CD81 and CD82 expressing tumor-infiltrating lymphocytes in the NSCLC tumor microenvironment play a crucial role in T-cell activation and cytokine production

Abstract

Introduction: To understand the immune system within the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC), it is crucial to elucidate the characteristics of molecules associated with T cell activation.

Methods: We conducted an in-depth analysis using single-cell RNA sequencing data obtained from tissue samples of 19 NSCLC patients. T cells were classified based on the Tumor Proportion Score (TPS) within the tumor region, and molecular markers associated with activation and exhaustion were analyzed in T cells from high TPS areas.

Results: Notably, tetraspanins CD81 and CD82, belonging to the tetraspanin protein family, were found to be expressed in activated T cells, particularly in cytotoxic T cells. These tetraspanins showed strong correlations with activation and exhaustion markers. In vitro experiments confirmed increased expression of CD81 and CD82 in IL-2-stimulated T cells. T cells were categorized into CD81^highCD82^high and CD81^lowCD82^low groups based on their expression levels, with CD81^highCD82^high T cells exhibiting elevated activation markers such as CD25 and CD69 compared to CD81^lowCD82^low T cells. This trend was consistent across CD3⁺, CD8⁺, and CD4⁺ T cell subsets. Moreover, CD81^highCD82^high T cells, when stimulated with anti-CD3, demonstrated enhanced secretion of cytokines such as IFN-γ, TNF-α, and IL-2, along with an increase in the proportion of memory T cells. Bulk RNA sequencing results after sorting CD81^highCD82^high and CD81^lowCD82^low T cells consistently supported the roles of CD81 and CD82. Experiments with overexpressed CD81 and CD82 showed increased cytotoxicity against target cells.

Discussion: These findings highlight the multifaceted roles of CD81 and CD82 in T cell activation, cytokine production, memory subset accumulation, and target cell cytolysis. Therefore, these findings suggest the potential of CD81 and CD82 as promising candidates for co-stimulatory molecules in immune therapeutic strategies for cancer treatment within the intricate TME.

Keywords: T lymphocyte; cell therapy; immunotherapy; tetraspanins; tumor-infiltrating lymphocyte.

Figure 1

(A) UMAP1-UMAP2 dimension plot visualizing a complex cellular landscape with 173,023 cells from 19 patients, focusing on T and NK cells, highlighted with a green cluster outlined using a dashed line. (B) UMAP1-UMAP2 Dimension plot closely examining cell clusters derived from T and NK cells identified in (A). A total of 84,286 cells were examined across 10 distinct clusters, providing insights into the diverse T and NK cell subtypes. (C) TPS-dependent gene expression transition exploring gene expression within T cells, considering the influence of tumor proportion score (TPS). Cells are categorized based on TPSs (below and above 50%), with a bar plot illustrating cluster proportions. (D) NMF cluster analysis revealing re-clustering of T cells into NMF1 and NMF2, based on Supplementary Figure 2C, demonstrating the spatial distribution of TPSs above and below 50% among T-cell subsets. Bar plot quantifies the TPS categories within NMF-defined clusters. (E) Activation and exhaustion marker heatmap assessing the activation and exhaustion status by reflecting gene enrichment in activated T cells. Dot plot indicates the expression percentages of these genes in a TPS-dependent manner. (F) Activation and exhaustion marker heatmap in T-cell clusters within the 10 T-cell clusters from (B), illustrating activation and exhaustion marker enrichment, elucidating their distribution across diverse T-cell subtypes. (G) Tetraspanin gene expression heatmap focusing on eight selected genes among 33 tetraspanins, showcasing their expression percentages within the cell population. (H) Tetraspanin gene expression in T-cell clusters similar to (F), dissecting tetraspanin gene enrichment within the 10 T-cell clusters, highlighting differential expression patterns. (I) CD81 and CD82 expression violin plot emphasizing the expression levels of CD81 and CD82 genes in relation to NMF clustering, providing insights into their roles in the T-cell response. Significance was evaluated using t-tests (*p ≤ 0.05). (J) Correlation plot revealing intricate relationships between activation markers, exhaustion markers, and CD81 and CD82 genes. Three major clusters were identified with distinct correlation patterns.

Figure 2

T cells displaying heightened levels of CD81 and CD82 expression demonstrate higher levels of activation markers. (A) T cells were stratified into CD81^lowCD82^low and CD81^highCD82^high subsets. The blue square denotes CD81^lowCD82^low, whereas the orange square indicates CD81^highCD82^high. (B) Comparison of the expression levels of activation markers, CD25 and CD69, within the CD81^lowCD82^low and CD81^highCD82^high regions across the total number of CD3⁺ T, CD8⁺ T, and CD4⁺ T cells. Blue region signifies gating for low CD81 and CD82 expression, whereas the orange region designates gating for the simultaneous high expression of CD81 and CD82. This comparison is presented as a histogram and bar graph to display fold changes. In the bar graph, mean fluorescence intensity (MFI) values under baseline conditions are represented using white bars, and MFI values following IL-2 treatment are indicated using black bars. Data is derived from three independent wells. (C) Comparison of the expression of exhaustion markers PD-1 and LAG-3 within the CD81^lowCD82^low and CD81^highCD82^high regions across the total number of CD3⁺ T, CD8⁺ T, and CD4⁺ T cells. Blue region depicts gating for low CD81 and CD82 expression, whereas the orange region signifies gating for the simultaneous high expression of CD81 and CD82. This comparison is displayed as a histogram and bar graph to demonstrate fold changes. In the bar graph, MFI values without stimulation are denoted using white bars, and MFI values after IL-2 treatment are indicated using black bars. Data is based on three independent wells. Statistical significance between groups was assessed using two-way ANOVA followed by Tukey’s multiple comparison test (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001).

Figure 3

The presence of CD81 and CD82 in T cells elevates the expression of cytokines associated with TCR-related signaling. (A) Illustration of the experimental procedure involving IL-2 stimulation of T cells, followed by sorting into CD81^lowCD82^low and CD81^highCD82^high T-cell subsets and subsequent stimulation with anti-CD3 antibody (0.5 μg/mL). (B) Evaluation of activation marker expression levels, CD25 and CD69, in various T-cell subtypes cultured using the procedure described in (A). Blue histogram represents CD81^low and CD82^low, and red histogram depicts CD81^high and CD82^high. Bar graph illustrates the fold change in CD81^lowCD82^low and CD81^highCD82^high populations. (C) Comparative analysis of the cytokine concentrations (pg/mL) of IFN-γ and TNF-α secreted by T cells cultured using the method outlined in (A). (D) Assessment of the transition from naive T cells to central memory subtype in both CD4⁺ and CD8⁺ T cells cultured using the method described in (A). This analysis explores the memory subset transition in CD81^lowCD82^low and CD81^highCD82^high T cells after stimulation with anti-CD3 antibody (0.5 μg/mL). (E) Investigation of the differentiation in cytokine and chemokine secretion at the immune synapse, comparing stimulation by anti-CD3 antibody (0.5 μg/mL) with concomitant stimulation by anti-CD3 and anti-CD82 antibodies (5 μg/mL). (F) Comparative examination of IL-2 secretion in CD4⁺ T cells with various co-stimulatory molecules, all added at a concentration of 5 μg/mL. Statistical significance between groups was calculated using two-way ANOVA followed by Tukey’s multiple comparison test (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001). ns, not significant.

Figure 4

Heatmap clustering and gene set enrichment analysis (GSEA) to examine the differences and associations between T cells with high CD81 and CD82 expression and those with low CD81 and CD82 expression. (A) Schematic of cell sorting and RNA sequencing process. (B) Heatmaps showed the IFN-γ and IL-2 signaling pathway related gene set in four groups. Individual gene expression was displayed as colored boxes (red = 1, yellow = 0.5, blue = 0). (C, D) GSEA and KEGG pathway. Among the statistically significant gene set pathways, IFN-γ signaling, T-cell cytokine production, and IL-2-mediated signaling pathways were particularly observed. (E) In the CD81^highCD82^high group, although the expression of each set of 198 hallmark genes related to IFN-γ and IL-2/STAT5 was similar or decreased in some areas, most areas exhibited an increase.

Figure 5

Association between CD81 and CD82 with T-cell activation and proliferation is observed in the signature gene expression analysis, aligning with the previous in vitro data. (A) Heatmap of hallmark genes serves as a visual representation of hallmark gene expression patterns, as outlined in Supplementary Table 2 . The x-axis of this heatmap is organized in an ascending order of CD81 and CD82 expression levels, providing a clear and structured view of how these genes relate to specific cellular characteristics and functions. (B) Focusing on CD81^high region in a UMAP1-UMAP2 dimension plot, our analysis focuses on the CD81^high region observed within the clusters of T cells. This region, prominently distinguished by elevated CD81 expression, is highly significant in our investigation. A dashed line demarcates an enriched area, shedding light on its distinctive gene expression profile and cellular attributes. (C) Pathway expression insights of UMAP1-UMAP2 dimension plot unveil the expression of hallmark-related pathways. Four major categories were explored, including the IFN-γ response (upper left), IL-2 and STAT5 signaling pathways (upper right), PI3K, AKT, and mTOR signaling pathways (lower left), and protein secretion (lower right). Each of these regions is carefully separated and highlighted using dashed lines, contributing to a comprehensive understanding of the complex molecular interactions and signaling cascades at play in the cellular landscape. These enriched regions provide valuable insights into the interplay of various pathways and how they collectively influence the function and behavior of these cells.

Figure 6

Increased levels of CD81 and CD82 expression within T-cell clusters boost their cytotoxic potential. (A) Overview of the experimental method and materials used. (B) Schematic of the bispecific T-cell engager, incorporating binding regions for CD3 and CEA. (C) Flow cytometry to assess transduction efficiency. Cells were separated into CD4⁺ and CD8⁺ T-cell subsets. Gray graph represents the baseline expression level, red graph illustrates the expression of CD81 after transduction with CMV-CD81, and blue graph shows the expression of CD82 after transduction with CMV-CD82. Data was analyzed 3 days after transduction. (D) Cytolysis graph comparing naive CD8⁺ T cells with CD81 and CD82 overexpression. Statistical significance was calculated at 48 h. The Effector : Target (E:T) ratio is 5:1, and target cells were seeded at 1E4, with bispecific engager added at 25 nM. (E) Statistical significance calculated for the area under the curve of the cytolysis data presented in (D). (F) Cytolysis graph comparing naive CD4⁺ T cells with CD81 and CD82 overexpression. Statistical significance was calculated at 48 h. The E:T ratio is 5:1, and target cells were seeded at 1E4, with bispecific engager added at 25 nM. (G) Statistical significance calculated for the area under the curve of the cytolysis data presented in (F). Statistical significance between groups was calculated using two-way ANOVA followed by Tukey’s multiple comparison test (**p ≤ 0.01, ****p ≤ 0.0001).

Figure 7

Schematic representation of the summary.