Now this is pretty cool. Figuring out which microbes are performing a certain type of metabolism is an age old challenge. The main competing technologies represent a trade-off between scale (e.g., PCR targeting specific genes or a metagenome that looks at a system's entire genetic complement) and specificity (e.g., single cell genomes). With the former, you can sample the enormous diversity that characterizes most microbial systems; with the latter, you can connect identity to a range of functions by seeing which genes are linked with a given 16S rRNA gene.
A new approach called Emulsion, Paired Isolation and Concatenation PCR (or, clearly, epicPCR) tries to bridge the gap. The method isolates individual cells and performs a snazzy fusion PCR to link the 16S rRNA gene with a functional gene of choice. In a survey of sulfate reducer diversity, two million fused dsrB-16S rRNA fusions were observed, exposing novel diversity at high-throughput.
Full paper [here], abstract below:
Many microbial communities are characterized by high genetic diversity. 16S ribosomal RNA sequencing can determine community members, and metagenomics can determine the functional diversity, but resolving the functional role of individual cells in high throughput remains an unsolved challenge. Here, we describe epicPCR (Emulsion, Paired Isolation and Concatenation PCR), a new technique that links functional genes and phylogenetic markers in uncultured single cells, providing a throughput of hundreds of thousands of cells with costs comparable to one genomic library preparation. We demonstrate the utility of our technique in a natural environment by profiling a sulfate-reducing community in a freshwater lake, revealing both known sulfate reducers and discovering new putative sulfate reducers. Our method is adaptable to any conserved genetic trait and translates genetic associations from diverse microbial samples into a sequencing library that answers targeted ecological questions. Potential applications include identifying functional community members, tracing horizontal gene transfer networks and mapping ecological interactions between microbial cells.