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Review
. 2014 May;19(5):304-10.
doi: 10.1016/j.tplants.2013.12.003. Epub 2014 Jan 13.

Advanced Imaging Techniques for the Study of Plant Growth and Development

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Free PMC article
Review

Advanced Imaging Techniques for the Study of Plant Growth and Development

Rosangela Sozzani et al. Trends Plant Sci. .
Free PMC article

Abstract

A variety of imaging methodologies are being used to collect data for quantitative studies of plant growth and development from living plants. Multi-level data, from macroscopic to molecular, and from weeks to seconds, can be acquired. Furthermore, advances in parallelized and automated image acquisition enable the throughput to capture images from large populations of plants under specific growth conditions. Image-processing capabilities allow for 3D or 4D reconstruction of image data and automated quantification of biological features. These advances facilitate the integration of imaging data with genome-wide molecular data to enable systems-level modeling.

Keywords: imaging techniques; plant growth and development; systems-level modeling.

Conflict of interest statement

Conflict of interests

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Schematic representation of the RootArray microfluidics device [71]. (A). Front view of 64 Arabidopsis thaliana seedlings grown over several days without manual intervention thanks to gaseous and liquid exchanges through the tubes on the top left and bottom right. (B). Side view of the microfluidics device. Red circle highlights roots imaged by confocal microscopy. (C). Root stained with propidium iodide expressing a transgenic reporter line and tracked over time.
Figure 2
Figure 2
A typical workflow for the acquisition and processing of images. (A) An imaging device can produce multidimensional raw data such as z-stacks or time series of images that need to be saved to a disk. (B) Extracting information from those images requires a suitable algorithm and appropriate computing resources. (C) The features quantified from the images must be organized in some data structure such as a database in a manner that facilitates integration with other data types that will be combined to capture key features of the system under study (D). (E) These integrative analyses, collectively called modeling, can be computationally demanding. (F) Databases that can present the raw data as well as the modeled results to the broad scientific community, including image scientists, statisticians, mathematicians, bioinformaticists, as well as plant scientists would be very enabling for modelling biological processes.

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