High Throughput Algorithms for Mapping the Topology of Neuronal and Glial Networks

Complex networks underlie the very foundation of neuroscience and the mammalian nervous system, yet high throughput computationally intensive measurements of molecular signaling between large groups of cells and the extraction of statistically significant quantitative information about the underlying network structure is not possible given current techniques. We have developed novel computational algorithms designed for high throughput characterization and analysis of complex neural cell networks (i.e. both neuronal and glial) that are applicable to measurements of inter- and intracellular fluorescence signaling in vitro using real time live cell fluorescence or confocal microscopy and in vivo using multi-photon (e.g. two photon) microscopy. The practical implementation of these algorithms is based on an integrated software suite we have developed called CellMap, which facilitates mapping and analyzing the functional structure (i.e. architecture or topology) of cellular networks. Here, we have focused on calcium mediated intercellular signaling in spontaneously forming astrocyte macroglial networks. We show that the underlying structure of these networks formed in vitro have non-random topologies consistent with a potential scale-free type topology as described by network theory, which provides a unifying mathematical basis for the structure of complex networks (e.g. from electrical power grids to human social networks to metabolic networks in cells). This work represents the first indication that intercellular chemical signaling in CNS cells has a previously unknown underlying topological structure. A quantitative understanding of the structure of CNS networks would most likely influence and produce novel conceptual hypotheses regarding how neural information is represented and processed