Transcriptome‐based profiling of yolk sac‐derived macrophages reveals a role for Irf8 in macrophage maturation
2016; Springer Nature; Volume: 35; Issue: 16 Linguagem: Inglês
10.15252/embj.201693801
ISSN1460-2075
AutoresNora Hagemeyer, Katrin Kierdorf, Kathrin Frenzel, Jia Xue, Marc Ringelhan, Zeinab Abdullah, Isabelle Godin, Peter Wieghofer, Marta Joana Costa Jordão, Thomas Ulas, Gülden Yorgancıoğlu-Budak, Frank Rosenbauer, Percy A. Knolle, Mathias Heikenwälder, Joachim L. Schultze, Marco Prinz,
Tópico(s)Immune Response and Inflammation
ResumoArticle13 July 2016free access Transparent process Transcriptome-based profiling of yolk sac-derived macrophages reveals a role for Irf8 in macrophage maturation Nora Hagemeyer Nora Hagemeyer Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Katrin Kierdorf Katrin Kierdorf Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Kathrin Frenzel Kathrin Frenzel Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Jia Xue Jia Xue Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany Search for more papers by this author Marc Ringelhan Marc Ringelhan Institute of Virology, Technische Universität München/Helmholtz-Zentrum Munich, Munich, Germany Second Medical Department, Klinikum rechts der Isar, Technische Universität München, Munich, Germany Search for more papers by this author Zeinab Abdullah Zeinab Abdullah Institute of Experimental Immunology, University Bonn, Bonn, Germany Search for more papers by this author Isabelle Godin Isabelle Godin Gustave Roussy, INSERM U1170, Université Paris-Saclay, Villejuif, France Search for more papers by this author Peter Wieghofer Peter Wieghofer Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Marta Joana Costa Jordão Marta Joana Costa Jordão Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Thomas Ulas Thomas Ulas Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany Search for more papers by this author Gülden Yorgancioglu Gülden Yorgancioglu Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Frank Rosenbauer Frank Rosenbauer Institute of Molecular Tumor Biology, University of Muenster, Muenster, Germany Search for more papers by this author Percy A Knolle Percy A Knolle Institute of Molecular Immunology, Technische Universität München, Munich, Germany Search for more papers by this author Mathias Heikenwalder Mathias Heikenwalder Institute of Virology, Technische Universität München/Helmholtz-Zentrum Munich, Munich, Germany Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Joachim L Schultze Joachim L Schultze Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases, University of Bonn, Bonn, Germany Search for more papers by this author Marco Prinz Corresponding Author Marco Prinz [email protected] orcid.org/0000-0002-0349-1955 Institute of Neuropathology, University of Freiburg, Freiburg, Germany BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany Search for more papers by this author Nora Hagemeyer Nora Hagemeyer Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Katrin Kierdorf Katrin Kierdorf Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Kathrin Frenzel Kathrin Frenzel Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Jia Xue Jia Xue Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany Search for more papers by this author Marc Ringelhan Marc Ringelhan Institute of Virology, Technische Universität München/Helmholtz-Zentrum Munich, Munich, Germany Second Medical Department, Klinikum rechts der Isar, Technische Universität München, Munich, Germany Search for more papers by this author Zeinab Abdullah Zeinab Abdullah Institute of Experimental Immunology, University Bonn, Bonn, Germany Search for more papers by this author Isabelle Godin Isabelle Godin Gustave Roussy, INSERM U1170, Université Paris-Saclay, Villejuif, France Search for more papers by this author Peter Wieghofer Peter Wieghofer Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Marta Joana Costa Jordão Marta Joana Costa Jordão Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Thomas Ulas Thomas Ulas Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany Search for more papers by this author Gülden Yorgancioglu Gülden Yorgancioglu Institute of Neuropathology, University of Freiburg, Freiburg, Germany Search for more papers by this author Frank Rosenbauer Frank Rosenbauer Institute of Molecular Tumor Biology, University of Muenster, Muenster, Germany Search for more papers by this author Percy A Knolle Percy A Knolle Institute of Molecular Immunology, Technische Universität München, Munich, Germany Search for more papers by this author Mathias Heikenwalder Mathias Heikenwalder Institute of Virology, Technische Universität München/Helmholtz-Zentrum Munich, Munich, Germany Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Joachim L Schultze Joachim L Schultze Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases, University of Bonn, Bonn, Germany Search for more papers by this author Marco Prinz Corresponding Author Marco Prinz [email protected] orcid.org/0000-0002-0349-1955 Institute of Neuropathology, University of Freiburg, Freiburg, Germany BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany Search for more papers by this author Author Information Nora Hagemeyer1,‡, Katrin Kierdorf1,‡, Kathrin Frenzel1,‡, Jia Xue2, Marc Ringelhan3,4, Zeinab Abdullah5, Isabelle Godin6, Peter Wieghofer1, Marta Joana Costa Jordão1, Thomas Ulas2, Gülden Yorgancioglu1, Frank Rosenbauer7, Percy A Knolle8, Mathias Heikenwalder3,9, Joachim L Schultze2,10 and Marco Prinz *,1,11 1Institute of Neuropathology, University of Freiburg, Freiburg, Germany 2Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, Germany 3Institute of Virology, Technische Universität München/Helmholtz-Zentrum Munich, Munich, Germany 4Second Medical Department, Klinikum rechts der Isar, Technische Universität München, Munich, Germany 5Institute of Experimental Immunology, University Bonn, Bonn, Germany 6Gustave Roussy, INSERM U1170, Université Paris-Saclay, Villejuif, France 7Institute of Molecular Tumor Biology, University of Muenster, Muenster, Germany 8Institute of Molecular Immunology, Technische Universität München, Munich, Germany 9Division of Chronic Inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany 10Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases, University of Bonn, Bonn, Germany 11BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany ‡These authors contributed equally to this work *Corresponding author. Tel: +49 761 270 51050; Fax: +49 761 270 50500; E-mail: [email protected] The EMBO Journal (2016)35:1730-1744https://doi.org/10.15252/embj.201693801 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Recent studies have shown that tissue macrophages (MΦ) arise from embryonic progenitors of the yolk sac (YS) and fetal liver and colonize tissues before birth. Further studies have proposed that developmentally distinct tissue MΦ can be identified based on the differential expression of F4/80 and CD11b, but whether a characteristic transcriptional profile exists is largely unknown. Here, we took advantage of an inducible fate-mapping system that facilitated the identification of CD45+c-kit−CX3CR1+F4/80+ (A2) progenitors of the YS as the source of F4/80hi but not CD11bhi MΦ. Large-scale transcriptional profiling of MΦ precursors from the YS stage to adulthood allowed for building computational models for F4/80hi tissue macrophages being direct descendants of A2 progenitors. We further identified a distinct molecular signature of F4/80hi and CD11bhi MΦ and found that Irf8 was vital for MΦ maturation. Our data provide new cellular and molecular insights into the origin and developmental pathways of tissue MΦ. Synopsis In vivo fate mapping combined with transcriptomics shows the existence of different mouse macrophage subsets, and an important role for the transcription factor Irf8 in regulating their development. Two distinct macrophage subsets are characterized by differential expression of F4/80 and CD11b. F4/80hi but not CD11bhi macrophages originate from the yolk sac. Distinct gene profile of F4/80hi and CD11bhi macrophages from embryogenesis until adulthood. Irf8 is expressed in F4/80hi and CD11bhi macrophages during ontogeny. Irf8 regulates tissue macrophage maturation. Introduction Macrophages (MΦ) play an important role in maintaining tissue integrity and contribute to adaptive and innate immune responses. They are part of the mononuclear phagocyte system and were classically thought to derive from blood monocytes (van Furth & Cohn, 1968). This concept prevailed for nearly half a century (Ginhoux & Jung, 2014). Accumulating evidence, however, including recent studies using sophisticated fate-mapping approaches, has determined that some tissue MΦ and their precursors are established embryonically in the yolk sac (YS) and fetal liver before the onset of hematopoiesis in the bone marrow (Ginhoux et al, 2010; Hoeffel et al, 2012; Schulz et al, 2012; Epelman et al, 2014; Molawi et al, 2014; Gomez et al, 2015). However, conflicting reports have recently been published suggesting a different origin for all tissue macrophages except microglia from sources of the definitive hematopoiesis (Sheng et al, 2015). Thus, the origin of tissue macrophages still awaits its full discovery. We therefore adapted our recently established Cx3cr1CreER fate-mapping system (Goldmann et al, 2013; Yona et al, 2013) to unequivocally determine the temporal and spatial origin of tissue macrophages and to investigate their full transcriptional program during ontogeny from the YS until adulthood. Regardless of their origin, tissue MΦ can maintain themselves in adulthood through self-renewal and thus independently of blood monocytes (Ajami et al, 2007; Mildner et al, 2007; Hashimoto et al, 2013; Sieweke & Allen, 2013; Yona et al, 2013). Recent studies have further proposed that the expression levels of the markers F4/80 and CD11b can be used to identify ontogenetically distinct MΦ populations in various tissues, but failed to determine genetic traits underlying this supposed heterogeneity (Schulz et al, 2012; Bain et al, 2014). Previous gene expression profiling of MΦ populations from several tissues has established that only a small number of mRNA transcripts were associate with all MΦ but not DCs. However, a core gene signature of all macrophages could be detected (Gautier et al, 2012). The impact of the tissue environment on MΦ signatures is now beginning to be unraveled. Epigenetic modifications are one of the conduits through which the organ-specific environment can influence the established MΦ identities. The chromatin landscape, among other epigenomic features of a differentiated cell type, reflects both its developmental origin as well as its future potential. Two recent studies compared the tissue MΦ enhancer repertoire that revealed a tissue-specific signature with considerable plasticity (Gosselin et al, 2014; Lavin et al, 2014). However, the regulatory networks, the precise ancestry, and the mechanisms that describe tissue MΦ development from the YS stage to adulthood are largely unknown. By combining large-scale transcriptome profiling with fate mapping, immunophenotyping, and confocal imaging, we provide a detailed description of F4/80hi tissue MΦ development from uncommitted erythromyeloid precursors (EMP; CD45−c-kit+) via the MΦ ancestor populations A1 (CD45+c-kitloCX3CR1loF4/80lo) and A2 (CD45+c-kit−CX3CR1+F4/80+) in the YS to embryonic MΦ and then fully differentiated MΦ during adulthood. By using an A2 cell type-based fate-mapping system that tracks distinct YS cells, we can furthermore show that in contrast to a recent report (Sheng et al, 2015) several tissues contain F4/80hi MΦ that are derived from the YS. Moreover, besides anticipated tissue environment-related genetic differences, we could identify a core signature in the embryonic and adult F4/80hi and CD11bhi MΦ population. We further identified a transcriptional factor that is essential for both F4/80hi and CD11bhi MΦ homeostasis in vivo. There are several transcription factors proven to be important for myeloid cell development including Pu.1, which affects multiple lineages of the hematopoiesis in an early stage including the development of MΦ (Rosenbauer & Tenen, 2007). Additional transcription factors have been reported to play a role in the differentiation of myeloid cells such as interferon regulatory factor (Irf) 8 as a transcriptional factor of the IRF family (Rosenbauer & Tenen, 2007). IRF-8 interacts with other transcription factors such as IRF-1, IRF-2, IRF-4 and PU.1 (Tamura & Ozato, 2002). It is known that Irf8 has critical roles in the differentiation of myeloid cells, promoting monocyte over granulocyte differentiation (Tamura & Ozato, 2002). It is also a crucial regulator of many aspects of DC development, differentiation, and function (Tamura et al, 2008), thus holding an essential role in the establishment of innate immune responses. Although Irf8 is critical for the regulation of several myeloid cell lineages derived from hematopoietic stem cells (HSCs) including bone marrow-derived macrophages (Tamura & Ozato, 2002), its role for tissue macrophages emerged from prenatal precursors from the YS or fetal liver, independent from HSCs, is unclear. In fact, recent work proposed a vital role of IRF-8 only for circulating monocytes but not for tissue macrophages (Hambleton et al, 2011). In contrast to that report we show that both MΦ subsets (F4/80hi and CD11bhi) are strongly dependent on the transcription factor Irf8, which in our study influenced MΦ homeostasis. Our findings substantially increase the understanding of the regulatory processes that characterize tissue MΦ development in a highly dynamic microenvironment from the YS stage until adulthood. Results Temporospatial in vivo fate mapping and transcriptomics defines F4/80hi MΦ as descendants of A2 progenitors In mice, the first hematopoietic progenitors appear in the extra-embryonic YS where they generate nucleated erythrocytes and MΦ starting at embryonic day (E) 7.5–8.0 (Bertrand et al, 2005; Cumano & Godin, 2007). From E 8.0, multilineage erythromyeloid progenitors (EMPs) emerge in the YS as a second wave that are thought to give rise to tissue MΦ in the brain (Kierdorf et al, 2013; Prinz & Priller, 2014) and other tissues as well (Gomez et al, 2015). After E 9.0, the intra-embryonic mesoderm commits to the hematopoietic lineage and new waves of hematopoietic progenitors emerge: first in the para-aortic splanchnopleura (P-Sp) region and then in the aorta-gonad-mesonephros (AGM) region (Cumano & Godin, 2007). To follow the fate of myeloid cells during early embryogenesis, we first used Cx3cr1GFP/WT mice, which have a GFP knock-in on one allele of the Cx3cr1 gene. CX3CR1 is expressed in mature monocytes as well as many tissue MΦ and their precursors (Yona et al, 2013). To determine at which time point the YS contains progenitor cells with MΦ features, we analyzed E 9.0 embryos by thorough confocal microscopy (Fig 1A and B). We detected two distinct populations in the YS at this time point: CD31+c-kit+ EMPs and CX3CR1+F4/80+ early MΦ (Bertrand et al, 2005). Notably, CX3CR1+F4/80+ cells were found in the YS but not in the P-Sp region indicating a largely YS-centered localization of early MΦ progenitors at this developmental stage. By separation of CD45+ cells, we were able to discriminate between two MΦ precursor subsets, CD45+CX3CR1loF4/80lo A1 cells and CD45+CX3CR1hiF4/80hi A2 progenitors as described before (Bertrand et al, 2005; Kierdorf et al, 2013) (Fig 1C). Figure 1. Temporospatial in vivo fate mapping of yolk sac MΦ and transcriptional ancestry of F4/80hi tissue MΦ during ontogeny Overview images of hematopoietic sites in an E 9.0 embryo depicting the yolk sac (YS) and para-aortic splanchnopleura region (P-Sp) with phase contrast (upper image) and direct fluorescence microscopy (lower image). Stars indicate CX3CR1+ cells (green) and arrowhead points to c-kit+/CD31+ EMPs (blue/white) in the YS. Scale bar represents 100 μm. One representative picture out of five independent experiments is displayed. Higher magnifications of the YS and P-Sp. CX3CR1-GFP expression (green) is found in the YS but not in the P-Sp, where only background staining is present. F4/80 (red), CD31 (blue), c-kit (white). Scale bars represent 50 μm. Representative pictures out of five independent experiments are shown. Flow cytometric analysis of MΦ precursors in the YS of E 9.0 Cx3cr1GFP/WT animals (A1: CD45+c-kitloCX3CR1loF4/80lo, A2: CD45+c-kit−CX3CR1+F4/80+). Mean fluorescent intensity (MFI) is presented. Four mice were investigated showing similar results. Fate-mapping strategy to target the A2 progenitor population preferentially in the YS. Male Cx3cr1CreER:R26-yfp mice were bred to wild-type animals, and plug-positive mice were injected at E 9.0 with tamoxifen (TAM). Offspring was analyzed at E 16.0, P0 or P42. Flow cytometry of F4/80hiCD11blo and F4/80loCD11bhi tissue MΦ in adult (P42) Cx3cr1CreER:R26-yfp mice. Cre-negative mice served as a control. Representative histograms from three independent experiments are displayed. Quantification of YFP labeling in tissue MΦ at E 16.0, P0, and P42 after TAM application at E 9.0 in plug-positive Cx3cr1CreER:R26-yfp mice. Data are normalized to YFP+ microglia and presented as mean ± s.e.m. At least one experiment out of two is shown, and each symbol presents one mouse. Hierarchical clustering of the 1,000 most variable probes within the dataset of embryonic development of yolk sac-derived tissue MΦ (EMP: erythromyeloid precursor, A1/A2: yolk sac MΦ progenitors, Emb F4/80 MG: embryonic microglia, Emb F4/80 liver: embryonic Kupffer cells, Emb F4/80 kidney: embryonic F4/80 kidney cells). Heat map displays z-transformed log2-expression values from red to blue via white. Above: Sample-wise co-regulation network of embryonic MΦ development. Pearson correlation threshold of 0.92 was used to generate the network. Below: scheme thereof. Download figure Download PowerPoint From our previous studies, we know that microglia derive from the A2 progenitors of the YS (Kierdorf et al, 2013). Recently, it was suggested that the progenitor for microglia and other tissue MΦ are different from each other (Hoeffel et al, 2015; Sheng et al, 2015). However, our hypothesis was that F4/80hi MΦ derive from the A2 progenitors of the YS as well. To perform fate mapping and subsequent gene profiling of tissue MΦ during ontogeny, we next adapted our recently developed Cx3cr1CreER mouse system (Goldmann et al, 2013; Yona et al, 2013) to target A2 progenitors only (Fig 1D). Male Cx3cr1CreER: Rosa26-fl-STOP-fl-yfp (Cx3cr1CreER:Rosa26-yfp) mice were crossed to female wild-type mice, and plug-positive animals were injected with a single intraperitoneal dose of tamoxifen (TAM) at E 9.0. The application of TAM at E 9.0 induced the recombination of CX3CR1+ progenitor cells and therefore also targeted A2-derived microglia (Fig EV1A and B). Importantly, to prove the specificity of our approach, we additionally investigated the recombination efficiency in microglia of E 16.0 embryos after TAM application at E 7.0 and E 8.0 (Fig EV1B). TAM application at E 8.0 but not at E 7.0 was able to target a small population of A2-derived microglia (YFP+ cells = 17.7 ± 1.3%) as well as liver (YFP+ cells = 13.2 ± 1.4%) and kidney (YFP+ cells = 15 ± 3.1%) F4/80hi MΦ. This result shows that the TAM-induced Cre recombination is restricted to a very limited time window of maximal 1 day. No CX3CR1+ HSCs were present in the E 10.0/E 10.5 AGM or fetal liver proving that we do not target HSCs in our approach (Fig EV1C and D). Therefore, specific targeting of A2 progenitors by TAM injection at E 9.0 induced efficient YFP labeling of 41.6 ± 0.6% brain microglia, 20.8 ± 2.5% F4/80hi Kupffer cells, 15.8 ± 2.2% F4/80hi kidney MΦ, and 14.2 ± 1.9% MHC II+ CD11b+skin MΦ at E 16.0, indicating robust labeling efficiency of various A2-derived MΦ progeny (Fig 1F). Microglia in 6-week-old animals retained the YFP label, demonstrating that A2-derived cells persists until adulthood, whereas labeling of Kupffer cells, Langerhans cells, and F4/80hi kidney cells was present but dropped after birth (Figs 1E and F, EV1E and EV2). This might be due to a replacement by fetal liver-derived cells as suggested by others (Hoeffel et al, 2012; Gomez et al, 2015). In contrast, CD11bhi tissue MΦ were not labeled in this approach (Fig 1E and F). Collectively, these data suggest that F4/80hi tissue MΦ are initially derived from CX3CR1+ A2 cells in the YS, whereas CD11bhi tissue MΦ develop independently from A2 cells. Click here to expand this figure. Figure EV1. Targeting of A2 precursors in the yolk sac labels F4/80hi MΦ (related to Fig 1) Flow cytometric quantification of YFP+ A1 and A2 yolk sac cells at E 10.5 after TAM application at E 9.0 in pregnant Cx3cr1CreER:R26yfp mice. Each symbol represents one mouse. Data represent mean ± s.e.m. Two independent experiments are depicted. Efficacies of microglia targeting (YFP+CD11b+CD45lo) at E 16.0 after TAM application either at E 7.0, E 8.0, or E 9.0 in plug-positive Cx3cr1CreER:R26-yfp mice. One representative blot is shown. Gray area depicts fluorescence signal in MΦ of non-transgenic littermates. At least three mice with similar results were investigated. Representative images of the fetal liver in E 10.5 Cx3cr1GFP/WT embryos. CX3CR1-GFP expression (green), F4/80 (red), CD31 (blue), c-kit, or CD41 (white). Arrow points to a c-KIT- and CD31-positive cell. No co-localization of CD31, CD41, or c-KIT with CX3CR1-GFP+ cells was detected. Scale bars represent 50 μm. Representative pictures out of two embryos are shown. Representative images of the AGM in E 10.0 Cx3cr1GFP/WT embryos. CX3CR1-GFP expression (green), CD31 (white or red), F4/80 (red). No CX3CR1-GFP signal is observed close to native HSC (* marks the HSC cluster in the aorta). CX3CR1-GFP+ cells are close to vessels; some are also F4/80+ (arrows). Scale bars represent 100 μm. Representative pictures out of two embryos are shown. Immunofluorescence of YFP (green) and Iba-1 (red) from brain, liver, and kidney of adult (P42) Cx3cr1CreER:R26-yfp mice that received TAM at E 9.0. Scale bars represent 25 μm. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Gating strategy used for flow cytometry of MΦ (related to Figs 1, 2, 3, 4, 5, 6)Single-cell suspensions were prepared using enzymatic digestion, density gradient centrifugation, or mechanical dissociation. Side scatter (SSC) and forward scatter (FSC) discrimination of cells of interest was followed by the gating for the respective MΦ population. Microglia were discriminated by CD45 and CD11b. Liver cells were gated for CD45-positive cells; CD146, lineage- (CD3, CD19, NK1.1) and Gr1-positive cells were excluded to select CD11bhi and F4/80hi MΦ as described recently (Huang et al, 2013). Kidney cells were gated on CD45+ cells followed by the exclusion of lineage markers (CD3, CD19, NK1.1) and Gr1+ cells to select the CD11bhi and F4/80hi MΦ. Epidermal cells were first selected by a living dye stain, followed by selection of CD45+, EpCAM+, Ly6C−, MHCII+, and CD11b+ characteristics. Download figure Download PowerPoint After having established an A2 YS-dependent labeling system, we isolated EMP from E 8.0 YS, A1 and A2 cells from E 9.0 YS, and embryonic F4/80hi MΦ from E 14.5 brain, liver, and kidney and performed a whole-genome expression analysis using the Affymetrix Mouse Gene 2.1 ST Array. Subsequent investigation of the 1,000 most variable transcripts by hierarchical clustering (Fig 1G) and generating a co-regulation network based on all expressed transcripts (Fig 1H) revealed that EMPs represent an early step in MΦ hierarchy followed by the YS populations A1 and A2, while A2 cells obviously represent a nodal point of tissue MΦ development (Fig 1G and H). These data suggest that the MΦ populations analyzed gain their own tissue-specific signature once they have left the YS and that A2 YS progenitors are ancestors of embryonic F4/80hi tissue MΦ. Difference in maintenance of F4/80hi and CD11bhi MΦ after birth Once established in the CNS, microglia persist throughout the entire life of the organism without any significant input from circulating blood cells due to their longevity and their capacity for self-renewal (Ajami et al, 2007; Mildner et al, 2007). We therefore sought to use this unique feature of microglia to compare the kinetics of persistence with F4/80hi and CD11bhi cells from the kidney and liver. For this purpose, we induced adult Cx3cr1CreER:R26-yfp animals with TAM and determined MΦ labeling at several time points after application (Fig 2A–C). FACS analysis demonstrated long-term labeling of microglia and F4/80hi kidney MΦ with virtually no turnover in microglia and low turnover in F4/80hi kidney MΦ (exchange rate: approx. 2%/week). In contrast, YFP labeling rapidly dropped in CD11bhi cells in liver and kidney indicating higher turnover rates (approx. 32%/week for both organs). Notably, Kupffer cells dynamically express CX3CR1 during development but lose their expression shortly after birth which is why their turnover cannot be monitored during adulthood (Yona et al, 2013). To circumvent these limitations, we adapted our Cx3cr1CreER:R26-yfp system and induced recombination shortly after birth when F4/80hi Kupffer cells still express CX3CR1 (Fig 2C). We successfully targeted at least a subpopulation of the F4/80hi Kupffer cells. Similar to their relatives in the brain, this Kupffer cell population remained a remarkably stable population until 9 months of age. To evaluate a possible contribution of blood cells to the macrophage populations, we evaluated the contribution of both Ly6Chi and Ly6Clo monocytes. We first measured the expression of monocyte-related CCR2 that was absent on F4/80hi Kupffer cells, only partially present on F4/80hi kidney MΦ but clearly present on CD11bhi MΦ (Fig 2D). Furthermore, we provide genetic evidence that Ly6Chi monocytes contributed to the homeostasis of F4/80hi kidney MΦ as well as CD11bhi MΦ shown by reduced cell numbers in mice lacking Ccr2 (Fig 2E). In contrast, Ly6Clo monocytes were dispensable for F4/80hi and CD11bhi MΦ development and homeostasis as mice lacking Nr4a1 presented with normal amounts of MΦ subsets in liver and kidney (Fig 2E). These data indicate that adult microglia and Kupffer cells are stable populations which do not undergo significant exchange with blood cells within the first 9 months of life. In contrast, adult F4/80hi kidney MΦ are partially replaced by blood monocytes, whereas adult CD11bhi MΦ found in the liver and kidney have a fast and continuous exchange with circulating CCR2-dependent progenitors, most likely Ly6Chi monocytes. Figure 2. Differences in turnover of CX3CR1+ CD11bhi and F4/80hi tissue MΦ after birth A. Scheme of fate mapping in Cx3cr1CreER:R26-yfp mice that were either treated at 6 weeks of age (for B) or at postnatal day 1 (for C) and analyzed at 6 days, 2, 4, and 35 weeks (for B) and 6 days, 6 and 35 weeks (for C). B, C. Flow cytometric quantifications of YFP+ MΦ in the brain, liver, and kidney at the indicated time points after TAM application in Cx3cr1CreER:R26-yfp mice. Data are summarized from two independent experiments (7–8 mice each) and represented as mean ± s.e.m. D. CCR2 expression on F4/80hi and CD11bhi MΦ in the liver or kidney of adult Ccr2RFP/WT mice, respectively. Gray area depicts fluorescence signal in MΦ of non-transgenic littermates. Representative cytometric graphs are shown. Four mice were investigated showing similar results. E. Flow cytometric analysis of MΦ populations in the liver or kidney in adult mice lacking Ccr2 or Nr4a1. Each symbol represents one mouse. Data are summarized from three independent experiments and represent mean ± s.e.m. Significant differences were determined by an unpaired t-test. n.s.: not significant. *P < 0.05, **P < 0.01. Download figure Download PowerPoint Distinct gene profiles of F4/80hi and CD11bhi MΦ during ontogeny The results presented above suggested that microglia and F4/80hi MΦ from liver and kidney are derived from CD45+CX3CR1hiF4/80hi A2 progenitors in the YS, whereas CD11bhi MΦ seem to originate from later hematopoietic sources, for example, elements from the definitive hematopoiesis (Ginhoux & Jung, 2014). Thus, we examined whether this divergent origin is mirrored by a distinct gene profile. To do so, we performed an unsupervised clustering analysis of genomewide expression arrays from embryonic (Fig 3A) and adult (Fig 3B) F4/80hi MΦ
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