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tgf β neutralizing antibodies  (PeproTech)

 
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    PeproTech tgf β neutralizing antibodies
    Tgf β Neutralizing Antibodies, supplied by PeproTech, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of <t>(DN)</t> <t>TILs</t> in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of <t>(DN)</t> <t>TILs</t> in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of <t>(DN)</t> <t>TILs</t> in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of <t>(DN)</t> <t>TILs</t> in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of <t>(DN)</t> <t>TILs</t> in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .
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    tgf-β monoclonal mouse neutralizing antibody tgfmab clone 1d11  (Bio X Cell)
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of <t>(DN)</t> <t>TILs</t> in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .
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    Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of (DN) TILs in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .

    Journal: Cell Reports Medicine

    Article Title: Tumor-infiltrated double-negative regulatory T cells predict outcome of T cell-based immunotherapy in nasopharyngeal carcinoma

    doi: 10.1016/j.xcrm.2025.102096

    Figure Lengend Snippet: Study design and single-cell transcriptomic landscape of TIL infusion products (A) Graphical overview of the experimental design and bioinformatics workflow. In our clinical trial cohort, bulk RNA sequencing was performed on tumor tissue from NPC patients before CCRT+TIL ( n = 22) or CCRT alone ( n = 40), and TIL infusion products were isolated from pretreatment tumor tissue from these patients, expanded ex vivo , and subsequently prepared for single-cell (sc)RNA sequencing ( n = 26) and flow cytometry ( n = 47). (B) Uniform manifold approximation projection (UMAP) plot of 65,978 quality-controlled T cells colored by 14 T cell subsets with the distinct G1 phase, S phase, and G2M phase. The cell-cycle phase of 14 T cell subsets was labeled in the UMAP plot and determined by the cell-cycle phase scores calculated by the Seurat package. (C) Heatmap showing the expression of the top 50 differentially expressed genes among cell subsets obtained by cell type annotation utilizing the expression of canonical marker genes in 14 cell subgroups. Information on the 14 cell subsets is displayed on the right. (D) UMAP plots showing the distribution of indicative T cell subsets between the non-progression group and progression group. (E) Dot plot showing the composition of CD3 + CD4 − CD8 − , CD3 + CD4 + , and CD3 + CD8 + T cells in NPC TIL infusion products according to flow cytometry ( n = 47). Scatter dot plots show individual values and mean ± SEM. (F) Bar plot comparing the frequency of 14 cell subgroups in the TIL infusion products from NPC patients in the non-progression ( n = 15) and progression groups ( n = 11). The data are presented as mean ± SEM. The Wilcoxon rank-sum test was used to determine the significance. ∗ p < 0.05; ns, not significant; adjusted for multiple comparisons using the Benjamini-Hochberg procedure. (G) Boxplot comparing the frequency of (DN) TILs in the non-progression ( n = 28) and progression groups ( n = 19) according to flow cytometry. Median and interquartile were shown in the boxplots. Wilcoxon rank-sum test was used to determine the significance. (H) Kaplan-Meier survival curves for progression-free survival (PFS) and overall survival (OS) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of the C8_DN_ T_cells cluster inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. Optimal cutoff values were defined using the “survminer” R package. See also and .

    Article Snippet: To evaluate the suppression mechanism of DN TILs, neutralizing antibodies targeting TGF-β (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHB215) and IL-10 (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHA915) were added, along with a Fas-FasL signaling antagonist (10 μM; MedChemExpress, HY- P10102 ) and GITR inhibitor (20 nM; KKL Med Inc., KM2426) at an effector-to-target (E:T) ratio of 1:1.

    Techniques: RNA Sequencing, Isolation, Ex Vivo, Flow Cytometry, Labeling, Expressing, Marker

    Transcriptional characterization and developmental trajectory of CD3 + CD8 − CD4 − (DN) TILs (A) Heatmap showing the expression levels of genes encoding NK cell markers, Treg-related molecules, immune checkpoint proteins, cytokines, and (DN) T cell-related markers among CD8 + , CD4 + , and (DN) TIL clusters. (B) Gene set enrichment analysis (GSEA) showing significant upregulation of TGF-β (left) and IL-10 (right) signaling pathways in (DN) TILs compared with other TIL subsets. (C) Violin plot showing the expression of a regulatory T cell signature across (DN) TIL, CD4 + TIL, and CD8 + TIL clusters. A dashed line indicates the median of the signature score of the (DN) TIL cluster. Median and interquartile were shown in the boxplots. Non-parametric Kruskal-Wallis test was used to determine the significance. (D) Representative overlap histogram for the expression of IL-10, TGF-β, IKZF2, FOXP3, and CTLA4 among (DN) TILs, CD4 + TILs, and CD8 + TILs determined by flow cytometry. (E) Developmental trajectory of 16,978 (DN) T cells from tumor tissues and peripheral blood of naive treated NPC patients ( n = 10) inferred by Monocle 2 and CytoTRACE algorithm. Solid and dotted lines denote distinct cell fates based on expression profiles, with colors indicating the origin of (DN) T cells, pseudo-time, and CytoTRACE score (GEO: GSE162025 ). (F) Dot plot showing the correlation between (DN) TIL signature scores of TMEs in this study and each state of (DN) T cells (GEO: GSE162025 ; PB, Cell Fate_1, and Cell Fate_2). The sizes and colors of the circles represent the strength of the relationship, assessed using Spearman’s correlation test. PB, peripheral blood. (G) Representative overlap histogram for the expression of IL-10, TGF-β, IKZF2, FOXP3, and CTLA4 in the (DN) T cells from TIL infusion products, peripheral blood of NPC patients, or healthy donors determined by flow cytometry. See also and .

    Journal: Cell Reports Medicine

    Article Title: Tumor-infiltrated double-negative regulatory T cells predict outcome of T cell-based immunotherapy in nasopharyngeal carcinoma

    doi: 10.1016/j.xcrm.2025.102096

    Figure Lengend Snippet: Transcriptional characterization and developmental trajectory of CD3 + CD8 − CD4 − (DN) TILs (A) Heatmap showing the expression levels of genes encoding NK cell markers, Treg-related molecules, immune checkpoint proteins, cytokines, and (DN) T cell-related markers among CD8 + , CD4 + , and (DN) TIL clusters. (B) Gene set enrichment analysis (GSEA) showing significant upregulation of TGF-β (left) and IL-10 (right) signaling pathways in (DN) TILs compared with other TIL subsets. (C) Violin plot showing the expression of a regulatory T cell signature across (DN) TIL, CD4 + TIL, and CD8 + TIL clusters. A dashed line indicates the median of the signature score of the (DN) TIL cluster. Median and interquartile were shown in the boxplots. Non-parametric Kruskal-Wallis test was used to determine the significance. (D) Representative overlap histogram for the expression of IL-10, TGF-β, IKZF2, FOXP3, and CTLA4 among (DN) TILs, CD4 + TILs, and CD8 + TILs determined by flow cytometry. (E) Developmental trajectory of 16,978 (DN) T cells from tumor tissues and peripheral blood of naive treated NPC patients ( n = 10) inferred by Monocle 2 and CytoTRACE algorithm. Solid and dotted lines denote distinct cell fates based on expression profiles, with colors indicating the origin of (DN) T cells, pseudo-time, and CytoTRACE score (GEO: GSE162025 ). (F) Dot plot showing the correlation between (DN) TIL signature scores of TMEs in this study and each state of (DN) T cells (GEO: GSE162025 ; PB, Cell Fate_1, and Cell Fate_2). The sizes and colors of the circles represent the strength of the relationship, assessed using Spearman’s correlation test. PB, peripheral blood. (G) Representative overlap histogram for the expression of IL-10, TGF-β, IKZF2, FOXP3, and CTLA4 in the (DN) T cells from TIL infusion products, peripheral blood of NPC patients, or healthy donors determined by flow cytometry. See also and .

    Article Snippet: To evaluate the suppression mechanism of DN TILs, neutralizing antibodies targeting TGF-β (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHB215) and IL-10 (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHA915) were added, along with a Fas-FasL signaling antagonist (10 μM; MedChemExpress, HY- P10102 ) and GITR inhibitor (20 nM; KKL Med Inc., KM2426) at an effector-to-target (E:T) ratio of 1:1.

    Techniques: Expressing, Protein-Protein interactions, Flow Cytometry

    Transcriptomic characterization and association of CD56 − (DN) TILs with clinical outcome (A) UMAP plot depicting 2,609 quality-controlled (DN) TILs colored by six cell subsets. (B) UMAP plot of (DN) TILs, with cells colored based on the relative normalized expression of NCAM1 (encoding CD56), FCGR3A (encoding CD16), and IKZF2 . (C) Volcano plot showcasing differentially expressed genes (DEGs) between CD56 − (DN) TILs and CD56 + (DN) TILs. Red dots and blue dots indicate up-regulated genes ( n = 770) and down-regulated genes ( n = 316) in CD56 − (DN) TILs, respectively. Selected genes were labeled. (D) Heatmap showing the expression of genes encoding TCR proteins, NK cell markers, immune checkpoint proteins, cytokines, and (DN) T cell-related markers among CD56 − and CD56 + (DN) TIL clusters. (E) GSEA plots showing that Treg signaling pathways were significantly up-regulated in CD56 − (DN) TILs compared with CD56 + (DN) TILs. (F) Violin plot showing the expression of an activated DN Treg signature across four (DN) TIL clusters (CD56 − IKZF2 + , CD56 − IKZF2 - , CD56 + IKZF2 - , and CD56 + IKZF2 + (DN) TILs). A dashed line indicates the median of the signature score of the CD56 − IKZF2 + (DN) TIL. Median and interquartile were shown in the boxplots. Kruskal-Wallis test was used to determine the significance. (G) Kaplan-Meier survival curves for PFS (top) and OS (bottom) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of CD56 − IKZF2 + (DN) TILs inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. See also .

    Journal: Cell Reports Medicine

    Article Title: Tumor-infiltrated double-negative regulatory T cells predict outcome of T cell-based immunotherapy in nasopharyngeal carcinoma

    doi: 10.1016/j.xcrm.2025.102096

    Figure Lengend Snippet: Transcriptomic characterization and association of CD56 − (DN) TILs with clinical outcome (A) UMAP plot depicting 2,609 quality-controlled (DN) TILs colored by six cell subsets. (B) UMAP plot of (DN) TILs, with cells colored based on the relative normalized expression of NCAM1 (encoding CD56), FCGR3A (encoding CD16), and IKZF2 . (C) Volcano plot showcasing differentially expressed genes (DEGs) between CD56 − (DN) TILs and CD56 + (DN) TILs. Red dots and blue dots indicate up-regulated genes ( n = 770) and down-regulated genes ( n = 316) in CD56 − (DN) TILs, respectively. Selected genes were labeled. (D) Heatmap showing the expression of genes encoding TCR proteins, NK cell markers, immune checkpoint proteins, cytokines, and (DN) T cell-related markers among CD56 − and CD56 + (DN) TIL clusters. (E) GSEA plots showing that Treg signaling pathways were significantly up-regulated in CD56 − (DN) TILs compared with CD56 + (DN) TILs. (F) Violin plot showing the expression of an activated DN Treg signature across four (DN) TIL clusters (CD56 − IKZF2 + , CD56 − IKZF2 - , CD56 + IKZF2 - , and CD56 + IKZF2 + (DN) TILs). A dashed line indicates the median of the signature score of the CD56 − IKZF2 + (DN) TIL. Median and interquartile were shown in the boxplots. Kruskal-Wallis test was used to determine the significance. (G) Kaplan-Meier survival curves for PFS (top) and OS (bottom) in NPC patients stratified according to the abundance (high abundance vs. low abundance) of CD56 − IKZF2 + (DN) TILs inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. See also .

    Article Snippet: To evaluate the suppression mechanism of DN TILs, neutralizing antibodies targeting TGF-β (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHB215) and IL-10 (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHA915) were added, along with a Fas-FasL signaling antagonist (10 μM; MedChemExpress, HY- P10102 ) and GITR inhibitor (20 nM; KKL Med Inc., KM2426) at an effector-to-target (E:T) ratio of 1:1.

    Techniques: Expressing, Labeling, Protein-Protein interactions, RNA Sequencing

    Associations of CD8 + TIL subsets with (DN) TILs and clinical outcome (A) Dot color represents the communication probability of the specific ligand-receptor pairs, including TGF-β, IL-10, and Fas-FasL signaling between the indicated sender cluster and receiver clusters. (B) Representative histograms and statistical graph showing carboxyfluorescein succinimidyl ester (CFSE) dilution (left) and proliferation inhibition rates (right) of (DN) TILs with CD4 + and CD8 + naive T cells at a ratio of 4:1, 2:1, 1:1, and 1:2. The data are presented as mean ± SD, and three biological replicates were included. (C) Representative histograms and statistical graph showing CFSE dilution (left) and proliferation inhibition rates (right) of (DN) TILs with CD4 + and CD8 + naive T cells at a 1:1 ratio with neutralizing antibodies against TGF-β and IL-10, as well as the presence or absence of a Fas-FasL signaling antagonist. The data are presented as mean ± SD, and three biological replicates were included. p values were evaluated by t test (two-sided). ∗∗ p < 0.01; ∗∗∗ p < 0.001. (D) Bubble plot showing the correlation between the frequency of (DN) TILs and other TIL subsets in TIL infusion products ( n = 26). p values were determined by Spearman correlation analysis. ∗ p < 0.05. (E) Dot plot showing the correlation between the frequency of (DN) TILs and CD8 + TILs in TIL infusion products ( n = 26). p values were determined by Spearman correlation analysis. (F) Representative histograms and statistical graph showing CFSE dilution (left) and proliferation inhibition rates (right) of (DN) TILs with autologous expanded CD8 + TILs at ratios of 4:1, 2:1, and 1:1. Conventional CD4 + (i)Tregs were included as a control. The data are presented as mean ± SD, and three biological replicates were included. (G–K) Kaplan-Meier survival curves of PFS and OS in NPC patients stratified according to the abundance (high vs. low) of total CD8 + TILs (G), C2_proliferation_CD8_T_cells-PCNA (H), C4_MHC_II_CD8_T_cells (I), C6_proliferation_CD8_T_cells-TOP2A (J), and C12_tissue_resident_memory_CD8_T_cells (K) inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. See also and .

    Journal: Cell Reports Medicine

    Article Title: Tumor-infiltrated double-negative regulatory T cells predict outcome of T cell-based immunotherapy in nasopharyngeal carcinoma

    doi: 10.1016/j.xcrm.2025.102096

    Figure Lengend Snippet: Associations of CD8 + TIL subsets with (DN) TILs and clinical outcome (A) Dot color represents the communication probability of the specific ligand-receptor pairs, including TGF-β, IL-10, and Fas-FasL signaling between the indicated sender cluster and receiver clusters. (B) Representative histograms and statistical graph showing carboxyfluorescein succinimidyl ester (CFSE) dilution (left) and proliferation inhibition rates (right) of (DN) TILs with CD4 + and CD8 + naive T cells at a ratio of 4:1, 2:1, 1:1, and 1:2. The data are presented as mean ± SD, and three biological replicates were included. (C) Representative histograms and statistical graph showing CFSE dilution (left) and proliferation inhibition rates (right) of (DN) TILs with CD4 + and CD8 + naive T cells at a 1:1 ratio with neutralizing antibodies against TGF-β and IL-10, as well as the presence or absence of a Fas-FasL signaling antagonist. The data are presented as mean ± SD, and three biological replicates were included. p values were evaluated by t test (two-sided). ∗∗ p < 0.01; ∗∗∗ p < 0.001. (D) Bubble plot showing the correlation between the frequency of (DN) TILs and other TIL subsets in TIL infusion products ( n = 26). p values were determined by Spearman correlation analysis. ∗ p < 0.05. (E) Dot plot showing the correlation between the frequency of (DN) TILs and CD8 + TILs in TIL infusion products ( n = 26). p values were determined by Spearman correlation analysis. (F) Representative histograms and statistical graph showing CFSE dilution (left) and proliferation inhibition rates (right) of (DN) TILs with autologous expanded CD8 + TILs at ratios of 4:1, 2:1, and 1:1. Conventional CD4 + (i)Tregs were included as a control. The data are presented as mean ± SD, and three biological replicates were included. (G–K) Kaplan-Meier survival curves of PFS and OS in NPC patients stratified according to the abundance (high vs. low) of total CD8 + TILs (G), C2_proliferation_CD8_T_cells-PCNA (H), C4_MHC_II_CD8_T_cells (I), C6_proliferation_CD8_T_cells-TOP2A (J), and C12_tissue_resident_memory_CD8_T_cells (K) inferred from (sc)RNA sequencing data ( n = 26). p values were determined based on the two-sided log rank test. See also and .

    Article Snippet: To evaluate the suppression mechanism of DN TILs, neutralizing antibodies targeting TGF-β (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHB215) and IL-10 (5 μg/mL; Sanyou Biopharmaceuticals Co. Ltd, CHA915) were added, along with a Fas-FasL signaling antagonist (10 μM; MedChemExpress, HY- P10102 ) and GITR inhibitor (20 nM; KKL Med Inc., KM2426) at an effector-to-target (E:T) ratio of 1:1.

    Techniques: Inhibition, Control, RNA Sequencing