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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-18638

Helmuth, J A; Burckhardt, C J; Greber, U F; Sbalzarini, I F (2009). Shape reconstruction of subcellular structures from live cell fluorescence microscopy images. Journal of Structural Biology, 167(1):1-10.

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Abstract

Live imaging of subcellular structures is indispensible to advance our understanding of cellular processes. The blurred digital images acquired in light microscopy are, however, complex to analyze, and identification and reconstruction of subcellular structures from such images remains a major challenge. We present a novel, model-based image analysis algorithm to reconstruct outlines of subcellular structures using a sub-pixel representation. The algorithm explicitly accounts for the optical properties of the microscope. We validate the reconstruction performance on synthetic data and apply the new method to fluorescence microscopy images of endosomes identified by the GTPase EGFP-Rab5. The benefits of the new algorithm are outlined by comparison to standard techniques. We demonstrate that the new algorithm leads to better discrimination between different endosomal virus entry pathways and to more robust, accurate, and self-consistent quantification of endosome shape features. This allows establishing a set of features that quantify endosome morphology and robustly capture the dynamics of endosome fusion.

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
DDC:570 Life sciences; biology
Uncontrolled Keywords:Virus entry; Endosomal trafficking; Image analysis; Shape reconstruction; Deconvolution
Language:English
Date:7 July 2009
Deposited On:25 May 2009 06:53
Last Modified:27 Nov 2013 20:30
Publisher:Elsevier
ISSN:1047-8477
Publisher DOI:10.1016/j.jsb.2009.03.017
PubMed ID:19358891
Citations:Web of Science®. Times Cited: 20
Google Scholar™
Scopus®. Citation Count: 20

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