Poster Session 5, 3:15 PM - 4:00 PM: Campus Center Auditorium [A87]

Presenter: Alina Shkurikhina

Faculty Sponsor: Elizabeth Vierling

School: UMass Amherst

Research Area: Biochemistry and Molecular Biology

ABSTRACT

Phenotypic analysis is an essential step in studying the molecular processes behind plant stress responses. Many of the phenotypic traits analyzed in plant assays, such as root length, leaf size, and plant color, currently rely upon manual measurement methods for quantification. In recent years, computation-based tools have given rise to more automated, noninvasive, and precise plant phenotyping. However, many modern phenotyping platforms are cost-prohibitive or require extensive engineering experience to use, which makes them inaccessible to many laboratories. Thus, there is significant interest in developing a versatile, cost-effective solution that can be readily repurposed for a variety of phenotyping applications. The goal of my project was to design an automated imaging chamber that can collect quantitative data on plant phenotypes. This device, powered by a Raspberry Pi, captures RGB plant images at regular intervals and analyzes them using Python scripts to measure phenotypic properties like leaf size and greenness over time. This allows for reproducible and quantitative phenotypic experiments, which will help characterize the functions of organellar proteins in the model plant Arabidopsis thaliana. Specifically, this device is being used to explore the function of mitochondrial ATPase family AAA domain-containing 3 (ATAD3) proteins, which are proposed to span both mitochondrial membranes and are crucial for plant viability, and to characterize the chloroplast small heat shock protein Hsp25.3, a molecular chaperone that plays a role in maintaining chloroplast homeostasis. This project demonstrates how quantitative phenotypic analysis can shed light on the functions of vital proteins in plants.