Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers

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

Identifying T Cell Receptors from High-Throughput Sequencing: Dealing with Promiscuity in TCR<i>α</i> and TCR<i>β</i> Pairing

<div><p>Characterisation of the T cell receptors (TCR) involved in immune responses is important for the design of vaccines and immunotherapies for cancer and autoimmune disease. The specificity of the interaction between the TCR heterodimer and its peptide-MHC ligand derives largely from the juxtaposed hypervariable CDR3 regions on the TCR<i>α</i> and TCR<i>β</i> chains, and obtaining the paired sequences of these regions is a standard for functionally defining the TCR. A brute force approach to identifying the TCRs in a population of T cells is to use high-throughput single-cell sequencing, but currently this process remains costly and risks missing small clones. Alternatively, CDR3<i>α</i> and CDR3<i>β</i> sequences can be associated using their frequency of co-occurrence in independent samples, but this approach can be confounded by the sharing of CDR3<i>α</i> and CDR3<i>β</i> across clones, commonly observed within epitope-specific T cell populations. The accurate, exhaustive, and economical recovery of TCR sequences from such populations therefore remains a challenging problem. Here we describe an algorithm for performing frequency-based pairing (alphabetr) that accommodates CDR3<i>α</i>- and CDR3<i>β</i>-sharing, cells expressing two TCR<i>α</i> chains, and multiple forms of sequencing error. The algorithm also yields accurate estimates of clonal frequencies.</p></div