Contextual control of CD8⁺ T cell priming by dendritic cell subsets in tumor and inflammatory microenvironments

Contextual control of CD8⁺ T cell priming by dendritic cell subsets in tumor and inflammatory microenvironments | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Contextual control of CD8⁺ T cell priming by dendritic cell subsets in tumor and inflammatory microenvironments Victoria P Schuster , Kasidy Brown , Julia R Unsworth , Naomi Berkowitz , Megan K Ruhland doi: https://doi.org/10.1101/2025.10.31.685861 Victoria P Schuster 1 Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University , Portland, Oregon, 97201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kasidy Brown 1 Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University , Portland, Oregon, 97201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julia R Unsworth 1 Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University , Portland, Oregon, 97201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Naomi Berkowitz 1 Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University , Portland, Oregon, 97201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Megan K Ruhland 1 Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University , Portland, Oregon, 97201 2 Department of Dermatology. Oregon Health & Science University , Portland, Oregon, 97201 3 Knight Cancer Institute, Oregon Health & Science University , Portland, Oregon, 97201 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ruhland{at}ohsu.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Conventional dendritic cells orchestrate adaptive immunity by trafficking peripheral antigens to draining lymph nodes and presenting peptide–MHC complexes to prime naïve T cells. Migratory and lymph node resident conventional dendritic cell subsets occupy distinct anatomical niches and have been shown to shape T cell activation in a variety of immunologic contexts including infection, vaccination and cancer. How peripheral tissue context and dendritic cell subset specific transcriptional programs collaborate to determine CD8 + T cell priming remains incompletely defined. Using fluorescent antigen, we tracked antigen distribution, dendritic cell transcriptional programming, and functional cross-presentation across tumor, inflammatory, and steady state tissue contexts. We find that skin tumor antigen is more widely distributed amongst draining lymph node conventional dendritic cells than skin antigen derived from either steady state or inflamed skin tissue. Comparing across dendritic cell subsets, migratory type 1 dendritic cells display higher expression of MHCI antigen presentation machinery and genes associated with cross-presentation compared with lymph node resident type 1 dendritic cells in tumor and inflamed tissue contexts. Similarly, they exhibited superior per-cell cross-presentation and stronger induction of naïve CD8 + T cell responses. We find that both antigen access in the lymph node and cell-intrinsic cross-presentation efficiency together predict the magnitude and quality of CD8 + T cell priming regardless of tissue context. These results identify migratory dendritic cells, particularly type 1, as central mediators of antitumor CD8 + T cell responses and support therapeutic strategies that either restrict antigen dispersal from migratory dendritic cells or augment the efficiency of resident dendritic cell cross-presentation. SYNOPSIS Antigen transfer from migratory to resident conventional dendritic cell subsets is increased in melanoma compared to steady state or inflamed skin draining lymph nodes. Superior T cell proliferation and expression of cross-presentation machinery by migratory dendritic cells following antigen uptake of tumor suggests retaining antigen within these dendritic cell subsets may improve antitumor CD8 + T cell responses. INTRODUCTION Conventional dendritic cells (cDCs) regulate induction of T cell responses through presentation of tissue-derived antigen alongside tolerogenic or activating signals to appropriately activate T cells. Priming of naïve antigen-specific T cells occurs in the lymph nodes (LNs) and migratory cDCs have been shown to traffic exogenous antigen from the peripheral tissue to the LN to drive a productive T cell response ( 1 ). In order to generate CD8 + T cell responses to exogenous antigens, migratory cDCs process and present peptide-MHCI complexes on the cell surface through a process termed cross-presentation. Migratory type 1 cDCs (mDC1s) are particularly adept at stimulating CD8 + T cell responses through cross presentation. This function is critical for antiviral responses, antitumor immunity, and maintenance of self tolerance ( 1 – 3 ). While migratory type 2 cDCs (mDC2s) are generally considered to be more efficient at driving CD4 + T cell responses, they have also been shown to cross-present tumor antigen to CD8 + T cells ( 4 – 6 ). However, in the context of solid tumors, mDC1s have been shown to be indispensable for stimulating antitumor CD8 + T cell responses across many murine tumor models ( 6 , 7 ). Importantly, this is also reflected in human biology where high mDC1 signatures, not mDC2 signatures, correlate with improved CD8 + T cell tumor infiltration and patient survival ( 3 , 8 ). Following the transport of antigen to the LN, migratory cDC subsets are capable of transferring antigen as discrete vesicular packages to LN resident type 1 (rDC1) or type 2 (rDC2) cDCs ( 9 , 10 ). During this transfer of antigen from migratory to resident cDC, contextual cues derived from the tissue can also be passed including co-transfer of receptors within the vesicular package ( 11 ). This additional information regarding tissue context can facilitate synchronous T cell priming by both LN resident and migratory cDCs. However, understanding of the patterns and degree of antigen transfer from migratory to resident cDCs in different inflammatory contexts remains limited. In model systems where transfer has been well-characterized, it is clear that the state of the peripheral tissues heavily influences they dynamics of antigen passing in the draining LN (dLN) ( 12 ). These findings suggest that resident cDCs play a differential role in instructing the immune response that is dependent on the state of the peripheral tissue. Studies in viral immunity suggest that antigen transfer to rDC1s is essential for immunity ( 9 , 13 ). However, in anti-tumor immunity, studies suggest that rDC1s drive suboptimal CD8 + T cell responses ( 10 ). In any context, two main factors contribute to the ability of a given cDC to prime a naïve CD8 + T cell: 1) access to antigen, and 2) efficiency of cross-presentation. Understanding both of these aspects of cDC biology will be essential for designing therapeutic strategies that optimize T cell priming. Here we used model systems that enable antigen tracking to determine how antigen transfer to LN resident cDCs contributes to CD8 + T cell priming in different skin tissue contexts and evaluate the efficiency and cell-intrinsic differences in cross-presentation across migratory and resident cDC subsets. While previous work has studied cDC subsets within individual models, here we compare CD8 + T cell activation across tumor, inflammation and steady state to identify characteristics that may enable context-specific, therapeutic targeting of cDCs. MATERIALS AND METHODS Mice Mice were housed in a specific pathogen-free environment at the Oregon Health and Science University (OHSU) mouse facility and all experimentation methods were approved by the The Institutional Animal Care and Use Committee (IACUC) at OHSU. Mice used for the study include: C57BL/6 (bred in house or purchased from Jackson Laboratory, Strain #000664), OTI (C57BL/6-Tg(TcraTcrb)1100Mjb/J, bred in house or purchased from Jackson Laboratory, Strain #003831), K14-Cre; lox-stop-lox-ZsGreen mice were a kind gift from M. Krummel ( 10 ), and K14-Cre; lox-stop-lox-ZsGreen; K14-OVA mice were generated by crossing K14-ZsGreen animals with C57BL/6-Tg(KRT14-OVAL*)15Sika/J from Jackson Labs, Strain #026562 (K14-OVA). A combination of 6-12 week old male and female mice were used for experiments. Cell lines B16-ZsGreen and B16-ZsGreen-OVA cell lines were generated and validated as previously described ( 10 ). Briefly, the B16-F10 cell line (ATCC, CRL-6475) was virally transduced with constructs for ZsGreen or ZsGreen-OVA, respectively, and FACS sorted to select for stable genetic expression. Cell lines were housed in incubators at 37°C, 5% CO 2 , and 95% humidity. Cell lines were maintained with D10 media: DMEM (Gibco, 11995065), 10% heat-inactivated FBS (HyClone, SH3039603), and 1% Penicillin-Streptomycin-Glutamine (Gibco, 10378-016). Tumor injections B16-ZsGreen or B16-ZsGreen-OVA lines were harvested at confluency, washed with PBS twice and resuspended to 10 7 cells/mL in RPMI1640 (Gibco, 11875-119). Cells were diluted with Matrigel matrix (Corning, 356231) for a 1:1 mixture. Mixture was used to perform bilateral injections subcutaneously in both flanks of C57BL/6 mice at 50 μL, 2.5×10 5 cells per injection. B16-ZsGreen injections were grown for 14 days and B16-ZsGreen-OVA injections were grown for 21 days prior to harvest of skin draining lymph nodes. Topical applications Dorsal skin of K14-ZsGreen or K14-ZsGreen-OVA mice were shaved one day prior to beginning of topical applications. A 1:1 mixture of dibutyl-phthalate, DBP (Sigma Aldrich, NC1014483) and acetone was made, and 200 μL of the DBP mixture or acetone vehicle control was applied dropwise to the shaved backs of the mice. Topical applications were performed daily for a total of 7 days prior to harvest of skin dLNs. Lymph node digest Inguinal, axillary, and brachial LNs were harvested from tumor bearing C57BL/6 mice, treated K14-ZsGreen, or treated K14-ZsGreen-OVA mice. LN harvests and digests were performed as previously described ( 6 ). Briefly, for each mouse, LNs were harvested and placed into one well of a 24 well plate (Corning, 3524) containing 1mL RPMI1640 (Gibco, 11875-119) on ice. For each mouse, 1mL digest buffer was prepped in RPMI1640: 100U Collagenase, Type I (Worthington Biochemical, LS004197), 500U Collagenase, Type IV (Worthington Biochemical, LS004189), and 20 μg DNAse I (Roche, 10104159001). Cleaned LNs were punctured with sharp forceps to open capsule and placed into 1mL digest buffer in a well of the 24 well plate. Plates were incubated at 37°C for 30 minutes, pipetting up and down each well halfway through incubation time. LNs were then washed and filtered through a 70 μm cell strainer (Corning 431751) with MACs buffer: PBS (Gibco, 14190144) supplemented with 2% heat-inactivated FBS (HyClone, SH3039603), 1mM EDTA (Fisher BioReagents, BP2482100), 1% Penicillin-Streptomycin-Glutamine (Gibco, 10378-016), and 2mM HEPES solution (MilliporeSigma, H0887-100ML). Cells for flow cytometry were then stained in MACs buffer. Cells for FACS were negatively selected for CD2 using a biotin-conjugated anti-CD2 antibody (BioLegend, 100104) alongside an EasySep Mouse Streptavidin RapidSpheres Isolation Kit (STEMCELL Technologies, 19860) and magnetic depletion (STEMCELL Technologies, 18000), per manufacturer’s protocol, and then stained. Cell staining For intracellular staining, cells in a 96 well v-bottom plate (Corning, 3894) were spun down and resuspended in R10: RPMI1640 (Gibco, 11875-119), 10% heat-inactivated FBS (HyClone, SH3039603), 1% Penicillin-Streptomycin-Glutamine (Gibco, 10378-016), 0.1% 2-mercaptoethanol (Gibco, 21985023). GolgiPlug (BD Biosciences, 555029) was added to the resuspension media at 1 μL/mL and returned to incubator for 5 hours. Cells were then spun down and incubated for 15 minutes on ice with viability dye (BioLegend, 423105). Cells were then stained for surface markes for 20 minutes on ice with Fc Block (BD Biosciences, 553142) followed by permeabilization (if applicable) by resuspending cells in Cytofix/Cytoperm Solution (BD Biosciences, 554655) for 20 minutes on ice. Permeabilized cells were then stained with intracellular antibodies and Fc Block diluted in the Cytofix/Cytoperm Wash Buffer for 1 hour on ice. All samples were washed and resuspended in MACs buffer before acquisition with flow cytometry or cell sorting. Flow cytometry and sorting Cell sorting was performed with the BD FACSymphony S6 (BD Biosciences) with the 100 μm size nozzle and BD FACSDiva Software (BD Biosciences, v.9). Flow cytometry was performed with the BD FACSymphony A5 (BD Biosciences). FlowJo Software (Treestar, v.10) was used for flow cytometry analysis and proliferation modeling. T cell stimulation assays Spleens and skin draining lymph nodes were harvested from OTI mice. Tissues were mashed with syringe plunger. Splenic tissues were incubated in ACK lysing buffer (Gibco, A1049201) briefly and all samples were brought up to 10 mL with MACs buffer. Cells were pelleted and filtered through a 70 μm cell strainer (Corning, 431751) and CD8 + T Cells were isolated using a CD8 + T cell isolation kit (STEMCELL Techologies, 19853). Isolated T cells were incubated with 1 μM Violet Proliferation Dye (BD Biosciences, 562158) in PBS for 15 minutes at for at 37°C. Cells were resuspended to 5×10 5 cells/mL in R10. Sorted cDCs were washed and resuspended to 5×10 4 cells/mL in R10. T cell stimulation assays were set up at a 1:10 ratio in v-bottom plates with 100uL from each cell suspension (200uL total per well). Plates were incubated at 37°C, 5% CO 2 , and 95% humidity for 3 days prior to harvest. T cell activation was based off the gating strategy outlined in Figure 2A . RNA-sequencing ZsGreen + cDC subsets were sorted as outlined in Figure 1A , and the QIAshredder (Qiagen, 79654) was used to homogenize samples after lysis. RNA was extracted from 1×10 4 cell sorted cells with the RNeasy Micro Kit (Qiagen, 74004), per manufacturer’s protocol. Concentration and integrity measurement of each RNA sample was assessed using the Agilent Bioanalyzer 2100 with the Pico Chip assay. Library preparations and sequencing were performed by the Integrated Genomics Laboratory at Oregon Health and Science University with the SMARTSeq v4 PLUS Low Input RNA Kit (Takara) and the Illumina NextSeq 500 with 50 million read pairs per library. Sequencing files were uploaded to the Galaxy online platform at usegalaxy.org for processing ( 14 ). Within the Galaxy platform, Trimmomatic was used to trim adapter sequences of paired-end reads, HISAT2 was used to align reads to the mouse reference genome (GRCm38, mm10), htseq-count was used to count aligned reads in a BAM file, and DESeq2 was used for differential gene expression. The enrichment tool ShinyGo, v.0.82 ( https://bioinformatics.sdstate.edu/go/ ) was used for Gene Ontology enrichment analysis. Download figure Open in new tab Figure 1. Peripheral tissue state determines antigen dispersal amongst cDC subsets in dLNs. (A) Schematic for flow cytometry analysis of conventional dendritic cell (cDC) subsets from steady state K14-cre; lox-stop-lox-ZsGreen (K14-ZsGreen) or C57Bl/6 mice bearing subcutaneous B16-F10 tumors expressing ZsGreen (B16-ZsGreen). (B) Gating strategy of cDC subsets from draining lymph nodes (dLNs). Migratory type 1 cDC, mDC1. Migratory type 2 cDC, mDC2. Resident type 1 cDC, rDC1. Resident type 2 cDC, rDC2. (C - E) Flow cytometry analysis of (C) total cDC composition and (D) %ZsGreen + (ZsG + ) cDC composition of dLN cDC subsets from steady state or tumor conditions. (E) Mean fluorescence intensity (MFI) of ZsGreen + cDC populations from dLNs of tumor bearing mice. (F) Schematic for topical application of 50% (v/v) dibutyl phthalate (DBP) or vehicle (acetone) control. cDC subsets were harvested from skin dLNs for analysis. (G) %ZsGreen + cDC composition of dLN cDC subsets from steady state or inflamed conditions. (H) MFI of ZsGreen + cDC populations from dLNs of inflamed mice. (I) Relative proportion of ZsGreen + of cDC subsets. %ZsGreen + migratory or resident cDC subsets harvested from dLNs of steady state, tumor and inflamed skin tissue. (J) Relative antigen load of cDC subsets normalized to MFI of ZsGreen + mDC1 subsets in steady state, tumor or inflamed tissue conditions. (C - D, G) Ordinary two-way ANOVA with Šídák’s multiple comparisons test. (E, H) Ordinary one-way ANOVA with Tukey’s multiple comparisons test. (J) Ordinary two-way ANOVA with Tukey’s multiple comparisons test. In all plots: steady state control animals are same for tumor and inflamed comparisons. Data are plotted as mean +/− SEM. N = 6 from 3 independent experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. RESULTS Skin tumor antigen is more dispersed than antigen from steady state or inflamed skin in dLN cDCs To evaluate the dynamics of antigen dispersal in dLN cDC subsets, we used K14-Cre; lox-stop-lox-ZsGreen mice (K14-ZsGreen) or subcutaneous implantation of B16F10 melanoma cells expressing ZsGreen (B16-ZsGreen) to track skin sourced fluorescent antigen (keratinocyte or tumor, respectively) ( 6 , 10 ). Skin dLNs (inguinal, axillary and brachial) were harvested from mice, digested and stained with an antibody panel to identify cDC subsets ( Fig. 1A ). Similarly, dLNs were harvested from tumor-bearing animals 16 days post implantation, digested and stained ( Fig. 1A ). Flow cytometry was used to quantify differences in antigen + (i.e. ZsGreen + ) cDCs based on subset ( Fig. 1B , Fig S1A). In our evaluation of skin dLNs, we found that the proportion of cDC subsets is independent of peripheral tissue context ( Fig. 1C ). Indeed, mDC2 represent the dominant subset of cDC in dLNs in both steady state and tumor-draining conditions ( Fig. 1C ). However, the proportion of cDCs bearing skin or tumor derived antigen showed striking differences. The majority of antigen present within the steady state skin dLNs resided within the mDC1 subset ( Fig. 1D ). At steady state, approximately 50% of mDC1s were antigen bearing whereas less than 20% of mDC2s contained detectable antigen. rDC1s and rDC2s had negligible levels of antigen ( Fig. 1D ). Notably, while the proportion of mDCs loaded with antigen was significantly different, mDCs that had acquired antigen did so to equivalent levels based on mean fluorescence intensity (MFI) (Fig. S1B). This pattern of antigen loading was in stark contrast to skin tumor dLNs. There, antigen distribution was nearly uniform across the cDC subsets with no significant difference in loading between the two major migratory cDC subsets, mDC1 and mDC2 ( Fig. 1D ). Only rDC1s showed a significantly lower proportion of antigen loading than the migratory cDC subsets. Unlike the even distribution of antigen quantity within the individual cDC subsets at steady state (Fig. S1A), the quantity of antigen by MFI varied by subset in the tumor context ( Fig. 1E ). These findings suggest that even at steady state, migratory cDCs, particularly mDC1s, are continuously sampling cells from the periphery and draining to the LN. However, as previously shown, antigen passing to dLN resident cDCs was limited under steady state conditions ( Fig. 1D , S1B) ( 10 ). Conversely, tumor dLNs showed robust antigen dispersal throughout cDC subsets, with an increased antigen load in mDC2s and resident populations compared to steady state conditons ( Fig. 1D , Fig. 1E ). Given that tumor dLNs exhibit antigen dispersal patterns distinct from homeostatic skin, we next asked if an inflammatory peripheral environment was sufficient to induce antigen dispersal to resident cDC subsets. To this end, we applied dibutyl phthalate (DBP) 1:1 with acetone topically to the dorsal skin of K14-ZsGreen mice to induce inflammation. DBP is a commonly used murine skin sensitizing agent that facilitates the activation of Th2 inflammatory responses ( 12 , 15 ). Topical application occurred daily over the course of 7 days at which time animals were sacrificed, skin dLNs isolated, digested and stained to identify cDC subsets ( Fig. 1B , 1F , S1A). Similar to the tumor setting, the proportion of migratory and resident populations within the dLNs in the context of induced skin inflammation remained unchanged compared to steady state dLNs (Fig. S1C). While mDC1s displayed the highest proportion of antigen uptake from both non-inflamed and inflamed skin, the frequency of antigen acquisition by mDC1 and mDC2 was greater in the setting of skin inflammation ( Fig. 1G ), similar to what was observed in the tumor context ( Fig. 1D ). mDC1s in inflammatory conditions also acquired a significantly greater amount of antigen compared to the other the other ZsGreen + subsets, based on MFI ( Fig. 1H ), consistent with the important role of mDC1s in transporting antigen to the lymph nodes in response to peripheral inflammation ( 9 , 16 ). However, the amount of antigen transfer to rDCs was significantly less in the contexts of non-inflamed and inflamed skin than was observed in tumor dLNs ( Fig. 1I , S1D, S1E). Indeed, mDC1s in tumor dLNs were neither the population by proportion nor by antigen load to harbor the most tumor antigen, suggesting a high degree of antigen transfer to rDC1s and rDC2s ( Fig. 1D , 1J ) as suggested previously ( 10 ). Therefore, both peripheral tumor and inflammatory conditions increase antigen uptake by mDCs, while dispersal to resident populations occurs to the greatest extent in the context of tumors. Together, these data suggest that within tumor dLNs, mDC1s have less antigen available for cross-presentation than other cDC subsets and this phenomenon differs from an induced inflammatory condition. Tissue environment and cDC subtype both contribute to CD8 + T cell priming DLNs displayed different antigen dispersal patterns amongst cDC subsets based on peripheral environment ( Fig. 1D - 1J ). Given the critical role of antigen availability for cDC mediated CD8 + T cell priming, we next sought to determine whether the differential antigen dispersal to resident cDCs impacted CD8 + T cell priming characteristics. In order to track antigen-specific CD8 + T cell priming while still allowing for in vivo cDC antigen loading and dispersal, we used the model antigen ovalbumin (OVA). Specifically, we generated a K14-cre; lox-stop-lox-ZsGreen; K14-OVA mouse model where K14-expressing keratinocytes express both ZsGreen and OVA in order to evaluate T cell priming against skin derived antigen. We leveraged the B16-ZsGreen-OVA tumor cell line to study tumor antigen specific T cell priming ( 6 ). We treated mice with either vehicle (steady state) or DBP (inflamed) or implanted B16-ZsGreen-OVA tumors. ZsGreen + cDC subsets from each condition were isolated via FACS and cocultured with proliferation dye-labelled, OVA-specific, OTI CD8 + T cells ( Fig 2A , S2A). Following 72 hours of coculture, we next sought to determine whether the proliferating T cells were functionally different based on the subtype and peripheral tissue conditions of the cross-presenting cDC. Due to the very low numbers of ZsGreen + resident cDCs at steady state ( Fig. 1D , 1G ), it was not possible to obtain sufficient numbers of resident cDCs for T cell stimulation assays. To determine how priming of naïve CD8 + T cells by cDC subsets from different tissue contexts impacts the T cells, we used surface and intracellular antibody staining to evaluate T cell functional markers. Expression and nuclear localization of transcription factors T-bet and Eomes augment CD8 + T cell effector function, in part through their competitive, repressive binding within the promoter of Pdcd1 , a gene encoding PD-1 ( 17 – 19 ). At steady state, priming of naïve CD8 + T cells by mDC1s led to lower levels of both Eomes and PD-1 expression compared to mDC2 priming (Fig. S2B). Within the inflamed conditions, mDC1 priming also drove decreased PD-1 and Eomes expression in proliferating CD8 + T cells compared to T cells primed by resident cDCs (Fig. S2C). However, in tumor conditions, the increase in Eomes, a weak repressor of PD-1, was not observed, but an increase in T-bet coinciding with the increased PD-1 expression was found on CD8 + T cells primed by rDC1s compared to mDC1s (Fig. S2D). Combined, these data may suggest a minor improvement in differentiation of T cell effector population from priming by mDC1s ( 17 – 19 ). However, this difference was lost when comparing across the subsets and tissue contexts, indicating context dependent differences between subset priming may ultimately be minimal ( Fig. 2B ). Beyond differentiation, we also sought to determine functional differences of CD8 + T cells by assessing expression levels of effector cytokines IFNγ, TNFα, and Granzyme B. Both at steady state (Fig. S2E) and in inflamed conditions (Fig. S2F), proliferating CD8 + T cell production of effector molecules was not affected by cDC subsets. While an increase in the the percentage of IFNγ + T cells was observed when T cells were primed by tumor dLN derived rDC1s compared to mDC1s, this did not correspond with an increase in other effector molecules (Fig S2G). Furthermore, when comparing across subsets and tissues, there were no significant differences between the effector cytokine profiles of the proliferating CD8 + T cells ( Fig. 2C ). Download figure Open in new tab Figure 2. mDC subsets drive productive CD8 + T cell proliferation in steady state, inflamed and tumor contexts. (A) Schematic for proliferation assay of OTI CD8 + T cells cocultured with ZsGreen + cDC subsets sorted from skin dLNs of mice under steady state, inflamed, or tumor conditions. (B - C) Fold change of proliferating CD8 + T cells positive for (B) (top) Eomes, (middle) Tbet, (bottom) PD-1, (C) (left) interferon γ (IFNγ), (middle) granzyme B (GzmB), and (right) tumor necrosis factor α (TNFα) by flow cytometry. Mean -/+ SEM of 3 independent experiments normalized to mDC1 cocultured condition. Proliferation analysis by cDC subsets sorted from (D) inflamed conditions and (E) tumor conditions as shown by (left) representative flow cytometry plots and (right) quantification of % T cell proliferation. n = 6-8 from 3 independent experiments. (F) Fold change proliferation of T cells cocultured with cDCs sorted from steady state, tumor, or inflamed conditions. Mean -/+ SEM of 3 independent experiments normalized to mDC1 cocultured condition. (G) Representative proliferation histograms comparing mDC1 and rDC1 from (left) inflamed or (right) tumor conditions. (H - J) Proliferative modeling by cDC subset from (H) inflamed or (I) tumor conditions, and (J) quantification of highly proliferative CD8 + T cells across tissue conditions. Highly proliferative as defined by ≥ 4 proliferative events, indicated by shaded regions in (H) and (I). (D, E) Ordinary one-way ANOVA with Tukey’s multiple comparisons test. (B, C, F, J) Ordinary two-way ANOVA with Tukey’s multiple comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. While effector cytokine and transcription factor expression did not appear to differentiate cDC subset primed T cells, proliferation rates showed distinct patterns. Following 72 hours of coculture, we found that steady state ZsGreen + mDC1s drove only a modest level of OTI CD8 + T cell proliferation though significantly more than ZsGreen + mDC2s (Fig. S2H). Next, we evaluated T cell proliferation driven by cDCs isolated from the inflammatory condition. We found that antigen-loaded migratory cDCs drove increased OTI CD8 + T cell proliferation compared to steady state derived migratory cDCs with mDC1s acheiving the most potent T cell priming as measured by T cell proliferation ( Fig. 2D ). While rDC1s and rDC2s were capable of driving CD8 + T cell proliferation when isolated from the inflammatory context, this was at a significantly lower level than either mDC1s or mDC2s. In agreement with previous observations ( 6 , 10 ), tumor antigen-loaded migratory cDCs drove potent OTI T cell proliferation ( Fig. 2E ). Resident cDC subsets from tumor dLNs were also capable of driving CD8 + T cell proliferation, but as seen in the inflammatory condition, proliferation was significantly less robust. Interestingly, there was no significant difference between the priming efficiencies of a given cDCs subset across inflammatory and tumor conditions ( Fig. 2F ). This suggests that maturation signals from both the inflammatory and tumor environment are sufficient to induce antigen presentation on MHCI regardless of antigen origin. When comparing within conventional subsets (i.e. mDC1 vs rDC1) we found that the migratory subset consistently drove the highest level of cumulative OTI T cell proliferation in both inflammatory and tumor contexts ( Fig. 2G ). In both contexts, CD8 + T cells primed by resident cDCs underwent a reduced number of proliferative events compared to migratory cDC counterparts. Proliferative modeling to analyze the number of induced cell divisions from activated CD8 + T cells indicated mDC1s are the most robust at priming CD8 + T cells as shown by their ability to induce the greatest number of cell divisions ( Fig. 2H , 2I ). Under inflamed conditions, few activated CD44 + T cells remained undivided and most had undergone ∼4 divisions when cocultured with mDC1s ( Fig. 2H ). However, in mDC2 and resident cDC cocultures, the majority of activated T cells had undergone 0-1 divisions within the same timeframe. Similarly, mDC1s derived from tumor conditions drive ∼4 divisions of naïve OTI T cells within 72 hours ( Fig. 2I ). In contrast, while some CD8 + T cells again failed to proliferate in coculture with mDC2s isolated from tumor dLNs, those that did proliferate underwent a similar number of proliferative events as those cocultured with tumor dLN derived mDC1( Fig. 2H , 2I ). Regardless of the context from which the cDCs were isolated (inflamed versus tumor), when OTIs were primed by rDC1s or rDC2s, most often they only underwent a single round of division ( Fig. 2H , 2I ). Taken together, these data suggest that the cross-presenting cDC subset primarily impacts the kinetics of CD8 + T cell proliferation, with less impact on effector phenotype. These results underscore the distinct deficit in priming capacity by antigen-loaded resident cDCs compared to migratory populations, suggesting the increased antigen dispersal in tumor contexts ( Fig. 1I , 1J ) to these subsets in vivo may lead to less productive antitumor CD8 + T cell responses. mDC antigen uptake from tumor leads to preferential increased expression of antigen presentation proteins and cytokines Resident cDCs isolated from either inflammatory or tumor contexts were less efficient at priming naïve CD8 + T cells in vitro than migratory cDC populations ( Fig. 2D - 2J ). To discern transcriptional differences within cDC subsets between different tissue contexts, we performed bulk RNA sequencing from sorted dLN cDC subsets that were positive for peripheral antigen (ZsGreen + ) from either steady state, inflamed or tumor tissue ( Fig. 3A ). Given the noted transcription factor and surface level PD-1 differences from T cells primed by mDC1s compared to rDC1s within individual tissue states (Fig. S2C, S2D), we first looked within these subsets for gene expression differences between tumor and inflamed conditions. We compared expression of genes known to promote productive T cell proliferation, including genes involved in cross-presentation of cell-derived antigen, costimulation and cytokine production. Notably, we found expression of key inflammatory cytokines Il1b and IL12b were signficantly upregulated specifically in mDC1s (p-value of 0.049 and 0.0015, respectively), but not rDC1s, from tumor dLNs ( Fig. 3B , 3C ). Production of these cytokines, particularly IL12p70, produced by cDCs have been shown to be key for inducing Th1 polarization and are important for antitumor immunity ( 20 – 22 ). Beyond cytokine differences, we found that antigen loaded migratory cDC subsets isolated from tumor dLNs had significantly increased expression of multiple MHC genes compared to antigen loaded migratory cDCs isolated from inflamed contexts ( Fig. 3D , 3E ). The same MHC transcripts in tumor dLN rDCs were not found to be upregulated compared to rDCs from inflamed dLNs ( Fig. 3F , 3G ). Given the increase of MHC gene expression in migratory cDCs isolated from tumor dLNs, we next asked if MHCI expression is dependent on tissue context within each subset. Using flow cytometry, we found that the increase in surface level MHCI does not vary when comparing within cDC subsets between the different tissue environments ( Fig. 3H - 3K ), consistent with the similar levels of T cell proliferation observed between inflamed and tumor conditions ( Fig. 2B , 2C , 2F ). Download figure Open in new tab Figure 3. mDCs more effectively upregulate antigen presentation components in tumor conditions. (A) Schematic describing bulk RNA sequencing of ZsGreen + cDC subsets sorted from dLNs from steady state, inflamed, or tumor conditions. (B - C) Heatmap of normalized counts for gene expression from (B) mDC1s or (C) rDC1s derived from tumor versus inflamed conditions. (D - G) Differentially expressed genes from tumor vs inflamed conditions for antigen presentation genes of sorted (D) mDC1s, (E) mDC2s, (F) rDC1s, (G) rDC2s from dLNs. (H - K) Quantification of flow cytometry analysis of H2-K difference from ZsGreen + and ZsGreen - counterparts of cDC subsets (H) mDC1s, (I) mDC2s, (J) rDC1s, (K) rDC2s from dLNs of steady state, inflamed or tumor bearing mice. (L - N) Representative histograms (left) and quantification (right) of flow cytometry analysis of H2-K from ZsGreen + cDC subsets at (L) steady state, (M) inflamed, and (N) tumor. (O - R) (left) Quantification flow cytometry analysis and (right) estimation plot of H2-K on ZsGreen - and ZsGreen + paired subsets of (O) mDC1s, (P) mDC2s, (Q) rDC1s, (R) rDC2s from dLNs of tumor bearing mice. (H - N) Ordinary one-way ANOVA with Tukey’s multiple comparisons test. (O - R) Gaussian paired two-tailed t test. In all plots: steady state control animals are same for tumor and inflamed comparisons. Data are plotted as mean -/+ SEM. (H - R) n = 6 from 3 independent experiments. (L - N) Data are plotted as mean +/− SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Previous work highlighted the importance of antigen transfer in vaccine models suggesting that sharing antigen across cDC populations in the LN boosts antigen availability and drives increased T cell priming ( 9 , 23 ). The contribution of antigen passing to antitumor T cell priming in the tumor dLN remains unclear ( 10 , 11 ). When comparing across model systems, we found that antigen bearing mDCs have significantly higher MHCI surface levels compared to rDC subsets, independent of tissue context ( Fig. 3L - 3N ). These findings supported the data showing that mDCs drove the greatest proliferation in CD8 + T cells ( Fig. 2F , 2J ). Notably, in tumor conditions, surface level MHCI on mDC1s was significantly higher than that on mDC2 populations harboring tumor antigen, agreeing with previous work that suggests mDC1s are crucial for CD8 + T cell priming in the context of tumor ( Fig. 3N ) ( 6 , 8 , 24 ). We next asked how the uptake of antigen impacted surface MHCI levels on a per subset basis within the tumor dLN. We found that mDC1s have the highest increase in surface MHCI following antigen uptake ( Fig. 3O ), at over 40%, compared to mDC1s without tumor antigen. This increase was over 2-fold more than the mDC2 increase ( Fig. 3P ), and over 4-fold more than the increase seen for either resident cDC population ( Fig. 3Q , 3R ) This subset specific difference in increased surface MHCI levels upon antigen uptake highlights the specialization of mDC1 for driving antitumor CD8 + T cell priming. Taken together, these data show that antigen uptake drives subset specific increases in MHC gene expression and cytokine expression specific to mDC1s within the context of tumor ( Fig 3B , 3D ). In contrast, rDC1 expression of MHCI remains constant between tumor and inflamed tissue states ( Fig. 3C , 3F ). Further, migratory cDC populations, specifically mDC1s, are particularly well-suited for presenting antigen from tumor environments compared to resident populations ( Fig. 3N - 3R ). Therefore the high rate of antigen transfer from mDC1s to rDCs in tumor dLNs ( Fig. 1E , 1I , 1J ) may lead to overall decreased MHCI antigen presentation of tumor antigens, impeding optimal antitumor CD8 + T cell responses. MHCI machinery and associated molecular pathways are upregulated in mDC1s vs rDC1s in tissues Type 1 cDCs are strongly implicated in CD8 + T cell responses in both inflammatory and tumor conditions ( 24 – 26 ). Our intra-subset comparison of cDC1s between tissue contexts revealed rDC1s have less variable expression of cross presentation genes between tumor and inflamed tissue states compared to the increased expression of those genes in tumor draining mDC1s. Given evolving insights into migratory and resident cDC1 subsets and their progenitor populations ( 27 – 29 ), parsing out subset intrinsic gene expression differences across different tissue contexts may help refine their relative contributions to initiating crucial CD8 + T cell responses. Using RNA sequencing, we compared the mDC1s versus rDC1s within individual tissue states. We first compared mDC1s to rDC1s in an inflammatory tissue response ( Fig. 4A ). ShinyGo (v.0.82) was used to determine which cellular components were being upregulated in mDC1s. Compared to rDC1s, mDC1s primarily upregulated expression of the MHCI protein complex and associated proteins ( Fig. 4B ). mDC1s also had higher expression of the CD40 costimulatory molecule complex. Both of these components are important for T cell activation and antigen-specific immune responses. This was also reflected in the top biological pathways upregulated in mDC1s versus rDC1s from inflammatory conditions. Specifically, the pathways involved in peptide transport and formation of peptide-MHCI and peptide-MHCI-TCR bound complexes were upregulated in mDC1s ( Fig. 4C ). Further analysis of the cellular components involved in the peptide-MHCI loading complex showed specific gene upregulation primarily of the MHCI molecules in mDC1s and much of the associated machinery ( Fig. 4D ). Together these upregulated cellular components, pathways and MHCI genes suggest mDC1s may have increased interactions with CD8 + T cells through MHCI binding, likely contributing to improved CD8 + T cell priming ( Fig. 2B ). Download figure Open in new tab Figure 4. Type I mDCs display superior upregulation of peptide loading and presentation pathways compared to rDC. (A) Volcano plot of differentially expressed genes from mDC1s compared to rDC1s from dLNs of inflamed tissue microenvironments. (B) Pathway analysis of cellular components and (C) molecular function upregulated in mDC1s vs rDC1s from dLNs of inflamed tissues. (D) Normalized counts of antigen presentation genes from mDC1s and rDC1s from dLNs of inflamed tissues. (E) Volcano plot of differentially expressed genes from mDC1s compared to rDC1s from dLNs of tumor microenvironment. (F) Pathway analysis of cellular components and (G) molecular function upregulated in mDC1s vs rDC1s from dLNs of tumor tissues. (H) Normalized counts of antigen presentation genes from mDC1s and rDC1s from dLNs of tumor tissues. (D, H) Gaussian unpaired two-tailed t test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Given the large degree of antigen transfer from mDCs to rDCs in tumor dLNs ( Fig. 1E , 1K ), determining differences between mDC1s and rDC1s is important for understaning how antigen transfer from migratory to resident subsets in tumor dLNs impacts antitumor T cell responses. Thus, we next compared corresponding mDC1s and rDC1s derived from tumor dLNs ( Fig. 4E ). Similar analysis of these cellular components showed mDC1s were enriched in MHCI protein components and the costimulatory CD40 receptor complex compared to rDC1s ( Fig. 4F ). Top pathways in tumor dLN mDC1s were similar to those seen in the dLN mDC1s from inflammatory peripheral tissue states, primarily involved in peptide-MHCI loading and binding ( Fig. 4G ). As similar pathways and components were upregulated in mDC1s compared to rDC1s independent of inflammatory or tumor tissue, we investigated the context independent overlap between the cDC1 subsets by looking at upregulated genes in mDC1s from both tissue states (Fig. S3A). Top cellular components and pathways in both peripheral tissue environments supported the conclusion that MHCI machinery and binding are key differences between mDC1s and rDC1s (Fig. S3B, S3C). These components and pathways have the potential to promote T cell proliferation through MHCI loading and presentation of antigen. Importantly, these differences in the expression of genes regulating peptide MHCI loading and MHCI gene expression, alongside the MHCI surface protein differences ( Fig. 3N - 3R ) between cDC subsets could impact the overall quantity of antitumor T cell priming following antigen transfer to resident cDC populations. In summary, migratory cDCs, particularly mDC1s, are transcriptionally and functionally superior in cross-presenting skin tumor antigen compared to steady state or inflamed skin antigen. Resident cDCs, including rDC1s, fail to upregulate MHCI antigen presentation pathways in tumor dLNs to the same degree as mDC1s. Further, rDC1s exhibit lower baseline expression of MHCI machinery than mDC1s, which corresponds with reduced per-cell capacity to prime naïve CD8 + T cells. Tumor-driven dispersal of antigen through the dLN reallocates antigen from optimally equipped mDC1s to less capable resident cDC subsets, a process that is likely to blunt the magnitude and quality of antitumor CD8+ T cell responses. These findings identify antigen dispersal in the LN as a potential mechanism of suboptimal T cell priming in cancer and underscore the importance of preserving or selectively harnessing mDC1 antigen access and cell-intrinsic cross-presentation programs. These data support the development of immunotherapies that limit detrimental antigen dispersal, enhance resident cDC cross-presentation efficiency, or selectively target antigen vaccines to mDC1 subsets in an effort to boost antitumor immunity. Author Contributions V.P.S. and M.K.R. designed and conducted most experiments, data analysis, and drafted the manuscript. K.B., J.R.U. and N.B. supported animal experiments and discussed data and project direction. Acknowledgments We thank M. Krummel for providing the K14-ZsGreen mice and B16F10 reporter cell lines. We thank E. Alspach for discussion and support. Funding: This work was supported by Harry J. Lloyd Charitable Trust (M.K.R.), the V Foundation V Scholar Award (M.K.R), the Concern Foundation Conquer Cancer Now Award (M.K.R.), T32AI170496 (V.P.S.), T32GM142619 (K.B.) We acknowledge the OHSU Flow Cytometry Shared Resource (RRID SCR_009974) for assistance generating flow cytometry data and the Massively Parallel Sequencing Shared Resource (RRID SCR_009984) for generating the RNA sequencing data. Research reported here was supported in part by the Knight Cancer Institute CCSG grant NIH P30CA069533. Funder Information Declared Harry J. Lloyd Charitable Trust, https://ror.org/011ze6662 V Foundation for Cancer Research, https://ror.org/00kbbk236 Concern Foundation, https://ror.org/01hwjc346 National Cancer Institute, https://ror.org/040gcmg81 , P30CA069533 National Institute of General Medical Sciences, https://ror.org/04q48ey07 , T32GM142619 National Institute of Allergy and Infectious Diseases, https://ror.org/043z4tv69 , T32AI170496 Footnotes Authors’ disclosures: Authors declare no competing interests. REFERENCES 1. ↵ Ng SL , Teo YJ , Setiagani YA , Karjalainen K , Ruedl C . Type 1 Conventional CD103+ Dendritic Cells Control Effector CD8+ T Cell Migration , Survival, and Memory Responses During Influenza Infection. Front Immunol; Frontiers ; 2018 21 ; 9 : 3043 . OpenUrl 2. Joeris T , Gomez-Casado C , Holmkvist P , Tavernier SJ , Silva-Sanchez A , Klotz L , et al. Intestinal cDC1 drive cross-tolerance to epithelial-derived antigen via induction of FoxP3+CD8+ Tregs . Sci Immunol. 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Share Contextual control of CD8⁺ T cell priming by dendritic cell subsets in tumor and inflammatory microenvironments Victoria P Schuster , Kasidy Brown , Julia R Unsworth , Naomi Berkowitz , Megan K Ruhland bioRxiv 2025.10.31.685861; doi: https://doi.org/10.1101/2025.10.31.685861 Share This Article: Copy Citation Tools Contextual control of CD8⁺ T cell priming by dendritic cell subsets in tumor and inflammatory microenvironments Victoria P Schuster , Kasidy Brown , Julia R Unsworth , Naomi Berkowitz , Megan K Ruhland bioRxiv 2025.10.31.685861; doi: https://doi.org/10.1101/2025.10.31.685861 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Immunology Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41913) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13372) Ecology (19889) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22483) Immunology (17728) Microbiology (40365) Molecular Biology (17163) Neuroscience (88540) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15130) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9818) Zoology (2269)