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

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  • Jun Feng

    (State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
    Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
    Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, Jianghan University, Wuhan 430056, China
    School of Intelligent Manufacturing, 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
    Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
    School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China)

  • Chun Bao

    (State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
    Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
    Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, 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
    Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
    Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, 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
    Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
    Key Laboratory of Optoelectronic Chemical Materials and Devices, Ministry of Education, School of Optoelectronic Materials & Technology, Jianghan University, Wuhan 430056, China)

  • Yu Zheng

    (State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
    Hubei Province Research Center of Legume Plants, School of Life Science, Institute for Interdisciplinary Research, Jianghan University, Wuhan 430056, China
    Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, Jianghan University, Wuhan 430056, China)

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.

Suggested Citation

  • Jun Feng & Zhenting Li & Shizhen Zhang & Chun Bao & Jingxian Fang & Yun Yin & Bolei Chen & Lei Pan & Bing Wang & Yu Zheng, 2023. "A Microimage-Processing-Based Technique for Detecting Qualitative and Quantitative Characteristics of Plant Cells," Agriculture, MDPI, vol. 13(9), pages 1-16, September.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1816-:d:1240963
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    References listed on IDEAS

    as
    1. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    2. Matthew R. Bennett & Wyming Lee Pang & Natalie A. Ostroff & Bridget L. Baumgartner & Sujata Nayak & Lev S. Tsimring & Jeff Hasty, 2008. "Metabolic gene regulation in a dynamically changing environment," Nature, Nature, vol. 454(7208), pages 1119-1122, August.
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