Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Atypical chemokine receptor 1 on nucleated erythroid cells regulates hematopoiesis

Abstract

Healthy individuals of African ancestry have neutropenia that has been linked with the variant rs2814778(G) of the gene encoding atypical chemokine receptor 1 (ACKR1). This polymorphism selectively abolishes the expression of ACKR1 in erythroid cells, causing a Duffy-negative phenotype. Here we describe an unexpected fundamental role for ACKR1 in hematopoiesis and provide the mechanism that links its absence with neutropenia. Nucleated erythroid cells had high expression of ACKR1, which facilitated their direct contact with hematopoietic stem cells. The absence of erythroid ACKR1 altered mouse hematopoiesis including stem and progenitor cells, which ultimately gave rise to phenotypically distinct neutrophils that readily left the circulation, causing neutropenia. Individuals with a Duffy-negative phenotype developed a distinct profile of neutrophil effector molecules that closely reflected the one observed in the ACKR1-deficient mice. Thus, alternative physiological patterns of hematopoiesis and bone marrow cell outputs depend on the expression of ACKR1 in the erythroid lineage, findings with major implications for the selection advantages that have resulted in the paramount fixation of the ACKR1 rs2814778(G) polymorphism in Africa.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: ACKR1 is expressed in the bone marrow by ECs and NECs.
Figure 2: ACKR1 deficiency alters HSPC populations.
Figure 3: ACKR1 deficiency changes HSPC transcriptomes.
Figure 4: ACKR1 on erythroid cells but not on ECs regulates HSPCs.
Figure 5: ACKR1 on BM erythroid cells regulates HSPCs.
Figure 6: ACKR1 on NECs promotes direct cell interactions with HSCs.
Figure 7: Lack of ACKR1 affects neutrophil phenotype.
Figure 8: Lack of ACKR1 on NECs together with its expression in ECs causes neutropenia.

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Rot, A. & von Andrian, U.H. Chemokines in innate and adaptive host defense: basic chemokinese grammar for immune cells. Annu. Rev. Immunol. 22, 891–928 (2004).

    Article  CAS  Google Scholar 

  2. Griffith, J.W., Sokol, C.L. & Luster, A.D. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu. Rev. Immunol. 32, 659–702 (2014).

    Article  CAS  Google Scholar 

  3. Luther, S.A. & Cyster, J.G. Chemokines as regulators of T cell differentiation. Nat. Immunol. 2, 102–107 (2001).

    Article  CAS  Google Scholar 

  4. Krathwohl, M.D. & Kaiser, J.L. Chemokines promote quiescence and survival of human neural progenitor cells. Stem Cells 22, 109–118 (2004).

    Article  CAS  Google Scholar 

  5. Ulvmar, M.H., Hub, E. & Rot, A. Atypical chemokine receptors. Exp. Cell Res. 317, 556–568 (2011).

    Article  CAS  Google Scholar 

  6. Bachelerie, F. et al. New nomenclature for atypical chemokine receptors. Nat. Immunol. 15, 207–208 (2014).

    Article  CAS  Google Scholar 

  7. Nibbs, R.J.B. & Graham, G.J. Immune regulation by atypical chemokine receptors. Nat. Rev. Immunol. 13, 815–829 (2013).

    Article  Google Scholar 

  8. Bachelerie, F. et al. Update on the extended family of chemokine receptors and introducing a new nomenclature for atypical chemokine receptors. Pharmacol. Rev. 66, 1–79 (2013).

    Article  Google Scholar 

  9. Pruenster, M. et al. The Duffy antigen receptor for chemokines transports chemokines and supports their pro-migratory activity. Nat. Immunol. 10, 101–108 (2009).

    Article  CAS  Google Scholar 

  10. Ulvmar, M.H. et al. The atypical chemokine receptor CCRL1 shapes functional CCL21 gradients in lymph nodes. Nat. Immunol. 15, 623–630 (2014).

    Article  CAS  Google Scholar 

  11. Lee, K.M. et al. The chemokine receptors ACKR2 and CCR2 reciprocally regulate lymphatic vessel density. EMBO J. 33, 2564–2580 (2014).

    Article  CAS  Google Scholar 

  12. Cruz-Orengo, L. et al. CXCR7 influences leukocyte entry into the CNS parenchyma by controlling abluminal CXCL12 abundance during autoimmunity. J. Exp. Med. 208, 327–339 (2011).

    Article  CAS  Google Scholar 

  13. Novitzky-Basso, I. & Rot, A. Duffy antigen receptor for chemokines and its involvement in patterning and control of inflammatory chemokines. Front. Immunol. 3, 266 (2012).

    Article  Google Scholar 

  14. Rot, A. Contribution of Duffy antigen to chemokine function. Cytokine Growth Factor Rev. 16, 687–694 (2005).

    Article  CAS  Google Scholar 

  15. Mei, J. et al. CXCL5 regulates chemokine scavenging and pulmonary host defense to bacterial infection. Immunity 33, 106–117 (2010).

    Article  CAS  Google Scholar 

  16. Schnabel, R.B. et al. Duffy antigen receptor for chemokines (Darc) polymorphism regulates circulating concentrations of monocyte chemoattractant protein 1 and other inflammatory mediators. Blood 115, 5289–5299 (2010).

    Article  CAS  Google Scholar 

  17. Reutershan, J., Harry, B., Chang, D., Bagby, G.J. & Ley, K. DARC on RBC limits lung injury by balancing compartmental distribution of CXC chemokines. Eur. J. Immunol. 39, 1597–1607 (2009).

    Article  CAS  Google Scholar 

  18. Miller, L.H., Mason, S.J., Dvorak, J.A., McGinniss, M.H. & Rothman, I.K. Erythrocyte receptors for (Plasmodium knowlesi) malaria: Duffy blood group determinants. Science 189, 561–563 (1975).

    Article  CAS  Google Scholar 

  19. Horuk, R. et al. A receptor for the malarial parasite Plasmodium vivax: the erythrocyte chemokine receptor. Science 261, 1182–1184 (1993).

    Article  CAS  Google Scholar 

  20. Nedelec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).

    Article  CAS  Google Scholar 

  21. Quach, H. et al. Genetic adaptation and neandertal admixture shaped the immune system of human populations. Cell 167, 643–656 (2016).

    Article  CAS  Google Scholar 

  22. Thobakgale, C.F. & Ndung'u, T. Neutrophil counts in persons of African origin. Curr. Opin. Hematol. 21, 50–57 (2014).

    Article  Google Scholar 

  23. Reich, D. et al. Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLoS Genet. 5, e1000360 (2009).

    Article  Google Scholar 

  24. Sanger, R., Race, R.R. & Jack, J. The Duffy blood groups of New York negroes: the phenotype Fy (a–b–). Br. J. Haematol. 1, 370–374 (1955).

    Article  CAS  Google Scholar 

  25. Howes, R.E. et al. The global distribution of the Duffy blood group. Nat. Commun. 2, 266 (2011).

    Article  Google Scholar 

  26. Tournamille, C., Colin, Y., Cartron, J.P. & Le Van Kim, C. Disruption of a GATA motif in the Duffy gene promoter abolishes erythroid gene expression in Duffy-negative individuals. Nat. Genet. 10, 224–228 (1995).

    Article  CAS  Google Scholar 

  27. Peiper, S.C. et al. The Duffy antigen/receptor for chemokines (DARC) is expressed in endothelial cells of Duffy-negative individuals who lack the erythrocyte receptor. J. Exp. Med. 181, 1311–1317 (1995).

    Article  CAS  Google Scholar 

  28. Doss, J.F. et al. A comprehensive joint analysis of the long and short RNA transcriptomes of human erythrocytes. BMC Genomics 16, 952 (2015).

    Article  Google Scholar 

  29. Thiriot, A. et al. Differential immunostaining of DARC (ACKR1) distinguishes venular from nonvenular endothelial cells in murine tissues. BMC Biol. 15, 45 (2017).

    Article  Google Scholar 

  30. Dawson, T.C. et al. Exaggerated response to endotoxin in mice lacking the Duffy antigen/receptor for chemokines (DARC). Blood 96, 1681–1684 (2000).

    CAS  PubMed  Google Scholar 

  31. Itkin, T. et al. Distinct bone marrow blood vessels differentially regulate hematopoiesis. Nature 532, 323–328 (2016).

    Article  CAS  Google Scholar 

  32. Morrison, S.J. & Scadden, D.T. The bone marrow niche for hematopoietic stem cells. Nature 505, 327–334 (2014).

    Article  CAS  Google Scholar 

  33. Mendelson, A. & Frenette, P.S. Hematopoietic stem cell niche maintenance during homeostasis and regeneration. Nat. Med. 20, 833–846 (2014).

    Article  CAS  Google Scholar 

  34. Fossati, G., Moots, R.J., Bucknall, R.C. & Edwards, S.W. Differential role of neutrophil Fcγ receptor IIIB (CD16) in phagocytosis, bacterial killing and responses to immune complexes. Arthritis Rheum. 46, 1351–1361 (2002).

    Article  CAS  Google Scholar 

  35. Gao, H., Henderson, A., Flynn, D.C., Landreth, K.S. & Ericson, S.G. Effects of the protein tyrosine phosphatase CD45 on FcγRIIa signaling and neutrophil function. Exp. Hematol. 28, 1062–1070 (2000).

    Article  CAS  Google Scholar 

  36. Minten, C. et al. DARC shuttles inflammatory chemokines across the blood–brain barrier during autoimmune central nervous system inflammation. Brain 137, 1454–1469 (2014).

    Article  Google Scholar 

  37. Martin, C. et al. Chemokines acting via CXCR2 and CXCR4 control the release of neutrophils from the bone marrow and their return following senescence. Immunity 19, 583–593 (2003).

    Article  CAS  Google Scholar 

  38. Busch, K. et al. Fundamental properties of unperturbed hematopoiesis from stem cells in vivo. Nature 518, 542–546 (2015).

    Article  CAS  Google Scholar 

  39. Yu, V.W. et al. Epigenetic memory underlies cell-autonomous heterogeneous behavior of hematopoietic stem cells. Cell 167, 1310–1322 e1317 (2016).

    Article  CAS  Google Scholar 

  40. Sun, J. et al. Clonal dynamics of native hematopoiesis. Nature 514, 322–327 (2014).

    Article  CAS  Google Scholar 

  41. Pietras, E.M. et al. Functionally distinct subsets of lineage-biased multipotent progenitors control blood production in normal and regenerative conditions. Cell Stem Cell 17, 35–46 (2015).

    Article  CAS  Google Scholar 

  42. Bandyopadhyay, S. et al. Interaction of KAI1 on tumor cells with DARC on vascular endothelium leads to metastasis suppression. Nat. Med. 12, 933–938 (2006).

    Article  CAS  Google Scholar 

  43. Hur, J. et al. CD82 (KAI1) maintains the dormancy of long-term hematopoietic stem cells through interaction with DARC-expressing macrophages. Cell Stem Cell 18, 508–521 (2016).

    Article  CAS  Google Scholar 

  44. Youn, B.S., Mantel, C. & Broxmeyer, H.E. Chemokines, chemokine receptors and hematopoiesis. Immunol. Rev. 177, 150–174 (2000).

    Article  CAS  Google Scholar 

  45. Sugiyama, T., Kohara, H., Noda, M. & Nagasawa, T. Maintenance of the hematopoietic stem cell pool by CXCL12–CXCR4 chemokine signaling in bone marrow stromal cell niches. Immunity 25, 977–988 (2006).

    Article  CAS  Google Scholar 

  46. Gardner, L., Patterson, A.M., Ashton, B.A., Stone, M.A. & Middleton, J. The human Duffy antigen binds selected inflammatory but not homeostatic chemokines. Biochem. Biophys. Res. Commun. 321, 306–312 (2004).

    Article  CAS  Google Scholar 

  47. Ménard, D. et al. Plasmodium vivax clinical malaria is commonly observed in Duffy-negative Malagasy people. Proc. Natl. Acad. Sci. USA 107, 5967–5971 (2010).

    Article  Google Scholar 

  48. Miller, L.H., Mason, S.J., Clyde, D.F. & McGinniss, M.H. The resistance factor to Plasmodium vivax in blacks. The Duffy-blood-group genotype, FyFy. N. Engl. J. Med. 295, 302–304 (1976).

    Article  CAS  Google Scholar 

  49. Wright, D.E., Wagers, A.J., Gulati, A.P., Johnson, F.L. & Weissman, I.L. Physiological migration of hematopoietic stem and progenitor cells. Science 294, 1933–1936 (2001).

    Article  CAS  Google Scholar 

  50. Seita, J. et al. Gene Expression Commons: an open platform for absolute gene expression profiling. PLoS One 7, e40321 (2012).

    Article  CAS  Google Scholar 

  51. Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).

    Article  Google Scholar 

Download references

Acknowledgements

J.D. and I.N.-B. are joint first authors, and A.T., M.C.-A., M.B., S.L.E., E.H. and K.N. contributed equally to this work. We thank M. Ulvmar, E. Ross, G. Volpe, P. Cauchy and A. Cunningham for their advice, R. Bird and S. Kissane for their assistance with cell sorting and microarray experiments, respectively, H. Vyas and P. Kelay for help with laboratory work and D. Santovito for help with statistical analysis. We thank M. Mack (University of Regensburg) and M. Uchikawa (Japanese Red Cross) for their generous gifts of antibodies specific for mouse CCR2 and human ACKR1 antibody, respectively, and J. Allen (University of Manchester) and M. Bader (Max Delbruck Center) for critical reading of the manuscript and their suggestions. A.R. is grateful to M. Tsaloumas, A. Denniston and N. Glover for the vision. Supported by Medical Research Council grant G0802838 (A.R.), a Senior Visiting Fellowship of the Center for Advanced Studies LMU, Munich (A.R.), Wellcome Trust grant WT090962MA (I.N.-B., A.R. and P.M.), Deutsche Zentrum Für Herz-Kreislauf-Forschung 86X2600229 (J.D. and C.W.), a Marie Curie Actions Intra-European Fellowship ATHEROCHEMOKINE (J.D.), Deutsche Forschungsgemeinschaft grants SFB1123/A1 (C.W.), SFB1123/Z1 (M.B. and R.T.A.M.) and INST 409/150-1 FUG (C.W. and R.T.A.M.), European Research Council grant ERC AdG °692511 (C.W.), Swiss National Science Foundation Sinergia grant CRSII3_160719 (E.H. and A.R.), a TransCard PhD fellowship in Translational Cardiovascular and Metabolic Medicine of the Helmholtz International Research School (K.N.), an ERA-EDTA short-term fellowship (K.A.) and Ministry of Economy, Industry and Competitiveness (MINECO) grant AF2015-65607-R (A.H.). The CNIC is supported by MINECO and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (MINECO award).

Author information

Authors and Affiliations

Authors

Contributions

A.R. conceived the study; J.D., I.N.-B., R.T.A.M., U.H.v.A., A.H., C.W. and A.R. designed the experiments; J.D., I.N.-B., A.T., M.C.-A., M.B., S.L.E., E.H., K.N., K.A. and T.R. performed the experiments and evaluated the data; J.D., I.N.-B., K.E., J.C., P.M., R.T.A.M., U.H.v.A., A.H., C.W. and A.R. interpreted the data; and A.R., J.D. and C.W. wrote the manuscript.

Corresponding authors

Correspondence to Christian Weber or Antal Rot.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Validation of the monoclonal antibody to mouse ACKR1 and expression of ACKR1 by BM hematopoietic cells.

(a to d) Comparison of immunostaining of BM cells by anti-mouse ACKR1 antibodies: new validated monoclonal (a and c, Thiriot A et al Ref. 29) and non-validated polyclonal (b and d, FAB6695P from R&D Systems used by Hur et al., Ref. 43, to report ACKR1 expression in BM macrophages). (a and b) ACKR1 immunoreactivity in nucleated erythroid cells (NECs; CD71+Ter119+), neutrophils (Neutro; CD11b+CD115F4/80Ly6G+), monocytes (Mono; CD11b+ CD115+F4/80Ly6G) and macrophages (Macro; CD11b+CD115F4/80+Ly6G) in BM of wild-type (WT) mice. Left, representative flow cytometry histogram plots; right, quantitative analysis. n=3. (c and d) ACKR1 immunoreactivity and negative control staining (NC) in NECs and macrophages (Macro) in BM of WT (blue) and ACKR1-deficient (KO, red) mice. Left, representative flow cytometry histogram plots; right, quantitative analysis. n=3. BM immunostaining using validated monoclonal antibody (a and c) show that ACKR1 is expressed in the BM by erythroid cells only but not by macrophages. Polyclonal non-validated antibody failed to immunoreact with NECs but at high concentration stained BM macrophages, (b) however non-specifically as (d) to the same extent in WT and KO mice. All data are Mean±SEM. One-Way ANOVA (a and b). Two-Way ANOVA (c and d). *P<0.001. (e) Relative expression of ACKR1 in hematopoietic and stromal populations as determined by microarray analysis based on the data in the BM cells transcriptome database (https://gexc.riken.jp/models). Microarray dataset of adult definitive murine erythroblasts were retrieved from ArrayExpress (https://www.ebi.ac.uk/arrayexpress; E-MTAB-1035) and implemented in Gene Expression Commons database (link to this model https://gexc.riken.jp/models/1649). Proerythroblasts (ProE), early normoblasts (BasoE), late normoblasts (PolyE), reticulocytes (Ret) and Endothelial cells (BM EC) in BM express ACKR1 whereas BM macrophages (BM Macro) do not.

Supplementary Figure 2 ACKR1 is expressed by endothelial cells and nucleated erythroid cells in the BM but not by other hematopoietic cells.

(a) Immunofluorescence micrographs of wild-type (WT) and ACKR1-deficient (KO) BM stained with anti-ACKR1 (red), anti-CD31 (endothelial cells, blue), anti-Ter119 (erythroid cells, yellow) antibodies and DAPI (nuclei, turquoise). (b) ACKR1 expression (MFI) on HSPCs, megakaryocyte progenitors (MkP) and leukocytes (Leu) as compared to the subpopulations of erythroid cells at different stages of development (I-VI) in WT (red) and KO (blue) BM (Mean±SEM; n=3). Scale bar, 30 μm (c) Human ACKR1 mRNA BM expression data retrieved from DMAP (http://portals.broadinstitute.org/dmap/home). Subpopulations of erythroid cells (ERY1-4) were defined as follows: CD71hi GlyA(Ery1), CD71hi GlyA+(Ery2), CD71lo GlyA+(Ery3) and CD71GlyA+(Ery4). Mean±SEM, One-Way ANOVA; *P < 0.001.

Supplementary Figure 3 ACKR1 deficiency does not affect erythroid parameters in BM and blood.

(a) BM cellularity in in WT (blue) and ACKR1-deficient (KO; red) mice (Mean±SEM; n=9). (b) Percentage of proerythroblasts (I), early normoblasts (II), intermediate normoblasts (III), late normoblasts (IV), reticulocytes (V) and mature red cells (VI) in BM of WT and KO mice (Mean±SEM; n=3). (c) Erythrocyte parameters in blood. RBC: Red blood cell counts; HCT: Haematocrit; Hb: haemoglobin. (Mean±SEM; n=9).

Supplementary Figure 4 Flow cytometry analysis of HSPCs, CD48+ subpopulations of LSK cells and CLP cells.

(a) Gating strategy to identify HSPC populations. LSK, defined as LinSca-1+c-Kit+ were subdivided into LSK CD48+ and LSK CD48+. Myeloid progenitor cells (MPC) were defined as LinSca-1c-Kit+. (b) Frequency of LSK CD48+ cells in in WT (blue) and ACKR1-deficient (KO, red) BM. (c) Relative distribution of their MPP2, and lineage-restricted progenitor (LRP) 1 and LRP2 sub-populations. Representative dot plots, left; quantitative analysis, right. (d) Frequency of MPP2, LRP1 and LRP2 in the BM of WT and KO mice. (e) Frequency of common lymphoid progenitors (CLP) defined as LinSca-1loc-Kitlo (G1 gate in a) and IL7Rɑ+Ftl3+. Representative dot plots, left; quantitative analysis, right. (b-d) n=12 in four independent experiments. (f) n=5 in two independent experiments. All data show Mean±SEM. Two-tailed Student’s t-test; ***p < 0.001.

Supplementary Figure 5 Gene expression in LSKs and GMPs from WT and ACKR1-deficient mice.

(a) Microarray heatmap of genes expressed in LSKs and GMPs from wild-type (WT) and ACKR1-deficient (KO) BMs. Each row represents a gene and columns show individual cell populations, in duplicates. Two clusters defined by hierarchical clustering analysis reflect specific gene expression in LSK and GMP. The levels of expression of specific LSK and GMP enriched genes were similar in WT and KO BM. (b) Left, blue: Comparative gene expression in neutrophils (PMNs) and GMPs from Immgen dataset (www.immgen.org). The expression of the 444 genes was higher in PMNs vs. GMPs on average >10 times. Three genes with the highest increase, Ltf, Ly6G and Mmp8, were expressed ca. 100 times more in PMN, than in GMP. Right, red: comparative gene expression in GMPs from WT vs. KO BM expressed as fold change. The level of expression of the overwhelming majority of genes was the same in GMPs from WT and KO BMs (mean fold change 1) but some were expressed orders of magnitude higher in KO GMPs. The transcripts for three most overexpressed genes Rentlg, Camp and Ngp, were in excess of 1000 times higher in KO vs. WT GMPs. These genes were not highly upregulated during the differentiation of GMPs towards PMNs (left panel, blue). This excluded a potential contamination of the GMP with PMNs and was consistent with the induction of a specific transcriptional program of a subset of neutrophil specific genes (also see Fig. 3c) in GMPs rather than only their conventional differentiation to PMNs. (c) Microarray heatmap from Fig. 3a with the list of genes. (d) Microarray heatmap from Fig. 3b with the list of genes.

Supplementary Figure 6 BM and blood cell parameters in reciprocal irradiation BM chimeric and parabiotic wild-type and ACKR1-deficient mice.

(a) Schematic representation of experimental groups of irradiation BM chimeric mice. (b,c) Confirmation of successful BM chimerism by flow cytometric measurement of ACKR1 expression on erythroid cells in BM and in blood. (b) Quantitative analysis of ACKR1 expression on subpopulations of erythroid cells (I-VI) in BM. (c) Percentages of ACKR1+and ACKR1 RBC in peripheral blood. (d) Relative distribution of MPP2, LRP1 and LRP2 subpopulation of LSK CD48+ in irradiation BM chimeric mice: wild-type (WT) BM cells reconstituted into WT mice (blue), WT BM cells reconstituted into ACKR1-deficient (KO) mice (light blue), KO BM cells reconstituted into KO mice (red) and KO BM cells reconstituted into WT mice (pink); right, representative dot plots; left, quantitative analysis. (e) Schematic representation of experimental groups in parabiosis experiments. (f) Confirmation of shared circulation in parabiosis by measurement of plasma CCL2 levels in individual naïve and parabiotic mice. WT with WT parabionts (blue), KO with KO parabionts (red), WT parabiont in WT with KO pair (purple) and KO parabiont in WT with KO pair (orange) (g) Relative distribution of MPP2, LRP1 and LRP2 subpopulation of LSK CD48+, right, representative dot plots; left, quantitative analysis. All data are n=4 from two independent experiments, Mean±SEM. One-way ANOVA; *P < 0.05, **P < 0.01 and ***P < 0.001.

Supplementary Figure 7 Interactions of HSCs and nucleated erythroid cells in the BM.

(a) Representative flow cytometry histograms showing staining by antibodies against CD48, Lineage (Lin; B220, CD3, CD11b, Gr1), CD150 and CD71 used for identification of HSCs and nucleated erythroid cells (NECs). The HSCs (LSK CD150+ CD48) are distinguished as Lin CD150+CD48CD71; the NECs (CD71+ Ter119+) are CD71+LinCD150CD48; the MPCs and CD48+LSK subset are LinCD48+ CD150CD71 and the lineage cells are Lin+CD48+. (b) Application of the above combination of antibodies to the two-photon microscopy of the whole-mounted femur. HSCs (LinCD150+CD71; green) and NECs (LinCD150 CD71+; red) appear clearly distinct. HSCs (in the upper rows of magnification insets, green; in lower rows of insets, only outlined in white) did not stain for lineage or CD71. Scale bars, 50 μm and 40 μm (c) Representative two photon microscopy images of the whole-mounted femurs from ACKR1-deficient mice used for the measurements of the distance between NECs and HSCs. (d) Quantitative analysis of HSC, MMP1, MPP2 and LRP populations interacting with NECs assessed by flow cytometry. Wild-type (WT, blue) and ACKR1-deficient (KO, red). Two-tailed Student’s t-test, (n=3). All numeric data are Mean±SEM. ***P < 0.001.

Supplementary Figure 8 Effect of ACKR1 on the expression of cell surface markers and cell numbers in BM and blood.

(a) CD62L, CD11a, CD16/32, CD45 and CD11b expression in BM neutrophils (CD11b+Ly6G+) from wild-type (WT, blue) and ACKR1-deficient (KO, red) mice; NC (white), negative control staining. Top, representative flow cytometry histograms; bottom, quantitative analysis. (b) Representative flow cytometry histograms of staining blood neutrophils (CD11b+Ly6G+) from WT and KO mice with antibodies against CD62L and CD11a and (c) antibodies against CXCR2, CCR1, CCR2, CCR3 and CCR5. (d) Representative flow cytometry histograms of CXCR1, CXCR2 and CCR2 antibodies staining of blood neutrophils from Duffy-positive and Duffy-negative individuals, grey and black histograms, respectively, negative control staining, NC (white). (e) PMN (CD11b+Ly6G+CD115), monocyte (CD11b+Ly6GCD115+) and B lineage cell (B220+CD19+) counts in BM of WT and ACKR1-deficient mice (n=6). (f) Monocyte (CD45+CD11b+Ly6G CD115+) and lymphoid cell (CD45+ and B220+ or CD3+) counts in blood of WT and ACKR1-deficient mice (n=6). (g) Platelet counts in blood of WT and ACKR1-deficient mice (n=16). (h) Expression of CD16/32 on blood neutrophils (CD11b+Ly6G+CD115) in reciprocal BM chimeric mice; WT BM cells reconstituted into WT mice (blue), WT BM cells reconstituted into KO mice (light blue), KO BM cells reconstituted into KO mice (red) and KO BM cells reconstituted into WT mice (pink); n=4 from two independent experiments. (i) Representative immunofluorescence micrograph of human spleen stained with monoclonal anti-ACKR1 (Fy6, red) antibody. All data show Mean±SEM. (a and e-g) two-tailed Student’s t-test; *P < 0.05, **P < 0.01. (h) one-way ANOVA; **P < 0.01.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Table 1.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Duchene, J., Novitzky-Basso, I., Thiriot, A. et al. Atypical chemokine receptor 1 on nucleated erythroid cells regulates hematopoiesis. Nat Immunol 18, 753–761 (2017). https://doi.org/10.1038/ni.3763

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3763

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing