Intrinsic and extrinsic cues determine developmental trajectories of hematopoietic stem cells (HSCs) towards erythroid, myeloid and lymphoid lineages. Using two newly generated transgenic mice that report and trace the expression of terminal deoxynucleotidyl transferase (TdT), transient induction of TdT was detected on a newly identified multipotent progenitor (MPP) subset that lacked self-renewal capacity but maintained multilineage differentiation potential. TdT induction on MPPs reflected a transcriptionally dynamic but uncommitted stage, characterized by low expression of lineage-associated genes. Single-cell CITE-seq indicated that multipotency in the TdT+ MPPs is associated with expression of the endothelial cell adhesion molecule ESAM. Stable and progressive upregulation of TdT defined the lymphoid developmental trajectory. Collectively, we here identify a new multipotent progenitor within the MPP4 compartment. Specification and commitment are defined by downregulation of ESAM which marks the progressive loss of alternative fates along all lineages.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Sankaran, V. G. et al. Human fetal hemoglobin expression is regulated by the developmental stage-specific repressor BCL11A. Science 322, 1839–1842 (2008).
Sawai, C. M. et al. Hematopoietic stem cells are the major source of multilineage hematopoiesis in adult animals. Immunity 45, 597–609 (2016).
Eaves, C. J. Hematopoietic stem cells: concepts, definitions, and the new reality. Blood 125, 2605–2613 (2015).
Ikuta, K. & Weissman, I. L. Evidence that hematopoietic stem cells express mouse c-kit but do not depend on steel factor for their generation. Proc. Natl Acad. Sci. USA 89, 1502–1506 (1992).
Morrison, S. J. & Weissman, I. L. The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661–673 (1994).
Ogawa, M. et al. B cell ontogeny in murine embryo studied by a culture system with the monolayer of a stromal cell clone, ST2: B cell progenitor develops first in the embryonal body rather than in the yolk sac. EMBO J. 7, 1337–1343 (1988).
Adolfsson, J. et al. Upregulation of Flt3 expression within the bone marrow Lin–Sca1+c-kit+ stem cell compartment is accompanied by loss of self-renewal capacity. Immunity 15, 659–669 (2001).
Christensen, J. L. & Weissman, I. L. Flk-2 is a marker in hematopoietic stem cell differentiation: a simple method to isolate long-term stem cells. Proc. Natl Acad. Sci. USA 98, 14541–14546 (2001).
Kiel, M. J. et al. SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 121, 1109–1121 (2005).
Yang, L. et al. Identification of Lin–Sca1+kit+CD34+Flt3– short-term hematopoietic stem cells capable of rapidly reconstituting and rescuing myeloablated transplant recipients. Blood 105, 2717–2723 (2005).
Arinobu, Y. et al. Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell 1, 416–427 (2007).
Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).
Oguro, H., Ding, L. & Morrison, S. J. SLAM family markers resolve functionally distinct subpopulations of hematopoietic stem cells and multipotent progenitors. Cell Stem Cell 13, 102–116 (2013).
Ooi, A. G. et al. The adhesion molecule esam1 is a novel hematopoietic stem cell marker. Stem Cells 27, 653–661 (2009).
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).
Rodriguez-Fraticelli, A. E. et al. Clonal analysis of lineage fate in native haematopoiesis. Nature 553, 212–216 (2018).
Wilson, A. et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 135, 1118–1129 (2008).
Wilson, N. K. et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712–724 (2015).
Yamamoto, R. et al. Clonal analysis unveils self-renewing lineage-restricted progenitors generated directly from hematopoietic stem cells. Cell 154, 1112–1126 (2013).
Yokota, T. et al. The endothelial antigen ESAM marks primitive hematopoietic progenitors throughout life in mice. Blood 113, 2914–2923 (2009).
Ng, S. Y., Yoshida, T., Zhang, J. & Georgopoulos, K. Genome-wide lineage-specific transcriptional networks underscore Ikaros-dependent lymphoid priming in hematopoietic stem cells. Immunity 30, 493–507 (2009).
Mansson, R. et al. Molecular evidence for hierarchical transcriptional lineage priming in fetal and adult stem cells and multipotent progenitors. Immunity 26, 407–419 (2007).
Herman, J. S., Sagar & Grun, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15, 379–386 (2018).
Gilfillan, S., Dierich, A., Lemeur, M., Benoist, C. & Mathis, D. Mice lacking TdT: mature animals with an immature lymphocyte repertoire. Science 261, 1175–1178 (1993).
Alberti-Servera, L. et al. Single-cell RNA sequencing reveals developmental heterogeneity among early lymphoid progenitors. EMBO J. 36, 3619–3633 (2017).
Balciunaite, G., Ceredig, R., Massa, S. & Rolink, A. G. A B220+CD117+CD19− hematopoietic progenitor with potent lymphoid and myeloid developmental potential. Eur. J. Immunol. 35, 2019–2030 (2005).
Klein, F. et al. Accumulation of multipotent hematopoietic progenitors in peripheral lymphoid organs of mice over-expressing interleukin-7 and Flt3-ligand. Front Immunol. 9, 2258 (2018).
Dress, R. J. et al. Plasmacytoid dendritic cells develop from Ly6D+ lymphoid progenitors distinct from the myeloid lineage. Nat. Immunol. 20, 852–864 (2019).
Rodrigues, P. F. et al. Distinct progenitor lineages contribute to the heterogeneity of plasmacytoid dendritic cells. Nat. Immunol. 19, 711–722 (2018).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Melania Barile, K. B. et al. Hematopoietic stem cells self-renew symmetrically or gradually proceed to differentiation. Preprint at CellPress https://doi.org/10.2139/ssrn.3787896 (2021).
Gazit, R. et al. Transcriptome analysis identifies regulators of hematopoietic stem and progenitor cells. Stem Cell Rep. 1, 266–280 (2013).
Heng, T. S. et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).
Carrelha, J. et al. Hierarchically related lineage-restricted fates of multipotent haematopoietic stem cells. Nature 554, 106–111 (2018).
Mitjavila-Garcia, M. T. et al. Expression of CD41 on hematopoietic progenitors derived from embryonic hematopoietic cells. Development 129, 2003–2013 (2002).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Ishibashi, T. et al. ESAM is a novel human hematopoietic stem cell marker associated with a subset of human leukemias. Exp. Hematol. 44, 269–281 e261 (2016).
Sudo, T. et al. The endothelial antigen ESAM monitors hematopoietic stem cell status between quiescence and self-renewal. J. Immunol. 189, 200–210 (2012).
Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014).
Busch, K. et al. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature 518, 542–546 (2015).
Sommerkamp, P. et al. Mouse multipotent progenitor 5 cells are located at the interphase between hematopoietic stem and progenitor cells. Blood 137, 3218–3224 (2021).
Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017).
Boyer, S. W., Schroeder, A. V., Smith-Berdan, S. & Forsberg, E. C. All hematopoietic cells develop from hematopoietic stem cells through Flk2/Flt3-positive progenitor cells. Cell Stem Cell 9, 64–73 (2011).
Buza-Vidas, N. et al. FLT3 expression initiates in fully multipotent mouse hematopoietic progenitor cells. Blood 118, 1544–1548 (2011).
Drexler, H. G., Sperling, C. & Ludwig, W. D. Terminal deoxynucleotidyl transferase (TdT) expression in acute myeloid leukemia. Leukemia 7, 1142–1150 (1993).
Cuneo, A. et al. Clinical review on features and cytogenetic patterns in adult acute myeloid leukemia with lymphoid markers. Leuk. Lymphoma 9, 285–291 (1993).
Campagnari, F., Bombardieri, E., de Braud, F., Baldini, L. & Maiolo, A. T. Terminal deoxynucleotidyl transferase, TdT, as a marker for leukemia and lymphoma cells. Int. J. Biol. Markers 2, 31–42 (1987).
Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).
Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).
Trichas, G., Begbie, J. & Srinivas, S. Use of the viral 2A peptide for bicistronic expression in transgenic mice. BMC Biol. 6, 40 (2008).
Jacobi, A. M. et al. Simplified CRISPR tools for efficient genome editing and streamlined protocols for their delivery into mammalian cells and mouse zygotes. Methods 121–122, 16–28 (2017).
Haueter, S. et al. Genetic vasectomy-overexpression of Prm1-EGFP fusion protein in elongating spermatids causes dominant male sterility in mice. Genesis 48, 151–160 (2010).
Klein, F. et al. The transcription factor Duxbl mediates elimination of pre-T cells that fail beta-selection. J. Exp. Med. 216, 638–655 (2019).
Pronk, C. J. et al. Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy. Cell Stem Cell 1, 428–442 (2007).
von Muenchow, L. et al. Permissive roles of cytokines interleukin-7 and Flt3 ligand in mouse B-cell lineage commitment. Proc. Natl Acad. Sci. USA 113, E8122–E8130 (2016).
Nakano, T., Kodama, H. & Honjo, T. Generation of lymphohematopoietic cells from embryonic stem cells in culture. Science 265, 1098–1101 (1994).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).
Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).
Murtagh, F. L. P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J. Classification 31, 274–295 (2014).
Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).
Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912.e20 (2019).
Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018).
Dong, F. et al. Differentiation of transplanted haematopoietic stem cells tracked by single-cell transcriptomic analysis. Nat. Cell Biol. 22, 630–639 (2020).
Rodriguez-Fraticelli, A. E. et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature 583, 585–589 (2020).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2018).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
We dedicate this work to the memory of T. Rolink, who has been a great mentor and a friend to all of us. His vision and passion for research will remain. We acknowledge C. Engdahl, G. Capoferri, M. Burgunder and S. Sikanjic for their contributions. We thank A. Offinger and L. Davidson and both teams of animal caretakers at the DBM Basel and NIDCR USA for constant support. Further we would like to acknowledge the Genomics Facility Basel (D-BSSE ETH Zürich) for generating the CITE-seq dataset. Calculations were performed at sciCORE (http://scicore.unibas.ch/) scientific computing center at the University of Basel. We also thank Y. Belkaid, G. Trinchieri, A. Bhandoola and C. Dunbar for their inputs and discussion. This work was in part supported by the SNF grants PP00P3_179056, 310030_185193 and by the Research Fund of the University of Basel for the promotion of excellent junior researchers (FK). This research was in part supported by the Intramural Research Program of the NIH, NIDCR (ZIADE000752-02).
The authors declare no competing interests.
Peer review information
Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Ioana Visan, in collaboration with the Nature Immunology team. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
(a) Schematic Fig. illustrating the generation of TdThCD4 and TdTiCre mouse lines. First Cas9, guided by a specific crRNA and the common trcrRNA, introduced a double-strand break before the stop-codon of the Dntt gene. Homology directed repair resulted in the integration of ssDNA repair templates containing 200 base-pair homology regions and the P2A self-cleaving peptide followed by either the hCD4 or iCre coding sequence. Small arrows spanning the final constructs indicate primers used in (b). (b) LSK cells isolated from the BM of 6-8 weeks old TdThCD4/YFP mice were sorted based on the expression of hCD4 as indicated. Shown are bar graphs depicting the relative mRNA expression of Dntt, hCD4, iCre as well as inter-spanning transcripts of Dntt-hCD4 and Dntt iCre using primer pair combinations shown in (a) Error bars indicate s.e.m. (n=5-6). (c-d) LSK cells subdivided based on the expression of hCD4 as indicated were analyzed for intracellular TdT expression. Shown are representative histograms (c) and cumulative bar graphs of the mean fluorescence intensities (d) depicting intracellular TdT (left, n=5) and surface hCD4 (right, n=6) in TdThCD4 BM LSK. Error bars indicate s.e.m. (e) Shown is the gating strategy used to identify Ly6D+ EPLMs in the BM.
(a-d) Shown are representative FACS plots depicting the gating strategies used to identify (a) lymphocytes as B cells, CD4/CD8 T cells, gd-T cells and NK cells in the spleen; (b) pro-erythrocytes in the BM; (c) platelets in peripheral blood; (d) pDC, pDC-like, cDC1, cDC2, monocytes and granulocytes in the spleen. (e) Shown is a representative FACS plots depicting the expression of CD150 and CD48 on MPP4s gated as shown in Fig. 2c. (f, g) Shown are representative FACS plots depicting the gating strategies used to identify (f) MkPs, CFU-Es, GMPs and (g) cMoPs, MDPs and CDPs in the BM. (h-i) Shown are representative FACS plots depicting the expression of (h) intracellular TdT against hCD4 (h) and surface hCD4 plotted against CD150 (i) on the indicated progenitor subsets gated as shown in Fig. 2c.
(a-d) Shown is the distribution of Tomato+ and GFP+ in TdTmTmG mice on the indicated progenitor subsets gated as in Fig. 2c. Data are derived from 2 independent experiments (n=3). Error bars indicate s.e.m. (a) Shown is the distribution of Tomato+ (red), Double positive (orange) and GFP+ single positive (green) cells within each subset as indicated. (b) Shown is the percent distribution of GFP+Tomatohigh, GFP+Tomatoint and GFP+Tomatolow as indicated. (c) Shown is the percent distribution of cells that are Tomatohigh, Tomatoint and Tomatolow within GFP+ progenitor subsets as indicated. (d) Shown is the expression of Tomato and GFP for the indicated subsets. (e) Shown is the expression of CD150 and CD48 on IL7R+ cells pre-gated as LSK (Lin-FLT3-CD117+ Sca1+). (f-h) Shown are the gating strategies to identify and quantify (f) donor derived B cells, (g) myeloid cells, (h) and platelets in peripheral blood of recipient mice. (i) 4000 GFP+ MPP4, GFP+ or GFP- were transferred into sub-lethally irradiated WT recipients. Shown are percent reconstitution in the bone marrow of recipient animals of Tomato+ (red bars) and GFP+ (green bars) MPP2s, MPP3s and MPP4s after 2 and 4 weeks. Cumulative from 2-3 independent experiments for each timepoint (n=4-8). Error bars indicate s.e.m. (j-k) 1500 LT- (j) and ST-HSCs (k) were sorted from the BM of 6-8 weeks old WT mice and transferred i.v. in competition with 1500 YFP- MPP4s sorted from the BM of 6-8 weeks old TdTYFPCD45.1/2 mice into lethally irradiated WT CD45.1 recipient mice. 3x105 WT CD45.1 BM cells were co-injected as support cells. Shown is the percent peripheral blood reconstitution for CD11b+CD3-NK1.1- myeloid and CD19+CD11b- B cells at the indicated timepoints after transfer. Data were collected from 2 independent experiments ((j) n=7; (k) n=8). Error bars indicate s.e.m.
LSK cells isolated from BM of four 6–8 weeks old TdThCD4/YFP double reporter mice were used for single-cell RNA sequencing in combination with CITE-Seq labelling as described in the methods. (a-c) Hierarchical clustering analysis was performed and projected in a 2-dimensional space using UMAP as explained in the methods. Each color represents a specific cluster as indicated. (a) Hierarchical clustering identified 12 clusters. (b) Cell-cycle phase, represented by the colors yellow (G1), orange (S) and red (G2), of each cell was determined as described in the methods. (c) Unique molecular identifiers (UMIs) coming from the mitochondrial (MT) genome were quantified across cells, reflected by the color intensity. Contour lines display the 2D cell density on the UMAP space. (d) Shown is a bar plot distribution indicating the frequency of cells in the different phases of the cell cycle across subsets obtained for the “a posteriori gating defined as in Fig. 4b. (e) UMAP and bar graphs illustrating the scaled expression of the Flt3, Slamf1, and Cd48 mRNA (left panels), as well as the expression of their corresponding surface markers used for CITE-Seq (right panels). The colors represent cells from the different clusters. Dot size and color intensity indicate expression levels. Bar height in bar graphs indicate the average expression across cells from each biological replicate across clusters. (f) A posteriori gating strategy used to define HSC and MPP populations within the CITE-Seq data. For MPP4: Flt3 > 2.5, CD135 > 3.5, and CD150 < 4. For MPP3: Flt3 < 2, CD135 < 3, CD150 < 4, and CD48 > 7. For MPP2: Flt3 < 2.5, CD135 < 3.5, CD150 > 5.5, and CD48 > 7. For LT-HSC: Flt3 < 2.5, CD135 < 3.5, CD150 > 5.5, and CD48 < 6.5. For ST-HSC: Flt3 < 2, CD135 < 3, CD150 < 4, and CD48 < 6.5. The colors represent cells from the different clusters. (g) Heatmap displaying the centered and scaled expression of the top differentially expressed genes between the gated populations defined as in (e), resulting in a list of 96 markers. Cells were ordered following the hierarchical clustering tree. Cluster assignment and similarity score of each cell to reference ImmGen RNA-seq samples is shown on top of the heatmap. (g) Shown is the UMAP distribution of our single cell dataset assigned using the previously published bulk RNA-Seq obtained from sorted progenitors (see gates below) from ref. 29. (i) Shown is the UMAP distribution for the integrated analysis of our dataset with the previously published scRNA-Seq obtained from ref. 34 obtained using the Seurat package (findIntegrationAnchors function)35. On the left the overlay of the two data sets on the right the scRNA-Seq obtained from ref. 34.
(a-d) UMAP and bar graphs illustrating the scaled expression for selected (a) stem cell-related markers and surface receptors (Cd34, CD48, CD9, ESAM); (b) erythroid (Gata1, Klf1, Vwf, and Pf4), (c) myeloid (Mpo, Irf8, Ctsg, Elane), (d) lymphoid (Ighm, Ighd, Notch1, Lck). The colors represent cells from the different clusters. Dot size and color intensity indicate expression levels. Below is the distribution across clusters where bar height indicates the average expression across cells from each biological replicate across clusters.
(a) UMAP and bar graphs illustrating the scaled expression of the Dntt mRNA, hCD4 surface marker, and YFP. The colors represent cells from the different clusters. Dot size and color intensity indicate expression level. Bar height in bar graph indicate the average expression across cells from each biological replicate across clusters. (b) Compiled data showing hCD4−YFP−, hCD4+YFP−, hCD4+YFP+, and hCD4−YFP+ MPP3s, annotated based on a posteriori gating as in Fig. 4b, in relation to their cluster distribution defined as in Fig. 4a; the similarity score to reference samples from the ImmGen dataset defined as in Fig. 4c; centered and scaled expression for the top 26 markers differentially expressed between subsets. (c) UMAP plot illustrating the distribution of hCD4−YFP−(grey), hCD4+YFP− (blue), hCD4+YFP+ (green), and hCD4−YFP+ (orange) MPP3 cells. (d) Bar graph showing the distribution of the subsets as in (c) across clusters defined as in Fig. 4a. The colors represent the different clusters. (e) hCD4−YFP−, hCD4+YFP+, and hCD4−YFP+ MPP3s were sorted from the BM of 6–8 weeks old TdThCD4/YFP mice and 1500 cells were transferred i.v. in competition with 1500 ST-HSCs sorted from the BM of 6–8 weeks old WT-CD45.1/2 mice into sub−lethally irradiated WT-CD45.1 recipients. Shown are the percent peripheral blood reconstitutions for B cells (CD19+CD11b-) and myeloid cells (CD11b+, CD3−, NK1.1-) at the indicated timepoints after transfer. Data were collected from 2 independent experiments (hCD4−YFP− MPP3 n = 5; hCD4+YFP+ MPP3 n = 6; hCD4−YFP+ MPP3 n = 6). A multiple two-tailed unpaired Student’s t test was performed. (Myeloid cells: hCD4−YFP− and hCD4+YFP+ day 10 P = 0.026, day 14 P = 0.03, day 18 P = 0.049; hCD4−YFP− and hCD4−YFP+ day 10 P = 0.005, day 14 P = 0.026; hCD4+YFP+ and hCD4−YFP+ day 10 P = 0.004). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. Error bars indicate s.e.m. (f) UMAP plot illustrating the distribution of hCD4−YFP−(grey), hCD4+YFP− (blue), hCD4+YFP+ (green), and hCD4−YFP+ (orange) MPP4 cells. (g) Bar graph showing the distribution of the subsets as in (f) across clusters defined as in Fig. 4a. The colors represent the different clusters. (h) hCD4−YFP−, hCD4+YFP+, and hCD4−YFP+ MPP4s were sorted from the BM of 6–8 weeks old TdThCD4/YFP mice and 1500 cells were transferred i.v. in competition with 1500 ST-HSCs sorted from the BM of 6–8 weeks old WT-CD45.1/2 mice into sub−lethally irradiated WT-CD45.1 recipients. Shown are the percent peripheral blood reconstitutions for B cells (CD19+CD11b-) and myeloid cells (CD11b+, CD3−, NK1.1-) at the indicated timepoints after transfer. Data were collected from 2 independent experiments (hCD4−YFP− MPP4 n = 5; hCD4+YFP+ MPP4 n = 6; hCD4−YFP+ MPP4 n = 7). A multiple two-tailed unpaired Student’s t test was performed (B cells: hCD4−YFP− and hCD4+YFP+ day 14 P = 0.008; hCD4+YFP+ and hCD4−YFP+ day 14 P = 0.002, day 18 P = 0.03; Myeloid cells: hCD4−YFP− and hCD4−YFP+ day 10 P = 0.011, day 14 P = 0.00003, day 18 P = 0.000004; hCD4+YFP+ and hCD4−YFP+ day 10 P = 0.00021, day 14 P = 0.0012, day 18 P = 0.002). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. Error bars indicate s.e.m.
(a) Two color histograms depicting the expression of YFP and ESAM from BM cells of 6–8 weeks old TdThCD4/YFP mice. Cells are pre-gated as shown in Fig. 2c. (b-c) Progenitors were defined using the “a posteriori” gating (Extended Data Fig. 4f) as ESAM negative and positive MPP2s (top) MPP3s (middle) and MPP4s (bottom). (b) Shown is the distribution with the frequency across clusters for each subset. (c) Shown are volcano plots projecting the difference in gene expression for each subset. Genes downregulated in the ESAM− fractions with an FDR < 0.01 are marked in blue and genes upregulated in the ESAM+ fractions are marked in red. Genes with an abs log2 FC > 1 are labeled. (d) 4000 ESAM negative and positive MPP2s (top) MPP3s (middle) and MPP4s (bottom) were isolated from the BM of 6–8 weeks old Rosa26mTmG mice and transferred i.v. into sub-lethally irradiated WT recipients. Shown are cumulative data with percent reconstitution of recipient animals for mature subsets (gated as shown in Extended Data Fig. 2a-d) after four weeks in the bone marrow (pro-Erythrocytes), peripheral blood (platelets) and spleen (all other subsets). Data were collected from 3 independent experiments. A multiple two-tailed unpaired Student’s t test was performed (MPP4: B cells P = 0.003, CD4+ T cells P = 0.0097, CD8+ T cells P = 0.005, pDCs P = 0.000005, monocytes P = 0.000001, pro-erythrocytes P = 0.006). **, P < 0.01; ****, P < 0.0001. Error bars indicate s.e.m. (e) Compiled data showing ESAM+hCD4−, ESAM+hCD4+, ESAM−hCD4+, and ESAM−hCD4−MPP4s, annotated based on the “a posteriori” gating as in (Extended Data Fig. 4f), in relation to their cluster distribution defined as in Fig. 4a; the similarity score to reference samples from the ImmGen dataset defined as in Fig. 4c; The top 38 differentially expressed markers and genes between subsets is centered and scaled. (f) Representative histograms illustrating the expression levels of hCD4 within LT-HSCs, ESAM+ and ESAM−MPP4s, and CLPs isolated from the BM of 6–8 weeks old TdThCD4 mice. (g) Shown is the expression of Sca-1 and ESAM for LT- and ST-HSCs (Left plot) gated as shown in Fig. 2c. LT- (in black) and ST-HSCs (color scale) are projected into a t-SNE plot. ST-HSCs are projected indicating the expression of ESAM. (h-k) CD48+ LSKs isolated from the BM of 6–8 weeks old TdThCD4 mice were sorted based on the expression of hCD4 as indicated. (h) 2000 cells were analyzed for erythroid colony forming potential (BFU-E). Shown are the number of colonies obtained. Data were collected from 3 independent experiments (n = 6). Statistical analysis was done with two-tailed unpaired Student’s t test (hCD4-neg and hCD4-int P = 0.002). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. Error bars indicate s.e.m. (i) Myeloid precursor frequency was determined for the indicated subsets by in vitro limiting dilution analysis, as described in methods. Shown is one representative experiment (n = 3). Error bars indicate s.e.m. (j, k) 4000 cells were transferred i.v. into sub-lethally irradiated WT-CD45.1 recipients in competition with 4000 hCD4-neg CD48+ LSK cells sorted from TdThCD4-CD45.1/2 mice. Shown is the percent peripheral blood reconstitution for CD11b+CD3−NK1.1- myeloid (j) and CD19+CD11b− B cells (k) at the indicated timepoints after transfer. Data were collected from 3 independent experiments. Error bars indicate s.e.m.
(a,b) Shown is the improved gating strategy depicting the use of ESAM to identify LT-, ST-HSCs, bona fide MPPs and oligopotent progenitors (OPP) with prominent myeloid and limited erythroid OPP-Me or prominent erythroid and limited myeloid OPP-Em potential. Lin− progenitors are pre-gated as IL-7R− and referred as LEKs (Lineage−ESAM+cKit+). LEKs are further subdivided based on FLT3 expression as bona fide MPPs (FLT3+ LEK), ST-HSCs (FLT3−CD48−CD150− LEK), LT-HSCs (FLT3−CD48−CD150+ LEK), Oligopotent Progenitors with prominent Myeloid- and limited erythroid (OPP-Me, FLT3−CD48+CD150− LEK) or prominent Erythroid and limited myeloid potential (OPP-Em, FLT3−CD48+CD150+ LEK) (c). Progenitors pre-gated as IL-7R−and referred as LSK (lineage−Sca1+ckit+) can be further subdivided based on FLT3 expression as MPP4s (FLT3+). Within this MPP4 subset the expression of ESAM in C57BL/6 mice identifies bona fide MPPs (FLT3+ESAM+ LSK), whereas the combined use of ESAM and hCD4 in TdThCD4 reporter mice allows for the identification of Lymphoid Progenitors (LP, FLT3+hCD4highESAM− LSK), and Oligopotent Progenitors with Llymphoid- and Myeloid potential (OPP-LM) (FLT3+hCD4int/lowESAM− LSK) besides MPPs (FLT3+ESAM+ LSK). FLT3−LSK can be further subdivided as shown into ST-HSCs (FLT3−ESAM+CD48−CD150− LSK), LT-HSCs (FLT3−CD48−CD150+ LSK), OPP-Me (FLT3−ESAM+CD48+CD150− LSK), OPP-Em (FLT3−ESAM+CD48+CD150+ LSK), Myeloid Progenitors (MP, FLT3−ESAM−CD48+CD150− LSK) and Erythroid Progenitors (EP, FLT3−ESAM−CD48+CD150+ LSK) as shown (d). Highlighted are the newly identified subsets. (c) Each pregated subset as obtained from Extended Data Fig. 8a,b is color coded according to their transcriptionally most similar cluster as defined in Fig. 4a and gated using selected markers (upper panel) or projected in a two-dimensional t-SNE (lower panel) plot. (d,e) Schematic model of steady state (d) and emergency (e) hematopoiesis illustrating the proposed hierarchy as observed for HSCs and MPPs in TdT-reporter and lineage tracer mice. Progenitors are labeled for hCD4, YFP, ESAM and FLT3 expression allowing for the new subset’s definition: OPP-LM, OPP-Me, OPP-Em, LP, MP and EP.
About this article
Cite this article
Klein, F., Roux, J., Cvijetic, G. et al. Dntt expression reveals developmental hierarchy and lineage specification of hematopoietic progenitors. Nat Immunol 23, 505–517 (2022). https://doi.org/10.1038/s41590-022-01167-5