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A Novel Algorithm for the Determination of Bacterial Cell Volumes That is Unbiased by Cell Morphology

Published online by Cambridge University Press:  13 September 2011

M. Zeder ,
E. Kohler ,
L. Zeder  and
J. Pernthaler
M. Zeder*
Affiliation:
Max Planck Institute for Marine Microbiology, Department of Molecular Ecology, Celsiusstrasse 1, 28359 Bremen, Germany Technobiology GmbH, Rütiweidhalde 7a, 6033 Buchrain, Switzerland Department of Limnology, Institute of Plant Biology, University of Zürich, Seestrasse 187, 8802 Kilchberg, Switzerland
E. Kohler
Affiliation:
Department of Limnology, Institute of Plant Biology, University of Zürich, Seestrasse 187, 8802 Kilchberg, Switzerland
L. Zeder
Affiliation:
Technobiology GmbH, Rütiweidhalde 7a, 6033 Buchrain, Switzerland
J. Pernthaler
Affiliation:
Department of Limnology, Institute of Plant Biology, University of Zürich, Seestrasse 187, 8802 Kilchberg, Switzerland
*
Corresponding author. E-mail: [email protected]
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Abstract

The determination of cell volumes and biomass offers a means of comparing the standing stocks of auto- and heterotrophic microbes of vastly different sizes for applications including the assessment of the flux of organic carbon within aquatic ecosystems. Conclusions about the importance of particular genotypes within microbial communities (e.g., of filamentous bacteria) may strongly depend on whether their contribution to total abundance or to biomass is regarded. Fluorescence microscopy and image analysis are suitable tools for determining bacterial biomass that moreover hold the potential to replace labor-intensive manual measurements by fully automated approaches. However, the current approaches to calculate bacterial cell volumes from digital images are intrinsically biased by the models that are used to approximate the morphology of the cells. Therefore, we developed a generic contour based algorithm to reconstruct the volumes of prokaryotic cells from two-dimensional representations (i.e., microscopic images) irrespective of their shape. Geometric models of commonly encountered bacterial morphotypes were used to verify the algorithm and to compare its performance with previously described approaches. The algorithm is embedded in a freely available computer program that is able to process both raw (8-bit grayscale) and thresholded (binary) images in a fully automated manner.

Keywords

cell volumehigh-throughput microscopymodel-based object-oriented image analysismicrobial ecologyscreening
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 17 , Issue 5 , October 2011 , pp. 799 - 809
Copyright
Copyright © Microscopy Society of America 2011

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