A microfluidic platform for the simultaneous quantification of methanogen populations in anaerobic digestion processes

Prince P. Mathai, Hannah M. Dunn, Kaushik Venkiteshwaran, Daniel H. Zitomer, James S. Maki, Satoshi Ishii, Michael J. Sadowsky

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Methanogens are a diverse group of archaea that play a critical role in the global carbon cycle. The lack of appropriate molecular tools to simultaneously quantify numerous methanogenic taxa, however, has largely limited our ability to study these communities in a wide variety of habitats, such as anaerobic digesters (ADs). In this study, 34 probe-based quantitative PCR (qPCR) assays were designed to target all known methanogenic genera within the archaeal phylum Euryarchaeota. These qPCR assays were adapted to a high-throughput microfluidic platform, which allowed for the simultaneous detection and absolute quantification of numerous taxa in a single run. The resulting microfluidic qPCR (MFQPCR) platform was successfully used to decipher structure–function relationships among methanogenic communities in four laboratory-scale digesters exposed to a transient organic overload. Twelve of the 34 genera targeted in the MFQPCR were detected in the ADs, similar to results obtained using high-throughput sequencing. The MFQPCR platform and conventional qPCR assays also generated similar quantitative results. The MFQPCR tool developed here will help optimize AD technologies for efficient waste treatment and enhanced biogas production and can facilitate studies that will increase our understanding of methanogenic communities in other environments.

Original languageEnglish (US)
Pages (from-to)1798-1808
Number of pages11
JournalEnvironmental microbiology
Issue number5
StatePublished - May 2019

Bibliographical note

Funding Information:
The authors thank Melinda Nicholes for technical assistance and the operators of full-scale anaerobic digesters for providing biomass samples. This work was carried out, in part, using computing resources at the University of Minnesota Supercomputing Institute. This work was supported, in part, by WE Energies, Marquette University, and the University of Minnesota Agricultural Experiment Station.

Publisher Copyright:
© 2019 Society for Applied Microbiology and John Wiley & Sons Ltd.


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