Obesity is a serious health issue currently plaguing 72 million Americans. While the genetic origin has yet to be fully understood, the risk has been linked to fat distribution and storage. The nematode, C. elegans, is a convenient model to understand key fat storage mechanisms conserved through evolution, which can give insight towards pathways controlling these processes in humans. Fat in C. elegans is primarily stored in organelles called lipid droplets, which when labeled can be imaged in vivo through the transparent animal. Classification of various lipid droplet phenotypes in worms would allow for discovery of important genes associated with lipid storage and metabolism. However, current limitations in imaging and analysis of lipid droplets in worms, such as low throughput and labor intensive quantification, are some of the factors that have hindered further growth in lipid research. Engineering methods and tools to improve object quantification would dramatically enhance the ability to study lipid droplets in C. elegans.
To address these issues, we use a microfluidic device to facilitate imaging and developed a modified granulometry algorithm to rapidly detect and quantify lipid droplets. We integrated technologies to investigated genes that affect lipid droplets and their storage by performing an automated real-time on-chip forward genetic screen of C. elegans for phenotypes with varying lipid droplet size distributions. With this platform, we automatically screened over 3,000 animals and successfully sorted and recovered various mutant strains in a much higher throughput manner, approximately two orders of magnitude faster, than previously achievable. The development of this integrated experimental platform has enabled the potential discovery of factors that contribute to fat accumulation that are currently unknown.
See more of this Group/Topical: Food, Pharmaceutical & Bioengineering Division