Abstract
During inflammation, Ly6Chi monocytes are rapidly mobilized from the bone marrow (BM) and are recruited into inflamed tissues, where they undergo monocyte-to-phagocyte transition (MTPT). The in vivo developmental trajectories of the MTPT and the contribution of individual cytokines to this process remain unclear. Here, we used a murine model of neuroinflammation to investigate how granulocyte–macrophage colony-stimulating factor (GM-CSF) and interferon-γ (IFNγ), two type 1 cytokines, controlled MTPT. Using genetic fate mapping, gene targeting and high-dimensional single-cell multiomics analyses, we found that IFNγ was essential for the gradual acquisition of a mature inflammatory phagocyte phenotype in Ly6Chi monocytes, while GM-CSF was required to license interleukin-1β (IL-1β) production, phagocytosis and oxidative burst. These results suggest that the proinflammatory cytokine environment guided MTPT trajectories in the inflamed central nervous system (CNS) and indicated that GM-CSF was the most prominent target for the disarming of monocyte progenies during neuroinflammation.
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Data availability
The scRNA-seq datasets generated in this study are available at https://doi.org/10.5281/zenodo.5722108, https://doi.org/10.5281/zenodo.5722170, https://doi.org/10.5281/zenodo.5722172 and https://doi.org/10.5281/zenodo.5711708. Source data are provided with this paper.
Code availability
Scripts to reproduce the data will be made available upon request.
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Acknowledgements
We thank M. Lutz and P. Zwicky for technical assistance. We wish to thank L. Robinson of Insight Editing London for critical review of the manuscript. This work was supported by the Swiss National Science Foundation (733 310030_170320, 310030_188450 and CRSII5_183478 to B.B. and BSSGI0_155832, 310030_184915 to M.G.), the Clinical Research Priority Program ImmunoCure (B.B.), the University of Zurich postdoctoral fellowship (D.D.F.), the Swiss Multiple Sclerosis Society (B.B. and D.D.F.) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant agreement No 882424 (B.B.).
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A.A., D.D.F. and E.F. designed and performed experiments, evaluated and interpreted data and wrote the manuscript. F.I. and C.D.A. performed bioinformatic analyses. S.K. and M.A. supported flow cytometry experiments and data analysis. C.A.W. performed histological analyses. Z.L. and F.G. shared the Ms4a3 fate mapping system. F.G. and M.G. interpreted data and edited the manuscript. B.B. supervised and financed the study and wrote the manuscript.
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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Ioana Visan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Extended data
Extended Data Fig. 1 Experiment setup and pre-gating strategy for downstream analysis of the CD11b + myeloid compartment in sdLNs and CNS during EAE.
a, Schematic representation of the experimental approach and the EAE time course used for the experiment. C57BL/6 mice were actively immunized with MOG35-55 emulsified in CFA (day 0, s.c.) and pertussis toxin (days 0 and 2) to induce EAE. Cells were isolated from the sdLN and the CNS from EAE mice at the indicated time points from day 0 to day 20 p.i. and flow cytometry analysis was carried out, n = 2 day 0 and n = 5 day 2−20. b, Gating strategy for myeloid cells in sdLN and CNS of MOG35-55 immunized mice before dimensionality reduction and clustering. c, Representative flow cytometry plots showing the strategy to gate on Ly6Chi monocytes, moDCs and moMACs in sdLN and in the CNS at different stages of EAE development. d, Representative contour plots showing NOS2, pro-IL−1β and Arginase-1 producing CD11b + and CD88 + cells in the CNS. e, Frequencies of pro-IL-1β, Arginase-1 and NOS2 among Ly6Chi monocytes, moMACs and moDCs in the CNS across the indicated time points (n = 2 for day 0 and n = 5 day 2 to 20 p.i.). f, Continuous time course of relative frequencies of pro-IL-1β + , Arginase-1+ and NOS2 + cells among CD11b + cells from sdLN and CNS, the mean value (centerline) ± s.d. (colored area) (n = 2, day 0 and n = 5, day 2-20 p.i.).
Extended Data Fig. 2 Characterization of Ccr2Ai14 and Ms4a3Ai14 targeting in the BM and blood at steady-state and during EAE.
a, Gating strategy of BM myeloid precursors and monocytes in nonimmunized and EAE from Ms4a3Ai14 and Ccr2Ai14 mice. Ccr2Ai14 mice received tamoxifen (5 mg) via oral gavage (nonimmunized: 48 h before analysis; EAE onset: at 2 and 5 d.p.i.; EAE peak: at 2, 5 and 10 d.p.i.). b, Bubble plot summarizing tdTomato+ cell frequencies per subset in BM and blood at day 0, 7 (EAE onset) and 12 p.i. (EAE peak) Ms4a3Ai14 mice (n = 4 per group). c, Bubble plots summarizing tdTomato+ cell frequencies per subset in the BM and blood at day 0, day 7 (EAE onset) and day 12 p.i. (EAE peak) Ccr2Ai14 mice (n = 4 per group). The size of the bubble indicates a mean value of relative frequencies; the bubble color shows negative log-transformed p-value. p-values are calculated using one-way ANOVA with Benjamini-Hochberg method. Data are representative of two experiments.
Extended Data Fig. 3 Characterization of Ccr2Ai14 and Ms4a3Ai14 targeting in the sdLNs and CNS at steady-state and during EAE.
a, Gating strategy of sdLN mononuclear phagocytes from N.I and EAE Ms4a3Ai14 and Ccr2Ai14 mice. sdLNs and CNS were obtained from the same Ms4a3Ai14 and Ccr2Ai14 mice and have been equally treated as in the Extended Data Fig. 2. Bubble plot summarizing tdTomato+ cell frequencies per subset and stage of disease in the sdLNs at day 0, 7 (EAE onset) and 12 p.i. (EAE peak). b, Gating strategy of CNS HSC-derived mononuclear phagocytes in Ms4a3Ai14 and Ccr2Ai14 EAE mice. Bubble plot summarizing tdTomato+ cell frequencies per subset and stages of disease, of the CNS inflammatory infiltrate at day 7 (EAE onset) and 12 p.i. (EAE peak) of Ms4a3Ai14 and Ccr2Ai14 mice (n = 4). The bubble size indicates a mean value of relative frequencies, the bubble color shows negative log-transformed p-value. p-values are calculated using one-way ANOVA with Benjamini-Hochberg method. Data are representative of two experiments.
Extended Data Fig. 4 HSC-derived phagocyte composition during steady state and EAE.
a-c, scRNA-seq analysis of HSC-derived phagocytes sorted from the BM, peripheral blood, sdLN, and the CNS of Ms4a3Ai14 mice at the day 0, 7 and 12 after EAE induction, n = 1 mice per time point. a, Gating strategy used to sort HSC-derived phagocytes. b, Relative frequencies among HSC-derived phagocytes from BM, blood, sdLN, and CNS. c, GSVA score displayed in red overlayered to the UMAP map of merged CNS at onset and peak of EAE, also shown in Fig. 4. The GSVA score was calculated using the list of genes expressed per cluster published in Giladi et. al. Each red dot corresponds to a cell expressing the same genes on Giladi et. al cluster list.
Extended Data Fig. 5 Chimerism assessment, sorting strategy, post-sequencing quality and antigen presentation score of Mdcs from the CNS of EAE BM chimeras.
a, Mixed BM chimeric mice (CD45.2 Csf2rb−/−: CD45.1 Csf2rb + /+ and CD45.2 Ifngr1−/−: CD45.1 Ifngr1 + /+) n = 6 per group were immunized with MOG35-55, 6 weeks after chimerism induction. At the peak of EAE (14 d.p.i.), CNS-infiltrating phagocytes were analyzed by high dimensional flow cytometry. The mean values ± s.d. of clinical severity are plotted against time. b, Gating strategy for CD45.1+ and CD45.2 + Ly6Chi monocytes in the CNS of Csf2rb + /+ (CD45.1+): Csf2rb−/− (CD45.2+) and Ifngr + /+(CD45.1+): Ifngr−/−(CD45.2+) BM chimeras. Remaining host cells (CD45.1 + CD45.2+) were gated out. c, The mean value ± s.d. normalized to the ratio of CD45.1+ and CD45.2+ from BM, blood, sdLN and CNS Ly6Chi monocytes of Csf2rb + /+:Csf2rb−/− and Ifngr + /+:Ifngr−/−, respectively. n = 10/group, from 4 independent experiments. d, Representative flow cytometry plots depict the gating strategy to sort CD11b + NK1.1-Ly6G- CD44hiCX3CR1int cells from the CNS of cytokine-receptor mixed BM chimeras at the peak of EAE (7 d.p.i). e, Violin plot showing number of genes, number of UMIs, mitochondria count percentage, and UMI per gene of all QC-passed cells in different genotype for all cells (top) and Mdcs (bottom). f, UMAP map of all CNS-infiltrating leukocytes, colored according to results of Seurat-guided clustering (left) or genotype (right). g, Violin plots displaying expression of selected genes across Mdcs clusters. h, Violin plot showing the scores for the antigen processing and presentation calculated as the average normalized expression of antigen processing and presentation-related genes for M1-10 clusters. For the box plot within each violin plot, middle lines indicate median values, box range from the 25th to 75th percentiles, and upper/lower whiskers extend from the hinge to the largest/smallest value no further than 1.5 times the interquartile range (IQR) from the hinge. Colored areas indicate density distribution of data. Significance was determined by one way ANOVA and Benjamini–Hochberg-corrected p-values are summarized in Supplementary Table 24. Antigen processing and presentation score projected on UMAP displaying M1-10 clusters. Antigen processing and presentation score is computed on individual genes listed in Supplementary Table 6.
Extended Data Fig. 6 GM-CSF and IFNγ dependent genes and pathways in CNS MdC subsets.
a, Correlation between integrated and non-integrated scRNA-seq dataset from WT, Csf2rb−/− and Ifngr1−/− CNS-invading mononuclear phagocyte dataset (see also Fig. 6 and Extended Data Fig. 5). Shown are the fraction of indicated scRNA-seq defined integrated clusters in each previously annotated non-integrated clusters. Each row is normalized by row sums. b, Volcano plots displaying genes that are up- (blue) or downregulated (red) in WTvsCsf2rb−/− for the indicated CNS MdC clusters. Dashed lines denote fold change thresholds used when identifying DEGs. c, Volcano plots displaying genes that are up- (blue) or downregulated (red) in WT vs Ifngr1−/− for the indicated CNS MdC clusters. Dashed lines denote fold change thresholds used when identifying DEGs. d, Gene ontology (GO) analysis of DEGs from b; e, Gene ontology (GO) analysis of DEGs from c. (See also Supplementary Table 2). Top5 GO BP terms with Benjamini-Hochberg-corrected p-values < 0.05 are shown.
Extended Data Fig. 7 Gating strategy and analysis of the CD11b + myeloid cells in CNS and sdLN of mixed BM chimeras during EAE; marker expression of Ccr2Ai14 CD11b + cells at peak EAE and targeting of Ccr2CreERT2xIabfl in sdLNs during EAE induction.
a, Gating strategy for the myeloid cells in sdLN and CNS of MOG35-55 immunized mice before dimensionality reduction and clustering. from the CNS of Csf2rb + /+ (CD45.1+): Csf2rb−/− (CD45.2+) and Ifngr1 + /+(CD45.1+): Ifngr1−/− (CD45.2+) BM chimeras. b, Median expression of the markers selected to define MdC populations, overlaid to the UMAP of CNS Mdcs from Ccr2Ai14 mice at peak EAE (14 d.p.i.). c, Representative plots are shown for: CD45 + Ly6G-CD11b + Ly6C+moDCs +and CD45 + Ly6G-Ly6C-CD11b + CD11c + CD26 + XCR1 + cDCs. Data represents 2 independent experiments (n = 5 per group), p values were calculated using an unpaired two-tailed t test with Welch’s correction. **p < 0.0079. Data are shown as means ± SEM. n.s., not significant.
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Amorim, A., De Feo, D., Friebel, E. et al. IFNγ and GM-CSF control complementary differentiation programs in the monocyte-to-phagocyte transition during neuroinflammation. Nat Immunol 23, 217–228 (2022). https://doi.org/10.1038/s41590-021-01117-7
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DOI: https://doi.org/10.1038/s41590-021-01117-7
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