(454d) Microfluidics-Based Quantitative Screening of C. Elegans with Adaptive Algorithm

Forward genetic screening based on mutagenesis is a powerful tool to identify components in pathways in model organisms such as the nematode C. elegans.  This screening usually requires imaging a large number of animals to reach saturation and/or to overcome the rare chance to find mutants.  Conventional manual microscopy is usually labor-intensive and time-consuming.  Recent development of microfluidic systems has allowed for increases in the throughput of experiments with minimized human intervention.  However, most devices are so complicated that their fabrication is hard, especially for non-experts.  Here, we present a simple single-layer microfluidic device for imaging and sorting C. elegans. Worms are loaded by pressure-driven flow and positioned in the imaging zone by controlling side valves.  Once worms are loaded, they are immobilized by low temperature of side cooling channel.  After complete immobilization, worms are imaged and then they can be sorted as either wild-type or mutant.  This operation is semi-automated by developed program in MATLAB.  This yields a throughput of 100-500 worms/hr, 1-2 orders of magnitude higher than conventional approach.  In addition to microfluidics, here we present the simple algorithm that finds target mutants.  Target mutants are sorted usually based on the changes of certain morphological markers, such as GFP transcriptional markers.  This selection in some scenarios is straightforward and many genetic screens are already performed with qualitative analysis of gene expressions.  However, some changes are too subtle to be identified by eyes even at high magnification.  Furthermore, since gene expressions may be heterogeneous due to stochasticity, and certain environmental fluctuations such as culture temperature and bacterial food conditions can change gene expressions from day to day, a simple threshold of GFP intensity is insufficient in selecting putative mutants with an acceptable small false positive rate.  To look for mutants with subtle changes, we used the adaptive quantification algorithm.  First, fifty mutagenized worms are imaged, and assuming the rate of finding relevant mutant is low (e.g. below 2%), the minimum value of target reporter expression in this group is set as a screening criterion.  Putative mutant worms having lower expression level compared to the sorting criterion were sorted.  The sorting criterion is updated after each 50 additional worms such that a reasonable sorting rate is maintained.  This algorithm is easy to implement and minimize efforts for verifying selected targets.  We use this system and the algorithm to quantify the regulation of tryptophan hydroxylase, a key enzyme for serotonin synthesis to understand how environmental signal can be integrated for various metabolic adjustments.  Using this high-throughput and quantitative method, we found several putative mutants, which would have been difficult and taken longer to find.