AuthorsS. Dahal-Koirala, G. Balaban, R. S. Neumann, L. Scheffer, K. E. A. Lundin, V. Greiff, L. M. Sollid, S. Qiao and G. Sandve
TitleTCRPower: Quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences
AfilliationScientific Computing
Project(s)Department of Computational Physiology
StatusAccepted
Publication TypeJournal Article
Year of Publication2022
JournalBriefings in Bioinformatics
PublisherOxford Academic
KeywordsAdaptive Immune Receptor Repertoire sequencing, Bulk T-cell receptor sequencing, computational model, Spike-in standards, T-cell receptor, TCRpower
Abstract

T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases, and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to mis-diagnosis if diagnostically-relevant TCRs remain undetected. To address this issue, we developed TCRPower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth, and read cut-off. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones as spike-in TCRs. We sequenced the spike-in TCRs from T-cell clones, together with TCRs from peripheral blood, using a 5’ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cut-off, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0%, TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease relevant TCRs for diagnostic applications. 

Citation Key28301

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