XCR1+ and IRF4+ migratory dendritic cells cooperate for the cross-priming of intratumoral CD8+ T cells with a tissue-resident memory phenotype.

XCR1+ and IRF4+ migratory dendritic cells cooperate for the cross-priming of intratumoral CD8+ T cells with a tissue-resident memory phenotype. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article XCR1 + and IRF4 + migratory dendritic cells cooperate for the cross-priming of intratumoral CD8 + T cells with a tissue-resident memory phenotype. Nathan Vaudiau, Pierre Bourdely, Agathe Ok, Maria Semitekolou, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6455825/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The composition and diversity of tumor-infiltrating CD8 + T cell populations have important consequences on the development of anti-tumor immunity. In a murine model of lung cancer, we have addressed the role of dendritic cell subsets on the generation of various types of tumor-infiltrating CD8 + T cells. We show that CD44 + PD1 - effector and PD1 + TIM3 + exhausted, tumor-infiltrating CD8 + T cells require XCR1 + DCs but not IRF4-dependent DCs. By contrast, CD103 + CXCR6 + T RM -like, tumor-infiltrating CD8 + T cells require both XCR1 + DCs and IRF4-dependent DCs. The same requirement is found in tumor-draining lymph nodes where we identify CD103 + CXCR6 + T RM -like precursors that are dependent on both XCR1 + DCs and IRF4-dependent migratory DCs. Mechanistically, we evidence that both types of migratory DCs cooperate. Mild TCR triggering by low MHCI-peptide density at the surface of cross-presenting migratory DC2s and low IL-12 support TGFb-dependent T RM specification in lymph nodes. High TCR triggering and high MHCI-peptide density at the surface of cross-presenting migratory DC1s and high IL-12 support proliferative expansion and CXCR6 acquisition. Altogether, these findings highlight the induction of intratumoral T RM -like cells under the collective aegis of multiple DCs subsets within tumor-draining lymph nodes reconciliating T RM phenotype instruction with proliferative expansion. Immunology CD8+ T lymphocytes dendritic cells tissue-resident memory T cells lung cancer priming tumor-draining lymph-node cross-presentation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 MAIN Tumor-infiltrating CD8 + T cells (TILs) are critical mediators of anti-tumor immunity. CD8 + TILs are characterized by the pervasive expression of exhaustion-associated features initially characterized in chronic infection models, including reduced proliferative response to TCR triggering and reduced polyfunctional cytokine responses. Multiple studies suggest that the qualitative composition of CD8 + TILs in solid tumors predicts clinical outcome and response to immunotherapies 1 , 2 . In that line, a subset of CD8 + TILs with a tissue-resident memory phenotype (T RM ) has been identified in solid tumors including lung and breast cancer and correlates with improved prognosis and better response to immunotherapies 1 , 3 – 9 . Bona fide T RM have first been identified in peripheral tissues after clearance of viral infections 10 . Unlike other activated and memory CD8 + T cells, T RM do not recirculate but persist in tissues where they ensure immunosurveillance. T RM are characterized by a specific transcriptional program including the expression of transcription factors Hobit (encoded by Zfp683 ), Runx3 and Blimp-1 4,11,12 . T RM express αE integrin/CD103, which associates with integrin β7 to bind E-cadherin stabilizing contacts with epithelial cells, and low levels of S1P receptors ensuring tissue residency 13 . Also, T RM stably express the CD69 lectin mediating S1P desensitization, further contributing to tissue residency 14 . Similar to bona fide anti-infectious T RM , T RM -like TILs harbor canonical features of residency including the concomitant expression of CD103, CD69, Hobit and reduced levels of S1PR1. A recent study in a murine model of E0771 murine breast tumors suggests that CD69 + CD49a + P2Rx7 + T RM phenotype within CD8 + TILs may not indicate actual residency within tumors 15 . Still, several studies show that CD8 + T RM -like TILs cells can display potent anti-tumor function 16 as they secrete cytokines, exhibit cytotoxic activity 17 and can potentially generate a large repertoire of T cell subsets upon antigenic restimulation 18 , 19 or after immune checkpoint blockade 20 . T RM -like TILs express some features of exhausted T cells but compared to T EX -like TILs, T RM -like TILs display enhanced cytotoxicity and provide local protection in a murine model of breast cancer 20 . Consistently, numerous studies have identified a strong correlation between CD8 + TILs with a T RM phenotype and favourable outcomes and increased response rates to immune checkpoint inhibition in human lung cancer 1 , 3 , 5 – 7 , 9 , triple-negative breast cancer and other solid tumors 16 , 21 . This calls for a better understanding of the mechanisms ensuring the acquisition of the T RM program during T cell activation in solid tumors. In cancer, CD8 + T cells are initially activated in tumor-draining lymph nodes (tdLN) and terminally differentiate within the tumor 22 , 23 . However, the anatomical location where T RM -like fate specification occurs in cancer is unclear ( i.e. , during priming in tdLN or after infiltration in the TME). In a mouse model of skin vaccination, TGF-β activated by steady-state migratory DCs imprint an epigenetic state rendering naïve T cells poised to differentiate into T RM 24 . Other studies identify T RM -committed circulating progenitors during vaccination or viral infection, arguing in favor of early T RM fate decision 25 , 26 . Anti-infectious T RM can also be generated from circulating effector cells receiving T RM instruction after tissue infiltration 4 , 27 , 28 . Dendritic cells (DCs) are the sentinel cells of the immune system ensuring T cell activation and differentiation. Classical DCs are diverse and composed of 2 main families expressing and relying on IRF8 or IRF4 transcription factors. There is wide evidence that IRF8 + , BATF3-dependent XCR1 + DC1s control the activation of effector CD8 + T cell responses in the context of immunogenic tumors 29 – 31 and maintain the fitness of CD8 + T cell effector compartment by expanding TCF1 + T cells in tdLN 32 . Efficient phagocytosis of tumor-derived cell debris and efficient cross presentation of cell-associated antigens by MHCI 33 – 35 and MHCII presentation to helper T cells 36 all support the paramount importance of XCR1 + DC1s in shaping effector CD8 + T cell responses. By contrast, the nature of antigen presenting DC subtypes controlling T RM activation during T cell priming and infiltration is less clear and has mostly been investigated in the context of vaccination or infections. Skin XCR1 + Batf3-dependent DC1s are essential for the efficient activation of skin T RM after vaccination 37 . IRF4-dependent DC2s are essential for the priming of influenza-specific T RM in lungs 38 and the maintenance of CXCR3 + T RM against HSV cervical infection 27 . Also, human IRF4 + CD163 + CD1c + DCs are the most potent antigen-presenting cells for T RM specification in vitro or in humanized mice 39 , 40 . Despite numerous evidence of a role for CD8 + T RM -like TILs in non-small cell lung cancer (NSCLC) immunosurveillance 1 , 3 , 5 – 7 , 9 , 21 , little is known on i) the anatomical location where the T RM -like phenotype is specified and ii) if and how migratory DCs subsets participate to the induction of T RM -like CD8 + T cells within tumor-draining lymph nodes. In this study, we delineate the activation of tumor-infiltrating T RM -like cells in the KP ( Kras G 12 D , p53 −/− ) model of lung adenocarcinoma. We identify an unexpected contribution of IRF4-dependent migratory DCs in activating pre-T RM precursors in tdLNs to give rise to tumor-infiltrating T RM -like TILs. In addition, we show that the low density of MHCI-peptide complexes evoking a mild TCR triggering promote specification into T RM -like phenotype. RESULTS CD8 + T cells with a T RM -like phenotype infiltrate lung tumors. To determine the extent of T RM -like cells infiltrating the lungs after engraftment of tumor cells, we employ a cell line derived from Kras G 12 D p53 −/− mice 32,41,42 (Fig.S1A). We found that a large proportion of CD8 + TILs stained positive for both CD69 and CD103 expression (27.3%, Fig. 1A) which is a classical gating for T RM phenotype. CD8 + TILs also stained positive for PD1 and TIM3 (21.7%, Fig. 1B), a feature of exhausted T cells. In line with other studies highlighting the expression of exhaustion markers on T RM -like TILs 3 , 20 , we found that these two subsets were largely overlapping (Fig. 1C and S1B). This led us to investigate the correspondence between cell surface phenotype and transcriptional profile in unbiased settings using a single-cell RNA-sequencing (scRNA-seq) combined with CITE-Seq approach (Fig. 1D and S1C). scRNA-seq identified 5 clusters of Cd44 + Sell − cells (clusters 1, 4, 7, 8, 9) (Fig. 1D-E and S1D). Clusters 4 and 7 expressed Tcf7 and intermediate levels of Pdcd1 , aligning them to a T PExh phenotype. Clusters 1, 8 and 9 shared the expression of multiple exhaustion molecules ( Pdcd1, Lag3, Havcr2, Tigit, Tox) and scored high for an exhaustion signature (Fig. 1E-F). Among these exhausted clusters, cluster 1 expressed core T RM− associated genes (such as Itgae /CD103, Itga1 /CD49a and Cxcr6 ) and scored high for a residency signature. Using CITE-Seq antibodies, we identified T RM -like TILs (CD44 + CD62L − CD103 + CD69 + ) and found that they overlapped mostly with cluster 1 (Fig. 1G and S1E). By contrast, CD44 + CD62L − PD1 + activated cells distinct from CD103 + CD69 + comprised a variety of transcriptional phenotypes including cycling and Tox + exhausted clusters (clusters 8 and 9). To further confirm these results, we designed a gating strategy enabling the prospective isolation of TILs populations identified above. As already shown previously, CD69 + CD103 + T RM -like TILs were largely enriched in an exhausted phenotype PD1 + TIM3 + (68.0%, termed here as TIM3 + T RM ) but also contained a population of PD1 + TIM3 − (29.2%, termed here as TIM3 − T RM ) and a minor fraction of PD1 − TIM3 − cells (Fig. 1H-I). Conversely, we also identified PD1 + TIM3 + (37.0%, termed here as TIM3 + T Exh ) and PD1 + TIM3 − (42.9%, termed here as TIM3 − T PExh ) distinct from CD69 + CD103 + . PD1-negative CD8 + T cells also comprised CD62 − CD44 + activated effector T cells (20.4%, termed here as PD1 − T Eff ). Similar populations were found in the autochtonous KP cancer model which recapitulates the human physiopathology from hyperplasia to adenocarcinoma and the onset of T cell dysfunction 32 , 41 , 42 (Fig. S1F-I). Bulk RNA-seq profiling further validated the correspondence between the T RM -like gate and the enrichment in T RM -associated genes and signatures ( Itgae , Itga1, Rgs1, Cxcr6 e.g. ) and gene ontology pathways “regulation of cell migration”, “TGF-β signalling pathway”, “R-SMAD binding” (Fig. 1J-K and S1J-M). By contrast, T RM expressed lower levels of effector-associated genes ( Eomes, Gzmk , e.g.) and of genes ensuring recirculation ( Klf2 and S1pr1, e.g. ) 4 , 11 . Finally, we confirmed by FACS that T RM -associated proteins (Hobit in Hobit reporter mice 18 and CD49a encoded by Itga1 ) were expressed at higher levels in TIM3 + T RM while they express low levels of EOMES and KLRG1 (Fig. 1L-M). Also, we found that the human T RM signature was enriched in TIM3 + T RM as compared to TIM3 + T Exh (Fig.S1N), thereby validating their physiological relevance. Unlike other T cell subsets, T RM -like TILs (CD69 + CD103 + TIM3 +/− ) were not stained after intravenous injection of anti-CD45.2-PE antibodies labelling the vascular compartment. This demonstrates their localization in the lung parenchyma (Fig. 1N). We next focused our analysis on tumor antigen-specific cells after engraftment of a KP cell line expressing the ovalbumin protein (KP-OVA, Fig. 1O-P). Tumor-antigen specific tetramer-OVA + cells encompassed similar subsets but displayed a significant enrichment in TIM3-expressing cells including TIM3 + T EXH and TIM3 + T RM , unlike PD1 − T EFF that were mostly found in the tetramer-OVA − fraction (Fig. 1Q). Functionally, we found that both TIM3 + T RM (15.5%) and TIM3 + T EXH (11.9%) were able to produce pro-inflammatory cytokines IFNg and TNFa after ex vivo antigen-specific restimulation with the OVA 257 − 264 peptide (Fig. 1R). Of note, TIM3 − T RM (5.7%) but not TIM3 − T PEXH (1.3%) displayed some levels of polyfunctionality. Altogether, we conclude that the transplanted KP model recapitulates some features of human NSCLC in terms of dysfunctionality and T RM -like phenotype 1 , 3 , 7 , 9 , 43 . Our results define T RM -like TILs as a specific, separable subset within a larger population of CD8 + TILs expressing co-inhibitory receptors. Therefore, this model is suitable to investigate how the T RM -like phenotype is instructed in comparison to other PD1 + TILs. Tumor-draining lymph nodes contain a fraction of CD103 + activated CD8 + T cells poised to generate T RM -like TILs. The anatomical localization where T RM specification occurs is still a matter of debate. Therefore, we wondered whether CD8 + T RM -like TILs require egress from tumor-draining mediastinal lymph nodes (tdLN) or differentiate in situ from a T cell pool pre-existing tumor development. To address this, we inhibited lymph node egress using S1PR1 blockade with fingolimod (FTY720) and analysed T RM -like TILs at day 14 post-tumor engraftment (Fig. 2A). We found that FTY720 administration from day 1 to 14 or from day 7 to 14 blocked T RM infiltration in the lungs (Fig. 2B). We conclude that tdLNs represent an obligate site during the development of CD8 + T RM -like TILs. Having established a role for tdLN in T RM infiltration, we next sought to characterize tdLN CD8 + T cells after KP tumor engraftment. We found that KP tumor development induced the expansion of CD62L − CD44 + activated CD8 + T cells (“T Act ”) (Fig. 2C). Because CD103 expression has been widely used as a marker of T RM biased T cells in other settings 24 , we analysed CD103 expression and found that 41.3% of T Act expressed CD103 (Fig. 2C and S2A). CD103 + T ACT population was similarly observed in OVA-specific T Act after KP-OVA engraftment (15.0%) (Fig. 2D-F). Bulk RNA-sequencing analysis showed that CD103 + T Act were enriched in T RM core genes such as Cxcr6, Fgl2, Itga1 and Innp4b (Fig. 2G and S2B-D). On the contrary, CD103 − T act expressed more T effector ( Eomes , Gzmk ) and proliferation-associated genes ( Mki67 ). Consistently, GSEA analysis revealed that lymphocyte residency signatures were enriched in CD103 + T Act while signature of genes associated to circulating lymphocytes were enriched in CD103 − T Act 4,11 (Fig. 2H and S2E). CD103 + T Act were also enriched for genes associated to KP-bearing lung TIM3 + T RM while CD103 − T Act were enriched for genes associated to KP bearing-lung TIM3 + T Exh (Fig. 2I). At the protein level, CD103 + T Act expressed lower levels of effector-associated markers (T-BET, EOMES, KLRG1 and CX3CR1) (Fig. 2J and S2F) but higher T RM -enriched markers such as CD49a, Hobit, CTLA4 and CXCR6 (Fig. 2K). We therefore hypothesized that CD103 + T Act are biased to give rise to lung T RM -like TILs. To assess the relationship between CD103 + T cells in tdLN and lung T RM -like TILs, we took advantage of the CD103-cre ERT2 -ROSA LSL−tdTomato reporter mouse 44 (hereafter referred as CD103-tdTomato). Tamoxifen induction 8 days after KP injection efficiently labelled CD103 + T Act (45.6%) but not CD103 − T Act (1.5%) in tdLNs from CD103-tdTomato donor mice (Fig.S2G). We thus transferred whole tdLN containing genetically labelled CD103 + T cells in KP tumor-bearing CD45.1 + WT recipient mice (Fig. 2L). We assessed T RM phenotype in the progeny of tdTomato − or tdTomato + transferred tdLN CD8 + T cells in the lungs as controlled in endogenous CD8 + T cells from the host mice (Fig.S2G). We found that tdTomato + transferred T cells exclusively gave rise to T RM -like but not other CD44 + CD62L − CD8 + TILs (100.0%, Fig. 2M). By contrast, only a small fraction of tdTomato − transferred CD8 + T cells differentiated into T RM -like TILs (8.5% of CD44 + CD62L − CD8 + TILs). These results demonstrate that T RM specification is initiated in tdLNs where CD103 + T cells are pre-committed to differentiate into T RM -like TILs after seeding tumor-bearing lungs. Both XCR1 + and IRF4-dependent DCs contribute to the differentiation of CD103 + T Act in tdLN Because DCs are the main tumor antigen-presenting cells in the LN, we assessed their requirements for the generation of CD103 + T Act in tdLN. We found that KP tumor development triggered a massive increase of both resident and migratory XCR1 + DC1s and CD11b + DC2s compared to mediastinal LN from naïve mouse (Fig. 3A-B). To better characterise DC populations in tdLN, we performed scRNA-Seq of CD11c + MHCII + cells 14 days after KP tumor engraftment (Fig. 3C and S3A). We identified 2 clusters of XCR1 + resident DC1 (clusters 7, 14) and 3 clusters of SIRPa + resident DC2 (clusters 3, 9 ,17). Within the clusters expressing high levels of maturation markers ( Ccr7 , Cd80, Cd86, Cd200, Cd274, Pdcd1lg2 e.g. ) (clusters 2, 5, 8), CITE-Seq protein expression of XCR1 and SIRPa enabled to discriminate migratory DC1 (cluster 2) from migratory DC2 (cluster 8), although cluster 5 was composed of a mix of migratory DC1 and DC2 as reported earlier 45 (Fig. 3D-E). To assess the contribution of DC subsets in T RM differentiation, we engrafted KP tumors in Xcr1 cre x Rosa LsL − DTA ( Xcr1 DTA ; 46 ) or Cd11c cre x Irf4 fl/fl ( Cd11c Irf 4 ; 47 ) mice. Xcr1 DTA mice were deficient in XCR1 + DCs while Cd11c Irf 4 mice had reduced numbers of migratory but not resident DC2s (Fig. 3F and S3B-C). By performing scRNA-Seq in Cd11c Irf 4 mice, we confirmed that migratory DC2 cluster 2 was reduced as compared to WT mice, while most clusters including resident DC2 clusters 3, 9 and 17 were left unaffected (Fig. 3G-H). Consistent with a well-established role of DC1s in activating CD8 + T cell responses 29 , 30 , 48 , 49 , we found that tetramer-OVA + T Act were severely reduced in Xcr1 DTA mice bearing KP-OVA tumors (Fig.S3D), including CD103 + T Act (Fig. 3I). Conversely, depletion of IRF4-dependent DCs in Cd11c Irf 4 did not impact tetramer-OVA + CD8 + T cell activation (Fig.S3D) but led to a selective decrease in tetramer-OVA + CD103 + T Act numbers (Fig. 3I). Similar results were found after adoptive transfer of OVA-specific OT1 T cells prior to tumor engraftment (Fig.S3E). Analysis of OT1 in tdLN at day 9 revealed impaired CD103 expression in both Xcr1 DTA and Cd11c Irf 4 mice (Fig. 3J). Altogether, these results show that both XCR1 + DC1s and IRF4-dependent DC2s are required for the crosspriming of tumor-specific CD103 + T Act in tdLN that are poised to generate T RM -like TILs. CD103 + activated T cells in tdLN contain a rare population of CXCR6 + T RM -like cells. To better characterize the heterogeneity of tdLN activated CD103 + CD8 + T cells giving rise to lung T RM -like TILs in unbiased settings, we performed single-cell RNA-sequencing of a mixture of 80% of T Act and 20% of T Naive and T CM (Fig. 4A and S4A). We identified 4 clusters of activated Cd44 + Sell − CD8 + T cells (clusters 0, 3, 5 and 8)(Fig. 4B). Cluster 5 was highly proliferating ( Mki67 high ). Among the remaining activated clusters, Itgae expression was restricted to clusters 0 and 8. Strikingly, cluster 8 expressed high levels of Cxcr6 and scored high for residency but low for circulating signatures or stemness-associated genes ( Slamf6, Tcf7 )(Fig. 4B-C and S4B). Consistently, this cluster scored the highest for genes associated to KP-bearing lung TIM3 + T RM (Fig.S4B). CITE-Seq gating also showed that CD103 was most highly expressed by cluster 8 (Fig.S3C). However, the majority of CD103 + TAct cells were found in cluster 0 since cluster 8 contains very few cells (Fig. 3D). To further compare single-cell RNA-seq clusters obtained in both lung and tdLN locations, we projected lung clusters on the tdLN dataset. We found that T RM cluster 1 from the lung was almost exclusively projected on Cxcr6 + cluster 8 from the tdLN (84.25%, Fig. 4E), thereby validating their transcriptional alignment. Taking advantage of this information, we refined our FACS gating strategy to separate previously gated CD103 + T Act into CD103 + CXCR6 − and CD103 + CXCR6 + T Act (Fig. 4F). We found that CD103 + CXCR6 + T Act expressed low levels of SLAMF6 but higher levels of PD1 and Hobit than CD103 − or CD103 + CXCR6 − T Act (Fig. 3G and S4D). CD103 + CXCR6 + T Act were found highly enriched within tumor antigen-specific tetramer-OVA + T Act from KP-OVA bearing mice (2.1% of tetramer-OVA − T Act vs 15.2% of tetramer-OVA + T Act ) unlike CD103 + CXCR6 − that were almost exclusively tetramer-OVA − (31.5% of tetramer-OVA − T Act vs 0.9% of tetramer-OVA + T Act )(Fig. 4H-I). CD103 − T Act encompassed both tetramer-OVA + and tetramer-OVA − cells. Phenotyping tetramer-OVA + T Act led to similar results compared to endogenous T Act as CD103 + CXCR6 + T Act expressed high levels of T RM marker CD49a but low levels of SLAMF6 (Fig.S4E-F). In order to identify conserved T RM markers across anatomical locations, we combined our different datasets and found that 38 genes overlapped between lung T RM cluster 1, tdLN Cxcr6 + cluster 8 and genes associated to lung TIM3 + T RM as compared to TIM3 + T Exh (Fig.S4G). We validated at the level of protein expression that JAML, DNAM (encoded by Cd226 ) and CD49a were indeed expressed at higher levels by lung T RM and tdLN CD103 + CXCR6 + T Act as compared to other T cell populations (Fig.S4H). These results demonstrate the existence of a rare population of CD103 + CXCR6 + T RM -like cells in tdLN. We thus wondered whether this population would be similarly affected by lack of DC populations in Xcr1 DTA and Cd11c Irf 4 mice bearing KP tumors. Consistent with previous results obtained in antigen-specific assays (Fig. 3I-J), we found that all T Act populations including CD103 + CXCR6 + T Act were severely reduced in Xcr1 DTA mice (Fig. 4J-L). By contrast, total T Act numbers were similar in Cd11c Irf 4 mice but both CD103 + CXCR6 − and CD103 + CXCR6 + T Act were reduced. Altogether, these results demonstrate that CD103 + T Act contain a rare population of CXCR6 + T RM -like cells and that both XCR1 + and IRF4-dependent migratory DCs are required for the differentiation of CD103 + CXCR6 + T RM -like cells in tdLN. Efficient generation of T RM -like TILs involves both XCR1 + and IRF4-dependent DCs at the level of tdLN. We next sought to address the consequences of impaired T RM specification at the level of tdLN on the generation of lung T RM -like TILs. In a first approach, we engrafted KP tumors in WT, Flt3L −/− deficient in all classical DC subsets, and Ccr2 −/− mice deficient in inflammatory monocytes and assessed CD8 + T RM -like content. In accordance with a non-redundant role of classical DCs in CD8 + T cell immunity 29 , 50 , we found that CD8 + T RM -like infiltration was deeply inhibited in DC-deficient Flt3L −/− mice (Fig.S5A-B). However, T RM -like TILs accrual was not modified in Ccr2 −/− mice, excluding a role for monocyte-derived/inflammatory phagocytes in T RM generation. To further define the role of XCR1 and IRF4-dependent DCs, we analysed the differentiation of polyclonal CD8 + T cells in Xcr1 DTA and Cd11c Irf 4 mice engrafted with KP tumors. In Xcr1 DTA mice, a major defect in the infiltration of all CD8 + T cells subsets was observed (Fig. 5A-B). These results highlight general defects in CD8 + T cell activation in DC1-lacking mice 29 , 32 , 49 and correlate with the deep impairment in T cell activation observed in tdLN (Fig. 4J-L). By contrast, Cd11c Irf 4 mice only had a partial and selective defect in both TIM3 − and TIM3 + T RM populations while other populations of CD8 + TILs were left unaffected. Next, we intended to address the role of DC subsets on CD8 + T cell populations in tumor antigen-specific settings using KP-OVA tumors engrafted in WT, Xcr1 DTA and Cd11c Irf 4 mice. We found that after 14 days, tetramer + OVA-specific lung CD8 + T cells were severely reduced in Xcr1 DTA but not in Cd11c Irf 4 mice (Fig. 5C-D and S5C-D). However, tetramer + OVA-specific CD8 + T RM -like TILs were reduced in Cd11c Irf 4 mice and they represented the most impacted population of activated CD8 + T cells. Of note, PD1 − effector CD8 + T cells were not impacted in Cd11c Irf 4 , evidencing a differential regulation compared to T RM in antigen-specific context as well. We have shown above that both migratory DC subsets promote pre-T RM generation in tdLN (Fig. 4J-L). To assess the contribution of this tdLN-localized phenomenon on lung T RM accrual, we adoptively transferred total cell suspensions obtained from tdLN of WT, Xcr1 DTA or Cd11c Irf 4 mice to KP-bearing WT host mice (Fig. 5E). This setting allowed us to normalise the lung tumor-microenvironment in recipient mice as controlled by similar responses in endogenous CD45.2 + CD8 + T cells (Fig.S5E-F). As anticipated, total donor CD8 + T cells from Xcr1 DTA but not Cd11c Irf 4 tdLNs were decreased in host lungs 7 days post-transfer (Fig. 5F-G). However, the generation of T RM -like TILs was impaired when tdLN progenitors originated from Xcr1 DTA or Cd11c Irf 4 tdLNs. These results demonstrate that T RM -like TILs activation necessitate the presence of both migratory XCR1 + DC1s and IRF4 + CD11b + migratory DC2s in tdLN. By contrast, the generation of non-T RM , exhausted CD8 + T cells or PD1 − CD8 + T cell effector populations is more selectively dependent on DC1s and less impacted by DC2 deficiency. Low TCR triggering by migratory crosspriming DC2s favors CD103 expression while migratory DC1-derived IL-12 supports CXCR6 expression. Because both types of migratory DCs were required for T RM -specification in the LN, we wondered which could be the mechanism accounting for their respective contribution. We first analysed the ability of both types of migratory DCs to uptake tumor antigens. To do so, we engrafted mice with KP tumors expressing the lysosomal-stable ZsGreen fluorescent protein. We found that both XCR1 + and IRF4-dependent CD11b + migratory but not resident DCs had some levels of antigen uptake, although XCR1 + migratory DCs were more able to transport tumor antigens to the LN (52.3% ZsGreen + MigDC1s vs 13.4% ZsGreen + MigDC2s) (Fig. 6A). We next wondered if migratory DCs had acquired an immunogenic phenotype. Using IL-12 YFP reporter mice (Fig. 6B) and scRNAseq on migratory DCs (Fig.S6A), we showed that migratory DC1 expressed much higher levels of IL-12 (78.3%) than migratory DC2 (15.9%). Therefore, we conclude that XCR1 + migratory DCs had undergone some level of activation supporting their ability to prime T cells. We next assessed the ability of both types of migratory DCs to cross-present tumor-associated antigens to CD8 + T cells through MHC-I. To do so, we performed a staining on tdLN DCs from KP or KP-OVA tumors with an antibody detecting H2-K b -SIINFEKL (OVA immunodominant epitope) complexes arising from the cross-presentation of KP-associated OVA protein 51 . We found that both XCR1 + and CD11b + migratory DCs cross-presented tumor-associated OVA antigen, although XCR1 + DC1s did it more efficiently than CD11b + DC2s (319.8 vs 132.8 MFI H2K b -OVA) (Fig. 6C). Of note, no signal was detected on resident DCs. Functionally, we evaluated the cross-presentation ability of migratory or resident DCs sorted from KP-OVA tdLNs by co-cultivating them with Cell trace violet (CTV)-loaded OVA-specific OT-1 CD8 + T cells. Migratory but not resident DCs were able to induce the proliferation of OT-1 and upregulation of CD44 (66.4% vs 2.82% of CTV low CD44 + ) (Fig.S6B). We thus focused our study on migratory DC subsets only. FACS-sorted migratory XCR1 + DC1s and CD11b + DC2s from KP-OVA-bearing tdLNs were co-cultured with CTV-loaded OT-1 for 2 days (Fig. 6D). We found that DC1 induced a vigorous proliferation of OT-1 that were almost entirely CD103-negative while they expressed homogenously the CD44 activation marker (Fig. 6E-F). By contrast, activation by CD11b + DC2s was milder but CD44 + proliferating OT-1 expressed high levels of CD103. We wondered which factor could explain the differential ability of CD11b + DC2s to maintain CD103 expression. We showed that IL-12 was highly produced by migratory DC1 (Fig. 6B) and IL-12 silences TGF-β activated CD103 expression via TBET competition at SMAD binding elements within CD103 promoter 52 . Therefore, we tested the role of IL-12 ex vivo and found that addition of recombinant IL-12 blunted CD103 expression without affecting the proliferation of naïve OT-1 by KP-OVA-loaded CD11b + DC2s (Fig. 6E-F). By contrast, IL12 blockade during DC1-dependent activation did not restore CD103 on proliferating T cells. We therefore hypothesized that another parameter, distinct from IL-12, was associated to cross-priming by DC1s and limited CD103 expression in activated T cells. We hypothesized that higher levels of MHCI-peptide complexes on DC1s (Fig. 6C) could intrinsically limit CD103 persistence on activated T cells. To test this, we exposed CD11b + DC2s sorted from tdLNs to increasing amounts of SIINFEKL synthetic peptide ex vivo in the presence of naïve OT1s. We found that peptide add-back was sufficient to impair CD103 expression in CD44 + CTV low OT-1 T cells while it increased proliferative expansion, thereby mimicking T cell activation by XCR1 + DC1s (Fig. 6G-H). We next addressed the role of TCR avidity for MHCI-peptide complexes in controlling CD103 persistence on activated T cells. To this end, we primed naïve OT1s with various altered peptide ligands acting as weaker agonists for the OT1 TCR 53 . We observed a decrease of OT-I activation with low affinity peptides (SIINFEKL > Q4 > T4 > G4), associated to an increased expression of CD103 on CD44 + divided OT-I cells (Fig. 6I and S6C). Overall, we conclude that a mild TCR triggering and low levels of IL-12 are both required for the persistence of CD103 on T cells during their cross-priming by migratory DCs. However, CXCR6 expression is also a hallmark of early-activated pre-T RM s in tdLNs (Fig. 4). We found that IL-12 blockade in vivo blunted T cell activation while inhibiting CXCR6 expression in tdLNs (Fig. 6J-K). Consistently, IL-12 also promoted CXCR6 expression during the priming of naïve OT1 in vitro (Fig. 6L-M). This shows that IL-12, despite its inhibitory effect on CD103, is required to acquire the full T RM phenotype. Given the paramount role of TGF-β signaling in the specification of T RM 54 , we further tested the role of IL-12 in combination with TGF-β in vitro . We found that IL-12 inhibited TGF-β signaling supporting CD103 expression (Fig. 6M). Vice versa, TGF-β counteracted i) TCR-induced CD103 inhibition and ii) IL-12-induced CXCR6 expression (Fig. 6M). As a result, optimal generation of CD103 + CXCR6 + OT1s required both IL-12 and TGF-β and intermediate levels of antigen presentation ensuring mild TCR triggering (Fig. 6L-M). Altogether, we conclude that i ) both XCR1 + and CD11b + migratory DCs, (but not resident DCs) cross-present KP-associated antigens to T cells in tdLNs. ii ) XCR1 + DC1s are the most efficient at antigen uptake, cross-presentation and in triggering CD8 + T cell proliferative expansion concomitant with the loss of CD103 which is dependent on both efficient TCR triggering and IL-12 release. iii ) IL-12 low, CD11b + DC2s are inefficient at uptake, cross-presentation and in triggering CD8 + T cell proliferative expansion but the low efficiency of antigen cross-presentation by CD11b + migratory DC2s is required for CD103 persistence on CD44 + activated T cells by delivering a milder TCR signalling as compared to XCR1 + DC1s. iv ) despite its inhibitory effect on CD103 expression, IL-12 is crucially needed for CXCR6 expression. Cross-priming by migratory DC2s recapitulates the T RM transcriptional program. We next sought to analyse how the identity of migratory DCs subset controlled the transcriptional phenotype of CD8 + T cells during cross-priming. To this end, we performed bulk RNAseq on OT1 cells that did or did not upregulate CD103 after ex vivo crosspriming by migratory DC1 or DC2 sorted from tdLN of KP-OVA bearing mice (Fig. 7A). PCA analysis revealed a stronger segregation between DC1 and DC2-activated OT1 across PC1 (84% of the variance) compared to CD103 expression (Fig. 7B). We conclude that the identity of migratory DCs cross-priming T cells is the main factor driving their transcriptional phenotype. PC1 was positively driven by T RM -associated genes ( Itgae , Cd226 e.g. ) and stemness genes ( Klf2, Sell, Slamf6, Il7r, e.g. ) and negatively driven by effector-related genes ( Il12rb1 and Il12rb2, Tbx21, Gzmb, Eomes, e.g. ) and proliferation associated transcription factor Myc . T RM -associated signatures found in tdLNs pre-T RM s (by scRNASeq, DEG Cluster 8 Cxcr6 + or bulk RNA-Seq, CD103 + T Act > CD103 − T Act ) or lung T RM -like TILs (bulk RNA-Seq, TIM3 + T RM >TIM3 + T Exh ) associated primarily with CD103 + OT1 crossprimed by DC2s (CD103 + OT1 DC2 − Act , Fig. 7C). By contrast, signature of genes downregulated in pre-T RM (bulk RNA-Seq, CD103 − T Act > CD103 + T Act ) was poorly expressed by CD103 + OT1 DC2 − Act . GSEA analysis of CD103 + OT1 DC2 − Act compared to all other OT1 populations, hereafter referred as Rest, further confirmed these results (Fig. 7D). CD103 + OT1 DC2 − Act were enriched in T RM -associated genes Itgae , Cd226 , Cxcr6 or Inpp4b , stemness-related genes Il7r and Tcf7 and TGF-β receptor genes Tgfbr1 and Tgfbr2 (Fig. 6E). Conversely, CD103 + OT1 DC2 − Act expressed lower levels of effector-related genes Gzmb , Klrk1 , Eomes or Tbx21 , proliferation-associated transcription factor Myc and Il12r genes Il12rb1 and Il12rb2 (Fig. 7E). Consistently, GSEA scoring of a memory CD8 T cell signature revealed that CD103 + OT1 DC2 − Act had increased expression of memory-related genes (NES = 1.46, Fig. 7F). We also evaluated a signature of SMAD4 target genes, a central mediator of TGF-β signalling, and found that it was enriched in CD103 + OT1 DC2 − Act (NES = 1.32, Fig. 7F). Conversely, MTORC1 signaling (NES = -1.58) and MYC targets (NES = -1.57) signatures were strongly reduced in CD103 + OT1 DC2 − Act , consistent with reduced proliferation activity (Fig. 7G). Taken together, these results indicate that migratory DC2s recapitulate the differentiation of naïve OT1s into pre-T RM similar to those found in tdLNs of tumor-bearing mice. The specification of T RM program within naïve T cells is enabled by a stronger activation of TGF-β signalling pathway by migratory DC2s as compared to migratory DC1s. DISCUSSION Infiltration of solid tumors with CD8 + TILs with a T RM phenotype is predictive of improved survival and better response to immunotherapies 1,3,5–9 . Besides, T RM -like TILs show signs of superior anti-tumor activity as compared to other T cell populations 16,17,20,21 . Therefore, we have sought to address the mechanisms governing the acquisition of the T RM phenotype during T cell activation in a murine model of lung cancer. Using a combination of transcriptomic and flow cytometry approaches, we identified a population of T RM -like TILs specifically expressing canonical T RM markers and sharing the expression of exhaustion molecules with T EXH -like TILs. Fate mapping experiments show that CD103 expression during the early phases of T cell activation in tdLNs defines CD8 + T cells committed to generate T RM -like TILs. Previous studies similarly identified CD103 as a marker of T RM -commitment within naïve T cells in a model of skin vaccination 24 . Using unsupervised analysis of activated T cells in the tdLN, we improved this phenotypic characterization and identified a rare population of CXCR6 + CD103 + T RM -like cells. We further addressed the dendritic cell requirements for T RM phenotype specification. Induction of CXCR6 + T RM -like precursor cells in tdLNs is dependent on both migratory XCR1 + DC1s and IRF4-dependent DC2s. Consistently, lung T RM -like TILs accrual requires both types of migratory DCs at the level of tdLN. By contrast, effector and exhausted CD8 + T cells infiltrating tumors solely rely on XCR1 + DC1s. Finally, we asked which factors provided by DCs subsets could control T RM -like TILs generation. We provide evidence that T RM specification in tdLNs: requires sub-optimal TCR triggering by weak cross-presenter DC2s that display reduced densities of MHCI-peptide complexes. is inhibited by high amounts of IL-12 mainly produced by XCR1 + DC1s that inhibits TGF-b signalling and its CD103 downstream target. necessitates some levels of IL-12 as evidenced by the IL-12 function in upregulating CXCR6 during T cell cross-priming. relies on the proliferative expansion uniquely achieved by cross-priming DC1s. T RM specification occurs in tdLNs and involves both XCR1 + and IRF4-dependent migratory DCs. The existence of precursors pre-committed to differentiate into T RM has been demonstrated within naïve CD8 + T cells epigenetically poised to express T RM -associated genes 24 and within blood circulating activated T cells after skin vaccination 26 . Mani et al. showed that TGF-β activation by migratory DCs was required for T RM preconditioning at the level of naïve T cells in the LN. Whether this mechanism requires one or multiple DC subsets has not been addressed. Also, it is unknown which DC subset participate to the activation of T RM from T RM -poised naïve progenitors. Our study identifies a subset or early activated CD8 + T cells expressing selectively a transcriptional signature of tumor-infiltrating T RM in tdLN. We demonstrate that the activation of tdLN CD103 + CXCR6 + T cells rely on both migratory XCR1 + DC1s and IRF4-dependent DC2s. By contrast, activation of CD103 - CD8 + T cells is largely independent on the presence of IRF4-dependent migratory DCs. Furthermore, we demonstrate by adoptive transfer that genetically labelled CD103 + T cells from tdLNs uniquely give rise to lung T RM -like TILs. Therefore, we establish that the specification of lung T RM -like TILs actually starts within lymph nodes by a process orchestrated by both migratory DCs subsets. These findings might have a relevance in humans since CD1c + DCs have been shown to participate to the activation of CD8 + CD103 + T RM -like cells 39,40 . Of note, the contribution of IRF4-dependent DCs to the generation of memory but not effector T cell responses had been previously noted in the influenza infection model 38 and to the maintenance of anti-infectious T RM at mucosal sites in HSV infection 27 . Altogether, our results further extend the major contribution of XCR1 + DCs in anti-tumor CD8 T cell immunity to T RM 29,30,34,48,49 . Our findings are in line with pioneer studies highlighting the crucial role of XCR1 + DC1s in the activation of vaccine-induced T RM s 37 . However, it is important to underline that the role of IRF4-dependent DC2s had not been addressed in this context. In addition, we evidence a contribution of IRF4-dependent DCs to T RM generation at the level of tdLNs. Low TCR signal strength, IL12 and TGF-β synergize to differentiate T RM T RM development dependency on TGF-β has been well established in several studies 24,25,27 . However, both migratory DC subsets express TGF-β activating molecules at the transcriptomic level (e.g., Itgav ) 24 . Therefore, IRF4-dependent migratory DC2s might contribute to T RM specification i) by providing a milder TCR signal strength because they cross-present tumor associated antigens inefficiently; ii) by not providing IL-12 in contrast to XCR1 + DC1s; iii) by providing specific signals yet to be identified. Here, we provide evidence for i) and ii). We show that sub-optimal cross-presentation by DC2s enable CD8 + T cells to maintain CD103 expression during T cell activation in vitro , and this, independently of other factors. These results are in line with previous studies highlighting a negative impact of strong TCR signalling on T RM differentiation in vivo in the context of viral infections 58,59 . More recently, Shakiba et al. have shown that low affinity tumor-associated antigens prime T cells expressing higher levels of CD103 while presenting less features of dysfunctional/exhausted T cells as compared to T cells primed with high affinity antigens 60 . Finally, both mild TCR signalling 61 and migratory DC2s contribute to the priming of central memory CD8 + T cells 62 . In addition to optimal cross-priming efficiency, XCR1 + migratory DC1s also provide high levels of IL-12 which acts as an inhibitory signal erasing CD103 expression induced by IRF4-dependent migratory DC2s. These findings are in line with the regulatory role of TBET, downstream of IL-12 signalling at SMAD3 binding sites in the CD103 locus 52,63 . Bergsbaken et al. have noticed that IL-12 promotes CD103 - but not TGF-β dependent CD103 + T RM by a STAT4-dependent mechanism 64,65 . Our results clearly establish that TGF-β-dependent program is induced more efficiently by migratory DC2s than migratory, IL-12-producing DC1s. TGF-β transpresentation might play a role in this process, together with the regulatory role of IL-12 on TGF-β signalling (that can be visualized by IL-12 ability to counteract TGF-β-induced CD103). Functional features associated to the T RM phenotype in cancer T RM have been extensively studied in the context of infections or vaccination when memory is settled and maintained after antigen clearance. In this study, we have not addressed the functional ability of T RM -like cells to recirculate or generate memory in the absence of antigen. Recent studies addressing the functional ability of T RM -like cells to recirculate in a mouse model of E0771 breast cancer suggest that T RM phenotype does not necessarily correlate with tumor residence or correspond to neo-generated T RM -like cells from circulating precursors 15 . However, T RM -like cells in this model do not express CD103 which may be a major contributor of residence within the tumor through direct interaction with epithelial E-Cadherin which is not expressed in the E0771 cells investigated in this study 66,67 . Evaluating this possibility would require additional experiments. From a functional perspective, T RM -like could represent a pool of responsive cells, able to generate effector cells during challenge or immunotherapies 3,9,20 . This view is in line with the developmental plasticity of T RM upon antigenic restimulation that had been established in infection-induced T RM s 18,19 . Deeper functional studies are required to understand the determinants of the prognosis value of T RM -like phenotype in cancer and how their functionality could be activated. In conclusion, our study highlights the fine-tuning of anti-tumor CD8 + T cell diversity by a broad repertoire of migratory DCs subsets in tdLNs. We show that the crosspriming of CD8 + T cells reaching a dysfunctional/exhausted phenotype within tumors is uniquely dependent on DC1s and entirely independent on migratory DC2s. By contrast, induction of T RM -like cells requires both DC1 and DC2 subsets. These findings challenge the relevance of the “division of labor” across DCs subsets ascribing discrete, specific and exclusive immune functions to specific DCs subsets. Instead, we evidence a high level of cooperativity relevant for the induction of memory precursors in tumor-draining lymph nodes. In sum, our findings support the view that instruction of residency program and proliferative expansion rely on complementary yet distinct DCs subsets. These findings have important implications for the design of effective immunotherapies harnessing multiple types of CD8 + T cell immunity, id est effector vs tissue-resident memory cells. One correlates of the current findings that remain to be tested, is that optimal priming of diverse T cell phenotypes might rely on antigen delivery to multiple DCs subsets. METHODS Study design The aim of this study was to understand the dendritic cells requirements for T RM specification in lung cancer. To that end, we intravenously injected KP lung tumors into several mouse models in which specific dendritic cell populations can be manipulated. We thoroughly characterized polyclonal and antigen-specific CD8 T cell populations to model antigen OVA using a combination of transcriptomics (bulk RNA-seq, scRNA-seq, CITE-Seq) and flow cytometry approaches. To address the role of dendritic cells in the lymph nodes, we designed a series of adoptive cell transfer and ex vivo OT1-dendritic cell coculture experiments. Mice Mice were housed at the INSERM U1149 CRI, Medicine school site Bichat or at the Paris Institut Pasteur animal house facilities. Wild type C57BL/6J and CD45.1 mice were purchased from Janvier and kept in our facility. XCR1 cre and Rosa26-lox-stop-lox-DTA (B6.129P2 Gt(ROSA)26Sortm1(DTA)Lky/J) 68 mice were kindly provided by Dr. Marc Dalod (Center of Immunology Marseille-Luminy, Marseille). XCR1 DTA mice were generated as described 46 . OT-I RAG2 -/- CD45.1 mice were kindly provided by Dr. Sébastian Amigorena (Curie institute, Paris). Flt3L -/- mice were kindly provided by Dr. Guillaume Darrasse-Jèze (UMR-S 979, Paris). CD11c cre mice (B6.Cg-Tg(Itgax-cre)1-1Reiz/J; 69 ), IRF4 flox mice (B6.129S1-Irf4tm1Rdf/J; 70 ), and CCR2 −/− mice (Ccr2tm1/fc; 71 ) were kindly gifted by Dr. Emmanuel Gautier (UMR-S U1166, Paris). Hobit-tdTomato-DTR [B6-Tg (Zfp683-tdTomato-P2A-Cre-P2A-DTR); 18 ] mice were kindly provided by Dr. Klaas Van Gisbergen, (Champalimaud Fundation, Lisbon). The reporter/deleter cassette disrupts the Hobit allele, in consequence, heterozygous Hobit tdTomato-DTR mice were used in the experiments. KRAS LSL-G12D/+ ;p53 fl/fl mice 72 were kindly provided by Dr. Kairbaan Hodilvala-Dilke (Bart Institute Queen Mary University, London). CD103 cre-ERT2 ( Itgae -creER T2 ) and Rosa LSL-tdTomato (B6.Cg- Gt(ROSA)26Sor tm14(CAG-tdTomato)Hze /J) mice were kindly provided by Dr. Tessa Bergsbaken (Rutgers, New Brunswick) 44 . IL-12 YFP (C.129S4(B6)-Il12btm1.1Lky/J; 73 ) mice were kindly provided by Dr. Mikaël Pittet. The study was approved by the local ethics committee comité d’éthique Paris Nord no. 121 and the Ministère de l’enseignement supérieur, de la recherche et de l’innovation under the authorization number APAFIS#15373. Animal care and treatment were conducted with national and international laws and policies (European Economic Community Council Directive 86/609; OJL 358; December 12, 1987). All experiments were performed in accordance with the Federation of European Laboratory Animal Science Association (FELASA) guidelines institutional guidelines and the French law. Tumor cell lines The KP line has been isolated from primary lung tumors of C57BL/6 KP mice ( KRAS LSL-G12D/+ ;p53 fl/fl ) 74 . The line was kindly provided by Dr. Federica Benvenuti (ICGEB, Trieste), and has been used previously 35,75 . KP-OVA were generated by retroviral transduction of KP cells with a pBabe-OVA-IRES-Cherry vector. KP-ZsGreen were generated by lentiviral transduction of KP cells with a pLVX-IRES-ZsGreen vector. Tumor cell lines were cultured in RPMI 1640 medium, Glutamax (Thermo Fisher) supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher), penicillin-streptomycin (Thermo Fisher) and 55μM β-mercaptoethanol (Thermo Fisher) (complete RPMI) and maintained at 37°C and 5% CO2. Tumor models For the grafted tumor model, the KP, KP-OVA and KP-ZsGreentumor cell lines were grown in RPMI-Glutamax with 10% FCS, 55 μM 2-mercaptoethanol, Penicillin streptomycine and used for experiments when in exponential growth phase. The cells were detached using Trypsine/EDTA 0.25%, washed two times in PBS and counted in trypan blue. 8.10 5 tumor cells were intravenously injected in 100 mL RPMI without supplement. For the autochthonous KP model, C57BL/6 KP mice ( KRAS LSL-G12D/+ ;p53 fl/fl ) at 8 weeks of age, were intranasally inoculated with 2.5 × 10 7 infectious particles of a replication-deficient adenoviral vector with Cytomegalovirus promoter driving the expression of the Cre recombinase protein in order to sporadically induce mutations in lung cells, and promote lung tumor development as previously described 62 . Mice were sacrificed 16 weeks after the adenovirus inoculation. In vivo treatments For the FTY treatment, mice received intraperitoneal injections of 1 mg/kg FTY720 (fingolimod) (Sigma-Aldrich) in 150 μl of non-deionised H 2 0 (Versol) every 3 days from day 1 after tumor injection. For IL-12p40 blocking, mice received intraperitoneal injections of 200µg anti-IL-12p40 blocking antibody (Biolegend) in 200µl of PBS every 3 days from day 0 after tumor injection. Mouse tissues processing Mouse lungs were harvested and transferred to 3ml digestion buffer (Hank’s Balanced Salt Solution (HBSS) with calcium and magnesium (Thermo Fisher) and with 75 μg/mL of Liberase TL (Roche) and 0.02mg/ml DNase I (Thermo Fisher)). Lungs were dissociated using gentleMACS Octo Dissociator (Miltenyi) (program m_lung_01), incubated at 37°C for 45 minutes and dissociated again on the gentleMACS Octo Dissociator (Miltenyi) (program m_lung_02). The cell suspension was passed through a 70 μm cell strainer (Corning) and red blood cells were lysed using ACK lysing buffer (Thermo Fischer). The mediastinal lymph nodes were smashed in FACS buffer on a 70 μm cell strainer. The absolute number of live immune cells in each tissue cell suspension was assessed using AccuCheck Counting Beads (Thermo Fisher) along with anti-CD45 and DAPI staining on BD FACS Fortessa 20 (BD Biosciences). For OT-I CD8 + T cell preparation, LNs from OT-I CD45.1 RAG mice were passed through 70 μm cell strainers. The LN suspensions were stained with anti-CD8-APCCy7 and counted by FACS using counting beads (AccuCheck beads, Invitrogen). Flow cytometry analysis After tissue processing and cell counting, Fc-receptor were blocked using FcBlock (2.4G2, BD) for 15min at 4°C. For dead cell identification, the cells were stained with 7-AAD (Biolegend) for extracellular staining only or Zombie dye (Biolegend) for intracellular staining in PBS. Cells were then stained with fluorophore-conjugated antibodies in FACS buffer (See antibody list in the Key resource table), PBS 3% FCS (Gibco) 2mM EDTA (Gibco), during 30 minutes at 4°C. When needed, cell suspensions were subsequently fixed and stained in for nuclear protein (30min fixation and 30min intracellular staining) using fixation/permeabilization kit (eBiosciences transcription factor staining buffer) according to the manufacturer’s instructions. Labelling with H2K b -SIINFEKL tetramers (MBL) was performed at room temperature for 30 min. Data acquisition was performed using an LSR-Fortessa X20 (BD) and analysis were done using FlowJo software (TreeStar). Ex vivo T cell restimulation assay After tissue processing and cell counting, 1 million total CD45 + cells were incubated for 5 hours at 37°C in a restimulation cocktail composed of 50ng/ml PMA (Sigma), 1µg/ml Ionomycin (Sigma), 2µg/ml Golgi Plug (BD) and 2µg/ml Golgi stop (BD) in complete RPMI medium. Cells were then processed for flow cytometry analysis as described before. Bulk RNA-sequencing After tissue processing and cell count, cell suspensions were washed in cold FACS buffer (PBS + 3% BSA + 2mM EDTA), Fc-receptors were blocked ,15min at 4°C, using purified anti-CD16/32 (2.4G2) and, then, stained with fluorophore conjugated antibodies for 30 minutes at 4°C. CD8 + T cell subsets from tumor-bearing lungs and tdLNs were isolated using an Aria III (BD) and a MELODY (BD) cell sorter, respectively, and directly collected on lysing TCL buffer (QIAGEN) containing 0.1% of beta-mercaptoethanol before storage at -80°C. RNA were extracted and isolated using the Single Cell RNA purification kit (Norgen, Cat#51800) according to the manufacturer’s instructions. After extraction, total RNA was analyzed using Agilent RNA 6000 Pico Kit on the Agilent 2100 Bioanalyzer System. RNA quality was estimated based on capillary electrophoresis profiles using the RNA Integrity Number (RIN). RNA sequencing libraries were prepared using the SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara). The input quantity of total RNA was comprised between 1 and 22ng. This protocol includes a first step of RNA fragmentation, using a proprietary fragmentation mix at 94°C. The time of incubation was set up for each sample, based on the RNA quality, and according to the manufacturer’s recommendations. After fragmentation, indexed cDNA synthesis was performed. Then the ribodepletion step was performed, using probes specific to mammalian rRNA. PCR amplification was finally achieved to amplify the indexed cDNA libraries, with a number of cycles set up according to the input quantity of tRNA. Library quantification and quality assessment was performed using Qubit fluorometric assay (Invitrogen) with dsDNA HS (High Sensitivity) Assay Kit and LabChip GX Touch using a High Sensitivity DNA chip (Perkin Elmer). Libraries were then equimolarly pooled and quantified by qPCR using the KAPA library quantification kit (Roche). Sequencing was carried out on the NovaSeq 6000 (Illumina), targeting between 10 and 15M reads per sample and using paired-end 2 x 100 bp. Gene set enrichment analysis To statistically evaluate the enrichment of previously reported gene signatures and DEG calculated from pairwise comparison in this study (Gene Sets), we used pairwise comparisons using the gene set enrichment analysis (GSEA) 76 method from the Massachussets Institute of Technology ( https://www.broadinstitute.org/gsea ). Statistical analysis was performed by evaluation of nominal p value and false discovery rate (q value) based on 1,000 random permutations. Results were considered significant when the p value was below 0.05 and when the q value was below 0.25 (false discovery rate below 25%) accordingly to the recommendation from the software developers. scRNA-seq analysis For cell sorting experiments, tumor-bearing lungs and tdLN were collected and processed as previously described. The strategy used for the sort is depicted in the figures. The droplet-based approach of 10X Genomics platform was used to perform scRNA-seq. For each scRNA-seq sample, cell suspension from three individual mice were pooled and sorted in PBS containing 0,04%BSA. 10 000 cells were loaded on a Chromium Controller using the Chromium Next GEM Single Cell 3 Reagent Kits v3.1 according to manufacturer instructions. Raw sequencing reads were processed using the 10x Genomics software Cellranger. To obtain a cell count matrix, reads were mapped to the mouse GRCm38.84 reference genome. scRNA-seq analysis was performed using Seurat v4. For each dataset, cells with low genes per cell or with a high percentage of mitochondrial genes were removed from downstream analyses. Following data QC we obtained 4421 cells from the lungs KP CD8 + T cells dataset, 1404 cells for the tdLN KP WT CD8 + T cells dataset, 3340 cells for the tdLN KP CD11c IRF4 CD8 + T cells dataset, 2076 cells for the tdLN KP WT CD11c + MHCII + cells dataset and 3150 cells for the tdLN KP CD11c IRF4 CD11c + MHCII + cells dataset. RNA and ADT expression were normalized separately using the default Seurat LogNormalize and Centered Log Ratio (CLR) approaches, respectively. Top 2000 most variable genes were then determined by applying a VST (Variance Stabilizing Transformation). Datasets were analyzed separately or were integrated together before analysis. For integration, top 2000 most variable genes across datasets were used as anchors with the LogNormalize method. Dimensionality reduction was performed using PCA and the top informative principle components were determined after visualization with an elbow plot and dimensional reduction heatmaps, and used for the non-linear dimensionality reduction technique UMAP. Louvain clustering of the data was performed across a range of resolution parameters visualized using a clustree. Cluster of cells with low number of genes and in which no T lymphocyte cell identifying genes could be detected were not considered in the analysis. Subsequent analyses were performed using Seurat default parameters. Adoptive transfers Mediastinal lymph nodes from donor mice (CD45.2 + tumor-bearing or CD45.1 + OTI x Rag1 -/- tumor-free mice, respectively) were mechanically dissociated on a 70μm cell strainers in sterile FACS buffer. After two washes in PBS, the LN cell suspensions were counted as previously described and resuspended in 100μL of RPMI without supplement and intravenously injected in recipient mice (CD45.1 + or CD45.2 + tumor-bearing mice, respectively), according to the experimental designs described in figures 2L, S3E and 5E. Ex vivo Mouse T cell priming assay OT-1 cell preparation: OT-I CD8 + T cells were isolated from lymph nodes of CD45.1 + OTI x Rag1 -/- mice and labeled for proliferation assay with 2μM of CellTrace Violet (CTV, Invitrogen) at 37°C for 15 minutes, washed and counted before culture with DC subsets. Dendritic cells isolation from LNs: For the isolation of mediastinal lymph node DCs from KP- or KP-OVA bearing mice, minced mediastinal tumor-draining LNs were digested in 500μl HBSS with calcium and magnesium (Thermo Fisher) with 75 μg/mL of Liberase TL (Roche) and 0.02mg/ml DNase I (Thermo Fisher) and incubated at 37°C for 20 minute under agitation. Cell suspensions were passed through a 70μm cell strainers. Cell suspensions were then blocked in FcBlock and stained with CD11c-biotin followed by SAV-beads (Miltenyi). Cell suspension was applied on a LS column (Miltenyi) according to the manufacturer’s instructions. The CD11c-enriched fraction retained in the column was then stained for CD11c, MHCII, CD19, XCR1 and CD11b. Dendritic cells subsets were FACS-sorted on a Melody (BD) according to the gating strategy depicted on the Fig.s. DCs and T cell co-culture: 2500 DCs were plated in V-bottom 96 well plates and incubated with 25000 CTV-loaded OT-1 in RPMI medium (10% FCS + 1% Pencillin/Streptomycin + 1mM Sodium Pyruvate 55μM 2-mercaptoethanol and 10mM HEPES). When indicated, various doses of the SIINFEKL OVA peptide or mutated peptides (Q4, T4, G4) were added to the cultures at the indicated concentration. For some experiments, recombinant mouse TGF-β (20μg/mL) (Biolegend), recombinant mouse IL-12p70 (20μg/mL) (Biolegend) or anti-IL-12p40 blocking antibody (20μg/mL) (Biolegend) were added to the well. T cell activation analysis by flow cytometry : After 48h or 96h of co-culture as indicated in the figures, T cells were stained for CD45.1, CD8, CD44, CD62L, PD-1 and CD103 to analyze their activation and polarization status. CTV dilution was analyzed to assess T cell proliferation. Quantification and Statistical Analysis Statistical analysis was performed using Prism 10 (GraphPad Software Inc., USA). Each dot displayed on the graphs corresponds to one biological replicate and error bars represent standard deviations. When two experimental groups were compared, two-tailed unpaired or paired Student’s t test was used. When three or more groups were compared, statistically significant differences between means were determined using the one-way analysis of variance (ANOVA) method. Tukey’s multiple comparisons test was applied when comparing multiple conditions. A p-value of less than 0.05 was considered as significant, indicated with the following signs: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Declarations Acknowledgments We thank M. Pittet for providing IL-12 YFP mice. We thank the flow cytometry and the animal facility platforms from Centre de Recherche sur l’Inflammation (CRI), Faculté de Médecine Bichat and from Institut Pasteur. We thank the NGS platforms from Institut Curie U932 and from Institut Cochin. We thank Institut National de la Santé et de la Recherche Médicale U1149 and U1016 units, Centre National de la Recherche UMR3738 unit, Institut Pasteur. Funding Centre National de la Recherche Scientifique CNRS (PG) PhD Fellowship from Université de Paris (NV) PhD Fellowship from Fondation pour la Recherche Médicale (NV) Postdoctoral fellowship from Fondation pour la Recherche sur le Cancer ARC (PB) Cancer Research UK Institut National du Cancer INCA (PL-BIO22-147) Fondation pour la Recherche sur le Cancer ARC (PJA2021060003913) Fondation pour la Recherche Médicale (EQU202203014687) Author contributions Conceptualization: NV, PB, PG Methodology: NV, PB, PG Investigation: NV, PB, AO, MS, LG, RS, YG, FLRDC, AS, MV, AR, SB, KP, JB Software: NV, SB, YG Ressources: GDJ, ELG, MD, TB, KVG, KH, LS, JH, FB, PG Funding acquisition: ET, JH, PG Supervision: LS, JH, FB, PG Writing: NV, PB, PG Ethics declarations Competing interests: Authors declare that they have no competing interests. Data availability Bulk and single-cell RNA-Seq datasets are accessible here: https://figshare.com/s/6a9c76588d496aaaa768. All original data are available from the corresponding author upon request. References Guo X et al (2018) Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med 24:978–985 Siddiqui I et al (2019) Intratumoral Tcf1 + PD-1 + CD8 + T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy. Immunity 50:195–211e10 Corgnac S et al (2020) 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 Milner JJ et al (2017) Runx3 programs CD8 + T cell residency in non-lymphoid tissues and tumours. Nature 552:253–257 Djenidi F et al (2015) CD8 + CD103 + tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J Immunol 194:3475–3486 Hombrink P et al (2016) Programs for the persistence, vigilance and control of human CD8 + lung-resident memory T cells. Nat Immunol 17:1467–1478 Ganesan A-P et al (2017) Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat Immunol 18:940–950 Savas P et al (2018) Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 24:986–993 Clarke J et al (2019) Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer. J Exp Med 216:2128–2149 Gebhardt T et al (2009) Memory T cells in nonlymphoid tissue that provide enhanced local immunity during infection with herpes simplex virus. Nat Immunol 10:524–530 Mackay LK et al (2016) Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352:459–463 Fonseca R et al (2022) Runx3 drives a CD8 + T cell tissue residency program that is absent in CD4 + T cells. Nat Immunol 23:1236–1245 Skon CN et al (2013) Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8 + T cells. Nat Immunol 14:1285–1293 Shiow LR et al (2006) CD69 acts downstream of interferon-alpha/beta to inhibit S1P1 and lymphocyte egress from lymphoid organs. Nature 440:540–544 Gavil NV et al (2023) Chronic antigen in solid tumors drives a distinct program of T cell residence. Sci Immunol 8:eadd5976 Anadon CM et al (2022) Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cells. Cancer Cell 40:545–557e13 Park SL et al (2019) Tissue-resident memory CD8 + T cells promote melanoma-immune equilibrium in skin. Nature 565:366–371 Behr FM et al (2020) Tissue-resident memory CD8 + T cells shape local and systemic secondary T cell responses. Nat Immunol 21:1070–1081 Low JS et al (2020) Tissue-resident memory T cell reactivation by diverse antigen-presenting cells imparts distinct functional responses. J Exp Med 217 Virassamy B et al (2023) Intratumoral CD8 + T cells with a tissue-resident memory phenotype mediate local immunity and immune checkpoint responses in breast cancer. Cancer Cell 41:585–601e8 Nizard M et al (2017) Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nat Commun 8:15221 Huang Q et al (2022) The primordial differentiation of tumor-specific memory CD8 + T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell 185:4049–4066e25 Prokhnevska N et al (2023) CD8 + T cell activation in cancer comprises an initial activation phase in lymph nodes followed by effector differentiation within the tumor. Immunity 56:107–124e5 Mani V et al (2019) Migratory DCs activate TGF-β to precondition naïve CD8 + T cells for tissue-resident memory fate. Science 366 Mackay LK et al (2013) The developmental pathway for CD103 + CD8 + tissue-resident memory T cells of skin. Nat Immunol 14:1294–1301 Kok L et al (2020) A committed tissue-resident memory T cell precursor within the circulating CD8 + effector T cell pool. J Exp Med 217 Shin H, Kumamoto Y, Gopinath S, Iwasaki A (2016) CD301b + dendritic cells stimulate tissue-resident memory CD8 + T cells to protect against genital HSV-2. Nat Commun 7:13346 Slütter B et al (2017) Dynamics of influenza-induced lung-resident memory T cells underlie waning heterosubtypic immunity. Sci Immunol 2:eaag2031 Hildner K et al (2008) Batf3 deficiency reveals a critical role for CD8alpha + dendritic cells in cytotoxic T cell immunity. Science 322:1097–1100 Zelenay S et al (2015) Cyclooxygenase-Dependent Tumor Growth through Evasion of Immunity. Cell 162:1257–1270 López L et al (2024) Dendritic cell-targeted therapy expands CD8 T cell responses to bona-fide neoantigens in lung tumors. Nat Commun 15:2280 Schenkel JM et al (2021) Conventional type I dendritic cells maintain a reservoir of proliferative tumor-antigen specific TCF-1 + CD8 + T cells in tumor-draining lymph nodes. Immunity 54:2338–2353e6 Gutiérrez-Martínez E et al (2015) Cross-Presentation of Cell-Associated Antigens by MHC Class I in Dendritic Cell Subsets. Front Immunol 6:363 Theisen DJ et al (2018) WDFY4 is required for cross-presentation in response to viral and tumor antigens. Science 362:694–699 Caronni N et al (2021) TIM4 expression by dendritic cells mediates uptake of tumor-associated antigens and anti-tumor responses. Nat Commun 12:2237 Ferris ST et al (2020) cDC1 prime and are licensed by CD4 + T cells to induce anti-tumour immunity. Nature 584:624–629 Iborra S et al (2016) Optimal Generation of Tissue-Resident but Not Circulating Memory T Cells during Viral Infection Requires Crosspriming by DNGR-1 + Dendritic Cells. Immunity 45:847–860 Ainsua-Enrich E et al (2019) IRF4-dependent dendritic cells regulate CD8 + T-cell differentiation and memory responses in influenza infection. Mucosal Immunol 12:1025–1037 Yu CI et al (2013) Human CD1c + dendritic cells drive the differentiation of CD103 + CD8 + mucosal effector T cells via the cytokine TGF-beta. Immunity 38:818–830 Bourdely P et al (2020) Transcriptional and Functional Analysis of CD1c + Human Dendritic Cells Identifies a CD163 + Subset Priming CD8 + CD103 + T Cells. Immunity 53:335–352e8 DuPage M et al (2011) Endogenous T cell responses to antigens expressed in lung adenocarcinomas delay malignant tumor progression. Cancer Cell 19:72–85 DuPage M, Mazumdar C, Schmidt LM, Cheung AF, Jacks T (2012) Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482:405–409 Gueguen P et al (2021) Contribution of resident and circulating precursors to tumor-infiltrating CD8(+) T cell populations in lung cancer. Sci Immunol 6 Fung HY, Teryek M, Lemenze AD, Bergsbaken T (2022) CD103 fate mapping reveals intestinal CD103 – tissue-resident memory T cells are the primary responders to secondary infection. Sci Immunol 7:eabl9925 Maier B et al (2020) A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580:257–262 Mattiuz R et al (2018) Novel Cre-Expressing Mouse Strains Permitting to Selectively Track and Edit Type 1 Conventional Dendritic Cells Facilitate Disentangling Their Complexity in vivo. Front Immunol 9:2805 Bajana S, Turner S, Paul J, Ainsua-Enrich E, Kovats S (2016) IRF4 and IRF8 Act in CD11c + Cells To Regulate Terminal Differentiation of Lung Tissue Dendritic Cells. J Immunol 196:1666–1677 Böttcher JP et al (2018) NK Cells Stimulate Recruitment of cDC1 into the Tumor Microenvironment Promoting Cancer Immune Control. Cell 172:1022–1037e14 Ghislat G et al (2021) NF-κB–dependent IRF1 activation programs cDC1 dendritic cells to drive antitumor immunity. Sci Immunol 6:eabg3570 Meredith MM et al (2012) Expression of the zinc finger transcription factor zDC (Zbtb46, Btbd4) defines the classical dendritic cell lineage. J Exp Med 209:1153–1165 Porgador A, Yewdell JW, Deng Y, Bennink JR, Germain RN (1997) Localization, quantitation, and in situ detection of specific peptide-MHC class I complexes using a monoclonal antibody. Immunity 6:715–726 Laidlaw BJ et al (2014) CD4 + T cell help guides formation of CD103 + lung-resident memory CD8 + T cells during influenza viral infection. Immunity 41:633–645 Zehn D, Lee SY, Bevan MJ (2009) Complete but curtailed T-cell response to very low-affinity antigen. Nature 458:211–214 Mackay LK et al (2015) T-box Transcription Factors Combine with the Cytokines TGF-β and IL-15 to Control Tissue-Resident Memory T Cell Fate. Immunity 43:1101–1111 Luckey CJ et al (2006) Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells. Proc Natl Acad Sci U S A 103:3304–3309 Diamant I, Clarke DJB, Evangelista JE, Lingam N (2024) & Ma’ayan, A. Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources. Nucleic Acids Research gkae1080 10.1093/nar/gkae1080 Liberzon A et al (2015) The Molecular Signatures Database Hallmark Gene Set Collection. Cell Syst 1:417–425 Maru S, Jin G, Schell TD, Lukacher AE (2017) TCR stimulation strength is inversely associated with establishment of functional brain-resident memory CD8 T cells during persistent viral infection. PLoS Pathog 13:e1006318 Fiege JK et al (2019) The Impact of TCR Signal Strength on Resident Memory T Cell Formation during Influenza Virus Infection. J Immunol 203:936–945 Shakiba M et al (2022) TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion. J Exp Med 219:e20201966 Solouki S et al (2020) TCR Signal Strength and Antigen Affinity Regulate CD8 + Memory T Cells. J Immunol 205:1217–1227 Kim TS, Gorski SA, Hahn S, Murphy KM, Braciale TJ (2014) Distinct dendritic cell subsets dictate the fate decision between effector and memory CD8(+) T cell differentiation by a CD24-dependent mechanism. Immunity 40:400–413 Slütter B, Pewe LL, Kaech SM, Harty JT (2013) Lung airway-surveilling CXCR3(hi) memory CD8(+) T cells are critical for protection against influenza A virus. Immunity 39:939–948 Bergsbaken T, Bevan MJ, Fink PJ (2017) Local Inflammatory Cues Regulate Differentiation and Persistence of CD8 + Tissue-Resident Memory T Cells. Cell Rep 19:114–124 Fung HY, Espinal AM, Teryek M, Lemenze AD, Bergsbaken T (2023) STAT4 increases the phenotypic and functional heterogeneity of intestinal tissue-resident memory T cells. Mucosal Immunol 16:250–263 Mami-Chouaib F et al (2018) Resident memory T cells, critical components in tumor immunology. J Immunother Cancer 6:87 Regev O et al (2022) ICAM-1 on Breast Cancer Cells Suppresses Lung Metastasis but Is Dispensable for Tumor Growth and Killing by Cytotoxic T Cells. Front Immunol 13:849701 Voehringer D, Liang H-E, Locksley RM (2008) Homeostasis and effector function of lymphopenia-induced ‘memory-like’ T cells in constitutively T cell-depleted mice. J Immunol 180:4742–4753 Caton ML, Smith-Raska MR, Reizis B (2007) Notch-RBP-J signaling controls the homeostasis of CD8- dendritic cells in the spleen. J Exp Med 204:1653–1664 Klein U et al (2006) Transcription factor IRF4 controls plasma cell differentiation and class-switch recombination. Nat Immunol 7:773–782 Boring L et al (1997) Impaired monocyte migration and reduced type 1 (Th1) cytokine responses in C-C chemokine receptor 2 knockout mice. J Clin Invest 100:2552–2561 DuPage M, Dooley AL, Jacks T (2009) Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat Protoc 4:1064–1072 Reinhardt RL, Hong S, Kang S-J, Wang Z, Locksley RM (2006) Visualization of IL-12/23p40 In Vivo Reveals Immunostimulatory Dendritic Cell Migrants that Promote Th1 Differentiation1. J Immunol 177:1618–1627 Dimitrova N et al (2016) Stromal expression of miR-143/145 promotes neoangiogenesis in lung cancer development. Cancer Discov 6:188–201 Simoncello F et al (2022) CXCL5-mediated accumulation of mature neutrophils in lung cancer tissues impairs the differentiation program of anticancer CD8 T cells and limits the efficacy of checkpoint inhibitors. Oncoimmunology 11:2059876 Subramanian A et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550 Zheng L et al (2021) Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374:abe6474 Additional Declarations The authors declare no competing interests. Supplementary Files SuppFig1TRMlikeCD8Tcellsinfiltratelungtumors.jpg Fig. S1. T RM -like CD8 + T cells infiltrate lung tumors. (A) Schematic for the grafted model of KP lung adenocarcinoma. WT or genetically modified mice are injected intravenously with the KP cell line and tumoral lung microenvironment is assessed at 14 days post-injection. (B) Representative flow cytometry plots showing the proportion of PD1 + TIM3 + cells within CD69 + CD103 + CD8 + T cells or, vice versa, the proportion of CD69 + CD103 + cells within PD1 + TIM3 + CD8 + T cells in the lungs of KP tumor-bearing mice. (C) CD8 + T cells were sorted by flow cytometry from pooled lungs of 3 KP tumor-bearing mice before scRNA-seq analysis. Single cells were isolated using a 10X Chromium droplet-based approach and sequenced. (D) Violin Plots showing the scoring of indicated gene signatures across activated scRNA-seq clusters 4,11,16 . (E) Distribution of CITE-Seq gated CD44 + CD62L - PD1 + CD69 + CD103 + T RM , CD44 + CD62L - PD1 + Not(CD69 + CD103 + ) T Exh and CD44 + CD62L - PD1 - T Eff across Cd44 + Sell - clusters (F) Schematic for the endogenous lung adenocarcinoma mouse model. KRAS LSL-G12D p53 fl/fl mice were injected intratracheally with an adenovirus coding for the Cre recombinase (Adeno-Cre) 72 . Tumoral lung microenvironment was assessed at 8-weeks post-intra-tracheal adenovirus-Cre instillation. (G-H) Representative flow cytometry plots (G) and quantification (H) of lung CD44 + CD62L - CD8 + T cell populations in the endogenous KP model. (I) Boolean gating strategy was used on lung CD8 + T cells for analysis in the endogenous KP model. Pie charts represent the frequency of cells positive for the given number of co-inhibitory markers (PD1, TIGIT, LAG3, and TIM3). Arcs depict the relative frequency of cells specifically positive for PD1, TIGIT, LAG3 and/or TIM3 staining, as indicated (n = 8 mice). (J-N) Bulk RNA-sequencing analysis of sorted CD8 + T cell populations as defined in Figure 1H. (J) Volcano plot representing differentially expressed genes (DEGs) between TIM3 + T RM and TIM3 + T Exh . Genes with a Log2(fold change, FC) > ±2 and a false discovery rate (FDR)-adjusted p-value of less than 0.05 were considered significant. (K) Differential enrichment of gene pathways (Gene ontology Molecular function and Biological processes) comparing TIM3 + T RM and TIM3 + T Exh . (L) Relative expression of genes related to stemness, circulation, activation-cytotoxicity, exhaustion and tissue-residency. (M-N) GSEA of pairwise comparisons of TIM3+ TRM versus TIM3+ TEXH using genes from residency and circulating signatures4,11 (I) or of human terminally exhausted and human CXCR6+ TRM in pan-cancer context77 (J) (NES, normalized enrichment score). SuppFig2TumordraininglymphnodescontainafractionofCD103activatedCD8TcellspoisedtogenerateTRMlikeTILs.jpg Fig. S2. Tumor-draining lymph nodes contain a fraction of CD103 + activated CD8 + T cells poised to generate T RM -like TILs. (A) Representative flow cytometry plot for the gating of naïve (T Naive , CD44 - CD62L + ), activated (T Act , CD44 + CD62L - ), central memory (T CM , CD44 + CD62L + ) CD8 + T cells in KP day 14 tdLN, histogram of CD103 expression within these populations and associated quantifications (n = 6 KP mice, paired one-way anova (left graph) and paired t-test (right graph)). (B) Gating strategy to sort CD103 - T Act and CD103 + T Act for bulk RNA-sequencing. (C-D) Relative expression of genes related to stemness, circulation, activation-cytotoxicity, exhaustion, effector, cell cycle and tissue-residency for CD103 + and CD103 - T Act populations (C) and global signature scoring of these modules (D). (E) GSEA of pairwise comparisons of CD103 + T Act vs CD103 - T Act using genes from residency and circulating signatures 11 . (F) Representative flow cytometry plots for CD49a and Hobit expression in CD103 - T Act and CD103 + T Act . (G) Representative flow cytometry histogram and quantification of tdTomato-labelling within CD103 - T Act or CD103 + T Act from donor tdLN CD45.2 + CD103 ERcre x ROSA LSL-tdTomato pre-transfer (i.e. 1-day post-tamoxifen injection, day 9 post-KP injection) (n = 5 mice, paired t-test). (H) Representative flow cytometry plots showing the gating of endogenous host T RM -like TILs. SuppFig3BothXCR1andIRF4dependentDCscontributetothedifferentiationofCD103TActintdLN.jpg Fig. S3. Both XCR1 + and IRF4-dependent DCs contribute to the differentiation of CD103 + T Act in tdLN. (A) CD11c + MHCII + cells were sorted by flow cytometry from pooled tdLNs of 3 KP-bearing mice before scRNA-seq analysis. Single cells were isolated using a 10X Chromium droplet-based approach and sequenced. (B-C) Representative flow cytometry plots of resident (B) or migratory (C) DC1 and DC2s in WT, XCR1 DTA and CD11c IRF4 mice bearing KP tumors. (D) Representative flow cytometry plots and quantification of tdLN tetramer-OVA + CD8 + T cell populations in KP-OVA bearing WT, XCR1 DTA or CD11c IRF4 mice (n = 4 mice per group, one-way anova test). (E) Experimental design for the adoptive transfer of OT1 quantified in Figure 3J. CD45.1 + OT1 cells were transferred intravenously into WT, XCR1 DTA and CD11c IRF4 mice 1 day before KP-OVA tumor cells injection. 9 days later, tdLN were harvested to assess OT1 phenotype. SuppFig4CD103activatedTcellsintdLNcontainararepopulationofCXCR6TRMlikecells.jpg Fig. S4. CD103 + activated T cells in tdLN contain a rare population of CXCR6 + T RM -like cells. (A) CD8 + T cell subsets were sorted by flow cytometry from pooled tdLNs of 3 KP-bearing mice before scRNA-seq analysis. To improve the resolution of CD8 + T Act , the cellular input was enriched in T Act (80%) mixed with T CM and T Naive (20%). Single cells were isolated using a 10X Chromium droplet-based approach and sequenced. (B) Violin Plots showing the scoring of indicated gene signatures across activated clusters 4,11,16 . (C) Distribution of CITE-Seq gated CD103 - T Act and CD103 + T Act across T Act clusters. (D) Representative flow cytometry histogram and quantification of PD1 expression across tdLN T Act subsets (n = 5 mice, paired one-way ANOVA test). (E) Representative flow cytometry histogram and quantification of CD49a + cells across tdLN tetramer-OVA + T Act subsets from KP-OVA tumor bearing mice (n = 4 mice, paired one-way ANOVA test). (F) Representative flow cytometry histogram and quantification of SLAMF6 + cells across tdLN tetramer-OVA + T Act subsets from KP-OVA tumor bearing mice (n = 4 mice, paired one-way ANOVA test). (G) Venn diagram showing the overlap between genes upregulated in lung scRNA-seq cluster C1 (orange), tdLN scRNA-seq cluster C8 (dark blue) and DEGs from TIM3 + T RM > TIM3 + T Exh (green). Shared genes (38) are written on the right of the diagram. (H) Flow cytometry quantification of the expression of conserved T RM marker genes DNAM, JAML and CD49a across lung and tdLN subsets as defined previously (n = 4-5 mice, paired one-way ANOVA tests). SuppFig5EfficientgenerationofTRMlikeTILsinvolvesbothXCR1andIRF4dependentDCsattheleveloftumordraininglymphnodes.jpg Fig. S5. Efficient generation of T RM -like TILs involves both XCR1 and IRF4-dependent DCs at the level of tumor-draining lymph nodes. (A-B) Representative flow cytometry plots (A) and quantifications (B) of lung CD8 + T cell subsets in WT, Flt3l -/- or Ccr2 -/- mice bearing KP-tumors (n = 11 WT, n = 7 Ftl3l -/- and n = 8 Ccr2 -/- mice, one-way ANOVA tests). (C) Representative flow cytometry plots of lung tetramer-OVA + CD8 + T cell populations in KP-OVA bearing WT, XCR1 DTA or CD11c IRF4 mice. (D) Quantification of lung total tetramer-OVA + CD8 + T cells in WT, XCR1 DTA or CD11c IRF4 mice bearing KP-OVA tumors (n = 4 mice per group, one-way ANOVA tests). (E-F) Representative flow cytometry plots (F) and quantifications (G) of host CD45.1 + CD8 + T cell populations 7 days post-transfer of WT, XCR1 DTA or CD11c IRF4 tdLN (n = 6 WT, n = 4 XCR1 DTA and n = 6 CD11c IRF4 donor mice, one-way ANOVA tests). SuppFig6LowTCRtriggeringbymigratorycrossprimingDC2sfavorsCD103expressionwhilemigratoryDC1derivedIL12supportsCXCR6expression.jpg Fig. S6. Low TCR triggering by migratory crosspriming DC2s favors CD103 expression while migratory DC1-derived IL-12 supports CXCR6 expression. (A) Violin plot showing the RNA expression of Il12b across tdLN scRNAseq dendritic cell clusters. (B) Representative flow cytometry plots of CTV-loaded naïve OT-I cells cultured for 2 days with flow cytometry-sorted migratory DCs from tdLN of mice bearing KP-tumors or migratory and resident DC from tdLN of mice bearing KP-OVA tumors. (C) Representative flow cytometry plots of OT-I cells cultured for 4 days with SIINFEKL peptide and low affinity peptides SIIQFEKL (Q4), SIITFEKL (T4) and SIIGFEKL (G4) at 10 2 pM. See also Figure 6I. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Darasse-Jèze","email":"","orcid":"","institution":"Immunology-Immunopathology-Immunotherapy (i3), UMRS 959, Sorbonne Université, INSERM, Paris, France","correspondingAuthor":false,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Darasse-Jèze","suffix":""},{"id":443384905,"identity":"ce9a1fc4-4bf5-4ca6-b3b1-d2fc7926f47f","order_by":15,"name":"Emmanuel L Gautier","email":"","orcid":"","institution":"Sorbonne Université, INSERM UMR-S 1166, 75013 Paris, France","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"L","lastName":"Gautier","suffix":""},{"id":443384906,"identity":"95cf772e-08ec-4245-b7dc-00e600bec2d4","order_by":16,"name":"Marc Dalod","email":"","orcid":"","institution":"Aix-Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, Marseille, France.","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Dalod","suffix":""},{"id":443384907,"identity":"d70608e6-568a-43af-bbf0-50e0594a3406","order_by":17,"name":"Eric Tartour","email":"","orcid":"","institution":"Université de Paris Cité, PARCC, INSERM U970, 75006 Paris, France.","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Tartour","suffix":""},{"id":443384908,"identity":"2d98f8aa-edfc-4b20-b76e-67723330f1a3","order_by":18,"name":"Tessa Bergsbaken","email":"","orcid":"","institution":"Center for Immunity and Inflammation, Department of Pathology, Immunology, and Laboratory Medicine, New Jersey Medical School, Rutgers-the State University of New Jersey, Newark, NJ, USA.","correspondingAuthor":false,"prefix":"","firstName":"Tessa","middleName":"","lastName":"Bergsbaken","suffix":""},{"id":443384909,"identity":"9a0e0e5b-955c-4da7-9cfc-548623358649","order_by":19,"name":"Klaas P J M Van Gisbergen","email":"","orcid":"","institution":"Champalimaud Research Program, Champalimaud Centre for the Unknown, Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Klaas","middleName":"P J M Van","lastName":"Gisbergen","suffix":""},{"id":443384910,"identity":"430344a3-53f5-45ae-8d13-b8f845965470","order_by":20,"name":"Kairbaan Hodivala-Dilke","email":"","orcid":"","institution":"Barts Cancer Institute, Queen Mary University of London, London, UK","correspondingAuthor":false,"prefix":"","firstName":"Kairbaan","middleName":"","lastName":"Hodivala-Dilke","suffix":""},{"id":443384911,"identity":"b2f56ae8-de6e-45a0-b457-b9bba255928b","order_by":21,"name":"Loredana Saveanu","email":"","orcid":"","institution":"Université Paris Cité, INSERM UMR1149, CNRS EMR8252, Paris, France","correspondingAuthor":false,"prefix":"","firstName":"Loredana","middleName":"","lastName":"Saveanu","suffix":""},{"id":443384912,"identity":"72ea001f-4bfb-40b8-ab0b-49a7e8b766ee","order_by":22,"name":"Julie Helft","email":"","orcid":"","institution":"Université Paris Cité, Institut Cochin, INSERM U1016, CNRS UMR 8104, Paris, France","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"","lastName":"Helft","suffix":""},{"id":443384913,"identity":"c45ec7c6-dee5-438f-826c-2200d2f470ec","order_by":23,"name":"Federica Benvenuti","email":"","orcid":"","institution":"International Centre for Genome Engineering and Biotechnology, Trieste, Italy","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Benvenuti","suffix":""},{"id":443384914,"identity":"1dd55506-a84d-48ae-bedf-012fe81c8a2a","order_by":24,"name":"Pierre Guermonprez","email":"data:image/png;base64,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","orcid":"","institution":"Institut Pasteur, “Dendritic cells and adaptive immunity” Unit, Immunology Department, Paris, France","correspondingAuthor":true,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Guermonprez","suffix":""}],"badges":[],"createdAt":"2025-04-15 14:37:01","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6455825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6455825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80731834,"identity":"d0f296cb-cc5c-4235-ab38-71fc1b577a77","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1344800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells infiltrate lung tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) Representative flow cytometry plots and quantifications of CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e (\u003cstrong\u003eA\u003c/strong\u003e) and exhausted PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (\u003cstrong\u003eB\u003c/strong\u003e) in the lungs of tumor-free and KP tumor-bearing mice 14 days post-tumor cells injection (n = 4 tumor-free and n = 8 KP mice, unpaired t-tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram showing the overlap between CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e and PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in the lungs of KP-tumor bearing mice 14 days post-tumor cells injection (n = 10 mice).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD-G\u003c/strong\u003e) scRNAseq analysis of sorted total lung CD8\u003csup\u003e+\u003c/sup\u003e T cells from KP-bearing mice 14 days post-tumor cells injection (see also Fig.S1C).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) UMAP dimensionality reduction embedding the different clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) DotPlot showing the RNA expression of core marker genes across clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Violin Plots showing the scoring of indicated gene signatures across \u003cem\u003eCd44\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSell\u003c/em\u003e\u003csup\u003e\u003cem\u003e-\u003c/em\u003e\u003c/sup\u003e clusters\u003csup\u003e4,11,16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Distribution of \u003cem\u003eCd44\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSell\u003c/em\u003e\u003csup\u003e\u003cem\u003e-\u003c/em\u003e\u003c/sup\u003e clusters across CITE-Seq gated CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e, CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003eNot(CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e) T\u003csub\u003eExh\u003c/sub\u003e and CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003ePD1\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eEff\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) Representative flow cytometry gating strategy used to identify CD8\u003csup\u003e+\u003c/sup\u003e T cell populations infiltrating lung KP tumors.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Quantification of the proportions of CD62L\u003csup\u003e-\u003c/sup\u003eCD44\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell populations infiltrating lung KP tumors (n = 10 mice).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ-K\u003c/strong\u003e) Bulk RNA-sequencing analysis of sorted CD8\u003csup\u003e+\u003c/sup\u003eT cell populations as defined in Figure 1H.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) Signature scoring of gene modules related to stemness, circulation, activation-cytotoxicity, exhaustion and tissue-residency as shown in Figure S1L.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eK\u003c/strong\u003e) Principal-component analysis (PCA). Top high and low loading genes are indicated for PC1 and PC2.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eL\u003c/strong\u003e) Relative MFI protein expression of indicated marker genes across CD8\u003csup\u003e+\u003c/sup\u003e T cell populations as determined by flow cytometry in Figure 1H.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eM\u003c/strong\u003e) Hobit-tdTomato MFI expression across lung CD8\u003csup\u003e+\u003c/sup\u003e T cell populations quantified in Hobit-tdTomato reporter mice\u003csup\u003e18\u003c/sup\u003e bearing KP tumors (n = 5 mice, paired one-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eN\u003c/strong\u003e) Representative histogram and quantifications of CD45.2-labelled cells following intravenous injection of a CD45.2-fluorescent antibody 5min before sacrifice (n = 5 mice, paired one-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eO\u003c/strong\u003e) Representative flow cytometry plots showing the gating of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells infiltrating KP-OVA but not KP lung tumors.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eP\u003c/strong\u003e) Representative flow cytometry gating strategy showing the phenotype of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e (dark) or tetramer-OVA\u003csup\u003e- \u003c/sup\u003ecells (grey) infiltrating KP-OVA lung tumors.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eQ\u003c/strong\u003e) Quantification of the proportions of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e or tetramer-OVA\u003csup\u003e-\u003c/sup\u003e CD8\u003csup\u003e+ \u003c/sup\u003eT cell populations infiltrating lung KP-OVA tumors (n = 11 mice, paired two-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eR\u003c/strong\u003e) CD8\u003csup\u003e+\u003c/sup\u003e T cell populations were isolated from the lungs of KP-OVA bearing mice at day 14 post-injection and restimulated \u003cem\u003eex vivo\u003c/em\u003e with 1µM OVA-specific SIINFEKL peptide for 5-hours. The percentage of double-positive IFNg\u003csup\u003e+\u003c/sup\u003eTNFa\u003csup\u003e+\u003c/sup\u003e are quantified (n = 7 mice, paired one-way anova).\u003c/p\u003e","description":"","filename":"Fig1TRMlikeCD8Tcellsinfiltratelungtumors.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/ca86f7633fbf1a6dd07099ef.jpg"},{"id":80731836,"identity":"6b325241-ff90-40df-ba52-66279e5ddb9f","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":997414,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eM\u003c/strong\u003e) Representative flow cytometry plots and quantification of post-transfer CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003eT\u003csub\u003eRM\u003c/sub\u003e-like TILs differentiated from donor CD45.2\u003csup\u003e+\u003c/sup\u003e tdTomato\u003csup\u003e-\u003c/sup\u003e or tdTomato\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (n = 5 mice, paired t-test).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor-draining lymph nodes contain a fraction of CD103\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e activated CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells poised to generate T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like TILs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Experimental design for T cell trapping in the LN. Mice were injected i.v. with KP tumors. Mice were treated with or FTY720 to block lymphocyte egress from all lymphoid tissues every 2-3 days from day 1 or from day 7 post-KP-injection.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Quantification of T\u003csub\u003eRM\u003c/sub\u003e after FTY720 treatment at day 14 post-KP-injection (n = 9 vehicle, n = 4 FTY720 mice day 1 to 14 and n = 9 FTY720 day 7 to 14 mice, unpaired one-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Representative flow cytometry plots and quantification of CD103\u003csup\u003e+\u003c/sup\u003e and CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e in the LN of tumor-free and KP-tumor bearing mice (n = 3 naïve and n = 8 KP mice, unpaired t-test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Representative flow cytometry plots showing the gating of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells infiltrating KP-OVA but not KP lung tumors.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Representative flow cytometry gating strategy showing the phenotype of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e (dark) or tetramer-OVA\u003csup\u003e- \u003c/sup\u003ecells (grey) in the tdLN of KP-OVA tumor bearing mice.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Quantification of the proportions of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e or tetramer-OVA\u003csup\u003e-\u003c/sup\u003e CD8\u003csup\u003e+ \u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e populations in the tdLN of KP-OVA tumor bearing mice (n = 4 mice, paired two-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Volcano plot representing differentially expressed genes between CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e and CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e. Genes with Log2(fold change, FC) \u0026gt; ±2 and a false discovery rate (FDR)-adjusted p value of less than 0.05 were considered significant.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH-I\u003c/strong\u003e) GSEA of pairwise comparisons of CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e vs CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e using genes from residency and circulating signatures\u003csup\u003e4\u003c/sup\u003e (H) or differentially expressed genes between lung TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e and TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh \u003c/sub\u003e(I).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ-K\u003c/strong\u003e) Flow cytometry quantification of T\u003csub\u003eeffector\u003c/sub\u003e (J) and T\u003csub\u003eRM\u003c/sub\u003e (K) core markers in CD103\u003csup\u003e- \u003c/sup\u003eT\u003csub\u003eAct \u003c/sub\u003eand CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (n = 4-7 mice, paired t-tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eL-M\u003c/strong\u003e) Adoptive transfer of total tdLN from CD103\u003csup\u003eERcre\u003c/sup\u003e x ROSA\u003csup\u003eLSL-tdTomato\u003c/sup\u003e mice.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eL\u003c/strong\u003e) Experimental design. Donor CD45.2\u003csup\u003e+\u003c/sup\u003e CD103\u003csup\u003eERcre\u003c/sup\u003e x ROSA\u003csup\u003eLSL-tdTomato\u003c/sup\u003e and host WT CD45.1\u003csup\u003e+\u003c/sup\u003e mice were injected with KP cells. 8 days later, tomato labelling was induced in donor mice by intraperitoneal injection of tamoxifen. At day 9, donor tdLNs were transferred intravenously in host mice (i.e., each total donor tdLN was transferred into one recipient mice). Lungs from host mice were analyzed 5 days post-transfer to assess the progeny of tdTomato-labelled transferred cells.\u003c/p\u003e","description":"","filename":"Fig2TumordraininglymphnodescontainafractionofCD103activatedCD8TcellspoisedtogenerateTRMlikeTILs.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/9d0efdf86decc39db6c01912.jpg"},{"id":80731838,"identity":"c40cfe3f-f534-4acd-999c-88716d77a389","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1009328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoth XCR1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and IRF4-dependent DCs contribute to the differentiation of CD103\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eAct\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e in tdLN.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Representative flow cytometry plot for the gating of CD11c\u003csup\u003ehigh\u003c/sup\u003eMHCII\u003csup\u003eint\u003c/sup\u003e resident vs CD11c\u003csup\u003eint\u003c/sup\u003eMHCII\u003csup\u003ehigh\u003c/sup\u003e migratory dendritic cells in tdLNs of KP tumor-bearing mice.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Representative flow cytometry plots for the gating of migratory and resident DC1 and DC2 and quantification in tumor-free or KP tumor-bearing mice (n = 5 tumor-free and n = 5 KP mice, unpaired t-tests)\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC-E\u003c/strong\u003e) scRNAseq analysis of total CD11c\u003csup\u003e+\u003c/sup\u003eMHCII\u003csup\u003e+\u003c/sup\u003e cells from tdLN of KP-bearing mice 14 days post-tumor cells injection (see also Figure S3A).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) UMAP dimensionality reduction embedding the different clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Dot Plot showing the expression of core marker genes across clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Violin Plots showing the CITE-Seq protein expression of core DC1 (XCR1) or DC2 (SIRPa) markers across dendritic cell clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Quantification of resident DC1, resident DC2, migratory DC1 and migratory DC2s in WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice bearing KP tumors (n = 13 WT, n = 4 \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and n = 7 \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e\u0026nbsp; mice, one-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG-H\u003c/strong\u003e) Proportion of each tdLN scRNAseq dendritic cell clusters in WT or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e samples (G) or, vice versa, of WT or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e samples in each dendritic cell cluster (H).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Representative flow cytometry plots and quantification of CD103\u003csup\u003e+\u003c/sup\u003e and CD103\u003csup\u003e-\u003c/sup\u003e tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e in the LN of WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice bearing KP-OVA tumors (n = 4 WT, n = 4 \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and n = 4 \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice, one-way anova). Black tetramer-OVA\u003csup\u003e+\u003c/sup\u003e cells overlay grey tetramer-OVA\u003csup\u003e-\u003c/sup\u003e cells.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) Representative flow cytometry plots and quantifications of transferred tdLN CD103\u003csup\u003e+\u003c/sup\u003e OT1 as described in Figure S3E. Black CD45.1\u003csup\u003e+\u003c/sup\u003e OT1 overlay grey endogenous CD45.2\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e","description":"","filename":"Fig3BothXCR1andIRF4dependentDCscontributetothedifferentiationofCD103TActintdLN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/b1e2213e9c3f810cb7aff4af.jpg"},{"id":80731839,"identity":"88ef697e-6607-4de6-898c-3c7350e208f3","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1128467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD103\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e activated T cells in tdLN contain a rare population of CXCR6\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-E\u003c/strong\u003e) scRNAseq of 80%T\u003csub\u003eAct\u003c/sub\u003e and 20% T\u003csub\u003eCM\u003c/sub\u003e/T\u003csub\u003eNaive\u003c/sub\u003e sorted from the tdLN of 14 days KP-bearing mice (gating strategy in Figure S4A).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) UMAP dimensionality reduction embedding the different clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Dot Plot showing the expression of core marker genes across clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Violin Plots showing the scoring of indicated gene signatures across activated clusters\u003csup\u003e4,11,16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Distribution of T\u003csub\u003eAct\u003c/sub\u003e clusters across CITE-Seq gated CD103\u003csup\u003e- \u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e and CD103\u003csup\u003e+ \u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Projection of lung \u003cem\u003eCd44\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSell\u003c/em\u003e\u003csup\u003e\u003cem\u003e-\u003c/em\u003e\u003c/sup\u003e scRNA-seq clusters 1, 4, 7, 8 and 9 on tdLN T\u003csub\u003eAct\u003c/sub\u003e clusters 0,3,5 and 8.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Representative flow cytometry gating strategy to identify T\u003csub\u003eAct\u003c/sub\u003e populations and associated quantification (n = 12 mice).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Representative flow cytometry histograms and quantifications of SLAMF6 and Hobit-tdTomato expression across tdLN T\u003csub\u003eAct\u003c/sub\u003e subsets (n = 5 Hobit-tdTomato\u003csup\u003e \u003c/sup\u003emice, paired one-way ANOVA test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) Representative flow cytometry gating strategy showing the phenotype of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e (dark) or tetramer-OVA\u003csup\u003e- \u003c/sup\u003ecells (grey) in the tdLN of KP-OVA tumor bearing mice.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Quantification of the proportions of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e or tetramer-OVA\u003csup\u003e-\u003c/sup\u003e CD8\u003csup\u003e+ \u003c/sup\u003eT cell populations in the tdLN of KP-OVA tumor bearing mice (n = 4 mice, paired two-way anova).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ-L\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eJ\u003c/strong\u003e) and quantifications of total T\u003csub\u003eAct\u003c/sub\u003e (\u003cstrong\u003eK\u003c/strong\u003e) or T\u003csub\u003eAct\u003c/sub\u003e subsets (\u003cstrong\u003eL\u003c/strong\u003e) in WT,\u003cem\u003e XCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003emice bearing KP-tumors (n = 12 WT, n = 8 \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and n = 6 \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice, one-way ANOVA tests).\u003c/p\u003e","description":"","filename":"Fig4CD103activatedTcellsintdLNcontainararepopulationofCXCR6TRMlikecells.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/d5efc782844daaee76eb2fe2.jpg"},{"id":80731841,"identity":"9dabd0a9-6b6a-40cf-8985-00cbac345926","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":861888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEfficient generation of T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like TILs involves both XCR1 and IRF4-dependent DCs at the level of tumor-draining lymph nodes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eA\u003c/strong\u003e) and quantifications (\u003cstrong\u003eB\u003c/strong\u003e) of lung CD8\u003csup\u003e+\u003c/sup\u003e T cell populations in WT,\u003cem\u003e XCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003emice bearing KP-tumors (n = 22 WT, n = 10 \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and n = 22 \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice, one-way ANOVA tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC-D\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eC\u003c/strong\u003e) and quantifications (\u003cstrong\u003eD\u003c/strong\u003e) of lung tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell populations in WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice bearing KP-OVA tumors (n = 4 mice per group, one-way ANOVA tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Experimental design for the adoptive transfer of total tdLN. Donor CD45.2\u003csup\u003e+\u003c/sup\u003e mice (WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003e\u003cem\u003eDTA\u003c/em\u003e\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e) and host CD45.1\u003csup\u003e+\u003c/sup\u003e mice were injected with KP cells. 7 days later, total donor tdLN were transferred intravenously in host mice. Lungs from host mice were analyzed 7 days post-transfer.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF-G\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eH\u003c/strong\u003e) and quantifications (\u003cstrong\u003eI\u003c/strong\u003e) of donor CD45.2\u003csup\u003e+\u003c/sup\u003e total CD8\u003csup\u003e+\u003c/sup\u003eT cells and donor CD45.2\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e in the lungs of host CD45.1\u003csup\u003e+\u003c/sup\u003e mice bearing KP tumors 7 days post-transfer of WT,\u003cem\u003e XCR1\u003c/em\u003e\u003csup\u003e\u003cem\u003eDTA\u003c/em\u003e\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e tdLN (n = 6 WT, n = 4 \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and n = 6 \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e donor mice, one-way ANOVA test).\u003c/p\u003e","description":"","filename":"Fig5EfficientgenerationofTRMlikeTILsinvolvesbothXCR1andIRF4dependentDCsattheleveloftumordraininglymphnodes.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/5dc1cbc9182d1a5a9f77aedc.jpg"},{"id":80731844,"identity":"83dc7e70-597c-4ab8-9850-d8358795b406","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1097973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLow TCR triggering by migratory crosspriming DC2s favors CD103 expression while migratory DC1-derived IL-12 supports CXCR6 expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Representative flow cytometry plot and quantification of ZsGreen\u003csup\u003e+\u003c/sup\u003e resident and migratory DC1 and DC2 in tdLN of mice bearing KP-ZsGreen tumors (full line) (n = 5 mice, one-way ANOVA tests). Dotted lines represent control KP-injected mice for each subset.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Representative flow cytometry plot and quantification of IL-12-YFP\u003csup\u003e+\u003c/sup\u003e resident and migratory DC1 and DC2 in tdLN of IL-12\u003csup\u003eYFP\u003c/sup\u003e mice bearing KP tumors (n = 5 mice, one-way ANOVA tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Representative flow cytometry plot and quantification of H2K\u003csup\u003eb\u003c/sup\u003e-OVA expression on resident and migratory DC1 and DC2 in tdLN of mice bearing KP-OVA tumors (full line). H2Kb-OVA MFI were normalized with KP-injected mice (dotted line).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Experimental design for the \u003cem\u003eex vivo\u003c/em\u003e cocultures. Migratory DC subsets were FACS-sorted from tdLN of mice bearing KP-OVA tumors at day 9 and cocultured with CTV-loaded naïve OT-I cells for 2 days.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE-F\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eE\u003c/strong\u003e) and quantifications (\u003cstrong\u003eF\u003c/strong\u003e) of CTV-loaded naïve OT-I cells cultured for 2 days with flow cytometry-sorted migratory cDC1 or migratory DC2 from tdLN of mice bearing KP-OVA tumors in presence or not of recombinant IL-12p70 or anti-IL-12p40 neutralizing antibody.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG-H\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eG\u003c/strong\u003e) and quantifications (\u003cstrong\u003eH\u003c/strong\u003e) of CTV-loaded OT-I cells cultured for 2 days with flow cytometry-sorted migratory DC2 from tdLN of mice bearing KP tumors in presence of increasing SIINFEKL peptide concentration (1pM, 10pM, 10\u003csup\u003e2\u003c/sup\u003epM and 10\u003csup\u003e4\u003c/sup\u003epM) (n = 4 mice, paired one-way anova tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Naïve OT-I cells were cultured for 4 days with SIINFEKL peptide and low affinity peptides SIIQFEKL (Q4), SIITFEKL (T4) and SIIGFEKL (G4) at 10\u003csup\u003e2\u003c/sup\u003epM. CD44\u003csup\u003e+\u003c/sup\u003e activated OT1 and CD103 expression were quantified (n = 3 replicates, paired one-way anova tests). See also Figure S6C.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) Experimental design for the \u003cem\u003ein vivo\u003c/em\u003e IL-12 blockade. Mice were injected i.v. with KP tumors and treated with anti-IL-12p40 neutralizing antibody every 3 days from day 0 to 14.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eK\u003c/strong\u003e) Quantifications of the total number of tdLN T\u003csub\u003eAct\u003c/sub\u003e and CXCR6 expression in CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (n = 7 control and n = 5 anti-IL12 treated mice, unpaired t-tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eL-M\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eL\u003c/strong\u003e) and quantifications (\u003cstrong\u003eM\u003c/strong\u003e) of CTV-loaded OT-I cells cultured for 4 days in the presence of increasing SIINFEKL peptide concentrations (1pM, 10pM, 10\u003csup\u003e2\u003c/sup\u003epM, 10\u003csup\u003e3\u003c/sup\u003epM and 10\u003csup\u003e4\u003c/sup\u003epM) and cytokines TGF-β, IL-12 or both (n = 3 replicates). CD103 and CXCR6 expression were analyzed.\u003c/p\u003e","description":"","filename":"Fig6LowTCRtriggeringbymigratorycrossprimingDC2sfavorsCD103expressionwhilemigratoryDC1derivedIL12supportsCXCR6expression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/48f6c48340613f9793581948.jpg"},{"id":80732761,"identity":"19a5bf53-e9b8-4bd9-9aa8-3c93b7ca98ca","added_by":"auto","created_at":"2025-04-16 12:53:51","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":916355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrosspriming by migratory DC2s recapitulates the T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e transcriptional program.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) CTV-loaded naïve OT-I cells were cultured for 2 days with flow cytometry-sorted migratory DC1 or migratory DC2 from tdLN of mice bearing KP-OVA tumors. CD103\u003csup\u003e+\u003c/sup\u003e and CD103\u003csup\u003e-\u003c/sup\u003e divided OT1 activated by DC1 or DC2 were FACS-sorted and bulk-RNA sequencing was performed.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Principal-component analysis (PCA). Top high and low loading genes are indicated for PC1 and PC2.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Scoring of previously defined gene signatures for tdLN CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (bulk RNA-seq), tdLN Cluster 8 \u003cem\u003eCxcr6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e (scRNA-seq), lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs (bulk RNA-Seq) and tdLN CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct \u003c/sub\u003e(bulk RNA-seq).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) GSEA of pairwise comparisons of CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2-Act\u003c/sub\u003e vs all other OT1 populations (termed as Rest) for gene signatures of tdLN CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (bulk RNA-seq), tdLN Cluster 8 \u003cem\u003eCxcr6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e (scRNA-seq), lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs (bulk RNA-Seq) and tdLN CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct \u003c/sub\u003e(bulk RNA-seq).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Volcano plot representing differentially expressed genes (DEGs) between CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2-Act\u003c/sub\u003e and all other OT1 populations (Rest). Genes with a Log2(fold change, FC) \u0026gt; ±2 and a false discovery rate (FDR)-adjusted p-value of less than 0.05 were considered significant.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF-G\u003c/strong\u003e) GSEA of pairwise comparisons of CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2-Act\u003c/sub\u003e vs all other OT1 populations (Rest) for gene signatures of memory CD8 T cells\u003csup\u003e55\u003c/sup\u003e and SMAD4 targets \u003csup\u003e56\u003c/sup\u003e(\u003cstrong\u003eF\u003c/strong\u003e) or Hallmark MTORC1 signaling and MYC targets \u003csup\u003e57\u003c/sup\u003e(\u003cstrong\u003eG\u003c/strong\u003e) extracted from the MSigDB website.\u003c/p\u003e","description":"","filename":"Fig7CrossprimingbymigratoryDC2srecapitulatestheTRMtranscriptionalprogram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/7e72e4335c3bedff7acae952.jpg"},{"id":80734497,"identity":"64de215a-f9b9-4d19-a4bc-e23ce72d5f79","added_by":"auto","created_at":"2025-04-16 13:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9419991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/ddb32f84-d74d-480e-9349-a8d24cd116b7.pdf"},{"id":80732762,"identity":"84d5291c-8bd4-496b-86b8-86935155d57b","added_by":"auto","created_at":"2025-04-16 12:53:51","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6565039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1. T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells infiltrate lung tumors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic for the grafted model of KP lung adenocarcinoma. WT or genetically modified mice are injected intravenously with the KP cell line and tumoral lung microenvironment is assessed at 14 days post-injection.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Representative flow cytometry plots showing the proportion of PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+\u003c/sup\u003e cells within CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells or, vice versa, the proportion of CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e cells within PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in the lungs of KP tumor-bearing mice.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) CD8\u003csup\u003e+ \u003c/sup\u003eT cells were sorted by flow cytometry from pooled lungs of 3 KP tumor-bearing mice before scRNA-seq analysis. Single cells were isolated using a 10X Chromium droplet-based approach and sequenced.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Violin Plots showing the scoring of indicated gene signatures across activated scRNA-seq clusters\u003csup\u003e4,11,16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Distribution of CITE-Seq gated CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e, CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003eNot(CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e) T\u003csub\u003eExh\u003c/sub\u003e and CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003ePD1\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eEff\u003c/sub\u003e across \u003cem\u003eCd44\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSell\u003c/em\u003e\u003csup\u003e\u003cem\u003e-\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003eclusters\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Schematic for the endogenous lung adenocarcinoma mouse model.\u003cbr\u003e\nKRAS\u003csup\u003eLSL-G12D \u003c/sup\u003e\u003cem\u003ep53\u003c/em\u003e\u003csup\u003efl/fl \u003c/sup\u003emice were injected intratracheally with an adenovirus coding for the Cre recombinase (Adeno-Cre)\u003csup\u003e72\u003c/sup\u003e. Tumoral lung microenvironment was assessed at 8-weeks post-intra-tracheal adenovirus-Cre instillation.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG-H\u003c/strong\u003e) Representative flow cytometry plots (G) and quantification (H) of lung CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cell populations in the endogenous KP model.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Boolean gating strategy was used on lung CD8\u003csup\u003e+\u003c/sup\u003eT cells for analysis in the endogenous KP model. Pie charts represent the frequency of cells positive for the given number of co-inhibitory markers (PD1, TIGIT, LAG3, and TIM3). Arcs depict the relative frequency of cells specifically positive for PD1, TIGIT, LAG3 and/or TIM3 staining, as indicated (n = 8 mice).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ-N\u003c/strong\u003e) Bulk RNA-sequencing analysis of sorted CD8\u003csup\u003e+\u003c/sup\u003eT cell populations as defined in Figure 1H.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) Volcano plot representing differentially expressed genes (DEGs) between TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e and TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e. Genes with a Log2(fold change, FC) \u0026gt; ±2 and a false discovery rate (FDR)-adjusted p-value of less than 0.05 were considered significant.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eK\u003c/strong\u003e) Differential enrichment of gene pathways (Gene ontology Molecular function and Biological processes) comparing TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e and TIM3\u003csup\u003e+ \u003c/sup\u003eT\u003csub\u003eExh\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eL\u003c/strong\u003e) Relative expression of genes related to stemness, circulation, activation-cytotoxicity, exhaustion and tissue-residency.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eM-N\u003c/strong\u003e) GSEA of pairwise comparisons of TIM3+ TRM versus TIM3+ TEXH using genes from\u0026nbsp; residency and circulating signatures4,11 (I) or of human terminally exhausted and human CXCR6+ TRM in pan-cancer context77 (J) (NES, normalized enrichment score).\u003c/p\u003e","description":"","filename":"SuppFig1TRMlikeCD8Tcellsinfiltratelungtumors.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/c28a1b821b76f7a94ece5d3f.jpg"},{"id":80733348,"identity":"a7c21d82-8cb5-44c1-b6ef-ad3de5451902","added_by":"auto","created_at":"2025-04-16 13:01:51","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4167413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2. Tumor-draining lymph nodes contain a fraction of CD103\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e activated CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells poised to generate T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like TILs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Representative flow cytometry plot for the gating of naïve (T\u003csub\u003eNaive\u003c/sub\u003e, CD44\u003csup\u003e-\u003c/sup\u003eCD62L\u003csup\u003e+\u003c/sup\u003e), activated (T\u003csub\u003eAct\u003c/sub\u003e, CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e-\u003c/sup\u003e), central memory (T\u003csub\u003eCM\u003c/sub\u003e, CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e+\u003c/sup\u003e) CD8\u003csup\u003e+ \u003c/sup\u003eT cells in KP day 14 tdLN, histogram of CD103 expression within these populations and associated quantifications (n = 6 KP mice, paired one-way anova (left graph) and paired t-test (right graph)).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Gating strategy to sort CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e and CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e for bulk RNA-sequencing.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC-D\u003c/strong\u003e) Relative expression of genes related to stemness, circulation, activation-cytotoxicity, exhaustion, effector, cell cycle and tissue-residency for CD103\u003csup\u003e+\u003c/sup\u003e and CD103\u003csup\u003e- \u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e populations (\u003cstrong\u003eC\u003c/strong\u003e) and global signature scoring of these modules (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) GSEA of pairwise comparisons of CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e vs CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e using genes from residency and circulating signatures\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Representative flow cytometry plots for CD49a and Hobit expression in CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct \u003c/sub\u003eand CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Representative flow cytometry histogram and quantification of tdTomato-labelling within CD103\u003csup\u003e-\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e or CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e from donor tdLN CD45.2\u003csup\u003e+\u003c/sup\u003e CD103\u003csup\u003eERcre\u003c/sup\u003e x ROSA\u003csup\u003eLSL-tdTomato\u003c/sup\u003e pre-transfer (i.e. 1-day post-tamoxifen injection, day 9 post-KP injection) (n = 5 mice, paired t-test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) Representative flow cytometry plots showing the gating of endogenous host T\u003csub\u003eRM\u003c/sub\u003e-like TILs.\u003c/p\u003e","description":"","filename":"SuppFig2TumordraininglymphnodescontainafractionofCD103activatedCD8TcellspoisedtogenerateTRMlikeTILs.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/b979016dc8c28e8626eb6271.jpg"},{"id":80732763,"identity":"2cee8259-d32d-4c70-857d-68deaa549132","added_by":"auto","created_at":"2025-04-16 12:53:51","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":861480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3. Both XCR1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and IRF4-dependent DCs contribute to the differentiation of CD103\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eAct\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e in tdLN.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) CD11c\u003csup\u003e+\u003c/sup\u003e MHCII\u003csup\u003e+ \u003c/sup\u003ecells were sorted by flow cytometry from pooled tdLNs of 3 KP-bearing mice before scRNA-seq analysis. Single cells were isolated using a 10X Chromium droplet-based approach and sequenced.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB-C\u003c/strong\u003e) Representative flow cytometry plots of resident (B) or migratory (C) DC1 and DC2s in WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4 \u003c/em\u003e\u003c/sup\u003emice bearing KP tumors.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Representative flow cytometry plots and quantification of tdLN tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell populations in KP-OVA bearing WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice (n = 4 mice per group, one-way anova test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Experimental design for the adoptive transfer of OT1 quantified in Figure 3J. CD45.1\u003csup\u003e+ \u003c/sup\u003eOT1 cells were transferred intravenously into WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4 \u003c/em\u003e\u003c/sup\u003emice 1 day before KP-OVA tumor cells injection. 9 days later, tdLN were harvested to assess OT1 phenotype.\u003c/p\u003e","description":"","filename":"SuppFig3BothXCR1andIRF4dependentDCscontributetothedifferentiationofCD103TActintdLN.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/498b425ac13ba636ce12ee8e.jpg"},{"id":80731849,"identity":"87c3c62a-38c5-426c-bae1-dc7a77e037f2","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4554119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S4. CD103\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e activated T cells in tdLN contain a rare population of CXCR6\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) CD8\u003csup\u003e+ \u003c/sup\u003eT cell subsets were sorted by flow cytometry from pooled tdLNs of 3 KP-bearing mice before scRNA-seq analysis. To improve the resolution of CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e, the cellular input was enriched in T\u003csub\u003eAct\u003c/sub\u003e (80%) mixed with T\u003csub\u003eCM\u003c/sub\u003e and T\u003csub\u003eNaive\u003c/sub\u003e (20%). Single cells were isolated using a 10X Chromium droplet-based approach and sequenced.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Violin Plots showing the scoring of indicated gene signatures across activated clusters\u003csup\u003e4,11,16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Distribution of CITE-Seq gated CD103\u003csup\u003e- \u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e and CD103\u003csup\u003e+ \u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e across T\u003csub\u003eAct\u003c/sub\u003e clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Representative flow cytometry histogram and quantification of PD1 expression across tdLN T\u003csub\u003eAct\u003c/sub\u003e subsets (n = 5 mice, paired one-way ANOVA test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Representative flow cytometry histogram and quantification of CD49a\u003csup\u003e+\u003c/sup\u003e cells across tdLN tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e subsets from KP-OVA tumor bearing mice (n = 4 mice, paired one-way ANOVA test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Representative flow cytometry histogram and quantification of SLAMF6\u003csup\u003e+\u003c/sup\u003e cells across tdLN tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e subsets from KP-OVA tumor bearing mice (n = 4 mice, paired one-way ANOVA test).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Venn diagram showing the overlap between genes upregulated in lung scRNA-seq cluster C1 (orange), tdLN scRNA-seq cluster C8 (dark blue) and DEGs from TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM \u003c/sub\u003e\u0026gt; TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e (green). Shared genes (38) are written on the right of the diagram.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) Flow cytometry quantification of the expression of conserved T\u003csub\u003eRM\u003c/sub\u003e marker genes DNAM, JAML and CD49a across lung and tdLN subsets as defined previously (n = 4-5 mice, paired one-way ANOVA tests).\u003c/p\u003e","description":"","filename":"SuppFig4CD103activatedTcellsintdLNcontainararepopulationofCXCR6TRMlikecells.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/1823d97f0c795525d6ac2201.jpg"},{"id":80731852,"identity":"e360ea20-f2fd-4c3f-833b-dc0601ca1ab6","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2783713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S5. Efficient generation of T\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e-like TILs involves both XCR1 and IRF4-dependent DCs at the level of tumor-draining lymph nodes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eA\u003c/strong\u003e) and quantifications (\u003cstrong\u003eB\u003c/strong\u003e) of lung CD8\u003csup\u003e+\u003c/sup\u003e T cell subsets in WT,\u003cem\u003e Flt3l\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e or \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003emice bearing KP-tumors (n = 11 WT, n = 7 \u003cem\u003eFtl3l\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e and n = 8 \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e\u003cem\u003e-/-\u003c/em\u003e\u003c/sup\u003e mice, one-way ANOVA tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Representative flow cytometry plots of lung tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell populations in KP-OVA bearing WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Quantification of lung total tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells in WT, \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e mice bearing KP-OVA tumors (n = 4 mice per group, one-way ANOVA tests).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE-F\u003c/strong\u003e) Representative flow cytometry plots (\u003cstrong\u003eF\u003c/strong\u003e) and quantifications (\u003cstrong\u003eG\u003c/strong\u003e) of host CD45.1\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003eT cell populations 7 days post-transfer of WT,\u003cem\u003e XCR1\u003c/em\u003e\u003csup\u003e\u003cem\u003eDTA\u003c/em\u003e\u003c/sup\u003e or \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e tdLN (n = 6 WT, n = 4 \u003cem\u003eXCR1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and n = 6 \u003cem\u003eCD11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIRF4\u003c/em\u003e\u003c/sup\u003e donor mice, one-way ANOVA tests).\u003c/p\u003e","description":"","filename":"SuppFig5EfficientgenerationofTRMlikeTILsinvolvesbothXCR1andIRF4dependentDCsattheleveloftumordraininglymphnodes.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/100c174662db407facd37b31.jpg"},{"id":80731847,"identity":"2bae7ac7-7602-4325-a79c-2271e469ec53","added_by":"auto","created_at":"2025-04-16 12:45:51","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2452491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S6. Low TCR triggering by migratory crosspriming DC2s favors CD103 expression while migratory DC1-derived IL-12 supports CXCR6 expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Violin plot showing the RNA expression of \u003cem\u003eIl12b\u003c/em\u003e across tdLN scRNAseq dendritic cell clusters.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Representative flow cytometry plots of CTV-loaded naïve OT-I cells cultured for 2 days with flow cytometry-sorted migratory DCs from tdLN of mice bearing KP-tumors or migratory and resident DC from tdLN of mice bearing KP-OVA tumors.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Representative flow cytometry plots of OT-I cells cultured for 4 days with SIINFEKL peptide and low affinity peptides SIIQFEKL (Q4), SIITFEKL (T4) and SIIGFEKL (G4) at 10\u003csup\u003e2\u003c/sup\u003epM. See also Figure 6I.\u003c/p\u003e","description":"","filename":"SuppFig6LowTCRtriggeringbymigratorycrossprimingDC2sfavorsCD103expressionwhilemigratoryDC1derivedIL12supportsCXCR6expression.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455825/v1/a6b6a0e71e963ea600d2158f.jpg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eXCR1\u003csup\u003e+\u003c/sup\u003e and IRF4\u003csup\u003e+\u003c/sup\u003e migratory dendritic cells cooperate for the cross-priming of intratumoral CD8\u003csup\u003e+\u003c/sup\u003e T cells with a tissue-resident memory phenotype.\u003c/p\u003e","fulltext":[{"header":"MAIN","content":"\u003cp\u003eTumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells (TILs) are critical mediators of anti-tumor immunity. CD8\u003csup\u003e+\u003c/sup\u003e TILs are characterized by the pervasive expression of exhaustion-associated features initially characterized in chronic infection models, including reduced proliferative response to TCR triggering and reduced polyfunctional cytokine responses. Multiple studies suggest that the qualitative composition of CD8\u003csup\u003e+\u003c/sup\u003e TILs in solid tumors predicts clinical outcome and response to immunotherapies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In that line, a subset of CD8\u003csup\u003e+\u003c/sup\u003e TILs with a tissue-resident memory phenotype (T\u003csub\u003eRM\u003c/sub\u003e) has been identified in solid tumors including lung and breast cancer and correlates with improved prognosis and better response to immunotherapies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBona fide\u003c/em\u003e T\u003csub\u003eRM\u003c/sub\u003e have first been identified in peripheral tissues after clearance of viral infections\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Unlike other activated and memory CD8\u003csup\u003e+\u003c/sup\u003e T cells, T\u003csub\u003eRM\u003c/sub\u003e do not recirculate but persist in tissues where they ensure immunosurveillance. T\u003csub\u003eRM\u003c/sub\u003e are characterized by a specific transcriptional program including the expression of transcription factors Hobit (encoded by \u003cem\u003eZfp683\u003c/em\u003e), Runx3 and Blimp-1\u003csup\u003e4,11,12\u003c/sup\u003e. T\u003csub\u003eRM\u003c/sub\u003e express αE integrin/CD103, which associates with integrin β7 to bind E-cadherin stabilizing contacts with epithelial cells, and low levels of S1P receptors ensuring tissue residency\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Also, T\u003csub\u003eRM\u003c/sub\u003e stably express the CD69 lectin mediating S1P desensitization, further contributing to tissue residency\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilar to \u003cem\u003ebona fide\u003c/em\u003e anti-infectious T\u003csub\u003eRM\u003c/sub\u003e, T\u003csub\u003eRM\u003c/sub\u003e-like TILs harbor canonical features of residency including the concomitant expression of CD103, CD69, Hobit and reduced levels of S1PR1. A recent study in a murine model of E0771 murine breast tumors suggests that CD69\u003csup\u003e+\u003c/sup\u003eCD49a\u003csup\u003e+\u003c/sup\u003eP2Rx7\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e phenotype within CD8\u003csup\u003e+\u003c/sup\u003e TILs may not indicate actual residency within tumors\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Still, several studies show that CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like TILs cells can display potent anti-tumor function\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e as they secrete cytokines, exhibit cytotoxic activity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and can potentially generate a large repertoire of T cell subsets upon antigenic restimulation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e or after immune checkpoint blockade\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. T\u003csub\u003eRM\u003c/sub\u003e-like TILs express some features of exhausted T cells but compared to T\u003csub\u003eEX\u003c/sub\u003e-like TILs, T\u003csub\u003eRM\u003c/sub\u003e-like TILs display enhanced cytotoxicity and provide local protection in a murine model of breast cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Consistently, numerous studies have identified a strong correlation between CD8\u003csup\u003e+\u003c/sup\u003e TILs with a T\u003csub\u003eRM\u003c/sub\u003e phenotype and favourable outcomes and increased response rates to immune checkpoint inhibition in human lung cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, triple-negative breast cancer and other solid tumors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This calls for a better understanding of the mechanisms ensuring the acquisition of the T\u003csub\u003eRM\u003c/sub\u003e program during T cell activation in solid tumors.\u003c/p\u003e \u003cp\u003eIn cancer, CD8\u003csup\u003e+\u003c/sup\u003e T cells are initially activated in tumor-draining lymph nodes (tdLN) and terminally differentiate within the tumor\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, the anatomical location where T\u003csub\u003eRM\u003c/sub\u003e-like fate specification occurs in cancer is unclear (\u003cem\u003ei.e.\u003c/em\u003e, during priming in tdLN or after infiltration in the TME). In a mouse model of skin vaccination, TGF-β activated by steady-state migratory DCs imprint an epigenetic state rendering na\u0026iuml;ve T cells poised to differentiate into T\u003csub\u003eRM\u003c/sub\u003e\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Other studies identify T\u003csub\u003eRM\u003c/sub\u003e-committed circulating progenitors during vaccination or viral infection, arguing in favor of early T\u003csub\u003eRM\u003c/sub\u003e fate decision\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Anti-infectious T\u003csub\u003eRM\u003c/sub\u003e can also be generated from circulating effector cells receiving T\u003csub\u003eRM\u003c/sub\u003e instruction after tissue infiltration\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDendritic cells (DCs) are the sentinel cells of the immune system ensuring T cell activation and differentiation. Classical DCs are diverse and composed of 2 main families expressing and relying on IRF8 or IRF4 transcription factors. There is wide evidence that IRF8\u003csup\u003e+\u003c/sup\u003e, BATF3-dependent XCR1\u003csup\u003e+\u003c/sup\u003e DC1s control the activation of effector CD8\u003csup\u003e+\u003c/sup\u003e T cell responses in the context of immunogenic tumors\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and maintain the fitness of CD8\u003csup\u003e+\u003c/sup\u003e T cell effector compartment by expanding TCF1\u003csup\u003e+\u003c/sup\u003e T cells in tdLN\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Efficient phagocytosis of tumor-derived cell debris and efficient cross presentation of cell-associated antigens by MHCI\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003eand MHCII presentation to helper T cells\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e all support the paramount importance of XCR1\u003csup\u003e+\u003c/sup\u003e DC1s in shaping effector CD8\u003csup\u003e+\u003c/sup\u003e T cell responses. By contrast, the nature of antigen presenting DC subtypes controlling T\u003csub\u003eRM\u003c/sub\u003e activation during T cell priming and infiltration is less clear and has mostly been investigated in the context of vaccination or infections. Skin XCR1\u003csup\u003e+\u003c/sup\u003e Batf3-dependent DC1s are essential for the efficient activation of skin T\u003csub\u003eRM\u003c/sub\u003e after vaccination\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. IRF4-dependent DC2s are essential for the priming of influenza-specific T\u003csub\u003eRM\u003c/sub\u003e in lungs\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and the maintenance of CXCR3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e against HSV cervical infection\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Also, human IRF4\u003csup\u003e+\u003c/sup\u003e CD163\u003csup\u003e+\u003c/sup\u003eCD1c\u003csup\u003e+\u003c/sup\u003e DCs are the most potent antigen-presenting cells for T\u003csub\u003eRM\u003c/sub\u003e specification \u003cem\u003ein vitro\u003c/em\u003e or in humanized mice\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite numerous evidence of a role for CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like TILs in non-small cell lung cancer (NSCLC) immunosurveillance\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, little is known on i) the anatomical location where the T\u003csub\u003eRM\u003c/sub\u003e-like phenotype is specified and ii) if and how migratory DCs subsets participate to the induction of T\u003csub\u003eRM\u003c/sub\u003e-like CD8\u003csup\u003e+\u003c/sup\u003e T cells within tumor-draining lymph nodes.\u003c/p\u003e \u003cp\u003eIn this study, we delineate the activation of tumor-infiltrating T\u003csub\u003eRM\u003c/sub\u003e-like cells in the KP (\u003cem\u003eKras\u003c/em\u003e\u003csup\u003eG\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003eD\u003c/sup\u003e, \u003cem\u003ep53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e) model of lung adenocarcinoma. We identify an unexpected contribution of IRF4-dependent migratory DCs in activating pre-T\u003csub\u003eRM\u003c/sub\u003e precursors in tdLNs to give rise to tumor-infiltrating T\u003csub\u003eRM\u003c/sub\u003e-like TILs. In addition, we show that the low density of MHCI-peptide complexes evoking a mild TCR triggering promote specification into T\u003csub\u003eRM\u003c/sub\u003e-like phenotype.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eCD8\u003c/b\u003e \u003csup\u003e \u003cb\u003e+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eT cells with a T\u003c/b\u003e\u003csub\u003e\u003cb\u003eRM\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-like phenotype infiltrate lung tumors.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine the extent of T\u003csub\u003eRM\u003c/sub\u003e-like cells infiltrating the lungs after engraftment of tumor cells, we employ a cell line derived from \u003cem\u003eKras\u003c/em\u003e\u003csup\u003eG\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003eD\u003c/sup\u003e \u003cem\u003ep53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice\u003csup\u003e32,41,42\u003c/sup\u003e (Fig.S1A). We found that a large proportion of CD8\u003csup\u003e+\u003c/sup\u003e TILs stained positive for both CD69 and CD103 expression (27.3%, Fig.\u0026nbsp;1A) which is a classical gating for T\u003csub\u003eRM\u003c/sub\u003e phenotype. CD8\u003csup\u003e+\u003c/sup\u003e TILs also stained positive for PD1 and TIM3 (21.7%, Fig.\u0026nbsp;1B), a feature of exhausted T cells. In line with other studies highlighting the expression of exhaustion markers on T\u003csub\u003eRM\u003c/sub\u003e-like TILs\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we found that these two subsets were largely overlapping (Fig.\u0026nbsp;1C and S1B). This led us to investigate the correspondence between cell surface phenotype and transcriptional profile in unbiased settings using a single-cell RNA-sequencing (scRNA-seq) combined with CITE-Seq approach (Fig.\u0026nbsp;1D and S1C). scRNA-seq identified 5 clusters of \u003cem\u003eCd44\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSell\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u003c/em\u003e\u003c/sup\u003e cells (clusters 1, 4, 7, 8, 9) (Fig.\u0026nbsp;1D-E and S1D). Clusters 4 and 7 expressed \u003cem\u003eTcf7\u003c/em\u003e and intermediate levels of \u003cem\u003ePdcd1\u003c/em\u003e, aligning them to a T\u003csub\u003ePExh\u003c/sub\u003e phenotype. Clusters 1, 8 and 9 shared the expression of multiple exhaustion molecules (\u003cem\u003ePdcd1, Lag3, Havcr2, Tigit, Tox)\u003c/em\u003e and scored high for an exhaustion signature (Fig.\u0026nbsp;1E-F). Among these exhausted clusters, cluster 1 expressed core T\u003csub\u003eRM\u0026minus;\u003c/sub\u003eassociated genes (such as \u003cem\u003eItgae\u003c/em\u003e/CD103, \u003cem\u003eItga1\u003c/em\u003e/CD49a and \u003cem\u003eCxcr6\u003c/em\u003e) and scored high for a residency signature. Using CITE-Seq antibodies, we identified T\u003csub\u003eRM\u003c/sub\u003e-like TILs (CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003e) and found that they overlapped mostly with cluster 1 (Fig.\u0026nbsp;1G and S1E). By contrast, CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003ePD1\u003csup\u003e+\u003c/sup\u003e activated cells distinct from CD103\u003csup\u003e+\u003c/sup\u003eCD69\u003csup\u003e+\u003c/sup\u003e comprised a variety of transcriptional phenotypes including cycling and \u003cem\u003eTox\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e exhausted clusters (clusters 8 and 9).\u003c/p\u003e \u003cp\u003eTo further confirm these results, we designed a gating strategy enabling the prospective isolation of TILs populations identified above. As already shown previously, CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like TILs were largely enriched in an exhausted phenotype PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+\u003c/sup\u003e (68.0%, termed here as TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e) but also contained a population of PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e (29.2%, termed here as TIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e) and a minor fraction of PD1\u003csup\u003e\u0026minus;\u003c/sup\u003eTIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e cells (Fig.\u0026nbsp;1H-I). Conversely, we also identified PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+\u003c/sup\u003e (37.0%, termed here as TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e) and PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e (42.9%, termed here as TIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003ePExh\u003c/sub\u003e) distinct from CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e. PD1-negative CD8\u003csup\u003e+\u003c/sup\u003e T cells also comprised CD62\u003csup\u003e\u0026minus;\u003c/sup\u003eCD44\u003csup\u003e+\u003c/sup\u003e activated effector T cells (20.4%, termed here as PD1\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eEff\u003c/sub\u003e). Similar populations were found in the autochtonous KP cancer model which recapitulates the human physiopathology from hyperplasia to adenocarcinoma and the onset of T cell dysfunction\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (Fig. S1F-I).\u003c/p\u003e \u003cp\u003eBulk RNA-seq profiling further validated the correspondence between the T\u003csub\u003eRM\u003c/sub\u003e-like gate and the enrichment in T\u003csub\u003eRM\u003c/sub\u003e-associated genes and signatures (\u003cem\u003eItgae\u003c/em\u003e, \u003cem\u003eItga1, Rgs1, Cxcr6 e.g.\u003c/em\u003e) and gene ontology pathways \u0026ldquo;regulation of cell migration\u0026rdquo;, \u0026ldquo;TGF-β signalling pathway\u0026rdquo;, \u0026ldquo;R-SMAD binding\u0026rdquo; (Fig.\u0026nbsp;1J-K and S1J-M). By contrast, T\u003csub\u003eRM\u003c/sub\u003e expressed lower levels of effector-associated genes (\u003cem\u003eEomes, Gzmk\u003c/em\u003e, e.g.) and of genes ensuring recirculation (\u003cem\u003eKlf2\u003c/em\u003e and \u003cem\u003eS1pr1, e.g.\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Finally, we confirmed by FACS that T\u003csub\u003eRM\u003c/sub\u003e-associated proteins (Hobit in Hobit reporter mice\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and CD49a encoded by \u003cem\u003eItga1\u003c/em\u003e) were expressed at higher levels in TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e while they express low levels of EOMES and KLRG1 (Fig.\u0026nbsp;1L-M). Also, we found that the human T\u003csub\u003eRM\u003c/sub\u003e signature was enriched in TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e as compared to TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e (Fig.S1N), thereby validating their physiological relevance. Unlike other T cell subsets, T\u003csub\u003eRM\u003c/sub\u003e-like TILs (CD69\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+/\u0026minus;\u003c/sup\u003e) were not stained after intravenous injection of anti-CD45.2-PE antibodies labelling the vascular compartment. This demonstrates their localization in the lung parenchyma (Fig.\u0026nbsp;1N).\u003c/p\u003e \u003cp\u003eWe next focused our analysis on tumor antigen-specific cells after engraftment of a KP cell line expressing the ovalbumin protein (KP-OVA, Fig.\u0026nbsp;1O-P). Tumor-antigen specific tetramer-OVA\u003csup\u003e+\u003c/sup\u003e cells encompassed similar subsets but displayed a significant enrichment in TIM3-expressing cells including TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eEXH\u003c/sub\u003e and TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e, unlike PD1\u003csup\u003e\u0026minus;\u003c/sup\u003eT\u003csub\u003eEFF\u003c/sub\u003e that were mostly found in the tetramer-OVA\u003csup\u003e\u0026minus;\u003c/sup\u003e fraction (Fig.\u0026nbsp;1Q). Functionally, we found that both TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e (15.5%) and TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eEXH\u003c/sub\u003e (11.9%) were able to produce pro-inflammatory cytokines IFNg and TNFa after \u003cem\u003eex vivo\u003c/em\u003e antigen-specific restimulation with the OVA\u003csub\u003e257\u0026thinsp;\u0026minus;\u0026thinsp;264\u003c/sub\u003e peptide (Fig.\u0026nbsp;1R). Of note, TIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e (5.7%) but not TIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003ePEXH\u003c/sub\u003e (1.3%) displayed some levels of polyfunctionality.\u003c/p\u003e \u003cp\u003eAltogether, we conclude that the transplanted KP model recapitulates some features of human NSCLC in terms of dysfunctionality and T\u003csub\u003eRM\u003c/sub\u003e-like phenotype\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our results define T\u003csub\u003eRM\u003c/sub\u003e-like TILs as a specific, separable subset within a larger population of CD8\u003csup\u003e+\u003c/sup\u003e TILs expressing co-inhibitory receptors. Therefore, this model is suitable to investigate how the T\u003csub\u003eRM\u003c/sub\u003e-like phenotype is instructed in comparison to other PD1\u003csup\u003e+\u003c/sup\u003e TILs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTumor-draining lymph nodes contain a fraction of CD103\u003c/b\u003e \u003csup\u003e \u003cb\u003e+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eactivated CD8\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eT cells poised to generate T\u003c/b\u003e\u003csub\u003e\u003cb\u003eRM\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-like TILs.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe anatomical localization where T\u003csub\u003eRM\u003c/sub\u003e specification occurs is still a matter of debate. Therefore, we wondered whether CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like TILs require egress from tumor-draining mediastinal lymph nodes (tdLN) or differentiate \u003cem\u003ein situ\u003c/em\u003e from a T cell pool pre-existing tumor development. To address this, we inhibited lymph node egress using S1PR1 blockade with fingolimod (FTY720) and analysed T\u003csub\u003eRM\u003c/sub\u003e-like TILs at day 14 post-tumor engraftment (Fig.\u0026nbsp;2A). We found that FTY720 administration from day 1 to 14 or from day 7 to 14 blocked T\u003csub\u003eRM\u003c/sub\u003e infiltration in the lungs (Fig.\u0026nbsp;2B). We conclude that tdLNs represent an obligate site during the development of CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like TILs.\u003c/p\u003e \u003cp\u003eHaving established a role for tdLN in T\u003csub\u003eRM\u003c/sub\u003e infiltration, we next sought to characterize tdLN CD8\u003csup\u003e+\u003c/sup\u003e T cells after KP tumor engraftment. We found that KP tumor development induced the expansion of CD62L\u003csup\u003e\u0026minus;\u003c/sup\u003eCD44\u003csup\u003e+\u003c/sup\u003e activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (\u0026ldquo;T\u003csub\u003eAct\u003c/sub\u003e\u0026rdquo;) (Fig.\u0026nbsp;2C). Because CD103 expression has been widely used as a marker of T\u003csub\u003eRM\u003c/sub\u003e biased T cells in other settings\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, we analysed CD103 expression and found that 41.3% of T\u003csub\u003eAct\u003c/sub\u003e expressed CD103 (Fig.\u0026nbsp;2C and S2A). CD103\u003csup\u003e+\u003c/sup\u003eT\u003csub\u003eACT\u003c/sub\u003e population was similarly observed in OVA-specific T\u003csub\u003eAct\u003c/sub\u003e after KP-OVA engraftment (15.0%) (Fig.\u0026nbsp;2D-F). Bulk RNA-sequencing analysis showed that CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were enriched in T\u003csub\u003eRM\u003c/sub\u003e core genes such as \u003cem\u003eCxcr6, Fgl2, Itga1\u003c/em\u003e and \u003cem\u003eInnp4b\u003c/em\u003e (Fig.\u0026nbsp;2G and S2B-D). On the contrary, CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eact\u003c/sub\u003e expressed more T\u003csub\u003eeffector\u003c/sub\u003e (\u003cem\u003eEomes\u003c/em\u003e, \u003cem\u003eGzmk\u003c/em\u003e) and proliferation-associated genes (\u003cem\u003eMki67\u003c/em\u003e). Consistently, GSEA analysis revealed that lymphocyte residency signatures were enriched in CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e while signature of genes associated to circulating lymphocytes were enriched in CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e\u003csup\u003e4,11\u003c/sup\u003e (Fig.\u0026nbsp;2H and S2E). CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were also enriched for genes associated to KP-bearing lung TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e while CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were enriched for genes associated to KP bearing-lung TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e (Fig.\u0026nbsp;2I). At the protein level, CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e expressed lower levels of effector-associated markers (T-BET, EOMES, KLRG1 and CX3CR1) (Fig.\u0026nbsp;2J and S2F) but higher T\u003csub\u003eRM\u003c/sub\u003e-enriched markers such as CD49a, Hobit, CTLA4 and CXCR6 (Fig.\u0026nbsp;2K). We therefore hypothesized that CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e are biased to give rise to lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs. To assess the relationship between CD103\u003csup\u003e+\u003c/sup\u003e T cells in tdLN and lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs, we took advantage of the CD103-cre\u003csup\u003eERT2\u003c/sup\u003e-ROSA\u003csup\u003eLSL\u0026minus;tdTomato\u003c/sup\u003e reporter mouse\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (hereafter referred as CD103-tdTomato). Tamoxifen induction 8 days after KP injection efficiently labelled CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (45.6%) but not CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (1.5%) in tdLNs from CD103-tdTomato donor mice (Fig.S2G). We thus transferred whole tdLN containing genetically labelled CD103\u003csup\u003e+\u003c/sup\u003e T cells in KP tumor-bearing CD45.1\u003csup\u003e+\u003c/sup\u003e WT recipient mice (Fig.\u0026nbsp;2L). We assessed T\u003csub\u003eRM\u003c/sub\u003e phenotype in the progeny of tdTomato\u003csup\u003e\u0026minus;\u003c/sup\u003e or tdTomato\u003csup\u003e+\u003c/sup\u003e transferred tdLN CD8\u003csup\u003e+\u003c/sup\u003e T cells in the lungs as controlled in endogenous CD8\u003csup\u003e+\u003c/sup\u003e T cells from the host mice (Fig.S2G). We found that tdTomato\u003csup\u003e+\u003c/sup\u003e transferred T cells exclusively gave rise to T\u003csub\u003eRM\u003c/sub\u003e-like but not other CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e TILs (100.0%, Fig.\u0026nbsp;2M). By contrast, only a small fraction of tdTomato\u003csup\u003e\u0026minus;\u003c/sup\u003e transferred CD8\u003csup\u003e+\u003c/sup\u003e T cells differentiated into T\u003csub\u003eRM\u003c/sub\u003e-like TILs (8.5% of CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e TILs).\u003c/p\u003e \u003cp\u003eThese results demonstrate that T\u003csub\u003eRM\u003c/sub\u003e specification is initiated in tdLNs where CD103\u003csup\u003e+\u003c/sup\u003e T cells are pre-committed to differentiate into T\u003csub\u003eRM\u003c/sub\u003e-like TILs after seeding tumor-bearing lungs.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBoth XCR1\u003csup\u003e+\u003c/sup\u003e and IRF4-dependent DCs contribute to the differentiation of CD103\u003csup\u003e+\u003c/sup\u003eT\u003csub\u003eAct\u003c/sub\u003e in tdLN\u003c/h2\u003e \u003cp\u003eBecause DCs are the main tumor antigen-presenting cells in the LN, we assessed their requirements for the generation of CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e in tdLN. We found that KP tumor development triggered a massive increase of both resident and migratory XCR1\u003csup\u003e+\u003c/sup\u003e DC1s and CD11b\u003csup\u003e+\u003c/sup\u003e DC2s compared to mediastinal LN from na\u0026iuml;ve mouse (Fig.\u0026nbsp;3A-B). To better characterise DC populations in tdLN, we performed scRNA-Seq of CD11c\u003csup\u003e+\u003c/sup\u003eMHCII\u003csup\u003e+\u003c/sup\u003e cells 14 days after KP tumor engraftment (Fig.\u0026nbsp;3C and S3A). We identified 2 clusters of XCR1\u003csup\u003e+\u003c/sup\u003e resident DC1 (clusters 7, 14) and 3 clusters of SIRPa\u003csup\u003e+\u003c/sup\u003e resident DC2 (clusters 3, 9 ,17). Within the clusters expressing high levels of maturation markers (\u003cem\u003eCcr7\u003c/em\u003e, \u003cem\u003eCd80, Cd86, Cd200, Cd274, Pdcd1lg2 e.g.\u003c/em\u003e) (clusters 2, 5, 8), CITE-Seq protein expression of XCR1 and SIRPa enabled to discriminate migratory DC1 (cluster 2) from migratory DC2 (cluster 8), although cluster 5 was composed of a mix of migratory DC1 and DC2 as reported earlier\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;3D-E). To assess the contribution of DC subsets in T\u003csub\u003eRM\u003c/sub\u003e differentiation, we engrafted KP tumors in \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003ecre\u003c/sup\u003ex\u003cem\u003eRosa\u003c/em\u003e\u003csup\u003eLsL\u0026thinsp;\u0026minus;\u0026thinsp;DTA\u003c/sup\u003e (\u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e;\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e) or \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003ecre\u003c/sup\u003ex\u003cem\u003eIrf4\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e (\u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e;\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e mice. \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e mice were deficient in XCR1\u003csup\u003e+\u003c/sup\u003e DCs while \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice had reduced numbers of migratory but not resident DC2s (Fig.\u0026nbsp;3F and S3B-C). By performing scRNA-Seq in \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice, we confirmed that migratory DC2 cluster 2 was reduced as compared to WT mice, while most clusters including resident DC2 clusters 3, 9 and 17 were left unaffected (Fig.\u0026nbsp;3G-H). Consistent with a well-established role of DC1s in activating CD8\u003csup\u003e+\u003c/sup\u003e T cell responses\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, we found that tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were severely reduced in \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e mice bearing KP-OVA tumors (Fig.S3D), including CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (Fig.\u0026nbsp;3I). Conversely, depletion of IRF4-dependent DCs in \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e did not impact tetramer-OVA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell activation (Fig.S3D) but led to a selective decrease in tetramer-OVA\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e numbers (Fig.\u0026nbsp;3I). Similar results were found after adoptive transfer of OVA-specific OT1 T cells prior to tumor engraftment (Fig.S3E). Analysis of OT1 in tdLN at day 9 revealed impaired CD103 expression in both \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;3J).\u003c/p\u003e \u003cp\u003eAltogether, these results show that both XCR1\u003csup\u003e+\u003c/sup\u003e DC1s and IRF4-dependent DC2s are required for the crosspriming of tumor-specific CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e in tdLN that are poised to generate T\u003csub\u003eRM\u003c/sub\u003e-like TILs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCD103\u003c/b\u003e \u003csup\u003e \u003cb\u003e+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eactivated T cells in tdLN contain a rare population of CXCR6\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eRM\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-like cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo better characterize the heterogeneity of tdLN activated CD103\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells giving rise to lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs in unbiased settings, we performed single-cell RNA-sequencing of a mixture of 80% of T\u003csub\u003eAct\u003c/sub\u003e and 20% of T\u003csub\u003eNaive\u003c/sub\u003e and T\u003csub\u003eCM\u003c/sub\u003e (Fig.\u0026nbsp;4A and S4A). We identified 4 clusters of activated \u003cem\u003eCd44\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e\u003cem\u003eSell\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u003c/em\u003e\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (clusters 0, 3, 5 and 8)(Fig.\u0026nbsp;4B). Cluster 5 was highly proliferating (\u003cem\u003eMki67\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e). Among the remaining activated clusters, \u003cem\u003eItgae\u003c/em\u003e expression was restricted to clusters 0 and 8. Strikingly, cluster 8 expressed high levels of \u003cem\u003eCxcr6\u003c/em\u003e and scored high for residency but low for circulating signatures or stemness-associated genes (\u003cem\u003eSlamf6, Tcf7\u003c/em\u003e)(Fig.\u0026nbsp;4B-C and S4B). Consistently, this cluster scored the highest for genes associated to KP-bearing lung TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e (Fig.S4B). CITE-Seq gating also showed that CD103 was most highly expressed by cluster 8 (Fig.S3C). However, the majority of CD103\u003csup\u003e+\u003c/sup\u003e TAct cells were found in cluster 0 since cluster 8 contains very few cells (Fig.\u0026nbsp;3D). To further compare single-cell RNA-seq clusters obtained in both lung and tdLN locations, we projected lung clusters on the tdLN dataset. We found that T\u003csub\u003eRM\u003c/sub\u003e cluster 1 from the lung was almost exclusively projected on \u003cem\u003eCxcr6\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e cluster 8 from the tdLN (84.25%, Fig.\u0026nbsp;4E), thereby validating their transcriptional alignment. Taking advantage of this information, we refined our FACS gating strategy to separate previously gated CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e into CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e\u0026minus;\u003c/sup\u003e and CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (Fig.\u0026nbsp;4F). We found that CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e expressed low levels of SLAMF6 but higher levels of PD1 and Hobit than CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e or CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e (Fig.\u0026nbsp;3G and S4D). CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were found highly enriched within tumor antigen-specific tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e from KP-OVA bearing mice (2.1% of tetramer-OVA\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e vs 15.2% of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e) unlike CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e\u0026minus;\u003c/sup\u003e that were almost exclusively tetramer-OVA\u003csup\u003e\u0026minus;\u003c/sup\u003e (31.5% of tetramer-OVA\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e vs 0.9% of tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e)(Fig.\u0026nbsp;4H-I). CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e encompassed both tetramer-OVA\u003csup\u003e+\u003c/sup\u003e and tetramer-OVA\u003csup\u003e\u0026minus;\u003c/sup\u003e cells. Phenotyping tetramer-OVA\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e led to similar results compared to endogenous T\u003csub\u003eAct\u003c/sub\u003e as CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e expressed high levels of T\u003csub\u003eRM\u003c/sub\u003e marker CD49a but low levels of SLAMF6 (Fig.S4E-F). In order to identify conserved T\u003csub\u003eRM\u003c/sub\u003e markers across anatomical locations, we combined our different datasets and found that 38 genes overlapped between lung T\u003csub\u003eRM\u003c/sub\u003e cluster 1, tdLN \u003cem\u003eCxcr6\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e cluster 8 and genes associated to lung TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e as compared to TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e (Fig.S4G). We validated at the level of protein expression that JAML, DNAM (encoded by \u003cem\u003eCd226\u003c/em\u003e) and CD49a were indeed expressed at higher levels by lung T\u003csub\u003eRM\u003c/sub\u003e and tdLN CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e as compared to other T cell populations (Fig.S4H). These results demonstrate the existence of a rare population of CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like cells in tdLN. We thus wondered whether this population would be similarly affected by lack of DC populations in \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice bearing KP tumors. Consistent with previous results obtained in antigen-specific assays (Fig.\u0026nbsp;3I-J), we found that all T\u003csub\u003eAct\u003c/sub\u003e populations including CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were severely reduced in \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e mice (Fig.\u0026nbsp;4J-L). By contrast, total T\u003csub\u003eAct\u003c/sub\u003e numbers were similar in \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice but both CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e\u0026minus;\u003c/sup\u003e and CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e were reduced.\u003c/p\u003e \u003cp\u003eAltogether, these results demonstrate that CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e contain a rare population of CXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like cells and that both XCR1\u003csup\u003e+\u003c/sup\u003e and IRF4-dependent migratory DCs are required for the differentiation of CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like cells in tdLN.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEfficient generation of T\u003c/b\u003e \u003csub\u003e \u003cb\u003eRM\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e-like TILs involves both XCR1\u003c/b\u003e \u003csup\u003e \u003cb\u003e+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand IRF4-dependent DCs at the level of tdLN.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe next sought to address the consequences of impaired T\u003csub\u003eRM\u003c/sub\u003e specification at the level of tdLN on the generation of lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs.\u003c/p\u003e \u003cp\u003eIn a first approach, we engrafted KP tumors in WT, \u003cem\u003eFlt3L\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e deficient in all classical DC subsets, and \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice deficient in inflammatory monocytes and assessed CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like content. In accordance with a non-redundant role of classical DCs in CD8\u003csup\u003e+\u003c/sup\u003e T cell immunity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, we found that CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like infiltration was deeply inhibited in DC-deficient \u003cem\u003eFlt3L\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.S5A-B). However, T\u003csub\u003eRM\u003c/sub\u003e-like TILs accrual was not modified in \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice, excluding a role for monocyte-derived/inflammatory phagocytes in T\u003csub\u003eRM\u003c/sub\u003e generation. To further define the role of XCR1 and IRF4-dependent DCs, we analysed the differentiation of polyclonal CD8\u003csup\u003e+\u003c/sup\u003eT cells in \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice engrafted with KP tumors. In \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e mice, a major defect in the infiltration of all CD8\u003csup\u003e+\u003c/sup\u003e T cells subsets was observed (Fig.\u0026nbsp;5A-B). These results highlight general defects in CD8\u003csup\u003e+\u003c/sup\u003e T cell activation in DC1-lacking mice\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and correlate with the deep impairment in T cell activation observed in tdLN (Fig.\u0026nbsp;4J-L). By contrast, \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice only had a partial and selective defect in both TIM3\u003csup\u003e\u0026minus;\u003c/sup\u003e and TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e populations while other populations of CD8\u003csup\u003e+\u003c/sup\u003e TILs were left unaffected.\u003c/p\u003e \u003cp\u003eNext, we intended to address the role of DC subsets on CD8\u003csup\u003e+\u003c/sup\u003e T cell populations in tumor antigen-specific settings using KP-OVA tumors engrafted in WT, \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e and \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice. We found that after 14 days, tetramer\u003csup\u003e+\u003c/sup\u003e OVA-specific lung CD8\u003csup\u003e+\u003c/sup\u003eT cells were severely reduced in \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e but not in \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;5C-D and S5C-D). However, tetramer\u003csup\u003e+\u003c/sup\u003e OVA-specific CD8\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like TILs were reduced in \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice and they represented the most impacted population of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells. Of note, PD1\u003csup\u003e\u0026minus;\u003c/sup\u003e effector CD8\u003csup\u003e+\u003c/sup\u003eT cells were not impacted in \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, evidencing a differential regulation compared to T\u003csub\u003eRM\u003c/sub\u003e in antigen-specific context as well.\u003c/p\u003e \u003cp\u003eWe have shown above that both migratory DC subsets promote pre-T\u003csub\u003eRM\u003c/sub\u003e generation in tdLN (Fig.\u0026nbsp;4J-L). To assess the contribution of this tdLN-localized phenomenon on lung T\u003csub\u003eRM\u003c/sub\u003e accrual, we adoptively transferred total cell suspensions obtained from tdLN of WT, \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e mice to KP-bearing WT host mice (Fig.\u0026nbsp;5E). This setting allowed us to normalise the lung tumor-microenvironment in recipient mice as controlled by similar responses in endogenous CD45.2\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig.S5E-F). As anticipated, total donor CD8\u003csup\u003e+\u003c/sup\u003eT cells from \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e but not \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e tdLNs were decreased in host lungs 7 days post-transfer (Fig.\u0026nbsp;5F-G). However, the generation of T\u003csub\u003eRM\u003c/sub\u003e-like TILs was impaired when tdLN progenitors originated from \u003cem\u003eXcr1\u003c/em\u003e\u003csup\u003eDTA\u003c/sup\u003e or \u003cem\u003eCd11c\u003c/em\u003e\u003csup\u003e\u003cem\u003eIrf\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e tdLNs.\u003c/p\u003e \u003cp\u003eThese results demonstrate that T\u003csub\u003eRM\u003c/sub\u003e-like TILs activation necessitate the presence of both migratory XCR1\u003csup\u003e+\u003c/sup\u003e DC1s and IRF4\u003csup\u003e+\u003c/sup\u003e CD11b\u003csup\u003e+\u003c/sup\u003e migratory DC2s in tdLN. By contrast, the generation of non-T\u003csub\u003eRM\u003c/sub\u003e, exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells or PD1\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell effector populations is more selectively dependent on DC1s and less impacted by DC2 deficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLow TCR triggering by migratory crosspriming DC2s favors CD103 expression while migratory DC1-derived IL-12 supports CXCR6 expression.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBecause both types of migratory DCs were required for T\u003csub\u003eRM\u003c/sub\u003e-specification in the LN, we wondered which could be the mechanism accounting for their respective contribution. We first analysed the ability of both types of migratory DCs to uptake tumor antigens. To do so, we engrafted mice with KP tumors expressing the lysosomal-stable ZsGreen fluorescent protein. We found that both XCR1\u003csup\u003e+\u003c/sup\u003e and IRF4-dependent CD11b\u003csup\u003e+\u003c/sup\u003e migratory but not resident DCs had some levels of antigen uptake, although XCR1\u003csup\u003e+\u003c/sup\u003e migratory DCs were more able to transport tumor antigens to the LN (52.3% ZsGreen\u003csup\u003e+\u003c/sup\u003e MigDC1s vs 13.4% ZsGreen\u003csup\u003e+\u003c/sup\u003e MigDC2s) (Fig.\u0026nbsp;6A). We next wondered if migratory DCs had acquired an immunogenic phenotype. Using IL-12\u003csup\u003eYFP\u003c/sup\u003e reporter mice (Fig.\u0026nbsp;6B) and scRNAseq on migratory DCs (Fig.S6A), we showed that migratory DC1 expressed much higher levels of IL-12 (78.3%) than migratory DC2 (15.9%). Therefore, we conclude that XCR1\u003csup\u003e+\u003c/sup\u003e migratory DCs had undergone some level of activation supporting their ability to prime T cells. We next assessed the ability of both types of migratory DCs to cross-present tumor-associated antigens to CD8\u003csup\u003e+\u003c/sup\u003e T cells through MHC-I. To do so, we performed a staining on tdLN DCs from KP or KP-OVA tumors with an antibody detecting H2-K\u003csup\u003eb\u003c/sup\u003e-SIINFEKL (OVA immunodominant epitope) complexes arising from the cross-presentation of KP-associated OVA protein\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. We found that both XCR1\u003csup\u003e+\u003c/sup\u003e and CD11b\u003csup\u003e+\u003c/sup\u003e migratory DCs cross-presented tumor-associated OVA antigen, although XCR1\u003csup\u003e+\u003c/sup\u003e DC1s did it more efficiently than CD11b\u003csup\u003e+\u003c/sup\u003e DC2s (319.8 vs 132.8 MFI H2K\u003csup\u003eb\u003c/sup\u003e-OVA) (Fig.\u0026nbsp;6C). Of note, no signal was detected on resident DCs. Functionally, we evaluated the cross-presentation ability of migratory or resident DCs sorted from KP-OVA tdLNs by co-cultivating them with Cell trace violet (CTV)-loaded OVA-specific OT-1 CD8\u003csup\u003e+\u003c/sup\u003e T cells. Migratory but not resident DCs were able to induce the proliferation of OT-1 and upregulation of CD44 (66.4% vs 2.82% of CTV\u003csup\u003elow\u003c/sup\u003eCD44\u003csup\u003e+\u003c/sup\u003e) (Fig.S6B). We thus focused our study on migratory DC subsets only. FACS-sorted migratory XCR1\u003csup\u003e+\u003c/sup\u003e DC1s and CD11b\u003csup\u003e+\u003c/sup\u003e DC2s from KP-OVA-bearing tdLNs were co-cultured with CTV-loaded OT-1 for 2 days (Fig.\u0026nbsp;6D). We found that DC1 induced a vigorous proliferation of OT-1 that were almost entirely CD103-negative while they expressed homogenously the CD44 activation marker (Fig.\u0026nbsp;6E-F). By contrast, activation by CD11b\u003csup\u003e+\u003c/sup\u003e DC2s was milder but CD44\u003csup\u003e+\u003c/sup\u003e proliferating OT-1 expressed high levels of CD103. We wondered which factor could explain the differential ability of CD11b\u003csup\u003e+\u003c/sup\u003e DC2s to maintain CD103 expression. We showed that IL-12 was highly produced by migratory DC1 (Fig.\u0026nbsp;6B) and IL-12 silences TGF-β activated CD103 expression via TBET competition at SMAD binding elements within CD103 promoter\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Therefore, we tested the role of IL-12 \u003cem\u003eex vivo\u003c/em\u003e and found that addition of recombinant IL-12 blunted CD103 expression without affecting the proliferation of na\u0026iuml;ve OT-1 by KP-OVA-loaded CD11b\u003csup\u003e+\u003c/sup\u003e DC2s (Fig.\u0026nbsp;6E-F). By contrast, IL12 blockade during DC1-dependent activation did not restore CD103 on proliferating T cells. We therefore hypothesized that another parameter, distinct from IL-12, was associated to cross-priming by DC1s and limited CD103 expression in activated T cells. We hypothesized that higher levels of MHCI-peptide complexes on DC1s (Fig.\u0026nbsp;6C) could intrinsically limit CD103 persistence on activated T cells. To test this, we exposed CD11b\u003csup\u003e+\u003c/sup\u003e DC2s sorted from tdLNs to increasing amounts of SIINFEKL synthetic peptide ex vivo in the presence of na\u0026iuml;ve OT1s. We found that peptide add-back was sufficient to impair CD103 expression in CD44\u003csup\u003e+\u003c/sup\u003eCTV\u003csup\u003elow\u003c/sup\u003e OT-1 T cells while it increased proliferative expansion, thereby mimicking T cell activation by XCR1\u003csup\u003e+\u003c/sup\u003e DC1s (Fig.\u0026nbsp;6G-H). We next addressed the role of TCR avidity for MHCI-peptide complexes in controlling CD103 persistence on activated T cells. To this end, we primed na\u0026iuml;ve OT1s with various altered peptide ligands acting as weaker agonists for the OT1 TCR\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. We observed a decrease of OT-I activation with low affinity peptides (SIINFEKL\u0026thinsp;\u0026gt;\u0026thinsp;Q4\u0026thinsp;\u0026gt;\u0026thinsp;T4\u0026thinsp;\u0026gt;\u0026thinsp;G4), associated to an increased expression of CD103 on CD44\u003csup\u003e+\u003c/sup\u003e divided OT-I cells (Fig.\u0026nbsp;6I and S6C). Overall, we conclude that a mild TCR triggering and low levels of IL-12 are both required for the persistence of CD103 on T cells during their cross-priming by migratory DCs. However, CXCR6 expression is also a hallmark of early-activated pre-T\u003csub\u003eRM\u003c/sub\u003es in tdLNs (Fig.\u0026nbsp;4). We found that IL-12 blockade \u003cem\u003ein vivo\u003c/em\u003e blunted T cell activation while inhibiting CXCR6 expression in tdLNs (Fig.\u0026nbsp;6J-K). Consistently, IL-12 also promoted CXCR6 expression during the priming of na\u0026iuml;ve OT1 \u003cem\u003ein vitro\u003c/em\u003e (Fig.\u0026nbsp;6L-M). This shows that IL-12, despite its inhibitory effect on CD103, is required to acquire the full T\u003csub\u003eRM\u003c/sub\u003e phenotype. Given the paramount role of TGF-β signaling in the specification of T\u003csub\u003eRM\u003c/sub\u003e\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, we further tested the role of IL-12 in combination with TGF-β \u003cem\u003ein vitro\u003c/em\u003e. We found that IL-12 inhibited TGF-β signaling supporting CD103 expression (Fig.\u0026nbsp;6M). Vice versa, TGF-β counteracted i) TCR-induced CD103 inhibition and ii) IL-12-induced CXCR6 expression (Fig.\u0026nbsp;6M). As a result, optimal generation of CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e OT1s required both IL-12 and TGF-β and intermediate levels of antigen presentation ensuring mild TCR triggering (Fig.\u0026nbsp;6L-M).\u003c/p\u003e \u003cp\u003eAltogether, we conclude that \u003cb\u003ei\u003c/b\u003e) both XCR1\u003csup\u003e+\u003c/sup\u003e and CD11b\u003csup\u003e+\u003c/sup\u003e migratory DCs, (but not resident DCs) cross-present KP-associated antigens to T cells in tdLNs. \u003cb\u003eii\u003c/b\u003e) XCR1\u003csup\u003e+\u003c/sup\u003e DC1s are the most efficient at antigen uptake, cross-presentation and in triggering CD8\u003csup\u003e+\u003c/sup\u003e T cell proliferative expansion concomitant with the loss of CD103 which is dependent on both efficient TCR triggering and IL-12 release. \u003cb\u003eiii\u003c/b\u003e) IL-12 low, CD11b\u003csup\u003e+\u003c/sup\u003e DC2s are inefficient at uptake, cross-presentation and in triggering CD8\u003csup\u003e+\u003c/sup\u003e T cell proliferative expansion but the low efficiency of antigen cross-presentation by CD11b\u003csup\u003e+\u003c/sup\u003e migratory DC2s is required for CD103 persistence on CD44\u003csup\u003e+\u003c/sup\u003e activated T cells by delivering a milder TCR signalling as compared to XCR1\u003csup\u003e+\u003c/sup\u003e DC1s. \u003cb\u003eiv\u003c/b\u003e) despite its inhibitory effect on CD103 expression, IL-12 is crucially needed for CXCR6 expression.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCross-priming by migratory DC2s recapitulates the T\u003c/b\u003e \u003csub\u003e \u003cb\u003eRM\u003c/b\u003e \u003c/sub\u003e \u003cb\u003etranscriptional program.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe next sought to analyse how the identity of migratory DCs subset controlled the transcriptional phenotype of CD8\u003csup\u003e+\u003c/sup\u003e T cells during cross-priming. To this end, we performed bulk RNAseq on OT1 cells that did or did not upregulate CD103 after \u003cem\u003eex vivo\u003c/em\u003e crosspriming by migratory DC1 or DC2 sorted from tdLN of KP-OVA bearing mice (Fig.\u0026nbsp;7A). PCA analysis revealed a stronger segregation between DC1 and DC2-activated OT1 across PC1 (84% of the variance) compared to CD103 expression (Fig.\u0026nbsp;7B). We conclude that the identity of migratory DCs cross-priming T cells is the main factor driving their transcriptional phenotype. PC1 was positively driven by T\u003csub\u003eRM\u003c/sub\u003e-associated genes (\u003cem\u003eItgae\u003c/em\u003e, \u003cem\u003eCd226 e.g.\u003c/em\u003e) and stemness genes (\u003cem\u003eKlf2, Sell, Slamf6, Il7r, e.g.\u003c/em\u003e) and negatively driven by effector-related genes (\u003cem\u003eIl12rb1 and Il12rb2, Tbx21, Gzmb, Eomes, e.g.\u003c/em\u003e) and proliferation associated transcription factor \u003cem\u003eMyc\u003c/em\u003e. T\u003csub\u003eRM\u003c/sub\u003e-associated signatures found in tdLNs pre-T\u003csub\u003eRM\u003c/sub\u003es (by scRNASeq, DEG Cluster 8 \u003cem\u003eCxcr6\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e or bulk RNA-Seq, CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e \u0026gt; CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e) or lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs (bulk RNA-Seq, TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e \u0026gt;TIM3\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eExh\u003c/sub\u003e) associated primarily with CD103\u003csup\u003e+\u003c/sup\u003e OT1 crossprimed by DC2s (CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e, Fig.\u0026nbsp;7C). By contrast, signature of genes downregulated in pre-T\u003csub\u003eRM\u003c/sub\u003e (bulk RNA-Seq, CD103\u003csup\u003e\u0026minus;\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e \u0026gt; CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eAct\u003c/sub\u003e) was poorly expressed by CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e. GSEA analysis of CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e compared to all other OT1 populations, hereafter referred as Rest, further confirmed these results (Fig.\u0026nbsp;7D). CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e were enriched in T\u003csub\u003eRM\u003c/sub\u003e-associated genes \u003cem\u003eItgae\u003c/em\u003e, \u003cem\u003eCd226\u003c/em\u003e, \u003cem\u003eCxcr6\u003c/em\u003e or \u003cem\u003eInpp4b\u003c/em\u003e, stemness-related genes \u003cem\u003eIl7r and Tcf7\u003c/em\u003e and TGF-β receptor genes \u003cem\u003eTgfbr1\u003c/em\u003e and \u003cem\u003eTgfbr2\u003c/em\u003e (Fig.\u0026nbsp;6E). Conversely, CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e expressed lower levels of effector-related genes \u003cem\u003eGzmb\u003c/em\u003e, \u003cem\u003eKlrk1\u003c/em\u003e, \u003cem\u003eEomes\u003c/em\u003e or \u003cem\u003eTbx21\u003c/em\u003e, proliferation-associated transcription factor \u003cem\u003eMyc\u003c/em\u003e and Il12r genes \u003cem\u003eIl12rb1\u003c/em\u003e and \u003cem\u003eIl12rb2\u003c/em\u003e (Fig.\u0026nbsp;7E). Consistently, GSEA scoring of a memory CD8 T cell signature revealed that CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e had increased expression of memory-related genes (NES\u0026thinsp;=\u0026thinsp;1.46, Fig.\u0026nbsp;7F). We also evaluated a signature of SMAD4 target genes, a central mediator of TGF-β signalling, and found that it was enriched in CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e (NES\u0026thinsp;=\u0026thinsp;1.32, Fig.\u0026nbsp;7F). Conversely, MTORC1 signaling (NES = -1.58) and MYC targets (NES = -1.57) signatures were strongly reduced in CD103\u003csup\u003e+\u003c/sup\u003e OT1\u003csub\u003eDC2\u0026thinsp;\u0026minus;\u0026thinsp;Act\u003c/sub\u003e, consistent with reduced proliferation activity (Fig.\u0026nbsp;7G).\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that migratory DC2s recapitulate the differentiation of na\u0026iuml;ve OT1s into pre-T\u003csub\u003eRM\u003c/sub\u003e similar to those found in tdLNs of tumor-bearing mice. The specification of T\u003csub\u003eRM\u003c/sub\u003e program within na\u0026iuml;ve T cells is enabled by a stronger activation of TGF-β signalling pathway by migratory DC2s as compared to migratory DC1s.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eInfiltration of solid tumors with CD8\u003csup\u003e+\u003c/sup\u003e TILs with a T\u003csub\u003eRM\u003c/sub\u003e phenotype is predictive of improved survival and better response to immunotherapies\u003csup\u003e1,3,5\u0026ndash;9\u003c/sup\u003e. Besides, T\u003csub\u003eRM\u003c/sub\u003e-like TILs show signs of superior anti-tumor activity as compared to other T cell populations\u003csup\u003e16,17,20,21\u003c/sup\u003e. Therefore, we have sought to address the mechanisms governing the acquisition of the T\u003csub\u003eRM\u003c/sub\u003e phenotype during T cell activation in a murine model of lung cancer. Using a combination of transcriptomic and flow cytometry approaches, we identified a population of T\u003csub\u003eRM\u003c/sub\u003e-like TILs specifically expressing canonical T\u003csub\u003eRM\u003c/sub\u003e markers and sharing the expression of exhaustion molecules with T\u003csub\u003eEXH\u003c/sub\u003e-like TILs. Fate mapping experiments show that CD103 expression during the early phases of T cell activation in tdLNs defines CD8\u003csup\u003e+\u003c/sup\u003e T cells committed to generate T\u003csub\u003eRM\u003c/sub\u003e-like TILs. Previous studies similarly identified CD103 as a marker of T\u003csub\u003eRM\u003c/sub\u003e-commitment within na\u0026iuml;ve T cells in a model of skin vaccination\u003csup\u003e24\u003c/sup\u003e. Using unsupervised analysis of activated T cells in the tdLN, we improved this phenotypic characterization and identified a rare population of CXCR6\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like cells. We further addressed the dendritic cell requirements for T\u003csub\u003eRM\u003c/sub\u003e phenotype specification. Induction of CXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like precursor cells in tdLNs is dependent on both migratory XCR1\u003csup\u003e+\u003c/sup\u003e DC1s and IRF4-dependent DC2s. Consistently, lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs accrual requires both types of migratory DCs at the level of tdLN. By contrast, effector and exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells infiltrating tumors solely rely on XCR1\u003csup\u003e+\u003c/sup\u003e DC1s. Finally, we asked which factors provided by DCs subsets could control T\u003csub\u003eRM\u003c/sub\u003e-like TILs generation. We provide evidence that T\u003csub\u003eRM\u003c/sub\u003e specification in tdLNs:\u0026nbsp;\u003c/p\u003e\n\u003col start=\"1\" style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003erequires sub-optimal TCR triggering by weak cross-presenter DC2s that display reduced densities of MHCI-peptide complexes.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eis inhibited by high amounts of IL-12 mainly produced by XCR1\u003csup\u003e+\u003c/sup\u003e DC1s that inhibits TGF-b signalling and its CD103 downstream target.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003enecessitates some levels of IL-12 as evidenced by the IL-12 function in upregulating CXCR6 during T cell cross-priming.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003erelies on the proliferative expansion uniquely achieved by cross-priming DC1s.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003csub\u003eRM\u003c/sub\u003e specification occurs in tdLNs and involves both XCR1\u003csup\u003e+\u003c/sup\u003e and IRF4-dependent migratory DCs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe existence of precursors pre-committed to differentiate into T\u003csub\u003eRM\u003c/sub\u003e has been demonstrated within na\u0026iuml;ve CD8\u003csup\u003e+\u003c/sup\u003e T cells epigenetically poised to express T\u003csub\u003eRM\u003c/sub\u003e-associated genes\u003csup\u003e24\u003c/sup\u003e and within blood circulating activated T cells after skin vaccination\u003csup\u003e26\u003c/sup\u003e. Mani et al. showed that TGF-\u0026beta; activation by migratory DCs was required for T\u003csub\u003eRM\u003c/sub\u003e preconditioning at the level of na\u0026iuml;ve T cells in the LN. Whether this mechanism requires one or multiple DC subsets has not been addressed. Also, it is unknown which DC subset participate to the activation of T\u003csub\u003eRM\u003c/sub\u003e from T\u003csub\u003eRM\u003c/sub\u003e-poised na\u0026iuml;ve progenitors. Our study identifies a subset or early activated CD8\u003csup\u003e+\u003c/sup\u003e T cells expressing selectively a transcriptional signature of tumor-infiltrating T\u003csub\u003eRM\u003c/sub\u003e in tdLN. We demonstrate that the activation of tdLN CD103\u003csup\u003e+\u003c/sup\u003e CXCR6\u003csup\u003e+\u003c/sup\u003e T cells rely on both migratory XCR1\u003csup\u003e+\u003c/sup\u003e DC1s and IRF4-dependent DC2s. By contrast, activation of CD103\u003csup\u003e-\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells is largely independent on the presence of IRF4-dependent migratory DCs. Furthermore, we demonstrate by adoptive transfer that genetically labelled CD103\u003csup\u003e+\u003c/sup\u003e T cells from tdLNs uniquely give rise to lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs. Therefore, we establish that the specification of lung T\u003csub\u003eRM\u003c/sub\u003e-like TILs actually starts within lymph nodes by a process orchestrated by both migratory DCs subsets.\u003c/p\u003e\n\u003cp\u003eThese findings might have a relevance in humans since CD1c\u003csup\u003e+\u003c/sup\u003e DCs have been shown to participate to the activation of CD8\u003csup\u003e+\u003c/sup\u003eCD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like cells\u003csup\u003e39,40\u003c/sup\u003e. Of note, the contribution of IRF4-dependent DCs to the generation of memory but not effector T cell responses had been previously noted in the influenza infection model\u003csup\u003e38\u003c/sup\u003e and to the maintenance of anti-infectious T\u003csub\u003eRM\u003c/sub\u003e at mucosal sites in HSV infection\u003csup\u003e27\u003c/sup\u003e. Altogether, our results further extend the major contribution of XCR1\u003csup\u003e+\u003c/sup\u003e DCs in anti-tumor CD8 T cell immunity to T\u003csub\u003eRM\u003c/sub\u003e\u003csup\u003e29,30,34,48,49\u003c/sup\u003e. Our findings are in line with pioneer studies highlighting the crucial role of XCR1\u003csup\u003e+\u003c/sup\u003e DC1s in the activation of vaccine-induced T\u003csub\u003eRM\u003c/sub\u003es\u003csup\u003e37\u003c/sup\u003e. However, it is important to underline that the role of IRF4-dependent DC2s had not been addressed in this context. In addition, we evidence a contribution of IRF4-dependent DCs to T\u003csub\u003eRM\u003c/sub\u003e generation at the level of tdLNs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow TCR signal strength, IL12 and TGF-\u0026beta; synergize to differentiate T\u003csub\u003eRM\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT\u003csub\u003eRM\u003c/sub\u003e development dependency on TGF-\u0026beta; has been well established in several studies\u003csup\u003e24,25,27\u003c/sup\u003e. However, both migratory DC subsets express TGF-\u0026beta; activating molecules at the transcriptomic level (e.g., \u003cem\u003eItgav\u003c/em\u003e)\u003csup\u003e24\u003c/sup\u003e. Therefore, IRF4-dependent migratory DC2s might contribute to T\u003csub\u003eRM\u003c/sub\u003e specification i) by providing a milder TCR signal strength because they cross-present tumor associated antigens inefficiently; ii) by not providing IL-12 in contrast to XCR1\u003csup\u003e+\u003c/sup\u003e DC1s; iii) by providing specific signals yet to be identified. Here, we provide evidence for i) and ii). We show that sub-optimal cross-presentation by DC2s enable CD8\u003csup\u003e+\u003c/sup\u003e T cells to maintain CD103 expression during T cell activation \u003cem\u003ein vitro\u003c/em\u003e, and this, independently of other factors. These results are in line with previous studies highlighting a negative impact of strong TCR signalling on T\u003csub\u003eRM\u003c/sub\u003e differentiation in vivo in the context of viral infections\u003csup\u003e58,59\u003c/sup\u003e. More recently, Shakiba et al. have shown that low affinity tumor-associated antigens prime T cells expressing higher levels of CD103 while presenting less features of dysfunctional/exhausted T cells as compared to T cells primed with high affinity antigens\u003csup\u003e60\u003c/sup\u003e. Finally, both mild TCR signalling\u003csup\u003e61\u003c/sup\u003e and migratory DC2s contribute to the priming of central memory CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e62\u003c/sup\u003e. In addition to optimal cross-priming efficiency, XCR1\u003csup\u003e+\u003c/sup\u003e migratory DC1s also provide high levels of IL-12 which acts as an inhibitory signal erasing CD103 expression induced by IRF4-dependent migratory DC2s. These findings are in line with the regulatory role of TBET, downstream of IL-12 signalling at SMAD3 binding sites in the CD103 locus\u003csup\u003e52,63\u003c/sup\u003e. Bergsbaken et al. have noticed that IL-12 promotes CD103\u003csup\u003e-\u003c/sup\u003e but not TGF-\u0026beta; dependent CD103\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e by a STAT4-dependent mechanism\u003csup\u003e64,65\u003c/sup\u003e. Our results clearly establish that TGF-\u0026beta;-dependent program is induced more efficiently by migratory DC2s than migratory, IL-12-producing DC1s. TGF-\u0026beta; transpresentation might play a role in this process, together with the regulatory role of IL-12 on TGF-\u0026beta; signalling (that can be visualized by IL-12 ability to counteract TGF-\u0026beta;-induced CD103).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional features associated to the T\u003csub\u003eRM\u003c/sub\u003e phenotype in cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT\u003csub\u003eRM\u003c/sub\u003e have been extensively studied in the context of infections or vaccination when memory is settled and maintained after antigen clearance. In this study, we have not addressed the functional ability of T\u003csub\u003eRM\u003c/sub\u003e-like cells to recirculate or generate memory in the absence of antigen. Recent studies addressing the functional ability of T\u003csub\u003eRM\u003c/sub\u003e-like cells to recirculate in a mouse model of E0771 breast cancer suggest that T\u003csub\u003eRM\u003c/sub\u003e phenotype does not necessarily correlate with tumor residence or correspond to neo-generated T\u003csub\u003eRM\u003c/sub\u003e-like cells from circulating precursors\u003csup\u003e15\u003c/sup\u003e. However, T\u003csub\u003eRM\u003c/sub\u003e-like cells in this model do not express CD103 which may be a major contributor of residence within the tumor through direct interaction with epithelial E-Cadherin which is not expressed in the E0771 cells investigated in this study\u003csup\u003e66,67\u003c/sup\u003e. Evaluating this possibility would require additional experiments.\u003c/p\u003e\n\u003cp\u003eFrom a functional perspective, T\u003csub\u003eRM\u003c/sub\u003e-like could represent a pool of responsive cells, able to generate effector cells during challenge or immunotherapies\u003csup\u003e3,9,20\u003c/sup\u003e. This view is in line with the developmental plasticity of T\u003csub\u003eRM\u003c/sub\u003e upon antigenic restimulation that had been established in infection-induced T\u003csub\u003eRM\u003c/sub\u003es\u003csup\u003e18,19\u003c/sup\u003e. Deeper functional studies are required to understand the determinants of the prognosis value of T\u003csub\u003eRM\u003c/sub\u003e-like phenotype in cancer and how their functionality could be activated.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study highlights the fine-tuning of anti-tumor CD8\u003csup\u003e+\u003c/sup\u003e T cell diversity by a broad repertoire of migratory DCs subsets in tdLNs. We show that the crosspriming of CD8\u003csup\u003e+\u003c/sup\u003e T cells reaching a dysfunctional/exhausted phenotype within tumors is uniquely dependent on DC1s and entirely independent on migratory DC2s. By contrast, induction of T\u003csub\u003eRM\u003c/sub\u003e-like cells requires both DC1 and DC2 subsets. These findings challenge the relevance of the \u0026ldquo;division of labor\u0026rdquo; across DCs subsets ascribing discrete, specific and exclusive immune functions to specific DCs subsets. Instead, we evidence a high level of cooperativity relevant for the induction of memory precursors in tumor-draining lymph nodes. In sum, our findings support the view that instruction of residency program and proliferative expansion rely on complementary yet distinct DCs subsets.\u003c/p\u003e\n\u003cp\u003eThese findings have important implications for the design of effective immunotherapies harnessing multiple types of CD8\u003csup\u003e+\u003c/sup\u003e T cell immunity, \u003cem\u003eid est\u003c/em\u003e effector vs tissue-resident memory cells. One correlates of the current findings that remain to be tested, is that optimal priming of diverse T cell phenotypes might rely on antigen delivery to multiple DCs subsets.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to understand the dendritic cells requirements for T\u003csub\u003eRM\u003c/sub\u003e specification in lung cancer. To that end, we intravenously injected KP lung tumors into several mouse models in which specific dendritic cell populations can be manipulated. We thoroughly characterized polyclonal and antigen-specific CD8 T cell populations to model antigen OVA using a combination of transcriptomics (bulk RNA-seq, scRNA-seq, CITE-Seq) and flow cytometry approaches. To address the role of dendritic cells in the lymph nodes, we designed a series of adoptive cell transfer and \u003cem\u003eex vivo\u003c/em\u003e OT1-dendritic cell coculture experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMice were housed at the INSERM U1149 CRI, Medicine school site Bichat or at the Paris Institut Pasteur animal house facilities. Wild type C57BL/6J and CD45.1 mice were purchased from Janvier and kept in our facility. \u003cem\u003eXCR1\u003csup\u003ecre\u003c/sup\u003e\u003c/em\u003e and Rosa26-lox-stop-lox-DTA (B6.129P2 Gt(ROSA)26Sortm1(DTA)Lky/J)\u003csup\u003e68\u003c/sup\u003e mice were kindly provided by Dr. Marc Dalod (Center of Immunology Marseille-Luminy, Marseille). XCR1\u003csup\u003eDTA\u003c/sup\u003e mice were generated as described\u003csup\u003e46\u003c/sup\u003e. OT-I \u003cem\u003eRAG2\u003c/em\u003e\u003csup\u003e-/-\u003c/sup\u003e CD45.1 mice were kindly provided by Dr. Sébastian Amigorena (Curie institute, Paris). \u003cem\u003eFlt3L\u003c/em\u003e\u003csup\u003e-/-\u003c/sup\u003e mice were kindly provided by Dr. Guillaume Darrasse-Jèze (UMR-S 979, Paris). \u003cem\u003eCD11c\u003csup\u003ecre\u003c/sup\u003e\u003c/em\u003e mice (B6.Cg-Tg(Itgax-cre)1-1Reiz/J;\u003csup\u003e69\u003c/sup\u003e), \u003cem\u003eIRF4\u003csup\u003eflox\u003c/sup\u003e\u003c/em\u003e mice (B6.129S1-Irf4tm1Rdf/J;\u003csup\u003e70\u003c/sup\u003e), and \u003cem\u003eCCR2\u003csup\u003e−/−\u003c/sup\u003e\u003c/em\u003e mice (Ccr2tm1/fc;\u003csup\u003e71\u003c/sup\u003e) were kindly gifted by Dr. Emmanuel Gautier (UMR-S U1166, Paris). Hobit-tdTomato-DTR [B6-Tg (Zfp683-tdTomato-P2A-Cre-P2A-DTR);\u003csup\u003e18\u003c/sup\u003e] mice were kindly provided by Dr. Klaas Van Gisbergen, (Champalimaud Fundation, Lisbon). The reporter/deleter cassette disrupts the Hobit allele, in consequence, heterozygous Hobit tdTomato-DTR mice were used in the experiments. \u003cem\u003eKRAS\u003csup\u003eLSL-G12D/+\u003c/sup\u003e;p53\u003csup\u003efl/fl\u003c/sup\u003e\u003c/em\u003e mice\u003csup\u003e72\u003c/sup\u003e were kindly provided by Dr. Kairbaan Hodilvala-Dilke (Bart Institute Queen Mary University, London). CD103\u003csup\u003ecre-ERT2\u003c/sup\u003e (\u003cem\u003eItgae\u003c/em\u003e-creER\u003csup\u003eT2\u003c/sup\u003e) and Rosa\u003csup\u003eLSL-tdTomato\u0026nbsp;\u003c/sup\u003e(B6.Cg-\u003cem\u003eGt(ROSA)26Sor\u003csup\u003etm14(CAG-tdTomato)Hze\u003c/sup\u003e\u003c/em\u003e/J) mice were kindly provided by Dr. Tessa Bergsbaken (Rutgers, New Brunswick)\u003csup\u003e44\u003c/sup\u003e. IL-12\u003csup\u003eYFP\u003c/sup\u003e (C.129S4(B6)-Il12btm1.1Lky/J;\u003csup\u003e73\u003c/sup\u003e) mice were kindly provided by Dr. Mikaël Pittet. The study was approved by the local ethics committee comité d’éthique Paris Nord no. 121 and the Ministère de l’enseignement supérieur, de la recherche et de l’innovation under the authorization number APAFIS#15373. Animal care and treatment were conducted with national and international laws and policies (European Economic Community Council Directive 86/609; OJL 358; December 12, 1987). All experiments were performed in accordance with the Federation of European Laboratory Animal Science Association (FELASA) guidelines institutional guidelines and the French law.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe KP line has been isolated from primary lung tumors of C57BL/6 KP mice (\u003cem\u003eKRAS\u003csup\u003eLSL-G12D/+\u003c/sup\u003e;p53\u003csup\u003efl/fl\u003c/sup\u003e\u003c/em\u003e)\u003csup\u003e74\u003c/sup\u003e. The line was kindly provided by Dr. Federica Benvenuti (ICGEB, Trieste), and has been used previously\u003csup\u003e35,75\u003c/sup\u003e. KP-OVA were generated by retroviral transduction of KP cells with a pBabe-OVA-IRES-Cherry vector. KP-ZsGreen were generated by lentiviral transduction of KP cells with a pLVX-IRES-ZsGreen vector. Tumor cell lines were cultured in RPMI 1640 medium, Glutamax (Thermo Fisher) supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher), penicillin-streptomycin (Thermo Fisher) and 55μM β-mercaptoethanol (Thermo Fisher) (complete RPMI) and maintained at 37°C and 5% CO2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the grafted tumor model, the KP, KP-OVA and KP-ZsGreentumor cell lines were grown in RPMI-Glutamax with 10% FCS, 55 μM 2-mercaptoethanol, Penicillin streptomycine and used for experiments when in exponential growth phase. The cells were detached using Trypsine/EDTA 0.25%, washed two times in PBS and counted in trypan blue. 8.10\u003csup\u003e5\u0026nbsp;\u003c/sup\u003etumor cells were intravenously injected in 100 mL RPMI without supplement.\u003c/p\u003e\n\u003cp\u003eFor the autochthonous KP model, C57BL/6 KP mice (\u003cem\u003eKRAS\u003csup\u003eLSL-G12D/+\u003c/sup\u003e;p53\u003csup\u003efl/fl\u003c/sup\u003e\u003c/em\u003e) at 8 weeks of age, were intranasally inoculated with 2.5 × 10\u003csup\u003e7\u003c/sup\u003e infectious particles of a replication-deficient adenoviral vector with Cytomegalovirus promoter driving the expression of the Cre recombinase protein in order to sporadically induce mutations in lung cells, and promote lung tumor development as previously described\u003csup\u003e62\u003c/sup\u003e. Mice were sacrificed 16 weeks after the adenovirus inoculation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vivo\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;treatments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the FTY treatment, mice received intraperitoneal injections of 1 mg/kg FTY720 (fingolimod) (Sigma-Aldrich) in 150 μl of non-deionised H\u003csub\u003e2\u003c/sub\u003e0 (Versol) every 3 days from day 1 after tumor injection. For IL-12p40 blocking, mice received intraperitoneal injections of 200µg anti-IL-12p40 blocking antibody (Biolegend) in 200µl of PBS every 3 days from day 0 after tumor injection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse tissues processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse lungs were harvested and transferred to 3ml digestion buffer (Hank’s Balanced Salt Solution (HBSS) with calcium and magnesium (Thermo Fisher) and with 75\u0026nbsp;μg/mL of Liberase TL (Roche) and 0.02mg/ml DNase I (Thermo Fisher)). Lungs were dissociated using gentleMACS Octo Dissociator (Miltenyi) (program m_lung_01), incubated at 37°C for 45 minutes and dissociated again on the gentleMACS Octo Dissociator (Miltenyi) (program m_lung_02). The cell suspension was passed through a 70 μm cell strainer (Corning) and red blood cells were lysed using ACK lysing buffer (Thermo Fischer). The mediastinal lymph nodes were smashed in FACS buffer on a 70 μm cell strainer. The absolute number of live immune cells in each tissue cell suspension was assessed using AccuCheck Counting Beads (Thermo Fisher) along with anti-CD45 and DAPI staining on BD FACS Fortessa 20 (BD Biosciences). For OT-I CD8\u003csup\u003e+\u003c/sup\u003eT cell preparation, LNs from OT-I CD45.1 RAG mice were passed through 70 μm cell strainers. The LN suspensions were stained with anti-CD8-APCCy7 and counted by FACS using counting beads (AccuCheck beads, Invitrogen).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter tissue processing and cell counting, Fc-receptor were blocked using FcBlock (2.4G2, BD) for 15min at 4°C. For dead cell identification, the cells were stained with 7-AAD (Biolegend) for extracellular staining only or Zombie dye (Biolegend) for intracellular staining in PBS. Cells were then stained with fluorophore-conjugated antibodies in FACS buffer (See antibody list in the Key resource table), PBS 3% FCS (Gibco) 2mM EDTA (Gibco), during 30 minutes at 4°C. When needed, cell suspensions were subsequently fixed and stained in for nuclear protein (30min fixation and 30min intracellular staining) using fixation/permeabilization kit (eBiosciences transcription factor staining buffer) according to the manufacturer’s instructions. Labelling with H2K\u003csup\u003eb\u003c/sup\u003e-SIINFEKL tetramers (MBL) was performed at room temperature for 30 min. Data acquisition was performed using an LSR-Fortessa X20 (BD) and analysis were done using FlowJo software (TreeStar).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEx vivo\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;T cell restimulation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter tissue processing and cell counting, 1 million total CD45\u003csup\u003e+\u003c/sup\u003e cells were incubated for 5 hours at 37°C in a restimulation cocktail composed of 50ng/ml PMA (Sigma), 1µg/ml Ionomycin (Sigma), 2µg/ml Golgi Plug (BD) and 2µg/ml Golgi stop (BD) in complete RPMI medium. Cells were then processed for flow cytometry analysis as described before.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBulk RNA-sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter tissue processing and cell count, cell suspensions were washed in cold FACS buffer (PBS + 3% BSA + 2mM EDTA), Fc-receptors were blocked ,15min at 4°C, using purified anti-CD16/32 (2.4G2) and, then, stained with fluorophore conjugated antibodies for 30 minutes at 4°C. CD8\u003csup\u003e+\u003c/sup\u003eT cell subsets from tumor-bearing lungs and tdLNs were isolated using an Aria III (BD) and a MELODY (BD) cell sorter, respectively, and directly collected on lysing TCL buffer (QIAGEN) containing 0.1% of beta-mercaptoethanol before storage at -80°C. RNA were extracted and isolated using the Single Cell RNA purification kit (Norgen, Cat#51800) according to the manufacturer’s instructions. After extraction, total RNA was analyzed using Agilent RNA 6000 Pico Kit on the Agilent 2100 Bioanalyzer System. RNA quality was estimated based on capillary electrophoresis profiles using the RNA Integrity Number (RIN). RNA sequencing libraries were prepared using the SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara). The input quantity of total RNA was comprised between 1 and 22ng. This protocol includes a first step of RNA fragmentation, using a proprietary fragmentation mix at 94°C. The time of incubation was set up for each sample, based on the RNA quality, and according to the manufacturer’s recommendations. After fragmentation, indexed cDNA synthesis was performed. Then the ribodepletion step was performed, using probes specific to mammalian rRNA. PCR amplification was finally achieved to amplify the indexed cDNA libraries, with a number of cycles set up according to the input quantity of tRNA. Library quantification and quality assessment was performed using Qubit fluorometric assay (Invitrogen) with dsDNA HS (High Sensitivity) Assay Kit and LabChip GX Touch using a High Sensitivity DNA chip (Perkin Elmer). Libraries were then equimolarly pooled and quantified by qPCR using the KAPA library quantification kit (Roche). Sequencing was carried out on the NovaSeq 6000 (Illumina), targeting between 10 and 15M reads per sample and using paired-end 2 x 100 bp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo statistically evaluate the enrichment of previously reported gene signatures and DEG calculated from pairwise comparison in this study (Gene Sets), we used pairwise comparisons using the gene set enrichment analysis (GSEA)\u003csup\u003e76\u003c/sup\u003e method from the Massachussets Institute of Technology (\u003ca href=\"https://www.broadinstitute.org/gsea\"\u003ehttps://www.broadinstitute.org/gsea\u003c/a\u003e). Statistical analysis was performed by evaluation of nominal p value and false discovery rate (q value) based on 1,000 random permutations. Results were considered significant when the p value was below 0.05 and when the q value was below 0.25 (false discovery rate below 25%) accordingly to the recommendation from the software developers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escRNA-seq analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor cell sorting experiments, tumor-bearing lungs and tdLN were collected and processed as previously described. The strategy used for the sort is depicted in the figures. The droplet-based approach of 10X Genomics platform was used to perform scRNA-seq. For each scRNA-seq sample, cell suspension from three individual mice were pooled and sorted in PBS containing 0,04%BSA. 10 000 cells were loaded on a Chromium Controller using the Chromium Next GEM Single Cell 3 Reagent Kits v3.1 according to manufacturer instructions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRaw sequencing reads were processed using the 10x Genomics software Cellranger. To obtain a cell count matrix, reads were mapped to the mouse GRCm38.84 reference genome. scRNA-seq analysis was performed using Seurat v4. For each dataset, cells with low genes per cell or with a high percentage of mitochondrial genes were removed from downstream analyses. Following data QC we obtained 4421 cells from the lungs KP CD8\u003csup\u003e+\u003c/sup\u003e T cells dataset, 1404 cells for the tdLN KP WT CD8\u003csup\u003e+\u003c/sup\u003e T cells dataset, 3340 cells for the tdLN KP \u003cem\u003eCD11c\u003csup\u003eIRF4\u003c/sup\u003e\u003c/em\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells dataset, 2076 cells for the tdLN KP WT CD11c\u003csup\u003e+\u003c/sup\u003eMHCII\u003csup\u003e+\u003c/sup\u003e cells dataset and 3150 cells for the tdLN KP \u003cem\u003eCD11c\u003csup\u003eIRF4\u003c/sup\u003e\u003c/em\u003e CD11c\u003csup\u003e+\u003c/sup\u003eMHCII\u003csup\u003e+\u003c/sup\u003e cells dataset. RNA and ADT expression were normalized separately using the default Seurat LogNormalize and Centered Log Ratio (CLR) approaches, respectively. Top 2000 most variable genes were then determined by applying a VST (Variance Stabilizing Transformation). Datasets were analyzed separately or were integrated together before analysis. For integration, top 2000 most variable genes across datasets were used as anchors with the LogNormalize method. Dimensionality reduction was performed using PCA and the top informative principle components were determined after visualization with an elbow plot and dimensional reduction heatmaps, and used for the non-linear dimensionality reduction technique UMAP. Louvain clustering of the data was performed across a range of resolution parameters visualized using a clustree. Cluster of cells with low number of genes and in which no T lymphocyte cell identifying genes could be detected were not considered in the analysis. Subsequent analyses were performed using Seurat default parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdoptive transfers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMediastinal lymph nodes from donor mice (CD45.2\u003csup\u003e+\u003c/sup\u003e tumor-bearing or CD45.1\u003csup\u003e+\u003c/sup\u003e OTI x Rag1\u003csup\u003e-/-\u0026nbsp;\u003c/sup\u003etumor-free mice, respectively) were mechanically dissociated on a 70μm cell strainers in sterile FACS buffer. After two washes in PBS, the LN cell suspensions were counted as previously described and resuspended in 100μL of RPMI without supplement and intravenously injected in recipient mice (CD45.1\u003csup\u003e+\u003c/sup\u003e or CD45.2\u003csup\u003e+\u0026nbsp;\u003c/sup\u003etumor-bearing mice, respectively), according to the experimental designs described in figures 2L, S3E and 5E.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEx vivo Mouse T\u0026nbsp;cell priming assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOT-1 cell preparation:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOT-I CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated from lymph nodes of CD45.1\u003csup\u003e+\u003c/sup\u003e OTI x Rag1\u003csup\u003e-/-\u003c/sup\u003e mice and labeled for proliferation assay with 2μM of CellTrace Violet (CTV, Invitrogen) at 37°C for 15 minutes, washed and counted before culture with DC subsets.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDendritic cells isolation from LNs:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the isolation of mediastinal lymph node DCs from KP- or KP-OVA bearing mice, minced mediastinal tumor-draining LNs were digested in 500μl HBSS with calcium and magnesium (Thermo Fisher) with 75\u0026nbsp;μg/mL of Liberase TL (Roche) and 0.02mg/ml DNase I (Thermo Fisher) and incubated at 37°C for 20 minute under agitation. Cell suspensions were passed through a 70μm cell strainers. Cell suspensions were then blocked in FcBlock and stained with CD11c-biotin followed by SAV-beads (Miltenyi). Cell suspension was applied on a LS column (Miltenyi) according to the manufacturer’s instructions. The CD11c-enriched fraction retained in the column was then stained for CD11c, MHCII, CD19, XCR1 and CD11b. Dendritic cells subsets were FACS-sorted on a Melody (BD) according to the gating strategy depicted on the Fig.s.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDCs and T\u0026nbsp;cell co-culture:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e2500 DCs were plated in V-bottom 96 well plates and incubated with 25000 CTV-loaded OT-1 in RPMI medium (10% FCS + 1% Pencillin/Streptomycin + 1mM Sodium Pyruvate 55μM 2-mercaptoethanol and 10mM HEPES). When indicated, various doses of the SIINFEKL OVA peptide or mutated peptides (Q4, T4, G4) were added to the cultures at the indicated concentration. For some experiments, recombinant mouse TGF-β (20μg/mL) (Biolegend), recombinant mouse IL-12p70 (20μg/mL) (Biolegend) or anti-IL-12p40 blocking antibody (20μg/mL) (Biolegend) were added to the well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT cell activation analysis by flow cytometry\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003eAfter 48h or 96h of co-culture as indicated in the figures, T cells were stained for CD45.1, CD8, CD44, CD62L, PD-1 and CD103 to analyze their activation and polarization status. CTV dilution was analyzed to assess T cell proliferation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification and Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using Prism 10 (GraphPad Software Inc., USA). Each dot displayed on the graphs corresponds to one biological replicate and error bars represent standard deviations. When two experimental groups were compared, two-tailed unpaired or paired Student’s t test was used. When three or more groups were compared, statistically significant differences between means were determined using the one-way analysis of variance (ANOVA) method. Tukey’s multiple comparisons test was applied when comparing multiple conditions. A p-value of less than 0.05 was considered as significant, indicated with the following signs: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank M. Pittet for providing IL-12\u003csup\u003eYFP\u003c/sup\u003e mice. We thank the flow cytometry and the\u003c/p\u003e\n\u003cp\u003eanimal facility platforms from Centre de Recherche sur l\u0026rsquo;Inflammation (CRI), Facult\u0026eacute; de\u003c/p\u003e\n\u003cp\u003eM\u0026eacute;decine Bichat and from Institut Pasteur. We thank the NGS platforms from Institut\u003c/p\u003e\n\u003cp\u003eCurie U932 and from Institut Cochin. We thank Institut National de la Sant\u0026eacute; et de la\u003c/p\u003e\n\u003cp\u003eRecherche M\u0026eacute;dicale U1149 and U1016 units, Centre National de la Recherche UMR3738\u003c/p\u003e\n\u003cp\u003eunit, Institut Pasteur.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCentre National de la Recherche Scientifique CNRS (PG)\u003c/p\u003e\n\u003cp\u003ePhD Fellowship from Universit\u0026eacute; de Paris (NV)\u003c/p\u003e\n\u003cp\u003ePhD Fellowship from Fondation pour la Recherche M\u0026eacute;dicale (NV)\u003c/p\u003e\n\u003cp\u003ePostdoctoral fellowship from Fondation pour la Recherche sur le Cancer ARC (PB)\u003c/p\u003e\n\u003cp\u003eCancer Research UK\u003c/p\u003e\n\u003cp\u003eInstitut National du Cancer INCA (PL-BIO22-147)\u003c/p\u003e\n\u003cp\u003eFondation pour la Recherche sur le Cancer ARC (PJA2021060003913)\u003c/p\u003e\n\u003cp\u003eFondation pour la Recherche M\u0026eacute;dicale (EQU202203014687)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: NV, PB, PG\u003c/p\u003e\n\u003cp\u003eMethodology: NV, PB, PG\u003c/p\u003e\n\u003cp\u003eInvestigation:\u0026nbsp;NV, PB, AO, MS, LG, RS, YG, FLRDC, AS, MV, AR, SB, KP, JB\u003c/p\u003e\n\u003cp\u003eSoftware: NV, SB, YG\u003c/p\u003e\n\u003cp\u003eRessources:\u0026nbsp;GDJ, ELG, MD, TB, KVG, KH, LS, JH, FB, PG\u003c/p\u003e\n\u003cp\u003eFunding acquisition: ET, JH, PG\u003c/p\u003e\n\u003cp\u003eSupervision: LS, JH, FB, PG\u003c/p\u003e\n\u003cp\u003eWriting: NV, PB, PG\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests: Authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBulk and single-cell RNA-Seq datasets are accessible here: https://figshare.com/s/6a9c76588d496aaaa768. All original data are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo X et al (2018) Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med 24:978\u0026ndash;985\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiqui I et al (2019) Intratumoral Tcf1\u0026thinsp;+\u0026thinsp;PD-1\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy. Immunity 50:195\u0026ndash;211e10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorgnac S et al (2020) CD103\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;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\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilner JJ et al (2017) Runx3 programs CD8\u0026thinsp;+\u0026thinsp;T cell residency in non-lymphoid tissues and tumours. Nature 552:253\u0026ndash;257\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDjenidi F et al (2015) CD8\u0026thinsp;+\u0026thinsp;CD103\u0026thinsp;+\u0026thinsp;tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J Immunol 194:3475\u0026ndash;3486\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHombrink P et al (2016) Programs for the persistence, vigilance and control of human CD8\u0026thinsp;+\u0026thinsp;lung-resident memory T cells. Nat Immunol 17:1467\u0026ndash;1478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanesan A-P et al (2017) Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat Immunol 18:940\u0026ndash;950\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavas P et al (2018) Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 24:986\u0026ndash;993\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke J et al (2019) Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer. J Exp Med 216:2128\u0026ndash;2149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebhardt T et al (2009) Memory T cells in nonlymphoid tissue that provide enhanced local immunity during infection with herpes simplex virus. Nat Immunol 10:524\u0026ndash;530\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackay LK et al (2016) Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352:459\u0026ndash;463\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonseca R et al (2022) Runx3 drives a CD8\u0026thinsp;+\u0026thinsp;T cell tissue residency program that is absent in CD4\u0026thinsp;+\u0026thinsp;T cells. Nat Immunol 23:1236\u0026ndash;1245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkon CN et al (2013) Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8\u0026thinsp;+\u0026thinsp;T cells. Nat Immunol 14:1285\u0026ndash;1293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiow LR et al (2006) CD69 acts downstream of interferon-alpha/beta to inhibit S1P1 and lymphocyte egress from lymphoid organs. Nature 440:540\u0026ndash;544\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavil NV et al (2023) Chronic antigen in solid tumors drives a distinct program of T cell residence. Sci Immunol 8:eadd5976\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnadon CM et al (2022) Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cells. Cancer Cell 40:545\u0026ndash;557e13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark SL et al (2019) Tissue-resident memory CD8\u0026thinsp;+\u0026thinsp;T cells promote melanoma-immune equilibrium in skin. Nature 565:366\u0026ndash;371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehr FM et al (2020) Tissue-resident memory CD8\u0026thinsp;+\u0026thinsp;T cells shape local and systemic secondary T cell responses. Nat Immunol 21:1070\u0026ndash;1081\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLow JS et al (2020) Tissue-resident memory T cell reactivation by diverse antigen-presenting cells imparts distinct functional responses. J Exp Med 217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirassamy B et al (2023) Intratumoral CD8\u0026thinsp;+\u0026thinsp;T cells with a tissue-resident memory phenotype mediate local immunity and immune checkpoint responses in breast cancer. Cancer Cell 41:585\u0026ndash;601e8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNizard M et al (2017) Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nat Commun 8:15221\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Q et al (2022) The primordial differentiation of tumor-specific memory CD8\u0026thinsp;+\u0026thinsp;T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes. Cell 185:4049\u0026ndash;4066e25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProkhnevska N et al (2023) CD8\u0026thinsp;+\u0026thinsp;T cell activation in cancer comprises an initial activation phase in lymph nodes followed by effector differentiation within the tumor. Immunity 56:107\u0026ndash;124e5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMani V et al (2019) Migratory DCs activate TGF-β to precondition na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells for tissue-resident memory fate. Science 366\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackay LK et al (2013) The developmental pathway for CD103\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;tissue-resident memory T cells of skin. Nat Immunol 14:1294\u0026ndash;1301\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKok L et al (2020) A committed tissue-resident memory T cell precursor within the circulating CD8\u0026thinsp;+\u0026thinsp;effector T cell pool. J Exp Med 217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin H, Kumamoto Y, Gopinath S, Iwasaki A (2016) CD301b\u0026thinsp;+\u0026thinsp;dendritic cells stimulate tissue-resident memory CD8\u0026thinsp;+\u0026thinsp;T cells to protect against genital HSV-2. Nat Commun 7:13346\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSl\u0026uuml;tter B et al (2017) Dynamics of influenza-induced lung-resident memory T cells underlie waning heterosubtypic immunity. Sci Immunol 2:eaag2031\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHildner K et al (2008) Batf3 deficiency reveals a critical role for CD8alpha\u0026thinsp;+\u0026thinsp;dendritic cells in cytotoxic T cell immunity. Science 322:1097\u0026ndash;1100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelenay S et al (2015) Cyclooxygenase-Dependent Tumor Growth through Evasion of Immunity. Cell 162:1257\u0026ndash;1270\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez L et al (2024) Dendritic cell-targeted therapy expands CD8 T cell responses to bona-fide neoantigens in lung tumors. Nat Commun 15:2280\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchenkel JM et al (2021) Conventional type I dendritic cells maintain a reservoir of proliferative tumor-antigen specific TCF-1\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells in tumor-draining lymph nodes. Immunity 54:2338\u0026ndash;2353e6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuti\u0026eacute;rrez-Mart\u0026iacute;nez E et al (2015) Cross-Presentation of Cell-Associated Antigens by MHC Class I in Dendritic Cell Subsets. Front Immunol 6:363\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheisen DJ et al (2018) WDFY4 is required for cross-presentation in response to viral and tumor antigens. Science 362:694\u0026ndash;699\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaronni N et al (2021) TIM4 expression by dendritic cells mediates uptake of tumor-associated antigens and anti-tumor responses. Nat Commun 12:2237\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerris ST et al (2020) cDC1 prime and are licensed by CD4\u0026thinsp;+\u0026thinsp;T cells to induce anti-tumour immunity. Nature 584:624\u0026ndash;629\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIborra S et al (2016) Optimal Generation of Tissue-Resident but Not Circulating Memory T Cells during Viral Infection Requires Crosspriming by DNGR-1\u0026thinsp;+\u0026thinsp;Dendritic Cells. Immunity 45:847\u0026ndash;860\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsua-Enrich E et al (2019) IRF4-dependent dendritic cells regulate CD8\u0026thinsp;+\u0026thinsp;T-cell differentiation and memory responses in influenza infection. Mucosal Immunol 12:1025\u0026ndash;1037\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu CI et al (2013) Human CD1c\u0026thinsp;+\u0026thinsp;dendritic cells drive the differentiation of CD103\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;mucosal effector T cells via the cytokine TGF-beta. Immunity 38:818\u0026ndash;830\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourdely P et al (2020) Transcriptional and Functional Analysis of CD1c\u0026thinsp;+\u0026thinsp;Human Dendritic Cells Identifies a CD163\u0026thinsp;+\u0026thinsp;Subset Priming CD8\u0026thinsp;+\u0026thinsp;CD103\u0026thinsp;+\u0026thinsp;T Cells. Immunity 53:335\u0026ndash;352e8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuPage M et al (2011) Endogenous T cell responses to antigens expressed in lung adenocarcinomas delay malignant tumor progression. Cancer Cell 19:72\u0026ndash;85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuPage M, Mazumdar C, Schmidt LM, Cheung AF, Jacks T (2012) Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482:405\u0026ndash;409\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGueguen P et al (2021) Contribution of resident and circulating precursors to tumor-infiltrating CD8(+) T cell populations in lung cancer. Sci Immunol 6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung HY, Teryek M, Lemenze AD, Bergsbaken T (2022) CD103 fate mapping reveals intestinal CD103\u0026thinsp;\u0026ndash;\u0026thinsp;tissue-resident memory T cells are the primary responders to secondary infection. Sci Immunol 7:eabl9925\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaier B et al (2020) A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580:257\u0026ndash;262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattiuz R et al (2018) Novel Cre-Expressing Mouse Strains Permitting to Selectively Track and Edit Type 1 Conventional Dendritic Cells Facilitate Disentangling Their Complexity in vivo. Front Immunol 9:2805\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajana S, Turner S, Paul J, Ainsua-Enrich E, Kovats S (2016) IRF4 and IRF8 Act in CD11c\u0026thinsp;+\u0026thinsp;Cells To Regulate Terminal Differentiation of Lung Tissue Dendritic Cells. J Immunol 196:1666\u0026ndash;1677\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026ouml;ttcher JP et al (2018) NK Cells Stimulate Recruitment of cDC1 into the Tumor Microenvironment Promoting Cancer Immune Control. Cell 172:1022\u0026ndash;1037e14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhislat G et al (2021) NF-κB\u0026ndash;dependent IRF1 activation programs cDC1 dendritic cells to drive antitumor immunity. Sci Immunol 6:eabg3570\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeredith MM et al (2012) Expression of the zinc finger transcription factor zDC (Zbtb46, Btbd4) defines the classical dendritic cell lineage. J Exp Med 209:1153\u0026ndash;1165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorgador A, Yewdell JW, Deng Y, Bennink JR, Germain RN (1997) Localization, quantitation, and in situ detection of specific peptide-MHC class I complexes using a monoclonal antibody. Immunity 6:715\u0026ndash;726\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaidlaw BJ et al (2014) CD4\u0026thinsp;+\u0026thinsp;T cell help guides formation of CD103\u0026thinsp;+\u0026thinsp;lung-resident memory CD8\u0026thinsp;+\u0026thinsp;T cells during influenza viral infection. Immunity 41:633\u0026ndash;645\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZehn D, Lee SY, Bevan MJ (2009) Complete but curtailed T-cell response to very low-affinity antigen. Nature 458:211\u0026ndash;214\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackay LK et al (2015) T-box Transcription Factors Combine with the Cytokines TGF-β and IL-15 to Control Tissue-Resident Memory T Cell Fate. Immunity 43:1101\u0026ndash;1111\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuckey CJ et al (2006) Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells. Proc Natl Acad Sci U S A 103:3304\u0026ndash;3309\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiamant I, Clarke DJB, Evangelista JE, Lingam N (2024) \u0026amp; Ma\u0026rsquo;ayan, A. Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources. \u003cem\u003eNucleic Acids Research\u003c/em\u003e gkae1080 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkae1080\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkae1080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A et al (2015) The Molecular Signatures Database Hallmark Gene Set Collection. Cell Syst 1:417\u0026ndash;425\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaru S, Jin G, Schell TD, Lukacher AE (2017) TCR stimulation strength is inversely associated with establishment of functional brain-resident memory CD8 T cells during persistent viral infection. PLoS Pathog 13:e1006318\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiege JK et al (2019) The Impact of TCR Signal Strength on Resident Memory T Cell Formation during Influenza Virus Infection. J Immunol 203:936\u0026ndash;945\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShakiba M et al (2022) TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion. J Exp Med 219:e20201966\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolouki S et al (2020) TCR Signal Strength and Antigen Affinity Regulate CD8\u0026thinsp;+\u0026thinsp;Memory T Cells. J Immunol 205:1217\u0026ndash;1227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim TS, Gorski SA, Hahn S, Murphy KM, Braciale TJ (2014) Distinct dendritic cell subsets dictate the fate decision between effector and memory CD8(+) T cell differentiation by a CD24-dependent mechanism. Immunity 40:400\u0026ndash;413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSl\u0026uuml;tter B, Pewe LL, Kaech SM, Harty JT (2013) Lung airway-surveilling CXCR3(hi) memory CD8(+) T cells are critical for protection against influenza A virus. Immunity 39:939\u0026ndash;948\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergsbaken T, Bevan MJ, Fink PJ (2017) Local Inflammatory Cues Regulate Differentiation and Persistence of CD8\u0026thinsp;+\u0026thinsp;Tissue-Resident Memory T Cells. Cell Rep 19:114\u0026ndash;124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung HY, Espinal AM, Teryek M, Lemenze AD, Bergsbaken T (2023) STAT4 increases the phenotypic and functional heterogeneity of intestinal tissue-resident memory T cells. Mucosal Immunol 16:250\u0026ndash;263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMami-Chouaib F et al (2018) Resident memory T cells, critical components in tumor immunology. J Immunother Cancer 6:87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegev O et al (2022) ICAM-1 on Breast Cancer Cells Suppresses Lung Metastasis but Is Dispensable for Tumor Growth and Killing by Cytotoxic T Cells. Front Immunol 13:849701\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoehringer D, Liang H-E, Locksley RM (2008) Homeostasis and effector function of lymphopenia-induced \u0026lsquo;memory-like\u0026rsquo; T cells in constitutively T cell-depleted mice. J Immunol 180:4742\u0026ndash;4753\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaton ML, Smith-Raska MR, Reizis B (2007) Notch-RBP-J signaling controls the homeostasis of CD8- dendritic cells in the spleen. J Exp Med 204:1653\u0026ndash;1664\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein U et al (2006) Transcription factor IRF4 controls plasma cell differentiation and class-switch recombination. Nat Immunol 7:773\u0026ndash;782\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoring L et al (1997) Impaired monocyte migration and reduced type 1 (Th1) cytokine responses in C-C chemokine receptor 2 knockout mice. J Clin Invest 100:2552\u0026ndash;2561\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuPage M, Dooley AL, Jacks T (2009) Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat Protoc 4:1064\u0026ndash;1072\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinhardt RL, Hong S, Kang S-J, Wang Z, Locksley RM (2006) Visualization of IL-12/23p40 In Vivo Reveals Immunostimulatory Dendritic Cell Migrants that Promote Th1 Differentiation1. J Immunol 177:1618\u0026ndash;1627\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitrova N et al (2016) Stromal expression of miR-143/145 promotes neoangiogenesis in lung cancer development. Cancer Discov 6:188\u0026ndash;201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimoncello F et al (2022) CXCL5-mediated accumulation of mature neutrophils in lung cancer tissues impairs the differentiation program of anticancer CD8 T cells and limits the efficacy of checkpoint inhibitors. Oncoimmunology 11:2059876\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545\u0026ndash;15550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng L et al (2021) Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374:abe6474\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"12d72ceb-4c5b-4de3-a837-ecfbd42c870b","identifier":"10.13039/501100006364","name":"Institut National Du Cancer","awardNumber":"PL-BIO22-147","order_by":0},{"identity":"607b5282-ab41-4438-976c-e276a82f910a","identifier":"10.13039/100007391","name":"Association pour la Recherche sur le Cancer","awardNumber":"PJA2021060003913","order_by":1},{"identity":"f716de51-3696-4ccf-ae4b-dacd3a82845a","identifier":"10.13039/501100002915","name":"Fondation pour la Recherche Médicale","awardNumber":"EQU202203014687","order_by":2}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institut Pasteur","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CD8+ T lymphocytes, dendritic cells, tissue-resident memory T cells, lung cancer, priming, tumor-draining lymph-node, cross-presentation","lastPublishedDoi":"10.21203/rs.3.rs-6455825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6455825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe composition and diversity of tumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cell populations have important consequences on the development of anti-tumor immunity. In a murine model of lung cancer, we have addressed the role of dendritic cell subsets on the generation of various types of tumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells. We show that CD44\u003csup\u003e+\u003c/sup\u003ePD1\u003csup\u003e-\u003c/sup\u003e effector and PD1\u003csup\u003e+\u003c/sup\u003eTIM3\u003csup\u003e+ \u003c/sup\u003eexhausted, tumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells require XCR1\u003csup\u003e+\u003c/sup\u003e DCs but not IRF4-dependent DCs. By contrast, CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+\u003c/sup\u003e T\u003csub\u003eRM\u003c/sub\u003e-like, tumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells require both XCR1\u003csup\u003e+\u003c/sup\u003e DCs and IRF4-dependent DCs. The same requirement is found in tumor-draining lymph nodes where we identify CD103\u003csup\u003e+\u003c/sup\u003eCXCR6\u003csup\u003e+ \u003c/sup\u003eT\u003csub\u003eRM\u003c/sub\u003e-like precursors that are dependent on both XCR1\u003csup\u003e+\u003c/sup\u003e DCs and IRF4-dependent migratory DCs. Mechanistically, we evidence that both types of migratory DCs cooperate. Mild TCR triggering by low MHCI-peptide density at the surface of cross-presenting migratory DC2s and low IL-12 support TGFb-dependent T\u003csub\u003eRM\u003c/sub\u003e specification in lymph nodes. High TCR triggering and high MHCI-peptide density at the surface of cross-presenting migratory DC1s and high IL-12 support proliferative expansion and CXCR6 acquisition. Altogether, these findings highlight the induction of intratumoral T\u003csub\u003eRM\u003c/sub\u003e-like cells under the collective aegis of multiple DCs subsets within tumor-draining lymph nodes reconciliating T\u003csub\u003eRM\u003c/sub\u003e phenotype instruction with proliferative expansion.\u003c/p\u003e","manuscriptTitle":"XCR1+ and IRF4+ migratory dendritic cells cooperate for the cross-priming of intratumoral CD8+ T cells with a tissue-resident memory phenotype.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 12:45:46","doi":"10.21203/rs.3.rs-6455825/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49f937d1-19e9-47a9-af0b-ed5f5dd14f95","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47255546,"name":"Immunology"}],"tags":[],"updatedAt":"2025-04-16T12:45:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 12:45:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6455825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6455825","identity":"rs-6455825","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}