380127 Automated High-Throughput Imaging for Multidimensional High-Resolution Characterization of Synaptic Patterns in C. Elegans

Thursday, November 20, 2014: 8:30 AM
206 (Hilton Atlanta)
Adriana San-Miguel1, Peri Kurshan2, Kang Shen2 and Hang Lu1, (1)School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, (2)Stanford University, Stanford, CA

Fluorescently labeled proteins are now ubiquitous in biological assays. Fluorescent markers can inform us of the spatiotemporal location of functional proteins, and the defects these patterns display upon genetic or environmental perturbation. Studies that rely on fluorescent markers, however, have been largely biased towards the identification of features easily identifiable by human vision. We propose that images of fluorescent markers are content-rich beyond characteristics that can be visually detected. In the multicellular organism C. elegans, fluorescent markers can be visualized in live intact animals due to its transparency. C. elegans is an well-studied model organism which has been particularly relevant in neuroscience since, among other experimental advantages, its full stereotypical neuronal wiring has been mapped. Obtaining high-content, high-resolution images on live animals is, however, challenged by a large amount of manual labor related to worm handling and sample preparation.

            Here, we show that microfluidics and computer vision can be coupled to perform high-throughput imaging at a very high-resolution level. We benefit from microfluidic chips to enable easy worm handling and imaging. Additionally, machine-learning algorithms allow the extraction of relevant information from each image in an unbiased manner. We apply this platform to perform genetic screens in the search for modulators of synaptic patterning. 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 and imaging experiments 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.


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See more of this Session: Experimental Approaches in Systems Biology
See more of this Group/Topical: Food, Pharmaceutical & Bioengineering Division