravity at the level of neurite network density, cell morphology and motility as well as cytoskeleton properties in established two-dimensional mature neuronal networks. Image processing analysis of dense neuronal networks exposed to simulated microgravity and their subsequent recovery under ground conditions revealed different neuronal responses depending on the duration period of exposure. After short- and middle-term exposures to simulated microgravity, changes in neurite network, neuron morphology and viability were observed with significant alterations followed by fast recovery processes. Long exposure to simulated microgravity revealed a high adaptation of single neurons to the new gravity conditions as well as a partial adaptation of neuronal networks. This latter was concomitant to an increase of apoptosis. However, neurons and neuronal networks exposed for long-term to simulated microgravity required longer recovery time to re-adapt to the ground gravity. In conclusion, a clear modulation in neuronal plasticity was evidenced through morphological and physiological changes in primary neuronal cultures during and after simulated microgravity exposure. These changes were dependent on the duration of exposure to microgravity.

FiloDetect: automatic detection of filopodia from fluorescence microscopy images

Background

Filopodia are small cellular projections that help cells to move through and sense their environment. Filopodia play crucial roles in processes such as development and wound-healing. Also, increases in filopodia number or size are characteristic of many invasive cancers and are correlated with increased rates of metastasis in mouse experiments. Thus, one possible route to developing anti-metastatic therapies is to target factors that influence the filopodia system. Filopodia can be detected by eye using confocal fluorescence microscopy, and they can be manually annotated in images to quantify filopodia parameters. Although this approach is accurate, it is slow, tedious and not entirely objective. Manual detection is a significant barrier to the discovery and quantification of new factors that influence the filopodia system.

Results

Here, we present FiloDetect, an automated tool for detecting, counting and measuring the length of filopodia in fluorescence microscopy images. The method first segments the cell from the background, using a modified triangle threshold method, and then extracts the filopodia using a series of morphological operations. We verified the accuracy of FiloDetect on Rat2 and B16F1 cell images from three different labs, showing that per-cell filopodia counts and length estimates are highly correlated with the manual annotations. We then used FiloDetect to assess the role of a lipid kinase on filopodia production in breast cancer cells. Experimental results show that PI4KIII β expression leads to an increase in filopodia number and length, suggesting that PI4KIII β is involved in driving filopodia production.

Conclusion

FiloDetect provides accurate and objective quantification of filopodia in microscopy images, and will enable large scale comparative studies to assess the effects of different genetic and chemical perturbations on filopodia production in different cell types, including cancer cell lines.

Subbasal nerve morphology, corneal sensation, and tear film evaluation after refractive femtosecond laser lenticule extraction

BACKGROUND

The purpose of this study was to compare corneal subbasal nerve morphology, corneal sensation, and tear film parameters after femtosecond lenticule extraction (FLEX) and small-incision lenticule extraction (SMILE).

METHODS

A prospective, randomized, single-masked, paired-eye design clinical trial of 35 patients treated for moderate to high myopia with FLEX in one eye and SMILE in the other. In both techniques, an intrastromal lenticule was cut by a femtosecond laser and manually extracted. In FLEX, a LASIK-like flap allowed removal of the lenticule, whereas in SMILE, it was removed through a small incision. In-vivo confocal microscopy was used to acquire images of the central corneal subbasal nerve plexus, from which nerve density, total nerve number, and nerve tortuosity were analyzed. Corneal sensation was measured using Cochet-Bonnet esthesiometry. A visual analog scale, tear osmolarity, non-invasive tear film break-up time (keratograph) tear meniscus height (anterior segment OCT), Schirmer's test, and fluorescein tear film break-up time were used to evaluate tear film and ocular surface symptoms. Patients were examined before and 6 months after surgery.

RESULTS

There were no statistically significant differences in baseline parameters between FLEX and SMILE (p > 0.050). With regard to changes from before to 6 months after surgery, mean reduction in subbasal nerve density was 14.22 ± 6.24 mm/mm(2) in FLEX eyes, and 9.21 ± 7.80 mm/mm(2) in SMILE eyes (p < 0.05). The total number of nerves decreased more in FLEX eyes than in SMILE eyes (p < 0.05). No change was found when comparing tortuosity (p > 0.05). Corneal sensation was reduced with 0.38 ± 0.49 cm in FLEX eyes, and 0.10 ± 0.34 cm in SMILE eyes (p < 0.01). No differences were found between FLEX and SMILE in tear film evaluation tests (p > 0.05). Significantly more patients felt postoperative foreign body sensation in the FLEX eye within the first days after surgery, as compared to the SMILE eye.

CONCLUSIONS

Six months after surgery, the less invasive SMILE technique seemed better at sparing the central corneal nerves as compared to FLEX. Corneal sensation was only significantly reduced in FLEX eyes. There were no differences between FLEX and SMILE when comparing tear film evaluation tests 6 months after surgery.

Efficient multiple object tracking using mutually repulsive active membranes

Studies of social and group behavior in interacting organisms require high-throughput analysis of the motion of a large number of individual subjects. Computer vision techniques offer solutions to specific tracking problems, and allow automated and efficient tracking with minimal human intervention. In this work, we adopt the open active contour model to track the trajectories of moving objects at high density. We add repulsive interactions between open contours to the original model, treat the trajectories as an extrusion in the temporal dimension, and show applications to two tracking problems. The walking behavior of Drosophila is studied at different population density and gender composition. We demonstrate that individual male flies have distinct walking signatures, and that the social interaction between flies in a mixed gender arena is gender specific. We also apply our model to studies of trajectories of gliding Myxococcus xanthus bacteria at high density. We examine the individual gliding behavioral statistics in terms of the gliding speed distribution. Using these two examples at very distinctive spatial scales, we illustrate the use of our algorithm on tracking both short rigid bodies (Drosophila) and long flexible objects (Myxococcus xanthus). Our repulsive active membrane model reaches error rates better than 5 x 10(-6) per fly per second for Drosophila tracking and comparable results for Myxococcus xanthus.

Neuronize: a tool for building realistic neuronal cell morphologies

This study presents a tool, Neuronize, for building realistic three-dimensional models of neuronal cells from the morphological information extracted through computer-aided tracing applications. Neuronize consists of a set of methods designed to build 3D neural meshes that approximate the cell membrane at different resolution levels, allowing a balance to be reached between the complexity and the quality of the final model. The main contribution of the present study is the proposal of a novel approach to build a realistic and accurate 3D shape of the soma from the incomplete information stored in the digitally traced neuron, which usually consists of a 2D cell body contour. This technique is based on the deformation of an initial shape driven by the position and thickness of the first order dendrites. The addition of a set of spines along the dendrites completes the model, building a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.

Automated and accurate detection of soma location and surface morphology in large-scale 3D neuron images

Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.

APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree

MOTIVATION

Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images.

RESULTS

We developed all-path-pruning 2.0 (APP2) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. We also design a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on ~700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods.

AVAILABILITY

The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RipleyGUI: software for analyzing spatial patterns in 3D cell distributions

The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. To facilitate the quantification of neuronal cell patterns we have developed RipleyGUI, a MATLAB-based software that can be used to detect patterns in the 3D distribution of cells. RipleyGUI uses Ripley's K-function to analyze spatial distributions. In addition the software contains statistical tools to determine quantitative statistical differences, and tools for spatial transformations that are useful for analyzing non-stationary point patterns. The software has a graphical user interface making it easy to use without programming experience, and an extensive user manual explaining the basic concepts underlying the different statistical tools used to analyze spatial point patterns. The described analysis tool can be used for determining the spatial organization of neurons that is important for a detailed study of structure-function relationships. For example, neocortex that can be subdivided into six layers based on cell density and cell types can also be analyzed in terms of organizational principles distinguishing the layers.

Abl2/Arg controls dendritic spine and dendrite arbor stability via distinct cytoskeletal control pathways

Rho family GTPases coordinate cytoskeletal rearrangements in neurons, and mutations in their regulators are associated with mental retardation and other neurodevelopmental disorders (Billuart et al., 1998; Kutsche et al., 2000; Newey et al., 2005; Benarroch, 2007). Chromosomal microdeletions encompassing p190RhoGAP or its upstream regulator, the Abl2/Arg tyrosine kinase, have been observed in cases of mental retardation associated with developmental defects (Scarbrough et al., 1988; James et al., 1996; Takano et al., 1997; Chaabouni et al., 2006; Leal et al., 2009). Genetic knock-out of Arg in mice leads to synapse, dendritic spine, and dendrite arbor loss accompanied by behavioral deficits (Moresco et al., 2005; Sfakianos et al., 2007). To elucidate the cell-autonomous mechanisms by which Arg regulates neuronal stability, we knocked down Arg in mouse hippocampal neuronal cultures. We find that Arg knockdown significantly destabilizes dendrite arbors and reduces dendritic spine density by compromising dendritic spine stability. Inhibiting RhoA prevents dendrite arbor loss following Arg knockdown in neurons, but does not block spine loss. Interestingly, Arg-deficient neurons exhibit increased miniature EPSC amplitudes, and their remaining spines exhibit larger heads deficient in the actin stabilizing protein cortactin. Spine destabilization in Arg knockdown neurons is prevented by blocking NMDA receptor-dependent relocalization of cortactin from spines, or by forcing cortactin into spines via fusion to an actin-binding region of Arg. Thus, Arg employs distinct mechanisms to selectively regulate spine and dendrite stability: Arg dampens activity-dependent disruption of cortactin localization to stabilize spines and attenuates Rho activity to stabilize dendrite arbors.

Chapter 17: bioimage informatics for systems pharmacology

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

Neuronal morphology goes digital: a research hub for cellular and system neuroscience

The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of "digital reconstructions" of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research "ecosystem" as a central reference for experimental and computational neuroscience.

Three-dimensional analysis of spiny dendrites using straightening and unrolling transforms

Current understanding of the synaptic organization of the brain depends to a large extent on knowledge about the synaptic inputs to the neurons. Indeed, the dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by thin protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition. This paper presents a new method that facilitates the analysis of the 3D structure of spine insertions in dendrites, providing insight on spine distribution patterns. This method is based both on the implementation of straightening and unrolling transformations to move the analysis process to a planar, unfolded arrangement, and on the design of DISPINE, an interactive environment that supports the visual analysis of 3D patterns.

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.