(247a) On-Chip Automated Phenotyping of Synapses in C. Elegans for High-Throughput Fully Automated Genetic Screens

Authors: 
San Miguel, A., Georgia Institute of Technology
Crane, M., Georgia Institute of Technology
Kurshan, P., Stanford University
Shen, K., Stanford University
Lu, H., Georgia Institute of Technology


            C. elegans, a soil dwelling nematode, is a highly studied multi-cellular organism that offers many experimental advantages, including a 3-day generation time and ease of culture. Its full genome sequence is known, as well as its stereotypical neuronal wiring. Being transparent, this nematode is excellent to study neurological processes in vivo with fluorescent gene reporters. To understand gene function, genetic screens are performed where a C. elegans population is randomly mutagenized and its progeny is scored to isolate mutants with a phenotype of interest. In this manner the function of many genes have been elucidated. However, the isolation of animals with strikingly altered phenotypes has reached a practical limit and the focus has now shifted to isolating animals with very subtle phenotypic differences. Aside from the difficulty of typical screens that require manual handling and inspection of a very large number of animals, the current challenge lies on the identification of mutants with very subtle phenotypes difficult to identify by eye. Performing screens based on fluorescent reporters of micron-sized features, such as synapses, presents an exceptionally difficult scenario.

            Here, we benefit from microfluidic chips to enable easy worm handling and imaging in a worm-sorting device. Additionally, machine learning algorithms allow the extraction of relevant information from each image and consequent decision-making for worm sorting. This integrated approach is fully automated by incorporating external hardware control. With this platform, we are able to perform automated high-throughput imaging and sorting of thousands of worms based on quantitative synapse-related features. This method enables imaging, phenotyping and sorting about 100 times faster than manual handling. Not only does this method allow genetic screens to be performed in a simple, automated and fast manner, it also provides a platform for discovery of very subtle mutants with differences in micron-sized puncta distribution, which would otherwise be overlooked in a typical manual screen.