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Sepsis-trained macrophages promote antitumoral tissue-resident T cells

An Author Correction to this article was published on 07 October 2024

This article has been updated

Abstract

Sepsis induces immune alterations, which last for months after the resolution of illness. The effect of this immunological reprogramming on the risk of developing cancer is unclear. Here we use a national claims database to show that sepsis survivors had a lower cumulative incidence of cancers than matched nonsevere infection survivors. We identify a chemokine network released from sepsis-trained resident macrophages that triggers tissue residency of T cells via CCR2 and CXCR6 stimulations as the immune mechanism responsible for this decreased risk of de novo tumor development after sepsis cure. While nonseptic inflammation does not provoke this network, laminarin injection could therapeutically reproduce the protective sepsis effect. This chemokine network and CXCR6 tissue-resident T cell accumulation were detected in humans with sepsis and were associated with prolonged survival in humans with cancer. These findings identify a therapeutically relevant antitumor consequence of sepsis-induced trained immunity.

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Fig. 1: Decreased risk of cancer up to 10 years after cure from sepsis in humans.
Fig. 2: CXCR6+ tissue-resident T cells reduce cancer growth in sepsis-cured mice.
Fig. 3: Sepsis decreases tissue egress of proliferating CXCR6+ tissue-resident T cells.
Fig. 4: CXCR6 and CCR2-dependent tissue retention of proliferating T cells after sepsis cure.
Fig. 5: CXCR6 and CCR2-dependent modulation of tissue-resident T cell functions after sepsis cure.
Fig. 6: Reduction of T cell tissue egress and tumor growth after sepsis is mediated by resident AMs.
Fig. 7: Trained Res-AM increases can be mimicked by β-(1,3)-glucan treatment.
Fig. 8: Chemokine macrophagic network and CXCR6 tissue-resident T cell in humans.

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Data availability

Single-cell sequence data have been deposited in ENA under accession code PRJEB52332. All other data are present in the article and Supplementary Information or from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Methods for the bioinformatic analyses of scRNA and TCR-seq data can be found on GitLab (https://gitlab.univ-nantes.fr/gourain-v-1/cxcr6_lungsepsis) and Zenodo (https://doi.org/10.5281/zenodo.10715057)73.

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References

  1. GBD 2016 Lower Respiratory Infections Collaborators. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Infect. Dis. 18, 1191–1210 (2018).

    Article  Google Scholar 

  2. Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315, 801–810 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kaukonen, K.-M., Bailey, M., Pilcher, D., Cooper, D. J. & Bellomo, R. Systemic inflammatory response syndrome criteria in defining severe sepsis. N. Engl. J. Med. 372, 1629–1638 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Fajgenbaum, D. C. & June, C. H. Cytokine storm. N. Engl. J. Med. 383, 2255–2273 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Taquet, M. et al. Acute blood biomarker profiles predict cognitive deficits 6 and 12 months after COVID-19 hospitalization. Nat. Med. 29, 2498–2508 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Davis, H. E., McCorkell, L., Vogel, J. M. & Topol, E. J. Long COVID: major findings, mechanisms and recommendations. Nat. Rev. Microbiol. 21, 133–146 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cirovic, B. et al. BCG vaccination in humans elicits trained immunity via the hematopoietic progenitor compartment. Cell Host Microbe 28, 322–334 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Klein, J. et al. Distinguishing features of long COVID identified through immune profiling. Nature https://doi.org/10.1038/s41586-023−06651-y (2023).

  9. Rubio, I. et al. Current gaps in sepsis immunology: new opportunities for translational research. Lancet Infect. Dis. 19, e422–e436 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Netea, M. G. et al. Defining trained immunity and its role in health and disease. Nat. Rev. Immunol. 20, 375–388 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van Vught, L. A. et al. Incidence, risk factors, and attributable mortality of secondary infections in the intensive care unit after admission for sepsis. JAMA 315, 1469 (2016).

    Article  PubMed  Google Scholar 

  12. GBD 2019 Respiratory Tract Cancers Collaborators. Global, regional, and national burden of respiratory tract cancers and associated risk factors from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Respir. Med. 9, 1030–1049 (2021).

    Article  Google Scholar 

  13. Overwijk, W. W. & Restifo, N. P. B16 as a mouse model for human melanoma. Curr. Protoc. Immunol. 39, 20.1.1–20.1.29 (2000).

    Article  Google Scholar 

  14. Park, S. L. et al. Tissue-resident memory CD8+ T cells promote melanoma–immune equilibrium in skin. Nature 565, 366–371 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Gaborit, B. J. et al. Circulating Treg cells expressing TNF receptor type 2 contributes to sepsis-induced immunosuppression in patients during sepsis shock. J. Infect. Dis. https://doi.org/10.1093/infdis/jiab276 (2021).

    Article  PubMed  Google Scholar 

  16. Passarelli, A., Mannavola, F., Stucci, L. S., Tucci, M. & Silvestris, F. Immune system and melanoma biology: a balance between immunosurveillance and immune escape. Oncotarget 8, 106132–106142 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mueller, S. N. & Mackay, L. K. Tissue-resident memory T cells: local specialists in immune defence. Nat. Rev. Immunol. 16, 79–89 (2016).

    Article  CAS  PubMed  Google Scholar 

  18. Skon, C. N. et al. Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8+ T cells. Nat. Immunol. 14, 1285–1293 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhou, X. et al. Circuit design features of a stable two-cell system. Cell 172, 744–757 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Roquilly, A. et al. Alveolar macrophages are epigenetically altered after inflammation, leading to long-term lung immunoparalysis. Nat. Immunol. 21, 636–648 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Yao, Y. et al. Induction of autonomous memory alveolar macrophages requires T cell help and is critical to trained immunity. Cell 175, 1634–1650 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Li, F. et al. Monocyte-derived alveolar macrophages autonomously determine severe outcome of respiratory viral infection. Sci. Immunol. 7, eabj5761 (2022).

    Article  CAS  PubMed  Google Scholar 

  24. Aegerter, H. et al. Influenza-induced monocyte-derived alveolar macrophages confer prolonged antibacterial protection. Nat. Immunol. 21, 145–157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dick, S. A. et al. Three tissue resident macrophage subsets coexist across organs with conserved origins and life cycles. Sci. Immunol. 7, eabf7777 (2022).

    Article  CAS  PubMed  Google Scholar 

  26. Christo, S. N. et al. Discrete tissue microenvironments instruct diversity in resident memory T cell function and plasticity. Nat. Immunol. 22, 1140–1151 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Saeed, S. et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science 345, 1251086 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. DiNardo, A. R., Netea, M. G. & Musher, D. M. Postinfectious epigenetic immune modifications — a double-edged sword. N. Engl. J. Med. 384, 261–270 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Goodridge, H. S. et al. Activation of the innate immune receptor dectin-1 upon formation of a phagocytic synapse. Nature 472, 471–475 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).

    Article  CAS  PubMed  Google Scholar 

  31. Clarke, J. et al. Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer. J. Exp. Med. 216, 2128–2149 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cheuk, S. et al. CD49a expression defines tissue-resident CD8+ T cells poised for cytotoxic function in human skin. Immunity 46, 287–300 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Corgnac, S. et al. CD103+CD8+ TRM cells accumulate in tumors of anti-PD-1-responder lung cancer patients and are tumor-reactive lymphocytes enriched with Tc17. Cell Rep. Med. 1, 100127 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Matzinger, P. The danger model: a renewed sense of self. Science 296, 301–305 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. Xiao, W. et al. A genomic storm in critically injured humans. J. Exp. Med. 208, 2581–2590 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bauer, M., Weis, S., Netea, M. G. & Wetzker, R. Remembering pathogen dose: long-term adaptation in innate immunity. Trends Immunol. 39, 438–445 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Katzmarski, N. et al. Transmission of trained immunity and heterologous resistance to infections across generations. Nat. Immunol. 22, 1382–1390 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Guilliams, M., Thierry, G. R., Bonnardel, J. & Bajenoff, M. Establishment and maintenance of the macrophage niche. Immunity 52, 434–451 (2020).

    Article  CAS  PubMed  Google Scholar 

  40. Roquilly, A., Mintern, J. D. & Villadangos, J. A. Spatiotemporal adaptations of macrophage and dendritic cell development and function. Annu. Rev. Immunol. 40, 525–557 (2022).

    Article  CAS  PubMed  Google Scholar 

  41. Lei, T. et al. Defining newly formed and tissue-resident bone marrow-derived macrophages in adult mice based on lysozyme expression. Cell. Mol. Immunol. 19, 1333–1346 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kaur, S. et al. Role of bone marrow macrophages in controlling homeostasis and repair in bone and bone marrow niches. Semin. Cell Dev. Biol. 61, 12–21 (2017).

    Article  CAS  PubMed  Google Scholar 

  43. Chaumette, T. et al. Monocyte signature associated with herpes simplex virus reactivation and neurological recovery after brain injury. Am. J. Resp. Crit. Care 206, 295–310 (2022).

    Article  CAS  Google Scholar 

  44. Hoeres, T., Smetak, M., Pretscher, D. & Wilhelm, M. Improving the efficiency of Vγ9Vδ2 T-cell Immunotherapy in cancer. Front. Immunol. 9, 800 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Jin, C. et al. Commensal microbiota promote lung cancer development via γδ T cells. Cell 176, 998–1013 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ridker, P. M. et al. Effect of interleukin-1β inhibition with canakinumab on incident lung cancer in patients with atherosclerosis: exploratory results from a randomised, double-blind, placebo-controlled trial. Lancet 390, 1833–1842 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Novakovic, B. et al. β-Glucan reverses the epigenetic state of LPS-induced immunological tolerance. Cell 167, 1354–1368 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Divangahi, M. et al. Trained immunity, tolerance, priming and differentiation: distinct immunological processes. Nat. Immunol. 22, 2–6 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sylla, B. et al. Oligo-β-(1 → 3)-glucans: impact of thio-bridges on immunostimulating activities and the development of cancer stem cells. J. Med. Chem. 57, 8280–8292 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. GBD 2019 Diseases and Injuries Collaborators.Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1204–1222 (2020).

    Article  Google Scholar 

  51. Sekhon, J. S. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. J. Stat. Softw. 42, 1–52 (2011).

    Article  Google Scholar 

  52. Roquilly, A. et al. Local modulation of antigen-presenting cell development after resolution of pneumonia induces long-term susceptibility to secondary infections. Immunity 47, 135–147 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Xiong, Y., Mahmood, A. & Chopp, M. Animal models of traumatic brain injury. Nat. Rev. Neurosci. 14, 128 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Abidi, A. et al. Characterization of rat ILCs reveals ILC2 as the dominant intestinal subset. Front. Immunol. 11, 255 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhang, R., Atwal, G. S. & Lim, W. K. Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing. Patterns 2, 100211 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhang, Z. et al. SCINA: semi-supervised analysis of single cells in silico. Genes 10, 531 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database J. Biol. Databases Curation 2019, baz046 (2019).

    Google Scholar 

  60. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Wang, Y. et al. iTALK: an R package to characterize and illustrate intercellular communication. Preprint at bioRxiv https://doi.org/10.1101/507871 (2019).

  62. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Rusinova, I. et al. INTERFEROME v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 41, D1040–D1046 (2013).

    Article  CAS  PubMed  Google Scholar 

  64. Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2020).

    Article  PubMed Central  Google Scholar 

  65. Blanc, R. S. et al. Inhibition of inflammatory CCR2 signaling promotes aged muscle regeneration and strength recovery after injury. Nat. Commun. 11, 4167 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. She, S. et al. Functional roles of chemokine receptor CCR2 and its ligands in liver disease. Front. Immunol. 13, 812431 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Brunner, P. M. et al. CCL7 contributes to the TNF‐α‐dependent inflammation of lesional psoriatic skin. Exp. Dermatol. 24, 522–528 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Yang, J. et al. Diverse injury pathways induce alveolar epithelial cell CCL2/12, which promotes lung fibrosis. Am. J. Respir. Cell Mol. Biol. 62, 622–632 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Latypova, X. et al. Haploinsufficiency of the Sin3/HDAC corepressor complex member SIN3B causes a syndromic intellectual disability/autism spectrum disorder. Am. J. Hum. Genet. 108, 929–941 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Keerthivasan, S. et al. Homeostatic functions of monocytes and interstitial lung macrophages are regulated via collagen domain-binding receptor LAIR1. Immunity 54, 1511–1526 (2021).

    Article  CAS  PubMed  Google Scholar 

  73. Gourain, V. et al. Sepsis-trained macrophages promote anti-tumoral tissue-resident T cell. Zenodo https://doi.org/10.5281/zenodo.10715057 (2024).

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Acknowledgements

We thank L. Brusselle for helping with the preparation of single-cell libraries, M. Papot for preparing the L–R analyses and E. Sechet for administrative support (all from Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology). We thank L. Legentil and V. Ferrières (Univ. Rennes, Ecole Nationale Supérieure de Chimie de Rennes) for providing the laminarin and C. Louvet (Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology) for providing B16-OVA and LLC cell lines. We thank staff at the Cytometry Facility ‘Cytocell’, University of Nantes and the Genomics and Bioinformatics Core Facility of Nantes (GenoBiRD, Biogenouest) for their technical support. We acknowledge the IBISA MicroPICell facility (Biogenouest), a member of the national infrastructure France-Bioimaging supported by the French National Research Agency (ANR-10-INBS-04). We thank the biological resource center for biobanking (CHU Nantes). A.B. was supported by La Ligue contre le Cancer (Comites 22, 44 85) and INCA (grant 16690). S.C. and F.M.C. were supported by the Association pour la Recherche sur le Cancer (grant SIGN’IT20181007792). A.R. was supported by Agence National de la Recherche (grant ANR JCJC Progr-AM). We gratefully acknowledge the financial support of LabEx IGO.

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Authors and Affiliations

Authors

Contributions

A.B. performed experiments, contributed to the study design, data analyses, interpretation of results and writing and revising the manuscript. V.G. generated scRNA-seq data, performed bioinformatics analysis, contributed to the study design, data analyses, interpretation of results and writing and revising the manuscript. T.G. performed the epidemiological study and contributed to interpreting results, writing, and revising the manuscript. V.L.M., D.S., M.A., C.J., F.P.M., C.P., P.M., M.D., L.B., M.P., C.F., M.C., T.L., M.G., J.F.M. and T.C. performed experiments and contributed to the interpretation of results and revision of the manuscript. L.L. and V.F. provided laminarin and contributed to interpreting results, writing and revising the manuscript. H.E.G.M., T.L., C.H., E.S., S.C., F.M.C., N.M., J.A.V., P.A.G. and K.A. contributed to the interpretation of results and revision of the manuscript. J.P. and A.R. contributed to the study design, data analyses, result interpretations and manuscript writing and revision.

Corresponding authors

Correspondence to Jeremie Poschmann or Antoine Roquilly.

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The authors declare no competing interests.

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Nature Immunology thanks Maziar Divangahi, Rene van Lier, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Raw data of risk of cancer up to ten years after cure from sepsis and SIRS in humans.

A-C. Cumulative incidences of cancers in sepsis survivors and infection survivors after ten years of follow-up in the (A) crude sample of cohort 1, (B) narrow matching criteria samples, and (C) in patients with low (IGS-II < 25) vs. high severity score (IGS-II > 25). Curves represented as calculated incidence +/- SD. D. Relative risk (RR) of cancer in sepsis vs. infection survivors (green line) and nonseptic SIRS vs. no SIRS (orange line) at the indicated time after the index medical condition. RR > 1 means increased risk in the severe condition, and a RR < 1 means a reduced risk of cancer. Results were obtained in the matched samples of cohorts 1 and 2. E-G. Cumulative incidence of cancers in nonseptic SIRS and no-SIRS survivors after ten years of follow-up in the (E) crude, (F) matched and (G) narrow matching criteria samples. Curves represented as calculated incidence +/- SD, and comparisons are obtained using ANCOVA (A-C, E-G) or RR with a 95% confidence interval (D).

Source data

Extended Data Fig. 2 Sepsis-cured mice model and scRNA-seq validations.

A-B. Bacterial load in digested lung tissue and weight loss at the indicated time after E. coli intratracheal instillation (n = 2 for uninfected, n = 16 for day 1, n = 14 for day 2, n = 7 for day 3, n = 8 for day 7, n = 7 for day 10). Graph represents median ± IQR and is pooled data from 2 independent experiments. C. Lung section of uninfected mice, and at 3 and 7 days after E. coli pneumonia. Representative of 3 independent experiments. D. Respiratory sepsis was induced by intratracheal instillation of E. coli. LLC was injected iv. in uninfected and sepsis-cured wild-type or Cxcr6-deficient mice (infected 28 days prior), and the surfaces of lung tissue and metastasis were measured 4 weeks after LLC injection. Wild-type mice: n = 6 for uninfected, n = 7 for sepsis-cured. Cxcr6−/− mice: n = 6 for uninfected, n = 9 for sepsis-cured. Graphs represent median ± IQR pooled from 3 independent experiments. E. Rationale of single-cell RNA sequencing assay. F. Quality controls of the single-cell RNA sequencing data representing the distribution of detected genes (left panel), mapped reads (middle panel), and reads mapped against mitochondrial genes (right panel) per cell. G. Heatmap of top 10 markers per cluster. Cxcr6 and Cd3g are characteristic of cluster 5. Statistical significance was assessed by One-way analysis of variance with Sidak correction for multiple comparisons (D).

Source data

Extended Data Fig. 3 Lung CXCR6 T cells characteristics.

A. CXCR6 staining on γδ tissue-resident T cells in sepsis-cured wild-type or Cxcr6-deficient mice expressing GFP as gene reported. Representative of 10 independent experiments. B. Clustering (numbers) and annotation (colors) of immune cells from the scRNA-seq in mouse lung. C. Expression of Ccr7 (top) and S1pr1 (bottom) in immune cells of mice lung. D-E. PE-conjugated anti-CD45 antibody was injected intravenously 3 and 5 days after E. coli sepsis. Brilliant Violet (bv)480 conjugated anti-CD45 antibody was injected immediately before sampling to remove immune cells adherent to the endothelium. (D) The percentages of blood-recruited (bv480- PE + ) CXCR6 and CXCR6 + T cells were measured on day 7 (n = 3 uninfected and 5 septic mice). Graph represents median ± IQR and are pooled data from 2 independent experiments. (E) Percentages of blood-recruited (bv480- PE + ) myeloid F4/80+ cells were measured on day 3 after pneumonia (n = 3 mice/group). (F) Percentage of blood PE + T cells 60 minutes after anti-CD45-PE iv. injection. Representative of three independent experiments. G. Expression of CXCR6 on CD8, CD4, NK1.1+ and γδ T cells (left panel) and linear regression of T cell subsets increase and their CXCR6 expression in 7 days infection-cured mice (right panel). H. Number of CXCR6 and CXCR6+ tissue-resident T cells in uninfected and sepsis-cured mice. N= for uninfected and n = 7 for sepsis-cured mice. Graph represents median ± IQR, pooled data from 2 independent experiments. Statistical significance was assessed by Mann–Whitney test (D-E) and one-way analysis of variance with Bonferroni correction was used for multiple comparisons (H). Correlation was tested by the Pearson test (G).

Source data

Extended Data Fig. 4 Activated pathways in lung T cell clusters.

A-B. Expression of genes associated with the Wikipathway «Chemokine signaling» (WP2292) during (A) and after (B) sepsis. C-D. Expression of genes of TGFb pathway (KEGG mmu:04350) at (C) day 3 and (D) day 7 post-sepsis in T cell compartment. (E) Expression of the interferon type II pathway effector genes in the T cell compartment. The colors represent the cell–cell communication probability for each test R-L and the circle radius the significance. (F) Number of resident CXCR6+γδT cells in uninfected and sepsis-cured wild-type and IfnγR deficient mice. (n = 7 for wild-type and n = 11 for IfnγR deficient mice per group). Graphs represent median ± IQR and are representative of 2 independent experiments. The color gradient represents the level of expression, and the circle radius the percentage of expressing cells. Statistical significance was assessed by one-way analysis of variance with Bonferroni correction for multiple comparisons (F).

Source data

Extended Data Fig. 5 scRNA-sequencing of CXCR6pos and CXCR6neg T cells.

A. Expression of cytokine and chemokine signaling genes in the T cell compartment after sepsis. The color gradient represents the level of expression, and the circle radius the percentage of expressing cells. B. Cell-sorting strategy for scRNA sequencing of tissue- resident T cells in septic-cured wild-type and Cxcr6deficient mice. C. Expression of the genes associated with tissue residency (list from literature review). The colors represent average expression for each and the percentage of expressing cells. Graph represents median ± IQR and is pooled data from 2 independent experiments. D. Heatmap of differentially expressed genes in CD69high or CD69low resident γδ, CD4 + , CD8 + T cells from wild-type vs. Cxcr6deficient sepsis-cured mice (from scRNA-seq). E. Differential pathway expression levels between CD69high or CD69low resident γδ T cells from wild-type vs. Cxcr6deficient sepsis-cured mice. F. CXCR6 expression after 18 hours in vitro stimulation of T cells sorted from uninfected T cells with the stated CCR2 agonists. (n = 4 for untreated and n = 6 for treated conditions). Graph represents median ± IQR and is pooled data from 2 independent experiments. Statistics were calculated with the two-sided Wilcoxon test (D). P values were corrected with the Bonferroni test with the confident interval of 95%. Gene set enrichments were calculated with the permutation test of ranked values and corrected with the Benjamini-Hochberg test (E).

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Extended Data Fig. 6 CXCL16 blocking and resident AM depletion.

A. Number of CXCR6+ and CXCR6 resident γδ, CD4 and CD8 T cells in sepsis-cured mice treated with isotype and anti-CXCL16 blocking antibody (n = 5 mice/group). Graphs represent median ± IQR and are pooled from two experiments. B. Ki67 expression by CXCR6 and CXCR6 + T cells in septic-cured mice treated with anti-CXCL16 blocking antibody or isotype control. (n = 3 mice/group). Graph represents median ± IQR and is pooled data from 2 independent experiments. C. PE staining of resident AM from sepsis-cured mice by anti-CXCL16 antibody or isotype control (Representative of 3 independent experiments). D. Percentages of Cxcl16 expressing cells (pi-charts, n = 2 mice per time point) and Cxcl16 RNA expression levels (box-plots) in the clusters at the indicated time point after E. coli pneumonia. The ratio of positive (red) and negative (blue) cells is represented in the upper panel. (n = 2 mice per time point). E. Expression of cytokine receptors on CXCR6 T cells and cognate cytokines in the different cell-type compartments during (left panel) and after (right panel) sepsis. Pairs of cytokine and cytokine receptors were initially extracted from the KEGG database. The colors represent the average expression level, and the circle radius represents the percentage of expressing cells. Significance of R-L interactions was evaluated with the one-sided permutation test. F. Gating strategy to identify resident alveolar macrophages and monocyte-derived macrophages (mo-AM). Representative of 10 experiments. G. Number of resident AM and mo-AM in uninfected and sepsis-cured wild-type and Ccr2-deficient mice. For resident AM (top panel): n = 6 mice per group. For mo-AM (bottom panel): n = 13 mice per group for all groups except wild-type day 7: n = 9 mice. Graphs represent median ± IQR and are pooled from two experiments. H. Numbers of resident AM, and mo-AM, in uninfected and sepsis- cured mice treated on day 3 and day 5 blocking anti-CXCL16 antibody or isotype control. N = 2 mice per group for uninfected. n = 3 mice per group in sepsis-cured. Graphs represent median ± IQR and are one experiment. I. Numbers of resident AM in sepsis-cured mice injected with LLC and treated with blocking anti-CXCL16 antibody or isotype control. N = 6 mice per group for all groups except isotype Ab group: n = 5 mice. Graph represents median ± IQR and is pooled data from 2 independent experiments. J. Number and percentage of resident AM depletion in untreated mice, and 2, 7 and 14 days post clodronate injection (n = 6 / group). Graphs represent median ± interquartile range and are pooled data from 2 independent experiments. Statistical significance was assessed by Mann–Whitney test (A) and One-way analysis of variance with Sidak’s correction for multiple comparisons (B, G, H, I, J).

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Extended Data Fig. 7 CXCR3 and CCR2 expressions by lung tissue resident T cells, and epigenic regulation of resident AM.

A. Percentage of CXCR3+ on antigen-experienced resident memory T cells (CD44 + CD69 + T cells) and antigen-experienced non-resident memory T cells (CD44 + CD69- T cells) in the lungs of uninfected, primary infection-cured and secondary infection-cured mice. n = 2 mice per group in uninfected and n = 4 mice per group in sepsis-cured group. Graphs represent median ± interquartile range and are pooled data from 2 independent experiments. B. Mice were infected with E. coli; 14 d later, the mice were treated with anti-CXCR3 antibody (200 μg twice) or isotype control and were finally assessed 7 days after the induction of secondary lung sepsis. Enumeration of antigen-experienced non-resident memory T cells (CD44 + CD69- T cells) in the lungs of uninfected mice or of mice cured from a secondary sepsis (n = 2 mice per group in uninfected, n = 5 in isotype group and n = 4 in anti-CXCR3 group). Graphs represent median ± interquartile range and are pooled data from 2 independent experiments. C. Pie-charts represent percentages of Ccr2 expressing cells and Ccr2 expression levels in the T cell compartment at the indicated time point during primary and secondary sepsis. The ratio of positive (red) and negative (blue) cells is represented in the upper panel. (n = 2 mice per time point). D. Promoter and enhancer activity at Il7, Ccl3, and Ccl9 loci in resident alveolar macrophages before (black) and after (red) sepsis. Statistical significance was assessed by one-way analysis of variance with Bonferroni correction for multiple comparisons (A-B), two-sided Wilcoxon test (C).

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Extended Data Fig. 8 CXCR6 expression by T cells during S. aureus pneumonia, systemic sepsis and CpG.

A. Numbers of CXCR6 and CXCR6 + T cells in the lungs 7 days after S. aureus pneumonia (n = 3 uninfected, n = 2 sepsis-cured mice). Graphs represent median ± IQR and is issued from 1 experiment. B. Numbers of CXCR6 and CXCR6+ tissue-resident T cells in the lungs and the spleen 7 days after S. aureus septicemia (n = 7 mice/group). Graphs represent median ± IQR and are pooled data from 2 independent experiments. C. Numbers of CXCR6 CD3+ and CXCR6 + CD3+ cells in the bone marrow, the gut, and spleen 7 days after CpG iv administration (n = 6 mice per time point). Graph represents median ± IQR, pooled data from 2 independent experiments. One-way analysis of variance with Bonferroni correction was used for multiple comparisons (A-C).

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Extended Data Fig. 9 scRNA-sequencing of lung immune cells to traumatic brain injury.

A. Heatmap of top 10 markers in monocyte-derived alveolar macrophages (mo-AM) at day 3 and day 7 after E. coli pneumonia, and interstitial Macrophages (IntMac), resident alveolar macrophages (Res-AM) and macrophages 1 day after traumatic brain injury (TBI) by single-cell RNA sequencing analyses of lung CD45+ cells. B. IL-1a and IL-1b expression levels in the monocytes/macrophages cluster at the indicated time point after E. coli sepsis or after TBI (n = 2 independent biological replicate per time point). C. CD45+ cells were sorted from the lungs of control mice, mice with acute traumatic brain injury and 3–7 days after E. coli pneumonia. UMAP representing the macrophage compartment in mouse lung CD45+ cells annotated from the scRNA-seq. 10,542 single cells from 2 mice from all time points were analyzed. D. Volcano plots of the DEG in the macrophage compartment after TBI vs. 3 days (left) or 7 days (right) after E. coli pneumonia. E. Pie-chart representing percentages of Cxcl16 expressing cells and Cxcl16 expression levels in the monocytes/macrophages cluster at the indicated time point after E. coli sepsis or after TBI. (n = 2 mice per time point). F-G. Numbers of CXCL16+ and CCL2+ res-AM and mo-AM in uninfected mice after traumatic brain injury, 7 days after E. coli pneumonia or 7 days after S. aureus pneumonia. (n = 3-5 mice/group). Graphs represent median ± IQR and pooled from 2 independent experiments. H. Numbers of CXCR6+ and CXCR6 T cells in the lungs 7 days after traumatic brain injury (n = 3 sham, =5 TBI mice). I. Mice were depleted or not in res-AM (clodronate i.t.), then treated with laminarin (i.p.) or vehicle, and injected with LLC 7 days later. Mice survival was monitored up to 14 days post LLC- inoculation. The lung tumor surface was measured 4 weeks later. (n = 10 mice for vehicle and 11 for laminarin, and 4 for clodronate with lamarin). Graphs represent median ± IQR and are pooled from 2 independent experiments. Statistics were calculated with the Kruskal-Wallis test (B), the two-sided Wilcoxon test P values were corrected with the Bonferroni test (D), one-way analysis of variance with Bonferroni correction for multiple comparisons (E-H), and log-rank test for survival (E).

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Extended Data Fig. 10 CXCR6 expression by T cells in human cancer samples.

A. Expression levels of Cxcl16, Ccl7, and Ccl2 in the macrophages compartment from BAL of healthy humans and patients with moderate (infection) or severe (sepsis) COVID- 19 pneumonia from GSE145926. B-C. Immunostained sections. Data are representative of a single experiment. (B) four human lung cancer, and (C) five colon adenocarcinoma. Green: CD3, Red: CXCR6, Blue: DAPI. D. Correlation between Cxcr6 level and lung tumor TRM, lung TRM, and dermis TRM gene signatures from publicly available datasets. GSE111898 (33) for lungs and GSE83637 (34) for the dermis. E. Projection of the gene signature of human dermis tissue-resident T cells (from GSE83637) on the UMAP representation of scRNA-seq of lung tissue from septic-cured mice. F. Definition of a Cxcr6 tissue-resident T cells signature in sepsis-cured mice. Significance of difference was evaluated with the two-sided Wilcoxon test (F).

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Broquet, A., Gourain, V., Goronflot, T. et al. Sepsis-trained macrophages promote antitumoral tissue-resident T cells. Nat Immunol 25, 802–819 (2024). https://doi.org/10.1038/s41590-024-01819-8

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