报告题目：Dissecting the interconnection of Ca2+ and H+ signaling in plants with a novel biosensor for dual time-lapse imaging
报告人：Kunkun Li
报告时间：2022年1月15日17：00
报告地点：腾讯会议：936-494-775
报告对象：电气学院相关专业研究生、教师
主办单位：long8唯一官方网站
报告人简介：
Kunkun Li obtained her Ph.D degree from the Institute of Molecular Plant Physiology and Biophysics, Faculty of Biology, University of Wuerzburg in 2021. Her research interests focus on quantifying the interconnection of multiple variables and revealing their functions and coordination in regulating biological processes in plants. Dr. Li combined multidimensional knowledge and technologies in her researches including biosensor development and optimization, live-cell imaging, molecular biology, cell biology, electrophysiology and bioinformatics.
Dr. Li has published several papers as the first-author on highly-ranked journals including New Phytologist (top), iScience (minor revision), Hortscience, Acta Horticulturae Sinica and Journal of Tropical and Subtropical Botany. Meantime, Dr. Li has several other manuscripts in revision or preparation focusing on revealing mechanisms in plants under adverse stimuli using the biosensors and mathematical models.
报告摘要：
Plants as sessile organisms live in constantly dynamic environments facing adverse conditions. Improving plants resistance to stress is a major goal to promote agricultural productivity and environmental sustainability. Hence, how plants sense different stresses and initiate processes to adapt to the environment remains to be resolved. Second messengers such as Ca2+ and H+ encode information to define physiological processes in plants. However due to the lack of new tools and precise quantitative analysis methods, the two signals have not been precisely linked simultaneously in the same cell in vivo.
In this talk, I will present how we developed and optimized a novel biosensor named CapHensor to simultaneously monitor intracellular Ca2+ and H+ changes and then successfully applied this tool in plants. Distinct Ca2+ and H+ signatures are monitored by time-lapse live-cell images. Ca2+ and H+ dynamics with different frequency, duration, numbers or amplitudes are specific in different tissues to encode distinct physiological outputs. These time-series signals are better interpreted by wavelet analysis. The modified algorithms and machine learning unravel the interconnections between Ca2+, H+ and pollen tube growth or stomatal movement and shed light on relationships of multiple factors in organisms.
