BIC: An Innovation Partnership to Advance a High-Throughput Phenotype Screening Platform

Great strides have been made in the plant sciences through high-throughput DNA sequencing and analysis of plant genomes. The challenge now is to develop analogous high-throughput technologies to analyze a plant’s biochemical phenotype (phenome), which is the key to utilizing genomics to improve and develop new cultivars. This challenge is driven by the need to provide food, feed, fiber, and energy to a rapidly growing global population at a time when non-renewable energy sources are decreasing, and the global climate is changing rapidly and already affecting water availability and pathogen distribution. These global challenges are stressing our crop production systems, and they demand faster, more effective, and lower-cost technologies for plant-trait analysis that will help both basic plant science and applied plant breeding.

Our project will advance a high-throughput screening platform for determining the chemical phenotype of individual plants through a synergistic partnership at Iowa State University among plant scientists, geneticists, breeders and instrumentation scientists. The participants at ISU and several small businesses will form an interactive knowledge enhancement partnership (KEP). The project’s platform is based on two complementary components: 1) near infrared (NIR) molecular spectroscopy and chemometrics integrated with individual seed automation for high-throughput screening, and 2) customized methodologies for specific phenotypes and plant types to support its application.

Our long-term vision is to accelerate fundamental understanding in plant science and breeding with a screening platform that significantly reduces costs for selecting and developing new plant varieties. The immediate goal is to explore and re-define an NIR-based seed-screening platform in terms of both its performance and applications. The platform’s purpose is to select individual seeds or plants that will become breeding stock that best express desired biochemical phenotypic traits. Because one of our partners is interested in screening for anti-oxidant biochemicals expressed in leaves, we will also explore adapting the screening platform to assess leaf composition.