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Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting
Heyse, J.; Schattenberg, F.; Rubbens, P.; Müller, S.; Waegeman, W.; Boon, N.; Props, R. (2021). Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting. mSystems 6(5): e00551-21.
In: mSystems. American Society for Microbiology: Washington, DC. ISSN 2379-5077, more
Peer reviewed article  

Available in  Authors 

    Flow cytometry
Author keywords
    16S rRNA gene amplicon sequencing, cell sorting, machine learning, microbial community dynamics

Authors  Top 
  • Heyse, J., more
  • Schattenberg, F.
  • Rubbens, P., more
  • Müller, S.

    Microbiome management research and applications rely on temporally resolved measurements of community composition. Current technologies to assess community composition make use of either cultivation or sequencing of genomic material, which can become time-consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yielded an average accuracy of 88.13% ± 4.78% across the top 50 operational taxonomic units (OTUs) of our data set. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yielded an average R2 of 0.35 ± 0.24 across the top 50 OTUs of our data set. Application of the models to flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell sorting, we validated that the model correctly associates taxa to regions in the cytometric fingerprint, where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline to two other data sets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods.

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