Readers may find this paper by Bryan Howie and colleagues interesting. It solves an important problem (finding pairing pattern of TCR alpha and beta chains) using high-throughput sequencing and an elegant mathematical model.
The T cell receptor (TCR) protein is a heterodimer composed of an alpha chain and a beta chain. TCR genes undergo somatic DNA rearrangements to generate the diversity of T cell binding specificities needed for effective immunity. Recently, high-throughput immunosequencing methods have been developed to profile the TCR alpha (TCRA) and TCR beta (TCRB) repertoires. However, these methods cannot determine which TCRA and TCRB chains combine to form a specific TCR, which is essential for many functional and therapeutic applications. We describe and validate a method called pairSEQ, which can leverage the diversity of TCR sequences to accurately pair hundreds of thousands of TCRA and TCRB sequences in a single experiment. Our TCR pairing method uses standard laboratory consumables and equipment without the need for single-cell technologies. We show that pairSEQ can be applied to T cells from both blood and solid tissues, such as tumors.
Their probabilistic model is very familiar to me, because twelve years back I used it in a different context - for finding clustered proteins from noisy large-scale protein-protein interaction data. Here is the basic idea. If A has 10 friends and B has 10 friends, what is the probability that they have 9 common friends (by chance alone)? If the computed probability is very low and the actual measurement shows that they indeed have 9 common friends, that means A and B are strongly associated. In case of TCR alpha and beta chains in their experimental set-up, that strong association implies their combining to form heterodimers. For large-scale protein-protein interaction data in yeast, significant association appeared to show functional similarity.
The work for the above TCR paper was done by Adaptive Technologies, a very creative Seattle-based company that was founded by scientists from Fred Hutch Cancer Research Institute.