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

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Morphology of Dbx1 respiratory neurons in the preBotzinger complex and reticular formation of neonatal mice

The relationship between neuron morphology and function is a perennial issue in neuroscience. Information about synaptic integration, network connectivity, and the specific roles of neuronal subpopulations can be obtained through morphological analysis of key neurons within a microcircuit. Here we present morphologies of two classes of brainstem respiratory neurons. First, interneurons derived from Dbx1-expressing precursors (Dbx1 neurons) in the preBotzinger complex (preBotC) of the ventral medulla that generate the rhythm for inspiratory breathing movements. Second, Dbx1 neurons of the intermediate reticular formation that influence the motor pattern of pharyngeal and lingual movements during the inspiratory phase of the breathing cycle. We describe the image acquisition and subsequent digitization of morphologies of respiratory Dbx1 neurons from the preBotC and the intermediate reticular formation that were first recorded in vitro. These data can be analyzed comparatively to examine how morphology influences the roles of Dbx1 preBotC and Dbx1 reticular interneurons in respiration and can also be utilized to create morphologically accurate compartmental models for simulation and modeling of respiratory circuits

A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling.

Accurate 3D reconstruction of neurons is vital for applications linking anatomy and physiology. Reconstructions are typically created using Neurolucida after biocytin histology (BH). An alternative inexpensive and fast method is to use freeware such as Neuromantic to reconstruct from fluorescence imaging (FI) stacks acquired using 2-photon laser-scanning microscopy during physiological recording. We compare these two methods with respect to morphometry, cell classification, and multicompartmental modeling in the NEURON simulation environment. Quantitative morphological analysis of the same cells reconstructed using both methods reveals that whilst biocytin reconstructions facilitate tracing of more distal collaterals, both methods are comparable in representing the overall morphology: automated clustering of reconstructions from both methods successfully separates neocortical basket cells from pyramidal cells but not BH from FI reconstructions. BH reconstructions suffer more from tissue shrinkage and compression artifacts than FI reconstructions do. FI reconstructions, on the other hand, consistently have larger process diameters. Consequently, significant differences in NEURON modeling of excitatory post-synaptic potential (EPSP) forward propagation are seen between the two methods, with FI reconstructions exhibiting smaller depolarizations. Simulated action potential backpropagation (bAP), however, is indistinguishable between reconstructions obtained with the two methods. In our hands, BH reconstructions are necessary for NEURON modeling and detailed morphological tracing, and thus remain state of the art, although they are more labor intensive, more expensive, and suffer from a higher failure rate due to the occasional poor outcome of histological processing. However, for a subset of anatomical applications such as cell type identification, FI reconstructions are superior, because of indistinguishable classification performance with greater ease of use, essentially 100% success rate, and lower cost

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

CellRemorph : A toolkit for transforming, selecting, and slicing 3D cell structures on the road to morphologically detailed astrocyte simulations

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Semi-Automated Reconstruction of Curvilinear Structures in Noisy 2D images and 3D image stacks

We propose a new approach to semi-automated delineation of curvilinear structures in a wide range of imaging modalities. Earlier approaches lack robustness to imaging noise, do not provide radius estimates for the structures and operate only on single channel images. In contrast, ours makes use of the color information, when available, and generates accurate centreline location and radius estimates with minimal supervision. We demonstrate this on a wide range of datasets ranging from a 2D dataset of aerial images to 3D micrographs of neurites

The role of iron in the skin and cutaneous wound healing.

In this review article we discuss current knowledge about iron in the skin and the cutaneous wound healing process. Iron plays a key role in both oxidative stress and photo-induced skin damage. The main causes of oxidative stress in the skin include reactive oxygen species (ROS) generated in the skin by ultraviolet (UVA) 320-400 nm portion of the UVA spectrum and biologically available iron. We also discuss the relationships between iron deficiency, anemia and cutaneous wound healing. Studies looking at this fall into two distinct groups. Early studies investigated the effect of anemia on wound healing using a variety of experimental methodology to establish anemia or iron deficiency and focused on wound-strength rather than effect on macroscopic healing or re-epithelialization. More recent animal studies have investigated novel treatments aimed at correcting the effects of systemic iron deficiency and localized iron overload. Iron overload is associated with local cutaneous iron deposition, which has numerous deleterious effects in chronic venous disease and hereditary hemochromatosis. Iron plays a key role in chronic ulceration and conditions such as rheumatoid arthritis (RA) and Lupus Erythematosus are associated with both anemia of chronic disease and dysregulation of local cutaneous iron hemostasis. Iron is a potential therapeutic target in the skin by application of topical iron chelators and novel pharmacological agents, and in delayed cutaneous wound healing by treatment of iron deficiency or underlying systemic inflammation