Skip to main content

Enriching and Characterizing T Cell Repertoires from 3′ Barcoded Single-Cell Whole Transcriptome Amplification Products

  • Protocol
  • First Online:
T-Cell Repertoire Characterization

Abstract

Antigen-specific T cells play an essential role in immunoregulation and many diseases such as cancer. Characterizing the T cell receptor (TCR) sequences that encode T cell specificity is critical for elucidating the antigenic determinants of immunological diseases and designing therapeutic remedies. However, methods of obtaining single-cell TCR sequencing data are labor and cost intensive, typically requiring both cell sorting and full-length single-cell RNA-sequencing (scRNA-seq). New high-throughput 3′ cell-barcoding scRNA-seq methods can simplify and scale this process; however, they do not routinely capture TCR sequences during library preparation and sequencing. While 5′ cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at single-cell resolution, doing so requires specialized reagents which cannot be applied to samples previously processed using 3′ cell-barcoding methods.

Here, we outline a method for sequencing TCRα and TCRβ transcripts from samples already processed using 3′ cell-barcoding scRNA-seq platforms, ensuring TCR recovery at a single-cell resolution. In short, a fraction of the 3′ barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3′ scRNA-seq library is enriched for TCR transcripts using biotinylated probes and further amplified using the same universal primer sequence from WTA. Primer extension using TCR V-region primers and targeted PCR amplification using a second universal primer result in a 3′ barcoded single-cell CDR3-enriched library that can be sequenced with custom sequencing primers. Coupled with 3′ scRNA-seq of the same WTA, this method enables simultaneous analysis of single-cell transcriptomes and TCR sequences which can help interpret inherent heterogeneity among antigen-specific T cells and salient disease biology. The method presented here can also be adapted readily to enrich and sequence other transcripts of interest from both 3′ and 5′ barcoded scRNA-seq WTA libraries.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kirsch IR, Watanabe R, O’Malley JT et al (2015) TCR sequencing facilitates diagnosis and identifies mature T cells as the cell of origin in CTCL. Sci Transl Med 7:308ra158

    Article  Google Scholar 

  2. Lossius A, Johansen JN, Vartdal F et al (2014) High-throughput sequencing of TCR repertoires in multiple sclerosis reveals intrathecal enrichment of EBV-reactive CD8 T cells. Eur J Immunol. 44(11):3439–3452. https://doi.org/10.1002/eji.201444662

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  4. Taussig MJ (1980) Antigen-specific suppressor T cell factors. Cancer Immunol Immunother 9:23–30

    Article  Google Scholar 

  5. Farmanbar A, Kneller R, Firouzi S (2019) RNA sequencing identifies clonal structure of T-cell repertoires in patients with adult T-cell leukemia/lymphoma. NPJ Genom Med 4:10

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Tu AA, Gierahn TM, Monian B et al (2019) TCR sequencing paired with massively parallel 3’ RNA-seq reveals clonotypic T cell signatures. Nat Immunol 20:1692–1699

    Article  CAS  Google Scholar 

  8. Hwang B, Lee JH, Bang D (2018) Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 50:1–14

    Article  CAS  Google Scholar 

  9. Sims S, Willberg C, Klenerman P (2010) MHC-peptide tetramers for the analysis of antigen-specific T cells. Expert Rev Vaccines 9:765–774

    Article  CAS  Google Scholar 

  10. Huang J, Zeng X, Sigal N et al (2016) Detection, phenotyping, and quantification of antigen-specific T cells using a peptide-MHC dodecamer. Proc Natl Acad Sci U S A 113:E1890–E1897

    PubMed  PubMed Central  CAS  Google Scholar 

  11. Ranasinghe S, Lamothe PA, Soghoian DZ et al (2016) Antiviral CD8+ T cells restricted by human leukocyte antigen class II exist during natural HIV infection and exhibit clonal expansion. Immunity 45:917–930

    Article  CAS  Google Scholar 

  12. Dash P, McClaren JL, Oguin TH 3rd et al (2011) Paired analysis of TCRα and TCRβ chains at the single-cell level in mice. J Clin Invest 121:288–295

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Kebschull JM, Zador AM (2018) Cellular barcoding: lineage tracing, screening and beyond. Nat Methods 15:871–879

    Article  CAS  Google Scholar 

  15. Macosko EZ, Basu A, Satija R et al (2015) Highly parallel genome-wide expression profiling of individual cells using Nanoliter droplets. Cell 161:1202–1214

    Article  CAS  Google Scholar 

  16. Rodriques SG, Stickels RR, Goeva A et al (2019) Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363:1463–1467

    Article  CAS  Google Scholar 

  17. Klein AM, Mazutis L, Akartuna I et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201

    Article  CAS  Google Scholar 

  18. Gierahn TM, Wadsworth MH II, Hughes TK et al (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Protoc Exch. https://doi.org/10.1038/protex.2017.006a

  19. Hughes TK, Wadsworth MH, Gierahn TM, et al (2019) Highly efficient, massively-parallel single-cell RNA-Seq reveals cellular states and molecular features of human skin pathology. https://www.biorxiv.org/content/10.1101/689273v1

  20. van Galen P, Hovestadt V, Wadsworth MH II et al (2019) Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell 176(6):1265–1281.e24. https://doi.org/10.1016/j.cell.2019.01.031

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Miller TE, Lareau CA, Verga JA et al (2022) Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat Biotechnololgy

    Google Scholar 

  22. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

    Article  CAS  Google Scholar 

  23. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359

    Article  CAS  Google Scholar 

  24. Broad Institute Picard toolkit. http://broadinstitute.github.io/picard/

  25. Lefranc M-P, Lefranc G (2003) The T cell receptor FactsBook. Academic Press, London

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

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

Editor information

Editors and Affiliations

1 Electronic Supplementary Material

Supplementary Table 1

Example output of TCRGO pipeline (TSV 51 kb)

Supplementary Table 2

Biotinylated oligonucleotide probes for TCR enrichment for human (Homo sapiens), mouse (Mus musculus), and crab-eating macaque (Macaca fascicularis, abbreviated MacFas), respectively (CSV 1 kb)

Supplementary Table 3

UPS2_N50× primers with Illumina barcodes (CSV 5 kb)

Supplementary Table 4

Optimized custom TCR sequencing primers for human, mouse, and crab-eating macaque. Human/macaque primers are universal for human and macaque species (CSV 574 bytes)

Supplementary Table 5

(ac) Species-specific variable region primers: (a) human, (b) mouse, and (c) crab-eating macaque (MacFas) (CSV 6 kb)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Jivanjee, T. et al. (2022). Enriching and Characterizing T Cell Repertoires from 3′ Barcoded Single-Cell Whole Transcriptome Amplification Products. In: Huang, H., Davis, M.M. (eds) T-Cell Repertoire Characterization. Methods in Molecular Biology, vol 2574. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2712-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2712-9_7

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2711-2

  • Online ISBN: 978-1-0716-2712-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics