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Automated screening of C. elegans neurodegeneration mutants enabled by microfluidics and image analysis algorithms

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Abstract

Spinal muscular atrophy (SMA) is a degenerative disorder that selectively deteriorates motor neurons due to a deficiency of survival motor neuron protein (SMN). The illness is the leading genetic cause of death in infants and is difficult to study in complex biological systems such as humans. A simpler model system, such as the nematode C. elegans, can be used to study potential mechanisms underlying this disease; C. elegans expresses the smn-1 gene, a homologue of SMN; powerful genetic tools in C. elegans research can be used to discover novel genes whose effect on SMN remains unknown or uncharacterized. Currently, conventional screening methods are time-consuming and laborious, as well as being subjective and mostly qualitative. To address these issues, we engineer an automated system capable of performing genetic suppressor screens on C. elegans using microfluidics in combination with custom image analysis software. We demonstrate the utility of this system by isolating 21 alleles that significantly suppress motor neuron degeneration at a screening rate of approximately 300 worms per hour. Many of these mutants also have improved motor function. These isolated alleles can potentially be further studied to understand mechanisms of protection against neurodegeneration. Our system is easily adaptable, providing a means to saturate screens not only implicated in the smn-1 pathway, but also for genes involved in other neurodegenerative phenotypes.

Graphical abstract: Automated screening of C. elegans neurodegeneration mutants enabled by microfluidics and image analysis algorithms

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Publication details

The article was received on 17 May 2018, accepted on 01 Aug 2018 and first published on 01 Aug 2018


Article type: Paper
DOI: 10.1039/C8IB00091C
Citation: Integr. Biol., 2018, Advance Article
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    Automated screening of C. elegans neurodegeneration mutants enabled by microfluidics and image analysis algorithms

    I. de Carlos Cáceres, D. A. Porto, I. Gallotta, P. Santonicola, J. Rodríguez-Cordero, E. Di Schiavi and H. Lu, Integr. Biol., 2018, Advance Article , DOI: 10.1039/C8IB00091C

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