A novel flow cytometry based methodology for rapid, highthroughput characterization of microbiome dynamics in anaerobic systems
A key challenge in studying complex microbial communities in natural, controlled and engineered environments is the development of a label-free, high throughput technique to enable rapid, in-line monitoring of the structure and function of microbiomes sensitive to perturbations. Here, a novel multidimensional flow cytometry based method has been demonstrated to monitor and rapidly characterize the dynamics of the complex anaerobic microbiome associated with perturbations in external environmental factors.The present study indicates that an autocorrelation analysis between diverging microbial communities is a simple and rapid tool to monitor perturbations in anaerobic systems due to addition of various carbon sources. Exploiting multiple measurable dimensions in flow cytometry such as cell size (FSC or forward scatter), cell granularity/morphology (SSC or side scatter) and autofluorescence (corresponding to the same excitation/emission wavelength as in AmCyan standard dye), it is possible to monitor and rapidly characterize the dynamics of the complex anaerobic microbiome associated with perturbations in external environmental factors. Further, it is also possible to quantitatively discriminate between divergent microbiomes, in a manner analogous to community fingerprinting techniques using automated ribosomal intergenic spacer analysis (ARISA). While ARISA measures diversity at the genomic level and flow cytometry measures diversity at the morphological level, there was an observed correspondence between the two measures at the phylum-level. The present study also suggests that machine learning algorithms can be fruitful in the classification of cytometric fingerprints. With a limited dataset from the carbon source perturbed anaerobic microbiome, several machine learning algorithms were found to be fast and comparable in accuracy to traditional microbial ecology statistical analysis. A comparison between different algorithms based on predictive capabilities suggested that Deep Learning (DL) was best at predicting overall community but Distributed Random Forest (DRF) was best for predicting the most important putative microbial group(s) in the anaerobic digesters viz. Methanogens. The utility of flow cytometry based method has also been demonstrated in a fully functional industry scale anaerobic digester to distinguish between microbiome compositions caused by varying the hydraulic retention time (HRT). Potential utility of the proposed methodology has been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiome perturbed under nanotoxicity.
Flow cytometry, Anaerobic digestion, Microbial community, Microbial dynamics, Microbiome characterization, Cytometric fingerprinting, Machine learning
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign