Massively parallel metabarcoding of droplet co-cultures

A predictive understanding of microbiomes is necessary to engineer them for human and environmental health. While omics data provide insight into patterns of microbial diversity and function, we lack understanding on how microbial interactions contribute to community function and emergent ecological properties. Synthetic ecology approaches seek to address this by constructing and interrogating defined co-cultures of cultured representatives. This provides phenotypic observations on how interactions change in different contexts and contribute to overall community function. However, this cannot be applied to systems where relevant isolates are not available. Here we developed Cocoa-seq (combinatorial co-cultivation and amplicon sequencing), a microfluidic workflow utilizing the high throughput nature of nanoliter-scale, water-in-oil droplets to generate and profile co-cultures generated by the stochastic co-encapsulation of cells from samples, circumventing laborious axenic isolation. The workflow multiplexes over a thousand 16S amplicon libraries from droplet co-cultures into one, with potential for even higher scalability, to enable metabarcoding of each droplet community. To validate Cocoa-seq, we compared community distributions generated from Cocoa-seq with two benchmark communities, a model synthetic bi-culture and synthetic mock communities of various compositions, to those from other measurements or expectations. Benchmarking showed qualitative agreement with fluorescence microscopy and quantitatively similar compositions and beta-diversity patterns compared to expectations. We also introduced discrete spike-in molecular standards to estimate absolute abundance, but uneven amplification due to stochastic bias prevented accurate quantification. We discuss Cocoa-seqs limitations and recommend how Cocoa-seq can be employed to identify interactions or functionally critical consortia.