Fig 2: Impact of nano-vaccine mediated mouse DC immune responses on primary tumor development. Note: Mice were divided into four groups and received subcutaneous injections of PBS, dPEDE, M32+A, dPEDE-A, and dPEDE-A@M32 on days 0, 7, and 14 in the left flank. On day 21, serum, splenocytes, and iLNs cells were collected to assess the immunological effects of the nano-vaccine. (A) Schematic diagram of the mouse immune model and experimental procedure; (B) FCM analysis of the proportion of CD80+CD86+ positive DCs extracted from the iLNs of mice in different treatment groups; (C) Percentage of CD40+ positive DCs extracted from the iLNs of mice in different treatment groups; (D) ELISA measurements of TNF-α and IFN-γ levels in the serum of mice from different treatment groups; (E) Schematic diagram of the mouse immune tumor growth model and experimental procedure; (F) In vivo bioluminescence imaging of 4T1-luc tumors, showing three representative mice per group; (G) Tumor growth curves for different treatment groups; (H) Survival curves for different treatment groups. Each group in animal experiments consisted of six mice, and values are presented as mean ± SD, ** p < 0.01, *** p < 0.001.

Fig 3: Effect of ADT + Fc-enhanced anti-CTLA-4 on DCs(A) Violin plot of myeloid cluster frequencies as percentage of myeloid cells stratified by patient’s 2-year PSA recurrence status. No recurrence, n = 7; recurrence, n = 4. Two-tailed Student’s t test was utilized to test statistical significance. For this and all violin plots to follow, solid lines denote group medians, while dashed lines denote quartiles.(B) Kaplan-Meier curve representing time-to-PSA recurrence in patients stratified by dendritic cell frequency; 95% confidence intervals shown in the shaded areas. There was one recurrence event in DC-high patients and three recurrence events in DC-low patients. Log rank test was performed to evaluate statistical significance.(C) Schematic of pre-clinical validation experiment in the MycCaP model.(D) Percent change in tumor volume relative to baseline volume prior to ADT stratified by treatment group. Average values are represented by solid lines and standard deviations are represented by shaded areas.(E) Kaplan-Meier curve representing survival of mice shown in (D). Log rank (Mantel-Cox) test was used to evaluate statistical significance.(F) Left: supervised UMAP of live CD45+ cells from MycCaP tumors harvested following therapy as denoted in (D and E). Semi-supervised clustering was performed using FlowSOM and clusters were manually annotated according to expression of lineage-defining markers. Right: heatmap of lineage-defining markers across all clusters. Data represent geometric MFI for each marker normalized to the minimum and maximum values across all clusters.(G and H) (G) Frequency of TI-Tregs and (H) frequency of cDCs as percentage of all live CD45+ cells, stratified by treatment group.(I) Left: PaCMAP representing FlowSOM-derived cDC clusters. Clusters were manually annotated according to expression of canonical marker proteins. Right: pseudocolor representation of cDC phenotypes in PaCMAP space stratified by treatment group.(J) Violin plot representing frequency of PD-L2+ DCs as percentage of all DCs stratified by treatment group.(K) Violin plot representing frequency of CD40+ DCs as percentage of all DCs stratified by treatment group. Welch’s t test was used to assess statistical significance. Murine data shown are n = 8 mice per group and representative of two independent experiments each for survival and immune profiling studies.

Fig 4: scRNA-seq emphasizes heterogeneity in myeloid-specific responses to immunotherapy.(A) Schematic describing treatment regimen and sorting strategy for single-cell RNA-sequencing (scRNA-seq) data collection. WT C57Bl/6J mice were inoculated with YR1.7 melanomas at day 0 (4 tumors from 2 mice per treatment group). Treatments were administered a single time at day 7, and tumors were excised and dissociated for sequencing 24 hours post-treatment at day 8. Prior to sequencing, tumors were FACS-sorted into four populations: CD45− (tumor and stroma), CD45+CD3+ (T cells), CD45+CD3−CD19−XCR1+ and CD45+CD3−CD19−CCR7+ (DC subsets), and CD45+CD3−CD19−XCR1−CCR7− (other myeloid cells). These four populations were then recombined at a 1:2:1:1 ratio and submitted for scRNA-seq. Created with BioRender.com.(B) UMAP embedding of control, ICB lo-treated, ICB hi-treated, CD40ag-treated, and ICB lo + CD40ag-treated scRNA-seq samples (n=4 tumors per group) separated by treatment condition and colored by cell type assigned by a neural network-based classifier.(C) UMAP plot as in (B) colored by Cd40 expression. Cd40 transcripts were primarily expressed by myeloid cell types, including macrophages and dendritic cell subsets.(D) Scatter plots comparing macrophage-specific log-fold changes in mean gene expression (x-axis) and Fano factor (y-axis) for CD40ag monotherapy (left) or ICB lo + CD40ag (right) relative to control. Each dot represents a gene that was highly variable (mean ≥ 0.0125, dispersion ≥ 0.5) in macrophages across treatment conditions(E) Schematic depicting our computational strategy for inference and comparison of scRNA-seq-derived cell-cell communication networks. We use the novel combination of NICHES for cell-cell communication inference and Milo for differential abundance testing to efficiently compare predicted interaction networks across experimental conditions and identify treatment-specific ligand-receptor interactions from scRNA-seq. Created with BioRender.com.