Research ArticleMultiple Sclerosis

Functional inflammatory profiles distinguish myelin-reactive T cells from patients with multiple sclerosis

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Science Translational Medicine  13 May 2015:
Vol. 7, Issue 287, pp. 287ra74
DOI: 10.1126/scitranslmed.aaa8038
  • Fig. 1. Phenotypic analysis of oligoclonal libraries of myelin-reactive CD4+ T cells from a patient with MS and a healthy control (HC) subject.

    (A to C) Heat map comparing functional responses of (A) naïve, (B), CCR6 memory, and (C) CCR6+ memory CD4+ T cells cultured with irradiated autologous monocytes with or without myelin peptides (MBP85–99, MOG222–241, PLP30–49, and PLP129–148; or MOG97–109 and PLP180–199) or C. albicans. Proliferation was measured by [3H]thymidine incorporation on day 5, and culture supernatants were measured on day 7 by ELISA for IFN-γ, IL-17, GM-CSF, and IL-10. Data show 1 representative experiment (of 13) and were z score–normalized for each parameter. Each bar per column represents one oligoclonal library.

  • Fig. 2. PCA of functional phenotypes of myelin-reactive CCR6+ memory CD4+ T cells.

    Scatterplots show measured pentadimensional responses (proliferation, IFN-γ, IL-17, GM-CSF, and IL-10) for individual amplified T cell libraries (each dot) projected onto the first two principal components. (A to C) Analysis is shown for no peptide (A), C. albicans (B), and myelin peptides (C) from 13 healthy subjects and 13 MS patients. Projections of the vectors for each data class are also shown and annotated for reference. Statistically significant P values of myelin-reactive T cells for IL-17 (n = 13; P < 0.0001, ANOVA), GM-CSF (n = 13; P = 0.0114, ANOVA), IFN-γ (n = 13; P < 0.0001, ANOVA), IL-10 (n = 13; P = 0.0005, ANOVA), and proliferation (n = 13; P < 0.0001, ANOVA) are shown.

  • Fig. 3. Single-cell clonal analysis of myelin-reactive CCR6+ memory CD4+ T cells in HLA-DR4+ patients with MS and healthy control subjects.

    Tetramer-sorted single-cell clones (n = 144) were stimulated with DR4 myelin peptides (MOG97–109 and PLP180–199) to verify the specificity. Heat map shows functional profiles of individual clones measured on day 5 after stimulation. Data were z score–normalized within a given parameter and organized by hierarchical clustering. Clusters that separated in the dendrogram by a distance metric of 3 are shown in each box, and pie charts indicate proportion of clones within a given cluster derived from healthy (black) or MS (gray) subjects. The numbers next the pie charts refer to the clones classified into each proportion. Clones that did not respond after restimulation (42 healthy controls and 41 MS clones) are not shown here but did cluster together.

  • Fig. 4. Gene expression analysis of myelin-reactive CCR6+ memory T cells in HLA-DR4+ patients with MS and healthy subjects.

    CCR6+ memory CD4+ T cells from healthy subjects (n = 3) and MS patients (n = 5) were amplified by PHA and IL-2, scored for proliferation upon restimulation, and sorted as myelin tetramer+ and tetramer cells for RNA sequencing. (A) Gene sets enriched in the MS tetramer+ samples (yellow), healthy control tetramer+ samples (green), or both as determined by GSEA (FDR < 0.05). Representative gene sets from each category are shown. (B) Venn diagram summarizes the overlap of genes with the core pathologic EAE set (white), and the total genes in the leading edge (light gray), within the differentially expressed gene set reported by Lee et al. (dark gray) (16). The heat map (right) shows the z score–normalized log2FPKM (fragments per kilobase of exon model per million mapped reads) values for the indicated genes in MS tetramer+ or tetramer samples. Genes that are bold with asterisk are contained within the leading-edge gene set. (C) −Log(FDR) values of GSEA results for gene sets indicated. FDR values that were reported as 0 were set to 4 for display purposes (pathogenic TH17, TH17 differential expression, TH17 cytokines, and TH17 combinatorial core). Dashed line shows FDR >0.05. EM, effector memory. (D) A network representation of molecules enriched in MS tetramer+ samples. The color of each molecule shows fold change (FC) of MS tetramer+ relative to MS tetramer as indicated by the key. Solid (direct) or dashed (indirect) cyan lines denote known molecular interactions. Select molecules are labeled, and molecules highlighted in orange were differentially expressed.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/287/287ra74/DC1

    Fig. S1. Schematic representation of amplified T cell library assay.

    Fig. S2. Sorting strategy of each T cell subpopulation.

    Fig. S3. PCA of functional phenotypes of myelin-reactive CD4+CCR6 T cells.

    Fig. S4. Functional phenotypes of myelin-reactive CD4+ T cells.

    Fig. S5. Representative tetramer staining and sorting strategy of each library were chosen for single-cell cloning and RNA sequencing.

    Fig. S6. Specificity of myelin-reactive CD4+ T cells.

    Fig. S7. Phenotypic analysis of myelin-specific single-cell clones.

    Fig. S8. Cell proliferation of each well from MS patients and healthy controls chosen for RNA sequencing.

    Fig. S9. Correlation of RNA-seq data across biological replicates.

    Fig. S10. Differential expression analysis of myelin-reactive T cells in MS and healthy controls.

    Fig. S11. Correlation scatterplots.

    Fig. S12. Heat maps of selected enriched gene sets identified by GSEA in MS tetramer-positive samples.

    Fig. S13. Histogram showing distribution of the number of times each gene appeared across all gene sets.

    Fig. S14. Heat map of log2FPKM values for the 224-gene leading-edge set.

    Fig. S15. Enriched canonical pathways and network analysis.

    Fig. S16. Additional network analysis.

    Table S1. Patients with MS and paired healthy subjects information.

    Table S2. Myelin peptides and control peptides used in T cell library assays.

    Table S3. Percent variance explained by each principal component.

    Table S4. GSEA results (FDR < 0.25) for comparison of MS tetramer-positive to MS tetramer-negative.

    Table S5. GSEA results (FDR < 0.25) for comparison of healthy control tetramer-positive to healthy control tetramer-negative.

    Table S6. Curated gene signatures corresponding to Fig. 4C.

    Table S7. Gene sets used for leading-edge analysis to define pathogenic gene signature.

    Table S8. Summary of networks constructed by IPA from pathogenic signature.

    Table S9. Raw data of T cell library assays.

    Table S10. Statistical summary of T cell library data.

    References (51, 52)

  • Supplementary Material for:

    Functional inflammatory profiles distinguish myelin-reactive T cells from patients with multiple sclerosis

    Yonghao Cao, Brittany A. Goods, Khadir Raddassi, Gerald T. Nepom, William W. Kwok, J. Christopher Love, David A. Hafler*

    *Corresponding author. E-mail: david.hafler{at}yale.edu

    Published 13 May 2015, Sci. Transl. Med. 7, 287ra74 (2015)
    DOI: 10.1126/scitranslmed.aaa8038

    This PDF file includes:

    • Fig. S1. Schematic representation of amplified T cell library assay.
    • Fig. S2. Sorting strategy of each T cell subpopulation.
    • Fig. S3. PCA of functional phenotypes of myelin-reactive CD4+CCR6 T cells.
    • Fig. S4. Functional phenotypes of myelin-reactive CD4+ T cells.
    • Fig. S5. Representative tetramer staining and sorting strategy of each library were chosen for single-cell cloning and RNA sequencing.
    • Fig. S6. Specificity of myelin-reactive CD4+ T cells.
    • Fig. S7. Phenotypic analysis of myelin-specific single-cell clones.
    • Fig. S8. Cell proliferation of each well from MS patients and healthy controls chosen for RNA sequencing.
    • Fig. S9. Correlation of RNA-seq data across biological replicates.
    • Fig. S10. Differential expression analysis of myelin-reactive T cells in MS and healthy controls.
    • Fig. S11. Correlation scatterplots.
    • Fig. S12. Heat maps of selected enriched gene sets identified by GSEA in MS tetramer-positive samples.
    • Fig. S13. Histogram showing distribution of the number of times each gene appeared across all gene sets.
    • Fig. S14. Heat map of log2FPKM values for the 224-gene leading-edge set.
    • Fig. S15. Enriched canonical pathways and network analysis.
    • Fig. S16. Additional network analysis.
    • Table S1. Patients with MS and paired healthy subjects information.
    • Table S2. Myelin peptides and control peptides used in T cell library assays.
    • Table S3. Percent variance explained by each principal component.
    • Table S4. GSEA results (FDR < 0.25) for comparison of MS tetramer-positive to MS tetramer-negative.
    • Table S5. GSEA results (FDR < 0.25) for comparison of healthy control tetramerpositive to healthy control tetramer-negative.
    • Table S6. Curated gene signatures corresponding to Fig. 4C.
    • Table S7. Gene sets used for leading-edge analysis to define pathogenic gene signature.
    • Table S8. Summary of networks constructed by IPA from pathogenic signature.
    • References (51, 52)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S9 (Microsoft Excel format). Raw data of T cell library assays.
    • Table S10 (Microsoft Excel format). Statistical summary of T cell library data.

    [Download Tables S9 and S10]

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