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

Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function

The N-glycome of human embryonic stem cells

<p>Abstract</p> <p>Background</p> <p>Complex carbohydrate structures, glycans, are essential components of glycoproteins, glycolipids, and proteoglycans. While individual glycan structures including the SSEA and Tra antigens are already used to define undifferentiated human embryonic stem cells (hESC), the whole spectrum of stem cell glycans has remained unknown. We undertook a global study of the asparagine-linked glycoprotein glycans (N-glycans) of hESC and their differentiated progeny using MALDI-TOF mass spectrometric and NMR spectroscopic profiling. Structural analyses were performed by specific glycosidase enzymes and mass spectrometric fragmentation analyses.</p> <p>Results</p> <p>The data demonstrated that hESC have a characteristic N-glycome which consists of both a constant part and a variable part that changes during hESC differentiation. hESC-associated N-glycans were downregulated and new structures emerged in the differentiated cells. Previously mouse embryonic stem cells have been associated with complex fucosylation by use of SSEA-1 antibody. In the present study we found that complex fucosylation was the most characteristic glycosylation feature also in undifferentiated hESC. The most abundant complex fucosylated structures were Le^xand H type 2 antennae in sialylated complex-type N-glycans.</p> <p>Conclusion</p> <p>The N-glycan phenotype of hESC was shown to reflect their differentiation stage. During differentiation, hESC-associated N-glycan features were replaced by differentiated cell-associated structures. The results indicated that hESC differentiation stage can be determined by direct analysis of the N-glycan profile. These results provide the first overview of the N-glycan profile of hESC and form the basis for future strategies to target stem cell glycans.</p