Abstract
Adaptive immunity recognizes and responds to tumors, although they are part of the immunological “self.” T cells, both CD4+ and CD8+, play a key role in the process, and the specific set of receptors which recognize tumor antigens therefore has the potential to provide prognostic biomarkers for tracking tumor growth after cancer therapy, including immunotherapy. Most published data on the T cell repertoire continue to rely on commercial proprietary methods, which often do not allow access to the raw data, and are difficult to validate. We describe an open-source protocol for amplifying, sequencing, and analyzing T cell receptors which is economical, robust, sensitive, and versatile. The key experimental step is the ligation of a single-stranded oligonucleotide to the 3′ end of the T cell receptor cDNA, which allows easy amplification of all possible rearrangements using only a single set of primers per locus, while simultaneously introducing a unique molecular identifier to label each starting cDNA molecule. After sequencing, this molecular identifier can be used to correct both sequence errors and the effects of differential PCR amplification efficiency, thus producing a more accurate measure of the true T cell receptor frequency within the sample. Samples are then tagged with unique pairs of indices, facilitating robotic scale-up and significantly reducing cross-sample contamination from index hopping. This method has been applied to the analysis of tumor-infiltrating lymphocytes and matched peripheral blood samples from patients with a variety of solid tumors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Joshi K, Milighetti M, Chain BM (2021) Application of T cell receptor (TCR) repertoire analysis for the advancement of cancer immunotherapy. Curr Opin Immunol 74:1–8. https://doi.org/10.1016/j.coi.2021.07.006
Heather JM, Chain B (2016) The sequence of sequencers: the history of sequencing DNA. Genomics 107:1–8. https://doi.org/10.1016/j.ygeno.2015.11.003
Barennes P, Quiniou V, Shugay M et al (2021) Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nat Biotechnol 39:236–245. https://doi.org/10.1038/s41587-020-0656-3
Uddin I, Woolston A, Peacock T et al (2019) Quantitative analysis of the T cell receptor repertoire. Methods Enzymol 629:465–492. https://doi.org/10.1016/bs.mie.2019.05.054
Oakes T, Heather JM, Best K et al (2017) Quantitative characterization of the T cell receptor repertoire of Naïve and memory subsets using an integrated experimental and computational pipeline which is robust, economical, and versatile. Front Immunol 8:1267. https://doi.org/10.3389/fimmu.2017.01267
Thomas N, Heather J, Ndifon W et al (2013) Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine. Bioinformatics 29:542–550. https://doi.org/10.1093/bioinformatics/btt004
Peacock T, Heather JM, Ronel T, Chain B (2021) Decombinator V4: an improved AIRR compliant-software package for T-cell receptor sequence annotation? Bioinformatics (Oxford, England) 37:876–878. https://doi.org/10.1093/bioinformatics/btaa758
Weinstein JA, Jiang N, White RA et al (2009) High-throughput sequencing of the zebrafish antibody repertoire. Science (New York, NY) 324:807–810. https://doi.org/10.1126/science.1170020
Shugay M, Britanova OV, Merzlyak EM et al (2014) Towards error-free profiling of immune repertoires. Nat Methods 11:653–655. https://doi.org/10.1038/nmeth.2960
Mamedov IZ, Britanova OV, Zvyagin IV et al (2013) Preparing unbiased T-cell receptor and antibody cDNA libraries for the deep next generation sequencing profiling. Front Immunol 4:1–10. https://doi.org/10.3389/fimmu.2013.00456
Best K, Oakes T, Heather JM et al (2015) Computational analysis of stochastic heterogeneity in PCR amplification efficiency revealed by single molecule barcoding. Sci Rep 5:14629. https://doi.org/10.1038/srep14629
Costello M, Fleharty M, Abreu J et al (2018) Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms. BMC Genomics 19:332. https://doi.org/10.1186/s12864-018-4703-0
Gallard A, Foucras G, Coureau C, Guéry J-C (2002) Tracking T cell clonotypes in complex T lymphocyte populations by real-time quantitative PCR using fluorogenic complementarity-determining region-3-specific probes. J Immunol Methods 270:269–280
Lindsten T, June CH, Thompson CB (1988) Transcription of T cell antigen receptor genes is induced by protein kinase C activation. J Immunol (Baltimore, Md : 1950) 141:1769–1774
Paillard F, Sterkers G, Vaquero C (1990) Transcriptional and post-transcriptional regulation of TcR, CD4 and CD8 gene expression during activation of normal human T lymphocytes. EMBO J 9:1867–1872
Paillard F, Sterkers G, Vaquero C (1992) Correlation between up-regulation of lymphokine mRNA and down-regulation of TcR, CD4, CD8 and lck mRNA as shown by the effect of CsA on activated T lymphocytes. Biochem Biophys Res Commun 186:603–611
Acknowledgments
This research was funded by grants from Cancer Research UK, the UK MRC, and the Rosetrees Trust and supported by the National Institute for Health and Care Research UCL Hospitals Biomedical Research Centre.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Nageswaran, G., Byrne, S., Veeriah, S., Chain, B. (2022). The Intra-Tumoral T Cell Receptor Repertoire: Steps Towards a Useful Clinical Biomarker. 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_6
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2712-9_6
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