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RNase H–dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis

Nature Protocols volume 14, pages 2571–2594 (2019)Cite this article

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Abstract

RNase H–dependent PCR-enabled T-cell receptor sequencing (rhTCRseq) can be used to determine paired alpha/beta T-cell receptor (TCR) clonotypes in single cells or perform alpha and beta TCR repertoire analysis in bulk RNA samples. With the enhanced specificity of RNase H–dependent PCR (rhPCR), it achieves TCR-specific amplification and addition of dual-index barcodes in a single PCR step. For single cells, the protocol includes sorting of single cells into plates, generation of cDNA libraries, a TCR-specific amplification step, a second PCR on pooled sample to generate a sequencing library, and sequencing. In the bulk method, sorting and cDNA library steps are replaced with a reverse-transcriptase (RT) reaction that adds a unique molecular identifier (UMI) to each cDNA molecule to improve the accuracy of repertoire-frequency measurements. Compared to other methods for TCR sequencing, rhTCRseq has a streamlined workflow and the ability to analyze single cells in 384-well plates. Compared to TCR reconstruction from single-cell transcriptome sequencing data, it improves the success rate for obtaining paired alpha/beta information and ensures recovery of complete complementarity-determining region 3 (CDR3) sequences, a prerequisite for cloning/expression of discovered TCRs. Although it has lower throughput than droplet-based methods, rhTCRseq is well-suited to analysis of small sorted populations, especially when analysis of 96 or 384 single cells is sufficient to identify predominant T-cell clones. For single cells, sorting typically requires 2–4 h and can be performed days, or even months, before library construction and data processing, which takes ~4 d; the bulk RNA protocol takes ~3 d.

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Fig. 1: Critical features and schemes of rhTCRseq.
Fig. 2: Schema of the single-cell and bulk RNA rhTCRseq workflows.
Fig. 3: Expected results for library construction using the rhTCRseq protocol for single cells.

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Data availability

All TCR clonotype data for the results presented in Supplementary Figs. 1, 2, and 4 are provided in Supplementary Tables 3 and 5. All other data are available from the corresponding author upon reasonable request. Example data used for data analysis are publicly available from the GitHub repository at https://github.com/julietforman/rhTCRseq.

Code availability

The code and reference files used for data analysis are publicly available from the GitHub repository at https://github.com/julietforman/rhTCRseq.

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Acknowledgements

We thank W. Zhang for preparation of total RNA from PBMC samples. S.L., J.F., D.B.K., S.A.S., and K.J.L. were supported by NIH/NCI U24 CA224331. G.O. received generous support from the Mathers Family Foundation. J.F. was a participant in the Broad Cancer Genomics Scholars program at the Broad Institute. D.B.K. was supported by NIH/NCI R21 CA216772-01A1 and NCI-SPORE-2P50CA101942-11A1. S.A.S. was supported by NIH R50CA211482. C.J.W. was supported by NCI 1R01 CA155010-01A1 and Leukemia Lymphoma Translational Research Program Award 6460-15, and was a Scholar of the Leukemia and Lymphoma Society.

Author information

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Shuqiang Li, Juliet Forman & Catherine J. Wu

  2. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Shuqiang Li, Jing Sun, Rosa Allesøe, Giacomo Oliveira, Juliet Forman, Derin B. Keskin, Sachet A. Shukla, Catherine J. Wu & Kenneth J. Livak

  3. Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA

    Shuqiang Li, Juliet Forman, Derin B. Keskin, Sachet A. Shukla & Kenneth J. Livak

  4. Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark

    Rosa Allesøe & Lars Rønn Olsen

  5. Integrated DNA Technologies, Redwood City, CA, USA

    Krishnalekha Datta & Yun Bao

  6. Massachusetts Institute of Technology, Cambridge, MA, USA

    Roger Jin

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Contributions

S.L. and K.J.L. devised the rhTCRseq scheme. S.L. developed and performed the rhTCRseq experimentation. J.S., R.A., J.F., R.J., L.R.O., and S.A.S. devised and wrote the computational pipeline and performed the data analysis. K.D. and Y.B. developed the rhPCR protocols and provided rhPCR primers. G.O. and D.B.K. provided single cells sorted into plates for the development of the rhTCRseq protocol. G.O. performed the single-cell preparation and sorting for Supplementary Fig. 3. C.J.W. and K.J.L. directed the overall study design. S.L., J.F., C.J.W., and K.J.L. wrote the manuscript with comments and editing by all authors.

Corresponding author

Correspondence to Kenneth J. Livak.

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Competing interests

J.S. is currently an employee of Moderna Therapeutics. K.D. is an employee of IDT; Y.B. was an employee of IDT during the time of this work; K.J.L. was a paid consultant of IDT during a portion of this work; D.B.K. has previously advised Neon Therapeutics and owns equity in Aduro Biotech, Agenus Inc., Ampliphi BioSciences Corp., Biomarin Pharmaceutical Inc., Bristol-Myers Squibb Company, Celldex Therapeutics Inc., Editas Medicine Inc., Exelixis Inc., Gilead Sciences Inc., IMV Inc., Lexicon Pharmaceuticals Inc., Sangamo Therapeutics, and Stemline Therapeutics Inc.; C.J.W. is a founder of Neon Therapeutics and a member of its scientific advisory board. C.J.W. is subject to a conflict of interest management plan for the reported studies because of her competing financial interests in Neon Therapeutics. Under this plan, C.J.W. may not access identifiable human subjects data nor otherwise participate directly in the IRB-approved protocol reported herein. C.J.W.’s contributions to the overall program strategy and data analyses occurred on a de-identified basis. The remaining authors declare no competing interests.

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Keskin, D. B. et al. Nature 565, 234–239 (2019): https://doi.org/10.1038/s41586-018-0792-9.

Integrated supplementary information

Supplementary Figure 1 TCR repertoire analysis for five individuals.

RNA extracted from PBMCs of five healthy adult volunteers (anonymous donors designated N1-N5) was analyzed using the bulk RNA rhTCRseq protocol. The heat maps show the frequency distributions for TRAV (a) and TRBV (b) alleles for 320-ng samples. The allele designations are along the top of each heat map and the frequency of each allele is indicated by the color code.

Supplementary Figure 2 Reproducibility of the bulk RNA rhTCRseq protocol.

RNA extracted from PBMCs of anonymous donors N1-N5 was analyzed in eight replicates of 10 ng RNA and eight replicates of 40 ng per donor. Replicate results were combined to generate 80- and 320-ng samples. The plots show the clonotype-specific correlations for alpha (a) and beta (b) comparing the 80 ng and 320 ng results for sample N2. Correlation coefficients for the other four samples are reported in Supplementary Table 4. Correlation analysis was limited to comparing higher frequency clonotypes. For each individual, the frequency in the 80-ng results that corresponds to five UMI counts was used as the threshold frequency. Clonotypes in the 80-ng or the 320-ng data above this threshold were used for the correlation analysis. For clonotypes that are above the threshold in one dataset and below the threshold in the other, the actual frequency values below the threshold were used in the comparison.

Supplementary Figure 3 Gating strategy for isolating tumor-specific T cells.

The gating strategy for sorting T cells stimulated with autologous melanoma cells consisted of the following steps: (i) gating on single cells with lymphocyte physical parameter, (ii) gating on viable (Zombie Aqua negative) CD3+ CD4+ events, and (iii) gating on IL2-, TNFα-, and IFNγ-positive cells using unstimulated control sample to establish background signal. Single cells were sorted into plates using a 70-µm nozzle.

Supplementary Figure 4 Distribution of clones identified by single-cell TCR sequencing of tumor-stimulated T cells.

Histogram of number of cells per clone for 49 TCR clones identified using the rhTCRseq protocol for single cells.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Supplementary Note, and Supplementary Table 4

Reporting Summary

Supplementary Table 1

TCR primers

Supplementary Table 2

Barcode primers

Supplementary Table 3

Bulk RNA clonotypes

Supplementary Table 5

Single-cell clonotypes

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Li, S., Sun, J., Allesøe, R. et al. RNase H–dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis. Nat Protoc 14, 2571–2594 (2019). https://doi.org/10.1038/s41596-019-0195-x

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