Department of Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina 27695, USA; email:
Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa 50010, USA; email:
Annu Rev Plant Biol. 2020 Apr 29;71:789-816. doi: 10.1146/annurev-arplant-050718-100038. Epub 2020 Mar 2.
The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.
获取关于植物在不同时间和空间尺度上的发展的定量信息对于理解植物生长的机制至关重要。近年来，已经出现了一些成像方法，这些方法能够捕捉和分析植物的生长，从细胞内分子的动态到田间生长植物的形态和生理特征的测量。在某些情况下，这些成像方法可以在多个样本上并行化以提高通量。当高通量与高时间和空间分辨率相结合时，所得到的基于图像的数据集可以与分子大规模数据集相结合，从而能够实现前所未有的系统级计算建模。考虑到这里综述的基础工作的早期成功，预计在不久的将来，这种基于图像的功能基因组学研究将以更快的速度出现。我们介绍了新的成像模式，并回顾了它们如何使我们能够更好地从微观到宏观尺度理解植物的生长。
