Journal: Clinical and Translational Medicine

Article Title: ZC3H13‐mediated m6A stabilization of CCND1 promotes malignant progression and is associated with poor anti‐PD‐1 response in HNSCC

doi: 10.1002/ctm2.70750

Figure Lengend Snippet: The ZC3H13/CCND1 axis remodels the HNSCC immune microenvironment and the proposed mechanistic model. (A) Gene Ontology (GO) enrichment analysis revealing biological pathways related to the negative regulation of cell activation and apoptosis. (B) Bar plot illustrating the changes in the proportion of CD4 + T cells between the Pre‐ and Post‐immunotherapy groups. (C) Boxplot comparing the quantitative CytoTRACE scores before and after immunotherapy (Pre vs. Post). (D) UMAP feature plots displaying the spatial distribution of cell differentiation states (CytoTRACE scores) in Pre‐ and Post‐treatment samples. (E) Violin plot demonstrating the significant difference in CD4+ T cell exhaustion scores between the Pre and Post treatment groups. (F) The relative mRNA expression levels of key immune checkpoint molecules (PDCD1, LAG3, CTLA4) in CCND1‐low and CCND1‐high groups were determined by RT‐qPCR. (G) Representative multiplex immunofluorescence images showing CCND1, CD4, PD‐1 and DAPI staining in mouse HNSCC tissues from control and ZC3H13‐deficient groups. Scale bar, 20 µm. (H) The quantification for the multiplex immunofluorescence (mIF) staining in the tumour microenvironment. (I) Schematic mechanistic model. Data are presented as the mean ± SD. *** p < .001.

Article Snippet: Sections were incubated with the indicated primary antibodies at room temperature for 1 h: CD4 (HY‐ P83756 , MCE, 1:500), CCND1 (HY‐ P80098 , MCE, 1:50), and PD‐1 (84651, Cell Signaling Technology, 1:100) in the optimized order of CD4, CCND1, and PD‐1.

Techniques: Activation Assay, Cell Differentiation, Expressing, Quantitative RT-PCR, Multiplex Assay, Immunofluorescence, Staining, Control

Journal: Cancer cell

Article Title: Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression

doi: 10.1016/j.ccell.2026.03.015

Figure Lengend Snippet: (A) UMAP plots of human T cells ( n = 64) showing stage- and subtype-specific grouping (insets). (B) Quantification of T cell subsets (normal: n = 11; DCIS: n = 29; IBC: n = 24). Patients with <100 T cells were excluded. Unadjusted significance values were computed by Kruskal-Wallis test and Tukey’s multiple comparison. (C–E) Milo neighborhoods of human T cells (C) and DAs (Differential Abundances) in IBC vs. DCIS as shown by UMAP plot (D) or beeswarm plot (E). (F) UMAP plots of SD rat T cells ( n = 13) showing condition-specific grouping (inset). (G–I) Milo neighborhoods of rat T cells (G), enrichment in NMU-treated tumors vs. MGs (H), or beeswarm plot (I). Node size denotes cell numbers, and edges denote cells shared between connected neighborhoods. Beeswarm plots show LFC distribution at FDR 10% of neighborhoods between IBC vs. DCIS (E) or rat NMU-treated tumors vs. MGs (I). (J) UMAP plots of human CD4 + T cells. (K) Beeswarm plots of T cell neighborhoods and LFC distribution for pairwise comparisons between three IBC subtypes (IBC/HR + : n = 6; IBC/TNBC: n = 10; IBC/HER2 + : n = 8). UMAP, uniform manifold approximation projection; DAs,; LCF, log fold-change. See also and .

Article Snippet: Cd4 ( NM_012705 ) Rat Tagged ORF Clone , Origene , CAT#: RR210599.

Techniques: Comparison

Journal: Cancer cell

Article Title: Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression

doi: 10.1016/j.ccell.2026.03.015

Figure Lengend Snippet: (A) Schematic of ex vivo iTreg-DC co-culture. Bright-field images of dendriform morphologies typical of DCs. Scale bars, 10 μm. (B) Contour plots depicting CellTrace-CSFE dilution in dLN-derived iTreg following co-cultures with cDC1 or cDC2 derived from macroscopic tumors or microlesions, or with BM- or PBMC-derived moDCs. T 0 baseline (cyan) and iTreg monoculture (gray) are included. CSFE signal is visualized as contour overlays and as mode-normalized histogram. Asterisk indicates significant D4+ division shift in co-cultures with tumor-derived cDC2 (blue). (C) Line plots showing the proportions of dLN-derived iTreg entering division states D0-D4+ in monoculture or co-cultures with DC subsets, with/without blockade of immune synapse components (10 μg/mL per mAb) (across treatments: no DCs, n = 12; +tumor-cDC1 or cDC2, n = 6; +microlesion-cDC1 or cDC2, n = 9; +BM- or PBMC-derived DC, n = 12). Significance for each treatment was computed by two-way repeated-measures (RM) ANOVA and Dunnett’s multiple comparisons, with p values reported for D1 and D4+ states. Pooled-variance two-way ANOVA was applied for D4+ states to evaluate cross-treatment differences among DC subtypes. (D) The division index calculated as the peak-to-trough ratio of iTreg in D4+ relative to D2. Individual or interaction effects of DC co-culture and blockade conditions were assessed by two-way RM ANOVA. Tukey’s post-hoc analysis was used for TCR/MHC-II blockade in tumor-derived cDC2 co-cultures. (E) Trajectorial inference of human CD4 + grouped by progression stage (left) and Seurat clusters (right). Pseudotime values and molecular subtype are shown as insets. (F and G) TCR Shannon’s diversity index (F) and cytotoxic CD8 + scores (G) of primary breast tumors among PAM50 subtypes from the TCGA cohort (luminal A: n = 473; luminal B: n = 240; HER2 + : n = 90; basal: n = 189). Stratification was based on median cutoff of adjusted cycTreg score. Significance was computed using Wilcoxon rank-sum test. (H) cycTreg and non-cycTreg in pure DCIS (left) and IBC (right) classified as TN, HR + /HER2 − , and HR − /HER2 + (DCIS: TN, n = 13; HR + , n = 9; HR − /HER2 + , n = 6; IBC/DCIS: TN, n = 12; HR + , n = 11; HR − /HER2 + , n = 5). Dotted lines demarcate tumor-stroma borders. Rectangles denote magnified ROIs. Asterisks denote cycTreg. Scale bars, 250 μm and 10 μm (insets). (I and J) Multivariable linear regression of cycTreg stratified by DCIS or IBC, with tumor Ki67 + (I) and tumor size (J) as predicted outcomes. Covariate interactions were evaluated by least-square regression. (K and L) Logistic regression by nuclear grade outcomes (1: high vs. 0: low) based on cycTreg, with patients stratified by DCIS or IBC (K) or histologic subtypes (L). See also ; and .

Article Snippet: Cd4 ( NM_012705 ) Rat Tagged ORF Clone , Origene , CAT#: RR210599.

Techniques: Ex Vivo, Co-Culture Assay, Derivative Assay

Journal: Cancer cell

Article Title: Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression

doi: 10.1016/j.ccell.2026.03.015

Figure Lengend Snippet: (A) Pseudotime trajectory of rat T cells. (B) OX40 ligand-receptor expression across cell types within rat macroscopic tumors and microlesions. (C) % TIL subtypes with OX40, CTLA4, 4–1BB, and LAG3 positivity ( n = 15 tumors). (D) Schematic of rat immunotherapy study coupled with EdU/BrdU dual pulse chase. (E) Spaghetti plots of individual tumor growth curves (isotype: n = 16; αOX40: n = 15; αPD-L1: n = 16; combo: n = 23). (F) Derivative distribution of the growth rate of individual tumors quantified as negative (blue) or positive (red) peak area-under-curve (AUC). (G) Tumor volumes at 10-day post initial treatment. (H and I) Flow diagram of cycTreg density (H) and frequency of cycTreg and non-cycTreg (I). (J) Ratios of total Treg, cycTreg, and non-cycTreg to total infiltrated CD8 + T cells. (K) EdU/BrdU contours of TILs. Quadrants denote EdU + /BrdU + distribution. (L and M) Frequency of BrdU + , EdU + , or double-labeled cycTreg (L) and non-cycTreg (M). Significance was computed by two-way ANOVA and Dunnet’s multiple comparison relative to BrdU-labeling. (N) Linear regression of cycTreg with CD8 + Tconv (red) and CD4 + Tconv (green). Dots in each color represent individual tumors ( n = 64). Pearson’s best-fit parameters including R 2 , slope, and significance, and 95% CI are shown. In (C) and (F), significance was computed by two-way ANOVA and Dunn’s multiple comparison. In (E), values are shown in log 10 scale to show very small tumors in responding groups. Pre- or on-treatment tumors are specified. In (J), values are shown in log 2 scale to show tumors with low Treg infiltration. See also and .

Article Snippet: Cd4 ( NM_012705 ) Rat Tagged ORF Clone , Origene , CAT#: RR210599.

Techniques: Expressing, Pulse Chase, Labeling, Comparison

Journal: Cancer cell

Article Title: Identification of cycling regulatory T cell precursors as conductors of immune escape during breast carcinoma progression

doi: 10.1016/j.ccell.2026.03.015

Figure Lengend Snippet: (A and B) CellChat heatmap of outgoing signaling in treatment-naïve tumors (A) (FDR <0.05) and chord diagram (B) inferring Il33 (green) from CAFs to Il1rl1 (blue, encoding ST2) on cycTreg and eTreg. (C) Pearson’s correlations of proximity scores from MIBI dataset of DCIS epithelium edge to CD45 + immune cells and to CAFs. Best-fit R 2 was computed based on recurrence diagnosis. Each dot represents individual DCIS patients ( n = 55) with follow-up for IBC recurrence (red, n = 14) or lack thereof (blue, n = 41). (D) Linear regression of adjusted cycTreg score with IL33 (top) and IL1RL1 (bottom) expression from TCGA dataset ( n = 992 patients). (E and F) Tumor volumes (E) and % CGR (F) computed at tumor endpoints (isotype: n = 26; αIL-33: n = 26). Tumors with < 7 days of in vivo growth were excluded. Significance was computed using two-tailed Mann-Whitney U test. (G) UMAP plot of 29 TIL clusters inferred by Phenograph. Heatmap insets show scaled fluorescence values. (H–M) Quantification of total CD4 + FOXP3 + Treg (H), cycTreg (I), ratios of total Treg (J), cycTreg (K), and non-cycTreg (L) to total CD8 + , and Eomes hi CD45RC hi memory-effector CD8 + T cells (M). (N–Q) Contours of IRF8/IRF4 (N) and RT1B/RT1A (P) and quantified myeloid IRF8 (O) and RT1A (MHC-I) (Q) of intratumor cDC1, cDC2, TAMs, and TILs. (R and S) Pearson’s correlations of cDC2 with cycTreg (R) and non-cycTreg (S) following αIL-33 blocking. Pearson’s best-fit parameters including R 2 , significance, and 95% CI are shown. For αIL-33 study, individual tumors are shown (isotype: n = 38; αIL-33: n = 26 tumors). Significance of two-group comparisons (E, F, and H–M) was computed by two-tailed Student’s t test. Significance of multiple group comparisons (O and Q) was computed by two-way ANOVA and Sidak’s multiple comparison. See also and .

Article Snippet: Cd4 ( NM_012705 ) Rat Tagged ORF Clone , Origene , CAT#: RR210599.

Techniques: Biomarker Discovery, Expressing, In Vivo, Two Tailed Test, MANN-WHITNEY, Fluorescence, Blocking Assay, Comparison

Journal: iScience

Article Title: Single-cell transcriptomics reveals the role of antigen presentation in liver metastatic breast cancer

doi: 10.1016/j.isci.2024.108896

Figure Lengend Snippet: Overview of using scRNA technology to study cancer liver metastasis

Article Snippet: The sections were stained for three panels by using 4-color Novo-Light multiplex fluorescence immunohistochemistry: panel 1 including Rabbit anti-FCN3 (C-term) Polyclonal Antibody (abs101543, Absin), Rabbit anti-C1QC Polyclonal Antibody (A9227, Abclonal) and Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin); panel 2 including Rabbit anti-XCR1 (D2F8T) Monoclonal Antibody (44665S, CST), Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin), Rabbit anti-HLA-DQA1 Polyclonal Antibody (16918-1-AP, Proteintech) and Rabbit anti-HLA-DPA1 Polyclonal Antibody (16109-1-AP, Proteintech); panel 3 including Rabbit anti-LAMP3 Polyclonal Antibody (A2895, Abclonal) and Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin).

Techniques: Sequencing, RNA Sequencing, Migration

Journal: iScience

Article Title: Single-cell transcriptomics reveals the role of antigen presentation in liver metastatic breast cancer

doi: 10.1016/j.isci.2024.108896

Figure Lengend Snippet: Clinical verification of alteration and function of FCN3 + macrophage, cDC1 and LAMP3 + DC in LM (A) Ratio of FCN3 + C1QC + HLA-E + cells, XCR1 + HLA-E + cells, XCR1 + HLA-DQA1 + cells, XCR1 + HLA-DPA1 + cells and LAMP3 + HLA-E + cells in normal mammary ducts or lobules, primary tumor of invasive ductal breast carcinoma (above phase 3C, staged by AJCC 6 th Edition) and liver metastasis. (B) Representative immunofluorescence appearance of FCN3 + C1QC + HLA-E + cells, XCR1 + HLA-E + cells, XCR1 + HLA-DQA1 + cells, XCR1 + HLA-DPA1 + cells and LAMP3 + HLA-E + cells (above phase 3c, staged by AJCC 6 th Edition). Panel 1 (left): blue for DAPI, green for FCN3, purple for C1QC and red for HLA-E; panel 2 (middle): blue for DAPI, green for XCR1, red for HLA-E, purple for HLA-DQA1 and yellow for HLA-DPA1; panel 3 (right): blue for DAPI, green for LAMP3 and red for HLA-E. Scale bar: 20 μm. (C) Analysis of OS between patients with high and low levels of MHC signature score filtered by top and bottom 25% values of XCR1 and LAMP3 from the TCGA-BRCA cohort. (D) Analysis of OS between patients with high and low levels of MHC signature score filtered by top and bottom 25% values of LAMP3 from the TCGA-LIHC cohort. In bar plots, Student’s t test was performed. p < 0.05∗, p < 0.01∗∗, p < 0.001∗∗∗, p < 0.0001∗∗∗∗. In survival analysis, Log rank was used to calculate the p value, p < 0.05 was considered to be significant.

Article Snippet: The sections were stained for three panels by using 4-color Novo-Light multiplex fluorescence immunohistochemistry: panel 1 including Rabbit anti-FCN3 (C-term) Polyclonal Antibody (abs101543, Absin), Rabbit anti-C1QC Polyclonal Antibody (A9227, Abclonal) and Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin); panel 2 including Rabbit anti-XCR1 (D2F8T) Monoclonal Antibody (44665S, CST), Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin), Rabbit anti-HLA-DQA1 Polyclonal Antibody (16918-1-AP, Proteintech) and Rabbit anti-HLA-DPA1 Polyclonal Antibody (16109-1-AP, Proteintech); panel 3 including Rabbit anti-LAMP3 Polyclonal Antibody (A2895, Abclonal) and Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin).

Techniques: Immunofluorescence

Journal: iScience

Article Title: Single-cell transcriptomics reveals the role of antigen presentation in liver metastatic breast cancer

doi: 10.1016/j.isci.2024.108896

Figure Lengend Snippet:

Article Snippet: The sections were stained for three panels by using 4-color Novo-Light multiplex fluorescence immunohistochemistry: panel 1 including Rabbit anti-FCN3 (C-term) Polyclonal Antibody (abs101543, Absin), Rabbit anti-C1QC Polyclonal Antibody (A9227, Abclonal) and Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin); panel 2 including Rabbit anti-XCR1 (D2F8T) Monoclonal Antibody (44665S, CST), Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin), Rabbit anti-HLA-DQA1 Polyclonal Antibody (16918-1-AP, Proteintech) and Rabbit anti-HLA-DPA1 Polyclonal Antibody (16109-1-AP, Proteintech); panel 3 including Rabbit anti-LAMP3 Polyclonal Antibody (A2895, Abclonal) and Mouse anti-HLA-E Monoclonal Antibody (abs154728, Absin).

Techniques: Recombinant, Lysis, Staining, Software