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

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

    Research output: Contribution to journalArticleScientificpeer-review

    72 Citations (Scopus)


    Fluorescent staining coupled with flow cytometry (FCM) is often used for the monitoring, quantification and characterization of bacteria in engineered and environmental aquatic ecosystems including seawater, freshwater, drinking water, wastewater, and industrial bioreactors. However, infrequent grab sampling hampers accurate characterization and subsequent understanding of microbial dynamics in all of these ecosystems. A logic technological progression is high throughput and full automation of the sampling, staining, measurement, and data analysis steps. Here we assess the feasibility and applicability of automated FCM by means of actual data sets produced with prototype instrumentation. As proof-of-concept we demonstrate examples of microbial dynamics in (i) flowing tap water from a municipal drinking water supply network and (ii) river water from a small creek subject to two rainfall events. In both cases, automated measurements were done at 15-min intervals during 12-14 consecutive days, yielding more than 1000 individual data points for each ecosystem. The extensive data sets derived from the automated measurements allowed for the establishment of baseline data for each ecosystem, as well as for the recognition of daily variations and specific events that would most likely be missed (or miss-characterized) by infrequent sampling. In addition, the online FCM data from the river water was combined and correlated with online measurements of abiotic parameters, showing considerable potential for a better understanding of cause-and-effect relationships in aquatic ecosystems. Although several challenges remain, the successful operation of an automated online FCM system and the basic interpretation of the resulting data sets represent a breakthrough toward the eventual establishment of fully automated online microbiological monitoring technologies.

    Original languageEnglish
    Article number265
    JournalFrontiers in Microbiology
    Issue numberJUN
    Publication statusPublished - 2014


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

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