A Microimage-Processing-Based Technique for Detecting Qualitative and Quantitative Characteristics of Plant Cells

Jun Feng, Zhenting Li, Shizhen Zhang, Chun Bao, Jingxian Fang, Yun Yin, Bolei Chen, Lei Pan, Bing Wang and Yu Zheng ()
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Jun Feng: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
Zhenting Li: Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
Shizhen Zhang: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
Chun Bao: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
Jingxian Fang: Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
Yun Yin: Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
Bolei Chen: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
Lei Pan: Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
Bing Wang: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
Yu Zheng: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China

Agriculture, 2023, vol. 13, issue 9, 1-16

Abstract: When plants encounter external environmental stimuli, they can adapt to environmental changes through a complex network of metabolism–gene expression–metabolism within the plant cell. In this process, changes in the characteristics of plant cells are a phenotype that is responsive and directly linked to this network. Accurate identification of large numbers of plant cells and quantitative analysis of their cellular characteristics is a much-needed experiment for in-depth analysis of plant metabolism and gene expression. This study aimed to develop an automated, accurate, high-throughput quantitative analysis method, ACFVA, for single-plant-cell identification. ACFVA can quantitatively address a variety of biological questions for a large number of plant cells automatically, including standard assays (for example, cell localization, count, and size) and complex morphological assays (for example, different fluorescence in cells). Using ACFVA, phenomics studies can be carried out at the plant cellular level and then combined with ever-changing sequencing technologies to address plant molecular biology and synthetic biology from another direction.

Keywords: ACFVA; image recognition; plant cells (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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