Tools for connectomics in C. elegans

Abstract

Efforts to model computation in biological neural networks require knowledge of the structure of the network, the dynamics that play across it, and a network simple enough to be tractable to our incipient analyses. The simplicity of the 302-node nervous system of the nematode C. elegans and its transparency have made it an attractive model organism in neuroscience for several decades. Indeed, Caenorhabditis elegans has long been touted as the only species for which the connectome is known, reconstructed by hand from electron micrographs. However, while the number and identity of neurons in C. elegans appears fixed across animals, the variability in the connections between them has not been sufficiently characterized by the above efforts, which examined only a handful of animals and required many years of human labor. Such a characterization, and, moreover, an ability to accurately assess shifts in these neural graphs on timescales compatible with the pace and statistical rigor of scientific research would significantly accelerate efforts to understand neural computation. This thesis lays the groundwork for the development of such a framework. The expansion microscopy tissue preparation platform provided the basis for the set of experiments described within, in which strategies for molecular annotation of C. elegans and the subsequent protocols for readout are examined.