Depth and accuracy of <i>αβ</i> pairings generated by alphabetr, for a range of overall sample sizes, sampling strategies and underlying distributions of clone sizes.

<p>Simulations were performed using <i>in silico</i> data sets of one or five plates using six different sampling strategies (see text) and different degrees of skewness in clonal frequencies, as indicated by the number of clones comprising 50% of the population when ranked by frequency. ‘Threshold’ refers to the stringency of pair association, <i>T</i> (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005313#sec014" target="_blank">Methods</a>). <b>(A)</b> The proportion of the most abundant 50% of clones that were identified. <b>(B)</b> The proportion of the least abundant 50% of clones that were identified. <b>(C)</b> The overall depth was influenced strongly by the tail depth, indicating that data from one plate may be sufficient for recovering the most common clones. <b>(D)</b> The rate at which CDR3<i>α</i> and CDR3<i>β</i> sequences were incorrectly paired (false positive rate, FPR).</p

Comparison of well occupancy patterns of the clones identified by alphabetr and in ref. [22].

<p>For each method, TCR<i>αβ</i> pairs identified for all tumour samples were combined to estimate the distribution of the number of wells in which the chains co-appeared. The differences between these distributions indicate the relative efficiency with which the two algorithms identify clones, as a function of their abundance.</p

Recovery of tumour-infiltrating lymphocyte TCR pairs using alphabetr and data from ref. [22].

<p>The data were processed by associating chains with their tumour sources through exact matching of the CDR3 nucleotide sequences from the mixed tumour samples to CDR3 libraries obtained from blood samples from each patient. The data were then simplified by selecting only those chains associated with one tumour. We then used alphabetr to identify TCR<i>αβ</i> pairs. The numbers of pairs unambiguously identified in ref. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005313#pcbi.1005313.ref022" target="_blank">22</a>] were determined by directly matching nucleotide sequences to the CDR3 libraries, and only those pairs for which both chains could be directly associated with the corresponding tumour sample were included in the analysis.</p

Comparison of single-cell approaches and alphabetr.

<p>Single-cell sequencing was simulated by sampling from the same populations used to evaluate alphabetr and including both the dropping of chains and in-frame sequencing errors. In these simulations, the parent population contains 2100 clones with 25 clones representing the top 50% of the clones ranked by abundance. The results were evaluated for (A) top depth, (B) tail depth, and (C) overall depth. The dashed lines show the mean performance of alphabetr applied to five plates using the high-mixed sampling strategy and a threshold of 0.6 (values taken from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005313#pcbi.1005313.g003" target="_blank">Fig 3</a>). The single-cell sequencing results are averages of 200 simulations.</p

Assessment of the precision of clonal frequency estimation.

<p><b>(A)</b> Point estimates of clonal abundances generated by alphabetr, derived from representative simulations using five plates and distributions with 25 and 5 clones in the top 50% (left and right panels respectively). (B) The coefficient of variation (precision) of abundance estimates for a range of skewnesses of clone sizes and sampling strategies. Values quoted are averages over 100 simulations.</p

A summary of the degrees of sharing of CDR3<i>α</i> and CDR3<i>β</i> at the amino acid level across clones within epitope-specific T cell populations, found in published single-cell TCR sequencing data and our own.

<p>Unless indicated otherwise, the samples were obtained from influenza-infected mice. The data clearly demonstrate that sharing of both <i>α</i> and <i>β</i> chains within an individual occurs in different infection/inoculation settings.</p

Analysis of TCR<i>α</i> usage in human, YFV-specific peripheral-blood CD8⁺ T cells.

<p><b>(A)</b> Observed distribution of relative clone sizes within the population specific for the HLA-A02:01/LLWNGPMAV epitope. Clones expressing a unique CDR3<i>α</i> are shown in grey; clones that share a CDR3<i>α</i> are coloured, and the numbers in the coloured boxes represent the number of clones sharing each CDR3<i>α</i>. <b>(B)</b> The distributions of CDR3<i>α</i> nucleotide insertion lengths in clones with shared CDR3<i>α</i> (left hand panel) and unique CDR3<i>α</i> (right hand panel).</p

The mixed sampling strategies used in the simulations.

<p>The mixed sampling strategies used in the simulations.</p

Discriminating between dual TCR<i>α</i> and <i>β</i>-sharing clones.

<p>We assess the degree of recovery of dual TCR<i>α</i> clones with the ‘adjusted depth,’ which is the proportion of dual TCR<i>α</i> clones correctly assigned out of the list of candidate dual TCR<i>α</i> and TCR<i>β</i>-sharing clones. Panel (A) shows the adjusted depth of common clones; panel (B), rare clones. For common clones, we used likelihood-based discrimination; for rare clones we used a clustering approach. Both procedures are detailed in Methods. Panel (C) shows the false dual rate averaged over all clones—the proportion of identified dual TCR<i>α</i> that are incorrect. All results are shown for a threshold of 0.3 with 30% prevalence of dual TCR<i>α</i> and are averages over 100 simulations.</p

Duration of chronic cardiovascular disease or risk factor management studies identified in the DCP3 systematic review.

<p>Duration of chronic cardiovascular disease or risk factor management studies identified in the DCP3 systematic review.</p