Monitoring disturbances in microbial community structure (e.g., diversity) in natural, engineered and synthetic environments is important for ecosystem monitoring, (bio-)process performance, and hypothesis testing. Although conventional assays by means of molecular techniques have resulted in major advances, these procedures remain slow, labor-intensive and susceptible to multiple sources of laboratory and data processing bias. Growing interest in highly resolved temporal surveys of microbial community structure necessitates rapid, inexpensive and robust analytical platforms that require limited computational effort.
Methods & Results
Here we introduce Phenoflow, an R-based toolbox for the advanced “fingerprinting” analysis of microbial flow cytometry data. In a proof-of-concept, real-time flow cytometry (RT-FCM) data acquisition was combined with this novel and automated FCM fingerprinting method to track and quantify disturbances in microbial communities. Through this new approach we were able to resolve various natural community and single-species microbial contaminations in a flow-through drinking water reactor. Importantly, we found that multiple community FCM metrics based on different statistical approaches were required to identify all contaminations. Next to conventional FCM metrics, we applied metrics from a recently developed fingerprinting technique in order to gain additional insights into the microbial dynamics during contamination events (i.e., phenotypic diversity index, phenotypic community type). We found that for accurate inference from the FCM metrics (coefficient of variation ≤ 5%), at least 1,000 cells should be analyzed, thereby constraining the achievable temporal resolution to 10 - 60s depending on the studied system.
The integrated RT-FCM acquisition and analysis approach presented herein provides a considerable improvement in the temporal resolution by which microbial disturbances can be observed and simultaneously provides a multi-faceted toolset to characterize such disturbances. Phenoflow allows a fast, robust and low-cost analysis workflow for monitoring the microbial community structure of natural and engineered ecosystems. Furthermore, our approach offers perspectives for the development of online and in situ monitoring systems of bioprocesses.