Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases

Pierre Barennes, Valentin Quiniou, Mikhail Shugay, Evgeniy S. Egorov, Alexey N. Davydov, Dmitriy M. Chudakov, Imran Uddin, Mazlina Ismail, Theres Oakes, Benny Chain, Anne Eugster, Karl Kashofer, Peter P. Rainer, Samuel Darko, Amy Ransier, Daniel C. Douek, David Klatzmann, Encarnita Mariotti-Ferrandiz

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Monitoring the T cell receptor (TCR) repertoire in health and disease can provide key insights into adaptive immune responses, but the accuracy of current TCR sequencing (TCRseq) methods is unclear. In this study, we systematically compared the results of nine commercial and academic TCRseq methods, including six rapid amplification of complementary DNA ends (RACE)-polymerase chain reaction (PCR) and three multiplex-PCR approaches, when applied to the same T cell sample. We found marked differences in accuracy and intra- and inter-method reproducibility for T cell receptor α (TRA) and T cell receptor β (TRB) TCR chains. Most methods showed a lower ability to capture TRA than TRB diversity. Low RNA input generated non-representative repertoires. Results from the 5′ RACE-PCR methods were consistent among themselves but differed from the RNA-based multiplex-PCR results. Using an in silico meta-repertoire generated from 108 replicates, we found that one genomic DNA-based method and two non-unique molecular identifier (UMI) RNA-based methods were more sensitive than UMI methods in detecting rare clonotypes, despite the better clonotype quantification accuracy of the latter.

Original languageEnglish
Pages (from-to)236-245
Number of pages10
JournalNature Biotechnology
Volume39
Issue number2
Early online date7 Sep 2020
DOIs
Publication statusPublished - Feb 2021
Externally publishedYes

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