Abstract
Aging impairs immune function and reduces vaccine efficacy, but whether dendritic cells (DCs), which play a central role in initiating immune responses via antigen presentation, contribute to this decline remains unclear. Through single-cell RNA sequencing analysis of lymph node changes upon vaccination in young versus aged mice, here we identify defects in DC migration during aging, alongside a dysfunction-associated gene signature in migratory DCs, and implicate these defects in the diminished vaccine response observed in aging. Furthermore, we demonstrate that oral delivery of yeast-derived nanoparticles elevates expression of the chemokine receptor CCR7 in gut dendritic cells, facilitates their trafficking to lymph nodes in response to chemotactic signals after immunization, and thus enhances vaccine-induced immunity in aged animals. These findings reveal a key mechanism of immune decline in aging and offer a noninvasive strategy to improve dendritic cell function and vaccine efficacy in aging.
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Data availability
The scRNA-seq and RNA-seq data generated in this study have been deposited in the Sequence Read Archive of the National Center for Biotechnology Information under the BioProject accession number PRJNA1345517. All data in this article are provided in the source data files or are available from the corresponding authors upon reasonable request. Source data are provided with this paper.
References
Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: an expanding universe. Cell 186, 243–278 (2023).
Kousa, A. I. et al. Age-related epithelial defects limit thymic function and regeneration. Nat. Immunol. 25, 1593–1606 (2024).
de Magalhães, J. P. Cellular senescence in normal physiology. Science 384, 1300–1301 (2024).
Phan, H. V. et al. Host-microbe multiomic profiling reveals age-dependent immune dysregulation associated with COVID-19 immunopathology. Sci. Transl. Med. 16, eadj5154 (2024).
Dallan, B. et al. Age differentially impacts adaptive immune responses induced by adenoviral versus mRNA vaccines against COVID-19. Nat. Aging 4, 1121–1136 (2024).
Jiang, G., Zou, Y., Zhao, D. & Yu, J. Optimising vaccine immunogenicity in ageing populations: key strategies. Lancet Infect. Dis. 25, e23–e33 (2025).
Zhivaki, D. et al. Correction of age-associated defects in dendritic cells enables CD4(+) T cells to eradicate tumors. Cell 187, 3888–3903 e3818 (2024).
Wu, R., Sun, F., Zhang, W., Ren, J. & Liu, G. H. Targeting aging and age-related diseases with vaccines. Nat. Aging 4, 464–482 (2024).
Palacios-Pedrero, M. A. et al. Signs of immunosenescence correlate with poor outcome of mRNA COVID-19 vaccination in older adults. Nat. Aging 2, 896–905 (2022).
Cobey, S. Vaccination against rapidly evolving pathogens and the entanglements of memory. Nat. Immunol. 25, 2015–2023 (2024).
Inoue, T. & Kurosaki, T. Memory B cells. Nat. Rev. Immunol. 24, 5–17 (2024).
van Dorst, M. et al. Immunological factors linked to geographical variation in vaccine responses. Nat. Rev. Immunol. 24, 250–263 (2024).
Jo, N. et al. Impaired CD4(+) T cell response in older adults is associated with reduced immunogenicity and reactogenicity of mRNA COVID-19 vaccination. Nat. Aging 3, 82–92 (2023).
Silva-Cayetano, A. et al. Spatial dysregulation of T follicular helper cells impairs vaccine responses in aging. Nat. Immunol. 24, 1124–1137 (2023).
Xiao, C. et al. Insufficient epitope-specific T cell clones are responsible for impaired cellular immunity to inactivated SARS-CoV-2 vaccine in older adults. Nat. Aging 3, 418–435 (2023).
Bartleson, J. M. et al. SARS-CoV-2, COVID-19 and the ageing immune system. Nat. Aging 1, 769–782 (2021).
Binnewies, M. et al. Unleashing type-2 dendritic cells to drive protective antitumor CD4(+) T cell immunity. Cell 177, 556–571 e516 (2019).
Yin, X., Chen, S. & Eisenbarth, S. C. Dendritic cell regulation of T helper cells. Annu. Rev. Immunol. 39, 759–790 (2021).
Aw, D., Silva, A. B. & Palmer, D. B. Immunosenescence: emerging challenges for an ageing population. Immunology 120, 435–446 (2007).
Chen, J., Deng, J. C. & Goldstein, D. R. How aging impacts vaccine efficacy: known molecular and cellular mechanisms and future directions. Trends Mol. Med. 28, 1100–1111 (2022).
Carrasco, E. et al. The role of T cells in age-related diseases. Nat. Rev. Immunol. 22, 97–111 (2022).
Amon, L. et al. Guidelines for DC preparation and flow cytometry analysis of mouse lymphohematopoietic tissues. Eur. J. Immunol. 53, e2249893 (2023).
Ginhoux, F., Guilliams, M. & Merad, M. Expanding dendritic cell nomenclature in the single-cell era. Nat. Rev. Immunol. 22, 67–68 (2022).
Lopez, L. et al. Dendritic cell-targeted therapy expands CD8 T cell responses to bona-fide neoantigens in lung tumors. Nat. Commun. 15, 2280 (2024).
Jin, Y., Li, P. & Wang, F. β-glucans as potential immunoadjuvants: a review on the adjuvanticity, structure-activity relationship and receptor recognition properties. Vaccine 36, 5235–5244 (2018).
Xu, J. et al. Yeast-derived nanoparticles remodel the immunosuppressive microenvironment in tumor and tumor-draining lymph nodes to suppress tumor growth. Nat. Commun. 13, 110 (2022).
Powell, N., Walker, M. M. & Talley, N. J. The mucosal immune system: master regulator of bidirectional gut-brain communications. Nat. Rev. Gastroenterol. Hepatol. 14, 143–159 (2017).
Agirman, G., Yu, K. B. & Hsiao, E. Y. Signaling inflammation across the gut-brain axis. Science 374, 1087–1092 (2021).
Chougnet, C. A. et al. Loss of phagocytic and antigen cross-presenting capacity in aging dendritic cells is associated with mitochondrial dysfunction. J. Immunol. 195, 2624–2632 (2015).
Grolleau-Julius, A., Harning, E. K., Abernathy, L. M. & Yung, R. L. Impaired dendritic cell function in aging leads to defective antitumor immunity. Cancer Res. 68, 6341–6349 (2008).
Granot, T. et al. Dendritic cells display subset and tissue-specific maturation dynamics over human life. Immunity 46, 504–515 (2017).
Förster, R., Davalos-Misslitz, A. C. & Rot, A. CCR7 and its ligands: balancing immunity and tolerance. Nat. Rev. Immunol. 8, 362–371 (2008).
Eckert, N., Permanyer, M., Yu, K., Werth, K. & Forster, R. Chemokines and other mediators in the development and functional organization of lymph nodes. Immunol. Rev. 289, 62–83 (2019).
Song, J. H. et al. CCR7-CCL19/CCL21-regulated dendritic cells are responsible for effectiveness of sublingual vaccination. J. Immunol. 182, 6851–6860 (2009).
Choi, Y. et al. Immune checkpoint blockade induces gut microbiota translocation that augments extraintestinal antitumor immunity. Sci. Immunol. 8, eabo2003 (2023).
Krebs, C. F. et al. Autoimmune renal disease is exacerbated by S1P-receptor-1-dependent intestinal Th17 cell migration to the kidney. Immunity 45, 1078–1092 (2016).
Tatsumi, N., El-Fenej, J., Davila-Pagan, A. & Kumamoto, Y. CD301b(+) dendritic cell-derived IL-2 dictates CD4(+) T helper cell differentiation. Nat. Commun. 16, 2002 (2025).
Goronzy, J. J. & Weyand, C. M. Understanding immunosenescence to improve responses to vaccines. Nat. Immunol. 14, 428–436 (2013).
Gustafson, C. E., Kim, C., Weyand, C. M. & Goronzy, J. J. Influence of immune aging on vaccine responses. J. Allergy Clin. Immunol. 145, 1309–1321 (2020).
Zhang, X., Yang, B., Ni, Q. & Chen, X. Materials engineering strategies for cancer vaccine adjuvant development. Chem. Soc. Rev. 52, 2886–2910 (2023).
Shi, S. et al. Vaccine adjuvants: understanding the structure and mechanism of adjuvanticity. Vaccine 37, 3167–3178 (2019).
Pereira, B., Xu, X. N. & Akbar, A. N. Targeting inflammation and immunosenescence to improve vaccine responses in the elderly. Front. Immunol. 11, 583019 (2020).
Sanmarco, L. M. et al. Lactate limits CNS autoimmunity by stabilizing HIF-1α in dendritic cells. Nature 620, 881–889 (2023).
Agrawal, A. et al. Altered innate immune functioning of dendritic cells in elderly humans: a role of phosphoinositide 3-kinase-signaling pathway. J. Immunol. 178, 6912–6922 (2007).
Agrawal, A. & Gupta, S. Impact of aging on dendritic cell functions in humans. Ageing Res. Rev. 10, 336–345 (2011).
Shaw, A. C., Goldstein, D. R. & Montgomery, R. R. Age-dependent dysregulation of innate immunity. Nat. Rev. Immunol. 13, 875–887 (2013).
Wong, C. & Goldstein, D. R. Impact of aging on antigen presentation cell function of dendritic cells. Curr. Opin. Immunol. 25, 535–541 (2013).
Salminen, A. Immunosuppressive network promotes immunosenescence associated with aging and chronic inflammatory conditions. J. Mol. Med. 99, 1553–1569 (2021).
Reitsema, R. D., Kumawat, A. K., Hesselink, B. C., van Baarle, D. & van Sleen, Y. Effects of ageing and frailty on circulating monocyte and dendritic cell subsets. NPJ Aging 10, 17 (2024).
Othman, R. A., Moghadasian, M. H. & Jones, P. J. Cholesterol-lowering effects of oat β-glucan. Nutr. Rev. 69, 299–309 (2011).
Xu, Y. P. et al. Aging affects epidermal Langerhans cell development and function and alters their miRNA gene expression profile. Aging 4, 742–754 (2012).
Grewe, M. Chronological ageing and photoageing of dendritic cells. Clin. Exp. Dermatol. 26, 608–612 (2001).
Tang, H. et al. Rescuing dendritic cell interstitial motility sustains antitumour immunity. Nature 645, 244–253 (2025).
Lobel, G. P. et al. Glutamine is critical for the maintenance of type 1 conventional dendritic cells in normal tissue and the tumor microenvironment. Proc. Natl. Acad. Sci. USA 121, e2412157121 (2024).
Acknowledgements
This work was supported by the National Key R&D Program of China (grant no. 2022YFB3808100 to C. Wang). This work was supported by the National Natural Science Foundation of China (grant nos. T2321005, 32371476 and U25A20599 to C. Wang). CAMS Innovation Fund for Medical Sciences (grant no. 2024-I2M-TS-030I to Y.L.), Yunling Scholar Project and the Science and Technology Plan-biological Medical Special Project of Yunnan Province (grant no. 202302AA310005 to Y.L.). This work was partly supported by Collaborative Innovation Center of Suzhou Nano Science & Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project.
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C. Wang conceived and designed the project, supervised the research and revised the paper. H.X.D., R.S., B.W.X., F.X., X.Y.Y., C. Weng, C.L.Y., H.W., B.B.W., J.L.X., Y.Z., B.T. and X.L.S. performed the experiments and collected data. H.X.D. analyzed, interpreted the data and wrote the first draft of the paper. Y.M.W., Y.L. and C. Wang provided key reagents and technical support. C. Wang acquired funding and provided overall supervision. All authors discussed the results, critically reviewed, edited the paper and approved the final version.
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Extended data
Extended Data Fig. 1 Immune evaluation of blood and secondary lymphoid organs.
a, Experimental design and timeline. b, Representative plots for the gating strategy of blood T cells. c-e, The population of CD4+ and CD8+ T cells in the blood, mesenteric lymph nodes (MLNs), inguinal lymph nodes (LNs), and spleen in young and aged mice (n = 4, biological replicates per group). P values (from left to right): **P = 0.0034; NS, P = 0.1331; **P = 0.0088, *P = 0.0496, **P = 0.0085, **P = 0.0046, **P = 0.0044, *P = 0.0116. f-m, The population of CD44−CD62L+, CD44+CD62L− and CD44+CD62L+ in CD4+ or CD8+ T cells and corresponding quantification results (n = 4, biological replicates per group). (g-i) P values (from left to right): **P = 0.0055, **P = 0.0086, *P = 0.0146, *P = 0.0293, ***P = 0.0001, **P = 0.0042, ****P < 0.0001, *P = 0.0309, *P = 0.0326, **P = 0.0013; NS, P = 0.1570; *P = 0.0330. (k-m) P values (from left to right): **P = 0.0028, **P = 0.0088, *P = 0.0296, *P = 0.0424, **P = 0.0050, ***P = 0.0003, **P = 0.0010, *P = 0.0110, **P = 0.0029, **P = 0.0020, **P = 0.0026, *P = 0.0243. n, The expression of the exhaustion markers PD-1, TIM-3, LAG-3, and CTLA-4 in blood T cells (n = 4, biological replicates per group). P values (from left to right): ****P < 0.0001, ***P = 0.0006, *P = 0.0186, **P = 0.0042. o, Flow cytometry analysis of B cells (CD19+B220+CD45+) in the peripheral blood (n = 4, biological replicates per group). **P = 0.0030. p, Correlations between the proportion of circulating B cells and serum anti-RBD IgG binding titers (n = 22, biological replicates). q, r, Representative plots for the gating strategy and corresponding quantification results (n = 4, biological replicates per group) of DCs (CD11c+MHCII+Epcam−CD45+) in LNs. *P = 0.0197. s-u, The expression of the exhaustion markers TIM-3, LAG-3, and PD-L1 in DCs (8–10 wo, n = 6; 80–90 wo, n = 5, biological replicates). P values (from left to right): ****P < 0.0001, ****P < 0.0001, **P = 0.0015. Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
Extended Data Fig. 2 Lymph node dendritic cells analysis.
a, The quantitative results of the absolute total cell number of LNs (blank (8–10 wo), blank (80–90 wo), Vac (80–90 wo), n = 4; Vac (8–10 wo), n = 5, biological replicates). P values: Vac (80–90 wo) versus blank (80–90 wo) P = 0.4487; Vac (8–10 wo) versus blank (8–10 wo) *P = 0.5483; blank (80–90 wo) versus blank (8–10 wo) P = 0.6497; Vac (80–90 wo) versus Vac (8–10 wo) *P = 0.0489. b, c, The proportion and absolute number of immune cells (CD45+) in the LNs (blank (8–10 wo), Vac (80–90 wo), blank (80–90 wo), n = 4; Vac (8–10 wo), n = 5, biological replicates). (b) P values: Vac (80–90 wo) versus blank (80–90 wo) P = 0.1138; Vac (8–10 wo) versus blank (8–10 wo) P = 0.0815; blank (80–90 wo) versus blank (8–10 wo) **P = 0.0029; Vac (80–90 wo) versus Vac (8–10 wo) **P = 0.0028. (c) P values: Vac (80–90 wo) versus blank (80–90 wo) P = 0.6238; Vac (8–10 wo) versus blank (8–10 wo) P = 0.1625; blank (80–90 wo) versus blank (8–10 wo) P = 0.0816; Vac (80–90 wo) versus Vac (8–10 wo) *P = 0.0408. d, Correlations between the absolute number of CCR7+ DCs and TFH cells (n = 22, biological replicates; P < 0.0001). e, Correlations between the number of CCR7+ DCs, TFH cells and antibody titers (n = 22, biological replicates). The number of CCR7+ DCs significantly correlated with both antibody titers (R2 = 0.7785, P < 0.0001) and TFH cell numbers (R2 = 0.7876, P < 0.0001). Similarly, TFH cell numbers showed a strong positive correlation with antibody titers (R2 = 0.7490, P < 0.0001). f, LNs immune cell clustering genes. g, h, t-SNE map for regrouping analysis of CD45+ cells and corresponding quantitative results. i, j, t-SNE map for regrouping analysis of DCs and corresponding quantitative results. k, Highlight t-SNE map of representative genes in DCs. l, Violin plot of genes identified for DC subset. m, n, The absolute number of cDC2 (CD11b+XCR1−MHCII+CD11c+Epcam−CD45+) and correlations between cDC2 and serum anti-RBD IgG binding titers in the LNs (n = 4, biological replicates per group). *P = 0.0456. o, p, The absolute number of cDC1 (XCR1+CD11b−MHCII+CD11c+Epcam−CD45+) and correlations between cDC1 and antibody titers (Vac (8–10 wo), n = 5; Vac (80–90 wo), n = 4, biological replicates). q, r, The absolute number of LCs (CD207+Epcam+MHCII+CD11c+CD45+) and correlations between LCs and antibody titers (Vac (8–10 wo), n = 5; Vac (80–90 wo), n = 4, biological replicates). s, t, The absolute number mig cDC1 (XCR1+CD11b−MHCIIhighCD11c+Epcam−CD45+) and correlations between mig cDC1 and antibody titers (n = 4, biological replicates per group). u, v, The absolute number res cDC1 (XCR1+CD11b−MHCIIintCD11c+Epcam−CD45+) and correlations between res cDC1 and antibody titers (Vac (8–10 wo), n = 5; Vac (80–90 wo), n = 4, biological replicates). w, x, The absolute number res cDC2 (CD11b+XCR1−MHCIIintCD11c+Epcam−CD45+) and correlations between mig cDC1 and antibody titers (Vac (8–10 wo), n = 5; Vac (80–90 wo), n = 4, biological replicates). Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
Extended Data Fig. 3 Functional analysis of DC subsets.
a, Representative gene expression associated with endocytosis in different DC subsets. b, Representative gene expression associated with antigen processing and presentation in different DC subsets. c, Representative gene expression associated with chemokine signaling pathway in different DC subsets. d, KEGG enrichment analysis of upregulated genes in mig cDC2. e, f, KEGG enrichment analysis of downregulated and upregulated genes in res cDC2. g-i, Representative genes for each pathway of mig cDC2 and res cDC2 in young and aged mice. j, KEGG enrichment analysis of mig cDC2 and res cDC2 in young mice. k, KEGG enrichment analysis of mig cDC2 and res cDC2 in aged mice. Statistical tests: KEGG enrichment analysis was performed using the two-tailed hypergeometric test, with false discovery rate (FDR) correction for multiple comparisons. Adjusted P < 0.05 was considered statistically significant.
Extended Data Fig. 4 YNPs preparation, characterization and LN single-cell RNA sequencing analysis.
a, Schematic of preparation of yeast cell wall-derived particles. b, SEM and TEM images of Yeast and YNPs. c, Size and surface zeta potential of Yeast and YNPs measured by dynamic light scattering (n = 3, biological replicates per group). d, Components of Yeast. e, H&E slices of intestine. Scale bars = 100 μm. f, Intestinal permeability assessment after and before YNPs treatment (n = 3, biological replicates per group). NS, P = 0.8410. g, Analysis of intestinal representative inflammatory cytokines (8–10 wo, 80–90 wo, n = 3; 80–90 wo+YNPs, n = 4, biological replicates). P values (from left to right): *P = 0.0251; NS, P = 0.9044; *P = 0.0187; NS, P = 0.7988; NS, P = 0.8844. h, Representative plots for the gating strategy of Ki67+ DCs (CD11c+MHCII+CD45+) in the LNs on day 7 post-immunization. i, LNs immune cell clustering gene. j, k, t-SNE map for regrouping analysis of CD45+ cells and corresponding quantitative results. l, m, t-SNE map for regrouping analysis of DCs and corresponding quantitative results. n, Highlight t-SNE map of representative genes in DCs. o, Violin plot of genes identified for DC subset. Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
Extended Data Fig. 5 Lymph node single-cell RNA sequencing analysis.
a, Representative gene expression associated with endocytosis in different DC subsets. b, Representative gene expression associated with antigen processing and presentation in different DC subsets. c, Representative gene expression associated with chemokine signaling pathway in different DC subsets. d, Representative GSEA enrichment analysis of endocytosis, antigen processing and presentation, and chemokine signaling pathways in different DC subsets. GSEA was performed using a permutation test (1000 permutations) to calculate enrichment scores and nominal P values. FDR correction was applied for multiple comparisons. e, f, Comparison of gene expression related to functional defects in the DC subsets. g, h, KEGG enrichment analysis of downregulated and upregulated genes in res cDC2. Statistical tests: KEGG enrichment analysis was performed using the two-tailed hypergeometric test, with FDR correction for multiple comparisons. Adjusted P < 0.05 was considered statistically significant.
Extended Data Fig. 6 Cellchat analysis.
a, Cell communication analysis of each cell in young mice LNs. b, Cell communication analysis of each cell in aged mice LNs. c, Representative confocal images of LNs from untreated and YNP-treated mice 7 d after immunization with RBD protein in Freund’s complete adjuvant. Scale bars, 100 μm. LN sections were stained for DAPI (blue), CD4 (red), CD11c (white), and CCR7 (green). Vac (80–90 wo), Vac (80–90 wo)+YNPs, n = 5, biological replicates per group. Brightness and contrast were adjusted only for visualization purposes.
Extended Data Fig. 7 Single-cell RNA sequencing analysis of lymph node B cells and T cells.
a, Violin plot of genes identified for T cell subset. b, c, t-SNE map for regrouping analysis of T cells and corresponding quantitative results. d, Violin plot of representative exhaustion genes in T cells. e, Violin plot of genes identified for B cell subset. f, g, t-SNE map for regrouping analysis of B cells and corresponding quantitative results. h, Violin plot of representative exhaustion genes in B cells. i, Representative plots for the gating strategy of T cells. j, The population of CD8+ T cells, CD44−CD62L+ (naïve T cells), CD44+CD62L−, CD44+CD62L+ and IFN-γ+ (CTL) in CD8+ T cells (n = 4, biological replicates per group). k, The population of CD4+ T cells, CD44−CD62L+ (naïve T cells), CD44+CD62L−, CD44+CD62L+ and IFN-γ+ (Th1) in CD4+ T cells (n = 4, biological replicates per group). Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
Extended Data Fig. 8 Evaluation of innate and adaptive immunity.
a, Serum anti-OVA IgG binding titers detected by ELISA (n = 10, biological replicates per group). P values (from left to right): **P = 0.0025, **P = 0.0085, **P = 0.0067. b–d, Flow cytometry analysis of GC B cells (CD19+CD38−CD95+), TFH cells (PD-1+CXCR5+Foxp3−CD4+CD3+CD45+) and plasma cells (CD44+CD138+CD45+) in the LNs (n = 3, biological replicates per group). P values (from left to right): ****P < 0.0001, ***P = 0.0006, ***P = 0.0005. e, Violin plot of genes identified for immune cell subset. f, g, t-SNE map for regrouping analysis of immune cells and corresponding quantitative results. h, Violin plot of genes identified for T cell subset. i, Violin plot of genes identified for B cell subset. j, k, Corresponding quantification results of polyfunctionality (IFN-γ+TNF-α+IL-2+) CD4 T cells and CD8 T cells after YNP treatment (n = 5, biological replicates per group). P values (from left to right): *P = 0.0393, ***P = 0.0008. l, m, Major cytokines of IFN-γ, IL-2, TNF-α in CD4+ T cells and CD8+ T cells from the blood (n = 5, biological replicates per group). P values (from left to right): **P = 0.0094, *P = 0.0493, *P = 0.0285, **P = 0.0079, **P = 0.0039, *P = 0.0123. Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
Extended Data Fig. 9 RNA sequencing analysis of BMDCs, and intestinal retention of YNPs.
a, Flow cytometry analysis of BMDCs maturation from young or aged mice after YNP-treated (n = 3, biological replicates per group). P values (from left to right): *P = 0.0435, *P = 0.0241. b, Frequency of CD69+ and interferon (IFN)-γ+ in CD4+ T cells and CD8+ T cells (n = 3, biological replicates per group). P values (from left to right): *P = 0.0118, *P = 0.0494, *P = 0.0204. c, Representative genes of BMDCs activation and migration after YNP treatment (n = 3, biological replicates per group). P values (from left to right): ****P < 0.0001, ***P = 0.0002, *P = 0.0236, ***P = 0.0004, ****P < 0.0001. d, Analysis of correlation coefficient between UnTx and YNP-treated group. e, f, GO and KEGG enrichment analysis of RNA sequencing after YNP treatment. g, In vitro bioluminescence imaging of LNs at different points in time and quantification results (n = 3, biological replicates per group). h, i, In vitro bioluminescence imaging of feces at different points in time and quantification results (n = 3, biological replicates per group). j-l, Flow cytometry analysis of Cy5.5 in CD11c+, F4/80+, and CD19+ cells (n = 3, biological replicates per group). (j) P values: YNPs versus UnTx **P = 0.0025, YNPs versus Cy5.5 **P = 0.0090, YNPs versus Yeast **P = 0.0032; (k) P values: YNPs versus UnTx **P = 0.0084, YNPs versus Cy5.5 **P = 0.0047, YNPs versus Yeast *P = 0.0112; (l) P values: YNPs versus UnTx *P = 0.0193, YNPs versus Cy5.5 **P = 0.0232, YNPs versus Yeast *P = 0.0390. Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
Extended Data Fig. 10 Intestinal immune microenvironment analysis after YNP treatment.
a, Violin plot of genes identified for immune cell in the intestine. b, c, t-SNE map for regrouping analysis of immune cells and corresponding quantitative results. d-f, Highlight t-SNE map of representative genes in DCs. g, h, Representative plots for the gating strategy of DC subset and corresponding quantitative results (n = 5, biological replicates per group). P values (from left to right): **P = 0.0034, **P = 0.0092, *P = 0.0126, *P = 0.0482. i, Dotplot of representative gene Itgae expression in DC subsets. j, Representative migration and endocytosis related genes in DCs subset. k, KEGG enrichment analysis of upregulated genes in CD103+ cDC2. Data are means ± SEM. Statistical analysis was performed using Student’s t-test (two-tailed). NS, not significant.
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Dai, H., Sun, R., Xie, B. et al. Attenuating age-related decline in dendritic cell migration improves vaccine efficacy via gut-immune crosstalk. Nat Aging 6, 816–830 (2026). https://doi.org/10.1038/s43587-026-01068-4
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DOI: https://doi.org/10.1038/s43587-026-01068-4
