@article {Veley132787, author = {Kira M. Veley and Jeffrey C. Berry and Sarah J. Fentress and Daniel P. Schachtman and Ivan Baxter and Rebecca Bart}, title = {High-throughput profiling and analysis of plant responses over time to abiotic stress}, elocation-id = {132787}, year = {2017}, doi = {10.1101/132787}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G {\texttimes} E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G {\texttimes} E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.}, URL = {https://www.biorxiv.org/content/early/2017/07/26/132787}, eprint = {https://www.biorxiv.org/content/early/2017/07/26/132787.full.pdf}, journal = {bioRxiv} }