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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13496

Title: Flow cytometry for fast microbial community fingerprinting
Authors: De Roy, Karen
Clement, Lieven
Thas, Olivier
Wang, Yingying
Boon, Nico
Issue Date: 2012
Citation: WATER RESEARCH, 46 (3), p. 907-919
Abstract: Characterizing the microbial community of water is important in different domains, ranging from food and beverage production to wastewater treatment. Conventional methods, such as heterotrophic plate count, selective plating and molecular techniques, are time consuming and labor intensive. A flow cytometry based approach was developed for a fast and objective comparison of microbial communities based on the distribution of cellular features from single cells within these communities. The method consists of two main parts, firstly the generation of fingerprint data by flow cytometry and secondly a novel statistical pipeline for the analysis of flow cytometric data. The combined method was shown to be useful for the discrimination and classification of different brands of drinking water. It was also successfully applied to detect changes in microbial community composition of drinking water caused by changing environmental factors. Generally, the method can be used as a fast fingerprinting method of microbial communities in aquatic samples and as a tool to detect shifts within these communities. (C) 2011 Elsevier Ltd. All rights reserved.
Notes: [De Roy, Karen; Wang, Yingying; Boon, Nico] Univ Ghent, Fac Biosci Engn, Lab Microbial Ecol & Technol LabMET, B-9000 Ghent, Belgium. [Clement, Lieven; Thas, Olivier] Univ Ghent, Dept Math Modelling Stat & Bioinformat, B-9000 Ghent, Belgium. [Wang, Yingying] Nankai Univ, Coll Environm Sci & Engn, Minist Educ, Key Lab Pollut Proc & Environm Criteria, Tianjin 300071, Peoples R China. [Clement, Lieven] Katholieke Univ Leuven, B-3000 Louvain, Belgium. [Clement, Lieven] Univ Hasselt, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/13496
DOI: 10.1016/j.watres.2011.11.076
ISI #: 000299713200034
ISSN: 0043-1354
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

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