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Lung tumor MHCII immunity depends on in situ antigen presentation by fibroblasts

Kerdidani et al. show that immunological hot spots in lung tumors are sustained by fibroblasts. Lung fibroblasts present MHCII cancer antigens locally and at the same time prevent CD4 T cells from apoptosis by activating the complement receptor C1qbp.


Introduction
The series of immunological events that takes place between tumors and tumor-draining LNs forms a cyclic trajectory that is being referred to as the cancer-immunity cycle (Chen and Mellman, 2013). In the first step of these events, tumor antigens are carried to the tumor-draining LNs and are partly transferred to resident dendritic cells (DCs; Ruhland et al., 2020). In LNs, migratory and resident DCs present their antigenic cargo to antigen-inexperienced (naive) T cells, which become differentiated effector cells that egress from LNs and enter tumors. In tumors, CD8 cells exhibit direct killing activity against cancer cells, but they are seriously dependent on CD4 T cells for function and transition to memory cells (Ahrends et al., 2019;Binnewies et al., 2019;Śledzińska et al., 2020;Zander et al., 2019;Bos and Sherman, 2010;Schietinger et al., 2010). Although our current understanding of the functional space in the cancer-immunity cycle is that MHCII cancer antigen presentation primarily occurs in LNs, the contribution of in situ cancer antigen presentation has not been ruled out (Dammeijer et al., 2020;Oh et al., 2020).
A few studies have directly addressed the role of peripheral antigen presentation in CD4 T cell responses (Dammeijer et al., 2020;Doebis et al., 2011;Low et al., 2020;McLachlan and Jenkins, 2007;Schøller et al., 2019). In cancer, three lines of evidence support that the TCRs are stimulated in situ within solid tumors. First, the CD4 TCR repertoire is regionally shaped by the local neoantigen load (Joshi et al., 2019). Second, stemlike CD8 T cells reside in dense MHCII-expressing cell niches within tumors (Jansen et al., 2019). Third, right flank tumors that differ only in one MHCII neoantigen with left flank tumors are infiltrated by higher numbers of neoantigen-specific CD4 + T cells (Alspach et al., 2019). DCs are scarce and immature within solid tumors and are generally considered to exert their primary effects in LNs (Dammeijer et al., 2020;Ferris et al., 2020;Maier et al., 2020;Ruhland et al., 2020;Oh et al., 2020). Because structural tissue cells greatly outnumber professional antigenpresenting cells, express immune genes (Krausgruber et al., 2020), and can be induced to present antigens (Koyama et al., 2019;Low et al., 2020), we hypothesized that they are required for local antigen presentation and anti-tumor immunity.
Fibroblasts are largely considered to be immunosuppressive cells (Biffi and Tuveson, 2021). Accordingly, the recently identified MHCII + antigen-presenting cancer-associated fibroblasts (apCAFs) in pancreatic adenocarcinoma (PDAC) and breast carcinoma (BC) are presumed to induce immune tolerance (Dominguez et al., 2020;Kieffer et al., 2020;Elyada et al., 2019;Friedman et al., 2020;Sebastian et al., 2020). Here, we show that MHCII-expressing fibroblasts are frequently found in lung non-small cell carcinomas and that they anatomically define areas of high rather than low CD4 T cell density. In orthotopic murine models of lung cancer, fibroblast-specific targeted ablation of MHCII impaired local immunity and accelerated tumor growth. Human and murine apCAFs directly activated the TCRs of effector CD4 T cells and, in parallel, protected them from apoptosis via the C1q receptor C1qbp, while C1qbp overexpression expanded the pool of adoptively transferred CD4 T cells in tumors and reduced tumor burden. Our studies identify and characterize lung apCAFs as tumor-suppressive cells and propose a new conceptual framework under which tumor MHCII immunity can be seen: CD4 T cells require in situ antigen presentation within tumors for effective MHCII immunity to occur.

apCAFs activate adjacent CD4 T cells within human lung tumors
To elaborate on the fibroblastic identity of MHCII + nonhematopoietic cells, we enzymatically dispersed primary human tumors and analyzed known mesenchymal markers by FACS ( Fig. 1 A). MHCII + Lin − CAFs (apCAFs) were phenotypically similar to their MHCII − counterparts and largely coexpressed FAP, PDGFRa, and Podoplanin. Vimentin was expressed at variable levels, while aSMA was lowly expressed (Fig. 1 A). Accordingly, two-dimensional (2D) visualization of lineage − cells in t-distributed stochastic neighbor embedding (t-SNE) plots (input all mesenchymal markers) confirmed that apCAFs do not cluster separately from MHCII − cancer-associated fibroblasts (CAFs). To determine the spatial relationship between apCAFs and CD4 T cells, we analyzed whole-tumor tissue sections by immunofluorescence staining. apCAFs were identified as CD45 − cells that coexpressed MHCII and FAP. We noticed that FACS had impressively underestimated the frequencies of fibroblasts compared with imaging. This discrepancy likely reflected differences in detachment efficiency of different cell types following tissue digestion, with fibroblasts being more adherent to the extracellular matrix (ECM) than immune cells and thus more difficult to disconnect. Quantitative analysis showed similar densities of intratumoral MHCII + FAP + CD45 − relative to MHCII + FAP − CD45 + hematopoietic antigen-presenting cells, indicating a physiologically relevant abundance of apCAFs. CD4 + T cells accumulated in dense apCAF regions and were identified closer to apCAFs rather than MHCII + FAP − CD45 + cells (Fig. 1 B). We interrogated whether we could identify MHCII fibroblasts in healthy lungs. To rule out the possibility of contamination by non-fibroblastic cell populations, we gated fibroblasts as FSC-A high Lin − FAP + cells. MHCII was expressed by a subset of normal lung fibroblasts, but it was more frequently detected in CAFs ( Fig. 1 C). Collectively, the above data suggest that the tumor microenvironment drives antigen presentation in fibroblasts and that apCAFs create functional immunological spots within lung tumors that might sustain CD4 T cell populations.
Spatial heterogeneity of T cell clones suggests regional activation by neighboring antigen-presenting cells that present their cognate antigens (Joshi et al., 2019). We reasoned that apCAFs directly presented cancer MHCII peptides to adjacent CD4 T cells. To test this, we co-cultured primary human CD4 T cells and FAP + CAFs, purified from the same lung tumor fragment, and assessed phosphorylation of the key TCR/CD28signaling node, mTOR ( Fig. 1 D). Primary cell phenotypes are dramatically altered once they are isolated and cultured. To avoid this bias, we used freshly FACS-sorted primary tumor fibroblasts and freshly sorted autologous primary tumorinfiltrating T cells for all our co-cultures. We used the bulk of CAFs rather than MHCII + CAFs to avoid blocking TCR recognition and did not supplement the culture medium with exogenous cytokines. Under these stringent conditions, ∼1 in 10 intratumoral T cells responded to direct CAF contact by mTOR phosphorylation and CD44 up-regulation, and these effects were abolished with MHCII blockade (Fig. 1 D). Control cocultures of apCAF-depleted CAFs and CD4 T cells confirmed the dependence of CD4 T cell activation on the apCAF fraction of bulk CAFs (Fig. S1). Thus, human apCAFs acquire exogenous peptides within tumors in vivo and can directly prime adjacent CD4 T cells ex vivo.
apCAFs are detected in tumors of IFN-γ-sufficient mice To assess whether apCAFs were present in murine tissues, we analyzed two models of orthotopic lung cancer and one metastatic. We inoculated one commercially available Lewis Lung Carcinoma (LLC) cell line (LLC mCherry ) or an autochthonous cancer cell line (CULA zsGreen ) in the left lung lobe of syngeneic mice or injected melanoma cells (B16F10 mCherry ) in the tail vein of mice. Tumors were digested and subjected to FACS analysis. Intratumoral FSC-A high CD45 − EpCAM − CD31 − IAb(MHCII) + noncancer cells coexpressed Podoplanin and PDGFRa, but not aSMA (Fig. 2 A). Murine FAP antibodies showed nonspecific and unreliable staining and were thus excluded from the analysis. Similar to human apCAFs, 2D visualization of all non-cancer lineage − cells in t-SNE plots indicated that apCAFs are admixed with MHCII − CAFs. Immunofluorescence staining detected MHCII + Podoplanin + cells within the tumor bed ( Fig. 2 A). Similar to what we had observed in humans, MHCII was more frequently expressed by CAFs relative to healthy lung fibroblasts (Fig. 2 B). To determine whether fibroblasts depended on IFN-γ for MHCII expression, we inoculated LLC cells in the lungs of IL-12 p35 −/− , IL12p40 −/− , IFN-γR −/− , and IFN-γ −/− mice. MHCII expression was decreased in CAFs of IL-12-deficient mice and severely impaired in those of IFN-γ/IFN-γR-deficient versus wild-type mice (Fig. 2 B).
To probe the contribution of the tumor microenvironment in sustaining MHCII in fibroblasts, we developed an in vitro model system where we sorted PDGFRa + Podoplanin + fibroblasts from LLC mCherry tumors and cultured them in vitro. Although by day 3 all cultured CAFs had lost MHCII, exposure to fresh lung tumor, but not to healthy lung homogenates, restored MHCII expression (Fig. 2 C). It should be noted that MHCII induction was observed upon three-dimensional (3D) but not 2D culture conditions, consistent with previous observations that 2D cultures induce MHCII loss in PDAC fibroblasts and differentiation to myofibroblasts (Elyada et al., 2019). Notably, neutralizing IFN-γ in tumor homogenates restrained MHCII induction in primary CAFs (Fig. 2 C).
Human and murine apCAF transcriptomes reveal conserved immune phenotypes and epithelial signature To characterize human antigen-presenting fibroblasts, we leveraged a public single-cell RNA sequencing (scRNAseq) dataset of primary human lung tumor, adjacent unaffected lung, and computational analyses (Lambrechts et al., 2018). There is no standardized computational method for characterizing gene expression levels in a binary form of "high" and "low." Furthermore, gene expression levels correlate variably with corresponding protein levels. We hypothesized that we would be able to identify antigen-presenting fibroblasts in single fibroblast RNA sequencing (RNAseq) datasets if we used classical DCs (cDCs) as a metric for physiologically relevant MHCII gene expression. Re-clustering of 1,284 single cells annotated as either fibroblasts (798) or cross-presenting cDCs (486; E-MTAB-6149 and E-MTAB-6653; n = 3 patients) via k-means clustering, using as input only input MHCII genes (k = 3 clusters supported by Silhouette scoring), separated fibroblasts into two clusters that shared (MHCII + ) or did not share (MHCII − ) MHCII expression features with cDCs. Different dimensionality reduction and visualization approaches using as input all 1,785 expressed genes indicated that MHCII + fibroblasts do not form a distinct cluster among total fibroblasts (Fig. 3 A). Differential expression analysis (DEA) revealed 115 deregulated genes in MHCII + versus MHCII − fibroblasts, 67 of which were up-regulated (Fig. 3 B). Among the top up-regulated were signature genes of PDAC ap-CAFs (CD74, SLPI; Elyada et al., 2019), the IL6 gene (known as a prototype inflammatory CAF gene; Biffi and Tuveson, 2021), and components of the complement pathway (CFD, C1QA, C1QB). Among the top down-regulated genes was ACTA2 (encoding the myofibroblastic CAF protein aSMA; Biffi and Tuveson, 2021), a number of collagen genes (COL3A1, COL1A1, COL1A2), and other secreted ECM proteins (SPARC, POSTN, BGN). Comparing pathway activities between MHCII + and MHCII − fibroblasts revealed enrichment in up-regulated genes that are involved in immunological processes and down-regulated genes involved in ECM organization (Fig. 3 B). Although MHCII + fibroblasts were interspersed among MHCII − fibroblasts in 2D plots, it was striking that SFTC (encoding for surfactant protein C) was on the top of the list of up-regulated genes, raising the possibility that a significant fraction of MHCII + fibroblasts may arise from alveolar type II (ATII) cells, which constitutively express SFTC and MHCII (Hasegawa et al., 2017). Three other genes known to be highly expressed by epithelial cells (albeit not considered lineage markers for epithelial cells), CLU (clusterin) and the antimicrobial proteins lysozyme (LYZ) and SLPI, were among the 11 top up-regulated genes (Habiel et al., 2017;Leiva-Juárez et al., 2018). Altogether, our computational analysis of the transcriptome of MHCII + fibroblasts depicts (i) prominent immune functions, (ii) a profile closer to inflammatory rather than myofibroblastic CAFs (IL6 high, a-SMA low, ECM proteins low), and (iii) strong cues of an alveolar epithelial origin.
To investigate whether the human lung MHCII fibroblast signature was conserved across mice, we sorted MHCII + and MHCII − CAFs and performed bulk RNAseq (Fig. 3 C). DEA (inputting all expressed 15,675 genes, adjusted [adj.] P < 0.05, |Log 2 fold change [FC]| > 3) revealed a large number of de-regulated genes (486), almost all of which (483) were up-regulated in MHCII + CAFs (Fig. 3 C). As in humans, among the top upregulated genes were the two MHCII CAF signature genes CD74 and SLPI. Pathway analysis was in concordance with the immune-related pathways that were identified in our human dataset (Fig. 3 D). Intriguingly, the ATII-specific genes SFTPs and many alveolar epithelial cell genes were again found to be increased (CLAUDINs, KRTs, SLC34A, NAPSA, LYZ). ATII cells are long-lived cells that self-renew locally (Barkauskas et al., 2013). To further support the hypothesis that MHCII fibroblasts originate from alveolar epithelial cells, we used alveolar cell type signature gene sets (curated from cluster markers, Materials and methods) and used them as inputs for gene set enrichment analysis (GSEA). GSEA comparing MHCII + versus MHCII − fibroblasts of mice and humans demonstrated that MHCII + fibroblasts were significantly enriched in genes that characterized alveolar epithelial cells in both species (Fig. 3 E). Primary ATII cells from healthy mice were induced to express known epithelial-to-mesenchymal transition (EMT) markers in response to tumor homogenates, adding more evidence to our hypothesis that apCAFs may evolve from ATII cells (Fig. 3 F). To align the gene signatures expressed across MHCII fibroblasts in mice and humans, we co-clustered genes based on their transcriptional profile in each species, using genes that were conserved and variable across MHCII fibroblasts in the human dataset ( Fig. 3 G). Briefly, 71 out of the 79 genes that were found up-regulated were successfully translated into their mouse homologies using biomaRt. Out of the 71 genes, 67 were found expressed in our mouse dataset. Thus, our analysis denoted conservation of the MHCII fibroblast profile across the two species. Altogether, these results indicate that the process of EMT regulates the in situ differentiation of ATII cells to a subset of fibroblasts with antigen-presenting functions.
Deletion of MHCII in fibroblasts has a local impact on tumorinfiltrating CD4 T cells We asked whether cancer antigen presentation by CAFs was a bystander, suppressor, or driver of tumor progression in vivo. We set up pilot experiments starting with three murine lines expressing Cre recombinase (Cre) driven off Twist (Šošić et al., 2003), collagen VI (Armaka et al., 2008), and collagen 1a2 (Col1a2) promoters (Zheng et al., 2002), which are lineage markers for mesenchymal cells. Lineage-restricted expression in LLC lung tumors was determined by Cre-driven GFP expression. An important fraction of intratumoral Twist-driven GFPexpressing cells were CD45 + cells consistent with cells of the hematopoietic lineage, while collagen VI-driven GFP-expressing cells could not be identified in lung tumors (Fig. S2 A). Col1a2driven GFP-expressing cells in lung tumors of Col1a2-CreER mice were CD45 − CD31 − EpCAM − and largely Podoplanin + , indicating that they were bona fide fibroblasts (Fig. S2 B). Therefore, we used Col1a2-CreER mice for our subsequent studies. IAb is the MHCII molecule expressed in C57BL/6 mice, and its expression can be deleted from Cre-expressing cell lineages using a floxed IAb gene (I-A b-fl/fl ; Hashimoto et al., 2002). We inoculated LLC mCherry cells that expressed the model antigen ovalbumin (Ova) in the lungs of Col1a2-CreER + I-Ab fl/fl and I-Ab fl/fl mice. Tumors appeared immunologically colder, with fewer CD4 T cells, CD8 T cells, and Ova-specific CD8 T cells, accompanied by increased tumor burden and decreased survival rates (Fig. 4,A and B). It could be hypothesized that invariant chain overload upon MHCII deletion induced lysosomal stress in apCAFs and impaired their function. To exclude this, we deleted MHCII from apCAFs of I-Ab fl/fl mice using Cre lentiviral vectors and studied their lysosomal function ex vivo. Labeling and FACS tracking of acidic organelles by LysoTracker showed no difference between MHCII-deleted and control apCAFs (Fig. S3). Similar results were obtained when proteolysis of DQ-BSA was quantified (Fig.  S3). Thus, we consider it unlikely that impaired apCAF function upon MHCII deletion is due to lysosomal stress.
To strengthen our data on the tumor-suppressive function of lung apCAFs in a different in vivo experimental setting, we established a cotransplantation model, where we coinjected cancer cells with primary in vitro expanded low-passage lung apCAFs. We reasoned that, similar to what others have shown, possible confounding effects from resident CAFs would be minimal (Hutton et al., 2021). Compared with control apCAF-depleted lung CAFs (non-apCAFs), lung apCAFs dramatically restricted subcutaneous tumor growth from two different cancer cell lines.
Thus, the tumor suppressive effect of lung apCAFs is stable and dominant after in vitro expansion (Fig. 4 C). Intriguingly, these findings could not be reproduced when BC-derived primary apCAFs were used for the cotransplantation studies, raising the possibility that the immunological functions of apCAFs depend on the tissue/cell type of origin (Fig. 4 D).
MHCII antigen presentation is expected to directly affect CD4 T cells. Thus, we sought to explore whether CD4 T cells were required for the in vivo tumor-promoting effect of fibroblastspecific MHCII deletion. CD4 T cell-depleting antibodies abolished differences in tumor burden between Col1a2-CreER + I-Ab fl/fl and I-Ab fl/fl mice, indicating that MHCII fibroblasts were strongly dependent on CD4 T cells for anti-tumor function (Fig. 5  A). Functionally, purified tumor-infiltrating lymphocytes (TILs) from Col1a2 CreER + I-Ab fl/fl versus I-Ab fl/fl mice demonstrated inferior cancer cell-specific cytotoxicity (Fig. 5 B). In addition, cancer-specific CD4 T cells were induced to produce high levels of effector cytokines upon ex vivo co-culture with primary lung apCAFs via MHCII (Fig. 5 C). Thus, MHCII antigen presentation contributes to the tumor-suppressive effects of apCAFs by increasing the effector functions of cancer-specific CD4 T cells.
RNAseq analysis of FACS-sorted TIL CD4 showed a high number of down-regulated genes in Col1a2-CreER + I-Ab fl/fl versus I-Ab fl/fl mice (Fig. 5 G). Among top down-regulated were genes involved in gene transcription (CRCT2, KAT7, EP300), cellular energy metabolism (GLUL, LIPA, PRKAG2), and protein ubiquitination and proteosomal degradation (PSMD6, CDC26, TRIM26; Fig. 5 G). Signature genes of T helper type 1 (Th1), Th2, Th17, and Th9 and regulatory T cells (Tregs), cytotoxicity, or exhaustion marker genes were not different between the two groups ( Fig. 5 F). Likewise, pathway activities of down-regulated genes showed significant enrichment in macromolecular metabolic processes. This agrees with the known fundamental role of TCR activation in T cell metabolism and effector functions (van der Windt and Pearce, 2012). In addition, when we looked into The Cancer Genome Atlas database, we found a strong positive correlation between the apCAF and CD4 effector/effector memory T cell signature genes in patients with non-small cell lung cancer (Fig. 5 H). Taken together, the results described above suggest that antigen presentation by lung MHCII fibroblasts is a critical driver of anti-tumor MHCII immunity.
LN fibroblastic reticular cells (FRCs) have contradictory roles in immune responses, such as inhibition of T cell proliferation via nitric oxide (Lukacs-Kornek et al., 2011) and T cell reprogramming toward memory T cells via IL6 (Brown et al., 2019). Albeit FRCs in LNs acquire transcriptional programs typically associated with tumor escape (Riedel et al., 2016), we considered the possibility that immune evasion in Col1a2-Cre + I-Ab fl/fl mice resulted from MHCII deletion in FRCs. We assessed mesenchymal-specific genes. Immunofluorescence for IAb (MHCII) and Podoplanin identifies MHCII fibroblasts. Samples were analyzed by confocal microscopy with a 40× objective. Bars, 50 µm. (B) Top: Representative FACS plots of IAb in FSC-A High Lin − Podoplanin + PDGFRa + fibroblasts of a CULA lung tumor and a healthy lung of wild-type C57BL/6 mice. Bottom left: Cumulative data on IAb expression of LLC, CULA, and B16F10 lung tumors and healthy lungs of wild-type C57BL/6 mice (n = 6-8 per group). Bottom right: Cumulative data on IAb expression of LLC lung tumors of cytokine-deficient mice (n = 4-7 per group). (C) FSC-A High Lin − Podoplanin + PDGFRa + fibroblasts were isolated from pooled LLC lung tumors and cultured in 3D, as indicated (n = 4 experiments). FSC-A, forward scatter-A; ISO, isotype control; Lung_homo, lung homogenate; Tumor_homo, tumor homogenate. (B and C) *, P < 0.05; **, P < 0.01. Error bars, mean ± SEM; two-tailed unpaired or paired t test.  (Fig. S4). However, there were no gross differences in nodal CD4 + , CD8 + T cells, and B cells (Fig. S4). We questioned whether antigen-presenting CAFs could sustain MHCI and MHCII immunity in the absence of continuous T cell migration from LNs. Therefore, we treated mice with an S1PR antagonist FTY720, which blocks egress of T cells from LNs, for 5 consecutive days, starting on day 7 after LLC mcherryOva transplantation. CD4 and CD8 T cells were again found to be decreased, while Ova peptide-MHCI tetramers stained less of CD8 T cells in lung LLC mcherryOva tumors of FTY720-treated MHCII conditional KO mice (Fig. 5,D and E). These results suggest that targeting MHCII in fibroblasts facilitates immune escape by acting locally and that T cell immunity in Col1a2-CreER + I-Ab fl/fl mice is compromised by MHCII loss in CAFs rather than FRCs.

apCAFs rescue intratumoral CD4 cells from apoptosis via the C1q receptor C1qbp
Effector T cells have less stringent and incompletely understood activation requirements compared with naive T cells (Chen and Flies, 2013). Since we could barely detect any of the classical costimulatory molecules (CD80, CD86, CD40) in MHCII fibroblasts, we mined their single-cell transcriptome looking for other immune-related molecules with ligand-receptor functions. Human MHCII + fibroblasts overexpressed TYROPB and CD47, which ligate to SIRP receptors CD52, and ANXA1, which bind to SIGLEC10 and ALX/FPR2, respectively, and C1q, which binds to cC1qR/calreticulin and gC1qR/C1qbp. None but the receptor of the globular head of C1q (i.e., gC1qr/C1qbp) was found to be expressed on the surface of primary human TIL CD4 cells (Fig. 6 A). C1qbp is located in most cell types in the mitochondria, where it is responsible for maintaining transcription of mitochondrial proteins. In the cytoplasm, it acts as a regulator of RNA stability and plays a critical role in mRNA splicing. The role of C1q as an extracellular signaling molecule and C1qbp as a plasma membrane receptor on T cells has been rarely reported (Kouser et al., 2015;Ling et al., 2018;West et al., 2018) and is largely unexplored. We set out to investigate whether MHCII fibroblast-derived C1q amplified TCR signaling on CD4 T cells via membrane C1qbp. First, we confirmed increased C1q gene expression by apCAFs versus MHCII − CAFs (non-apCAFs) in distinct patients (Fig. 6 A). There was also lower C1q gene expression in TIL CD4 than in apCAFs ( Fig. 6 A). Then, we cocultured human effector CD4 TILs and CAFs in the presence of C1qbp-blocking antibodies. Surprisingly, we did not observe any effect on TCR signaling, but a decrease in the numbers of viable T cells (Fig. 6 B). This led us to hypothesize that C1q is an extrinsic pro-survival signal for CD4 T cells. To test this, we cultured stimulated peripheral blood CD4 T cells for 1 h under serum starvation conditions. Purified C1q acted directly on T cells to almost completely rescue them from apoptosis in a dosedependent manner (Fig. 6 C). Moreover, aC1qbp substantially inhibited the pro-survival effect of C1q on T cells (Fig. 6 D).
To study the dependence of intratumoral CD4 T cells on apCAF-derived C1q, we depleted apCAFs from the CAF-TIL CD4 co-cultures. apCAF depletion abrogated the apoptotic effect of aC1qbp, indicating that aC1qbp blocks apCAF-derived C1q ( Fig. 6 E). We consider it unlikely that these results have been confounded by an autocrine effect of T cell intrinsic C1q because of the low C1q in TIL CD4 ( Fig. 6 A). To further substantiate the pro-survival effect of apCAF-derived C1q on T cells, we sorted primary murine lung apCAFs and knocked out C1qa via CRISPR-Cas9. CD4 T cells showed higher apoptosis rates when co-cultured with apCAF c1qaKO versus control apCAFs. (Fig. 7  A). Accordingly, subcutaneous tumors grew bigger when ap-CAF c1qaKO (versus control apCAFs) and cancer cells were cotransplanted, accompanied by lower CD4 T cell numbers (Fig. 7 B). In addition, when cancer-specific OTII T cells were induced to overexpress C1qbp and were adoptively transferred in lung tumor-bearing mice, an increased number of cancer-specific T cells was identified within lung tumors, accompanied by a decrease in tumor burden ( Fig. 7 C). Altogether, these data suggest that CD4 T cells depend on C1qbp for their survival within tumors and point to apCAFs as a potent C1q source.

Discussion
A functional dichotomy is currently thought to exist between draining LNs and tumors, whereby T cells are primed in LNs and exert effector functions in tumors. Here, we show that effector CD4 T cells require in situ stimulation by a subset of fibroblasts for effective immunity to occur in lung cancer. Fibroblastspecific-targeted ablation of MHCII induced an LN-independent impairment in local immunity, accelerating tumor growth. Primary human lung fibroblasts directly activated the TCRs of tumor-infiltrating CD4 T cells and, at the same time, protected them from apoptosis via the C1q receptor C1qbp, which unveiled a previously unrecognized function of C1q/C1qbp as an anti-apoptotic ligand/receptor pair for CD4 T cells. Our studies introduce fibroblasts as key peripheral cancer antigenpresenting cells and propose a new concept in tumor immunity: the final outcome of LN CD4 T cell priming depends on intratumoral MHCII antigen-presenting cells. patients). (B) Heatmap of differentially expressed genes (adj. P values < 0.05) between MHCII + and MHCII − fibroblasts. GO enrichment analysis was performed using DAVID. Network visualization was conducted by Cytoscape's EnrichmentMap. Nodes represent GO biological processes, and edges connect functional terms with overlapping genes. Clusters of related nodes are circled and given labels reflecting broad biological processes. GO terms with an FDR < 0.1 are shown. (C) Multidimensional scaling representation of bulk RNAseq data of sorted MHCII + and MHCII − fibroblasts from pooled mouse LLC lung tumors (n = 3 experiments). Differential expression between MHCII + and MHCII − fibroblasts. Deregulated genes with adj. P values < 0.05 are indicated. (D) GO enrichment analysis using up-regulated genes with |Log 2 FC| > 3 as input. (E) GSEA enrichment plots for alveolar epithelial gene sets. (F) Murine ATII cells were FACS sorted from healthy lungs and exposed ex vivo to tumor homogenate (TUMOR_HOMO). Expression of EMT genes was quantified by qPCR (n = 2 or 3, pooled from two experiments). (G) Homology analysis of gene expression across mouse and human MHCII fibroblasts. Tumor-homo, Tumor homogenate. (F) **, P < 0.01; ****, P < 0.0001. Error bars, mean ± SEM; two-tailed unpaired t test. LPS, lipopolysaccharides; Ag, antigen; NES, negative normalized enrichment score.
Journal of Experimental Medicine In tumor slides, MHCII fibroblastic cells were not identified within macroscopically visible tertiary lymphoid structures (TLSs), suggesting that intratumoral fibroblastic antigenpresenting niches are distinct to TLSs. It is likely that TLSs are mainly sites of B cell somatic hypermutation and plasma cell generation (Cabrita et al., 2020;Helmink et al., 2020;Petitprez et al., 2020), while intratumoral antigen-presenting fibroblasts form immunological niches for effector T cells. MHCII fibroblasts did not express classical costimulatory molecules but were still able to activate the TCR/CD28 pathway in effector T cells and enhance T cell metabolism. We consider two possibilities that may explain this contradiction: (i) costimulatory and coinhibitory receptors of effector T cells display great diversity and promiscuity (thus, MHCII fibroblasts mediate effector T cell activation via nonclassical costimulatory molecules; Chen and Flies, 2013), and (ii) antigen recognition suffices to activate effector T cells (Dubey et al., 1996).
C1q is known to regulate basic metabolic processes of T cells (Kouser et al., 2015;Ling et al., 2018;West et al., 2018) but has been disregarded as an extracellular signal. This is also related to the fact that the receptor of its globular head, C1qbp, has a very short intra-cytoplasmic tail and must complex with other membrane molecules to trigger intracellular signaling (Hosszu et al., 2012;Shi et al., 2017). Our results provide the first, to the best of our knowledge, demonstration that a complement component is anti-apoptotic, pointing specifically to exogenous C1q as a critical pro-survival ligand for CD4 T cells via C1qbp. The outcome of T cell therapy is highly dependent on T cell survival within tumors (Charo et al., 2005;Yamamoto et al., 2019). We showed that C1qbp overexpression increases the numbers of adoptively transferred tumor-specific CD4 T cells within tumors. Conceivably, it is likely that the increased efficacy of C1qbp-OTII cells is not only the result of apCAF-derived C1q. Nonetheless, our results showing that (i) apCAFs express high levels of C1q, (ii) they are a key antigen-presenting cell within lung tumors, (iii) apCAF-derived C1q acts directly on T cells to rescue them from apoptosis, and (iv) TIL CD4 cells reside within apCAF spots altogether suggest an important role for apCAFderived C1q as a pro-survival factor for TIL CD4 cells.
Single-cell/bulk RNAseq approaches revealed a conserved transcriptome signature among murine and human lung apCAFs that was enriched for alveolar epithelial genes, suggesting that lung antigen-presenting fibroblasts may arise from alveolar epithelial cells through the process of EMT. Albeit no safe conclusions can be made without experimental evidence from lineage tracing studies, the following arguments indicate that lung apCAFs originate from ATII rather than cancer or mesothelial cells, the last being considered as likely precursors of PDAC apCAFs (Dominguez et al., 2020;Hutton et al., 2021). (i) In the human dataset, tumor cells clustered away from fibroblasts, while in the mouse dataset, cancer cells were brightly fluorescent and negatively selected. Furthermore, we never detected fluorescence in murine fibroblasts that were cultured up to passage 5. (ii) In both human and mouse datasets, the most upregulated genes were surfactant proteins, which are highly specific for ATII cells. (iii) Constitutive MHCII expression is a hallmark of ATII cells (Hasegawa et al., 2017). (iv) ATII cells can transit to fibroblasts through the process of EMT (Goldmann et al., 2018;Hill et al., 2019). (v) Murine ATII cells underwent EMT ex vivo when exposed to tumor homogenate. The antigenpresenting functions of ATII cells are well recognized, but their immunological impact has not been extensively investigated (Wosen et al., 2018). Although we did not extensively study the function of other types of apCAFs, purified BC apCAFs did not show tumor-suppressive properties in our cotransplantation studies. Thus, lung antigen-presenting fibroblasts might be unique in their ability to activate effector T cells, and it is tempting to speculate that this could be due to their ATII origin, if such is proven to be. If the apCAF function greatly depends on tissue/cell type of origin, several other questions are raised. Are there specific cell types that are more likely to give rise to stimulatory apCAFs? What are the unique features of stimulatory versus nonstimulatory apCAFs? Are there stimulatory versus nonstimulatory subsets within the same tumor? Given the emergence of tumor stroma as a novel immunotherapeutic target, these are clearly therapeutically relevant and not merely theoretical questions.
One would expect that should lung apCAFs originate from ATII cells, they would create a distinct cluster when plotted in 2D scRNAseq plots with other fibroblasts. There are a few explanations as to why this was not observed. First, apCAFs are rare among total fibroblasts, and most dimensionality reduction approaches are biased in favor of frequent cell states. This is also supported by the fact that in most PDAC and BC scRNAseq studies, apCAFs do not form a separate cluster, but are rather found enriched in a cluster (Dominguez et al., 2020;Elyada et al., 2019;Friedman et al., 2020;Kieffer et al., 2020;Sebastian et al., 2020). Second, the identity of ATII cells is closer to that of fibroblasts after they have undergone EMT transition. Indeed, at the protein level, they expressed equal levels of key fibroblasts markers, such as FAP and PDGFRa and no EpCAM. They also displayed a characteristic fibroblastic morphology, and they grew easily in culture (primary alveolar epithelial cells are notoriously difficult to grow; Sahai et al., 2020).
The apCAFs that were recently identified in PDAC and BC are presumed to induce T cell anergy or differentiation to T regulatory cells (Elyada et al., 2019;Friedman et al., 2020;Sebastian et al., 2020). This presumption opposes the increase in Tregs that has been observed upon fibroblast deletion in PDAC (Özdemir et al., 2014). In our models, MHCII deletion in fibroblasts did not alter CD4 T cell subsets. However, it caused a profound decrease in the metabolic rate of tumor-infiltrating CD4 T cells. This finding fits perfectly well with the pivotal role of T cell metabolism in antigen-specific responses (van der Windt and Pearce, 2012). Further supporting the idea that nonhematopoietic cells can act as stimulating rather than tolerizing antigen-presenting cells, MHCII intestinal epithelial cells activate CD4 T cells in graft-versus-host disease, and diverse types of non-hematopoietic lung antigen-presenting cells prime T resident memory cells (Koyama et al., 2019;Low et al., 2020). IFN-γ drives MHCII expression in epithelial cells (Koyama et al., 2019). Our results also demonstrate that the tumor microenvironment controls MHCII in fibroblasts via IFN-γ. That said, positive clinical responses to immune checkpoint blockade are associated with increased IFN-γ and MHCII expression (Grasso et al., 2020;Johnson et al., 2016;Rodig et al., 2018). Accordingly, BC responses to aTGFβ are associated with increased MHCII expression in fibroblasts (Grauel et al., 2020). Thus, on the basis of our models, another mechanism behind the success of checkpoint inhibitors in lung cancer could be their ability to sustain IFN-γ-dependent apCAF spots, which in turn activate the unleashed effector T cells within tumors.
Current immunotherapeutic treatments largely focus on immune cells. Our works point to mesenchymal cells as key partners in cancer therapy. We already envisage a few possible scenarios for the immunotherapeutic exploitation of our findings: in vivo delivery of tumor antigens to apCAFs (in vivo vaccination) and amplification of the antigen-presenting fibroblastic spots with IFN-γ-inducing drugs, such as checkpoint inhibitors or aTGFβ. Conceivably, the above strategies are expected to require combinatorial T cell-directed therapies for maximum clinical benefits.

Materials and methods
Human samples Paired human lung tumor and macroscopically healthy lung resection specimens were obtained from patients with lung adenocarcinoma and lung squamous cell carcinoma at Sotiria Chest Hospital. The study was approved by the Sotiria Chest Hospital ethical committee board, and informed consent was obtained from all patients. lung adenocarcinomas and lung squamous cell carcinomas were clinically scored and staged according to the International Union against Cancer tumor, node, metastasis (TNM) staging system. The clinical and pathological characteristics of all patients included in this study are summarized (Table S1). locus. For T cell responses against Ova-expressing cancer cells, OTII mice that express the mouse α-chain and β-chain T cell receptor that pairs with the CD4 coreceptor and is specific for the chicken Ova 323-339 peptide in the context of I-Ab were crossed to Rag1 KO mice and mice that express the CD45.1 (Ly5.1 PTP) alloantigen. For reporter studies, Col1a2-CreER, ColVI-Cre, and Twist2-Cre were crossed to mTmG mice.

Cell lines
The LLC cell line, obtained from American Type Culture Collection, the C57BL/6-derived urethane-induced lung adenocarcinoma CULA cell line, and Ad-Cre-treated KRAS G12D ;Trp53 f/f C57BL/6-derived cell line KPM, provided by G. Stathopoulos cells were assessed by FACS using counting beads. (B) Col1a2 CreER + I-Ab fl/fl versus I-Ab fl/fl mice bearing LLC Ova lung tumors were adoptively (a.t.) transferred with OTII T cells. TILs were FACS sorted and incubated with CFSE-loaded LLC Ova (CSFE high = 5 μM CFSE) plus LLC (CSFE low = 0.5 μM CFSE) cells at a 60:1: 1 ratio. Cytotoxicity was determined at 16 h by quantifying the ratio of LLC Ova to LLC (n = 2-5 per group, pooled from two experiments). Representative and cumulative FACS data are shown. (C) CAFs from LLC Ova lung tumor-bearing mice were sorted and co-cultured with OTII T cells from OTII RAG −/− mice at a 1: 1 ratio plus MHCII-blocking antibody or isotype (ISO) control. Representative FACS plots of intracellular cytokines (n = 2 or 3 per group, representative of two experiments). (D) Depletion of circulating T cells upon FTY720 treatment. Representative FACS plots. (E) Lung tumor Col1a2 CreER + I-Ab fl/fl versus I-Ab fl/fl mice were treated with FTY720. Lung tumors were excised and analyzed by FACS (n = 6 or 7 per group, pooled from two experiments). (F and G) Intratumoral lung CD4 T cells were purified from Col1a2 CreER + I-Ab fl/fl versus I-Ab fl/fl mice (n = 7) and analyzed by bulk RNAseq. Expression heatmaps (mean values) of marker genes (F). MA (M [log ratio] and A [mean average]) plots and GO term enrichment analysis, with nodes in the networks representing biological processes and edges connecting terms with overlapping genes (G). Adj. P values > 0.05, except from: Stat6 0.0042, Stat3:0.0053 (F). Deregulated genes with FDR < 0.01 and GO terms with an FDR < 0.1 are shown (G).  T cells and CAFs were sorted from the same tumor fragment and co-cultured with aC1qbp-blocking antibody or isotype (ISO) control. Absolute (Abs.) numbers of live T cells were assessed by FACS with counting beads (n = 5 patients). (C) Peripheral blood (PB) CD4 T cells from a healthy donor were sorted and activated with aCD3/aCD2/aCD28 beads before undergoing serum starvation with or without purified C1q. Representative FACS plots of two experiments. (D) As in C, plus aC1qbp-blocking antibody or isotype control. Cumulative data of cell viability measured by Annexin V//dead stain by FACS (n = 6 donors). Representative FACS plots of caspase-3 activity. (E) CD4 T cells, total CAFs, and MHCII − CAFs were sorted from the same tumor fragment. T-CAFs were co-cultured with aC1qbp-blocking antibody or isotype control. T cell apoptosis was assessed using Annexin V and PI staining. Representative FACS plots and cumulative data are shown (n = 3 patients). (A-E) *, P < 0.05; ***, P < 0.001; ****, P < 0.0001. Error bars, mean ± SEM; one-tailed unpaired or paired t test. FSC-A, forward scatter-A; PBMC, peripheral blood mononuclear cell.   For Annexin V/propidium iodide (PI) stain, up to 5 × 10 4 cells were resuspended in Binding Buffer (0.01 M Hepes, pH 7.4, 0.14 M NaCl, and 2.5 mM CaCl 2 ) and stained with Annexin V FITC (lot# B284572, cat. no. 640906) for 30 min at 4°C. Cells were washed and resuspended in PI-Binding Buffer solution (0.1 mg/ml; Sigma-Aldrich; cat. no. P4170). For Caspase-3 stain, the NucView 488 Caspase-3 Assay Kit for Live Cells (Biotium) was used according to the manufacturer's instructions.
For murine 3D CAF cultures, CAFs sorted as FSC-A High Cancer cells zsGreen − CD45 − CD31 − EpCAM − PDPN + PDGFRa + cells were resuspended in liquified Matrigel (Corning; 354254) at 4°C. Approximately 30,000 cells/well were seeded in a 25-μl drop of Matrigel in the center of wells of a 48-well tissue culture plate. The matrix was allowed to set for 15 min at 37°C before adding 400 μl complete RPMI into each well. At day 5 of culture, RPMI medium was replaced with fresh RPMI supplemented with 30% healthy lung or tumor lung homogenate plus/minus anti-IFN-γ (10 μg/ml; Biolegend; cat. no. 505705). To obtain tissue homogenates, tumors or healthy lungs were excised, weighed, and homogenized in PBS (30% weight/volume) on ice. Samples were centrifuged at 13,000 rpm for 10 min at 4°C, and supernatants were added in wells containing Matrigel-encapsulated cells.
For ex vivo cytotoxicity assays, 4 d after adoptive transfer of OTII cells to LLC mcherryOva lung tumor-bearing mice, lung tumors were excised, and untouched total intratumoral T cells were FACS sorted from tumor single-cell suspensions as FSC-A low LIVECancercell mcherry . LLC mcherryOva and LLC cells were preconditioned with IFN-γ (20 U/ml) overnight to up-regulate MHC expression and the next day were labeled with 5 μΜ or 0.5 CFSE, respectively. Cells were mixed and plated together with T cells at a 60:1:1 ratio on V-bottom 96-well plates, centrifuged at 1,000 rpm for 10 min, and incubated for 16 h. Cytotoxicity was calculated as cytotoxicity = 100% × {1 − [(CFSE low /CSFE high ) control /(CFSE low /CSFE high ) experimental ]}.
For assessment of lysosomal stress, apCAFs from I-Ab fl/fl mice sorted as FSC-A High Cancer cells-zsGreen − CD45 − CD31 − EpCAM − PDPN + PDGFRa + MHCII + cells were infected with CRE mcherry or control lentiviruses. Low-passaged apCAF I-Ab fl/fl and apCAF I-Ab fl/fl-Cre were exposed to tumor homogenate for 72 h in 3D cultures supplemented with 30% tumor homogenate, as above, and then incubated with LysoTracker Green DND-26 (Thermo Fisher Scientific; L7526) for 1 h and DQ Green BSA (Thermo Fisher Scientific; D12050) for 2 h, following the manufacturer's instructions.
To study EMT in ATII cells, lungs from healthy mice were harvested and digested. ATII cells were sorted as CD45 − CD31 − EpCam + MHCII + and cultured in SAGM medium (Small Airway Epithelial Cell Growth Medium; LONZA; CC-3118) for 5 d. On day 5, medium was supplemented with 20% tumor homogenate for 48 h. For assessment of EMT, total RNA was extracted from ATII cells as described below.
In vivo studies Gender-and age-matched mice over 8 wk old were used for all studies. All mice were housed under standard special pathogenfree conditions at BSRC Al. Fleming. All animal procedures were approved by the Veterinary Administration Bureau, Prefecture of Athens, Greece, under compliance with the national law and the EU Directives and were performed in accordance with the guidance of the Institutional Animal Care and Use Committee of BSRC Al. Fleming.
For the lung cancer model, mice were anesthetized via i.p. injection with xylazine and ketamine. Cancer cells (2 × 10 5 ) resuspended in 50 μl DMEM (Gibco) and enriched with 20% growth-factor-reduced ECM (Matrigel; BD Biosciences) were intrapleurally injected in the lung parenchyma of mice using a 29G needle (BD Biosciences). For the metastatic cancer model, mice were injected i.v. with 4 × 10 5 B16F10 cells in 100 µl DMEM medium. For the orthotopic breast tumor model, 4T1 (10 5 ) cells resuspended in 50 μl of PBS were implanted into the third mammary fat pad. For all experiments with inducible Col1a2-CreER mice, tamoxifen (50 mg/kg; Sigma-Aldrich; T5648) dissolved in corn oil (Sigma-Aldrich; C8267) was given i.p. once per day every day except weekends for the duration of the experiment (5 injections/week, 2 wk), starting on day 0 of cancer cell injection in the lungs.
Monoclonal antibody aCD4/GK1.5 (American Type Culture Collection/TIB-207) was administered i.p. 2 d before tumor implantation and continued three times per week for the duration of the study (150 μg/mouse). FTY720 (Cayman Chemicals) was administered i.p. for 5 consecutive days, starting on day 7 after tumor cell transplantation at 20 mg per mouse.
For the adoptive T cell transfer experiments, T cells from OT-II/Rag1/CD45.1 mice were isolated and in vitro expanded/activated as above. For the cytotoxicity assay on day 7, 1,000,000 T cells per mouse were injected i.v. in LLC mcherryOva tumorbearing mice. For the C1qbp-OTII experiments on day 4, OTII cells were transduced with C1qbp lentiviral particles (Lenti ORF, C1qbp; Origene; NM_007573) versus Lenti-ORF Control Particles at a multiplicity of infection of 2. The transduction was performed for 24 h in medium supplemented with 8 μg/ml polybrene (Sigma-Aldrich). 2 d after lentiviral transduction, cells were placed in puromycin selection for 2 wk (2 μg/ml; Sigma-Aldrich; P8833). On day 22, 100,000 T cells per mouse were injected i.v. in LLC-OVA tumor-bearing mice.
Flow cytometry FACS analysis or sorting was performed using FACSCANTO II or BD FACSARIA III or BD FACSCelesta (BD Biosciences), and data were analyzed using Flowjo software. For calculation of absolute numbers of tumor cells (burden) or immune cells, counting beads (123Count eBeads; Thermo Fisher Scientific) were used. Ebeads are 7-µm microparticles that have higher side scatter than cell populations and contain fluorescent dyes excited by the blue (488 nm) or violet (405 nm) laser line. They are supplied at a known concentration (∼1,000 beads/µl). Ebeads were thoroughly mixed by vortexing and were carefully suspended and mixed with cell suspensions. The following equation was used for calculation of absolute counts according to the manufacturer's recommendations: Absolute count (cells/µl) = [(cell count × ebead volume)/(ebead count × cell volume)] × ebead concentration.
Immunofluorescence in T cells for the expression and localization of C1qbp were conducted in sorted intratumoral T cells. After sorting, T cells were washed twice and cytospun at 400 g for 4 min. After cytospin, slides were let dry for 5-10 min, and then cells were fixed for 15 min using 4% paraformaldehyde.

Image acquisition and analysis
Images were acquired using a TCS SP8X confocal system (Leica). We selected a fluorophore panel, which allowed for simultaneous visualization of three targets, and a nuclear stain (DAPI). During acquisition, fluorophores were excited with 405 nm (UV Laser), 488 nm (Argon), 555 nm, 594 nm, and 647 nm (White Light Laser). For images shown in Fig. 1, analysis was performed using Leica LASX. Tile scanning was performed in slides stained for pan-MHCII, CD45, CD4, and FAP, and nuclei were stained using DAPI. Autostitching using 10% overlap was performed by Imaris Stitcher. Imaris 9.6 was used for subsequent image manipulations. After creating a colocalization channel between pan-MHCII, CD45 (or CD45 inverted),and FAP (or FAP inverted), all channels were used to define "primary objects" (surfaces-spots) used to analyze the image (distances, cell number, XY positioning, etc.). Shortest distance calculation, average distance to nearest neighbor, and object identification tools were used for data acquisition and image analysis. Cell density analysis was performed by identifying each "object" in fields of 250,000 µm 2 . Data exported from Imaris 9.6, including XY location of CD4 + , MHCII + FAP + CD45 − , and MHCII + FAP − CD45 + objects, were used for density plot creation using custom R scripts.

Identification of human MHCII fibroblasts in scRNAseq datasets
Processed data were collected from publicly available, paired human lung tumor and healthy lung scRNAseq datasets (E-MTAB-6149 and E-MTAB-6653). The log 2 cpm values of the 22,180 ensembl gene IDs were used. The transition from the raw sequence read data to the gene expression table can be found in the original publication (Lambrechts et al., 2018). Processed cells annotated as fibroblasts were included in the analysis. Patient #1 was excluded from the analysis because of the small number of fibroblasts (nine cells). Patient #2 was excluded from the analysis because of the small number of healthy fibroblasts and DCs (5 and 11 cells, respectively). Only fibroblasts of patients #3, #4, and #5 were analyzed (n = 643, 155, and 486, respectively). To identify MHCII + and MHCII − fibroblasts, cells annotated as cross-presenting DCs in the same dataset were used as positive control for MHCII expression. The nine MHCII genes (HLA-DRA, HLA-DRB5, HLA-DRB1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DPA1, and HLA-DPB1) were used for reclustering fibroblasts and DCs with k-means function of cluster R package (Mächler et al., 2012) with k = 3 (tuned for k = 3:10), supported by mean Silhouette information scoring, calculated by Silhouette function of cluster R package. This resulted in the splitting of the cells into three groups (low, middle, and high MHCII expression). Fibroblasts of the middle and high groups were considered MHCII + fibroblasts (206 cells), while those of the low group were considered MHCII − fibroblasts. Gene IDs that had zero expression in all fibroblasts were removed from the analysis (16,877 IDs were kept). 15,487 of them were successfully translated into gene names using the biomaRt R package. After the characterization of MHCII + and MHCII − fibroblasts, genes with non-zero expression values in more than 25% of each group were kept (1,699 genes for apCAFs and 1,388 genes for non-ap-CAFs). The union of both lists was considered the list of the genes that are expressed in at least one group. This resulted in 1,785 genes.
Differential expression and pathway analysis of human datasets To identify the differentially expressed genes between the MHCII + and MHCII − , we used the FindAllMarkers function of the Seurat R package (Satija et al., 2015), giving as input nonscaled read values of the two clusters. We kept cells that had at least 10 features and removed ribosomal and mitochondrial genes. This resulted in 79 statistically significant up-regulated genes (adj. P value < 0.05, 67 with logFC > 1) and 55 statistically significant down-regulated genes (adj. P value < 0.05, 48 with logFC < −1). The 67 up-regulated and the 55 down-regulated genes were submitted to DAVID (Huang et al., 2009) for gene ontology (GO) term enrichment analysis. DAVID analysis was performed as previously described (Huang et al., 2009) using the official gene names (hg19) as background. GO using DAVID (6.8; Huang et al., 2009) was performed for terms classified as biological processes. An enrichment map was created in Cytoscape (v3.7.2; Shannon et al., 2003) using all significantly enriched GO terms (false discovery rate [FDR] < 0.1), with a gene set size between 10 and 1,000. Overlap was used as a metric, and the cutoff was set to 0.5. Clustering was performed using Au-toAnnotate (v1.3.2; Kucera et al., 2016) with overlap used as edge weight values.
Bulk mouse RNAseq data acquisition, processing, and analysis Intratumoral CD45 + CD3 + CD4 + T cells were FACS sorted in RL buffer (lysis buffer of cells before RNA isolation). Total RNA was isolated by using NucleoSpin RNA (Macherey-Nagel). RNA integrity was assessed on an Agilent Bioanalyzer RNA 6000 Pico Chip, and the library was constructed using NEB Next Ultra RNA Library Prep Kit. The sequencing platform Ion Torrent PROTON and 39-untranslated region sequencing strategy was used. Quality of FASTQ files, obtained after Ion Proton sequencing, was assessed using FastQC (https://www.bioinformatics. babraham.ac.uk/projects/fastqc/) following the software recommendations. Alignment of sequencing reads to the reference genome was performed using the software HISAT2 (version 2.1.0; Kim et al., 2019) with the genome reference version mm10. Bam files containing reads that were uniquely aligned were summarized to read counts table using the GenomicRanges package (Lawrence et al., 2013) through MetaseqR2 pipeline (Fanidis and Moulos, 2021) and default settings for 39untranslated region data. The resulting gene counts table was subjected to DEA with metaseqr2 function, using DESeq2 algorithm for the normalization and statistical testing. Genes with less than five counts in 75% of the samples were excluded from downstream analysis. DEA thresholds were set for FDR equal to 0.01 and for logFC + −1, returning 365 down-regulated and 19 upregulated genes. The 365 down-regulated genes were submitted to DAVID for GO term enrichment analysis. DAVID analysis was performed as previously described (Huang et al., 2009) using the official gene names (mm10) as background. GO using DAVID (6.8; Huang et al., 2009) was performed for terms classified as biological processes, as described earlier.
MHCII + CAFs and MHCII − CAFs from tumor-bearing mice were FACS sorted as FSC-A Ηigh CD45 − CD31 − EpCAM − mCherry − PDPL + PDGFRa + MHCII + and FSC-A Ηigh CD45 − CD31 − EpCAM − mCherry − PDPL + PDGFRa + MHCII − cells, respectively. Total RNA was isolated by using Single Cell RNA Purification Kit (Norgen Biotek) according to the manufacturer's instructions. RNA integrity was assessed on an Agilent Bioanalyzer RNA 6000 Pico Chip, and the library was constructed using NEB Next Ultra RNA Library Prep Kit. The sequencing platform Illumina Novaseq 6000 and Pair-end 150 strategy were used. Quality of FASTQ files was assessed using FastQC, following the software recommendations. Alignment of sequencing reads to the reference genome was performed using a two-way alignment procedure of initial mapping with STAR (version 2.7.3; Dobin et al., 2013) and remapping of the unmapped reads with bowtie2 (version 2.3.5.1; Langmead and Salzberg, 2012) packages with the genome reference version mm10. The raw bam files were summarized to read counts table using the GenomicRanges through MetaseqR2 R package pipeline, and the resulting gene counts table was subjected to DEA using the R package DESeq2 again through MetaseqR2 pipeline. DEA thresholds set for P value equal to 0.05 and for logFC 1.5 returned 2,216 down-regulated and 164 up-regulated genes. The 418 down-regulated genes with logFC > 3 were submitted to DAVID for GO term enrichment analysis, as described earlier.
GSEA of CAFs for alveolar genes GSEA was performed with (v4.1.0) software. The normalized counts table produced by DESeq2 was used for the GSEA according to software recommendations on the standard GSEA run. For human CAFs, the gene set included marker genes of an alveolar cluster that was identified in the same dataset (Lambrechts et al., 2018). For murine CAFs, we used a gene set that included marker genes of a murine alveolar cluster that was identified in another dataset (Strunz et al., 2020). The rest of the analysis was performed with the default thresholds.

Cross-species homology analysis
Up-regulated genes in MHCII + versus MHCII − fibroblasts were identified independently for each species, as above. The gene symbols of the genes found up-regulated in the human MHCII + fibroblast (67 genes) were translated to their mouse homologues through the biomaRt R package ("useMart" and "getLDS" functions). This returned 63 gene homology pairs. For each gene that returned a homologue, the relative gene expression value for each species was indicated by dividing the average gene expression value in the MHCII + cluster across each species plus a regularization constant (10e −4 ) by the average expression of all single fibroblasts (human dataset) or all samples (mouse dataset) plus a regularization constant (10e −4 ). The normalized expression matrices were log 2 -normalized, and their correlation was calculated by Pearson correlation distance.

Statistics and reproducibility
For the G-squared tests of independence, the g2Test_univariate function of Rfast R package was used. The P value was calculated only for the upper tail through the pchisq function of the same package. The Pearson correlation values were calculated with cor base R function.

Visualization
Principal component analysis was performed using the prcomp base R function, with centering but no scaling. Multidimensional scaling plots were drawn in R with ggplot2. Box plots were generated using the ggplot2 R package and default parameters. MA (M [log ratio] and A [mean average])plots and GO terms dot plot were generated using the geom_point function of ggplot2 R package. Plots were generated using the ggplot2 with dropping. Heatmaps were generated using the pheatmap function of pheatmap R package. Three different dimensionality reduction/ visualization methods were applied to the gene expression matrix of all human single-cell fibroblasts (1,294 cells): PHATE (Moon et al., 2019) was applied with the "phateR" R package with default parameters (knn = 5, decay = 40, t = auto), returning a 2D embedding. Uniform Manifold Approximation and Projection (Becht et al., 2019) was applied with the "umap" R package with default configuration parameters (n_neighbors = 15, metric = euclidean, n_epochs = 200, min_dist = 0.1), returning a 2D embedding. t-SNE (van der Maaten and Hinton, 2008) was applied with the "Rtsne" R package with default parameters (perplexity = 30, theta = 0.5, max_iter = 1,000), returning a 2D embedding.
Online supplemental material Fig. S1 shows the gating strategy for sorting untouched CD4 T cells and CAFs and co-cultures of human MHCII − CAFs with tumor-infiltrating CD4 T cells. Fig. S2 illustrates the lung CAF specificity of the Col1a2-CreER, ColVI-Cre, and Twist2-Cre mouse strains. Fig. S3 shows the lysosomal processes of MHCIIdeleted apCAFs. Fig. S4 shows the FACS analysis results of tumordraining LNs of the Col1a2-CreER strain. Table S1 lists the main data from the patients involved in this study.

Data availability
Murine CD4 T cell bulk RNAseq and CAF RNAseq data generated in this study are available through National Center for Biotechnology Information Gene Expression Omnibus (GEO) repository with accession numbers GSE164659 and GSE164653, respectively. Human scRNAseq data used in this study are available through ArrayExpress database (E-MTAB-6149, E-MTAB-6653; Lambrechts et al., 2018). Other relevant data are available from the corresponding author upon reasonable request. Figure S1. Human MHCII − CAF-tumor infiltrating CD4 T cell co-cultures. (A) Gating strategy for sorting untouched CD4 T cells and CAFs from the same tumor fragment. CAFs are sorted as live FSC-A high Lin − CD45 − FAP + and untouched CD4 T cells as FSC-A low Lin − CD45 + . (B) Purified CD4 T cells and FSC-A high Lin − CD45 − FAP + MHCII − CAFs were co-cultured with panMHCII-blocking antibody or isotype (ISO) control. Representative FACS plots and cumulative data on phospho-mTOR (pmTOR) and CD44 (n = 5 patients). *, P < 0.05; **, P < 0.01. Error bars, mean ± SEM; two-tailed paired t test. aMHCII, pan-MHCII blocking antibody; FSC-A, forward scatter-A; MFI, mean fluorescence intensity; SSC-A, side scatter-A.

Supplemental material
Kerdidani et al.
Journal of Experimental Medicine S1 Fibroblasts sustain lung tumor MHCII immunity https://doi.org/10.1084/jem.20210815   Table S1 is provided online and lists main patient data, including number of patients enrolled in the study, age, gender, histology, smoking history, and TNM stage.