Monitoring extracellular glutamate in hippocampal slices with a microsensor.

The direct local assessment of glutamate in brain slices may improve our understanding of glutamatergic neurotransmission significantly. However, an analytical technique that monitors glutamate directly in brain slices is currently not available. Most recording techniques either monitor derivatives of glutamate or detect glutamate that diffuses out of the slice. Microsensors provide a promising solution to fulfill this analytical requirement. In the present study we have implanted a 10 mu m diameter hydrogel-coated microsensor in the CAl area of hippocampal slices to monitor extracellular glutarnate levels. The influence of several pharmacological agents, which facilitate glutamate release from neurons or astrocytes, was investigated to explore the applicability of the microsensor. It was observed that KCl, veratradine, alpha-latrotoxine (LTX), DL-threo-beta-benzyloxyaspartate (DL-TBOA) and L-cystine rapidly increased the extracellular glutamate levels. As far as we know this is the first study in which a microsensor is applied to monitor dynamic changes of glutarnate in brain slices and in our opinion this type of research may contribute greatly to improve our understanding of the physiology of glutamatergic neurotransmission. (c) 2006 Elsevier B.V. All rights reserved

Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis

Abstract. We present a novel approach for extracting cluttered objects based on their morphological properties 1. Specifically, we address the problem of untangling C. elegans clusters in high-throughput screening experiments. We represent the skeleton of each worm cluster by a sparse directed graph whose vertices and edges correspond to worm segments and their adjacencies, respectively. We then search for paths in the graph that are most likely to represent worms while minimizing overlaps. The worm likelihood measure is defined on a low-dimensional feature space that captures different worm poses, obtained from a training set of isolated worms. We test the algorithm on 236 microscopy images, each containing 15 C. elegans worms, and demonstrate successful cluster untangling and high worm detection ratio.