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Multifactorial heterogeneity of virus-specific T cells and association with the progression of human chronic hepatitis B infection

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Science Immunology  08 Feb 2019:
Vol. 4, Issue 32, eaau6905
DOI: 10.1126/sciimmunol.aau6905
  • Fig. 1 Comprehensive epitope mapping against HBV using highly multiplexed combinatorial pMHC tetramer strategy.

    (A) Experimental workflows. The 562-plex pMHC tetramer library was generated from the deep sequencing of virus and epitope prediction. The library included 484 putative A*11:01-restricted HBV peptides and 78 known control peptides derived from other common virus or self-antigens. One thousand one unique combinations of quadruple SAv-metal codings were used to code the entire library. A self-validated tetramer deconvolution algorithm automatically identified the signals on patient’s T cells with statistical measurements. Validated antigen-specific CD8+ T cells targeting four viral epitopes were shown. (B) Mean frequency of HBV-specific CD8+ T cells from all patients tested across four different viral proteins. Plot only shows the detectable epitopes. Numbers at the bottom indicate the numbers of epitopes detected/screened for each viral protein. (C) Epitope nomenclature and annotation used in this report are shown. “*” indicates a peptide cluster that contained more than one peptide (table S1). Peptide sequences in boldface are previously unpublished sequences based on Immune Epitope Database. (D) Frequencies of four antigen-specific CD8+ T cells across various patient groups (color-coded). (E) Expression of cellular markers on four HBV-specific CD8+ T cells in heatmaps. Boxes highlight the discriminative markers for each patient group.

  • Fig. 2 Multifactorial memory atlas of HBVpol387- and HBVcore169-specific CD8+ T cells linked to HBV clinical stages.

    (A) Unsupervised Phenograph clustering of cellular subsets on all detected antigen-specific CD8+ T cells across patient groups. n = 20, 4 patients per group. Nineteen cellular clusters objectively identified by Phenograph were color-coded as indicated, and the expression levels of probed cellular proteins are shown. (B) Visualization of the Phenograph clustering of nine major cellular clusters of HBVpol387-specific CD8+ T cells. The proportion of cellular clusters within HBVpol387-specific CD8+ T cells in individuals across various patient groups is shown. (C) Same analytical strategy for HBVcore169-specific CD8+ T cells. (D) Bar graph indicates the discrepancy of T cell memory–associated markers (CD27, CD28, CD45RO, CD127, and CXCR3), inhibitory receptors (PD-1 and TIGIT), and CD57 expressed on HBVcore169-specific CD8+ T cells. Error bars are median and range, and values from individuals were imposed. (E) Representative contour plots show the expression level of markers on HBVcore169-specific CD8+ T cells between patient groups. Patients were color-coded as indicated. (F) Top: Logistic regression of eight phenotypic markers showing that significant difference between patient groups was stacked against pseudotime imputed using Scorpius. Bottom: Logistic regression (black solid line) was used to visualize the trend of these eight cellular markers. Dots are individuals color-coded by clinical stages and showed the expression levels of these markers on HBVcore169-specific CD8+ T cells.

  • Fig. 3 Unsupervised analyses uncovered the complex model of inhibitory receptors (exhaustion markers) in CHB.

    (A) One-SENSE objectively related three different T cell categories (Differentiation + TNFR, Inhibitory, and Trafficking) in 2D plots with visualization of the expression levels of cellular proteins. Dots are selected virus-specific CD8+ T cells (color-coded). Boxes annotated the epitopes that were enriched in the given regions. (B) Average fractions of the numbers of coexpressed inhibitory receptors on four HBV-specific CD8+ T cells across patient groups. Plots were from a representative experiment with all nine inhibitory receptors. (C) Average coexpressed inhibitory receptors on four HBV-specific CD8+ T cells across patient groups. Plots were composed of three experiments with simultaneous measurements of eight inhibitory receptors (without TIGIT). Each dot represents an individual.

  • Fig. 4 Nonlinear correlations of multifunctionalities and inhibitory receptors by One-SENSE.

    (A) Patient’s PBMCs were stimulated with corresponding viral peptides for 10 days of in vitro culture to measure the functional capacity. Categorical (Function, Inhibitory, and Differentiation + TNFR) analysis of One-SENSE revealed the diverse multifunctional virus-specific CD8+ T cells subsets and their corresponding coexpression of inhibitory receptors. Dots are different virus-specific CD8+ T cells as annotated. Five different major functional subsets were labeled on the basis of the aligned heatplots and color-coded as indicated. (B) The expression levels of T cell functions, inhibitory receptors, and TNFR costimulatory receptors were compared between these five functional subsets. GM-CSF, granulocyte-macrophage colony-stimulating factor. (C) Bar graphs showed the proportion of each functional subset in HBVpol387- and HBVcore169-specific CD8+ T cells across patient groups. n = 5 per group, except for IT = 4.

  • Fig. 5 Unsupervised quantifications of HBV-specific TCR were associated with disease stages in an epitope-dependent manner.

    (A) TCRdist measurements of epitope-specific TCRs were clustered by unsupervised Phenograph analysis and then projected by t-SNE. Each dot represents one TCR clone. Twenty-eight TCR sequence clusters on t-SNE map were labeled. (B) Sequence motifs (dashed boxes labeled with size) of representative TCR sequence clusters. Average linkage dendrogram for each TCR in the given cluster was presented and color-coded by generation probability. TCR logos display the frequency of V and J segments with CDR3β sequence in the middle. Bottom bars are source regions as indicated. Light gray is V region. Red is N insertion. Black is D for diversity. Dark gray is J region. (C) Percentages of the receptor in total epitope-specific TCRs for 28 TCR sequence clusters. (D) Proportion of TCR C27 and C15 in four different epitope-specific TCRs. (E) TCRdiv diversity measures for each epitope-specific TCR across patient groups. (F) Stacked bar charts show the top 11 TCR clones in individual patient. The frequencies and sequences of public TCR clones were presented. (G) 3D PCA projection delineated patient’s clinical stage using the epitope-specific TCR repertoires, tetramer response, and cellular profiles from the same individuals. (H) Correlation between the frequency and TCRdiv diversity measures of HBVcore169-specific CD8+ T cells.

  • Fig. 6 The phenotypic dynamics and the machine learning–aided modeling of HBVcore169-specific CD8+ T cells.

    (A) A total of 14 patients (n = 8 for HBeAg and n = 6 for HBeAg+) were included in the longitudinal cohort. The average frequency of all detectable HBVcore169-specific CD8+ T cells across different time points was shown. Each dot represents one patient who had detectable HBVcore169-specific CD8+ T cells. (B) Dynamics of HBVcore169-specific CD8+ T cells in two representative patients. (C) Phenotypic dynamics of HBVcore169-specific CD8+ T cells using One-SENSE. Numbers are frequencies, and boxes are annotated as indicated. (D) Fractions of memory (blue boxes) and terminal effector (red boxes) cells in individual patients across longitudinal time points. (E) Plot showed the changes of selective cellular marker expression on HBVcore169-specific CD8+ T cells across patient’s longitudinal time points [early and late, thick stacked bars in (D)]. Two time points (early and late) were picked to roughly match the time points between patients based on the drug intervention. “Early” are pretreatment time points besides one patient (HBeAg+04, whose earliest time point was 3 months after treatment), and “Late” are roughly 30 months after treatment. Plots showed the patients who had detectable HBVcore169-specific CD8+ T cells in both early and late time points. Statistical analysis was used to compare the cellular marker expression between two time points (early and late, solid lines) or patient groups (HBeAg+ and HBeAg, dashed lines). (F) Logistic model (dashed gray lines) of cellular marker expression (dependent variable) against SVM-predicted pseudotime (independent variable). Dots represent the expression levels of cellular markers on HBVcore169-specific CD8+ T cells across different patient’s longitudinal time points. (G) Statistical analysis of SVM-predicted pseudotime during the progression of patient’s longitudinal time points. A nonparametric paired t test was used.

Supplementary Materials

  • immunology.sciencemag.org/cgi/content/full/4/32/eaau6905/DC1

    Materials and Methods

    Fig. S1. Comprehensive epitope mapping strategy and experimental workflow.

    Fig. S2. Quality and detection of antigen-specific CD8+ T cells using highly multiplexed combinatorial pMHC tetramer staining and mass cytometry.

    Fig. S3. Overall magnitudes of antigen-specific CD8+ T cell response in various clinical stages during HBV infection.

    Fig. S4. Validation of highly multiplexed combinatorial pMHC tetramer strategy in HLA-A*11:01 and non–HLA-A*11:01 donors.

    Fig. S5. Validation and reproducibility of antigen-specific CD8+ T cells using flow cytometry and the serological measurement of HDs.

    Fig. S6. In vitro expansion of antigen-specific CD8+ T cells upon peptide stimulation.

    Fig. S7. Expression levels of nine different inhibitory receptors on antigen-specific CD8+ T cells.

    Fig. S8. Epitope frequencies in longitudinal patient cohorts of HBeAg seroconverters.

    Fig. S9. Cellular profiles of subset clusters of HBV-specific CD8+ T cells identified by Phenograph and the enrichment strategy.

    Fig. S10. Unsupervised Phenograph clustering analysis identified multifactorial T cell heterogeneity of HBV-specific CD8+ T cells.

    Fig. S11. Coexpression of inhibitory receptors on virus-specific CD8+ T cells.

    Fig. S12. Heterogeneous multifunctional subsets of virus-specific CD8+ T cells.

    Fig. S13. Diverse characteristics of epitope-specific TCRβ repertoire using TCRdist.

    Fig. S14. Dynamics of cellular response and viral mutation of HBVcore169-specific CD8+ T cells in a longitudinal patient cohort.

    Fig. S15. Staining quality of cellular markers including nine inhibitory receptors using mass cytometry.

    Table S1. List of screened HLA-A*11:01–restricted epitopes and the detected frequency.

    Table S2. List of the antibody staining panels used for mass cytometry and high-dimensional cytometric data analysis.

    Table S3. List of the patient samples and the clinical and serological information.

    Table S4. Frequency of viral mutation on selective epitopes in longitudinal patient cohort across HBeAg seroconversion.

    Movie S1. 3D image of categorical analysis of HBV-specific CD8+ T cells using One-SENSE.

    References (7279)

  • Supplementary Materials

    The PDF file includes:

    • Materials and Methods
    • Fig. S1. Comprehensive epitope mapping strategy and experimental workflow.
    • Fig. S2. Quality and detection of antigen-specific CD8+ T cells using highly multiplexed combinatorial pMHC tetramer staining and mass cytometry.
    • Fig. S3. Overall magnitudes of antigen-specific CD8+ T cell response in various clinical stages during HBV infection.
    • Fig. S4. Validation of highly multiplexed combinatorial pMHC tetramer strategy in HLA-A*11:01 and non–HLA-A*11:01 donors.
    • Fig. S5. Validation and reproducibility of antigen-specific CD8+ T cells using flow cytometry and the serological measurement of HDs.
    • Fig. S6. In vitro expansion of antigen-specific CD8+ T cells upon peptide stimulation.
    • Fig. S7. Expression levels of nine different inhibitory receptors on antigen-specific CD8+ T cells.
    • Fig. S8. Epitope frequencies in longitudinal patient cohorts of HBeAg seroconverters.
    • Fig. S9. Cellular profiles of subset clusters of HBV-specific CD8+ T cells identified by Phenograph and the enrichment strategy.
    • Fig. S10. Unsupervised Phenograph clustering analysis identified multifactorial T cell heterogeneity of HBV-specific CD8+ T cells.
    • Fig. S11. Coexpression of inhibitory receptors on virus-specific CD8+ T cells.
    • Fig. S12. Heterogeneous multifunctional subsets of virus-specific CD8+ T cells.
    • Fig. S13. Diverse characteristics of epitope-specific TCRβ repertoire using TCRdist.
    • Fig. S14. Dynamics of cellular response and viral mutation of HBVcore169-specific CD8+ T cells in a longitudinal patient cohort.
    • Fig. S15. Staining quality of cellular markers including nine inhibitory receptors using mass cytometry.
    • Table S1. List of screened HLA-A*11:01–restricted epitopes and the detected frequency.
    • Table S2. List of the antibody staining panels used for mass cytometry and high-dimensional cytometric data analysis.
    • Table S3. List of the patient samples and the clinical and serological information.
    • Table S4. Frequency of viral mutation on selective epitopes in longitudinal patient cohort across HBeAg seroconversion.
    • Legend for movie S1
    • References (7279)

    Download PDF

    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mov format). 3D image of categorical analysis of HBV-specific CD8+ T cells using One-SENSE.

    Files in this Data Supplement:

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