Benchmarking a massively-multiplexed amplicon sequencing strategy for 16S profiling 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. To address this, 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 sequencing library, with potential for even higher scalability. We benchmarked Cocoa-seq with a model synthetic co-culture as well as mock communities of varying rank abundances and found that beta-diversity distributions obtained with Cocoa-seq qualitatively recapitulate expectations. This BioProject contains the raw reads from the benchmarking, as described in the paper.