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TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data

Nature Methods volume 18, pages 627–630 (2021)Cite this article

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Abstract

We introduce the TRUST4 open-source algorithm for reconstruction of immune receptor repertoires in αβ/γδ T cells and B cells from RNA-sequencing (RNA-seq) data. Compared with competing methods, TRUST4 supports both FASTQ and BAM format and is faster and more sensitive in assembling longer—even full-length—receptor repertoires. TRUST4 can also call repertoire sequences from single-cell RNA-seq (scRNA-seq) data without V(D)J enrichment, and is compatible with both SMART-seq and 5′ 10x Genomics platforms.

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Fig. 1: The performance of TRUST4 on bulk RNA-seq data.
Fig. 2: The performance of TRUST4 on scRNA-seq data.

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Data availability

The original scripts for generation and evaluation of in silico RNA-seq data are available at https://github.com/milaboratory/mixcr-rna-seq-paper.

The six bulk RNA-seq samples for BCR evaluation are available in the SRA repository, accession code PRJNA492301, and their matched iRepertoire data are available at https://bitbucket.org/liulab/ng-bcr-validate/src/master/iRep. SMART-seq data are available in the SRA repository, accession code SRP126429. 10x Genomics scRNA-seq data are available at https://support.10xgenomics.com/single-cell-vdj/datasets/3.1.0/vdj_nextgem_hs_pbmc3, https://support.10xgenomics.com/single-cell-vdj/datasets/2.2.0/vdj_v1_hs_nsclc_5gex and https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_protein_v3_nextgem.

Code availability

TRUST4 source code is available at https://github.com/liulab-dfci/TRUST4. Evaluation code for this work is available at https://github.com/liulab-dfci/TRUST4_manuscript_evaluation.

References

  1. Lee, J. et al. Molecular-level analysis of the serum antibody repertoire in young adults before and after seasonal influenza vaccination. Nat. Med. 22, 1456–1464 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kiyotani, K. et al. Characterization of the B-cell receptor repertoires in peanut allergic subjects undergoing oral immunotherapy. J. Hum. Genet. 63, 239–248 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Liu, S. et al. Direct measurement of B-cell receptor repertoire’s composition and variation in systemic lupus erythematosus. Genes Immun. 18, 22–27 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Kurtz, D. M. et al. Noninvasive monitoring of diffuse large B-cell lymphoma by immunoglobulin high-throughput sequencing. Blood 125, 3679–3687 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li, B. et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. Nat. Genet. 48, 725–732 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Li, B. et al. Ultrasensitive detection of TCR hypervariable-region sequences in solid-tissue RNA-seq data. Nat. Genet. 49, 482–483 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hu, X. et al. Landscape of B cell immunity and related immune evasion in human cancers. Nat. Genet. 51, 560–567 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cao, Y. et al. Potent neutralizing antibodies against SARS-CoV-2 identified by high-throughput single-cell sequencing of convalescent patients’ B cells. Cell 182, 73–84 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mose, L. E. et al. Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V’DJer. Bioinformatics 32, 3729–3734 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bolotin, D. A. et al. Antigen receptor repertoire profiling from RNA-seq data. Nat. Biotechnol. 35, 908–911 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chen, S.-Y., Liu, C.-J., Zhang, Q. & Guo, A.-Y. An ultrasensitive T-cell receptor detection method for TCR-seq and RNA-seq data. Bioinformatics 36, 4255–4262 (2020).

  13. Mandric, I. et al. Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing. Nat. Commun. 11, 3126 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sulea, T. et al. Structure-based engineering of pH-dependent antibody binding for selective targeting of solid-tumor microenvironment. mAbs 12, 1682866 (2020).

    Article  PubMed  Google Scholar 

  15. Chi, X. et al. A neutralizing human antibody binds to the N-terminal domain of the Spike protein of SARS-CoV-2. Science 369, 650–655 (2020).

  16. Upadhyay, A. A. et al. BALDR: a computational pipeline for paired heavy and light chain immunoglobulin reconstruction in single-cell RNA-seq data. Genome Med. 10, 20 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Canzar, S., Neu, K. E., Tang, Q., Wilson, P. C. & Khan, A. A. BASIC: BCR assembly from single cells. Bioinformatics 33, 425–427 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Rizzetto, S. et al. B-cell receptor reconstruction from single-cell RNA-seq with VDJPuzzle. Bioinformatics 34, 2846–2847 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Stuart, T. et al. Comprehensive Integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lefranc, M.-P. IMGT, the international ImMunoGeneTics information system. Cold Spring Harb. Protoc. 2011, 595–603 (2011).

    Article  PubMed  Google Scholar 

  26. Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Series B Stat. Methodol. 39, 1–22 (1977).

    Google Scholar 

  27. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Huang, W., Li, L., Myers, J. R. & Marth, G. T. ART: a next-generation sequencing read simulator. Bioinformatics 28, 593–594 (2012).

    Article  PubMed  Google Scholar 

  29. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sharonov, G. V., Serebrovskaya, E. O., Yuzhakova, D. V., Britanova, O. V. & Chudakov, D. M. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat. Rev. Immunol. 20, 294–307 (2020).

    Article  CAS  PubMed  Google Scholar 

  31. Bunker, J. J. & Bendelac, A. IgA responses to microbiota. Immunity 49, 211–224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank B. Li and C. Wang for the helpful discussions. We also acknowledge funding from NIH (grant U01CA226196) and China Scholarship Council (Z.O. and Y.C.) to support this work. The study used data generated by the TCGA Research Network that are not otherwise cited: https://www.cancer.gov/tcga

Author information

Authors and Affiliations

  1. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Li Song, David Cohen, Xihao Hu & X. Shirley Liu

  2. Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Li Song, Xihao Hu & X. Shirley Liu

  3. Department of Biotechnology, Beijing Institue of Radiation Medicine, Beijing, China

    Zhangyi Ouyang

  4. College of Life Sciences, Sichuan University, Chengdu, China

    Yang Cao

  5. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    X. Shirley Liu

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  1. Li Song
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Contributions

L.S., X.H. and X.S.L conceived the project. L.S. designed and implemented the methods. L.S., D.C., Z.O., Y.C., X.H. and X.S.L. evaluated the methods and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to X. Shirley Liu.

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Competing interests

X.S.L. is a cofounder, scientific advisory board (SAB) member and consultant of GV20 Oncotherapy and its subsidiaries, SAB memner of 3DMedCare, consultant for Genentech, stockholder of AMGN, JNJ, MRK and PFE and receives sponsored research funding from Takeda and Sanofi. X.H. conducted the work while a postdoctorate fellow at DFCI, and is currently a full-time employee of GV20 Oncotherapy.

Additional information

Peer review information Nature Methods thanks Aly Azeem Khan, Gur Yaari and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Table 1 and Figs. 1–7.

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Song, L., Cohen, D., Ouyang, Z. et al. TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nat Methods 18, 627–630 (2021). https://doi.org/10.1038/s41592-021-01142-2

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