MAIT cells can discriminate between pathogen-derived ligands in a clonotype-dependent manner, and the TCR repertoire is distinct within individuals, indicating that the MAIT cell repertoire is shaped by prior microbial exposure.

The clone size distribution of the human naive T-cell receptor (TCR) repertoire is an important determinant of adaptive immunity. We estimated the abundance of TCR sequences in samples of naive T cells from blood using an accurate quantitative sequencing protocol. We observe most TCR sequences only once, consistent with the enormous diversity of the repertoire. However, a substantial number of sequences were observed multiple times. We detect abundant TCR sequences even after exclusion of methodological confounders such as sort contamination, and multiple mRNA sampling from the same cell. By combining experimental data with predictions from models we describe two mechanisms contributing to TCR sequence abundance. TCRα abundant sequences can be primarily attributed to many identical recombination events in different cells, while abundant TCRβ sequences are primarily derived from large clones, which make up a small percentage of the naive repertoire, and could be established early in the development of the T-cell repertoire.

Motivation: High Throughput Sequencing (HTS) has enabled researchers to probe the human T cell receptor (TCR) repertoire, which consists of many rare sequences. Distinguishing between true but rare TCR sequences and variants generated by polymerase chain reaction (PCR) and sequencing errors remains a formidable challenge. The conventional approach to handle errors is to remove low quality reads, and/or rare TCR sequences. Such filtering discards a large number of true and often rare TCR sequences. However, accurate identification and quantification of rare TCR sequences is essential for repertoire diversity estimation.Results: We devised a pipeline, called Recover TCR (RTCR), that accurately recovers TCR sequences, including rare TCR sequences, from HTS data (including barcoded data) even at low coverage. RTCR employs a data-driven statistical model to rectify PCR and sequencing errors in an adaptive manner. Using simulations, we demonstrate that RTCR can easily adapt to the error profiles of different types of sequencers and exhibits consistently high recall and high precision even at low coverages where other pipelines perform poorly. Using published real data, we show that RTCR accurately resolves sequencing errors and outperforms all other pipelines.Availability and Implementation: The RTCR pipeline is implemented in Python (v2.7) and C and is freely available at http://uubram.github.io/RTCR/along with documentation and examples of typical usage.Contact: b.gerritsen@uu.nl

The human naive T-cell receptor (TCR) repertoire is extremely diverse and accurately estimating its distribution is challenging. We address this challenge by combining a quantitative sequencing protocol of TCRA and TCRB sequences with computational modelling. We observed the vast majority of TCR chains only once in our samples, confirming the enormous diversity of the naive repertoire. However, a substantial number of sequences were observed multiple times within samples, and we demonstrated that this is due to expression by many cells in the naive pool. We reason that α and β chains are frequently observed due to a combination of selective processes and summation over multiple clones expressing these chains. We test the contribution of both mechanisms by predicting samples from phenomenological and mechanistically modelled repertoire distributions. By comparing these with sequencing data, we show that frequently observed chains are likely to be derived from multiple clones. Still, a neutral model of T-cell homeostasis cannot account for the observed distributions. We conclude that the data are only compatible with distributions of many small clones in combination with a sufficient number of very large naive T-cell clones, the latter most likely as a result of peripheral selection.