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Rescuing dendritic cell interstitial motility sustains antitumour immunity

Nature volume 645, pages 244–253 (2025)Cite this article

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Abstract

The dendritic cell (DC)-initiated and sustained cancer immunity cycle is indispensable for effective endogenous and therapeutically mobilized antitumour T cell responses1,2,3,4,5,6,7,8. This necessitates the continuous migration of antigen-carrying DCs from the tumour microenvironment (TME) to the tumour draining lymph nodes (tdLNs)7,8,9,10,11,12,13. Here, through longitudinal analysis of human and mouse tumours, we observed a progressive decrease in migratory conventional DCs (mig-cDCs) in the tdLNs during tumour progression. This decline compromised tumour-specific T cell priming and subsequent T cell supply to the TME. Using a genome-wide in vivo CRISPR screen, we identified phosphodiesterase 5 (PDE5) and its substrate cyclic guanosine monophosphate (cGMP) as key modulators of DC migration. Advanced tumours disrupted cGMP synthesis in DCs to decrease their motility, while PDE5 perturbation preserved the cGMP pool to restore DC migration. Mechanistically, cGMP enhanced myosin-II activity through Rho-associated factors, extending the paradigm of cGMP-regulated amoeboid migration from Dictyostelium to mammalian immune cells. Pharmacological inhibition of PDE5 using sildenafil restored mig-cDC homing to late-stage tdLNs and sustained antitumour immunity in a DC-dependent manner. Our findings bridge fundamental DC interstitial motility to antitumour immunity, revealing that its disruption in chaotic TME promotes immune evasion, and its enhancement offers a promising direction for DC-centric immunotherapy.

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Fig. 1: mig-cDCs steadily decline in tdLNs during tumour progression.
Fig. 2: DC in vivo CRISPR screening and validation.
Fig. 3: Progressed tumour dampens DC interstitial motility through PDE5–sGC-maintained cGMP.
Fig. 4: cGMP enhances DC motility through the RhoA–ROCK–myosin-II axis independently of PKG.
Fig. 5: Restoring DC motility by sildenafil enhances DC-based antitumour efficacy.

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Data availability

The data supporting the findings of this study are available in the Article and its Supplementary Information. RNA-seq data files are available at the Gene Expression Omnibus (GEO) under accession number GSE246849. All other data are available in the Article and its Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We thank all of the members of the Ting Zhou and Yongdeng Zhang laboratories for discussions and support; the staff at the Laboratory Animal Resources Center, High-Performance Computing Center, Flow Cytometry Core, Genomic Core and Microscopy Core at Westlake University for support with techniques; and K. Guan, Q. Ma, D. Li, J. Wang, X. Zhou, D. He, H. Xu and C. Rosen for discussions and suggestions. This study was supported by National Natural Science Foundation of China grant 32270968, the Noncommunicable Chronic Diseases-National Science and Technology Major Program (2023ZD0500402 to T.Z.), the ‘High Risk High Impact’ (HRHI) program of Westlake Laboratory of Life Sciences and Biomedicine W101110566022301, ‘Pioneer’ and ‘Leading Goose’ R&D Program of the Department of Science and Technology of Zhejiang Province 2023SDXHDX0001, Westlake Multidisciplinary Research Initiative Center (MRIC) Seed Fund MRIC20210202 (T.Z.) and the Education Foundation of Westlake University.

Author information

Author notes

  1. These authors contributed equally: Haichao Tang, Zongfang Wei

Authors and Affiliations

  1. School of Medicine, Zhejiang University, Hangzhou, China

    Haichao Tang

  2. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

    Haichao Tang, Zongfang Wei, Bei Zheng, Peihan Wu, Lulu Wu, Xiaohe Ma, Yanqin Chen, Jinmin Xu, Yu Qiao, Ying Zhang, Juju Miao, Zijing Yu, Yaodong Zhao, Zhen Xia, Qi Xie, Luming Zhao, Xu Li, Yongdeng Zhang & Ting Zhou

  3. School of Medicine, Westlake University, Hangzhou, China

    Haichao Tang, Zongfang Wei, Lulu Wu, Xiaohe Ma, Jinmin Xu, Ying Zhang, Juju Miao, Yaodong Zhao, Luming Zhao & Ting Zhou

  4. School of Life Sciences, Westlake University, Hangzhou, China

    Haichao Tang, Zongfang Wei, Bei Zheng, Peihan Wu, Lulu Wu, Xiaohe Ma, Yanqin Chen, Jinmin Xu, Yu Qiao, Ying Zhang, Juju Miao, Zijing Yu, Yaodong Zhao, Zhen Xia, Qi Xie, Luming Zhao, Xu Li, Yongdeng Zhang & Ting Zhou

  5. Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Yumeng Cai, Si Su & Huanwen Wu

  6. Hangzhou Cancer Hospital, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou, China

    Rongjing Zhou & Bing Xia

  7. Department of Breast Surgery, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou, China

    Jian Liu & Jufeng Guo

  8. Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Zhaoyuan Liu & Florent Ginhoux

  9. Gustave Roussy Cancer Campus, Villejuif, France

    Florent Ginhoux

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Contributions

H.T. and Z.W. performed all of the experiments. Z.W. and H.T. performed imaging experiments. L.W. and H.T. analysed RNA-seq data. Z.W., B.Z., Y. Chen, Z.Y., Y.Q. and Yongdeng Zhang performed super-resolution imaging. H.W., Y. Cai, S.S., B.X., R.Z., J.L. and J.G. collected and prepared human patient samples. H.T. and X.M. performed western blot experiments. P.W., X.L. and H.T. performed biotin–cGMP pull-down assays and MS analysis. J.X. and Y. Zhao helped to maintain the mice. Z.L. and F.G. provided DC-related genetic mice. Ying Zhang, J.M., Z.X. and Q.X. helped to provide agents and assays. T.Z., H.T. and Z.W. wrote the paper. T.Z., H.T., Z.W. and L.Z. edited and discussed the paper. T.Z. supervised the research. T.Z. conceived the project.

Corresponding author

Correspondence to Ting Zhou.

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Nature thanks Ana-Maria Lennon-Duménil, Caetano Reis e Sousa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Decreased migratory cDCs in both human and mouse tdLNs.

a,b, Immunofluorescent staining of cDCs (CD11C and LAMP3), T cells (CD3), or B cells (CD19) in early- or late-stage SLNs from breast cancer (n = 5 vs 6 patients) (a) and colon cancer (n = 5 vs 5 patients) (b) patients. Scale bars, overview, 1 mm; zone a and b, 100 μm. Quantifications on the right panel were CD11C/LAMP3 double positive area out of the total SLN area (%) (upper), or area value of 2 randomly selected areas (500 × 500 μm2) within each sample (lower) (n = 10 vs 12 areas in a; n = 10 areas each in b from corresponding patients). Lower quantification is displayed as area per 10 μm2. c,d, Quantifications of tumour volume and tdLN mig-cDCs (CD11c+MHC-IIhi) at day 7, 10, and 14 (c, B16F10, n = 4, 4, 6 mice; d, MC38, n = 4 mice per group). e, Quantification of IFNγ and TOX frequencies in CD44+PD1+CD8+ T cells in B16F10 tumour (n = 3 vs 6 mice). f, Quantifications of mig-cDCs, mig-CD103+ cDCs, or mig-CD11b+ cDCs in tdLNs of MC38 tumour-bearing mice (n = 5 vs 6 mice). g, Flow cytometric gating strategy for DC populations in tdLNs (sample derived from B16F10-OVA-ZsGreen tumour bearing mice). ZsGreen negative control was indicated as grey. Migratory cDC1s or cDC2s were pre-gated on migratory cDCs (CD11c+MHC-IIhi). h, Quantifications of migratory XCR1+ and SIRPα+ cDCs in tdLNs of B16F10-OVA-ZsGreen -bearing mice (n = 6 mice per group). i, Quantifications of migratory CD103+ and CD11b+ cDCs in non-tdLNs of B16F10-OVA-ZsGreen-bearing mice (n = 6 mice per group). j, Quantifications of total CD8+ T cells, CD44+CD8+ T cells and PD1+CD8+ T cells in tdLNs from mice bearing early- or late-stage tumours (n = 6 mice per group). Data are merged (a, b) or representative (c-j) of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Student’s unpaired t-test.

Source data

Extended Data Fig. 2 DC frequency in TME and in vivo CRISPR screening QC.

a, Flow cytometric gating strategy for DC populations in TME. b,c, Quantifications of total cDCs, CD103+ cDCs, and CD11b+ cDCs in TME from early or late stage (b, B16F10, n = 5 mice; c, MC38, n = 6 mice). d, LAMP3+ DC signatures in early- or late-stage cancer patients based on TCGA database. e,i, All the mapped sgRNA hits for the pre-round (e) or major-round (i). Data are merged from 5 (e) or 3 (i) independent experiments. The x-axis indicates sgRNA numbers and y-axis indicates the calculated log2 fold change. f,j, Coverage calculation in each step from pre-round (f) or major-round (j) screening. g,k, Gini-Index analysis for pre-round (g) or major-round (k) screening. h, Minipool gRNAs selection strategy after the pre-round screening. l, Distribution of the enriched sgRNAs for top-hit genes from the major-round screening. m, Western blot analysis of Pde5 expression in BMDCs derived from Pde5+/+ or Pde5−/− mice. β-actin was used as control. n, Frequencies of CTV-labelled Pde5+/+ and CFSE-labelled Pde5−/− DCs in the TME two days post peritumour transfer (n = 6 mice). Quantifications on right panel were normalized to the mixture ratio before cell transfer. o, Whole-mount tissue 3D reconstitution (left) and cross-section image (middle) of tdLNs from mice co-transferred with Pde5+/+ and Pde5−/− DCs. Cells were surface rendered and DC signals were processed as dots. Scale bars, 500 μm. Data quantified from full tissue sections from each mouse (n = 2 mice). p, Representative staining and quantification of CTV-labelled Pde5+/+ and CFSE-labelled Pde5−/− mature BMDCs in mouse ear explants (n = 7 lymphatic vessels). Scale bar, 100 μm. Data are representative (b, c, m, n) or merged (e, g, i, k, l, o, p) of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Student’s unpaired (b, c) or paired (n, p) t-test or two tailed Mann–Whitney U-tests (d).

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Extended Data Fig. 3 Late-stage tumours dampen DC interstitial motility.

a, Gene set enrichment analysis of down-regulated genes associated with cytoskeletal protein binding in late-stage tumoural DCs. NES, normalized enrichment score. b, Gene ontology enrichment analysis of downregulated pathways in late-stage tumoural DCs. c, Differentially expressed genes associated with GTPase in early- or late-stage tumoural DCs. d, Representative images and quantifications of tumoural DCs within early- or late-stage tumour margin lymphatic vessels (white arrowheads; n = 57 lymphatic vessels). Scale bars, 50 μm. e, Transcriptional analysis of Ccr7 (left, n = 2 biological samples) and flow cytometric quantification of CCR7 on tumoural DCs (right, n = 6 vs 5 mice). f,g, Representative trajectories of BMDC migration (corresponding to experiments and velocity statistics in Fig. 3f,g). h, ELISA examining intracellular cGMP levels in Pde5+/+ and Pde5−/− BMDCs (n = 3 biological replicates). i, Mean velocities of BMDCs treated with vehicle (n = 169 cells), 10 μM (n = 157 cells), 100 μM (n = 182 cells), or 500 μM 8-Br-cGMP (n = 142 cells) in 3D migration assay. j,k, Trajectories and mean velocities of mature BMDCs in 3D collagen gel assay migration assay treated with vehicle (n = 181 cells) or 500 μM 8-Br-cGMP (n = 175 cells) (j), or using Pde5−/− BMDCs (Pde5+/+, n = 193 cells; Pde5−/−, n = 116 cells) (k). l, Schematic of cGMP pathway components in regulating DC interstitial motility. The diagrams were created in BioRender. Zhou, T. (2025) https://BioRender.com/lyhhvye. m, t-SNE plot visualization of flow cytometric quantified cell populations (left) and NOS2 fluorescence intensity (right) in B16F10 TME. n, Representative trajectories of BMDC migration (corresponding to Fig. 3l). o, Trajectories and mean velocities of mature BMDCs either treated with vehicle (n = 188 cells) or 100 μM DETA NONOate (n = 154 cells). Data are representative (e, h, m) or merged (d, f, g, i-k, n, o) of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Mann–Whitney U-tests (d, j, k, o), two-tailed Student’s unpaired t-test (e, h) or Kruskal–Wallis test with multiple comparisons (i). Trajectories were randomly selected from cells after quantifying their velocities under the same conditions.

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Extended Data Fig. 4 Pde5 perturbation restores DC migration through cGMP-regulated myosin-II activity.

a, pMLC signal visualization using DNA-PAINT imaging at nanoscale resolution in whole cell (overlay) or y-z slice view (x = 200 nm) of BMDCs treated with vehicle or 8-Br-cGMP. pMLC signal quantifications are presented on the right (n = 75 steps per group, 1 step = 50 nm). White arrows indicate enhanced pMLC signals in peri-nuclear regions. Colour bar denotes depth of z axis. Scale bars, 1 μm. b, pMLC signal visualization in mature BMDCs treated with vehicle or 500 μM 8-Br-cGMP. Colour bar denotes depth. Scale bar, 1 μm. c, Quantifications of the pMLC signal ratio between the back and front of DCs treated with vehicle or 500 μM 8-Br-cGMP (n = 20 cells per group) (see Methods). d, Western blot analysis of pMLC expression in mature BMDCs treated with 500 μM 8-Br-cGMP for varying durations (0, 5, and 15 min). GAPDH was used as control. e-f, Western blot analysis of pMLC and pROCK levels in mature BMDCs treated with 500 μM 8-Br-cGMP plus 10 μM Y27632 (e) or 50 μM Rhosin hydrochloride (f) for 5 min. g, Quantifications of RhoA activity in BMDCs treated with vehicle (n = 26 cells) or 500 μM 8-Br-cGMP (n = 50 cells) using FRET sensor, with FRET ratios plotted. h, Mean velocities of BMDCs treated with vehicle (n = 125 cells), 500 μM 8-Br-cGMP (n = 158 cells), 8-Br-cGMP plus 50 μM Rhosin (n = 100 cells) or 10 μM Y27632 (n = 132 cells). i,j, Transcript analysis of PRKG1 and PRKG2 in different cell populations of Pan-cancer (i) or colorectal cancer (CRC) (j) patients (see Methods). k, Mean velocities of mature BMDCs treated with vehicle (n = 132 cells), 500 μM 8-Br-cGMP (n = 115 cells), 30 μM sildenafil (n = 137 cells), or each plus 86 μM PKG inhibitor (n = 121, 130, 123 cells correspondingly) in 3D collagen gel assay. Data are representative (a-f) or merged (g, h, k) of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Mann–Whitney U-tests (g) or Kruskal–Wallis test with multiple comparisons (h, k).

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Extended Data Fig. 5 Sildenafil enhances antitumour immunity.

a,b, Trajectories and mean velocities of immature BMDCs (n = 147 vs 136 cells) (a) or mature BMDCs (n = 150 vs 174 cells) (b) treated with 30 μM sildenafil in 3D collagen gel assay. c, Mean velocities of BMDCs treated with vehicle (n = 151 cells), 10 μM (n = 137 cells), 30 μM (n = 173 cells), or 50 μM sildenafil (n = 137 cells) in 3D migration assay. d-i, Comparative characteristics between B16F10/B16F10Pde5−/− (d-f) and MC38/MC38Pde5−/− tumour cells (g-i) (n = 3 biological replicates for d, g; n = 6 mice per group for e, f, h, i). j,k, Tumour growth and Kaplan–Meier survival curves of MC38Pde5−/− bearing mice treated with 7 doses of vehicle or sildenafil every other day via intraperitoneal (i.p.) injection (n = 10 vs 13 mice) (j), or oral gavage (gava.) (n = 11 vs 10 mice) (k). l, Tumour growth curves of MC38Pde5−/− bearing mice treated with vehicle or sildenafil at specified doses (vehicle, n = 7; 200 μg sildenafil, n = 6; 400 μg sildenafil, n = 7). m, Tumour growth curves of Batf3+/− and Batf3−/− mice bearing MC38Pde5−/− tumour and treated with vehicle or sildenafil (Batf3+/− + vehicle, n = 5; Batf3+/− + sildenafil, n = 6; Batf3−/− + vehicle, n = 5; Batf3−/− + sildenafil, n = 6). n, Tumour growth curves of MC38Pde5−/− bearing mice treated with vehicle plus isotype, sildenafil plus isotype, vehicle plus anti-CD8, or sildenafil plus anti-CD8 antibody (n = 3, 3, 6, 4 mice). o, Quantifications of mig-CD103+ or CD11b+ cDCs in tdLNs from each group (n = 5, 3, 4 mice). p, Tumour growth curve of MC38Pde5−/− bearing mice treated with vehicle, sildenafil, sildenafil plus 50 μM blebbistatin, or 50 μM Y27632 (n = 3, 3, 6, 6 mice). q, IF staining of DCs in tumoural lymphatic vessels (n = 22, 31, 30 vessels). Scale bar, 50 μm. r, Quantifications of H-2Kb/SIINFEKL, CD80 and CD86 levels on mig-cDCs in tdLNs from each group (n = 5, 5, 3, 4 mice). Control, resident cDCs. Data are merged (a-c, j-l, q) or representative (d-i, m-p, r) of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Mann–Whitney U-tests (a, b), two-tailed Student’s unpaired t-test (d, f, g, i, o) or Kruskal–Wallis test with multiple comparisons (c, q), two-way ANOVA with multiple comparisons corrected using Šidák’s multiple comparison test (d, e, g, h and j–n) or one-way ANOVA with Tukey’s multiple-comparison test (r).

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Extended Data Fig. 6 Sildenafil specifically acts on DCs.

a, Quantifications of TCF1+TIM3− CD8+ T cells in the TME in each group (n = 6 vs 8 mice). b, Quantifications of CD44+PD-1+TIM3−CD8+ T cells, IFNγ+CD44+PD-1+CD8+ T cells, and CD44+PD-1+IFNγ+CD8+ T cells in the TME in each group (n = 6 vs 8 mice). c, Tumour growth and survival curves of B16F10Pde5−/− bearing mice treated with 7 doses of vehicle or sildenafil every other day via subcutaneous (s.c.) injection (n = 10 vs 12 mice). d, Kaplan–Meier survival curve of previously cured mice rechallenged with a double dose of MC38Pde5−/− cells (WT, n = 4; cured, n = 5). Age- and sex-matched mice served as controls. e, Quantifications of CD44+CD8+ and Ki67+CD8+ T cells in tdLNs from each group (n = 6). f, Representative flow cytometric histograms of IFNγ or GZMB in CD44+PD-1+CD8+ and quantifications of CD44+PD-1+CD8+ T cells and GZMB+CD44+PD-1+CD8+ T cells in the TME from each treatment group (n = 6 per group). GZMB, Granzyme B. g,h, TCGA pan-cancer analysis using DC gene signature combined with PDE5 expression (n = 1066 patients). Kaplan–Meier survival curve of patients stratified by median PDE5 expression into high or low (n = 537 vs 529 patients) DC gene signature groups (g). Correlation between PDE5 and CD8 T cell infiltration levels (h). i, Quantifications of mig-CD103+ and CD11b+ cDCs in Zbtb46crePde5fl/fl mice bearing MC38Pde5−/− tumour and treated with vehicle or sildenafil (vehicle, n = 5; sildenafil, n = 4). j, Quantifications of tdLNs CD44+CD8+ or TME IFNγ+CD44+PD-1+CD8+ T cells in Zbtb46crePde5fl/fl mice bearing tumour and treated with vehicle or sildenafil (n = 5 vs 4 mice). k, Schematic of the design strategy for LSL-Pde5 mice. l, Survival curves corresponding to results in Fig. 5o. m, Quantifications of tdLN mig-CD103+ and CD11b+ cDCs in Zbtb46crePde5LSL mice bearing MC38Pde5−/− tumour and treated with vehicle or sildenafil (n = 6 per group). n, Quantifications of tdLN CD44+CD8+ or IFNγ+CD44+PD-1+CD8+ T cells in Zbtb46crePde5LSL mice bearing MC38Pde5−/− tumour and treated with vehicle or sildenafil (n = 6 per group). o, Tumour growth curves of Zbtb46cre or Zbtb46crePde5LSL mice inoculated with MC38Pde5−/− tumour. Data were re-analysed from Fig. 5o (Zbtb46cre, n = 8; Zbtb46crePde5LSL, n = 7). p,q, Quantifications of tdLN migratory cDCs (p), or migratory CD103+ and CD11b+ cDCs (q) in Zbtb46cre and Zbtb46crePde5LSL mice (n = 4 per group). r,s, Quantifications of CD44+CD8+ T cells in tdLNs (r) or IFNγ+CD44+PD-1+CD8+ T cells in the TME (s) in Zbtb46cre and Zbtb46crePde5LSL mice (n = 4 per group). Data are representative (a, b, d, e-j, m, n, p-s) or merged (c, l, o) of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Student’s unpaired t-test (a, b, i, j, m, n, p-s), two-way ANOVA with multiple comparisons corrected using Šidák’s multiple comparison test (c, o), or one-way ANOVA with Tukey’s multiple-comparison test (e, f).

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Extended Data Fig. 7 Pde5-cGMP-mediated DC migration in inflammation and homeostasis.

a, Representative flow cytometric plots and quantifications of FITC+ migratory and resident cDCs in tdLNs from Pde5fl/fl and Zbtb46crePde5fl/fl mice (Pde5fl/fl, n = 6; Zbtb46crePde5fl/fl, n = 7). Mice were painted with FITC-LPS mixture and tdLNs were analysed 48 h post painting to assess FITC uptake. b, Quantifications of cDC percentages, CCR7 expression, or surface CD80/CD86 expression from Pde5+/+ and Pde5−/− BMDCs differentiated with FLT3L for 9 days (n = 3 biological replicates per group). c,d, Quantifications of migratory cDCs (c), and migratory XCR1+ and SIRPα+ cDCs (d) in mediastinal LNs from Pde5fl/fl and Zbtb46crePde5fl/fl mice at homeostasis (n = 6 per group). e, Quantification of total cDCs in lung tissue from Pde5fl/fl and Zbtb46crePde5fl/fl mice (n = 6 per group). Cells were gated on live CD45+CD3−B220−Gr1−F4/80−. Data are representative of at least two to three independent experiments and present as mean ± s.e.m. Statistical analysis was performed using two-tailed Student’s unpaired t-test.

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Supplementary information

Supplementary Figs. 1 and 2

Supplementary Fig. 1: the gating strategies for flow cytometry analysis. a, The gating strategy for mig-cDCs, CD103+ cDCs, CD11b+ cDCs, XCR1+ cDCs, SIRPα+ cDCs and ZsG+ cDCs in tdLNs. b, The gating strategy for analysis of immune cells in the TME. Supplementary Fig. 2: uncropped immunoblot images with size marker indications. The loading control of β-actin or GAPDH was run on the same gels.

Reporting Summary

Supplementary Table 1

Results from the pre-round and major-round in vivo CRISPR screens. This table includes gene-level and sgRNA-level results from both the pre-round and major-round in vivo CRISPR library screens, as well as the composition of the minipool used in the major round. LFC, log2 fold change; RRA, robust rank aggregation [RRA] lo value, reflecting positive selection.

Supplementary Table 2

Identification of cGMP-binding proteins by MS. This table lists proteins enriched in the cGMP-pull-down assay, including protein identities and their corresponding MS intensities. NC, negative control group treated with vehicle.

Supplementary Table 3

Patient information for SLN staining and DC motility analysis. This table presents the clinical and pathological characteristics of the patient cohort evaluated for SLN staining and DC motility.

Supplementary Table 4

Flow cytometry antibodies used in this study and the associated product information.

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Tang, H., Wei, Z., Zheng, B. et al. Rescuing dendritic cell interstitial motility sustains antitumour immunity. Nature 645, 244–253 (2025). https://doi.org/10.1038/s41586-025-09202-9

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