key: cord-0812087-rujs0clj authors: Korniotis, Sarantis; Saichi, Melissa; Trichot, Coline; Hoffmann, Caroline; Amblard, Elise; Viguier, Annick; Grondin, Sophie; Noel, Floriane; Mattoo, Hamid; Soumelis, Vassili title: GM-CSF-activated human dendritic cells promote type1 T follicular helper cells (Tfh1) polarization in a CD40-dependent manner date: 2022-04-28 journal: bioRxiv DOI: 10.1101/2022.04.28.489850 sha: 8fc9572913319224119138cc5fb1d5517ddc2898 doc_id: 812087 cord_uid: rujs0clj T follicular helper (Tfh) cells are specialized CD4+ T cells that regulate humoral immunity by providing B cell help. Tfh1 sub-population was recently identified and associated with severity in infection and autoimmune diseases. The cellular and molecular requirements to induce human Tfh1 differentiation are unknown. Our work investigated the role of human dendritic cells (DC) in promoting Tfh1 differentiation and their physiopathological implication in mycobacterium tuberculosis and mild COVID-19 infection. Activated human blood CD1c+ DC were cocultured with allogeneic naive CD4+ T cells. Single-cell RNA sequencing was then used alongside protein validation to define the induced Tfh lineage. DC signature and correlation with Tfh1 cells in infected patients was established through bioinformatic analysis. Our results show that GM-CSF-activated DC drove the differentiation of Tfh1 cells, displaying typical Tfh molecular features, including 1) high levels of PD-1, CXCR5, and ICOS expression; 2) BCL6 and TBET co-expression; 3) IL-21 and IFN-γ secretion. Mechanistically, GM-CSF triggered the emergence of two distinct DC sub-populations defined by their differential expression of CD40 and ICOS-ligand (ICOS-L), and distinct phenotype, morphology, transcriptomic signature, and function. We showed that Tfh1 differentiation was efficiently and specifically induced by CD40highICOS-Llow DC in a CD40-dependent manner. Tfh1 cells were positively associated with a CD40highICOS-LLow DC signature in patients with latent mycobacterium tuberculosis and mild COVID-19 infection. Our study uncovers a novel CD40-dependent human Tfh1 axis. Immunotherapy modulation of Tfh1 activity might contribute to control diseases where Tfh1 are known to play a key role, such as infections. Significance Statement Dendritic cells (DC) play a central role in triggering the adaptive immune response due to their T cell priming functions. Among different T cell subsets, it is still not clear how human type1 T follicular helper cells (Tfh1) differentiate. Tfh1 cells are implicated in several physiopathological conditions, including infections. Here we show that GM-CSF induces diversification of human DC. Only CD40highICOS-LLow DC were able to drive Tfh1 cell differentiation. We found that CD40highICOS-LLow DC signature was associated to Tfh1 cells in mycobacterium tuberculosis and COVID-19 patients. Our data reveal a previously undescribed pathway leading to human Tfh1 cell differentiation and highlight the importance of GM-CSF and CD40 as potential targets for the design of anti-infective therapies. Tfh cells provide critical help to B cells for proliferation, somatic hypermutation, class-switch recombination, and differentiation into antibody (Ab)-producing plasma cells (1, 2) . They have been associated to several human diseases, including viral and bacterial infections (3) (4) (5) (6) (7) (8) asthma (9) (10) (11) , cancer (12) (13) (14) and autoimmune diseases (15) (16) (17) , but the mechanisms underpinning their development and functions are not well characterized. Circulating Tfh cells were divided into sub-populations sharing key phenotypic and functional characteristics with other T helper lineages such as Th1, Th2 and Th17 and displaying different capacity in regulating B cell responses (18) . The nature of the inflammatory microenvironment affects Tfh differentiation programs, which may subsequently regulate B cell immunity. Tfh1 cells were identified based on their cytokine profile, which is characterized by the co-production of IL-21 and IFN-γ, and specific phenotypic features: PD-1 + ICOS + CXCR5 + CXCR3 + CCCR6 - (18) . Tfh1 cells were increased in HIV infection where the Tfh1 population represents a major fraction of the viral reservoir (19) (20) (21) . Studies in mycobacterium tuberculosis infection (MTB) have revealed a role of Tfh1 cells and association with disease severity (22) (23) (24) (25) . Mouse studies showed an important role for Tfh1 in controlling Zika virus infection, as well as Lymphocytic Choriomeningitis Virus (LCMV) infection, mainly through IFN-γ secretion (26, 27) . Additionally, it has been recently shown that circulating Tfh1 cells (cTfh1) are positively correlated with the magnitude of viral specific antibodies in both influenza and COVID-19 patients (5) (6) (7) 28, 29) . In line with the results from the influenza vaccination, recently published findings in mice revealed that immunization with SARS-CoV-2 mRNA elicited potent viral-specific Tfh1 cells needed to produce long-lived plasma cells (29) (30) (31) . Tfh differentiation requires cooperation between antigen-specific interactions and signaling pathways, co-stimulation, cytokines, and chemokine receptors (32) . Dendritic cells (DC)-derived cytokines, such as IL-6, IL-12, IL-23 and TGF- promote surface CXCR5 expression on Tfh cells, facilitating their migration towards the T-B cell interface within the secondary lymphoid organ germinal centers (GC) (33, 34) . Additionally, human Tfh differentiation is driven by Activin-A in a SMAD2 and SMAD3 dependent way (35) . Given the established Tfh subset diversity, it is now of critical physio pathological and therapeutic importance to identify cellular and molecular mechanisms controlling specific Tfh differentiation pathways. We have previously shown that TSLP-activated DC could promote Tfh2 differentiation through OX40 ligand (36) . However, the factors inducing human Tfh1 differentiation remain elusive. Here, single-cell RNA sequencing (scRNAseq) analysis of human CD4 + T cells differentiated by GM-CSF-activated blood DC (GM-CSF-DC) revealed the presence of bona fide Tfh1 cells. Mechanistically, GM-CSF induced diversification of human DC into two phenotypically, transcriptionally, and functionally distinct subsets. Only CD40 high ICOS-L Low DC could efficiently drive Tfh1 polarization, in a CD40-dependent manner. Moreover, we found that Tfh1 cells were positively correlated with a signature of CD40 high ICOS-L Low DC in two different clinical settings of infection, mycobacterium tuberculosis and active COVID-19 patients. Overall, our results define a novel Tfh1 differentiation pathway, with potential molecular targets for its pharmacological manipulation. We decided to revisit human Th cell differentiation induced by GM-CSF-DC in a comprehensive manner using single-cell RNA sequencing (scRNAseq). Primary human naive CD4 + T lymphocytes were co-cultured 6 days with allogeneic primary blood cDC2 previously activated for 24h with LPS (LPS-DC), GM-CSF (GM-CSF-DC) or cultured in medium only (Medium-DC). scRNAseq was performed in sorted T cells after six days of co-culture, using the 10X-Genomics platform, on an average 5,600 cells per DC condition. This led to a total of 17,070 high quality sequenced cells, with an average 4,000 detected genes per cell. To probe the dataset for prototypical Th subsets, we used knowledge-driven signatures characteristic of Th1, Th2, Th17, and Tfh cells (Table S1 ). To be able to dissect Th diversity, we applied those signatures to CD4 + T cells generated with GM-CSF-DC. We dimensionality reduced the data using Principal Component Analysis (PCA) and we visualized them using Uniform Manifold Approximation and Projection (UMAP). Th1 and Th2 signatures were enriched in distinct cell clusters, representing 11.6% and 12.0% of cells, respectively (Fig. 1A) . The Th17 signature was not enriched in the whole dataset, indicating that GM-CSF-DC do not have the potential to induce Th17 differentiation program. Interestingly, two clusters were enriched in the Tfh signature. One of them co-expressed a Th1 signature, suggestive of Tfh1 cells (11.6%), while the other (16.5%) did not overlap neither with Th1-nor Th2-enriched clusters (Fig. 1A) . Tfh-enriched clusters were not detected in T cells activated by either Medium-DC or LPS-DC (fig. S1A). To better define the specific contribution of Tfh markers to the Tfh signature enrichment in the different cell clusters, we represented the expression of four key Tfh genes: BCL6, PDCD1 (PD-1), CXCR5 and IL21. All genes were highly expressed exclusively in CD4 + T cells differentiated by GM-CSF-DC (Fig. 1B) (p=0, comparing GM-CSF-DC and LPS-DC, GM-CSF-DC and Medium-DC, permutation test). Interestingly, the Tfh1-enriched cluster displayed higher expression for both the phenotypic markers PD-1 and CXCR5, the transcription factor BCL6 and the key cytokine IL-21, suggesting the induction of a stronger Tfh polarization within Tfh1 cells (Fig. 1C) . The induction of Tfh1 cells by GM-CSF-DC was additionally validated by using Pearson pairwise correlation matrices for phenotypic markers, transcription factors (TF) and cytokines known to characterize the three major T helper lineages, Th1, Th2 and Th17. We also observed a high correlation between the cytokines and TF, typical of Th1 and Tfh cells, within the in vitro generated Tfh cells. IL21 expression was strongly associated with Th1-related cytokines IFNG and TNFA, but not with IL4 or IL17A, Th2-and Th17-related cytokines, respectively. Similarly, the master TF of Tfh cells, BCL6, was highly associated with the expression of TBX21, TF of Th1 cells, but not with GATA3, TF of Th2 cells. Additionally, the classical phenotypic markers of Tfh cells, PDCD1 and CXCR5, displayed high correlation with IL-21, IFNG, TNFA, BCL6 and TBX21 but not IL4, IL13, IL17A and GATA3 (Fig. 1D) . When the expression level of these genes was represented separately, we observed that GATA3 and IL-4 were exclusively expressed in the Th2-enriched cluster without major expression of IL-13 ( fig. S1 , B and C). TNFA, IFNG and TBX21 displayed higher levels within the Tfh1-enriched cluster, which highlighted the importance of their coexpression in promoting efficient Tfh1 differentiation ( Fig. S1B and C) . Overall, our scRNAseq analysis revealed that GM-CSF-DC polarized a significant fraction of naive CD4 + T cells into Tfh1 at the transcriptomic level. 3E and 3F). The expression of RΟRγt might be only a result of transient activation of T cells since the expression of this TF was not associated with significant secretion of Th17-related cytokines, as IL-17A and IL-17F, both in supernatants or intracellularly ( Fig. S2B and S2C ). The percentage of cells co-expressing BCL6 and GATA3 was low (27.40%±2.56) , fitting well with the absence of IL-4 secretion. Conversely, in both Tlow (PD-1 low ) and DN cells, the co-expression of BCL6 with any of these three TF was significantly reduced, confirming that the in vitro induced PD1 high CXCR5 + Tfhlike cells were the only population displaying phenotypic features of Tfh cells ( Fig. 3E and 3F ). Next, we wanted to verify that the production of Tfh1-related cytokines (IFN-γ and IL-21) derived exclusively from the Tfh-like population. Sorted Tfh-like and Tlow cells were stimulated with PMA/Ionomycin and Brefeldin A for 4h and stained intracellularly for IL-21, TNF-α, IFN-γ, TBET and BCL6. As expected, only Tfh-like cells co-produced IL-21 and TNF-α ( Fig. S2D and S2E ). Among them, 19.60%±3.80 of cells expressed IFN-γ. Those cells were also positive for both BCL6 and TBET, confirming the Tfh1 polarization. Functionally, we asked whether the GM-CSF-DCgenerated Tfh-like cells were able to induce the differentiation of memory B cells into plasma cells. Sorted Tfh-like and Tlow (PD-1 low ) cells were co-cultured with both allogeneic naive and memory B cells (Fig. S3A ). After 10 days of co-culture, we identified significant proportions of CD19 low CD38 high CD27 high cells, standing for plasma cells, only in the Tfh-like co-culture condition. More specifically, we found that Tfh-like cells induced differentiation of memory B cells into plasma cells. The extent of this plasma cell differentiation was comparable to the one obtained with CpG-B-activated memory B cells, clearly showing the ability of Tfh-like cells in exerting prototypical Tfh functions. On the other hand, Tlow (PD-1 low ) cells promoted very low levels of plasma cell differentiation with both naive and memory B cells ( Fig. S3A and S3B ). These findings confirm that GM-CSF-DC is a new experimental condition allowing the induction of both phenotypical and functional Tfh-like cells. To explore further the mechanism used by GM-CSF-DC to induce Tfh1 differentiation, we sought to characterize the maturation profile of GM-CSF-DC. After 48 hours of activation, GM-CSF induced strong up-regulation of CD40, ICOS-L, CD86 and PD-L1 and intermediate upregulation of CD80, HLA-DR, CD25 and Nectin-II as compared to Medium ( Fig. 4A and 4B ). The presence of two peaks of expression for some of these markers over time suggested the existence of two DC subpopulations. T-SNE analysis of total GM-CSF-DC at day 1, day 2 and day 3 showed that GM-CSF induced the emergence of two different activated sub-populations from day 2 (Fig. 4C ). One population expressed high levels of CD40, and low levels of ICOS-L and PD-L1 and was labeled ICOS-L Low ; whereas the second population expressed low levels of CD40, and high levels of ICOS-L and PD-L1 and was labeled ICOS-L High (Fig. 4C) . Interestingly, the percentage of ICOS-L Low DC was higher than ICOS-L High DC at both day 2 and day 3 of GM-CSF stimulation (45. 180%±4.14 at day 2 and 39.45%±4.0 at day 3 for ICOS-L High , 54.06%±4.0 at day 2 and 59.12%±2.2 at day 3 for ICOS-L Low ) (Fig. S4A) . For further analysis, we sorted the two sub-populations at day 2 using CD40 and ICOS-L staining (Fig. 4D ). Using a Flow-Stream Imaging approach, we detected morphological differences between the two sub-populations. The ICOS-L High DC displayed a typical morphology of activated DC with high FSC/SSC levels, typical dendrites, and very low levels of circularity. The ICOS-L Low DC were rounder, FSC/SSC Low , with no dendrites, suggesting a less mature stage of differentiation ( Fig. 4E and Fig. S4B ). To address the question of cell plasticity, sorted ICOS-L High and ICOS-L Low DC at day 2 were re-stimulated with GM-CSF for additional 24h and 48h. We observed a stable phenotype of ICOS-L High DC after 24h as well as 48h of stimulation (Fig 4F and 4G ). On the contrary, ICOS-L Low DC were more plastic since 56.3%±9.9 of cells at 24h and 49.8%±6.0 of cells at 48h acquired a phenotype of ICOS-L High DC (Fig. 4F , 4G, 4H and 4I). We then thought to analyze the expression of GM-CSF receptor a chain (CD116) on the two subpopulations of GM-CSF-DC, as well as in Medium-DC and LPS-DC. ICOS-L High DC displayed higher levels of CD116 compared to ICOS-L Low DC, which could contribute to the lower secondary response of ICOS-L Low DC to GM-CSF exposure (Fig. 4J ). We observed that total GM-CSF-DC displayed two peaks of CD116 expression at both day 1 and day 2, suggesting that this marker could be also used to separate the two GM-CSF-induced sub-populations. ICOS-L High (CD116 + ) DC expressed lower levels of GM-CSF-R compared to LPS-DC but comparable levels to Medium-DC, whereas the ICOS-L Low DC (CD116 low ) down-regulated the expression of GM-CSF-R (Fig. S4C ). We hypothesized that the emergence of these two sub-populations could also be dose dependent. To address that, we activated DC for 48h with increasing doses of GM-CSF (10ng/ml to 100ng/ml). The emergence of these two sub-populations was dose-independent since the ratio between ICOS-L High and ICOS-L Low DC (10:6) did not vary between the different doses tested (Fig. 4K ). These data raised the question whether the GM-CSF-DC-induced sub-populations displayed also functional differences in promoting Tfh polarization. We performed 4-day and 6-day co-cultures of sorted ICOS-L High and ICOS-L Low cells with allogeneic naive CD4 + T cells. At day 4, we observed that ICOS-L Low DC were more efficient in driving Tfh differentiation since 29.6%±4.6 of cells displayed a Tfh phenotype compared to 13.5%±1.3 of cells in the ICOS-L High DC condition ( Fig. 5A and 5B ). Within Tfh-like cells, ICOS-L Low DC induced higher co-expression of BCL6 with either TBET (75.4%±3.7) or ICOS (42.7%±4.3), as compared to ICOS-L High DC-activated T cells (64.28%±3.69 for BCL6/TBET and 31.8%±2.7 for BCL6/ICOS). GATA3 was also co-expressed with BCL6, but at lower levels (58.4%±3.2 for ICOS-L Low and 61.9%±4.2 for ICOS-L High ) ( Fig. 5A and 5B). The higher percentage of PD1 high CXCR5 + Tfh-like cells induced by ICOS-L Low DC together with a significant co-expression of BCL6, ICOS and TBET within this population, suggested that this subset displayed the strongest potential in inducing a Tfh1 phenotypic profile. Next, we studied the cytokine production of T cells activated by each GM-CSF-DC sub-population at day 6, either in supernatants or by ICS. We observed that ICOS-L Low DCactivated T cells secreted high amounts of IL-21, IFN-γ, TNF-α and IL-12p70 whereas ICOS-L High DC-activated T cells secreted more CXCL13, IL-4, IL-10, IL-9 and IL-13 ( Fig. 5C and Fig. S5 ). Additionally, ICS showed that ICOS-L Low DC induced higher co-secretion of IL-21 and TNF-α by T cells (17.5%±3.1) as compared to ICOS-L High DC condition (8.4%±1.5). Among IL-21-producing T cells, ICOS-L Low DC promoted a high production of IFN-γ (46.6%±4.7) but not IL-4 (0.4%±0.1). Conversely, ICOS-L high DC induced significant IL-4-secreting Tfh-like cells (2.0%±0.5) but much lower levels of IFN-γ (7.5%±2.5) as compared to ICOS-L Low DC. (Fig. 5D and 5E ). Based on the cytokine profile of T cells, we concluded that ICOS-L Low DC were efficient inducers of Tfh polarization, favoring the differentiation into Tfh1, whereas ICOS-L High DC only promoted few Tfh-Th2 cells. There was no significant difference in the T cell polarization induced by total GM-CSF-DC as compared to the one induced by ICOS-L Low DC suggesting that this sub-population dominated over the ICOS-L High DC. This can be explained either by the higher number of ICOS-L Low DC (Fig. S4A ) or a cytokine competition, which favored the induction of a Tfh1 profile. To further explore the mechanisms by which these two phenotypically, morphologically, and functionally distinct sub-populations of GM-CSF-DC induced Tfh polarization, we performed RNAsequencing (RNAseq) analysis of sorted GM-CSF-induced ICOS-L High and ICOS-L Low DC (100.000 cells/sample, n=3 donors) (Fig. 6A) . We detected 5,118 significantly Differentially Expressed Genes (DEG) between the two sub-populations with an absolute fold-change higher than two. More specifically, 2,414 genes were up regulated in ICOS-L High and 2,704 in ICOS-L Low DC ( Fig. 6B and 6C). Focusing on checkpoints and maturation markers, we observed that ICOS-L Low DC expressed more HLA-DRB1 and HLA-DRA, CD276 (B7H3), and CD40, as expected. ICOS-L High DC expressed more negative checkpoints such as CD274 (PD-L1), PDCD1LG2 (PD-L2), TNFRSF9 (4-1BB), IDO1, IDO2, but also the positive checkpoint TNFSF4 (OX40-L), and the maturation molecules CD70, CD80, CD83, CD86 (Fig. 6D ). Among secreted molecules, ICOS-L High DC preferentially expressed IL-15, IL-7, IL-32 and the CCR7 ligand CCL19, whereas ICOS-L Low DC expressed more IL-16, IL-1B, TNF, TRAIL, and CCL4 (Fig. 6E ). These data raised the question whether some of the DEG were involved in the distinct Tfh polarization programs driven by the two GM-CSF-DC sub-populations. Both CD40 and ICOS-L have been already shown to participate in the crosstalk between DC and T cells (41) (42) (43) . Since their expression differed in the two sub-populations of GM-CSF-DC, we performed CD40 and ICOS-L blocking experiments to evaluate their respective role in Tfh differentiation. ICOS-L High and ICOS-L Low DC were incubated for 60min with anti-human CD40 and ICOS-L blocking antibodies before the co-culture with allogeneic naive CD4 + T cells. Considering our results, we tested the hypothesis whether GM-CSF-DC sub-populations are associated with the presence of Tfh1 cells in human infections. To explore a possible correlation between GM-CSF-DC signatures and Tfh subsets in vivo, we used two different clinical settings of infection: 1) whole blood microarray data from TB infected patients (38) and 2) scRNAseq of PBMCs from COVID-19 patients (mild, severe, and asymptomatic) alongside severe influenza patients (44) . Both studies included healthy controls. Based on our transcriptomic analysis of GM-CSF-DC sub-populations, we selected the top 9-10 genes expressed by each group and we created in-house signatures for ICOS-L High (DC-High) and ICOS-L Low (DC-Low) GM-CSF-DC (Table S2) . First, we applied a deconvolution method to assess the presence of all major immune cell types in the microarray data of whole blood from active and latent TB and healthy controls (Fig. S6A) , which allowed us to proceed to inter sample and inter cell type comparisons (Fig. S6B ). Using Pearson pairwise correlation matrices of DC-High and DC-Low signatures confronted with T effector (Th1, Th2, Th17 and Tfh) cell signatures (Table S3 ) we observed that Tfh cells were significantly associated only with DC-Low in active and latent TB patient samples, but not in healthy controls (Fig. 8A ). Next, we focused on a possible association with the diverse Tfh subsets. Interestingly, we identified a positive correlation between DC-Low and Tfh1 signatures in latent (r = 0.44), but not in active TB, and a negative correlation between DC-High and Tfh1 in both latent (r = 0.54) and active TB (r = 0.50) (Fig. 8B) . Neither DC-High nor DC-Low signatures had any significant positive correlation with other subsets of Tfh cells (Tfh2 or Tfh17) ( Fig. S6C and S6D ) emphasizing the important relation between the sub-population of ICOS-L Low GM-CSF-DC and Tfh1 cells, which might be needed to provide a protective environment in latent TB. In addition, we tried to validate our new experimental findings in a clinical setting of COVID-19 infection (Fig. S7A) . We sought to figure out whether we could detect any positive correlation between Tfh1 cells and DC-Low. First, the use of CD3 and CD4 as universal markers for the identification of T cells allowed us to detect them in all the disease groups. We also recovered the T-helper cell subsets using in-house constructed signatures ( Fig. 7B and Table S3 ). Th1, Th2 and Tfh signatures were observed in sufficient numbers in all patient groups whereas very few cells expressed Th17 signature. Interestingly, mild COVID-19 patients revealed higher Th1 numbers as compared to both severe COVID-19 and influenza. The same trend was observed for both Th2 and Tfh cells, suggesting a more efficient adaptive immune response in mild COVID-19 patients ( fig. S7C ). Since the focus of our study was on Tfh subsets, we used again in-house constructed signatures to identify Tfh1, Tfh2 and Tfh17 cells within the Tfh cluster. The distribution of the corresponding module scores revealed higher positivity for Tfh1 as compared to both Tfh2 and Tfh17 cells (Fig. 8C ). This observation was additionally confirmed by statistical comparison of the percentage of positive cells for each signature among total Tfh cells at the patient level. Tfh1 cells are increased mostly in mild COVID-19 patients (Fig. 8D) . However, the small cohort size did not allow the conduction of statistically significant tests. In parallel, we looked for the two DC signatures in CD11c + BDCA1 + cells (Fig. S7D) . We could not detect any cells expressing the DC-High signature in all disease groups whereas cells displaying DC-Low signature were present (Fig. S7D) . The violin plot representation revealed that enough DC-Low positive cells could be mostly detected in mild COVID-19 patients even if the ratio within the total DC cells is almost the same in all disease groups (Fig. S7E) . The estimation of both DC and Tfh subsets is defined by the ratio detected in each patient. Finally, we sought to correlate the percentages of Tfh subsets and DC-Low cells in each disease severity group by applying Pearson correlation test associated to p-value. The only condition where we detected a statistically significant (p<0.1) positive correlation is in mild COVID-19 patients and only between Tfh1 and DC-Low signatures. (Fig. 8E ). This observation might be explained by the better response of these patients which need a more activated Tfh1 profile for a more efficient differentiation of plasma cells. The presence of GM-CSF-ICOS-L Low DC might be necessary for inducing efficient Tfh1 responses. In this study, we provided evidence for the key role of GM-CSF-DC and CD40 in inducing polarization of human naive CD4 + T cells into bona fide Tfh1. BCL6 was shown to be the lineage-defining transcription factor of Tfh cells regulating their functional properties (45) (46) (47) (48) . Co-expression of BCL6 with other Th transcription factors may imprint Tfh cells with additional functions playing a crucial role in regulating B-cell induced immunity. More specifically, it was shown that viral infections promote TBET expression in Tfh cells, which contributes to IFN-γ secretion, and the type of antibodies produced by plasma cells (26, 49) . Even a transient TBET expression is sufficient to make Tfh cells secrete IFN-γ for long time (50). We had previously shown that TSLP-DC induce polarization of human naive CD4 + T cells into Tfh cells expressing high levels of GATA3 and secreting significant amounts of IL-4 (36) . More recently, a new study provided evidence for the existence of Tfh cells (TFH13) with an unusual cytokine profile (IL-13 high IL-4 high IL-5 high IL-21 low ) co-expressing BCL6 and GATA3, affecting high affinity IgE production, and subsequent allergen-induced anaphylaxis (10) . In support of these findings, we demonstrated that GM-CSF-DC-induced Tfh cells secreted large amounts of IFN-γ, and expressed high levels of TBET, confirming a strong relationship between transcription factors and subsequent cytokine secretion. However, the identification of two distinct sub-populations of GM-CSF-DC with different functions on T cell polarization raised the question whether the cytokine production can be dissociated from transcription factors. ICOS-L High -induced Tfh cells secreted higher levels of IL-4 and much lower amounts of IFN-γ as compared to ICOS-L Low -induced Tfh cells, but without any major differences in the expression of either GATA3 or TBET. These observations suggest that the expression of transcription factors might not be always sufficient to identify distinct Tfh subsets since the cytokine profile could reflect a different polarization program. This is not very surprising since ICOS-L:ICOS interactions between the Tfh and germinal center B cells or Tfh and DCs can lead to cytokine secretion that can signal through the Tfh leading to a transcription factor aside from GATA3 and/or TBET. We cannot exclude the possibility that our in vitro system might lead to a transient activation of other transcription factors. Further in vitro and ex vivo studies are needed to better understand the role of transcription factors in the functions of distinct Tfh subsets. GM-CSF was among the first cytokines shown to efficiently promote DC development in vitro from monocytes and hematopoietic progenitor cells (51) (52) (53) (54) . It is considered as a regulator of granulocyte, monocyte and DC lineage at all stages of maturation, with effects on cytokine secretion, cytotoxicity and antigen presentation capability (55) (56) (57) . In our study, we used single cell approaches to show that GM-CSF induces the diversification of human blood DC into distinct subpopulations with different phenotype, morphology, transcriptomic signature, and function. Interestingly, the function of total GM-CSF-DC in activating CD4 + T cells was like the effect of ICOS-L Low DC sub-population, showing that the latter had a dominant role in T cell polarization. Previous studies of bulk GM-CSF-DC may have been biased by the function of dominant DC sub-populations and hindered an underlying functional heterogeneity. CD40 expression on DC was shown to be necessary for promoting survival, cytokine production, and activation of naive T cells (58, 59) . CD40 is known to induce secretion of IL-12 resulting in enhanced Th1 immune responses (60) (61) (62) , but there is no direct link with other Th differentiation programs. Interestingly, the reduced Tfh cells in patients with immune deficiencies caused by mutations in CD40 ligand (63) raised the question about a potential role of CD40 in both the generation and maintenance of Tfh cells. In our study we showed by both transcriptomic and single cell protein approaches that CD40 was highly expressed by ICOS-L Low DC, the sub-population of GM-CSF-DC that induced a strong Tfh1 responses. Blocking of CD40 in GM-CSF-DC resulted in reduced frequencies of the common Tfh phenotypic markers PD-1 and CXCR5, together with decreased levels of their key effector cytokine, IL-21. Additionally, inhibition of CD40 expression induced a dramatic decrease in IFN-γ by IL-21-producing Tfh cells, favoring the secretion of IL-4. Altogether, these data suggest that the expression of CD40 by GM-CSF-DC is involved in both Tfh and Tfh1 polarization programs. Its absence might allow other co-stimulatory molecules to dominate their function creating a microenvironment with different effects on the type of Tfh differentiation. CD40 could be considered as a new therapeutic target for the manipulation of Tfh/Tfh1 cells. (5, 6, 67) . The severity of COVID-19 infection might be also associated with the function of Tfh1 cells since it has been recently shown that in active severe COVID-19 patients there is a loss of BCL6 + Tfh cells and GC, together with an increase in TBET + Th1 cells (68) . The huge production of cytokines in severe COVID-19 patients might block GC development, inhibiting the transformation of Th1 cells into Tfh cells (68, 69) . Taking advantage of publicly available data (70) we identified a positive correlation between Tfh1 cells and ICOS-L Low GM-CSF-DC exclusively in mild COVID-19 patients. Those patients are characterized by a better response to SARS-COV-2; by avoiding a strong cytokine storm, their immune system might allow to an efficient generation of Tfh cells and GC development. Additionally, their Tfh responses could be enhanced by the presence of DC displaying a specific activation profile favoring the polarization of Tfh1 cells. SARS-CoV-2 can make host cells secrete GM-CSF, which could enhance the ability of DC to better prime naive T cells during antigen-specific immune responses (71, 72) . Additionally, the capacity of GM-CSF to maintain pulmonary function and lung cell-mediated immunity, together with its protective functions in mouse models of influenza, suggested that GM-CSF administration is a possible therapy against COVID-19 (73) (74) (75) . Indeed, several clinical trials are already ongoing (73) . DC were cultured in RPMI 1640 Medium Gluta-MAX (Life Technologies) containing 10% Fetal Calf Serum (Hyclone), 100 U/ml Penicillin/Streptomycin (Gibco), MEM Non-Essential Amino Acids (Gibco), and 1 mM NA pyruvate (GIB CO). DC were cultured at 106 cells/ml in flat bottom plates for 24h or 48h in the presence of 50 ng/ml rhGM-CSF (Prospec) (unless differently specified) or 100 ng/ml ultrapure LPS (InvivoGen). For co-culture, activated-DC were washed twice in PBS 1X and put in culture with allogeneic naive CD4 + T cells (10 4 DC and 5x10 4 T) in X-VIVO 15 media (LONZA) for the indicated time. For coculture, CD4 + T cells were freshly purified from PBMC the day after DC purification or 2 days later, depending on the experimental condition. Coupling exclusively a single DC donor with a single CD4 + T cell donor was used to perform each co-culture experiment. DC were stimulated either with rhGM-CSF or LPS for 24h to activate total cells or only with rhGM-CSF for 48h/72h to induce the emergence of two different sub-populations. After 2 days of activation, sub-populations of DC were electronically sorted (ARIA III, BD) based on the expression of ICOS-L and CD40. Total activated DC or both sub-types of GM-CSF-DC were put in co-culture with allogeneic naive CD4 + T cells with the same ratio as mentioned before. DC were incubated at 37°C with 50 ng/ml anti-human CD40 antibody, or 50ng/ml anti-human ICOS-L or 50ng/ml of the corresponding isotype control (Biolegend). After 60 min, CD4 + naive T cells were added to the culture. Antibodies were maintained for the duration of the co-culture. At indicated time points, cells were either FACS sorted or used for surface or intracellular staining or washed and reseeded at 10 6 cells/ml and re-stimulated with aCD3/aCD28 beads (LifeTech) for 24h, after which supernatants and cells were collected for analysis. At day 4, T cells were counted and analyzed for the induction of a Tfh profile. At day 6, T cells were counted and analyzed for their cytokine production by FACS. Samples were sequenced at QuickBiology, Pasadena CA. Briefly, RNA integrity was checked by Agilent Bioanalyzer 2100. Library for RNA-Seq was prepared according to KAPA Stranded mRNA-Seq poly(A) selected kit with 201-300bp insert size (KAPA Biosystems, Wilmington, MA) using 250 ng total RNAs as input. Library quality and quantity were analyzed by Agilent Bioanalyzer 2100 and Life Technologies Qubit 3.0 Fluorometer. 150 bp paired-end reads were sequenced on Illumina HiSeq 4000 (Illumnia Inc., San Diego, CA). The reads were first mapped to the hg19 UCSC transcript set using Bowtie2 version 2.1.0 and the gene expression level was estimated using RSEM v1.2.15. Downstream analyses were performed using R (v3.6.0) and DESeq2 package (v1.26.0). Differentially expressed genes were determined with an absolute log-fold change threshold at 2 and an adjusted p-value below 0.01 We loaded the normalized dataset using GEOquery R package, and GSE19904 as studyID. We selected the top 9 differentially expressed genes between ICOS-L High DC and ICOS-L Low DC from the previous bulk transcriptomic analysis and used the T subtypes signatures previously used. We estimated the fraction of detected cell types in the samples using Quanti Seq deconvolution algorithm from the immunedeconv R package. For each DC and CD4 + T cells subtype, we calculated a signature score as the median expression values of the set of genes of a given signature. To assess the relationships between DC subsets and Tfh-Th1 polarization, we plotted the corresponding subtype signature scores and fitter the scatter graphs using linear regression (lm() function on R). We performed the analysis on R (version 4.1) using the Seurat R toolkit package for single cell data analysis. Associated metadata of the whole analyzed cells were loaded and added to the Metadata slot of the Seurat object. First, we created a subset Seurat object containing only CD4 + T cells. Next, we defined T helper (Th) subsets (Th1, Th2, Th17 and Tfh) using as input our in-house signature genes, and constructed scores for each Th subset for each individual cell, using "AddModuleScore" Seurat function, setting both the number of bins and control genes to n=100. A similar procedure was applied to retrieve CD1c + DC, using the expression levels of canonical markers (positive values for CD1C and ANPEP genes, and absence of expression of THBD gene). Similarly, we applied both DC-Low (ICOSL Low GM-CSF-DC) and DC-High (ICOS-L High GM-CSF-DC) signatures on the DC and followed the same procedure to estimate the percentage of DC for each patient. The analysis code of this work is available on Github (https://github.com/MelissaSaichi/Tfh_GMCSF-DC). (B) Percentages of PD-1 high CXCR5 + cells from data as shown in A; mean ± SEM from n=9. (C) and (D) Intracellular FACS staining for T cell cytokines induced either by ICOS-L high or ICOS-L Low DC incubated with blocking antibodies against CD40, ICOS-L and isotype control. One representative experiment is shown. IFN-γ and IL-4 production is gated within IL-21 + TNF-α + cells. Percentages of (E) IL-21 + TNF-α + cells, (F) IFN-γ + IL-4gated in IL-21 + TNF-α + cells and (G) IL-4 + IFN-γcells gated in IL-21 + TNF-α + cells from data as shown in D; mean ± SEM from n=8. Supplementary text Figures S1 to S7 Tables S1 to S3 Apheresis blood from healthy human blood donors were obtained from Etablissement Francais du Sang (French Blood Establishment) after written informed consent and in conformity with Institute Curie and Research Institute Saint-Louis ethical guidelines. Gender identity and age from anonymous donors were not available, but all donors were between 18 and 70 years old (age limits for blood donation in France). Peripheral blood mononuclear cells (PBMC) were isolated by centrifugation on a density gradient (Lymphoprep, StemCell Technologies) following standard protocols. Primary blood DC were purified according to an established protocol (37) . In brief, total PBMC were enriched in DC using the EasySep Human Pan-DC Pre-Enrichment kit (StemCell Technologies). Enriched DC were then sorted to obtain 98% purity on an MoFloAstrios Cell Sorting (Beckman Coulter) or FACS ARIA III (BD Technologies), as Lineage-(CD3, CD14, CD16, CD56, CD20 and CD19) (Miltenyi Biotech), CD4+ (Biolegend), CD11c+ (BioLegend), CD1c+ (eBioscience). After enrichment from total PBMC using the CD4 + T cell isolation kit (StemCell Technologies), naive CD4 + T cells were magnetically isolated. Purity was at least 95%. Antibodies were titrated on the relevant human PBMC population and matched isotypes controls were used at the same final concentrations. For intracellular cytokine staining, CD4 + T cells were stimulated with 100ng/ml PMA plus 500 ng/ml Ionomycin plus Brefeldin A (eBioscience) for 4 hours. When cells were sorted before intracellular staining, they were cultured overnight in X-VIVO medium at 10 6 cells/ml before PMA and Ionomycin stimulation. To exclude dead cells, CD4 + T cells were stained using the LIVE/DEAD Fixable yellow dead cell stain kit, following manufacturer's instructions (ThermoFischer Scientific). Cells were fixed and permeabilized using the IC Fix and Permeabilization buffers (eBioscience). Intracellular cytokines were revealed with fluorescently conjugated antibodies against IL-21 (Biolegend), TNF-(BioLegend), IL-4 (eBioscience), IFN-γ (eBioscience), and IL-17A (RD Technologies) or matched isotype controls (eBioscience) and acquired on a LSR Fortessa instrument (BD). For transcription factor intra-nuclear staining, dead cells were first stained with a yellow dye (BioLegend), followed by PD1 (Biolegend) and CXCR5 (BD) staining. After fixation and permeabilization using the FOXP3 IC buffer kit (eBioscience), cells were stained with an anti-BCL6 antibody (BD), TBET, GATA3, RORC, C-MAF, or SAP antibodies (eBioscience) and acquired on a LSR Fortessa instrument. As a control for intracellular staining of transcription factors, cells were stained with matched isotype controls at the same concentration as the transcription factor antibodies. The fluorescence obtained in each channel and in each population in the presence of the isotype control antibody (Fluorescence minus one [FMO] ) was subtracted from the fluorescence obtained by the specific staining of transcription factors in each population. After 4 days of co-culture with total GM-CSF-DC, CD4 + T cells were FACS sorted as PD-1 high CXCR5 + (Tfh-like cells) or PD-1 low CXCR5 + (Tlow cells). Allogeneic PBMC were thawed and, after a round of human memory B cell Enrichment, memory B cells were magnetically sorted using the EasySep Human Memory B cell isolation kit (StemCell Technologies). T and B cells were cocultured in X-VIVO medium in round-bottom plates (2.5 x 10 5 T cells and 2.5 x 10 5 memory B cells). At day 10 of culture, cells were harvested for FACS analysis. We performed single cell RNA sequencing analysis using Chromium 10X technology of naive CD4 + T cells stimulated with Medium-DC, LPS-DC or GM-CSF-DC. Cell Ranger Software was used to generate fastq files and align them on Grch38 human reference genome. The expression matrix datasets were loaded on R (version 4.0.0) and the whole analysis was performed using Seurat package (version 3.2.0 https://github.com/satijalab/seurat). The three datasets corresponding to each condition were analyzed separately. For each sample, cells expressing at least 100 genes were kept to discard debris cells. Pre-processing steps were applied to remove cells with less than 100 expressed genes or having more than 20% of mitochondrial transcripts. Upper cutoffs of 8,000 and 90,000 were manually set for the nFeatures and nUMI respectively for each sample. In contrast, lower cutoffs of 3,500 and 20,000 were also set for the nFeatures and nUMI, respectively. Normalization to 10,000 reads, centering, and scaling were sequentially applied on the expression matrices to correct for the sequencing depth variability. We used MAGIC (Markov Affinity-based Graph Imputation of Cells) (https://github.com/KrishnaswamyLab/MAGIC) imputation method to denoise the data and correct for the dropouts, and stored the corrected expression matrices into the "imputed" slot of the Seurat objects. To construct Th-related gene signatures, we used "AddModuleScore" Seurat function using 10 control genes and 4 bins. Cells, which overexpressed (or downexpressed) a given module, were attributed positive (or negative) scores. This strategy allowed us to point out cells, which coexpress the genes, used to identify cell types. The analysis code is deposited on Github: https://github.com/MelissaSaichi/Tfh_GMCSF-DC. The normalized count matrices were used to identify the highly variable genes within each dataset separately using the "mvp" method implemented in the "FindVariableFeatures" function. For each sample, PCA dimension reduction was applied on the top 3,000 genes, and the first 20 principal components (PC) were used for further steps, including cell community detection, clustering and non-linear dimension reduction. Cell clusters were identified using a shared nearest neighbor (SNN) clustering algorithm, which consists in a calculation of the k-nearest neighbors (k=30) then identification of cell communities (clusters) with a resolution parameter of 0.4. Non-linear dimensionality reduction methods, Uniform Manifold Approximation and Projection (UMAP) was used to explore and visualize the datasets, given as input the top 20 principal component genes. Samples were acquired with ImageStreamX MkII technology (Amnis/Luminex). Lasers power were set as 25mW for the 405 nm, 80 mW for the 488 nm and 200 mW for the 561 nm to excite respectively DAPI viability dye in channel 7 (430-505 nm filter), anti-CD40 FITC in channel 2 (480-560 nm filter) and anti-ICOS-L-PE in channel 3 (560-595 nm filter). Channels 1 (430-480 nm filter) and 9 (570-595 nm filter) were both used to collect bright fields. Data were acquired with the 60× magnifications, a 7 µm core size and low flow rate. Analysis was performed with IDEAS software to calculate Brightfield circularity (arbitrary unit) for each live population of interest selected based on their Mean Fluorescence Intensities of CD40 and ICOS-L. T cells were then put in co-culture with allogeneic memory or naive B cells for 10 days. Extracellular staining for the expression of CD19, CD27 and CD38 for one representative experiment (n=3). (B) Percentages of CD19 low CD27 high cells gated in total CD19 + cells and CD38 high CD27 high gated in CD19 low CD27 high cells from data as shown in A (n=3), *, P < 0.05; **, P < 0.01; ***, P < 0.001, by Wilcoxon or Student's t test. FIG 4D (n=5). (B) Histograms comparing the circularity levels of both ICOS-L High and ICOS-L Low GM-CSF-DC at day 2 by Imaging Flow Cytometry approach (n=3). (C) Expression of CD116 by FACS of human DC right after sorting (light grey histogram) or DC activated with LPS (blue histogram) or GM-CSF (red histogram) for 48h as compared to non-activated DC (orange histogram) and isotype control (dark grey histogram) for one representative experiment (n=4), *, P < 0.05; **, P < 0.01; ***, P < 0.001, by Wilcoxon or Student's test. 9 , IL-13, IL-12p70, and IL-6) assay for the measurement of cytokines in the supernatants of CD4 + T cells differentiated either by ICOS-L High or ICOS-L low GM-CSF-activated DC, after additional 24h stimulation with aCD3/aCD28 beads. Data are mean ± SEM from 5 independent experiments (n=5), *, P < 0.05; **, P < 0.01; ***, P < 0.001, by Wilcoxon or Student's test. Tfh Th1 Th2 Th17 Tfh1 Tfh2 Tfh17 ICOS PDCD1 CXCR5 IL21 BCL6 TNF IFNG TBX21 IL4 IL5 GATA3 IL17A IL17F RORC ICOS PDCD1 CXCR5 IL21 BCL6 TNF IL4 IL5 GATA3 ICOS PDCD1 CXCR5 ICOS PDCD1 CXCR5 IL21 BCL6 IL17A Table S3 . Top-9 differentially expressed genes characterizing the two in-vitro generated GM-CSF-activated DC subsets; ICOS-L High (DC-High) and ICOS-L Low (DC-Low). A dynamic T cell-limited checkpoint regulates affinity-dependent B cell entry into the germinal center Germinal center entry not selection of B cells is controlled by peptide-MHCII complex density The elusive identity of CXCR5(+) CD8 T cells in viral infection and autoimmunity: Cytotoxic, regulatory, or helper cells? Abortive T follicular helper development is associated with a defective humoral response in Leishmania infantum-infected macaques Humoral and circulating follicular helper T cell responses in recovered patients with COVID-19 Peripheral CD4+ T cell subsets and antibody response in COVID-19 convalescent individuals Spike-specific circulating T follicular helper cell and cross-neutralizing antibody responses in COVID-19-convalescent individuals Circulating T(FH) cells, serological memory, and tissue compartmentalization shape human influenzaspecific B cell immunity Helper Cell Subsets and the Associated Cytokine IL-21 in the Pathogenesis and Therapy of Identification of a T follicular helper cell subset that drives anaphylactic IgE Effect of follicular helper T cells on the pathogenesis of asthma CXCL13-producing TFH cells link immune suppression and adaptive memory in human breast cancer Circulating Tfh1 (cTfh1) cell numbers and PD1 expression are elevated in low-grade Distribution of circulating follicular helper T cells and expression of interleukin-21 and chemokine C-X-C ligand 13 in gastric cancer Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis Spatial and functional heterogeneity of follicular helper T cells in autoimmunity Human blood cxcr5(+)cd4(+) t cells are counterparts of t follicular cells and contain specific subsets that differentially support antibody secretion Frequencies of Circulating Th1-Biased T Follicular Helper Cells in Acute HIV-1 Infection Correlate with the Development of HIV-Specific Antibody Responses and Lower Set Point Viral Load Immunological Fingerprints of Controllers Developing Neutralizing HIV-1 Antibodies Tfh1 Cells in Germinal Centers During Chronic HIV/SIV Infection T cells and adaptive immunity to Mycobacterium tuberculosis in humans Mycobacterium tuberculosis-Specific IL-21+IFN-γ+CD4+ T Cells Are Regulated by IL-12 Decreased frequencies of circulating CD4+ T follicular helper cells associated with diminished plasma IL-21 in active pulmonary tuberculosis CXCR5+ T helper cells mediate protective immunity against tuberculosis STAT4 and T-bet control follicular helper T cell development in viral infections ZIKV infection induces robust Th1-like Tfh cell and long-term protective antibody responses in immunocompetent mice Circulating CXCR5(+)CXCR3(+)PD-1(lo) Tfh-like cells in HIV-1 controllers with neutralizing antibody breadth Induction of ICOS+CXCR3+CXCR5+ TH cells correlates with antibody responses to influenza vaccination SARS-CoV-2 mRNA Vaccines Foster Potent Antigen-Specific Germinal Center Responses Associated with Neutralizing Antibody Generation A Single Immunization with Nucleoside-Modified mRNA Vaccines Elicits Strong Cellular and Humoral Immune Responses against SARS-CoV-2 in Mice T follicular helper cell differentiation, function, and roles in disease Inhibition of IL-2 responsiveness by IL-6 is required for the generation of GC-T(FH) cells Follicular helper CD4 T cells (TFH) Activin A programs the differentiation of human TFH cells TSLPactivated dendritic cells induce human T follicular helper cell differentiation through OX40-ligand Purification of Human Dendritic Cell Subsets from Peripheral Blood An interferoninducible neutrophil-driven blood transcriptional signature in human tuberculosis Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNAseq data Mechanism of Follicular Helper T Cell Differentiation Regulated by Transcription Factors A p85α-osteopontin axis couples the receptor ICOS to sustained Bcl-6 expression by follicular helper and regulatory T cells ICOS maintains the T follicular helper cell phenotype by down-regulating Krüppel-like factor 2 Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19 orchestrates Tfh cell differentiation via multiple distinct mechanisms Bcl6 expression specifies the T follicular helper cell program in vivo Bcl6 middle domain repressor function is required for T follicular helper cell differentiation and utilizes the corepressor MTA3 T(FH) differentiation by regulating differentiation circuits upstream of the transcriptional repressor Bcl6 Context-Dependent Role for T-bet in T Follicular Helper Differentiation and Germinal Center Function following Viral Infection Generation of large numbers of dendritic cells from mouse bone marrow cultures supplemented with granulocyte/macrophage colony-stimulating factor Pillars Article: Efficient presentation of soluble antigen by cultured human dendritic cells is maintained by granulocyte/macrophage colony-stimulating factor plus interleukin 4 and downregulated by tumor necrosis factor α CD34+ hematopoietic progenitors from human cord blood differentiate along two independent dendritic cell pathways in response to GM-CSF+TNF alpha GM-CSF and TNF-alpha cooperate in the generation of dendritic Langerhans 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The role of CD40-CD40L interactions in Th1 T-cell responses Dendritic cells produce IL-12 and direct the development of Th1 cells from naive CD4+ T cells Critical role of IL-12 in dendritic cell-induced differentiation of naive B lymphocytes ICOS deficiency is associated with a severe reduction of CXCR5+CD4 germinal center Th cells Disruption of granulocyte macrophage-colony stimulating factor production in the lungs severely affects the ability of mice to control Mycobacterium tuberculosis infection Enhanced immunogenicity of BCG vaccine by using a viralbased GM-CSF transgene adjuvant formulation Heterogeneous GM-CSF signaling in macrophages is associated with control of Mycobacterium tuberculosis Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19 Viral persistence redirects CD4 T cell differentiation toward T follicular helper cells Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19 From Growth Factor to Central Mediator of Tissue Inflammation Alveolar macrophages develop from fetal monocytes that differentiate into long-lived cells in the first week of life via GM-CSF GM-CSF-based treatments in COVID-19: reconciling opposing therapeutic approaches Pulmonary alveolar proteinosis Paired Wilcoxon or t test were applied to compare two groups. Mann-Whitney test was used for non-paired analysis. Significance was retained for P < 0.05. The asterisks in the figures show the statistical significance as compared to the condition without any asterisks Programmed cell death 1 (PD-1); CXCR5: C-X-C chemokine receptor type 5; IL21: Interleukin-21; BCL6: B-cell lymphoma 6 protein Th2: T helper 2 cells Interleukin-17F; RORC: RAR related orphan receptor C; Tfh1: type 1 T follicular helper cells; CXCR3: C-X-C Motif Chemokine Receptor 3; Tfh2: type 2 T follicular helper cells We would like to thank the NGS platform of Institute Curie for generating the single-cell RNA sequencing data, the Genomics Facility of Sanofi-Boston for generating the transcriptomic data and the technology platform of IRSL for their critical assistance in cell sorting and Flow cytometry analysis. We wish to thank also Dr. Jasna Medvedovic and Dr. Cristina Ghirelli for her critical review on the data and manuscript.