Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant
Quantitative studies in plant developmental biology require monitoring and measuring the changes in cells and tissues as growth gives rise to intricate patterns. The success of these studies has been amplified by the combined strengths of two complementary techniques, namely live imaging and computational image analysis. Live imaging records time-lapse images showing the spatial-temporal progress of tissue growth with cells dividing and changing shape under controlled laboratory experiments. Image processing and analysis make sense of these data by providing computational ways to extract and interpret quantitative developmental information present in the acquired images. Manual labeling and qualitative interpretation of images are limited as they don't scale well to large data sets and cannot provide field measurements to feed into mathematical and computational models of growth and patterning. Computational analysis, when it can be made sufficiently accurate, is more efficient, complete, repeatable, and less biased. In this chapter, we present some guidelines for the acquisition and processing of images of sepals and the shoot apical meristem of Arabidopsis thaliana to serve as a basis for modeling. We discuss fluorescent markers and imaging using confocal laser scanning microscopy as well as present protocols for doing time-lapse live imaging and static imaging of living tissue. Image segmentation and tracking are discussed. Algorithms are presented and demonstrated together with low-level image processing methods that have proven to be essential in the detection of cell contours. We illustrate the application of these procedures in investigations aiming to unravel the mechanical and biochemical signaling mechanisms responsible for the coordinated growth and patterning in plants.
© 2012 Elsevier Inc. Available online 5 April 2012. We acknowledge funding support from the Gordon and Betty Moore Foundation Cell Center (http://www.cellcenter.caltech.edu/) (AC and AHKR), the Department of Energy Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences grant DE-FG02-88ER13873 (EMM), National Science Foundation grant IOS-0846192 (EMM), and NIH NRSA Postdoctoral Fellowship F32-GM090534 (PTT). Work of EM was partially supported by NIH R01 GM086883, and NSF's Frontiers in Biological Research (FIBR) program Award No. EF-0330786.