Background: Cold stress injury to plants is a highly complex process and a significant cost to agricultural food production. This stress adversely affects metabolism, growth and productivity of plants. Timely visualization of plants’ cold stress is important for identifying injury to the plants and for predicting a plant's survival. While there is a developed understanding of physiology and biology associated with this condition, early detection and assessment of the injury remain difficult. A rapid, remote method for quantitatively measuring cold stress in situ will aid producers in selecting cold-tolerant plants for breeding and for identifying appropriate remedies.
Methods: Standard methods such as electrolyte leakage assays correctly and quantitatively evaluate the damage. However, these methods are laborious and costly, not applicable for non-invasive highthroughput screening of plants in the field. To address this problem, we have evaluated a new sensitive method based on the fluorescence lifetime imaging (FLIM) that can be used for injury assessment.
Results: Standard methods such as electrolyte leakage assays correctly and quantitatively evaluate the damage. However, these methods are laborious and costly, not applicable for non-invasive highthroughput screening of plants in the field. To address this problem, we have evaluated a new sensitive method based on the fluorescence lifetime imaging (FLIM) that can be used for injury assessment. We have demonstrated that the fluorescence lifetime of chlorophyll’s autofluorescence in intact leaves from Periwinkle (Vinca Minor) plants is correlated with the degree of injury. Nonlinear regression identifies the long-lifetime component of the fluorescence decay, showing a high sensitivity for detecting injury mere minutes after plant exposure to -20°C, while no gross visual differences could be distinguished. Moreover, conventional color imaging, reflection, or and steady-state fluorescence intensity showed lower sensitivity in detecting cold stress.
Conclusion: FLIM was shown to be more sensitive than visual or camera-based inspection and can be potentially used for rapid and remote monitoring of the health of individual plants and crops in the field and will aid in the selection of cold-tolerant crop variants.
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