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Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells

Nature Immunology volume 19, pages 85–97 (2018)Cite this article

Abstract

The hierarchy of human hemopoietic progenitor cells that produce lymphoid and granulocytic–monocytic (myeloid) lineages is unclear. Multiple progenitor populations produce lymphoid and myeloid cells, but they remain incompletely characterized. Here we demonstrated that lympho-myeloid progenitor populations in cord blood — lymphoid-primed multi-potential progenitors (LMPPs), granulocyte-macrophage progenitors (GMPs) and multi-lymphoid progenitors (MLPs) — were functionally and transcriptionally distinct and heterogeneous at the clonal level, with progenitors of many different functional potentials present. Although most progenitors had the potential to develop into only one mature cell type (‘uni-lineage potential’), bi- and rarer multi-lineage progenitors were present among LMPPs, GMPs and MLPs. Those findings, coupled with single-cell expression analyses, suggest that a continuum of progenitors execute lymphoid and myeloid differentiation, rather than only uni-lineage progenitors’ being present downstream of stem cells.

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Fig. 1: Human CB lympho-myeloid populations have distinct functional potential in vitro.
Fig. 2: CB LMPPs and GMPs are lympho-myeloid progenitors, while MLPs are mainly lymphoid progenitors in clonal in vitro assays.
Fig. 3: Human CB LMPPs, MLPs and GMPs have distinct differentiation potential in vivo.
Fig. 4: Distinct transcriptional patterns of human CB HSPC populations.
Fig. 5: Transcriptional heterogeneity of CB lympho-myeloid progenitor cells by single-cell RNA sequencing.
Fig. 6: New flow-cytometry sorting strategy to purify functional potential in the CB LMPP compartment.
Fig. 7: New flow-cytometry sorting strategy to purify functional potential in the CB GMP compartment.

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Acknowledgements

We thank J.C. Zuniga-Pflucker for OP9-DL4 cells used for initial experiments; and the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics (funded by Wellcome Trust (090532/Z/09/Z)) for generation of sequencing data. Supported by the MRC (MHU Award G1000729 and MRC Disease Team Award 4050189188 to P.V.; PhD studentship to Z.A. and F.H.; Single Cell Award MR/M00919X/1), CRUK (Program Grant C7893/A12796 to P.V.; Program Grant C1163/A21762 to B.G.; Development Fund CRUKDF0176-DK to D.K. and P.V.; and CRUK Development Fund C5255/A20758 to B.S. and P.V.), Bloodwise (Specialist Program 13001 and Project Grant 12019), the Oxford Partnership Comprehensive Biomedical Research Centre (NIHR BRC Funding scheme), the US National Institutes of Health (R01CA188055 and U01HL099999 to R.M.), the New York Stem Cell Foundation (Robertson Investigator, R.M.), the Leukemia and Lymphoma Society (Scholar Award to R.M.) and the Austrian Science Fund (FWF) (Erwin-Schroedinger Research fellowship to A.R.).

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Author notes

  1. Dimitris Karamitros and Bilyana Stoilova contributed equally to this work.

Authors and Affiliations

  1. MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK

    Dimitris Karamitros, Bilyana Stoilova, Zahra Aboukhalil, Marina Samitsch, Lynn Quek, Georg Otto, Emmanouela Repapi, Jessica Doondeea, Batchimeg Usukhbayar, Stephen Taylor, Nicolas Goardon, Catherine Porcher & Paresh Vyas

  2. Oxford Biomedical Research Centre, University of Oxford, Oxford, UK

    Dimitris Karamitros, Bilyana Stoilova, Lynn Quek, Jessica Doondeea, Batchimeg Usukhbayar & Paresh Vyas

  3. Department of Haematology and Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Fiona Hamey & Berthold Göttgens

  4. Division of Hematology, Stanford Institute for Stem Cell Biology and Regenerative Medicine, California, USA

    Andreas Reinisch & Ravindra Majeti

  5. Department of Hematology, OUH NHS Trust, Oxford, UK

    Lynn Quek & Paresh Vyas

  6. UMR967 INSERM/CEA, Université Paris 7/Université Paris 11, Paris, France

    Julien Calvo & Francoise Pflumio

  7. UMR1163, Imagine Institute, Paris Descartes -Sorbonne Paris Cité University, Paris, France

    Emmanuelle Six

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Contributions

D.K. and B.S. contributed equally to this work and are listed alphabetically; F.H. and A.R. contributed equally to this work; D.K., B.S., Z.A. and P.V. designed the experiments; D.K., B.S., Z.A., A.R., M.S., L.Q. and N.G. performed experiments and analyzed data; F.H., G.O., Z.A., E.R. and S.T. performed bioinformatics and statistical analysis; J.D. and B.U. prepared samples; J.C., E.S., F.P., C.P., R.M. and B.G. provided reagents and materials; D.K., B.S. and P.V. wrote the paper; and all authors edited the manuscript.

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Correspondence to Paresh Vyas.

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Supplementary Figure 1 Immunophenotypic separation of eight distinct human hemopoietic stem–progenitor cell (HSPC) populations and in vitro functional lympho-myeloid potential of LMPPs, MLPs and GMPs

(a) Representative plot of flow sorting of human CB HSPCs. Population frequencies shown are the mean from 44 CB donors calculated as a percentage of Lin-CD34+ compartment. HSC, hemopoietic stem cell; MPP, multipotent progenitor; LMPP, lymphoid-primed multi-potential progenitor; MLP, multi-lymphoid progenitor; CMP, common myeloid progenitor; GMP, granulocyte-monocyte progenitor; MEP, megakaryocyte-erythroid progenitor; B/NK, B-NK cell progenitor. (b) Representative flow cytometric analysis of the B cell, NK, monocytic and granulocytic output of CB LMPP, MLP and GMP cultured for 2 weeks on SGF15/2. Frequencies shown are mean from 3 CB donors calculated as a percentage of human CD45+ cells. (c) Representative flow cytometric analysis of T cell output of CB LMPP, MLP and GMP cultured for 5 weeks on SF7a. Frequencies shown are average from 5 CB donors calculated as a percentage of human CD45+ cells.

Supplementary Figure 2 LMPPs and GMPs are lympho-myeloid progenitors, while MLPs are mainly lymphoid progenitors in quantitative in vitro assays

(a) Experimental strategy for sorting, culture and analysis for quantitative in vitro assays in SGF15/2. Similar strategy was used for the other culture conditions. (b) LDA and single cell in vitro culture conditions used in the study. (c) Representative flow cytometric profiles of the outputs from the quantitative in vitro assays. Plots are gated on human CD45+ cells. (d) LDA plots showing the frequency (f: 1 in X cells can give rise to) of LMPP, MLP and GMP cells with B-cell, NK cell, monocytic and granulocytic potential. Plots are generated in R and the lines represent the estimates calculated using ELDA software. (e) Total cloning efficiency (left) of single LMPP, MLP and GMP in S7T2GM/G/M culture (LMPP: 69/96 cells, MLP: 4/52, GMP: 98/110). Significance defined using Fisher’s exact test. Cloning efficiency of lymphoid (Ly, middle) and myeloid lineages (My, right). Bars indicate total cloning efficiency; filled portion indicates the proportion of lymphoid (lymphoid plus mixed clones) or myeloid potential (myeloid plus mixed clones). Mean ± SD is shown. Significance is defined using student’s t-test. (f) Single-, (g) bi- and (h) multi-lineage single cell outputs in S7T2GM/G/M culture, presented as percentage of the positive wells. (i) Lymphoid (Ly), myeloid (My) and lympho-myeloid (Ly-My) outputs presented as percentage of all plated single cell LMPP, MLP and GMP wells in S7T2GM/G/M culture. (j) Summary of lineage outputs from single LMPP, MLP and GMP. For the LDA data were obtained from 4 CB donors, for the single cell assay in S7T2GM/G/M culture data were obtained from 2 CB donors.

Supplementary Figure 3 Human LMPPs, MLPs and GMPs have distinct differentiation potential in vivo

(a) Experimental strategy for generation of human ossicles in NSG mice and human progenitor transplantation. (b) Gating strategy for the sorting of human progenitors for in vivo transplantation. (c) Number of cells of LMPP, MLP and GMP populations injected per ossicle. (d) Correlation between the number of transplanted cells and the myeloid/lymphoid ratio of the output cells.

Supplementary Figure 4 Transcriptional profiling of human HSPCs in bulk shows the distinct transcriptional patterns of human progenitor populations

(a) Heatmap showing hierarchical clustering and the expression of all genes by HSPC populations. Expression values are normalized per gene (per row). (b) Plot comparing the eigenvalues for each principle component (PC) for the PC analysis (PCA) of HSPC using the top 300 ANOVA genes (black) and 300 randomly selected genes from a randomized expression matrix (red). (c) 3D PCA plot showing HSPC populations using the top 300 ANOVA genes. Percentage variance for each PC is shown. (d) PCA plots showing HSPC populations using between 1,000 and 10,000 genes with the highest variance across all HSPC populations. Percentage variance for each PC is shown. (e) Table of differentially expressed genes in one versus one comparisons of HSPCs. Genes are upregulated in a population column versus row. (f) Heatmap showing the expression of genes recently identified as being expressed by lineage primed hemopoietic cells25. Genes are color-coded according to their classification. Expression values are normalized per gene. (g-h) Heatmap showing the expression of (g) hemopoietic-related transcription factors that were differentially expressed between the MLP and GMP and (h) cytokine and chemokine related genes by the LMPP, GMP and MLP. Genes affiliated with the lymphoid or myeloid lineages are color-coded (lymphoid: orange, myeloid: green) and genes associated with immune function are labeled in black. Expression values are normalized per gene.

Supplementary Figure 5 Transcriptional heterogeneity of single lympho-myeloid progenitor cells (a) Heatmap showing hierarchical clustering on genes (rows) and single cells (columns), based on qRT-PCR gene expression analysis

(b) Top, cell type composition of the 3 clusters identified in the dendrogram in (a). Middle, differentially expressed genes between cluster pairs. Bottom, expected number of cells per cluster, based on the functional assays. (c) Diffusion map dimensionality reductions colored in by cell type (left) and cluster membership (right), based on qRT-PCR gene expression analysis. (d) Heatmap showing clustering of single LMPP, GMP and MLP using the 55 most highly and variably expressed genes between clusters. Single cell RNA-sequencing data from two donors were used to generate the clusters. The heat map shows clustering from one of two. Data from the other donor is in Fig. 5b. Log-normalised gene expression (rows) for each single cell (columns) is shown. (e) Cell type composition of the 3 clusters identified in the heatmap in (d) and Fig. 5b. (f) PCA plot colored by cell type (top) or cluster membership (bottom). For single cell qRT-PCR gene expression analysis: data from 4 CB donors. For single cell RNA sequencing analysis: data from 2 donors (donor 1 in Fig. 5, donor 2 in current figure).

Supplementary Figure 6 Further functional purification of the lympho-myeloid progenitor populations (a) CD10 fluorescence intensity in LMPPs in the 3 clusters identified from clustering of single cell RNA Seq data presented in . Wilcoxon rank sum test is used to define significance.

(b) Histograms showing expression of CD10 and CD45RA for LMPP with different functional output. The percentage cut-offs used to define the new sorting strategy for LMPPly and LMPPmix are shown. Thresholds were defined based on maximum CD10 and CD45RA expression of LMPPs with myeloid output. (c) Total cloning efficiency (left) of single MLP, LMPP, LMPPly, LMPPmix and GMP in SF7b culture (LMPPly: 26/108 cells, LMPPmix: 48/100). Significance defined using Fisher’s exact test. Cloning efficiency of lymphoid (Ly, middle) and myeloid lineages (My, right). Bars indicate total cloning efficiency; filled portion indicates the proportion of lymphoid (lymphoid plus mixed clones) or myeloid potential (myeloid plus mixed clones). Mean ± SD is shown. Significance is defined using students t-test. (d) Single-, (e) bi- and (f) multi-lineage outputs from single cells in SF7b culture, presented as percentage of the positive wells. (g) CD38 fluorescence intensity in GMPs in the 3 clusters identified from clustering of single cell RNA Seq data presented in Fig. 5b. Wilcoxon rank sum test is used to define significance. (h) Correlation between expression of selected genes and cell surface marker expression in GMPs. ρ indicates Spearman’s rank correlation coefficient values and p is corresponding p-value from cor.test function in R. (i) Histograms showing expression of CD38 for GMP with different functional output. The percentage cut-off used to define the new sorting strategy for GMP CD38hi and CD38mid are shown. For functional assay based on the revised LMPP sorting strategy (SF7b culture) data are from 6 CB donors; for LMPP, MLP and GMP controls data from 9 CB donors (including 3 CB donors used for Fig. 2f-j).

Supplementary Figure 7 Models of human lympho-myeloid differentiation

(a) Lineage output of lympho-myeloid progenitors (b-c) Models of human hemopoietic differentiation and lineage diversification. Dashed arrows - hierarchical relationships not experimentally validated. (d) Novel model of human lympho-myeloid differentiation. Multiple, rare, functionally distinct bi- and multi-lineage lympho-myeloid progenitors (LMPs) could differentiate either directly from hemopoietic stem cells (HSC) or multi-potent progenitors (MPP). We have used the term LMP, rather than LMPP (lymphoid primed multi-potential progenitor) to describe these lympho-myeloid progenitors, as not all LMPs will be lymphoid biased. Multi-lineage LMPs are rare. Lymphoid only and myeloid only progenitors are shown below. Bi-lineage progenitors are more frequent and uni-potent progenitors are most common. The hierarchical relationships between LMP and lymphoid and myeloid only bi- and uni-lineage progenitors remain to be determined. This model also leaves open the question of when commitment to either lymphoid or myeloid fate occurs. Erythro-megakaryocytic differentiation leading to a MEP (megakaryocyte-erythroid progenitor), erythroid (BFU-E) and megakaryocytic progenitor (MkP) could occur directly from either HSC or MPP.

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Karamitros, D., Stoilova, B., Aboukhalil, Z. et al. Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells. Nat Immunol 19, 85–97 (2018). https://doi.org/10.1038/s41590-017-0001-2

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