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

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant