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.
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References
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
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
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
Taussig MJ (1980) Antigen-specific suppressor T cell factors. Cancer Immunol Immunother 9:23–30
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
Rosati E, Dowds CM, Liaskou E et al (2017) Overview of methodologies for T-cell receptor repertoire analysis. BMC Biotechnol 17:61
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
Hwang B, Lee JH, Bang D (2018) Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 50:1–14
Sims S, Willberg C, Klenerman P (2010) MHC-peptide tetramers for the analysis of antigen-specific T cells. Expert Rev Vaccines 9:765–774
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
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
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
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
Kebschull JM, Zador AM (2018) Cellular barcoding: lineage tracing, screening and beyond. Nat Methods 15:871–879
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
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
Klein AM, Mazutis L, Akartuna I et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201
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
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
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
Miller TE, Lareau CA, Verga JA et al (2022) Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat Biotechnololgy
Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359
Broad Institute Picard toolkit. http://broadinstitute.github.io/picard/
Lefranc M-P, Lefranc G (2003) The T cell receptor FactsBook. Academic Press, London
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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
(a–c) Species-specific variable region primers: (a) human, (b) mouse, and (c) crab-eating macaque (MacFas) (CSV 6 kb)
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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
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DOI: https://doi.org/10.1007/978-1-0716-2712-9_7
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