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Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and <t>CD86</t> surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and <t>CD86</t> surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and <t>CD86</t> surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and <t>CD86</t> surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.

Journal: Molecular Therapy Oncology

Article Title: Engineered Salmonella -mediated c-di-AMP delivery activates STING to remodel the tumor microenvironment

doi: 10.1016/j.omton.2026.201185

Figure Lengend Snippet: Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.

Article Snippet: The following antibodies were used: FITC anti-mouse F4/80 (clone CI: A3-1), APC anti-mouse CD86 (clone GL-1), FITC anti-mouse CD3 (clone 17A2), APC anti-mouse CD4 (clone GK1.5), and APC anti-mouse CD8 (clone YTS-169), all purchased from Elabscience Biotechnology Co., Ltd.

Techniques: Flow Cytometry