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High-throughput and high-dimensional single-cell analysis of antigen-specific CD8+ T cells

Nature Immunology volume 22, pages 1590–1598 (2021)Cite this article

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Abstract

Although critical to T cell function, antigen specificity is often omitted in high-throughput multiomics-based T cell profiling due to technical challenges. We describe a high-dimensional, tetramer-associated T cell antigen receptor (TCR) sequencing (TetTCR-SeqHD) method to simultaneously profile cognate antigen specificities, TCR sequences, targeted gene expression and surface-protein expression from tens of thousands of single cells. Using human polyclonal CD8+ T cells with known antigen specificity and TCR sequences, we demonstrate over 98% precision for detecting the correct antigen specificity. We also evaluate gene expression and phenotypic differences among antigen-specific CD8+ T cells and characterize phenotype signatures of influenza- and Epstein–Barr virus-specific CD8+ T cells that are unique to their pathogen targets. Moreover, with the high-throughput capacity of profiling hundreds of antigens simultaneously, we apply TetTCR-SeqHD to identify antigens that preferentially enrich cognate CD8+ T cells in patients with type 1 diabetes compared to healthy controls and discover a TCR that cross-reacts with diabetes-related and microbiome antigens. TetTCR-SeqHD is a powerful approach for profiling T cell responses in humans and mice.

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Fig. 1: Schematics of the TetTCR-SeqHD workflow.
Fig. 2: TetTCR-SeqHD validation in cultured human polyclonal CD8+ T cells.
Fig. 3: TetTCR-SeqHD enables combined gene expression, phenotype and TCR clonality comparison among antigen-specific CD8+ T cells.
Fig. 4: Gene expression and phenotype analysis of foreign antigen-specific T cells and cross-reactivity validation.
Fig. 5: Identification of three T cell specificities selectively enriched in patients with T1D and TCR specificity and cross-reactivity validation.

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Data availability

All TCR and peptide information is in the supplementary tables. The accession number for raw sequencing data is phs002441.v1.p1 on dbGaP. Source data are provided with this paper.

Code availability

Custom analysis code is available on GitHub (https://github.com/JiangLabSysImmune).

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Acknowledgments

We thank the patients with T1D for donating blood samples to our study. We also thank anonymous blood donors and staff members at We Are Blood for sample collection. We thank P. Parker for assistance with blood sample purification. We thank S. Chizari for assistance with uploading sequencing data to dbGaP and source code to GitHub. This work was supported by National Institutes of Health grants S10OD020072 (N.J.), R33CA225539 (N.J.) and R56AG064801 (N.J.); National Science Foundation CAREER award 1653866 (N.J.); Welch Foundation grant F1785 (N.J.); the Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation (N.J.); and the Chan Zuckerberg Initiative Neurodegeneration Challenge Network Ben Barres Early Career Acceleration Awards 191856 (N.J.). We would also like to acknowledge funding from the University of Texas at Austin Cockrell School of Engineering Fellowship (A.A.S.), Mario E. Ramirez Endowed Graduate Fellowship (A.A.S.) and the Harry and Rubye Gaston Graduate Scholarship (A.A.S.).

Author information

Author notes

  1. These authors contributed equally: Ke-Yue Ma, Alexandra A. Schonnesen.

Authors and Affiliations

  1. Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA

    Ke-Yue Ma & Ning Jiang

  2. Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA

    Alexandra A. Schonnesen, Chenfeng He, Eric Sun & Ning Jiang

  3. Department of Molecular Biosciences, The University of Texas atAustin, Austin, TX, USA

    Amanda Y. Xia

  4. Department of Nutritional Sciences, The University of Texas at Austin, Austin, TX, USA

    Eunise Chen

  5. Department of Internal Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, USA

    Katherine R. Sebastian & Mrinalini Kulkarni-Date

  6. McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, USA

    Yu-Wan Guo

  7. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Yu-Wan Guo & Ning Jiang

  8. Becton Dickinson, San Jose, CA, USA

    Robert Balderas

  9. Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA

    Ning Jiang

  10. Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Ning Jiang

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Contributions

K.-Y.M. and N.J. conceived and designed the study. K.-Y.M. designed and developed the technology platform; K.-Y.M. and A.A.S. performed and analyzed data for the majority of experiments; K.-Y.M. developed the pipeline to analyze tetramer DNA-barcode data; C.H. and K.-Y.M. developed the script for analyzing TCR sequence data; A.A.S., A.X., E.C., and Y.W.G. performed TCR transduction experiments; E.S. performed in vitro cell culture; A.A.S. and Y.W.G. performed tetramer staining and CD107α experiments. K.R.S. and M.K.-D. recruited patients with T1D and collected blood samples from them. R.B. provided help with Rhapsody-related experiments; K.-Y.M. and N.J. wrote the manuscript with help from all co-authors.

Corresponding author

Correspondence to Ning Jiang.

Ethics declarations

Competing interests

N.J. is a scientific advisor and holds equity interest in ImmuDX and Immune Arch, companies that are developing products related to the research reported. R.B. is an employee of Becton Dickinson, which provided some of the equipment and reagents used in the study. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks Iwijn De Vlaminck, Angela Wu, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. L. 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 Tetramer-positive CD8+ T cells from a mixture of pMHC tetramer-sorted polyclonal T cells cultured in vitro and quality check of gene expression.

a, Gating strategy for sorting tetramer-positive CD8+ T cells from a mixture of pMHC tetramer sorted polyclonal T cells cultured in vitro. b,c, Distribution of mRNA counts (log10) (b) and number of detected genes per cell (c) among different antigen-specific T cell populations. Horizontal lines represent 25th percentile, median and 75th percentile values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range.

Source data

Extended Data Fig. 2 Absolute expression (log10 of MID counts) of differentially expressed genes and surface proteins among different clusters.

a,b, Genes and surface protein plotted here are the same set as in Fig. 3d. c, Distributions of AbSeq MID counts for differentially expressed surface proteins shown in Fig. 3d. d, Density plot showing of all CD8 T cells by AbSeq MID counts of CD45RA vs. CD197, and CD28 vs. CD27, respectively. Colors represent the local density of cells on the two-dimensional space.

Source data

Extended Data Fig. 3 TetTCR-SeqHD enables combined gene expression, phenotype and TCR clonality comparison among antigen-specific CD8+ T cells.

a, UMAP of single cells among different donors. Grey dots represent all cells and colored dots are cells from different chips. b, Comparison of the distribution of phenotypes between tetramer-positive and tetramer-negative CD8+ T cells. c, Percentage of naive population in each antigen-specific CD8+ T cell group. d, Percentage breakdown of naive CD8+ T cells among all major antigen specificities. Only antigen specificities with a percentage greater than 1% within the naive population are shown. ‘NEG’ cells are tetramer-negative CD8+ T cells identified by tetramer MID counts. Cells were classified into filter category based on the following criteria: (1) more than one antigen binds to a single cell, and these antigens are a distance of more than 3 amino acids away from each other; (2) correlation of tetramer MID between single cells and the median of all cells with same TCR sequence is below 0.9, identified as described in Methods.

Source data

Extended Data Fig. 4 Distribution of representative tetramer MID counts.

a, Distribution of tetramer MID counts for eight antigen specificities, including EBV antigens (EBV-BLMF1, BZLF1-190-197, EBV-BRLF1 and EBV-LMP2A), influenza viral antigens (M1 and NP44-52), T1D-associated antigens (PTPRN-FGD-9 and ZNT8-115-123) or cross-reactive antigens (HCV, Mart1 and DUF5119-124-133/INSDRIP-1-9/PTPRN-797-805). For each cell in the group, the MID counts for each of the 280 antigens used in the experiment were tallied and then overlaid in the same order of the 280 antigens. Only the antigens that emerge after the filter are labeled on the x axis, and their position in the 280 antigen list is indicated by a tick on the x axis. Each panel with a sharp single peak indicated single antigen specificity, while panels with multiple sharp peaks indicated cross-reactive antigens. b, Comparison of tetramer MID counts among DUF5119-124-133/INSDRIP-1-9/PTPRN-797-805 cross-reactive and single antigen-specific cells.

Source data

Extended Data Fig. 5 Analysis of T cells with bound antigen specificity being mismatched HLA alleles.

a, Summary of percentage of antigen-specific T cells with mismatched HLA alleles in all donors. Combined percentages from two sources are presented (Discussion). b, Percentage of antigen-specific T cells with mismatched HLA alleles in each donor. Combined percentages of two sources are presented (Discussion). c, Comparison of phenotypes of cells with mismatched HLA alleles with the overall population. Gray dots represent all CD8+ T cells.

Source data

Extended Data Fig. 6 Distribution of viral antigen-specific CD8+ T cells among 12 primary CD8+ T cells clusters in all 18 donors when the tetramer-negative MID threshold was set to 15.

Distribution of viral antigen-specific CD8+ T cells among 12 primary CD8+ T cells clusters in all 18 donors when the tetramer-negative MID threshold was set to 15 (Methods).

Source data

Extended Data Fig. 7 Frequency of total T1D autoantigen-specific CD8+ T cells in healthy subjects and T1D patients.

a, Frequency of T1D autoantigen tetramer-positive CD8+ T cells in different donors for various HLA alleles. b, Comparison of total T1D autoantigen tetramer-positive CD8+ T cells between healthy and T1D donors for various HLA alleles. A two-sided Wilcoxon nonparametric test was performed. Horizontal lines represent 25th percentile, median and 75th percentile values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. The number of subjects with HLA-A01:01, A02:01 and B08:01 are 10, 12 and 10, respectively.

Source data

Extended Data Fig. 8 T1D autoantigens with different antigen-specific CD8+ T cell frequencies and clonality between healthy subjects and T1D patients.

a, Five T1D autoantigens were identified to have a significantly higher frequency of antigen-specific T cells in peripheral blood when the MID-negative threshold was set to 15. A two-sided Wilcoxon nonparametric test was performed. Horizontal lines represent 25th percentile, median and 75th percentile values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. The number of subjects with HLA-A01:01, A02:01 and B08:01 are 10, 12 and 10, respectively. b, TCR clonality heatmap of T1D antigenic-specific T cells for each antigen/donor combination. Grey, no T cells were detected.

Source data

Extended Data Fig. 9 Comparison of T1D antigen-specific CD8+ T cells between T1D patients and healthy subjects.

a, UMAP of T1D antigen-specific CD8+ T cells in T1D patients and healthy subjects respectively. Colored dots are T1D antigen-specific CD8+ T cells, and gray dots are other cells. b, Comparison of the distribution of phenotypes among T1D antigen-specific CD8+ T cells in each donor. Wilcoxon test was performed, with no significance between T1D and healthy subjects in any cluster.

Source data

Extended Data Fig. 10 TCR specificity and cross-reactivity validation by tetramer staining.

Bar plot showing the percentage of tetramer-positive cells gated on TCRβhi fraction of the cells, corresponding to Fig. 5b. Tetramer staining experiments were performed in triplicate. A two-tailed Student’s t test was performed between cognate tetramer and each negative control for all TCRs. EBV-BLMF1: GLCTLVAML; INSDRIP-1-9: MLYQHLLPL; DUF5119-124-133: MVWGPDPLYV; PTPRN-797-805: MVWESGCTV; PTPRN-FGD-9: FGDHPGHSY; INS-WMR-8: WMRLLPLL. ns, not significant; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; ****P ≤ 0.0001.

Source data

Supplementary information

Supplementary Information

Supplementary Figures 1-8

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41590_2021_1073_MOESM4_ESM.xlsx

Supplementary Table 1: Antigens used to sort and stimulate polyclonal CD8+ T cells. Supplementary Table 2: Sequencing metrics for all TetTCR-SeqHD experiments. Supplementary Table 3: Reference sequences of TCRβ for polyclonal CD8+ T cells. Supplementary Table 4: Endogenous and foreign antigens used in TetTCR-SeqHD experiments with primary CD8+ T cells. Supplementary Table 5: Healthy individuals without T1D and patients with T1D and T2D used in the TetTCR-SeqHD experiments. Supplementary Table 6: Number of antigens and cells detected for non-T1D endogenous, T1D, and viral antigens in each individual. Supplementary Table 7: Summary of transduced TCR sequences and their cognate antigen specificities. Supplementary Table 8: Oligonucleotide sequences used in TetTCR-SeqHD. Supplementary Table 9: Oligonucleotides used to label additional antibodies.

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Ma, KY., Schonnesen, A.A., He, C. et al. High-throughput and high-dimensional single-cell analysis of antigen-specific CD8+ T cells. Nat Immunol 22, 1590–1598 (2021). https://doi.org/10.1038/s41590-021-01073-2

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