The feasibility of automated online flow cytometry for In-situ monitoring of microbial dynamics in aquatic ecosystems

Frontiers in Microbiology

Volumn 5, Issue JUN, 2014, Pages

  Besmer, Michael D ^a,b   Weissbrodt, David G ^a,b   Kratochvil, Bradley E ^a   Sigrist, Jürg A ^a   Weyland, Mathias S ^a   Hammes, Frederik ^a  

^a SWISS FEDERAL INSTITUTE OF AQUATIC SCIENCE AND TECHNOLOGY   (Switzerland)

^b ETH ZURICH   (Switzerland)

Author keywords

Automation; Bacteria; Drinking water; Flow cytometry (FCM); Monitoring; Online; Sensors; Staining

Indexed keywords

EID: 84905158824     PISSN: None     EISSN: 1664302X     Source Type: Journal    
DOI: 10.3389/fmicb.2014.00265     Document Type: Article

References (50)

1. Abu-Absi, N. R., Zamamiri, A., Kacmar, J., Balogh, S. J., and Srienc, F. (2003). Automated flow cytometry for acquisition of time-dependent population data. Cytometry A. 51, 87-96. doi: 10.1002/cyto.a.10016
2. Arnoldini, M., Heck, T., Blanco-Fernandez, A., and Hammes, F. (2013). Monitoring of dynamic microbiological processes using real-time flow cytometry. PLoS ONE 8:117. doi: 10.1371/journal.pone.0080117
3. Borcard, D., Gillet, F. O., and Legendre, P. (2011). Numerical Ecology with R. New York; London: Springer.
4. Broger, T., Odermatt, R. P., Huber, P., and Sonnleitner, B. (2011). Real-time on-line flow cytometry for bioprocess monitoring. J. Biotechnol. 154, 240-247. doi: 10.1016/j.jbiotec.2011.05.003
5. Brognaux, A., Han, S., Sorensen, S., Lebeau, F., Thonart, P., and Delvigne, F. (2013). A low-cost, multiplexable, automated flow cytometry procedure for the characterization of microbial stress dynamics in bioreactors. Microb. Cell Fact. 12:100. doi: 10.1186/1475-2859-12-100
6. Butscher, C., Auckenthaler, A., Scheidler, S., and Huggenberger, P. (2011). Validation of a numerical indicator of microbial contamination for karst springs. Ground Water 49, 66-76. doi: 10.1111/j.1745-6584.2010.00687.x
7. Czechowska, K., Johnson, D. R., and van der Meer, J. R. (2008). Use of flow cytometric methods for single-cell analysis in environmental microbiology. Curr. Opin. Microbiol. 11, 205-212. doi: 10.1016/j.mib.2008.04.006
8. Day, A. (2007). heatmap.plus: Heatmaps with More Sensible Behavior. R Package Version 1.3. Vienna: R Foundation for Statistical Computing. Available online at: http://cran.r-project.org/web/packages/heatmap.plus/index.html
9. Dubelaar, G. B. J., Gerritzen, P. L., Beeker, A. E. R., Jonker, R. R., and Tangen, K. (1999). Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters. Cytometry 37, 247-254. doi: 10.1002/(SICI)1097-0320(19991201)37:4<247::AID-CYTO1>3.0.CO;2-9
10. Foladori, P., Bruni, L., Tamburini, S., and Ziglio, G. (2010). Direct quantification of bacterial biomass in influent, effluent and activated sludge of wastewater treatment plants by using flow cytometry. Water Res. 44, 3807-3818. doi: 10.1016/j.watres.2010.04.027
11. Gregori, G., Citterio, S., Ghiani, A., Labra, M., Sgorbati, S., Brown, S., et al. (2001). Resolution of viable and membrane-compromised bacteria in freshwater and marine waters based on analytical flow cytometry and nucleic acid double staining. Appl. Environ. Microbiol. 67, 4662-4670. doi: 10.1128/AEM.67.10.4662-4670.2001
12. Guadayol, O., Peters, F., Marrase, C., Gasol, J. M., Roldan, C., Berdalet, E., et al. (2009). Episodic meteorological and nutrient-load events as drivers of coastal planktonic ecosystem dynamics: a time-series analysis. Mar. Ecol. Prog. Ser. 381, 139-155. doi: 10.3354/meps07939
13. Gunther, S., Koch, C., Hubschmann, T., Roske, I., Muller, R. A., Bley, T., et al. (2012). Correlation of community dynamics and process parameters as a tool for the prediction of the stability of wastewater treatment. Environ. Sci. Technol. 46, 84-92. doi: 10.1021/es2010682
14. Hammes, F., Berney, M., Wang, Y. Y., Vital, M., Koster, O., and Egli, T. (2008). Flow-cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 42, 269-277. doi: 10.1016/j.watres.2007.07.009
15. Hammes, F., Broger, T., Weilenmann, H. U., Vital, M., Helbing, J., Bosshart, U., et al. (2012). Development and laboratory-scale testing of a fully automated online flow cytometer for drinking water analysis. Cytometry A. 81, 508-516. doi: 10.1002/cyto.a.22048
16. Hammes, F., and Egli, T. (2010). Cytometric methods for measuring bacteria in water: advantages, pitfalls and applications. Anal. Bioanal. Chem. 397, 1083-1095. doi: 10.1007/s00216-010-3646-3
17. Hayashi, M., Vogt, T., Machler, L., and Schirmer, M. (2012). Diurnal fluctuations of electrical conductivity in a pre-alpine river: Effects of photosynthesis and groundwater exchange. J. Hydrol. 450, 93-104. doi: 10.1016/j.jhydrol.2012.05.020
18. Hwang, C., Ling, F. Q., Andersen, G. L., Lechevallier, M. W., and Liu, W. T. (2012). Microbial community dynamics of an urban drinking water distribution system subjected to phases of chloramination and chlorination treatments. Appl. Environ. Microbiol. 78, 7856-7865. doi: 10.1128/AEM.01892-12
19. Koch, C., Fetzer, I., Harms, H., and Muller, S. (2013a). CHIC-an automated approach for the detection of dynamic variations in complex microbial communities. Cytometry A. 83, 561-567. doi: 10.1002/cyto.a.22286
20. Koch, C., Fetzer, I., Schmidt, T., Harms, H., and Muller, S. (2013b). Monitoring functions in managed microbial systems by cytometric bar coding. Environ. Sci. Technol. 47, 1753-1760. doi: 10.1021/es3041048
21. Krishnamurti, K., and Kate, S. R. (1951). Changes in electrical conductivity during bacterial growth. Nature 168, 170-170. doi: 10.1038/168170b0
22. Lautenschlager, K., Boon, N., Wang, Y. Y., Egli, T., and Hammes, F. (2010). Overnight stagnation of drinking water in household taps induces microbial growth and changes in community composition. Water Res. 44, 4868-4877. doi: 10.1016/j.watres.2010.07.032
23. Legendre, L., Courties, C., and Troussellier, M. (2001). Flow cytometry in oceanography 1989-1999: environmental challenges and research trends. Cytometry 44, 164-172. doi: 10.1002/1097-0320(20010701)44:3<164::AID-CYTO1108>3.0.CO;2-6
24. Lehtola, M. J., Laxander, M., Miettinen, I. T., Hirvonen, A., Vartiainen, T., and Martikainen, P. J. (2006). The effects of changing water flow velocity on the formation of biofilms and water quality in pilot distribution system consisting of copper or polyethylene pipes. Water Res. 40, 2151-2160. doi: 10.1016/j.watres.2006.04.010
25. Lindberg, W., Ruzicka, J., and Christian, G. D. (1993). Flow-injection cytometry-a new approach for sample and solution handling in flow-cytometry. Cytometry 14, 230-236. doi: 10.1002/cyto.990140217
26. Oksanen, J., Kindt, R., Legendre, P., O'Hara, B., Simpson, G. L., Solymos, P., et al. (2009). Vegan: Community Ecology Package. R Package Version 1.15-4. Vienna: R Foundation for Statistical Computing. Available online at: http://CRAN.R-project.org/package=vegan
27. Omann, G. M., Coppersmith, W., Finney, D. A., and Sklar, L. A. (1985). A convenient online device for reagent addition, sample mixing, and temperature control of cell-suspensions in flow-cytometry. Cytometry 6, 69-73. doi: 10.1002/cyto.990060113
28. Ploner, A. (2011). Heatplus: Heatmaps with Row and/or Column Covariates and Colored Clusters. R Package Version 2.1.0. Vienna: R Foundation for Statistical Computing. Available online at: http://www.bioconductor.org/packages/2.10/bioc/html/Heatplus.html
29. Pomati, F., Kraft, N. J. B., Posch, T., Eugster, B., Jokela, J., and Ibelings, B. W. (2013). Individual cell based traits obtained by scanning flow-cytometry show selection by biotic and abiotic environmental factors during a phytoplankton spring bloom. PLoS ONE 8:e71677. doi: 10.1371/journal.pone.0071677
30. Porter, J., Deere, D., Hardman, M., Edwards, C., and Pickup, R. (1997). Go with the flow-use of flow cytometry in environmental microbiology. FEMS Microbiol. Ecol. 24, 93-101. doi: 10.1016/S0168-6496(97)00038-X
31. Prest, E. I., Hammes, F., Kötzsch, S., Van Loosdrecht, M. C. M., and Vrouwenvelder, J. S. (2013). Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method. Water Res. 47, 7131-7142. doi: 10.1016/j.watres.2013.07.051
32. Pronk, M., Goldscheider, N., and Zopfi, J. (2006). Dynamics and interaction of organic carbon, turbidity and bacteria in a karst aquifer system. Hydrogeol. J. 14, 473-484. doi: 10.1007/s10040-005-0454-5
33. Read, S., Marzorati, M., Guimaraes, B. C. M., and Boon, N. (2011). Microbial Resource Management revisited: successful parameters and new concepts. Appl. Microbiol. Biotechnol. 90, 861-871. doi: 10.1007/s00253-011-3223-5
34. R Development Core Team (2008). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available online at: http://cran.r-project.org/
35. Sheikh, Q. I., Boxall, J. B., and Biggs, C. A. (2012). "A review of potential culture independent biological detection methods for the water industry-challenges of moving beyond the research lab," in The Significance of Faecal Indicators in Water: A Global Perspective, eds D. Kay and C. Fricker (Cambridge: RSC Publishing), 131-144. doi: 10.1039/9781849735421-00131
36. Signor, R. S., and Ashbolt, N. J. (2006). Pathogen monitoring offers questionable protection against drinking-water risks: a QMRA (Quantitative Microbial Risk Analysis) approach to assess management strategies. Water Sci. Technol. 54, 261-268. doi: 10.2166/wst.2006.478
37. SLMB. (2012). "Method 333.1: determining the total cell count and ratios of high and low nucleic acid content cells in freshwater using flow cytometry," in Schweizerisches Lebensmittelhandbuch (Bern: Federal Office for Public Health).
38. Smeets, P. W. M. H., Rietveld, L. C., Van Dijk, J. C., and Medema, G. J. (2010). Practical applications of quantitative microbial risk assessment (QMRA) for water safety plans. Water Sci. Technol. 61, 1561-1568. doi: 10.2166/wst.2010.839
39. Stadler, H., Klock, E., Skritek, P., Mach, R. L., Zerobin, W., and Farnleitner, A. H. (2010). The spectral absorption coefficient at 254 nm as a real-time early warning proxy for detecting faecal pollution events at alpine karst water resources. Water Sci. Technol. 62, 1898-1906. doi: 10.2166/wst.2010.500
40. Stadler, H., Skritek, P., Sommer, R., Mach, R. L., Zerobin, W., and Farnleitner, A. H. (2008). Microbiological monitoring and automated event sampling at karst springs using LEO-satellites. Water Sci. Technol. 58, 899-909. doi: 10.2166/wst.2008.442
41. Sundh, I., and Bell, R. T. (1992). Extracellular dissolved organic-carbon released from phytoplankton as a source of carbon for heterotrophic bacteria in lakes of different humic content. Hydrobiologia 229, 93-106. doi: 10.1007/BF00006993
42. Thyssen, M., Tarran, G. A., Zubkov, M. V., Holland, R. J., Gregori, G., Burkill, P. H., et al. (2008). The emergence of automated high-frequency flow cytometry: revealing temporal and spatial phytoplankton variability. J. Plankton Res. 30, 333-343. doi: 10.1093/plankt/fbn005
43. Van Den H.T., Kazner, C., Project Techneau (European Commission), and European Commission. (2009). TECHNEAU: Safe Drinking Water from Source to Tap State of the Art And Perspectives. London; New York: IWA Pub.
44. Vital, M., Dignum, M., Magic-Knezeu, A., Ross, P., Rietueld, L., and Hammes, F. (2012). Flow cytometry and adenosine tri-phosphate analysis: alternative possibilities to evaluate major bacteriological changes in drinking water treatment and distribution systems. Water Res. 46, 4665-4676. doi: 10.1016/j.watres.2012.06.010
45. Vives-Rego, J., Lebaron, P., and Nebe-Von C G. (2000). Current and future applications of flow cytometry in aquatic microbiology. FEMS Microbiol. Rev. 24, 429-448. doi: 10.1111/j.1574-6976.2000.tb00549.x
46. Vogt, T., Hoehn, E., Schneider, P., Freund, A., Schirmer, M., and Cirpka, O. A. (2010). Fluctuations of electrical conductivity as a natural tracer for bank filtration in a losing stream. Adv. Water Resour. 33, 1296-1308. doi: 10.1016/j.advwatres.2010.02.007
47. Wang, Y., Hammes, F., Boon, N., and Egli, T. (2007). Quantification of the filterability of freshwater bacteria through 0.45, 0.22, and 0.1 mu m pore size filters and shape-dependent enrichment of filterable bacterial communities. Environ. Sci. Technol. 41, 7080-7086. doi: 10.1021/es0707198
48. Weissbrodt, D. G., Shani, N., and Holliger, C. (2014). Linking bacterial population dynamics and nutrient removal in the granular sludge biofilm ecosystem engineered for wastewater treatment. FEMS Microbiol. Ecol. doi: 10.1111/1574-6941.12326. [Epub ahead of print].
49. World Health Organization. (2011). Guidelines for Drinking-Water Quality. Geneva: World Health Organization.
50. Zhao, R., Natarajan, A., and Srienc, F. (1999). A flow injection flow cytometry system for on-line monitoring of bioreactors. Biotechnol. Bioeng. 62, 609-617. doi: 10.1002/(SICI)1097-0290(19990305)62:5<609::AID-BIT13>3.0.CO;2-C