An automated high-resolution screening platform identifies regulators of anchor cell invasion in <i>C. elegans</i>.

Abstract

Microfluidic devices are valuable tools for live imaging. However, widespread adoption of microfluidic-based screening methods has been limited by the complexity of the existing techniques. Here, we introduce a user-friendly, high-throughput, and high-resolution automated imaging system for <i>C. elegans</i>. We demonstrate the system's capabilities in an RNA interference (RNAi) screen, combined with neural network-based phenotypic scoring. We evaluated the effects of RNAi targeting 193 candidate genes on anchor cell (AC) invasion, a model for basement membrane (BM) breaching that shares similarities with tumor cell invasion during cancer metastasis. Over 40,000 animals were imaged at subcellular resolution and scored using a custom neural network classifier with an accuracy of over 92%. The screen identified 41 of 52 genes previously known to control AC invasion, along with 51 additional regulators of invasion. This automated imaging and classification system enables researchers to perform forward mutagenesis, RNAi, and drug screens in <i>C. elegans</i> with much greater speed and higher resolution than previously possible.