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PLoS Comput Biol. 2008 Nov;4(11):e1000233. doi: 10.1371/journal.pcbi.1000233. Epub 2008 Nov 28.

PSICIC: noise and asymmetry in bacterial division revealed by computational image analysis at sub-pixel resolution.

Author information

1
Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America.

Erratum in

  • PLoS Comput Biol. 2009 Jul;5(7). doi: 10.1371/annotation/1087d9ce-af96-49c7-8074-8da2542cb005.

Abstract

Live-cell imaging by light microscopy has demonstrated that all cells are spatially and temporally organized. Quantitative, computational image analysis is an important part of cellular imaging, providing both enriched information about individual cell properties and the ability to analyze large datasets. However, such studies are often limited by the small size and variable shape of objects of interest. Here, we address two outstanding problems in bacterial cell division by developing a generally applicable, standardized, and modular software suite termed Projected System of Internal Coordinates from Interpolated Contours (PSICIC) that solves common problems in image quantitation. PSICIC implements interpolated-contour analysis for accurate and precise determination of cell borders and automatically generates internal coordinate systems that are superimposable regardless of cell geometry. We have used PSICIC to establish that the cell-fate determinant, SpoIIE, is asymmetrically localized during Bacillus subtilis sporulation, thereby demonstrating the ability of PSICIC to discern protein localization features at sub-pixel scales. We also used PSICIC to examine the accuracy of cell division in Esherichia coli and found a new role for the Min system in regulating division-site placement throughout the cell length, but only prior to the initiation of cell constriction. These results extend our understanding of the regulation of both asymmetry and accuracy in bacterial division while demonstrating the general applicability of PSICIC as a computational approach for quantitative, high-throughput analysis of cellular images.

PMID:
19043544
PMCID:
PMC2581597
DOI:
10.1371/journal.pcbi.1000233
[Indexed for MEDLINE]
Free PMC Article

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