Massively parallel metabarcoding of droplet co-cultures
A systems approach to microbial community ecology requires high-throughput tools for identifying key microbial interactions. While microfluidic droplets enable the high throughput generation, co-cultivation, and sorting of miniaturized co-cultures in sub-nanoliter, uniform water-in-oil emulsions, analyzing composition of individual droplet co-cultures at comparable throughputs has been challenging, particularly with environmentally isolated or less genetically tractable strains. To address this bottleneck, we present Cocoa-seq (combinatorial co-cultivation and amplicon sequencing), a droplet-based microfluidic workflow that enables 16S rRNA gene amplicon sequencing for thousands of droplet co-cultures in parallel. In summary, after co-cultivation of microbial co-cultures in agarose droplets, which are then set and recovered as discrete gel beads, Cocoa-seq pairs individual gel beads with barcoded primer beads in new droplets to produce multiplexed amplicon libraries via droplet PCR for sequencing. To benchmark Cocoa-seq, we used a model two-species co-culture and four-member mock communities with three different compositions. The community profiles derived from Cocoa-seq were qualitatively consistent with fluorescence microscopy-based estimates for the two-species co-culture and correlated well with mock community expectations, even for low-abundance representatives. Attempts to incorporate spike-in 16S standards for quantification of absolute abundance were hindered by PCR stochasticity at the single-molecule level, and we provide a simulation-based explanation of this bias and recommend against relying on spike-ins for quantitative inference. ImportanceResearchers in microbial ecology are increasingly utilizing sub-nanoliter microfluidic droplets to construct, grow, sort, and analyze miniaturized microbial co-cultures to identify and characterize critical microbial interactions. However, while the generation, incubation, and sorting of droplet co-cultures is scalable, analyzing the composition of these droplet co-cultures in large number is much more difficult. We demonstrate and benchmark a workflow for multiplexing 16S rRNA amplicon libraries derived from thousands of individual droplet co-cultures in a single sequencing run. This addition in the arsenal of growing microfluidic capabilities will support future study of critical interactions within complex microbial systems. Study fundingNational Science Foundation (Awards 2120909, 2426415) (awarded to XNL)