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FAQ
It is possible that a minority of the TCR alignments result in non-productive TCRs. A TCR is considered non-productive if it has an out of frame junction sequence and/or an early stop codon and/or any defect described in the initiation codon, splicing sites and/or regulatory elements. By default, we do not perform any specific filtering for non-productive TCRs, considering that the minority of results of this kind can be neglected. In addition, we have found it is more common to see non-productive rearrangements in alpha chains, but we often observe that these are clonal, so they are likely the result of dual alpha-chain-expressing clonotypes rather than poor quality sequencing data. In any case, the results table at transcript level (sample_barcode_umi_results.csv) includes a column named productive (see output tables wiki), by which the user can filter out non-productive TCRs.
Part of the T cells in an organism can have dual TCRs, this is, they express two rearranged TCR alleles for the alpha and/or beta chains. In principle, this event is prevented by the process of allelic exclusion, by which only one allele is expressed and the other is silenced. However, allelic exclussion is not 100% efficient, reason why dual TCR T cells arise. In addition, it is more common to find dual TCR α-chains than dual TCR β-chains, due to different mechanisms of allelic exclusion.
In WAT3R, we consider the possibility of dual TCR α-chains expression. For this reason, we include the two most common TRA alleles per cell barcode, ranking them by UMI counts and read counts.
However, wWe don’t do that for TRB, as we consider that it is a more uncommon event.
If the user is interested in exploring dual TCR expression in both the alpha and beta chains, it is possible to do so using the sample_barcode_umi_results.csv table. This results table contains the TCR alignments at the transcript level. That is, it registers every TRA and TRB alignment for a cell, even if there are multiple ones (see output tables description). By default, we select the most common TRB alignment and the two most common TRA alignments to create sample_barcode_results.csv. However, the user could choose to keep two TRB alleles per cell barcode.
For more information on dual TCR, see the following review.
When analyzing a mixture of cells including non-T cells, some TCRs may be reported in non-T cells, which is most likely a technical artifact. In our PBMCs analyzed with TREK-seq and WAT3R, 3% of non-T cells had TCR results (bioRxiv preprint). Since TCR transcripts were detected in non-T cells by both scRNA-seq and WAT3R, some TCR transcripts were already present in the 10x Genomics 3’ barcoded cDNA from which both modalities originate (paper under review at Bioinformatics). We propose three possible reasons: ambient mRNAs, doublets, or erroneous cell type classification. First, ambient mRNAs contained within the input solution may be captured; this contamination has been estimated to make up ~2-10% of transcripts (Young 2020, Yang 2020). While tools such as SoupX and DecontX can be used to eliminate ambient RNAs, these are not designed to work on enriched data such as the input for WAT3R. Second, the incorrect detection of TCRs could be caused by doublets. Users can apply doublet removal tools to scRNA-seq data (and not TREK-seq/WAT3R) in a way that is consistent with the experimental goals (DePasquale 2019, Wolock 2019, McGinnis 2019. Removing these cell barcodes from consideration should also minimize false positives from T cell-containing doublets. Third, TCR detection in non-T cells by WAT3R could be explained by cell type misclassification. In our data, cell types were assigned to each cell barcode using standard bioinformatic pipelines (i.e. Seurat), but all methods of classification are prone to erroneous labeling in a small proportion of cells. Given these possibilities and the evidentiary support from the scRNA-seq data, we believe that false positive non-T cells are a byproduct of upstream processes and not of the WAT3R pipeline.