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Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant

Cunha, Alexandre and Tarr, Paul T. and Roeder, Adrienne H. K. and Altinok, Alphan and Mjolsness, Eric and Meyerowitz, Elliot M. (2012) Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant. In: Computational Methods in Cell Biology. Methods in Cell Biology. No.110. Academic Press , pp. 285-323. ISBN 9780123884039.

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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.

Item Type:Book Section
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URLURL TypeDescription
Cunha, Alexandre0000-0002-2541-6024
Meyerowitz, Elliot M.0000-0003-4798-5153
Additional Information:© 2012 Elsevier Inc. Available online 5 April 2012. We acknowledge funding support from the Gordon and Betty Moore Foundation Cell Center ( (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.
Funding AgencyGrant Number
Gordon and Betty Moore FoundationUNSPECIFIED
Department of Energy (DOE)DE-FG02-88ER13873
NIH Postdoctoral FellowshipF32-GM090534
NIHR01 GM086883
Subject Keywords:Contrast; Denoising; Enhancement; Imaging; Meristem; Segmentation
Series Name:Methods in Cell Biology
Issue or Number:110
Record Number:CaltechAUTHORS:20120416-081534198
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Official Citation:Alexandre Cunha, Paul T. Tarr, Adrienne H.K. Roeder, Alphan Altinok, Eric Mjolsness, Elliot M. Meyerowitz, Chapter 12 - Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant, In: Anand R. Asthagiri and Adam P. Arkin, Editor(s), Methods in Cell Biology, Academic Press, 2012, Volume 110, Pages 285-323, ISSN 0091-679X, ISBN 9780123884039, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:30091
Deposited By: Tony Diaz
Deposited On:16 Apr 2012 18:43
Last Modified:09 Nov 2021 19:36

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