Bioinformatics advances in neuron imaging

Exploring the role of neuron's morphological change in brain function or neurological disorders is a long-time goal in many neuroscience fields. The arrival of digital microscopy, such as two-photon and confocal laser scanning microscopy, coupled with a large arsenal of fluorescent and other labeling methods to study the neuron 3D structure and dynamics in cell culture, brain tissue culture, and even live animals, has become a powerful technique that allows the neuroscientists to gather objective, quantitative and reproducible cell-based phenotype information. It offers tremendous value to localize, identify and characterize brain cells and subcellular components at different animal models. It enables better understanding of cellular changes underlying the pathogenesis of various neurological disorders. Nevertheless, there are significant bioinformatics challenges in processing, managing, analyzing, modeling, and mining large volumes of multi-dimensional neuronal images generated by high resolution optical microscopy in studying dynamic neuronal information. The existing bioimaging tools are extremely limited in their scope and capacity. The scientists have to rely on slow and restrictive manual analysis to extract information. Image informatics has become the rate-limiting factor in neuroscience imaging studies. We aim to fill this gap by developing an image informatics platform, neuronal image quantitator (NeuronIQ), with increased capacity to identify and track neuronal objects and to analyze and quantitate object features extracted from the large amounts of images generated by optical microscopy, providing a complete picture of the evolution of the morphological features and behaviors of neurons and related cellular objects in time and space, and under different experimental conditions. This would lead to rapid cell-based assays to obtain and analyze broad ranges of neuronal phenotypes and facilitate the extraction of previously undecipherable information from neuronal morphology. In this paper, the author presents some of the ongoing work and applications of NeuronIQ in deciphering biological mechanisms and behaviors of neurological disorders. Summary form only given.