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Population snapshots predict early haematopoietic and erythroid hierarchies

Nature volume 555, pages 54–60 (2018)Cite this article

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Abstract

The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages. We uncovered coupling between the erythroid and the basophil or mast cell fates, a global haematopoietic response to erythroid stress and novel growth factor receptors that regulate erythropoiesis. We defined a flow cytometry sorting strategy to purify early stages of erythroid differentiation, completely isolating classically defined burst-forming and colony-forming progenitors. We also found that the cell cycle is progressively remodelled during erythroid development and during a sharp transcriptional switch that ends the colony-forming progenitor stage and activates terminal differentiation. Our work showcases the utility of linking transcriptomic data to predictive fate models, and provides insights into lineage development in vivo.

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Figure 1: The early haematopoietic hierarchy predicted by scRNA-seq.
Figure 2: A novel sorting scheme isolates erythroid progenitors.
Figure 3: Predicted fate couplings confirmed by single-cell fate assays.
Figure 4: Stages of early erythropoiesis and the global erythroid stress response.
Figure 5: Novel growth factor regulators of early erythropoiesis.
Figure 6: Extensive remodelling of the cell cycle during erythroid development.

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Acknowledgements

This work was funded by a Leukemia and Lymphoma Society Scholar award (1728-13) and R01DK100915 and R01099281 (M.S.). A.M.K. is supported by a BW Fund CASI award and an Edward J Mallinckrodt Foundation Grant. S.L.W. and C.W. are supported by National Institutes of Health (NIH) training grant 5T32GM080177-07.

Author information

Author notes

  1. Betsabeh Khoramian Tusi, Samuel L. Wolock and Caleb Weinreb: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA

    Betsabeh Khoramian Tusi, Yung Hwang, Daniel Hidalgo & Merav Socolovsky

  2. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Samuel L. Wolock, Caleb Weinreb, Rapolas Zilionis & Allon M. Klein

  3. Institute for Molecular Medicine, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

    Ari Waisman

  4. Division of Immunology, Department of Microbiology and Immunobiology and Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts, USA

    Jun R. Huh

  5. Department of Pediatrics, Hematology/Oncology Division, University of Massachusetts Medical School, Worcester, Massachusetts, USA

    Merav Socolovsky

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Contributions

M.S. and A.M.K. designed the experiments and supervised the project. B.K.T., S.L.W., Y.H., D.H. and R.Z. performed experiments including inDrops (B.K.T., R.Z., S.L.W.), FACS and antibody screening (B.K.T., D.H.), single-cell fate assays and cell cycle analysis (B.K.T.), western blotting (Y.H.), qRT–PCR (B.T.K.), pSTAT3/5 (Y.H., D.H.) and colony assays for novel growth factors (Y.H.). S.L.W. and C.W. performed single-cell data analysis, informatics and PBA modelling. A.W. and J.R.H. provided Il17ra−/− mice. B.K.T., S.L.W., C.W., Y.H., D.H., A.M.K. and M.S. prepared figures and wrote the manuscript.

Corresponding authors

Correspondence to Allon M. Klein or Merav Socolovsky.

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A.M.K. is a co-founder of 1Cell-Bio.

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Reviewer Information Nature thanks B. Göttgens, F. Hamey and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 scRNA-seq of Kit+ haematopoietic progenitors for prediction of the early haematopoietic hierarchy.

a, Top, SPRING plot of 7,959 human peripheral blood mononuclear cells (PBMCs) from 10X Genomics (https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.0.1/pbmc8k). Clusters were generated by performing spectral clustering on the underlying kNN graph and annotated on the basis of marker genes. NK, natural killer. Random walks over kNN graphs for the PBMC (middle) and Kit+ bone marrow (bottom) datasets. Each plot shows the fraction of nodes (cells) visited for 1,000 simulated random walks. b, Top, SPRING plot of 2,855 Lin−Kit+SCA1− mouse HPCs from a previously published dataset12. Bottom, SPRING plot of 1,656 cells from three mouse haematopoietic progenitor populations (Lin–Kit+Sca1–, Lin–Kit+SCA1+, and Lin–Kit+SCA1+FLK2–CD34+) from a previously published dataset15. Coloured (non-grey) cells indicate expression of lineage-specific genes (see Supplementary Table 7). c, Schematic of the population balance law, which relates the dynamic velocities of cells to the distribution of states they are in at a moment in time. The law states that in steady state, after accounting for cell division and loss, the flux of cells entering any region of gene expression space equals the flux out of that region. d, Flow diagram of the inputs and outputs of the PBA algorithm. The population balance law is applied to inputs that include single-cell expression data and estimates of cell proliferation and loss rates at each point in gene expression space; inferred outputs include cell dynamics such as fate probabilities and pseudo-temporal ordering. e, SPRING plot of bone marrow Kit+ cells (Fig. 1) constructed using only the PBA-predicted fate probabilities and differentiation ordering as inputs (n = 4,763 cells from one inDrops experiment). Coloured cells indicate expression of lineage-specific genes as in Fig. 1b. f, SPRING plot of bone marrow Kit+ cells (Fig. 1), with cells coloured by library preparation batch.

Extended Data Figure 2 Predicting key regulators at haematopoietic choice points.

Candidate regulators of fate choice, identified by ranking transcription factors and transmembrane receptors by their correlation with PBA-predicted fate probabilities at key choice points in haematopoiesis. Top-ranked genes are shown; these include many canonical regulators. Candidate genes that have not been previously reported are marked with asterisks. Several candidates participate in more than one fate choice. Insets show SPRING plots coloured by expression of representative genes. Fog1 is also known as Zfpm1, B-myb is also known as Mybl2, PU.1 is also known as Spi1 and Oct2 is also known as Slc22a2.

Source data

Extended Data Figure 3 Mapping HPC subsets P1–P5 to the Kit+ SPRING plot using qRT–PCR and scRNA-seq.

a, Subpopulations P1–P5 map onto specific regions of the SPRING plot. SPRING plot heat maps for a panel of marker genes (left) and corresponding measured expression for each of the marker genes by qRT–PCR (middle), performed on sorted cell subsets P1–P5, and on EryA (cells undergoing ETD2). Probable mapping of each of the P1–P5 subpopulations on the basis of qRT–PCR is shown on the SPRING plot (right). Bars represent the mean of two independent experiments (circles, triangles or squares). Expression is shown normalized to Actb mRNA. b, c, SPRING plot of single-cell transcriptomes from freshly sorted P1–P5 subsets (Fig. 2a, b). Cells are coloured on the basis of sorted subpopulation (b) or the expression of lineage-specific marker genes (c) (Supplementary Table 7). d, e, SPRING plots of P1–P5 subpopulation cells, coloured on the basis of expression of basophil (d) and mast cell (e) marker genes. The larger number of cells in the P3 region of the graph resolves a split between the two lineages that was not observable in the original Kit+ dataset.

Source data

Extended Data Figure 4 Validation of PBA predictions.

a, Megakaryocytic colonies from sorted subsets P1–P5 and from Kit+CD55− cells, stained for the megakaryocytic marker acetylcholinesterase. Duplicate cultures are shown; representative of two independent experiments. b, Representative flow cytometry plots to assay fate output of single cells in liquid culture (see Fig. 3, including Fig. 3a for experimental design). Each row corresponds to a single clone, with the left column indicating the source subset (P1–P5, CD55−) of the clone and the cell type(s) produced, as inferred from the FACS plots in the remaining columns. These data are representative of 1,158 single-cell clonal cultures, pooled from three independent sorting experiments (complete dataset is shown in Fig. 3b). c, Bulk liquid cultures of freshly sorted P1–P5 subsets and Kit+CD55− cells in the presence of EPO and a mixture of cytokines supporting myeloid progenitors. On the indicated days, cells were labelled with antibodies as indicated and analysed by flow cytometry.

Source data

Extended Data Figure 5 The early erythroid trajectory.

a, qRT–PCR for expression of established erythroid regulators in sorted P1–P5 subsets. Expression of each gene is normalized to Actb. Bars represent the mean of two independent experiments (circles). b, Left, representative western blot of GATA1 expression in sorted P1 (subdivided into CD71medium and CD71high subsets), P2 and EryA (CD71+TER119+FSChigh cells, representative of ETD) cells. 3T3-GATA1, positive control 3T3 cells virally transduced with a GATA1 expression vector; untransduced 3T3 cells were used as negative control. Right, quantification of GATA1 expression (mean) by densitometry. Data points are from two independent western blots. For gel source data, see Supplementary Fig. 1. c, Density of FACS subsets P1–P5 along the erythroid trajectory. Single-cell transcriptomes from each subset were mapped to their most similar counterparts in the Kit+ data (Fig. 2a, b; cell numbers analysed for each subset are indicated in Fig. 2b). Shown here is the fraction of mapped cells after smoothing with a Gaussian kernel. Also included are CD71high P1 cells, constituting cells with the 30% highest CD71 expression in that subset (n = 752 cells post-filter). d, Distribution of CD71 expression in P1 (top) and P2 (bottom) cells immediately after sorting (grey) and after 24 h of in vitro differentiation (lavender). Data are representative of two independent experiments. e, Dynamically varying genes along the MPP-to-erythroid axis were clustered on the basis of their behaviour across three transition points. At each transition, gene expression is increased, decreased or unchanged, giving a total of 27 potential dynamic patterns across all three transitions, shown in red. The number of genes corresponding to each dynamic pattern is noted, and z-score-normalized expression traces for each individual gene are shown in black. Selected clusters were further analysed in Extended Data Fig. 6 and are marked with an asterisk and a representative gene.

Source data

Extended Data Figure 6 Gene set enrichment on dynamic gene clusters in early erythroid differentiation.

a–d, Nine key dynamic gene clusters along the MPP-to-erythroid progression (Extended Data Fig. 5e) are analysed further for Gene Ontology terms. Each cluster is identified by its dynamic pattern with a cartoon of nodes and edges. Each node represents a progenitor stage (in order, MPP, EBMP/EEP, CEP and ETD), connected to the next stage by an edge that either goes up (for increased expression) or down (for decreased expression) (see Extended Data Fig. 5e). a, Number and identity of transcription factors (TFs) whose targets are enriched in the dynamic clusters, as predicted by chromatin immunoprecipitation methods (ChIP-X) experiments62. b, Significance of enrichment for signalling pathways and Gene Ontology gene sets in the dynamic gene clusters (hypergeometric test with Benjamini–Hochberg correction for multiple hypothesis testing). Rep., representative. c, d, Enrichment of transcription factor targets. c, Heat map (−log10 of P value of hypergeometric test with Benjamini–Hochberg correction for multiple hypothesis testing) of target gene enrichment for transcription factors (rows) with targets significantly enriched (P < 0.05) in at least one of the nine dynamic clusters (columns, labelled on top) highlighted in Extended Data Fig. 5e. Note that the transcription factor targets shown are based on previous ChIP-X experiments62 and it is possible that unappreciated transcription factor targets occur in early erythropoiesis. d, Gene expression traces over the erythroid trajectory for the transcription factors from c. Rows match those in c.

Source data

Extended Data Figure 7 Quantification of absolute Kit+ cell number in bone marrow after in vivo administration of EPO.

a, b, Eight-week-old female mice were injected with either EPO (100 U per 25 g body weight) or saline (basal), once per day for two days. Bone marrow was collected at 48 h. Viable (trypan blue-negative) cells were counted using a TC20 automated cell counter (BIORAD) and stained for Kit, TER119 and CD71 and lineage markers. Data are from two independent experiments, with five mice analysed individually for each group (basal or EPO) in each experiment. a, Representative flow cytometric analysis of either basal or EPO-stimulated bone marrow, gating on Kit+Lin− cells (left) or on proerythroblasts (ProE) and TER119high cells (right; ProE and TER119high cells are sequential stages of ETD). b, Data summary (mean ± s.d.) for all mice (ten in each group). Top, the fraction of all bone marrow cells for each of the flow cytometric gates defined in a. Bottom, the absolute cell count in adult bone marrow for subsets defined in each flow cytometric gate, or for the total number of bone marrow cells. Significant (P < 0.05) P values are shown (two-tailed t-test, unequal variances).

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Extended Data Figure 8 Identification of stage-specific differential gene expression during the erythroid stress response.

a, Identification of genes that are differentially expressed in EEP cells of either EPO-stimulated bone marrow (eBM, left) or fetal liver (FL, right) samples, compared with basal bone marrow (bBM). One single-cell inDrop experiment per condition. P values were calculated using a binomial test for differential expression (see Methods) and measure the significance of the expression difference. The specific enrichment score (see Methods) measures the degree to which the differential expression is specific to this region of interest (EEPs); positive scores correspond to region-specific upregulation, and negative to region-specific downregulation. Selected genes are highlighted. b, The analysis in a applied to the CEP stage. One single-cell inDrop experiment per condition. c, Stage-specific differential gene expression during stress, comparing EPO-stimulated and fetal liver samples. The heat map shows the number of differentially expressed genes at each stage that show similar or different patterns of upregulation and downregulation in fetal liver and EPO-stimulated bone marrow samples. Representative gene traces are shown on the right.

Extended Data Figure 9 Localized gene expression and functional response of the erythroid lineage to stimulation of MST1, RYK and IL-17RA.

a, b, Predicted expression pattern (a) and confirmation by qRT–PCR (b) for Mst1r, Ryk and Il17ra in basal bone marrow. In a, traces show the smoothed scRNA-seq gene expression of cells from the basal bone marrow (bBM), fetal liver (FL) and EPO-stimulated bone marrow (eBM) samples, arranged along the erythroid trajectory. qRT–PCR data represent the mean (bars) of two independent experiments (circles). c, Complete results for CFU-e and BFU-e colony-formation assays in methylcellulose, supporting the data shown in Fig. 5. Curves show colony numbers in the presence of increasing concentrations of either EPO, or EPO with a ligand (MSP, WNT5A or IL-17A). Error bars show s.d. of two independent experiments, with four replicates per experiment. Where appropriate, data were fitted to a dose–response curve with a Hill coefficient of one. d, Western blot analysis shows that IL-17RA expression peaks in EEP P2 cells, dropping in CEP P1 cells and in the granulocytic branch (which contributes most of the CD55− cells), consistent with the SPRING plots in Fig. 5a. The western blot is representative of two independent experiments. For gel source images, see Supplementary Fig. 1.

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Extended Data Figure 10 Cell ordering independently of cell cycle genes, and evidence of an S-phase-dependent CEP-to-ETD transition in bone marrow erythropoiesis.

a, The computational ordering of cells from MPP to ETD is not sensitive to the inclusion or exclusion of annotated cell cycle genes (cell ordering correlation is R = 0.97). b, c, Reproduction of Fig. 6b, c after the exclusion of cell cycle genes shows that the computationally inferred expression dynamics of cell cycle genes during EEP to CEP differentiation are not sensitive to the inclusion or exclusion of annotated cell cycle genes when ordering cells. d–f, Activation of ETD is dependent on the S phase. d, Schematic of experiments shown in e and f that test the link between S-phase progression and the CEP-to-ETD transition. Kit+Lin−CD71− bone marrow cells were cultured in the presence of EPO for 28 h, with or without the presence of the DNA polymerase inhibitor aphidicolin (Aphi) for the first 10 h. e, Kit+Lin−CD71− bone marrow cells require the S phase to upregulate CD71, an early event in ETD. Left, CD71high cells fail to appear in the first 10 h if cells are exposed to aphidicolin; they appear as soon as aphidicolin is removed from the medium. Right, cell cycle analysis of the same cells shows that aphidicolin prevented S-phase progression; aphidicolin removal was followed by a full recovery of S-phase progression, with a high fraction of CD71high cells in S phase. Data are representative of three independent experiments. f, Aphidicolin exposure for 10 h delays induction of β-globin (Hbb-b1) by 10 h. Data are representative of two independent experiments. g, CD71 expression (top), cell cycle phase distribution (middle), and intra-S-phase DNA synthesis rate (bottom), for consecutive FACS gates of increasing CD71 in the early stages of erythropoiesis from the fetal liver (left, representative of four independent experiments) and EPO-simulated bone marrow (right, representative of two independent experiments) samples. See Fig. 6e, f for similar analysis of basal bone marrow samples. h, Western blots (top) and quantification by densitometry (bottom) showing an increase in S-phase proteins during progression from EEP (P2) to early CEP (P1-CD71low) and late CEP (P1-CD71high). Control 3T3 cells were either cycling or contact-inhibited (non-cycling), as indicated. Western blots are representative of three independent experiments. For gel source images, see Supplementary Fig. 1.

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Supplementary information

Life Sciences Reporting Summary (PDF 159 kb)

Supplementary Information

This file contains full legends for Supplementary Tables 1-7, gel source data and Supplementary Tables 1, 2 and 7. (PDF 5505 kb)

Supplementary Table 3

This table contains a list of significantly varying genes in the basal BM dataset - see Supplementary Information document for the full description. (XLSX 391 kb)

Supplementary Table 4

This table shows gene set enrichment analysis (GSEA) of dynamic gene clusters - see Supplementary Information document for the full description. (XLSX 230 kb)

Supplementary Table 5

This table shows genes differentially expressed in stress - see Supplementary Information document for the full description. (XLSX 443 kb)

Supplementary Table 6

This table shows genes correlated with progression through the CEP stage - see Supplementary Information document for the full description. (XLSX 265 kb)

Supplementary Data

This zipped file contains the input data files and code for running Population Balance Analysis on the Bone Marrow and Fetal Liver data sets. (ZIP 1146 kb)

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Tusi, B., Wolock, S., Weinreb, C. et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555, 54–60 (2018). https://doi.org/10.1038/nature25741

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