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A new lymphoid-primed progenitor marked by Dach1 downregulation identified with single cell multi-omics


A classical view of blood cell development is that multipotent hematopoietic stem and progenitor cells (HSPCs) become lineage-restricted at defined stages. Linc-Kit+Sca-1+Flt3+ cells, termed lymphoid-primed multipotent progenitors (LMPPs), have lost megakaryocyte and erythroid potential but are heterogeneous in their fate. Here, through single-cell RNA sequencing, we identify the expression of Dach1 and associated genes in this fraction as being coexpressed with myeloid/stem genes but inversely correlated with lymphoid genes. Through generation of Dach1–GFP reporter mice, we identify a transcriptionally and functionally unique Dach1–GFP subpopulation within LMPPs with lymphoid potential with low to negligible classic myeloid potential. We term these ‘lymphoid-primed progenitors’ (LPPs). These findings define an early definitive branch point of lymphoid development in hematopoiesis and a means for prospective isolation of LPPs.

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Fig. 1: scRNA-seq reveals heterogeneous subpopulations within LMPPs.
Fig. 2: Dach1–GFP expression among HSPC subpopulations.
Fig. 3: Dach1–GFP defined populations change with age.
Fig. 4: scRNA-seq reveals that Dach1–GFP levels demarcate a unique putative lymphoid-primed progenitor.
Fig. 5: LPPs are enriched for lymphoid potential, with negligible myeloid potential.
Fig. 6: LPPs generate lymphoid but not myeloid cells in vivo.
Fig. 7: Revised model of hematopoiesis.

Data availability

Raw sequencing data are available from GEO under accession numbers GSE136341 and GSE136225. Processed data from these analyses are available from The Haemopedia database can be accessed at The Immgen database is available from

Code availability

Code from these analyses is available from


  1. 1.

    Rothenberg, E. V. Transcriptional control of early T and B cell developmental choices. Annu. Rev. Immunol. 32, 283–321 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Adolfsson, J. et al. Identification of Flt3+ lympho-myeloid stem cells lacking erythro-megakaryocytic potential. Cell 121, 295–306 (2005).

    CAS  PubMed  Google Scholar 

  3. 3.

    Yoshida, T., Ng, S. Y., Zuniga-Pflucker, J. C. & Georgopoulos, K. Early hematopoietic lineage restrictions directed by Ikaros. Nat. Immunol. 7, 382–391 (2006).

    CAS  PubMed  Google Scholar 

  4. 4.

    Igarashi, H., Gregory, S. C., Yokota, T., Sakaguchi, N. & Kincade, P. W. Transcription from the RAG1 locus marks the earliest lymphocyte progenitors in bone marrow. Immunity 17, 117–130 (2002).

    CAS  PubMed  Google Scholar 

  5. 5.

    Berthault, C. et al. Asynchronous lineage priming determines commitment to T cell and B cell lineages in fetal liver. Nat. Immunol. 18, 1139–1149 (2017).

    CAS  PubMed  Google Scholar 

  6. 6.

    Lai, A. Y., Lin, S. M. & Kondo, M. Heterogeneity of Flt3-expressing multipotent progenitors in mouse bone marrow. J. Immunol. 175, 5016–5023 (2005).

    CAS  PubMed  Google Scholar 

  7. 7.

    Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    CAS  PubMed  Google Scholar 

  8. 8.

    Olsson, A. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698–702 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Nestorowa, S. et al. A single cell resolution map of mouse haematopoietic stem and progenitor cell differentiation. Blood (2016).

  10. 10.

    Giladi, A. et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat. Cell Biol. 20, 836–846 (2018).

    CAS  PubMed  Google Scholar 

  11. 11.

    Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016).

    PubMed  Google Scholar 

  13. 13.

    Lee, J. et al. Lineage specification of human dendritic cells is marked by IRF8 expression in hematopoietic stem cells and multipotent progenitors. Nat. Immunol. 18, 877–888 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Jacobsen, S. E. W. & Nerlov, C. Haematopoiesis in the era of advanced single-cell technologies. Nat. Cell Biol. 21, 2–8 (2019).

    CAS  PubMed  Google Scholar 

  15. 15.

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

    CAS  PubMed  Google Scholar 

  16. 16.

    Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS  PubMed  Google Scholar 

  17. 17.

    Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell (2019).

  20. 20.

    Choi, J. et al. Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans. Nucleic Acids Res. 47, D780–D785 (2019).

    CAS  PubMed  Google Scholar 

  21. 21.

    Chopin, M. et al. Transcription factor PU.1 promotes conventional dendritic cell identity and function via induction of transcriptional regulator DC-SCRIPT. Immunity 50, 77–90 (2019).

    CAS  PubMed  Google Scholar 

  22. 22.

    Aubrey, B. J. et al. An inducible lentiviral guide RNA platform enables the identification of tumor essential genes and novel tumor promoting mutations in vivo. Cell Rep. (2015).

  23. 23.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Dorshkind, K., Höfer, T. H. X., Montecino-Rodriguez, E., Pioli, P. D. & Rodewald, H.-R. Do haematopoietic stem cells age? Nat. Rev. Immunol. (2019).

  25. 25.

    Chung, S. S. & Park, C. Y. Aging, hematopoiesis, and the myelodysplastic syndromes. Hematology Am. Soc. Hematol. Educ. Program 2017, 73–78 (2017).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477–16 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    De Obaldia, M. E. et al. T cell development requires constraint of the myeloid regulator C/EBP-α by the Notch target and transcriptional repressor Hes1. Nat. Immunol. 14, 1277–1284 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Metcalf, D., Ng, A., Mifsud, S. & Di Rago, L. Multipotential hematopoietic blast colony-forming cells exhibit delays in self-generation and lineage commitment. Proc. Natl Acad. Sci. USA 107, 16257–16261 (2010).

    CAS  PubMed  Google Scholar 

  30. 30.

    Nicola, N. A. & Metcalf, D. The Hemopoietic Colony-Stimulating Factors. (Cambridge University Press, 1995).

  31. 31.

    Haas, S., Trumpp, A. & Milsom, M. D. Causes and consequences of hematopoietic stem cell heterogeneity. Cell Stem Cell 22, 627–638 (2018).

    CAS  PubMed  Google Scholar 

  32. 32.

    Guilliam, M., Mildner, A. & Yona, S. Developmental and functional heterogeneity of monocytes. Immunity 49, 595–613 (2018).

    Google Scholar 

  33. 33.

    Laurenti, E. & Göttgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 553, 418–426 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Naik, S. H. Dendritic Cell Development, Lineage Issues and Haematopoiesis at the Single Cell Level. Cell Determination During Hematopoiesis (Nova Press, 2009).

  35. 35.

    Bell, J. J. & Bhandoola, A. The earliest thymic progenitors for T cells possess myeloid lineage potential. Nature 452, 764–767 (2008).

    CAS  PubMed  Google Scholar 

  36. 36.

    Wada, H. et al. Adult T-cell progenitors retain myeloid potential. Nature 452, 768–772 (2008).

    CAS  PubMed  Google Scholar 

  37. 37.

    Bhandoola, A., von Boehmer, H., Petrie, H. T. & Zúñiga-Pflücker, J. C. Commitment and developmental potential of extrathymic and intrathymic T cell precursors: plenty to choose from. Immunity 26, 678–689 (2007).

    CAS  PubMed  Google Scholar 

  38. 38.

    Naik, S. H. Dendritic cell development at a clonal level within a revised ‘continuous’ model of haematopoiesis. Mol. Immunol. 124, 190–197 (2020).

    CAS  PubMed  Google Scholar 

  39. 39.

    Ji, H. et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature 467, 338–342 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wu, K. et al. DACH1 inhibits transforming growth factor-β signaling through binding Smad4. J. Biol. Chem. 278, 51673–51684 (2003).

    CAS  PubMed  Google Scholar 

  41. 41.

    Lee, J.-W. et al. DACH1 regulates cell cycle progression of myeloid cells through the control of cyclin D, Cdk 4/6 and p21Cip1. Biochem. Bioph. Res. Co. 420, 91–95 (2012).

    CAS  Google Scholar 

  42. 42.

    Lee, J.-W. et al. Regulation of HOXA9 activity by predominant expression of DACH1 against C/EBPα and GATA-1 in myeloid leukemia with MLL-AF9. Biochem. Bioph. Res. Co. 426, 299–305 (2012).

    CAS  Google Scholar 

  43. 43.

    Chu, V. T. et al. Efficient CRISPR-mediated mutagenesis in primary immune cells using CrispRGold and a C57BL/6 Cas9 transgenic mouse line. Proc. Natl Acad. Sci. USA 113, 12514–12519 (2016).

    CAS  PubMed  Google Scholar 

  44. 44.

    Kontgen, F., Suss, G., Stewart, C., Steinmetz, M. & Bluethmann, H. Targeted disruption of the MHC class II Aa gene in C57BL/6 mice. Int. Immunol. 5, 957–964 (1993).

    CAS  PubMed  Google Scholar 

  45. 45.

    Lin, D. S. et al. DiSNE movie visualization and assessment of clonal kinetics reveal multiple trajectories of dendritic cell development. Cell Rep. 22, 2557–2566 (2018).

    CAS  PubMed  Google Scholar 

  46. 46.

    Tian, L. et al. scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput. Biol. 14, e1006361 (2018).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Vallejos, C. A., Marioni, J. C. & Richardson, S. BASiCS: Bayesian analysis of single-cell sequencing. Data. 11, e1004333 (2015).

    Google Scholar 

  48. 48.

    Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    PubMed  Google Scholar 

  49. 49.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    de Graaf, C. A. et al. Haemopedia: an expression atlas of murine hematopoietic cells. Stem Cell Rep. 7, 571–582 (2016).

    Google Scholar 

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We thank the WEHI flow cytometry facility, the Single Cell Open Research Endeavour, S. Wilcox for genomics support and Animal Bioservices for animal husbandry. This work was supported by the National Health & Medical Research Council, with fellowships to S.H.N. (0516782), J.E.B (0637403), C.d.G (1035229), S.L.N. (1155342) and W.S.A (1058344); project grants (1062820, 1124812, 1085765, 1060179, 1122783); a program grant (1113577), the Australian Research Council’s special initiative Stem Cells Australia, an Australian Cancer Research Fund, Victorian State Government Infrastructure Support, Australian Government NHMRC IRIIS and CSL Limited.

Author information




D.Z. developed the analogous CEL-Seq2 protocol and performed scRNA-seq with the WEHI Single Cell Open Research Endeavour. L.T. with M.R. performed bioinformatics analysis, and C.G. performed the hematopoietic expression survey. Most in vivo and in vitro experiments were performed by J.S. and S.T. with assistance from A.P.N. and L.D. (myeloid colony assays), S.L.N, M.P.M. and J.T.J. (lymphoid colony assays) and D.S.L. (FACS characterization). Dach1–GFP reporter mice were generated by W.A., with initial characterization by J.S., K.A.F. and J.E.B. CRISPR construct generation and validation were performed by A.J., A.H. and M.D.M. S.H.N. designed and supervised the study and wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Shalin H. Naik.

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The authors declare no competing interests.

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Editor recognition statement Jamie D. K. Wilson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Dach1 expression amongst HSPCs.

Haematopoietic mRNA expression survey for Dach1 from a) Haemopedia Mouse RNA-Seq dataset53 and b) Immgen ULI RNA-Seq dataset19, as derived from haemosphere.org20. Expression shown is log2 transcripts per million (tpm) and cells are coloured by lineage.

Extended Data Fig. 2 Dach1 deficiency does not overtly affect haematopoiesis.

a, c-Kit-enriched cells from the BM of CD45.1 (WT) and CD45.2 (Cas9-Tg) mice were transduced with lentivirus expressing guide RNAs (Cherry+) that were b) previously validated by western blot to delete Dach1 expression in AML246 cells. These cells were co-injected into sub-lethally irradiated CD45.1/CD45.2 recipient mice and cellular output was analysed at different time-points; c) FACS data from days 20 and 55 is shown. d, For each cell type, the ratio of Cherry+/Cherry between donor WT and donor Cas9 Tg is shown at multiple times after transplantation as indicated (mean ± S.E.M, 1 experiment). No significant differences in this ratio between WT and Cas9 Tg were observed. Number of mice for each guide RNA and time point is shown.

Extended Data Fig. 3 scRNA-seq gating parameters.

a, Gating parameters of cells from scRNA-seq in Fig. 4. c-Kit+Sca-1+ LSKs, which contained IL-7R+ lymphoid progenitors, were index sorted from Dach1-GFP reporter mice. b, gating parameters based on the index sorted cells for the indicated HSPC subpopulations used for Fig. 4e–i. c, UMAP with all trajectories inferred by slingshot over cells annotated by transcriptional clusters from Fig. 4a.

Extended Data Fig. 4 Cytometry and gating strategy for limiting dilution assays.

Phenotype of progeny from limiting dilution experiments in Fig. 5b. Flt3hi, Flt3int, Dach1-GFP+ and LPPs were isolated and seeded in a) OP9-DL1 cultures to allow T cell generation and b) OP9 cultures to allow B cell generation. Progeny from wells were assessed after 3 weeks and 2 weeks respectively by flow cytometry. One representative well from the row seeded with the lowest dilution of cells is shown. The top row of panel a) and b) is the gating strategy used for all wells.

Extended Data Fig. 5 Flt3hi versus Flt3int separation of LMPPs does not separate myeloid and lymphoid fate.

a, Dach1+, Flt3hi, Flt3int LMPPs and LPPs were sorted from Dach1-GFP reporter mice (CD45.2+) as per Fig. 4a, then transferred into separate groups of sub-lethally irradiated recipients (CD45.1+). b, Myeloid and lymphoid output of donor-derived LMPP fractions is shown by FACS at day 13 and day 28 post transplantation from the blood of recipient mice. Numbers indicate percentage of cells from parent gate. c, Histograms show the percentage of contribution of donor-derived (CD45.2+) cells to differentiated lineages from spleen at day 13 post-transplantation. n = 4 mice from 2 experimental repeats (mean ± S.E.M). Two-tailed unpaired T-test * p < 0.01, ** p < 0.001, *** p < 0.0001. d, Differentiated lineages were also analysed from blood of recipient mice at different time points. Shown is the contribution of donor-derived CD45.1+ cells to each indicated cell type. n = 4 mice from 2 experimental repeats (mean ± S.E.M).

Supplementary information

Reporting Summary

Supplementary Table 1

Seurat-defined significant marker genes.

Supplementary Table 2

Full list of antibodies used for flow cytometry.

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Amann-Zalcenstein, D., Tian, L., Schreuder, J. et al. A new lymphoid-primed progenitor marked by Dach1 downregulation identified with single cell multi-omics. Nat Immunol 21, 1574–1584 (2020).

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