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Ye J, Song J, Gao Y, Lu X, Pei W, Li F, Feng H, Yang W. An automatic fluorescence phenotyping platform to evaluate dynamic infection process of Tobacco mosaic virus-green fluorescent protein in tobacco leaves. FRONTIERS IN PLANT SCIENCE 2022; 13:968855. [PMID: 36119566 PMCID: PMC9478445 DOI: 10.3389/fpls.2022.968855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Tobacco is one of the important economic crops all over the world. Tobacco mosaic virus (TMV) seriously affects the yield and quality of tobacco leaves. The expression of TMV in tobacco leaves can be analyzed by detecting green fluorescence-related traits after inoculation with the infectious clone of TMV-GFP (Tobacco mosaic virus - green fluorescent protein). However, traditional methods for detecting TMV-GFP are time-consuming and laborious, and mostly require a lot of manual procedures. In this study, we develop a low-cost machine-vision-based phenotyping platform for the automatic evaluation of fluorescence-related traits in tobacco leaf based on digital camera and image processing. A dynamic monitoring experiment lasting 7 days was conducted to evaluate the efficiency of this platform using Nicotiana tabacum L. with a total of 14 samples, including the wild-type strain SR1 and 4 mutant lines generated by RNA interference technology. As a result, we found that green fluorescence area and brightness generally showed an increasing trend over time, and the trends were different among these SR1 and 4 mutant lines samples, where the maximum and minimum of green fluorescence area and brightness were mutant-4 and mutant-1 respectively. In conclusion, the platform can full-automatically extract fluorescence-related traits with the advantage of low-cost and high accuracy, which could be used in detecting dynamic changes of TMV-GFP in tobacco leaves.
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Affiliation(s)
- Junli Ye
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuan Gao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xu Lu
- Key Laboratory of Horticulture Biology, Ministry of Education, College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, College of Horticulture, Hainan University, Haikou, China
| | - Wenyue Pei
- Key Laboratory of Horticulture Biology, Ministry of Education, College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
| | - Feng Li
- Key Laboratory of Horticulture Biology, Ministry of Education, College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
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