A Detailed Guide to Recording and Analyzing Arabidopsis thaliana Leaf Surface Potential Dynamics Elicited by...

Recordings of electric potential changes on plant surfaces have been utilized to identify the components and mechanisms involved in the formation and transmission of systemic signals elicited by stimuli such as herbivory, wounding, or burning. The recorded responses, commonly referred to as slow wave or variation potentials, exhibit striking variability in their waveform. The extent to which this variability is due to differences in experimental procedures or plant biological variability remains unclear. Here, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding. This protocol serves as a comprehensive tutorial covering plant growth, procedures for reproducible mechanical wounding, critical aspects of electrophysiological recordings, and statistical analysis of surface potential recordings. It particularly emphasizes the construction and maintenance of electrodes, placement of the reference or ground electrode, mechanisms for wounding, and data analysis. This protocol aims to promote and facilitate the adoption, standardization, and interoperability of plant surface potential recordings among research groups, thereby increasing the reproducibility and comparability of data within the field. Key features • Recording electric potential changes on the petiole of 5-week-old Arabidopsis plants using noninvasive surface electrodes, improving the wounding procedure, reproducibility, and data processing from [1]. • Genotype-independent method for phenotyping, including parallel recordings from multiple plants. • Guidelines for plant growth conditions, unambiguous leaf assignment by order of emergence, and detailed instructions for electrode fabrication and maintenance. • Instructions for constructing devices for standardized, reproducible mechanical wounding along with a custom script for unbiased and semi-automated data analysis.