Digging Deeper: Methodologies for High-Content Phenotyping and Knowledge-Abstraction in C. elegans

TL;DR

This paper reviews recent technological and computational advances in deep phenotyping of C. elegans, highlighting how these tools enhance understanding of complex biological traits in multicellular organisms.

Contribution

It provides a comprehensive overview of new methodologies and tools that improve high-content phenotyping and knowledge abstraction in C. elegans.

Findings

Advances in imaging and data analysis increase phenotyping throughput.

New tools enable detailed analysis of complex phenotypes.

Technological innovations facilitate understanding of genetic and environmental influences.

Abstract

Deep phenotyping is an emerging conceptual paradigm and experimental approach that seeks to measure many aspects of phenotypes and link them to understand the underlying biology. Successful deep phenotyping has mostly been applied in cultured cells, less so in multicellular organisms. Recently, however, it has been recognized that such an approach could lead to better understanding of how genetics, the environment, and stochasticity affect development, physiology, and behavior of an organism. Over the last 50 years, the nematode Caenorhabditis elegans has become an invaluable model system for understanding the role of the genes underlying a phenotypic trait. Recent technological innovation has taken advantage of the worm physical attributes to increase the throughput and informational content of experiments. Coupling these technical advancements with computational or analytical tools has enabled a boom in deep phenotyping studies of C. elegans. In this review, we highlight how these new technologies and tools are digging into the biological origins of complex multidimensional phenotypes seen in the worm.