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Aging is a fundamental biological process that is still not fully understood. As many of the most significant human diseases have aging as their greatest risk factor, a better understanding of aging potentially has enormous practical implications in treating these diseases. The nematode C. elegans is an exceptionally useful genetic model organism that had been used with great success to shed light on many genes and pathways that are involved in aging. Many of these pathways and mechanisms have been shown to be conserved through mammals. The standard methods for assaying survival in C. elegans to measure changes in lifespan are tedious and time consuming. This limits the throughput and productivity of C. elegans aging researchers. In recent years, many inroads have been made into automating various facets of the collection and analysis of C. elegans lifespan experimental data. The advances described in this review all work to ameliorate some of the hurdles that come with manual worm lifespan scoring, by automating or eliminating some of the most time consuming aspects of the assay. By greatly increasing the throughput of lifespan assays, these methods will enable types of experiments (e.g., drug library screens) whose scale is currently impractical. These methods have already proved exceptionally useful, and some of them are likely to be the predecessors of even more refined methods that could lead to breakthroughs in the ability to study lifespan in C. elegans. This could in turn potentially revolutionize our understanding of the basic biology of aging, and one day lead to treatments that could offset or delay age-related diseases in humans.

Plate-reader based methods for high-throughput measurement of growth rate, cellular survival, and chronological lifespan are a compelling addition to the already powerful toolbox of budding yeast Saccharomyces cerevisiae genetics. These methods have overcome many of the limits of traditional yeast biology techniques, but also present a new bottleneck at the point of data-analysis. Herein, we describe SPOCK (Survival Percentage and Outgrowth Collection Kit), an R-based package for the analysis of data created by high-throughput plate reader based methods. This package allows for the determination of chronological lifespan, cellular growth rate, and survival in an efficient, robust, and reproducible fashion.