Using ImageJ to Assess Neurite Outgrowth in Mammalian Cell Cultures: Research Data Quantification Exercises in Undergraduate Neuroscience Lab.

Brain functions rely critically upon the proper development of neuronal processes (axons and dendrites) and the formation of functional networks. Any genetic factors or environmental compounds that alter the morphological features of neurons may render the nervous system dysfunctional and result in neuronal disorders. In vitro cell culture is an important technique in assessing the effect of chemicals on neurite formation and growth of individual neurons in desired brain regions and has been fundamental in advancing our understanding of the nervous system development and functioning. Despite others offering excellent techniques in cell cultures (Catlin et al., 2016), there is a lack of available resources for teaching students how to analyze neurite outgrowth and run proper statistics on their data. Here, we first briefly discuss culturing cryopreserved mammalian neurons. We then give detailed options to aid upper level undergraduate neurobiology students to quantify neurite outgrowth using NeuronJ, a plugin in the free ImageJ package, Fiji, on both phase contrast and immunofluorescent images. This laboratory exercise provides students the opportunity to culture live neurons, quantify neuronal growth, experiment with the effects of common chemicals on neural development, and conduct statistical data analysis. Previous students expressed their great appreciation for the opportunity to work with live neurons and conduct data quantification and analysis like a true scientist. The ability to accurately measure and calculate the overall growth of neurons using the software ImageJ greatly enhanced students' confidence in presenting their results both in oral and written format.

NeuronCyto II: An automatic and quantitative solution for crossover neural cells in high throughput screening.

Microscopy is a fundamental technology driving new biological discoveries. Today microscopy allows a large number of images to be acquired using, for example, High Throughput Screening (HTS) and 4D imaging. It is essential to be able to interrogate these images and extract quantitative information in an automated fashion. In the context of neurobiology, it is important to automatically quantify the morphology of neurons in terms of neurite number, length, branching and complexity, etc. One major issue in quantification of neuronal morphology is the "crossover" problem where neurites cross and it is difficult to assign which neurite belongs to which cell body. In the present study, we provide a solution to the "crossover" problem, the software package NeuronCyto II. NeuronCyto II is an interactive and user-friendly software package for automatic neurite quantification. It has a well-designed graphical user interface (GUI) with only a few free parameters allowing users to optimize the software by themselves and extract relevant quantitative information routinely. Users are able to interact with the images and the numerical features through the Result Inspector. The processing of neurites without crossover was presented in our previous work. Our solution for the "crossover" problem is developed based on our recently published work with directed graph theory. Both methods are implemented in NeuronCyto II. The results show that our solution is able to significantly improve the reliability and accuracy of the neurons displaying "crossover." NeuronCyto II is freely available at the website: https://sites.google.com/site/neuroncyto/, which includes user support and where software upgrades will also be placed in the future. © 2016 The Authors. Cytometry Part A Published by Wiley Periodicals, Inc. on behalf of ISAC.

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth.

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.

Atypical Myosin Tunes Dendrite Arbor Subdivision.

Dendrite arbor pattern determines the functional characteristics of a neuron. It is founded on primary branch structure, defined through cell intrinsic and transcription-factor-encoded mechanisms. Developing arbors have extensive acentrosomal microtubule dynamics, and here, we report an unexpected role for the atypical actin motor Myo6 in creating primary branch structure by specifying the position, polarity, and targeting of these events. We carried out in vivo time-lapse imaging of Drosophila adult sensory neuron differentiation, integrating machine-learning-based quantification of arbor patterning with molecular-level tracking of cytoskeletal remodeling. This revealed that Myo6 and the transcription factor Knot regulate transient surges of microtubule polymerization at dendrite tips; they drive retrograde extension of an actin filament array that specifies anterograde microtubule polymerization and guides these microtubules to subdivide the tip into multiple branches. Primary branches delineate functional compartments; this tunable branching mechanism is key to define and diversify dendrite arbor compartmentalization.

Computing neurite outgrowth and arborization in superior cervical ganglion neurons.

Dendrites are the primary site of synaptic activity in neurons and changes in synapses are often the first pathological stage in neurodegenerative diseases. Molecular studies of these changes rely on morphological analysis of the imaging of somas and dendritic arbors of cultured or primary neurons. As research on preventing or reversing synaptic degeneration develops, demands increase for user-friendly 2D neurite analyzers without undermining accuracy and reproducibility. The most common method of 2D neurite analysis is manual by using ImageJ. This method relies completely on the user's ability to distinguish the shape and size of dendrites and trace morphology with a series of straight connected lines. Semi-automatic methods have also been developed, such as the NeuronJ plugin for ImageJ. These methods still rely on the user to identify the start and end of the dendrites, but automatically determine the shape, reducing the likelihood of user bias and speeding the process. Some automatic methods have been developed through image processing software, like ImagePro. These programs tend to be expensive, but have been shown to be fast and effective, limiting user interaction. In this study, we compare three methods of neurite analysis-ImageJ, NeuronJ, and ImagePro-in measuring the soma size, number of dendrites, and length of dendrites per cell of embryonic sympathetic rat neurons with BMP-7-induced dendritic growth. Our results indicate that ImageJ and NeuronJ measurements were of similar effectiveness and consistent throughout various images and multiple trials. NeuronJ required less user interaction in measuring the length of dendrites than the manual method and therefore, was faster and less labor intensive. Conversely, ImagePro tended to be inconsistent across images, overestimating both soma size and the number of dendrites per cell while underestimating the length of dendrites. Overall, NeuronJ, in conjunction with ImageJ, is the most reliable and efficient method of 2D neurite analysis tested in the present study.

Neurite analyzer: An original Fiji plugin for quantification of neuritogenesis in two-dimensional images.

BACKGROUND: In life sciences, there is a growing need for new informatics tools designed to provide automated solutions in order to analyze big amounts of images obtained from high-throughput imaging systems. Among the most widely used assays in neurotoxicity, endocrinology and brain diseases, the neurite outgrowth assay is popular. NEW METHOD: Cell-to-cell quantification of the main morphological features of neurite outgrowth assays remains very challenging. Here, we provide a new pipeline developed on Fiji software for analysis of series of two-dimensional images. It allows the automated analysis of most of these features. RESULTS: We tested the accuracy and usefulness of the software by confirming the effects of estradiol and hypoxia on in vitro neuronal differentiation, previously published by different authors with manual analysis methods. With this new method, we highlighted original interesting data. COMPARISON WITH EXISTING METHOD(S): The innovation brought by this plugin lies in the fact that it can process multiple images at the same time, in order to obtain: the number of nuclei, the number of neurites, the length of neurites, the number of neurites junctions, the number of neurites branches, the length of each branch, the position of the branch in the image, the angle of each branch, but also the area of each cell and the number of neurites per cell. CONCLUSIONS: This plugin is easy to use, highly sensitive, and allows the experimenter to acquire ready-to-use data coming from a vast amount of images.

Multiscale Analysis of Neurite Orientation and Spatial Organization in Neuronal Images.

The spatial organization of neurites, the thin processes (i.e., dendrites and axons) that stem from a neuron's soma, conveys structural information required for proper brain function. The alignment, direction and overall geometry of neurites in the brain are subject to continuous remodeling in response to healthy and noxious stimuli. In the developing brain, during neurogenesis or in neuroregeneration, these structural changes are indicators of the ability of neurons to establish axon-to-dendrite connections that can ultimately develop into functional synapses. Enabling a proper quantification of this structural remodeling would facilitate the identification of new phenotypic criteria to classify developmental stages and further our understanding of brain function. However, adequate algorithms to accurately and reliably quantify neurite orientation and alignment are still lacking. To fill this gap, we introduce a novel algorithm that relies on multiscale directional filters designed to measure local neurites orientation over multiple scales. This innovative approach allows us to discriminate the physical orientation of neurites from finer scale phenomena associated with local irregularities and noise. Building on this multiscale framework, we also introduce a notion of alignment score that we apply to quantify the degree of spatial organization of neurites in tissue and cultured neurons. Numerical codes were implemented in Python and released open source and freely available to the scientific community.