Fig 2: Mass cytometry analysis of peripheral blood and dissociated tumour cell (DTC) samples from renal and lung cancer patients shows limited co-expression of CD226 and TIGIT on T cells.a Frequency (left) and expression level (right) of TIGIT on CD45RA- CD4+ Tregs, CD4 + T cells, or CD8 + T cells in blood (blue) or tumour (red) samples. Relative TIGIT expression intensity was determined by median metal intensity (MMI) of TIGIT+ cells within each subset. Boxes depict the 25th-75th percentile with a line showing the median. Whiskers display minimum to maximum values. n = 14 matched patient samples. b Co-expression of TIGIT and CD226 was investigated on CD45RA- CD4+ Tregs, CD4+ T cells (non-Tregs), and CD8+ T cells from the blood and tumour. Plots display the frequency of singly or coexpressing populations on concatenated files of the matched blood or tumour samples. c Mean frequency of cells co-expressing TIGIT and CD226 in each CD45RA- T cell subset from blood and tumour. n = 14 matched samples (parent populations with <50 events excluded). d UMAP analysis performed on T cells from matched blood and tumour samples (n = 14 of each). Samples with greater than 3,000 T cell events (n = 19) were downsampled to 3,000 events prior to concatenation. UMAP projections show concatenated T cells from the blood (top; n = 35,691 events) or tumour (bottom; n = 42,000 events), highlighting contour (left) or median metal intensity (MMI) of TIGIT, CD226, and CD96 (right). Arbitrary dashed lines are used to highlight visual examples of discrete and co-expression patterns of TIGIT and CD226. e FlowSOM metaclusters were created on T cells concatenated from all matched samples and projected onto the same UMAP as in d. f Expression intensity heatmap of the indicated markers for each of the 12 FlowSOM metaclusters in e. Color scale indicates row-adjusted z-score expression for each marker. g Mean frequency (±SEM) of T cells in each metacluster for each individual matched sample (n = 14). Gray bars represent the frequency of TIGIT+ events (per biaxial gating) within each metacluster (blood and tumour samples are combined; n = 28, except where <50 events in parent population; statistical differences between blood and tumour are listed in Supplementary Table 3). h Plots displaying the correlation of the frequency of CD155 expression on myeloid antigen-presenting cells (mAPC) with the frequency of CD226+ (left), CD96+ (middle), and TIGIT+ (right) cells from CD4+ Tregs (red circles), CD4+ T cells (black circles), or CD8+ T cells (gray squares) in the tumour microenvironment (n = 44). Linear regression lines and Spearman’s Rho (rs) are shown for correlations that were significant (Two-tailed; P < 0.05, as indicated). Non-parametric matched-pairs two-tailed Wilcoxon tests were used to determine differences between PBMCs and DTCs. Source data are provided as a Source Data file.

Fig 3: Single molecule localisation microscopy reveals extent of TIGIT clustering upon ligation.a dSTORM imaging of TIGIT-GFP in Jurkat cells that have interacted with Planar Lipid Bilayers (PLBs) loaded with ICAM-1 (100 molecules/µm2), and the indicated nectin ligands (400 molecules/µm2) for 20 mins. Cells pre-incubated with an antagonistic TIGIT antibody (aT) or an isotype-matched control (iso) are shown, as indicated. Representative STORM images are shown in the top row, scale bar = 2 µm. Smaller regions (5 µm x 5 µm; dashed red boxes) were subjected to cluster analysis (see methods) and Getis and Franklin density maps and binary maps showing identified clusters are displayed below. b–e Quantitative analysis of the single molecule localisation images shown in a. n = =22 (representing a 25 µm2 region from a single cells), examined over 3 independent experiments, with adjusted P values from one-way ANOVA with Tukey’s multiple comparisons shown (ns = not significant). Mean density of TIGIT localisations (b), mean Ripley’s H function at different clustering radii (c), mean cluster area (d; with each datapoint representing the mean cluster size per region of a single cell) and mean density of events within clusters (e). f–g dSTORM imaging of TIGIT in primary peripheral blood-isolated CD4+ (f) and CD8+ (g) T cells on PLBs loaded with ICAM-1, and the indicated nectin ligands, as in a. Representative STORM images are shown in the top row, scale bar = 1 µm. Smaller regions (3 µm x 3 µm; dashed red boxes) were subjected to cluster analysis, as in a. h–k Quantitative analysis of the single molecule localisations depicted in f and g, as in b–e. n = =30 cells from 3 independent donors, as depicted through symbol shape. Adjusted P values from a one-way ANOVA with Tukey’s multiple comparisons are shown. Error bars represent standard deviation throughout. Source data are provided as a Source Data file.

Fig 4: ITT-like motif is fundamental to T-cell intrinsic TIGIT-mediated inhibition although functionally null-mutants can still co-cluster with the TCR.a Schematic depicting individual point mutations introduced into TIGIT-SNAP. b Representative confocal microscopy images showing WT and mutant forms of TIGIT-SNAP (green) on the surface of Jurkat T cells conjugated for 20 mins with different Raji B cell populations (either CD111- or CD155-expressing; stained via V5 and shown in magenta). A merged fluorescence-BF image is also provided. c Mean log2 fold change (±S.D., n = 3 independent experiments) in synaptic TIGIT enrichment in Jurkat T cells, from the conjugates shown in b. Adjusted P values from a one-way ANOVA with Šídák’s multiple comparisons are displayed, with differences from the WT-111 condition displayed in black and the WT-155 condition displayed in grey. d Representative TIRF microscopy images of WT and mutant forms of TIGIT-SNAP at the IS of Jurkat cells that have interacted with PLBs loaded with ICAM-1, and CD111 or CD155 for 20 mins, as in Fig. 2b. Intensities have been scaled equally, and colour scales provided. e Mean degree of TIGIT clustering measured from the images shown in d (±S.D., n = 3–4 independent experiments, as indicated). Adjusted P values from a one-way ANOVA with Dunnett’s multiple comparisons are displayed, and coloured as in c. f ELISA data showing the relative amount of IL-2 released from either parental or different forms of TIGIT-SNAP-expressing Jurkat cells after co-incubation with SEE-pulsed Raji cells. Data is shown as the mean log2 fold changes between Raji-CD155 conjugates compared to Raji-CD111 conjugates, ± S.D. (n = ?5 independent experiments with adjusted P values from a one-way mixed-effects analysis with a Dunnett’s multiple comparison test displayed). Differences from the parental condition are displayed above in black and from the WT condition displayed below in grey. g Western blot analysis of TIGIT using either Phos-tag SDS-PAGE (left) or standard SDS-PAGE to examine TIGIT phosphorylation in Raji-Jurkat conjugates, as labelled above. Data are representative of 3 independent experiments. h Representative TIRF microscopy images of different forms of TIGIT (SNAP labelled; magenta) and the TCR (OKT3 in PLB; green) in Jurkat cells upon interaction with PLBs containing ICAM-1, either CD111 or CD155, and fluorescently labelled OKT3 (100 molecules/µm2), for 10 mins. i Mean Pearson’s correlation coefficient (±S.D; n = ?50 cells from 2 independent experiments) between TIGIT and OKT3 from the images shown in h. Adjusted P values from a Kruskal-Wallis test with Dunn’s multiple comparisons are shown, and coloured as in c. All scale bars = 5 µm. Source data are provided as a Source Data file.

Fig 5: Super resolution microscopy reveals the nanoscale proximity of TIGIT and TCR clusters.a Two-colour dSTORM imaging of TIGIT-GFP (magenta) in Jurkat cells that have interacted with PLBs loaded with ICAM-1 (100 molecules/µm2), the indicated nectin ligands (400 molecules/µm2) and directly labelled, mono-biotinylated stimulatory TCR antibody OKT3 (green) for the indicated timepoints. Cells pre-incubated with an antagonistic TIGIT antibody (aT) or an isotype-matched control (iso) are shown, as indicated. Representative TIRF and dSTORM images are shown in the top and middle rows, respectively. b–d Quantitative analysis of the colocalisation between TIGIT and TCR in Jurkat cells as shown in a; n = =15 cells per condition, representative of 3 independent experiments, with adjusted P values from a one-way ANOVA with Tukey’s multiple comparisons (b) or Kruskal-Wallis tests with Dunn’s multiple comparisons (c, d) are shown. Positive controls represent TIGIT labelled with both a GFP nanobody and a TIGIT antibody, with the negative control representing the same data but with the XY coordinates of one channel being swapped (Supplementary Fig. 12). Mean (±S.D.) Spearman rank correlations (b) and Mander’s coefficients (c) for TIGIT-OKT3 localisations across each cell. d Mean (±S.D.) fraction of localisations that have a score of >0.8 from a coordinate-based colocalisation analysis across single cells. e Two-colour dSTORM imaging of TIGIT (magenta) and PLB-bound OKT3 (green) in primary peripheral blood-isolated CD4+ and CD8 + T cells on PLBs, as in a. Representative TIRF and dSTORM images are shown in the top and middle rows, respectively. f–h Quantitative analysis of the colocalisation between TIGIT and TCR in primary T cells as shown in e; n = =15 cells (depicted in blue or orange) from 3 individual donors (as shown in black and matched by shape) with adjusted P values from one-way ANOVA with Holm-Šídák’s multiple corrections on the means of each donor displayed. Mean (±S.D.) Spearman rank correlations (f) and Mander’s coefficients (g) for TIGIT-OKT3 localisations across each cell. h Mean (±S.D.) fraction of localisations that have a score of >0.8 from a coordinate-based colocalisation analysis across single cells. In a and e, white scale bars = 5 µm, zoomed regions (5 µm x 5 µm; dashed yellow boxes) are displayed below with yellow scale bars = 1 µm. Source data are provided as a Source Data file.