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Quantification of viable Legionella pneumophila cells using propidium monoazide combined with quantitative PCR.

By M. Adela Yáñez, Andreas Nocker, Elena Soria-Soria, Raquel Múrtula, Lorena Martínez and Vicente Catalán


One of the greatest challenges of implementing fast molecular detection methods as part of Legionella surveillance systems is to limit detection to live cells. In this work, a protocol for sample treatment with propidium monoazide (PMA) in combination with quantitative PCR (qPCR) has been optimized and validated for L. pneumophila as an alternative of the currently used time-consuming culture method. Results from PMA-qPCR were compared with culture isolation and traditional qPCR. Under the conditions used, sample treatment with 50 μM PMA followed by 5 min of light exposure were assumed optimal resulting in an average reduction of 4.45 log units of the qPCR signal from heat-killed cells. When applied to environmental samples (including water from cooling water towers, hospitals, spas, hot water systems in hotels, and tap water), different degrees of correlations between the three methods were obtained which might be explained by different matrix properties, but also varying degrees of non-culturable cells. It was furthermore shown that PMA displayed substantially lower cytotoxicity with Legionella than the alternative dye ethidium monoazide (EMA) when exposing live cells to the dye followed by plate counting. This result confirmed the findings with other species that PMA is less membrane-permeant and more selective for the intact cells. In conclusion, PMA-qPCR is a promising technique for limiting detection to intact cells and makes Legionella surveillance data substantially more relevant in comparison with qPCR alone. For future research it would be desirable to increase the method's capacity to exclude signals from dead cells in difficult matrices or samples containing high numbers of dead cell

Publisher: Elsevier Science B.V., Amsterdam.
Year: 2011
DOI identifier: 10.1016/j.mimet.2011.02.004
OAI identifier:
Provided by: Cranfield CERES

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