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Single-cell analysis of mixed-lineage states leading to a binary cell fate choice

Nature volume 537, pages 698–702 (2016)Cite this article

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An Author Correction to this article was published on 24 April 2019

Abstract

Delineating hierarchical cellular states, including rare intermediates and the networks of regulatory genes that orchestrate cell-type specification, are continuing challenges for developmental biology. Single-cell RNA sequencing is greatly accelerating such research, given its power to provide comprehensive descriptions of genomic states and their presumptive regulators1,2,3,4,5. Haematopoietic multipotential progenitor cells, as well as bipotential intermediates, manifest mixed-lineage patterns of gene expression at a single-cell level6,7. Such mixed-lineage states may reflect the molecular priming of different developmental potentials by co-expressed alternative-lineage determinants, namely transcription factors. Although a bistable gene regulatory network has been proposed to regulate the specification of either neutrophils or macrophages7,8, the nature of the transition states manifested in vivo, and the underlying dynamics of the cell-fate determinants, have remained elusive. Here we use single-cell RNA sequencing coupled with a new analytic tool, iterative clustering and guide-gene selection, and clonogenic assays to delineate hierarchical genomic and regulatory states that culminate in neutrophil or macrophage specification in mice. We show that this analysis captured prevalent mixed-lineage intermediates that manifested concurrent expression of haematopoietic stem cell/progenitor and myeloid progenitor cell genes. It also revealed rare metastable intermediates that had collapsed the haematopoietic stem cell/progenitor gene expression programme, instead expressing low levels of the myeloid determinants, Irf8 and Gfi1 (refs 9, 10, 11, 12, 13). Genetic perturbations and chromatin immunoprecipitation followed by sequencing revealed Irf8 and Gfi1 as key components of counteracting myeloid-gene-regulatory networks. Combined loss of these two determinants ‘trapped’ the metastable intermediate. We propose that mixed-lineage states are obligatory during cell-fate specification, manifest differing frequencies because of their dynamic instability and are dictated by counteracting gene-regulatory networks.

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Figure 1: ICGS ordering of the myeloid developmental hierarchy and derivation of regulatory states.
Figure 2: Counteracting gene regulatory network underlying myeloid cell fate determination.
Figure 3: Detection of rare transition state poised to undergo myeloid cell-fate determination.
Figure 4: Trapping rare myeloid transition state by removal of counteracting determinants.
Figure 5: Model of mixed-lineage transition states underlying myeloid cell-fate determination.

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Data deposits

Data are deposited in the Gene Expression Omnibus SuperSeries under accession number GSE70245.

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Acknowledgements

We thank H. C. Morse for supplying Irf8–GFP mice. We acknowledge the assistance of the Cincinnati Children’s Hospital Medical Center (CCHMC) Research Flow Cytometry Core (supported in part by NIH grants AR-47363, NIH DK78392 and NIH DK90971) and DNA Sequencing and Genotyping Core. We thank S. Smith and H. C. Liang for optimizing and generating scRNA-seq libraries. We thank S. Potter and J. Whitsett for advice on scRNA-seq experiments, P. Dexheimer and K. Chetal for assistance with RNA-seq data processing, and K. Pollard, L. Omberg and A. Khan for discussions. This work was partly funded by contributions from CCRF (H.S. and N.S.), CCHMC Divisions of Pathology and Oncology, and NIH R01HL122661 (H.L.G.).

Author information

Author notes

  1. Nathan Salomonis, Harinder Singh and H. Leighton Grimes: These authors jointly supervised this work.

Authors and Affiliations

  1. Division of Immunobiology and Center for Systems Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, 45229, Ohio, USA

    Andre Olsson, Viren K. Chaudhri, Harinder Singh & H. Leighton Grimes

  2. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, 45229, Ohio, USA

    Meenakshi Venkatasubramanian, Bruce J. Aronow & Nathan Salomonis

  3. Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, 45229, Ohio, USA

    H. Leighton Grimes

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Contributions

A.O., H.S. and H.L.G. designed and interpreted experiments. A.O. performed the experiments. N.S. conceived and developed the software with considerable input from B.J.A., H.S. and H.L.G. Bioinformatics data were analysed by M.V., V.K.C., B.J.A., N.S., H.S. and H.L.G. The paper was written by A.O., N.S., H.S. and H.L.G.

Corresponding authors

Correspondence to Nathan Salomonis, Harinder Singh or H. Leighton Grimes.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks L. Zon and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Experimental design, optimization, quality control, and validation of scRNA-seq data.

a, Schematic illustrating haematopoietic cell intermediates that constitute the myeloid developmental pathway in the bone marrow. Select cell-surface markers expressed by the various intermediates are indicated (top). The mouse bone-marrow populations used for scRNA-seq are colour coded and indicated at bottom of schematic. b, Flow cytometry strategy for isolation of LSK, CMP and GMP haematopoietic cell subsets. c, Sorting strategy for lineage-negative CD117+CD34+ (LK CD34+) myeloid progenitor-precursor populations. d, Optimization of size of scRNA-seq library fragments by varying the amount of input cDNA (amount shown on top of each electropherogram). e, An scRNA-seq library fragment distribution before and after optimization reveals an increase in reads that can be mapped to the genome (reads mapping to a single genomic/transcriptomic location using BowTie2/TopHat2; 61.8% before and 83.5% after optimization). f, scRNA-seq summary statistics. g, Distribution of number of aligned reads for scRNA-seq libraries. h, Scatter plot of the fraction of aligned reads (y axis: RSEM-transcriptome-aligned reads relative to the total number of sequenced reads) versus the total number of genes with a TPM >1 (x axis) for each cell. RNA-seq libraries in red were considered outliers and eliminated from downstream analyses. Of the 96 LSK, 96 CMP, 136 GMP and 66 LKCD34+ cell libraries that were sequenced, 3 LSK, 2 CMP, 4 GMP and 3 LKCD34+ samples failed the quality control analysis. i, Histogram showing the inverse-cumulative count of genes for each sequenced cell greater than the TPM cut-off in each bin (200 bins, red samples represent outliers from Extended Data Fig. 1h). j, Correlation between bulk RNA-seq (TRUseq Stranded mRNA HT kit) for each sorted population compared to average single-cell-gene-expression values from the same sort (for example, LSK, CMP or GMP cells).

Extended Data Figure 2 Analysis of scRNA-seq data with Monocle, SCUBA, RaceID and principle component analysis.

a, Pseudotemporal ordering of all 382 haematopoietic progenitors along the 9 identified Monocle cell states. These states were identified from the original four annotated flow cytometric gates, based on 5,110 Monocle-identified genes (P < 0.01). b, Hierarchical clustering of the 1,000 most significant genes for the 9 Monocle states (correlation and ward hierarchical clustering for genes). c, SCUBA pseudotemporal ordered cell states. d, Hierarchically clustering of the 1,000 most variable SCUBA genes. e, RaceID identified k-means cell clusters. f, RaceID t-SNE visualization of these cells and k-means cell states. g, Heat map of the t-SNE based cell-state ordering and the top 150 most significant RaceID genes (identified using RaceID R code, clustdiffgenes function p-value) associated with each of the 14 k-means clusters. Gene-set predictions are assigned on the left of the heat map following ImmGen (https://www.immgen.org/) gene-set enrichment analysis in AltAnalyze. h, Hierarchical clustering of 584 principle component analysis (PCA)-identified genes using the workflow outlined in ref. 48.

Source data

Extended Data Figure 3 Analysis of scRNA-seq data with Seurat, scLVM and ICGS.

a, Seurat-significant gene-weighted PCA, coloured by the sorted cell population annotations. b, Seurat t-SNE displayed output for the 9 predicted cell states. c, Seurat hierarchical relationships between the predicted cell states, based on 161 differentially expressed genes. d, Expanded 766 significant genes displayed along the Seurat ordered cell states. e, Uncorrected PCA using the top 2,361 scLVM variance (https://www.synapse.org/#!Synapse:syn4975059) genes (left). PCA of the scLVM-corrected normalized expression matrix (right). f, Hierarchically clustered heat map using the top 2,361 genes from the corrected scLVM-normalized expression matrix. g, ICGS analysis and integration of cell-type prediction analyses in AltAnalyze. ICGS-produced expression heat map for the haematopoietic progenitor scRNA-seq data. On the right of the ICGS heat map are the default predicted GO-Elite BioMarker enrichment predictions (60 top-genes for each of the 300 cells/tissue microarray data sets evaluated) for each HOPACH cluster. On the left is a similar set of gene-set enrichments derived in AltAnalyze for all mouse ImmGen profiles (enrichment analysis and visualization available through the AltAnalyze heat map viewer). Fisher-exact enrichment P values are displayed with each term, along with the associated HOPACH cluster number. Terms used to derive the final predicted cell-types are manually highlighted in red.

Source data

Extended Data Figure 4 Comparing ICGS with Monocle, SCUBA, RaceID, PCA, Seurat and scLVM for the analysis of scRNA-seq data.

a, Table comparing ICGS-derived cell population gene-set results using different ICGS parameters. These parameters include minimum cells differing between the highest and lowest values for a gene for an indicated minimum fold difference and minimum Pearson correlation threshold. The results indicated under ICGS Steps refer to the gene outputs from each step of ICGS or from the entire workflow with exclusion of step 2. b, PCA visualization of the first two principal components of all expressed genes (ICGS step 1), following Z-score normalization of all TPM values. Cells are coloured according to the flow cytometric gate (top left), according to their ICGS clusters (top right) or for cell populations indicated by the different evaluated algorithms (Monocle, SCUBA, RaceID and Seurat). c, Table comparing ICGS-derived cell population gene-sets for different scRNA-seq algorithms. d, Direct comparison of gene expression results from different scRNA-seq algorithms. All cells are ordered based on the ICGS output and genes are clustered by HOPACH. Gene-set enrichment analysis results for ICGS delineated gene populations (GO-Elite) are displayed to the left of each HOPACH gene cluster. e, Table comparing the relative overlap of the top significant gene sets produced by each of the scRNA-seq algorithms.

Source data

Extended Data Figure 5 Application of ICGS to diverse scRNA-seq data sets.

a, Schematic overview of published embryonic5, myoblast4 and intestinal organoid1 scRNA-seq data sets. b, HOPACH-generated clusters derived using ICGS for human scRNA-seq data for pre-implantation embryos and ES cells. c, HOPACH-generated clusters using ICGS for human myoblast differentiation, without the inclusion of cell-cycle-associated genes. d, HOPACH-generated clusters using ICGS for mouse intestinal organoids1 for the discovery of rare cell populations. Novel rare population markers reported by the original study authors are highlighted in red. To the left of the heat maps are predicted cell-types and tissues using AltAnalyze enrichment analysis (GO-Elite algorithm), using gene-sets from the embedded MarkerFinder database. Enriched terms are ordered based on significance from the bottom to top in each indicated HOPACH cluster. Genes to the right of the heat map are guide genes delineated by ICGS. The colour bars above the heat maps indicate either HOPACH clusters or input cell identities.

Source data

Extended Data Figure 6 Cell cycle, monocyte–dendritic precursor, and transcription factor–gene correlation analyses.

a–c, Activation of a mitotic gene expression programme in developmentally distinct cell populations. a, Heat map of single-cell ICGS-gene-expression clusters generated (in AltAnalyze using the HOPACH algorithm) with the allowed inclusion of cell-cycle regulators as guide genes. Each column represents a single cell library; each row represents a different gene. ICGS-identified guide genes are indicated to the right of each plot. ICGS-identified HOPACH clusters are indicated at the top. b, ICGS from a reordered by the gates used for flow cytometric isolation (indicated at the top). Cell types (to the left) were predicted using GO-Elite (AltAnalyze) and ToppGene enrichment analysis, in addition to prior literature knowledge. c, PCA visualization of the first two principal components of all expressed genes (ICGS step 1), following Z-score normalization of all TPM values. Cells shaded to signify the mean expression of cell-cycle-associated genes (GO: 0022402). d–f, MDP and nascent dendritic cells within myeloid progenitor cell gates. d, Column plots displaying the incidence and amplitude of expression of select genes (in Fig. 1b ICGS-clustered order is shown as ‘Clusters’ at the top). The origin (flow-cytometric-gate) of each cell is indicated. Expression of Flt3, Csf1r and Cx3cr1 identifies MDP, while expression of Batf3 and Ifi205 suggests dendritic cell differentiation. e, Flow cytometric analysis of lineage-negative Cx3CR1–GFP+ mouse bone marrow cells confirms the presence of phenotypic CD135+ (Flt3), CD115+ (Csf1r) MDP in CMP and GMP gates. f, Bar graph representing the relative abundance of MDP cells within each gate. Mean ± s.e.m. for three biological replicates. g–j, Transcription factor-to-gene correlation analysis. g, ICGS clustering of LSK cells (n = 93) with cell-cycle genes excluded. h, ICGS clustering of CMP cells (n = 94) with cell-cycle genes excluded. ICGS-selected guide genes are displayed on the right of each heat map. i, Heat map displays clustering of Pearson correlation coefficients among genes and transcription factors using HOPACH, with corresponding ICGS clusters from LSK in g. j, Heat map displays clustering of Pearson correlation coefficients among genes and transcription factors using HOPACH, with corresponding ICGS clusters from CMP cell population in h. Columns represent genes and rows transcription factors that are captured by ICGS analysis of CMP cells.

Source data

Extended Data Figure 7 Transcription factor to transcription factor correlations and transcription factor loss-of-function analyses.

a, Scatter plots reveal the single-cell structure underlying correlations between transcription factors. Scatter plots generated in R (using the pairs function) show TPM of select transcription factor pairs in individual GMP cell populations (colours corresponding to ICGS groups in Fig. 1d, top). Expression is given as TPM. Pearson correlation coefficients are indicated opposite to each plot. b, Plots displaying the incidence and amplitude of expression of select genes in Fig. 2a. Expression clusters of Irf8-high (blue) and Gfi1-high (green) or neither (Multi-Lin*; purple) are shown. Significant changes in the expression of key genes between Irf8−/− versus Irf8-high wild-type GMP, or Gfi1−/− versus Gfi1-high wild-type GMP cells are noted (*P < 0.05, **P < 0.01, ***P < 0.001; Benjamini–Hochberg adjusted). Note that Irf8−/− and Gfi1−/− GMP cells continue to express non-productive transcripts emanating from the mutant Gfi1 and Irf8 alleles. c, Gfi1−/− GMP cells show a significant increase in cell-cycle-related gene expression compared to wild-type or Irf8−/− GMP cells. HOPACH clustering of Gfi1−/− and Irf8−/− GMP cells using haematopoietic guide genes from Fig. 2a. All cells were first clustered by HOPACH and then grouped according to sorting gates. In agreement with our previous report that Gfi1 controls two genetically separable programmes; granulopoiesis and Hox-based myeloid progenitor proliferation25, Gfi1−/− GMP cells demonstrate significantly increased HSC and cell-cycle-associated gene expression. Cell-cycle-associated genes were enriched (Z > 1.96) in Gfi1−/− and depleted (Z < −1.96) in Irf8−/− GMP cells. d, e, Lsd1 inhibition results in monocytic colony formation and increased Irf8 expression. d, CFU assays performed with CD117+ bone marrow cells with and without Lsd1-inhibitor (GSK C-76) treatment. The y axis displays percentage distribution of colony types. Mean CFU number of three technical replicates shown. e, TaqMan analysis of Irf8 expression in CD117+ bone marrow cells with and without treatment with C-76 (16 h). Mean of 3 technical replicates with similar results from 3 biological replicates. Representative plot from one of the 3 independent experiments performed is displayed for both d and e. f, Heat map showing the expression of a subset of genes (214) associated with Gfi1- and Irf8-shared ChIP–seq peaks. All displayed genes are significantly differentially expressed (P < 0.05, Benjamini–Hochberg adjusted) among at least one of the four comparisons (Irf8−/− versus wild type; Irf8−/− versus Irf8-high wild-type; Gfi1−/− versus wild-type; Gfi1−/− versus Gfi1-high wild-type). Marked genes (−) are associated with ImmGen monocyte-dendritic-precursor genes sets, and named genes are associated with abnormal mononuclear cell morphology (Mouse Phenotype Ontology; GO-Elite).

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Extended Data Figure 8 Counteracting functions of Irf8 and Gfi1 in myeloid cell fate choice.

a, Gfi1, Irf8 and CEBPα ChIP–seq and RNA-seq tracks illustrating co-regulation at select loci. Gfi1 and Irf8 ChIP–seq was carried using crosslinked wild-type GMP cells, whereas RNA-seq was performed using non-crosslinked wild-type GMP cells. Cebpα ChIP–seq data was obtained from GEO record, accession number GSE43007. Significant peaks called by MACS are represented as bars under each ChIP–seq track. Regions that have called peaks overlapping for Gfi1, Irf8 and Cebpα are highlighted by a box. Strand-specific RNA-seq data displayed as black and grey peaks, respectively. Refseq gene structure presented at bottom for Irf8, Gfi1, Klf4, Per3, Zeb2 and Ets1. b–d, G3-tetracycline-inducible promoter-driven Gfi1 allele G3GV results in granulocytic differentiation. b, Schematic representation of the Col1a1 locus of KH2 ES cells engineered using FLP recombinase to harbour a G3-tetracycline-inducible promoter-driven Gfi1 allele. KH2 ES cells also contain a ROSA-allele which expresses the rtTA-M2 protein. Immunoblot of Gfi1 and Venus eYFP expression in ES cells. G3GV KH2 ES cells were treated with 1 μg ml−1 doxycycline for 48 h, then analysed for Gfi1 and Venus expression by immunoblotting. For gel source data, see Supplementary Fig. 1. c, TaqMan analysis of gene expression in Csf1r− and Csf1r+ GMP cells, with or without doxycycline induction of G3GV using one allele encoding rtTA-M2. Mean of two technical replicates represented. d, CFU assays using lineage-negative bone marrow cells from wild type B6 or G3GV knock-in mice. Cells were cultured with or without 1 μg ml−1 doxycycline, in methyl-cellulose media. The percentage distribution of colony types is displayed on the y axis. Mean CFU number (bottom) (n = 3 wells per condition). Representative plot from one of three independent experiments performed is displayed in c and d.

Extended Data Figure 9 Bipotential GG1 cells comprise transcriptionally distinct progenitor populations.

a, Colony appearance of CFU-G, CFU-M and CFU-GM respectively. Photos were taken with a 10× objective lens. b, ICGS analysis of GG1 cells with cells spanning the entire myeloid developmental spectrum (Fig. 1b). Cells were separated according to flow cytometric sort gates. c, Hierarchical clustering using genes in panel b that are expressed in GG1 cells (TPM >1) identifies four distinct sub clusters. d, Finding GG1-like cells in the existing scRNA-seq data set. HOPACH clustering of the same genes and cells from Extended Data Fig. 9b with arrows indicating the GG1 and 16 GG1-like cells identified in the other sort gates. GG1-like cells were identified by comparing centroids from c to those from Fig. 1b. HOPACH clusters, using the LineageProfiler classification option in AltAnalyze (n = 16) (Supplementary Methods). Arrows at the top of the heat map denote GG1 and GG1-like cells. e, f, Back-gating of sorted Irf8–GFP GMP cell subpopulations (e; IG1, IG2 and IG3) or Gfi1–GFP GMP cell subpopulations (f; GG1, GG2 and GG3) showing that all populations are phenotypically GMP cells (CD16/32high CD34high).

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Extended Data Figure 10 Clustering intermediates and Irf8−/− Gfi1−/− double-knockout GMP cells.

a–h, GMP cell subpopulations enriched for CFU-GM also contain eosinophil–granulocyte progenitors. a, Plots displaying the incidence and amplitude of expression of select genes (from Fig. 4a). b, TaqMan analysis of eosinophil gene expression (Il5ra, Epx and Prg2) in the GMP cell subpopulations from Gfi1–GFP heterozygous mice. c, CFU assays with GMP cell subsets in media containing IL-3, GM-CSF, IL-5, SCF and TPO. d, CFU assays with GMP cell subsets with media containing IL-5 and SCF (which supports eosinophil-granulocyte colonies). e, TaqMan analysis of eosinophil gene expression in colonies from GG1 cells. Mean CFU number of 2 technical replicates with similar results from 2 biological replicates. f, Cytospin analysis of eosinophils in GG1-derived CFU from i. g, Flow cytometry analysis for eosinophil–granulocyte markers CCR3 and SiglecF on colonies from GG1 cells. Nearly all the GG1-derived IL-5 and SCF CFUs are positive for eosinophil markers. Representative FACS plot shown. h, TaqMan analysis of eosinophil genes (Il5ra, Epx, Prg2) in the GMP cell subpopulations from Irf8–GFP heterozygous mice. i, ICGS of GG1 and IG2 cells with cells spanning the entire myeloid developmental spectrum (Fig. 1b). Cells were separated according to flow cytometry sort gates. j–k, GG1 and IG1 cells that are enriched for CFU-GM also preferentially express HSCP1- and HSCP2-cluster genes. j, TaqMan analysis of HSCP1–HSCP2 genes in the GMP GG subpopulations. k, TaqMan analysis of HSCP1–HSCP2 genes in sorted IG subpopulations. l, Clustering Irf8−/− Gfi1−/− double-knockout single-cell libraries. HOPACH hierarchical clustering of all cells from Fig. 1b, as well as IG2 and Irf8−/− Gfi1−/− double-knockout, single-cell libraries. Only genes from Fig. 1b and in the previously clustered results were included. Genes and cells outlined in the dotted box were re-clustered with HOPACH to delineate relationships between monocytic and granulocytic cell programming among the different indicated cell populations (Fig. 4c). Representative plot of the mean of 2 technical replicates from 1 of 3 independent experiments performed displayed in b–e, h, j and k.

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Supplementary Information

This file contains Supplementary Text and Data, Supplementary Table 1 and additional references (see Contents for details). (PDF 1869 kb)

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This file contains the uncropped immunoblot for Extended Data Figure 8. (PDF 355 kb)

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Olsson, A., Venkatasubramanian, M., Chaudhri, V. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698–702 (2016). https://doi.org/10.1038/nature19348

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