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S Figure 1. Cellular and Molecular Presence of B Cells, Inkt Cells, and Δγtcr T Cells in Allergic Inflammatory Tissue

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S Figure 1. Cellular and Molecular Presence of B Cells, Inkt Cells, and Δγtcr T Cells in Allergic Inflammatory Tissue Supplementary Figures: S Figure 1. Cellular and molecular presence of B cells, iNKT cells, and δγTCR T cells in allergic inflammatory tissue. A, CD45+ events isolated from biopsy tissue and autologous blood were gated and double-plotted for CD19 (for B cells) and CD3 (for T cells) in the context of normal (N) and active disease (A). B, Cellular presence of iNKT cells, identified by the invariant TCR Vα24-Jα18, as assessed in active disease biopsy and autologous blood tissue, with a spike-in of the iNKT cell line (upper panel) as the positive staining control. C, The % CD4 frequency of δγT cells was assessed by FACS (anti-δγTCR staining) in the context of normal vs. active disease. D, The frequencies of δγT cells were also assessed by extracting the entire TCR sequence pool from scRNA-seq of the 1088 tissue T cells, followed by computerized enumeration. N, normal; R, remission; A, active EoE. 1 S Figure 2. Gene ontology analysis of the tissue lymphocyte-specific genes in disease context. The full list of GO function nodes shown with two approaches, namely enrichment priority (A) and FDR- adjusted p value priority (B), with the radar scanning map representing the top contributing biofunction nodes derived from the 331 tissue-specific genes. C, The signatures of remission (R) tissue lymphocytes signature were compared to those of active (A) and normal (N) tissue lymphocytes genome wide (Mann- Whitney test, FDR-adjusted p < 0.05, fold change > 2), resulting in 217 and 333 significant genes, respectively. Heat maps in the context of all tissue and disease activities types were shown. The Venn gram for overlapping genes between the two cohorts were exhibited on the right. The gene lists were exhibited in Table S4 A and B. 2 S Figure 3. Ontology analysis of tissue T cells in disease context and integrin/chemokine receptor comparison in tissue context. A, Focusing on the 150 dysregulated genes shown in the volcano plot (147 being upregulated in CD3+ cells from patients with active allergic inflammation), major gene ontology (GO) functional nodes are shown on the radar scanning map. The blue radar indicates the fold change in GO enrichment, whereas the orange radar shows the FDR-adjusted p value of the given nodes. B, A set of 14 out 23 chemokine receptors and 10 out of 27 integrin (subunit) genes are significantly different between blood and tissue as shown by the heat diagrams (2-way ANOVA; FDR-adjusted p < 0.05). Disease status illustrated as, N, normal, R, remission and A, active EoE. 3 4 S Figure 4. Genetic interrogation of the expression phenotypes of T1-T8. A, A pipeline for dimensionality reduction of the genome-wide scRNA-seq data to obtain principal component (PC) genes defining T cells subsets using principal component analysis (PCA). B, Heatmap indicating the genetic separation of T1-T8 by the 1,114 PCA genes, with brief GO annotation of major gene clusters juxtaposed. C, The heatmap of the 1088 T cells stratified on disease activity based on the 1114 core genes identified by PCA. Within each disease activity bin, the T cell classification grouping is illustrated, also serving as absoluate cell counters for each cluster. T7 and T8 are separately spaced to emphasize their activated transcriptome phenotype and specific overrepresentation in the cohort with active EoE. 5 S Figure 5. Overall expression topology in the contexts of T1-T8, disease condition and severity, and CD8 dimerization component analyses. A, Global assessment of overall genome wide expression profiles. The non-supervised (genome-wide) expression percentile were graphed as a function of the unique T1-T8 clusters, disease activities and level of esophageal eosinophilia in the unit of RPKMs. Radar plots show the RPKM percentile gradient showing the overall expression abundancy for each parameter. B, On the basis of the expression RPKMs of CD4 and the two CD8 monomers, CD8A, and CD8B, the topology of all 1088 tissue T cells were collectively exhibited in a 3-D plot (top panel, pivoting animation), which were subsequently broken down to 3 disease activities to visually reveal their individual presence (N, normal; R, remission; A, active). Each dot represents a single T cell, and the system is heat labeled on x axis (CD4) to indicate presence of the CD4 component. 6 S Figure 6. CD4-CD8 and TRM topology analyses of T1-T8 clusters The CD4-CD8A distribution plots (A) and CD69-ITGAE (CD103) distribution plots (B) of T1-T8 are shown to illustrate their CD4-CD8A cellularity and tissue-resident memory T cell (TRM) distribution, respectively. All cells were included without disease context, and each dot represents a single T cell. 7 S Figure 7. The mutually exclusive pattern of CD103 and CD25 in T7 and T8 A, T7 and T8 cells were analyzed for their expression of CD25 and CD103 at the single-cell level with each data point representing a single cell. B, With each line (pairing two blue dots) representing a single T7/T8 cell, the single-cell level of CD25 and CD103 are shown with the blue data points. 8 S Figure 8. Extended panel of gene expression by tissue CD3+ T cells as a function of T cell cluster. Focusing on a panel of T cell cytokines relevant to allergy and Th2 activation, we analyzed their cluster- specific expression levels and patterns. In IFNG panel, a red Ʃ resembles the cumulative expression level within a given cluster referred to the right y axis. In the graphs of HPGDS and CD40LG/CD154, the red color was used to emphasize the unique high expression in reference to other clusters in blue. Data were graphed as Mean ± SEM with each dot representing a single T cell. 9 S Figure 9. Gene ontology analyses focusing on functionality of T7 and T8 Gene ontology analysis (FDR-adjusted p < 0.05, Fisher test–based) revealed major biological functions of T7 (A) and T8 (B) presented by radar map. The full functional lists are shown in Table S8 and S9. 10 S Figure 10. Transcriptional and translational properties of T7 and T8 clusters and disease-related clinical correlating genes. A, The expression levels of two key transcription factors determining the differentiation of TREG and peTh2, FOXP3 and GATA3, respectively, within the T7 and T8 cluster, are plotted at single-cell levels to illustrate their mutually exclusive pattern. B, With T7 and T8 cells double-plotted on FOXP3 and GATA3, a negative correlation (left panel, p < 0.0001, spearman correlation) and a largely mutually exclusive relationship (right panel) at single-cell resolution is found. C, Expression profiles of GATA3 and FOXP3 in all T8 clusters with 1088 T cells plotted. Each cell’s RPKM exhibited on left y axis and the RPKM Ʃ 11 in that particular T cluster shown with the red Ʃ tracked to the right y axis. (Mean ± SEM) D, FACS analysis of esophageal CD3+ T cells from active EoE biopsies. CD3+ CD4+ and CD3+CD8+ gated (internal negative control) events were double-plotted on FOXP3 and GATA3. E, A selected gene cohort whose expression correlated with EoE clinical severity (tissue eosinophilia), with the Pearson correlation p value plotted in the radar map. 12 S Figure 11. 2D-TRACER analysis for single-cell Th2 cytokine expression topology Left panel: CD3+ tissue T cells from active (A), remission (R), and normal (N) were quantitatively analyzed by CD4-CD8A expressions. CD4 T cells are defined by CD4 RPKM > 2.0 per single cell as shown in blue, with CD8 T cells by CD8 RPKM > 2.0 shown in orange and non-CD4/non-CD8 cells (Double negative) defined by RPKMs < 2.0 for both shown in grey. Right panel: following the same disease activity designation on the left, Th2 cytokine–positive cells are color-traced for the given Th2 cytokine by a red circle, with the area of that circle proportional to the Th2 cytokine RPKM at the single- cell level. A scale of a series of RPKM circles is shown on the bottom. 13 14 S Figure 12. The single-cell expression analysis for major TREG anti-inflammatory cytokines A, Across all T1-T8 clusters, expression pattern and levels of IL10 are shown at the single-cell level. Each data point represents a T cell that is tracked for RPKM expression on the left y axis. The cumulative/overall IL10 expression for each cluster derived is graphed on the right y axis with their cumulative RPKM levels indicated by the red Ʃ. B, In light of the TL1-TL2 (CD4-CD8) bifurcation determined by the SPADE (Spanning-tree Progression Analysis of Density-normalized Events) analysis (Fig. 6), the developmental tree nodes of IL-10 producers were illustrated by the same tree color-scaled on IL10 expression, with a representative double plot of IL-10/CD8 shown on the bottom. C, Scatter plots for single-cell TGFB1 production across T1-T8 (N, normal; R, remission; A, active) are shown. D, Scatter plots for single-cell IL10, TGFB1 and FOXP3 production as a function of disease status are shown. E, The single-cell expression histogram of IL10 and representative Th2 cytokine IL13 for the pool of T7 and T8 combined. F, Tissue T cell pool of 1088 cells were sorted by IL10 expression levels (blue) with highest values on the left (RPKMs), with TGFB1 and FOXP3 labeled by green and red on the same single-cell histogram, respectively. All data were shown as Mean ± SEM. 15 S Figure 13. Distinct transcription factor profiles across T1-T8 A, On the basis of the known 1391 human transcription factor as reported , the transcription factor expression profile was revealed by an averaged heatmap across T1-T8, with red being higher expression and blue being low in each row.
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