The Use of High-Throughput Phenotyping for Assessment of Heat Stress-Induced Changes in Arabidopsis
KAUST DepartmentBiological and Environmental Science and Engineering (BESE) Division
Center for Desert Agriculture
The Salt Lab
Water Desalination and Reuse Research Center (WDRC)
Permanent link to this recordhttp://hdl.handle.net/10754/664328
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AbstractThe worldwide rise in heatwave frequency poses a threat to plant survival and productivity. Determining the new marker phenotypes that show reproducible response to heat stress and contribute to heat stress tolerance is becoming a priority. In this study, we describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated noninvasive phenotyping system. Heat stress exposure resulted in an acute reduction of the quantum yield of photosystem II and increased leaf angle. In longer term, the exposure to heat also affected plant growth and morphology. By tracking the recovery period of the WT and mutants impaired in thermotolerance (hsp101), we observed that the difference in maximum quantum yield, quenching, rosette size, and morphology. By examining the correlation across the traits throughout time, we observed that early changes in photochemical quenching corresponded with the rosette size at later stages, which suggests the contribution of quenching to overall heat tolerance. We also determined that 6 h of heat stress provides the most informative insight in plant’s responses to heat, as it shows a clear separation between treated and nontreated plants as well as the WT and hsp101. Our work streamlines future discoveries by providing an experimental protocol, data analysis pipeline, and new phenotypes that could be used as targets in thermotolerance screenings.
CitationGao, G., Tester, M. A., & Julkowska, M. M. (2020). The Use of High-Throughput Phenotyping for Assessment of Heat Stress-Induced Changes in Arabidopsis. Plant Phenomics, 2020, 1–14. doi:10.34133/2020/3723916
SponsorsWe thank Dr. Andrew Yip for his discussion and technical assistance in machine learning modeling, Eunje Kim for the pilot experiment, and Growth Facility and KAUST Core Lab staff, Mr. Thomas Hoover, Mr. John Ramer, and Mr. Johnard Balangue for their assistance with the phenotyping facility. This work was supported by funding from King Abdullah University of Science and Technology (KAUST) from M.A.T. baseline funding.
Except where otherwise noted, this item's license is described as Ge Gao et al. Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).