Journal: Research

Article Title: Inverse Vaccine Nanomimics: In situ Antigen-Specific Immune Tolerance Induction for Durable Cardiac Repair Post-Myocardial Infarction

doi: 10.34133/research.1464

Figure Lengend Snippet: The fabrication of Gel@IVNMs, in vivo application and immunomodulatory mechanism for myocardial infarction (MI) therapy. (A) The stepwise fabrication of the PPM@rapamycin (Rapa) inverse vaccine nanomimics (IVNMs), Gel, and Gel@IVNMs. First, PPMal, PPMan, and Rapa are assembled into the IVNMs via the solvent evaporation method. Concurrently, oDex and gelatin are cross-linked to form a hydrogel network, where IVNMs are encapsulated. (B) The hydrogel is injected into the infarcted myocardium. As the Gel swells, cardiac lysate antigens (CLAs) released by necrotic cardiomyocytes diffuse into it and are captured by Mal on the surface of IVNMs to form antigen-loaded IVNMs (AIVNMs). Lactic acid accumulates at the infarcted site due to the activation of inflammation, followed by a decrease in pH, leading to a mild acidic microenvironment. In response to the mildly acidic pH, the Gel gradually degrades and releases AIVNMs. (C) Released AIVNMs can be taken up by CD206-expressing immune cells, including dendritic cells (DCs), and subsequently modulate DC phenotype by suppressing the mechanistic target of rapamycin (mTOR) pathway (via Rapa-mediated inhibition of mTOR–Rictor–FKBP12 signaling), reducing CD80 and CD86 expression, and increasing programmed death ligand 1 (PD-L1) expression and transforming growth factor-β (TGF-β) secretion. These DCs then present antigens and suppress T cell activation through PD-L1 signaling and TGF-β secretion, fostering regulatory T cell (Treg) expansion. Tregs further modulate macrophage polarization, shifting proinflammatory M1 macrophages toward anti-inflammatory M2 macrophages, thereby balancing proinflammatory and anti-inflammatory cytokine secretion. The established immune tolerance memory can rapidly respond and specifically inhibit inflammatory activation upon restimulation with CLAs. This cascade reduces cardiomyocyte apoptosis, promotes neovascularization, ameliorates myocardial fibrosis, and ultimately enhances heart pumping function, contributing to improved cardiac repair after MI.

Article Snippet: Cells were then surface-stained with fluorophore-conjugated anti-rat CD11b/c (AN00651E, Elabscience, China), MHC-II (AN00653D, Elabscience, China), and PD-L1 (E-AB-F1132UC, Elabscience, China) antibodies.

Techniques: In Vivo, Solvent, Evaporation, Injection, Activation Assay, Expressing, Inhibition

Journal: Research

Article Title: Inverse Vaccine Nanomimics: In situ Antigen-Specific Immune Tolerance Induction for Durable Cardiac Repair Post-Myocardial Infarction

doi: 10.34133/research.1464

Figure Lengend Snippet: The effect of mannose functionalization on the uptake of inverse vaccine nanomimics (IVNMs) by dendritic cells (DCs) in vitro, together with DC phenotypes and cytokine secretion after stimulation with cardiac lysate antigens (CLAs). (A) DC isolation and experimental workflow. The process by which IVNMs capture antigens and are internalized by DCs. After antigen-loaded IVNMs (AIVNMs) are internalized by DCs, the antigens activate mechanistic target of rapamycin (mTOR) signaling, whereas Rapa inhibits mTOR activation, which leads to decreased expression of molecules such as CD80 and CD86, and increased expression of programmed death ligand 1 (PD-L1). Additionally, the secretion of anti-inflammatory factors (such as interleukin-10 [IL-10] and transforming growth factor-β [TGF-β]) is enhanced. (B) Flow cytometry results for the uptake of IVNMs by DCs. (C) Quantification of the uptake of IVNMs by DCs. (D) Fluorescence images of the uptake of IVNMs by DCs, with red fluorescence indicating IVNMs and blue fluorescence indicating cell nuclei. (E and F) Quantification of DC phenotypes by flow cytometry. (G) Representative flow cytometry plots of DC phenotypes. (H) Enzyme-linked immunosorbent assay detection results of interferon-γ (IFN-γ), IL-2, IL-10, and TGF-β expression levels in DCs after CLA stimulation and drug administration. Data are presented as means ± SD, with individual points representing biological replicates. Statistical significance is indicated as ns, not significant; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Cells were then surface-stained with fluorophore-conjugated anti-rat CD11b/c (AN00651E, Elabscience, China), MHC-II (AN00653D, Elabscience, China), and PD-L1 (E-AB-F1132UC, Elabscience, China) antibodies.

Techniques: In Vitro, Isolation, Activation Assay, Expressing, Flow Cytometry, Fluorescence, Enzyme-linked Immunosorbent Assay

Journal: Research

Article Title: Inverse Vaccine Nanomimics: In situ Antigen-Specific Immune Tolerance Induction for Durable Cardiac Repair Post-Myocardial Infarction

doi: 10.34133/research.1464

Figure Lengend Snippet: Phenotypic changes and inflammatory factor secretion of T cells stimulated by cardiac lysate antigens (CLAs). (A) Coculture protocol for dendritic cells (DCs) and T cells. The tolerogenic DCs (tDCs) present antigens to T cells; meanwhile, programmed death ligand 1 binds to programmed cell death protein 1 on T cells and inhibits T cell activation, promoting the phenotypic conversion of T cells to regulatory T cells (Tregs). Furthermore, due to the lack of activation signals such as CD80 and CD86, some T cells exhibit a weak activation state after receiving antigen presentation, and under the regulation of key factors such as interleukin-10 (IL-10) and transforming growth factor-β (TGF-β), they undergo phenotypic transition to Tregs. Under restimulation, tDCs phagocytose antigens and present them to Tregs. Tregs are rapidly activated, express high levels of Forkhead box P3 (FOXP3), and directly inhibit T cell activation, while secreting large amounts of anti-inflammatory factors. (B) Flow cytometry (FC) results for T cell phenotype. (C) Quantification of T cell phenotypes by FC. (D) T cell phenotype immunofluorescence staining results, where blue represents cell nuclei, red represents CD127, and green represents CD4. (E) Expression of interferon-γ (IFN-γ), IL-2, IL-10, and TGF-β after coculture of DCs and T cells. (F) Western blot analysis of the activation status of the mechanistic target of rapamycin (mTOR)-p70S6K-S6 pathway after treatment with Gel@PPM, Rapa, inverse vaccine nanomimics (IVNMs), and Gel@IVNMs under CLA stimulation. (G) The percentage of phosphorylation of mTOR, p70S6K, and S6 was statistically analyzed based on the gray values of the western blot results (compared to the unphosphorylated proteins). Data are presented as means ± SD, with individual points representing biological replicates. Statistical significance is indicated as ns, not significant; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Cells were then surface-stained with fluorophore-conjugated anti-rat CD11b/c (AN00651E, Elabscience, China), MHC-II (AN00653D, Elabscience, China), and PD-L1 (E-AB-F1132UC, Elabscience, China) antibodies.

Techniques: Activation Assay, Immunopeptidomics, Flow Cytometry, Immunofluorescence, Staining, Expressing, Western Blot, Analysis, Phospho-proteomics

Journal: Research

Article Title: Inverse Vaccine Nanomimics: In situ Antigen-Specific Immune Tolerance Induction for Durable Cardiac Repair Post-Myocardial Infarction

doi: 10.34133/research.1464

Figure Lengend Snippet: Changes in dendritic cell phenotype and regulatory T cell responses in cardiac-draining lymph nodes after myocardial infarction (MI). (A) Representative flow cytometry plots showing CD11c + MHC-II + dendritic cells and programmed death ligand 1 (PD-L1) expression in the Control, Gel@PPM, Gel@PP@Rapa, Gel@OVA-IVNMs, and Gel@IVNMs groups at 7 and 56 d after MI. (B) Quantification of the proportion of PD-L1 + cells within the CD11c + MHC-II + dendritic cell population at 7 and 56 d. (C) Quantification of CD4 + CD25 + FOXP3 + regulatory T cells (Tregs) in lymph node-derived cells from the 5 treatment groups after 48 h of culture under unstimulated Control, OVA, or cardiac lysate antigen (CLA) stimulation at 7 and 56 d. (D) Representative flow cytometry plots of CD25 and Forkhead box P3 (FOXP3) expression within CD4 + T cells in the Gel@OVA-IVNMs and Gel@IVNMs groups following Control, OVA, or CLA stimulation at 7 and 56 d. Data are presented as means ± SD, with individual points representing biological replicates. Statistical significance is indicated as ns, not significant; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Cells were then surface-stained with fluorophore-conjugated anti-rat CD11b/c (AN00651E, Elabscience, China), MHC-II (AN00653D, Elabscience, China), and PD-L1 (E-AB-F1132UC, Elabscience, China) antibodies.

Techniques: Flow Cytometry, Expressing, Control, Derivative Assay

Journal: bioRxiv

Article Title: Neutrophils orchestrate pre-metastatic immunosuppressive bone marrow remodeling during prostate cancer metastasis

doi: 10.64898/2026.10.08.757800

Figure Lengend Snippet: (a) Schematic of bone marrow (BM) collection, single-cell RNA sequencing and downstream validation in the Benign (n = 6), HMP (n = 7) and Mets (n = 7) cohorts. HMP denotes patients without detectable bone metastasis but at high risk of metastatic progression; Mets denotes prostate cancer bone metastasis. (b) UMAP of all cells in the main cohort, colored by major cell type. (c) Cell-density maps on the integrated UMAP by disease group. (d) Proportions of major myeloid, lymphoid, stromal, erythroid and epithelial populations across groups. Statistical comparisons were performed using patient-level cell proportions. Values above comparisons show adjusted P values. (e) UMAP of neutrophil subpopulations and corresponding density maps by disease group. (f) Heatmap of differentially expressed genes across neutrophil states; values are row-scaled average expression. (g) Representative immunofluorescence images of benign and metastatic BM stained for DAPI, CCR3 and CD66b. Scale bars, 25 µm (main) and 5 µm (insets). (h) Neutrophil functional-module scores across subpopulations. (i) TAN signature scores in metastatic prostate cancer samples from the Stand Up To Cancer (SU2C) cohort, stratified by survival status. (j) Kaplan-Meier analysis of overall survival in SU2C samples stratified by the median TAN score. (k) Correlations between neutrophil ROS- or nitric-oxide-related programs and DNA-damage or nitrogen-response programs in CD8 T cells across the main cohort. Each point represents one patient sample. (l) UMAP of CD8 T cell subtypes and density maps by disease group. (m) Expression of dysfunction-related genes in CD8Temra cells across integrated healthy, Benign, HMP and Mets datasets; dot size indicates the expressing-cell fraction and color indicates scaled mean expression. (n) NicheNet-predicted ligands and target genes associated with CD8Temra alteration in HMP. (o) Relative SLAMF6 and SLAMF7 mRNA abundance in healthy male peripheral blood CD8 T cells after IL-1β stimulation. Data are presented as mean ± s.e.m.; n = 3 donors. (p, q) Diffusion-map and RNA-velocity analysis of CD8Temra-state transitions, with cell-density maps by disease group. (r) Fitted exhaustion and hypoxia scores along the inferred trajectories. (s) Regulon activity across the CD8Temra-to- CD8Trm and CD8Temra-to-CD8Thx trajectories. (t) CD8Thx and CD8Trm ssGSEA scores in SU2C samples stratified by survival status. (u) Correlation between the CD8Thx ssGSEA score and IL1B expression in SU2C samples. (v) Correlation between the proportion of CD8Thx cells and neutrophil IL1B expression in the main cohort. (w) Proposed model linking TANs with CD8 T cell dysfunction across HMP and metastatic BM. Unless otherwise indicated, bar plots with error bars show mean ± s.d. Statistical significance was assessed using two-sided unpaired Student’s t-tests (o), Wilcoxon rank-sum tests (d, i, t), a log-rank test (j), Pearson correlation (u) and Spearman correlation (k, v).

Article Snippet: Primary antibodies included anti-CD274 (Proteintech, 66248-1-Ig), anti-Ly6G (Cell Signaling Technology, 31469S) and anti-CCR3 (Abcam, ab318266).

Techniques: Single Cell, RNA Sequencing, Biomarker Discovery, Expressing, Immunofluorescence, Staining, Functional Assay, Analysis, Diffusion-based Assay, Activity Assay, Immunopeptidomics