Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures

Abstract

High-throughput 3′ single-cell RNA-sequencing (scRNA-seq) allows cost-effective, detailed characterization of individual immune cells from tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including variable sequences of T cell antigen receptors (TCRs) that confer antigen specificity. Here, we present a strategy that enables simultaneous analysis of TCR sequences and corresponding full transcriptomes from 3′-barcoded scRNA-seq samples. This approach is compatible with common 3′ scRNA-seq methods, and adaptable to processed samples post hoc. We applied the technique to identify transcriptional signatures associated with T cells sharing common TCRs from immunized mice and from patients with food allergy. We observed preferential phenotypes among subsets of expanded clonotypes, including type 2 helper CD4+ T cell (TH2) states associated with food allergy. These results demonstrate the utility of our method when studying diseases in which clonotype-driven responses are critical to understanding the underlying biology.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Strategy for TCR recovery from 3′-barcoded single-cell sequencing libraries.
Fig. 2: Recovery of OT-I Tcra and Tcrb CDR3 sequences.
Fig. 3: scRNA-seq and TCR analysis of HPV-E7-immunized mice.
Fig. 4: ScRNA-seq and TCR analysis of peanut-dependent activated T cells from one of the individuals with an allergy to peanut (patient 77) combined with pseudotemporal analysis.

Data availability

FASTQ file format data related to murine samples are available through GEO and BioProject under accession numbers GSE136028 and PRJNA560970. FASTQ file format data related to human samples is available through dbGaP under accession number phs001897.v1.p1. Source data files and associated metadata tables for Figs. 24 are available on http://shaleklab.com/resources/, https://github.com/mitlovelab/ or upon request. MSigDB results for Extended Data Fig. 1c, Extended Data Fig. 5a and Supplementary Fig. 3h are available as Supplementary Table 12. Full results from the differential gene expression comparison shown in Extended Data Fig. 2c are available as Supplementary Table 13. All recovered CDR3 sequences, and their frequencies, of TCR α and β chains from E7-immunized mice and patients with peanut allergy are available as Supplementary Tables 6 and 10. Gene expression matrices for E7-immunized mice and patients with peanut allergy are available as Supplementary Data 1 and 2.

Code availability

R scripts for generating all analysis, Matlab scripts for processing TCR sequencing data, as well as all updates, are available at http://shaleklab.com/resources/, https://github.com/mitlovelab/ or upon reasonable request.

References

  1. 1.

    Schrama, D., Ritter, C. & Becker, J. C. T cell receptor repertoire usage in cancer as a surrogate marker for immune responses. Semin. Immunopathol. 39, 255–268 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Lossius, A. et al. High-throughput sequencing of TCR repertoires in multiple sclerosis reveals intrathecal enrichment of EBV-reactive CD8+ T cells. Eur. J. Immunol. 44, 1–41 (2014).

    Article  CAS  Google Scholar 

  3. 3.

    Kirsch, I. R. et al. TCR sequencing facilitates diagnosis and identifies mature T cells as the cell of origin in CTCL. Sci. Transl. Med. 7, 1–13 (2015).

    Article  Google Scholar 

  4. 4.

    Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Crosby, E. J. et al. Complimentary mechanisms of dual checkpoint blockade expand unique T-cell repertoires and activate adaptive anti-tumor immunity in triple-negative breast tumors. Oncoimmunology 7, e1421891 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Khodadoust, M. S. et al. Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens. Nature 543, 723–727 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Avraham, R. et al. Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell 162, 1309–1321 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e36 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Dash, P. et al. Paired analysis of TCRα and TCRβ chains at the single-cell level in mice. J. Clin. Invest. 121, 288–295 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Gierahn, T. M. et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Saikia, M. et al. Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells. Nat. Methods 16, 59–62 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Singh, M. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat. Commun. 10, 3120 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Zemmour, D. et al. Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR. Nat. Immunol. 19, 291–301 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338–345 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Gupta, I. et al. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. Nat. Biotechnol. 36, 1197–1202 (2018).

    Article  CAS  Google Scholar 

  24. 24.

    Hughes, T. K. et al. Highly efficient, massively-parallel single-cell RNA-seq reveals cellular states and molecular features of human skin pathology. Preprint at bioRxiv https://doi.org/10.1101/689273 (2019).

  25. 25.

    Blüthmann, H. et al. T-cell-specific deletion of T-cell receptor transgenes allows functional rearrangement of endogenous α- and β-genes. Nature 334, 156–159 (1988).

    Article  Google Scholar 

  26. 26.

    Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Mousset, C. M. et al. Comprehensive phenotyping of T cells using flow cytometry. Cytometry A 95, 647–654 (2019).

    Article  Google Scholar 

  28. 28.

    Farber, D. L., Yudanin, N. A. & Restifo, N. P. Human memory T cells: generation, compartmentalization and homeostasis. Nat. Rev. Immunol. 14, 24–35 (2014).

    Article  CAS  Google Scholar 

  29. 29.

    Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 166, 1500–1511.e9 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Huang, W. & August, A. The signaling symphony: T cell receptor tunes cytokine-mediated T cell differentiation. J. Leukoc. Biol. 97, 477–485 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Padovan, E. et al. Expression of two T cell receptor alpha chains: dual receptor T cells. Science 262, 422–424 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Bacher, P. & Scheffold, A. Flow-cytometric analysis of rare antigen-specific T cells. Cytometry A 83A, 692–701 (2013).

    Article  Google Scholar 

  33. 33.

    Chattopadhyay, P. K., Yu, J. & Roederer, M. Live-cell assay to detect antigen-specific CD4+ T-cell responses by CD154 expression. Nat. Protoc. 1, 1–6 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Syed, A., Kohli, A. & Nadeau, K. C. Food allergy diagnosis and therapy: where are we now? Immunotherapy 5, 931–944 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Seumois, G. et al. Transcriptional profiling of Th2 cells identifies pathogenic features associated with asthma. J. Immunol. 197, 655–664 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Mueller, S. N., Gebhardt, T., Carbone, F. R. & Heath, W. R. Memory T cell subsets, migration patterns, and tissue residence. Annu. Rev. Immunol. 31, 137–161 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Nish, S. A. et al. CD4+ T cell effector commitment coupled to self-renewal by asymmetric cell divisions. J. Exp. Med. 214, 39–47 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Wei, G. et al. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30, 155–167 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Foletta, V. C., Segal, D. H. & Cohen, D. R. Transcriptional regulation in the immune system: all roads lead to AP-1. J. Leukoc. Biol. 63, 139–152 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Müller, U. et al. Lack of IL-4 receptor expression on T helper cells reduces T helper 2 cell polyfunctionality and confers resistance in allergic bronchopulmonary mycosis. Mucosal Immunol. 5, 299–310 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Upadhyaya, B., Yin, Y., Hill, B. J., Douek, D. C. & Prussin, C. Hierarchical IL-5 expression defines a subpopulation of highly differentiated human Th2 cells. J. Immunol. 187, 3111–3120 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ritvo, P.-G. et al. High-resolution repertoire analysis reveals a major bystander activation of Tfh and Tfr cells. Proc. Natl Acad. Sci. USA 115, 9604–9609 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Han, Q. et al. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl Acad. Sci. USA 109, 1607–1612 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Schumacher, T. N. M., Gerlach, C. & van Heijst, J. W. J. Mapping the life histories of T cells. Nat. Rev. Immunol. 10, 621–631 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Rosati, E. et al. Overview of methodologies for T-cell receptor repertoire analysis. BMC Biotechnol. 17, 61 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Hermiston, M. L., Xu, Z. & Weiss, A. CD45: a critical regulator of signaling thresholds in immune cells. Annu. Rev. Immunol. 21, 107–137 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Liberzon, A. et al. The Molecular Signatures Database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank P. Blainey and M. Birnbaum for fruitful discussions. This work was supported in part by the Koch Institute Support (core) NIH Grant P30-CA14051 from the National Cancer Institute, as well as the Koch Institute - Dana-Farber/Harvard Cancer Center Bridge Project. This work was also supported by the Food Allergy Science Initiative at the Broad Institute and the NIH (5P01AI039671, 5U19AI089992, U19AI095261). A.K.S. was supported by the Searle Scholars Program, the Beckman Young Investigator Program, the Pew-Stewart Scholars Program for Cancer Research, a Sloan Fellowship in Chemistry, the NIH (1DP2GM119419, 2U19AI089992, 2R01HL095791, 1U54CA217377, 2P01AI039671, 5U24AI118672, 2RM1HG006193, 1R33CA202820, 1R01AI138546, 1R01HL134539, 1R01DA046277, 1U2CCA23319501) and Agilent Technologies.

Author information

Affiliations

Authors

Contributions

A.A.T., T.M.G, A.K.S. and J.C.L. developed the concepts and designed the study. A.A.T. and T.M.G. performed the experiments. A.A.T. prepared the manuscript with input from all authors. A.A.T., T.M.G., B.M. and D.M.M. performed bioinformatics analyses. N.K.M. performed E7 immunization of mice. W.G.S. designed the clinical study and provided samples from peanut-allergic patients. B.R. performed stimulation and sorting of PBMCs from allergic patients.

Corresponding authors

Correspondence to Alex K. Shalek or J. Christopher Love.

Ethics declarations

Competing interests

A.A.T., T.M.G., J.C.L. and the Massachusetts Institute of Technology have filed a patent application (patent no. PCT/US2018/013443) that relates to T cell receptor recovery, compositions of matter and the outlined experimental and computational methods and uses thereof. J.C.L. and A.K.S. are co-founders and shareholders of Honeycomb Biotechnologies, Inc. T.M.G. is currently an employee of Honeycomb Biotechnologies, Inc.

Additional information

Peer review information Laurie A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Stimulated and ex vivo cells are transcriptionally distinct.

a, tSNE visualization of all cells colored based on stimulation condition (n = 6,912 stimulated cells, dark grey; 7,512 ex vivo cells, light grey). b, Proportions of stimulated and ex vivo cells in each of the computationally determined clusters shown in Fig. 3a. Dash line indicates expected proportions assuming even distributions of cells from both conditions. c, Enriched MsigDb signatures of the four modules of genes identified in Fig. 3d. FDR q-values represent Benjamini and Hochberg-corrected, one-tailed hypergeometric P values. 50, 49, 35, and 48 genes are included in Module 1, 2, 3, and 4, respectively for enrichment calculation. See Supplementary Table 12 for more details. Data represent combined data from four independent experiments of four mice total (a-c).

Extended Data Fig. 2 Group 1 and 2 clonotypes differ in expansion and gene expression upon stimulation.

a, Clonal sizes of Group 1 and 2 clonotypes in the stimulated and ex vivo conditions shown in Fig. 3d. P value calculated by two-sample Mann-Whitney U test (Stimulated: n = 74 clonotype groups in Group 1; 37 Group 2 clonotypes. ex vivo: n = 82 Group 1 clonotypes; 40 Group 2 clonotypes). Box and whisker plots indicate the (box) 25th and 75th percentile along with (whisker) + /− 1.5*interquartile range. Violin plots represent estimated density of clonotypes. b, Gene expression fold changes between stimulated and ex vivo cells in (x-axis) Group 1 clonotypes and (y-axis) Group 2 clonotypes. Each point represents a shared gene across Group 1 and 2 clonotypes. Red line indicates fitted linear model. P value calculated by one-tailed F statistics (F(1,5908)) of the linear regression. n = 5908 genes. c, Volcano plots of differentially expressed genes between Group 1 and 2 clonotypes in the (left) stimulated and the (right) ex vivo conditions. P values were determined using a two-tailed likelihood ratio test, and adjusted by Bonferroni correction. Top 10 genes with positive or negative fold changes are labeled. Cells in Group 1 and 2 have been downsampled to 300 each (n = 300 cells for each of the groups). See Supplementary Table 13 for more details.

Extended Data Fig. 3 Analysis of shared clonotypes across four E7-HPV immunized mice.

a, Venn diagram of shared unique Tcrb clones across the four mice. b, Amino acid logo plot of TCRβ sequences that show high similarity among public clones shared by at least three of four animals. Individual sequences are shown in c. c, TCRα and β matching of highly similar clonotypes found in public clones shared by at least three of four animals (Supplementary Table 9). Bold outline indicates dual TCRα chains found in the same cells (Supplementary Table 3). Structural amino acids shown in grey. Number of cells shown in parenthesis. TCRα and β sequences that were detected in less than two cells were excluded for visualization.

Extended Data Fig. 4 Distinct patterns of gene expression correlate with pseudotime.

a, Expression of top 100 most significant genes visualized across pseudotime. Genes were clustered via Ward.D2 based on their patterns of expression. Data represent an individual experiment with 1847 single-cells from one patient (patient 77).

Extended Data Fig. 5 Psuedotime correlates with effector T cell signatures.

a, MsigDB analysis of genes enriched early (cluster 3 in Extended Data Fig 4a; n = 38 genes) or late (cluster 4 in Extended Data Fig 4a; n = 123 genes) on the pseudotemporal trajectory. Description indicates cell state enriched with the corresponding gene set in comparison to another cell state. FDR q-values represent Benjamini and Hochberg-corrected, one-tailed hypergeometric P values. See Supplementary Table 12 for more details. b, Pseudotime distribution of expanded clones shown in Fig. 4d. Number of cells for each clonotype group indicated in parenthesis. A total of 16 clonotype groups are shown. All box and whisker plots indicate the (box) 25th and 75th percentile along with (whisker) + /− 1.5*interquartile range (b). Data represent an individual experiment with 1847 single-cells from one patient (patient 77; a,b).

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 and Tables 1,2,4,5,8 and 9.

Reporting Summary

Supplementary Tables

Supplementary Tables 3,6,7 and 10–13.

Supplementary Data 1

Digital gene expression matrices for data shown in Fig. 3.

Supplementary Data 2

Digital gene expression matrices for data shown in Fig. 4.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tu, A.A., Gierahn, T.M., Monian, B. et al. TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures. Nat Immunol 20, 1692–1699 (2019). https://doi.org/10.1038/s41590-019-0544-5

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing