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

Nature Biotechnology volume 39, pages 236–245 (2021)Cite this article

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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.

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Fig. 1: Performance statistics and VDJ rearrangement model of each method for Experiments A and B.
Fig. 2: TRBV usage comparison between flow cytometry and TCRseq.
Fig. 3: The reproducibility of detection of major TCR clonotypes by different methods.
Fig. 4: Sensitivity of TCR sequence detection by different methods.
Fig. 5: Sharing with robust and representative meta-repertoire.

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Data availability

All the fastq data obtained in this study, including the Jurkat Clone E6-­1 (ATCC TIB­152) cell line TCR α and β sequences, were deposited in the National Center for Biotechnology Information Sequence Read Archive repository following MiAIRR standard recommendations47 under the BioProject ID PRJNA548335. The aligned sequence data will be stored in an iReceptor Repository at Sorbonne Université as a repository in the AIRR Data Commons and can be explored and downloaded through the iReceptor Gateway65 (https://gateway.ireceptor.org). Source data for TCRVβ flow cytometry data are provided as Supplementary Fig. 4a,b. All other data are available from the corresponding author upon reasonable request.

Code availability

All software packages and programs are publicly available and open source. Scripts used to analyze the data with MiXCR are available from https://mixcr.milaboratory.com. Decombinator is available from https://github.com/innate2adaptive/Decombinator. MiGEC is available from https://github.com/mikessh/migec. Detailed VDJ rearrangement statistics scripts are available from https://github.com/antigenomics/repseq-protocol-comparison. There is no restriction on the use of the code or data.

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Acknowledgements

We are grateful to M. Barbié for providing the human samples. This work benefited from equipment and services from the iGenSeq core facility at ICM. This work was supported the ERC-Advanced TRiPoD (322856), LabEx Transimmunom (ANR-11-IDEX-0004-02) and RHU iMAP (ANR-16-RHUS-0001) grants to D.K. E.M.F. is funded by the European Research Area Network-Cardiovascular Diseases (JCT2018 and ANR-18-ECVD-0001) and iReceptorPlus (H2020 Research and Innovation Programme 825821) grants. M.S. and D.M.C. were supported by a grant from the Ministry of Science and Higher Education of the Russian Federation (075−15−2019-1789). This work was funded, in part, by the intramural program of the National Institute of Allergy and Infectious Diseases (to D.C.D.). B.C. was supported by the National Institute for Health Research UCL Hospitals Biomedical Research. A.E. was supported by DFG CRTD (FZ 111). A.N.D. was supported by the Ministry of Education, Youth and Sports of the Czech Republic under project CEITEC 2020 (LQ1601). K.K. and P.P.R. were supported by the European Research Area Network-Cardiovascular Diseases (JCT2018 and AIR-MI Consortium) program.

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Authors and Affiliations

  1. Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), Paris, France

    Pierre Barennes, Valentin Quiniou, David Klatzmann & Encarnita Mariotti-Ferrandiz

  2. AP-HP, Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi) and Inflammation-Immunopathology-Biotherapy Department (i2B), Paris, France

    Pierre Barennes, Valentin Quiniou, David Klatzmann & Encarnita Mariotti-Ferrandiz

  3. Center of Life Sciences, Skoltech, Moscow, Russia

    Mikhail Shugay & Dmitriy M. Chudakov

  4. Genomics of Adaptive Immunity Department, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia

    Mikhail Shugay, Evgeniy S. Egorov & Dmitriy M. Chudakov

  5. Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia

    Mikhail Shugay & Dmitriy M. Chudakov

  6. Adaptive Immunity Group, Central European Institute of Technology, Brno, Czechia

    Alexey N. Davydov & Dmitriy M. Chudakov

  7. Division of Infection and Immunity, University College London, London, UK

    Imran Uddin, Mazlina Ismail, Theres Oakes & Benny Chain

  8. DFG-Centre for Regenerative Therapies Dresden, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany

    Anne Eugster

  9. Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria

    Karl Kashofer

  10. Division of Cardiology, Medical University of Graz, Graz, Austria

    Peter P. Rainer

  11. BioTechMed-Graz, Graz, Austria

    Peter P. Rainer

  12. Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Samuel Darko, Amy Ransier & Daniel C. Douek

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  1. Pierre Barennes
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Contributions

P.B., V.Q., E.S.E., A.N.D., I.U., M.I., T.O., A.E., S.D., A.R., K.K. and P.R. performed the experiments and raw data pre-processing. P.B., V.Q., M.S. and M.I. analyzed the data. E.M.F., D.M.C., B.C., D.C.D. and D.K. designed the experiments. P.B., D.K. and E.M.F. wrote the manuscript with input from all authors. D.K. and E.M.F. conceived the study, which was supervised by E.M.F. D.K., B.C., A.E., D.C.D., D.M.C., K.K. and M.S. obtained funding for the study.

Corresponding author

Correspondence to Encarnita Mariotti-Ferrandiz.

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Competing interests

D.M.C. and M.S. are cofounders of MiLaboratory LLC. A.E., A.N.D., A.R., B.C., D.C.D., D.K., E.M.F., E.S.E., I.U., K.K., M.I., P.B., P.R., S.D., T.O. and V.Q. declare no conflicts of interest.

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Supplementary Information

Supplementary Figs 1–10, Tables 1 and 2, and Methods

Reporting Summary

Supplementary Data 1

Source data used for rank calculations displayed in Table 1. (Raw data sheet corresponds to raw data used for rank values calculation; rank summary values sheet corresponds to details of rank calculations and values displayed in Table 1.)

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Barennes, P., Quiniou, V., Shugay, M. et al. Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nat Biotechnol 39, 236–245 (2021). https://doi.org/10.1038/s41587-020-0656-3

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