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Colonyzer: automated quantification of micro-organism growth characteristics on solid agar

Lookup NU author(s): Dr Conor Lawless, Professor Darren Wilkinson, Dr Stephen Addinall, Professor David Lydall

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Abstract

Background: High-throughput screens comparing growth rates of arrays of distinct micro-organism cultures on solid agar are useful, rapid methods of quantifying genetic interactions. Growth rate is an informative phenotype which can be estimated by measuring cell densities at one or more times after inoculation. Precise estimates can be made by inoculating cultures onto agar and capturing cell density frequently by plate-scanning or photography, especially throughout the exponential growth phase, and summarising growth with a simple dynamic model (e. g. the logistic growth model). In order to parametrize such a model, a robust image analysis tool capable of capturing a wide range of cell densities from plate photographs is required. Results: Colonyzer is a collection of image analysis algorithms for automatic quantification of the size, granularity, colour and location of micro-organism cultures grown on solid agar. Colonyzer is uniquely sensitive to extremely low cell densities photographed after dilute liquid culture inoculation (spotting) due to image segmentation using a mixed Gaussian model for plate-wide thresholding based on pixel intensity. Colonyzer is robust to slight experimental imperfections and corrects for lighting gradients which would otherwise introduce spatial bias to cell density estimates without the need for imaging dummy plates. Colonyzer is general enough to quantify cultures growing in any rectangular array format, either growing after pinning with a dense inoculum or growing with the irregular morphology characteristic of spotted cultures. Colonyzer was developed using the open source packages: Python, RPy and the Python Imaging Library and its source code and documentation are available on SourceForge under GNU General Public License. Colonyzer is adaptable to suit specific requirements: e. g. automatic detection of cultures at irregular locations on streaked plates for robotic picking, or decreasing analysis time by disabling components such as lighting correction or colour measures. Conclusion: Colonyzer can automatically quantify culture growth from large batches of captured images of microbial cultures grown during genome-wide scans over the wide range of cell densities observable after highly dilute liquid spot inoculation, as well as after more concentrated pinning inoculation. Colonyzer is open-source, allowing users to assess it, adapt it to particular research requirements and to contribute to its development.


Publication metadata

Author(s): Lawless C, Wilkinson DJ, Young A, Addinall SG, Lydall DA

Publication type: Article

Publication status: Published

Journal: BMC Bioinformatics

Year: 2010

Volume: 11

Issue: 1

Pages: 287

Print publication date: 01/05/2010

Date deposited: 03/09/2010

ISSN (print): 1471-2105

ISSN (electronic):

Publisher: BioMed Central Ltd.

URL: http://dx.doi.org/10.1186/1471-2105-11-287

DOI: 10.1186/1471-2105-11-287


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