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.

  • Article
  • Published:

Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow

Nature Immunology volume 16, pages 718–728 (2015)Cite this article

Subjects

Abstract

Mouse conventional dendritic cells (cDCs) can be classified into two functionally distinct lineages: the CD8α+ (CD103+) cDC1 lineage, and the CD11b+ cDC2 lineage. cDCs arise from a cascade of bone marrow (BM) DC-committed progenitor cells that include the common DC progenitors (CDPs) and pre-DCs, which exit the BM and seed peripheral tissues before differentiating locally into mature cDCs. Where and when commitment to the cDC1 or cDC2 lineage occurs remains poorly understood. Here we found that transcriptional signatures of the cDC1 and cDC2 lineages became evident at the single-cell level from the CDP stage. We also identified Siglec-H and Ly6C as lineage markers that distinguished pre-DC subpopulations committed to the cDC1 lineage (Siglec-H−Ly6C− pre-DCs) or cDC2 lineage (Siglec-H−Ly6C+ pre-DCs). Our results indicate that commitment to the cDC1 or cDC2 lineage occurs in the BM and not in the periphery.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The transcriptional landscape of DC development.
Figure 2: Heterogeneity in the pre-DC compartment is detectable at the mRNA level.
Figure 3: BM, blood and splenic pre-DCs have heterogeneous surface protein profiles.
Figure 4: Pre-DC subsets arise from CDPs and show subset-specific transcriptome imprinting.
Figure 5: Identification of DC subset–specific progenitors.
Figure 6: Analysis of single MDPs, CDPs and pre-DCs facilitates visualization of dedicated cDC1 lineage– and cDC2 lineage–primed pre-DCs.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Schlitzer, A. & Ginhoux, F. Organization of the mouse and human DC network. Curr. Opin. Immunol. 26, 90–99 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Merad, M., Sathe, P., Helft, J., Miller, J. & Mortha, A. The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting. Annu. Rev. Immunol. 31, 563–604 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Schlitzer, A., McGovern, N. & Ginhoux, F. Dendritic cells and monocyte-derived cells: Two complementary and integrated functional systems. Semin. Cell Dev. Biol. doi:10.1016/j.semcdb.2015.03.011 (2015).

  4. Karsunky, H., Merad, M. & Cozzio, A. Flt3 ligand regulates dendritic cell development from Flt3+ lymphoid and myeloid-committed progenitors to Flt3+ dendritic cells in vivo. J. Exp. Med. 198, 305–313 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Fogg, D.K. et al. A clonogenic bone marrow progenitor specific for macrophages and dendritic cells. Science 311, 83–87 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Naik, S.H. et al. Development of plasmacytoid and conventional dendritic cell subtypes from single precursor cells derived in vitro and in vivo. Nat. Immunol. 8, 1217–1226 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Onai, N. et al. Identification of clonogenic common Flt3+M-CSFR+ plasmacytoid and conventional dendritic cell progenitors in mouse bone marrow. Nat. Immunol. 8, 1207–1216 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Sathe, P. et al. Lymphoid tissue and plasmacytoid dendritic cells and macrophages do not share a common macrophage-dendritic cell-Restricted progenitor. Immunity 41, 104–115 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Liu, K. & Nussenzweig, M.C. Development and homeostasis of dendritic cells. Eur. J. Immunol. 40, 2099–2102 (2010).

    Article  CAS  PubMed  Google Scholar 

  10. Ginhoux, F. et al. The origin and development of nonlymphoid tissue CD103+ DCs. J. Exp. Med. 206, 3115–3130 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Onai, N. et al. A clonogenic progenitor with prominent plasmacytoid dendritic cell developmental potential. Immunity 38, 943–957 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Swiecki, M. et al. Cell depletion in mice that express diphtheria toxin receptor under the control of SiglecH encompasses more than plasmacytoid dendritic cells. J. Immunol. 192, 4409–4416 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Kucharczak, J., Simmons, M.J., Fan, Y. & Gélinas, C. To be, or not to be: NF-κB is the answer–role of Rel/NF-κB in the regulation of apoptosis. Oncogene 22, 8961–8982 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. LeibundGut-Landmann, S. et al. Mini-review: Specificity and expression of CIITA, the master regulator of MHC class II genes. Eur. J. Immunol. 34, 1513–1525 (2004).

    Article  CAS  PubMed  Google Scholar 

  15. Miller, J.C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gautier, E.L. et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat. Immunol. 13, 1118–1128 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Liu, K. et al. In vivo analysis of dendritic cell development and homeostasis. Science 324, 392–397 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schlitzer, A. et al. Tissue-specific differentiation of a circulating CCR9− pDC-like common dendritic cell precursor. Blood 119, 6063–6071 (2012).

    Article  CAS  PubMed  Google Scholar 

  20. Satpathy, A.T. et al. Zbtb46 expression distinguishes classical dendritic cells and their committed progenitors from other immune lineages. J. Exp. Med. 209, 1135–1152 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Schlitzer, A. et al. IRF4 transcription factor-dependent CD11b+ dendritic cells in human and mouse control mucosal IL-17 cytokine responses. Immunity 38, 970–983 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Persson, E.K. et al. IRF4 transcription-factor-dependent CD103+CD11b+ dendritic cells drive mucosal T helper 17 cell differentiation. Immunity 38, 958–969 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Cisse, B. et al. Transcription factor E2–2 is an essential and specific regulator of plasmacytoid dendritic cell development. Cell 135, 37–48 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sakaue-Sawano, A. et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132, 487–498 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Tenenbaum, J.B., de Silva, V. & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).

    Article  CAS  PubMed  Google Scholar 

  26. Naik, S.H. et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496, 229–232 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Lara-Astiaso, D. et al. Immunogenetics. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Besson, A., Dowdy, S.F. & Roberts, J.M. CDK inhibitors: cell cycle regulators and beyond. Dev. Cell 14, 159–169 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Meister, P., Mango, S.E. & Gasser, S.M. Locking the genome: nuclear organization and cell fate. Curr. Opin. Genet. Dev. 21, 167–174 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hildner, K. et al. Batf3 deficiency reveals a critical role for CD8α+ dendritic cells in cytotoxic T cell immunity. Science 322, 1097–1100 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Kashiwada, M., Pham, N.-L.L., Pewe, L.L., Harty, J.T. & Rothman, P.B. NFIL3/E4BP4 is a key transcription factor for CD8α+ dendritic cell development. Blood 117, 6193–6197 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Prendergast, A.M. & Essers, M.A.G. Hematopoietic stem cells, infection, and the niche. Ann. NY Acad. Sci. 1310, 51–57 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Essers, M.A.G. et al. IFNalpha activates dormant haematopoietic stem cells in vivo. Nature 458, 904–908 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Baldridge, M.T., King, K.Y., Boles, N.C., Weksberg, D.C. & Goodell, M.A. Quiescent haematopoietic stem cells are activated by IFN-γ in response to chronic infection. Nature 465, 793–797 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Challen, G.A., Boles, N.C., Chambers, S.M. & Goodell, M.A. Distinct hematopoietic stem cell subtypes are differentially regulated by TGF-β1. Cell Stem Cell 6, 265–278 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mercher, T. et al. Notch signaling specifies megakaryocyte development from hematopoietic stem cells. Cell Stem Cell 3, 314–326 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lewis, K.L. et al. Notch2 receptor signaling controls functional differentiation of dendritic cells in the spleen and intestine. Immunity 35, 780–791 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wu, L. et al. RelB is essential for the development of myeloid-related CD8α− dendritic cells but not of lymphoid-related CD8α+ dendritic cells. Immunity 9, 839–847 (1998).

    Article  CAS  PubMed  Google Scholar 

  40. Kabashima, K. et al. Intrinsic lymphotoxin-β receptor requirement for homeostasis of lymphoid tissue dendritic cells. Immunity 22, 439–450 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Satpathy, A.T. et al. Notch2-dependent classical dendritic cells orchestrate intestinal immunity to attaching-and-effacing bacterial pathogens. Nat. Immunol. 14, 937–948 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gatto, D. et al. The chemotactic receptor EBI2 regulates the homeostasis, localization and immunological function of splenic dendritic cells. Nat. Immunol. 14, 446–453 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Yi, T. & Cyster, J.G. EBI2-mediated bridging channel positioning supports splenic dendritic cell homeostasis and particulate antigen capture. eLife 2, e00757 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Gentleman, R., Carey, V., Huber, W., Irizarry, R. & Dudoit, S. Bioinformatics and computational biology solutions using R and Bioconductor. Brief. Bioinformatics 8, 136–137 (2006).

    Article  Google Scholar 

  45. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  46. Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Pollen, A.A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Usoskin, D. et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Shalek, A.K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 1–22 (2014).

    Article  CAS  Google Scholar 

  51. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality inexpression and splicing in immune cells. Nature 498, 236–240 (2014).

    Article  CAS  Google Scholar 

  52. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ward, J.T.J. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).

    Article  Google Scholar 

Download references

Acknowledgements

We thank L. Robinson for critical review and editing of the manuscript; M.L. Ng, S.H. Tan, T.B. Lu, I. Low and N.B. Shadan for technical assistance; K. Murphy (University of Washington) for ZBTB46-GFP BM; and G. Belz (Walter & Eliza Hall Institute) for Id2-GFP BM. Supported by Singapore Immunology Network (F.G.), Agency for Science, Technology and Research (BMRC Young Investigator grant to A.S.), the Genomics Institute of Singapore (P.R.) and the Singapore Medical Research Council (NMRC/CBRG/0047/2013 to B.M.).

Author information

Author notes

  1. V Sivakamasundari, Jinmiao Chen, Paul Robson and Florent Ginhoux: These authors contributed equally to this work.

Authors and Affiliations

  1. Singapore Immunology Network, Agency for Science, Technology and Research, Singapore

    Andreas Schlitzer, Jinmiao Chen, Hermi Rizal Bin Sumatoh, Josephine Lum, Benoit Malleret, Sanqian Zhang, Anis Larbi, Francesca Zolezzi, Laurent Renia, Michael Poidinger, Evan W Newell & Florent Ginhoux

  2. Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore

    V Sivakamasundari & Paul Robson

  3. Molecular Medicine Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia

    Jaring Schreuder & Shalin Naik

  4. Department of Microbiology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore

    Benoit Malleret

  5. Department of Medical Biology, University of Melbourne, Parkville, Australia

    Shalin Naik

Authors
  1. Andreas Schlitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V Sivakamasundari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinmiao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hermi Rizal Bin Sumatoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jaring Schreuder
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Josephine Lum
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Benoit Malleret
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sanqian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Anis Larbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Francesca Zolezzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Laurent Renia
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Michael Poidinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Shalin Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Evan W Newell
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Paul Robson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Florent Ginhoux
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.S., P.R. and F.G. conceived of the study; A.S., S.V., H.R.B.S., J.S., J.L. and B.M. performed experiments; A.S., J.C., S.Z., M.P. and F.G. analyzed data; A.L., F.Z., L.R., S.N. and E.W.N. provided reagents and intellectual guidance; and A.S., S.V., J.C. and F.G. wrote the paper.

Corresponding author

Correspondence to Florent Ginhoux.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Single-cell transcriptomic workflow and quality control.

Experimental approach for analysis of gene expression in single MDPs, CDPs and pre-DCs using the Fluidgim C1 autoprep system (a). Box plot of log2 r.p.k.m. values of the 264 most commonly expressed genes in single MDPs, CDPs and pre-DCs. Single MDPs, CDPs and pre-DCs were ranked according to their median values of the gene expression of these 264 genes from high to low. The dashed horizontal line in red indicates the threshold used to predict the outlier cells. Red boxes indicate outlier cells that had median log2 r.p.k.m. values below the threshold due to poor sequencing efficiency (b). Principal component Analysis (PCA) plot of cells using the 264 most commonly expressed genes. Red boxes indicate outlier cells that were identified in Supplementary Fig. 1b (c). Bar plot of total number of reads, number of mapped reads and mapping rate in each individual MDP, CDP and pre-DC (d). Correlation of ERCC spike-in RNA and detected r.p.k.m values detected across all mRNA sequenced cells (R2: Pearson’s correlation coefficient, (e)). Assessment of capture batch effect on CDP single cell mRNA sequencing data. CDP color coded for different capture batches analyzed using hierarchical clustering (f). Assessment of capture batch effect on pre-DC single cell mRNA sequencing data. pre-DC color coded for different capture batches analyzed using hierarchical clustering (g). MDP was captured using 1 Fluidigm C1 IFC, CDP was captured using 5 Fluidigm C1 IFCs, pre-DC was captured using 3 Fluidigm C1 IFCs.

Supplementary Figure 2 Comparison of different sequencing depths in single CDP transcriptomes.

47 CDP libraries were sequenced to a read depth between 10 – 15 million reads per cell and compared to their counterparts already sequenced to 1 – 2 million reads per cell (a). Additionally, the number of expressed genes detected per single CDP was compared between the deeper sequencing depth and the depth used throughout our analysis (b). Hierarchical clustering of all MDPs, CDPs and pre-DCs based on all expressed genes (r.p.k.m > 1) before selection of the Top 87 PCA-loaded genes determined by ANOVA (c).

Supplementary Figure 3 Unsupervised clustering of the DC development continuum.

Heatmap depicts unsupervised clustering of Top 87 PCA-loaded genes, determined by ANOVA across all transcriptomes of single MDP, CDPs and pre-DCs (ANOVA followed by multiple test correction using the Benjamini & Hochberg method). This heatmap was then reprocessed with the NBOR algorithm (see methods) to generate the heatmap in Fig. 1a. Assigned landmarks depicted under the heatmap, genes can be found in Supplementary Table 2 (z-score normalized, (a)). Bioinformatics workflow for ordering single cells along a linear development continuum using the neighborhood-based ordering strategy (b).

Supplementary Figure 4 Workflow for identification of cDC1 lineage– or cDC2 lineage–primed progenitors using CMap.

Workflow depicts strategy to identify total DC, cDC1 or cDC2 lineage-primed progenitors using transcriptomic signatures derived from conventional transcriptome analysis combined with CMap analysis.

Supplementary Figure 5 Characterization of novel pre-DC subsets defined by expression of Siglec-H and Ly6C in mouse BM.

Flow cytometric analysis of CD24 expression on Siglec-H+Ly6C−, Siglec-H+Ly6C+, Siglec-H−Ly6C− or Siglec-H−Ly6C+ pre-DC subsets in BM, blood and spleen (representative data shown of 3 independent experiments with a total of 7 individual mice, (a)) (a). Representative scanning electron microscopy images of individual cells from each pre-DC subset following purification from the bone marrow by FACS (b). Flow cytometric analysis of intensity of GFP expression in ZBTB46, ID2 and CX3CR1 reporter mice in Siglec-H+Ly6C−, Siglec-H+Ly6C+, Siglec-H−Ly6C− or Siglec-H−Ly6C+ pre-DC subsets from mouse BM, number depicts mean fluorescent intensity (MFI) (representative data shown of 3 independent experiments with a total of 2 mice per strain per experiment, (c)). Frequency of pre-DC subsets among total pre-DCs in BM, blood and spleen (data representative of 3 independent experiments with 3 mice per experiment, mean ± SEM, (d)). Flow cytometric analysis of Ly6C and Siglec-H expression on the progeny of CD45.2+ pre-DC subsets (CD45.2+Siglec-H+Ly6C−, CD45.2+Siglec-H+Ly6C+, CD45.2+Siglec-H−Ly6C+ or CD45.2+Siglech-H−Ly6C− pre-DCs) added individually into day 2 in vitro CD45.1+ FLT3L-stimulated BM cultures. Cells depicted were harvested on day 3 and gated for Lin−B220−MHCII−CD45.2+CD11c+ expression (data representative of 2 independent experiments with 1 analyte per population, mean ± SEM, (e)).

Supplementary Figure 6 Identification of subset-specific DC progenitors in vitro and in vivo.

Percentage of proliferating cells among total Siglec-H+ BM pre-DC subsets and total Siglec-H− BM pre-DCs as determined by expression of Azami green expression in Fucci mice measured in vivo (data representative of 3 independent experiments with 7 mice in total, mean ± SEM, p: 0.0003, unpaired, two-tailed t-test, *p < 0.001, (a)). Flow cytometric analysis of B220 and MHC II expression on the progeny of CD45.2+ pre-DC subsets (CD45.2+Siglec-H+Ly6C−, CD45.2+Siglec-H+Ly6C+, CD45.2+Siglec-H−Ly6C+ or CD45.2+Siglech-H−Ly6C− pre-DCs) added individually into day 2 in vitro CD45.1+ FLT3L-stimulated BM cultures. Cells depicted were harvested on day 3 or 6 and are gated CD45.2+CD11c+ expression (data representative of 3 independent experiments with 1 analyte per population (b)). Pie charts depict flow cytometric analysis of single Siglec-H+Ly6C− pre-DC or Siglec-H+Ly6C+ pre-DC progeny cultured for 6 days on OP9 feeder cells in day 9 FLT3L-stimulated BM culture conditioned media. Progeny of single Siglec-H+Ly6C− pre-DC or Siglec-H+Ly6C+ pre-DC was analyzed for the presence of pDC, cDC1 and cDC2 (n depicts number of clones analyzed, Siglec-H+Ly6C− pre-DC n=24, Siglec-H+Ly6C+ pre-DC n=35 (c)). Flow cytometric analysis of phenotypes of splenic progeny from CD45.2+Siglec-H+Ly6C−, CD45.2+Siglec-H+Ly6C+, CD45.2+Siglec-H−Ly6C+ or CD45.2+Siglech-H−Ly6C− pre-DCs injected into femurs of recipient mice 5 days earlier (data representative of 3 independent experiments with 1 analyte per pre-DC population, (d)). Schematic depiction of the DC development cascade in the bone marrow; CDPs as well as Siglec-H+Ly6C− pre-DCs show potential to give rise to cDCs as well as pDCs, while acquisition of Ly6C on Siglec-H+Ly6C− pre-DCs leads to the loss of pDC potential. Siglec-H+Ly6C+ pre-DCs are further differentiating in Siglec-H−Ly6C+ as well as Siglec-H−Ly6C− pre-DC with selective potential for cDC2 or cDC1 respectively (e).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Tables 1–12 (PDF 10177 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schlitzer, A., Sivakamasundari, V., Chen, J. et al. Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow. Nat Immunol 16, 718–728 (2015). https://doi.org/10.1038/ni.3200

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3200

This article is cited by

Search

Advanced 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