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Resolving early mesoderm diversification through single-cell expression profiling

Abstract

In mammals, specification of the three major germ layers occurs during gastrulation, when cells ingressing through the primitive streak differentiate into the precursor cells of major organ systems. However, the molecular mechanisms underlying this process remain unclear, as numbers of gastrulating cells are very limited. In the mouse embryo at embryonic day 6.5, cells located at the junction between the extra-embryonic region and the epiblast on the posterior side of the embryo undergo an epithelial-to-mesenchymal transition and ingress through the primitive streak. Subsequently, cells migrate, either surrounding the prospective ectoderm contributing to the embryo proper, or into the extra-embryonic region to form the yolk sac, umbilical cord and placenta. Fate mapping has shown that mature tissues such as blood and heart originate from specific regions of the pre-gastrula epiblast1, but the plasticity of cells within the embryo and the function of key cell-type-specific transcription factors remain unclear. Here we analyse 1,205 cells from the epiblast and nascent Flk1+ mesoderm of gastrulating mouse embryos using single-cell RNA sequencing, representing the first transcriptome-wide in vivo view of early mesoderm formation during mammalian gastrulation. Additionally, using knockout mice, we study the function of Tal1, a key haematopoietic transcription factor, and demonstrate, contrary to previous studies performed using retrospective assays2,3, that Tal1 knockout does not immediately bias precursor cells towards a cardiac fate.

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Figure 1: Single-cell transcriptomics identifies ten populations relevant to early mesodermal development.
Figure 2: Transcriptional program associated with T induction in E6.5 epiblast cells.
Figure 3: Dimensionality reduction reveals transcriptional profiles associated with cell location in the embryo.
Figure 4: Inferring the transcriptional program underlying primitive erythropoiesis.
Figure 5: Analysis of Tal1−/− embryos suggests independent fate acquisition.

Accession codes

Data deposits

ChIP-seq data are available at the NCBI Gene Expression Omnibus portal under accession number GSE74994. Processed data are also available at http://codex.stemcells.cam.ac.uk. RNAseq data are available at Array Express under accession numbers E-MTAB-4079 and E-MTAB-4026. Processed RNAseq data are also available at http://gastrulation.stemcells.cam.ac.uk/scialdone2016.

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Acknowledgements

We thank M. de Bruijn, A. Martinez-Arias, J. Nichols and C. Mulas for discussion, the Cambridge Institute for Medical Research Flow Cytometry facility for their expertise in single-cell index sorting, and S. Lorenz from the Sanger Single Cell Genomics Core for supervising purification of Tal1−/− sequencing libraries. ChIP-seq reads were processed by R. Hannah. Research in the authors’ laboratories is supported by the Medical Research Council, Cancer Research UK, the Biotechnology and Biological Sciences Research Council, Bloodwise, the Leukemia and Lymphoma Society, and the Sanger-EBI Single Cell Centre, and by core support grants from the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute and by core funding from Cancer Research UK and the European Molecular Biology Laboratory. Y.T. was supported by a fellowship from the Japan Society for the Promotion of Science. W.J. is a Wellcome Trust Clinical Research Fellow. A.S. is supported by the Sanger-EBI Single Cell Centre. This work was funded as part of Wellcome Trust Strategic Award 105031/D/14/Z ‘Tracing early mammalian lineage decisions by single-cell genomics’ awarded to W. Reik, S. Teichmann, J. Nichols, B. Simons, T. Voet, S. Srinivas, L. Vallier, B. Göttgens and J. Marioni.

Author information

Authors and Affiliations

Authors

Contributions

A.S. and W.J. processed and analysed single-cell RNA sequencing (RNA-seq) data. A.S. and V.M. generated figures. Y.T. and W.J. performed embryo dissection. N.K.W., V.M. and I.C.M. performed single-cell RNA-seq experiments. Y.T. performed flow cytometry, ESC differentiation and in situ hybridization. V.M. performed ChIP-seq assays. A.S., W.J., Y.T., V.M., B.G. and J.C.M. interpreted results and wrote the paper. B.G. and J.C.M. supervised and conceived the study.

Corresponding authors

Correspondence to John C. Marioni or Berthold Göttgens.

Additional information

Reviewer Information Nature thanks A.-K. Hadjantonakis, P. Robson 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 FACS of single cells.

a, Flk1+ cells were sorted from three embryos at primitive streak and head fold stages and four embryos at neural plate stage. Labels such as ‘S4’ refer to the embryo number in the metadata available online at http://gastrulation.stemcells.cam.ac.uk/scialdone2016. b, CD41+Flk1 cells (red gate) and CD41+Flk1+ (green gate) cells were sorted from eight embryos each at neural plate and head fold stages (see Fig. 1 for cell numbers). Each stage was sorted on two occasions. Labels above FACS plots refer to the sort and embryo number in the metadata available online, as above. In all plots, pink text indicates the percentage of cells in that gate.

Extended Data Figure 2 Quality control of single-cell RNA-seq data.

a, Table showing numbers and estimates of numbers of cells of different phenotypes present in embryos between E6.5 and E7.75 (head fold stage) and numbers sorted for this study. *Total cell numbers for E6.5 are from Beddington and Robertson (1999)58. †Total numbers and numbers of Flk1+ cells are from Moignard et al., (2015)15. Percentages of cells expressing Flk1 and/or CD41 at neural plate and head fold stages are the average values from the embryos used in this study and were used to calculate the estimated numbers present in embryos from the total cell numbers. ND, not done. b, t-SNE representation of the five metrics used to assess the quality of all 2,208 sorted cells from the wild-type and Tal1 experiments. Only cells that passed all criteria (blue circles) were used for downstream analysis. c, Squared coefficient of variation (CV2) as a function of the mean normalized counts (μ) for genes across all cells. The green line shows the fit CV2 = a1/μ + α0. All highly variable genes (with an adjusted P value < 0.1) are marked by red circles. d, Number of genes detected (that is, with more than ten normalized read counts) in cells across the different clusters in the WT (left) and the Tal1−/− (right) mice. Boxes indicate the median and interquartile range.

Extended Data Figure 3 Identifying cell clusters.

The dynamic hybrid cut algorithm was used with all possible values of the ‘deepSplit’ parameter on 100 bootstrapped subsamples. a, b, To assess the quality of the clustering, the Pearson gamma (a) and the average silhouette width (b) were calculated. Higher values of these parameters correspond to better clustering. The Pearson gamma represents the correlation between the dissimilarity of samples and a binary variable that equals 0 for pairs of samples in the same cluster and 1 for samples in different clusters. The average silhouette width measures the average separation between neighbouring clusters43,44. At ‘deepSplit’ = 2 the Pearson gamma is highest whereas the average silhouette width begins to decrease. This suggests that at such a value of the ‘deepSplit’ parameter a good compromise between robustness and sensitivity is achieved. The Pearson gamma and the average silhouette width were computed with the R function ‘cluster.stats’ in the ‘fpc’ package (version 2.1-9). cf, Examples of marker genes for four clusters: Mesp1 for cluster 4 (top-ranked marker) (c), Hbb-bh1 for cluster 8 (fourth-ranked) (d), Alx1 for cluster 10 (top-ranked) (e) and Sox18 for cluster 6 (second-ranked) (f). The y axis shows the log10-normalized expression of the genes. For af, boxes indicate the median and interquartile range. g, Dendrogram showing the clustering of the cells in the first experiment. The colours at the bottom indicate the cluster each cell was assigned to by the dynamic hybrid cut algorithm. Cluster assignment was used to sort cells in Fig. 1b. h, Identities were assigned to the ten clusters in Fig. 1c on the basis of the expression of key genes20,50,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80 associated with various mesodermal lineages or spatial locations within the embryo.

Extended Data Figure 4 Expression of key marker genes in E7.0–7.75 embryos.

Schematic representations of expression patterns were generated from published in situ hybridization data (see citations) for key markers of clusters 1 (magenta, visceral endoderm59), 2 (pink, extra-embryonic ectoderm61), 6 (red, yolk sac endothelium62), 9 (black, allantois63,64) and 10 (green, second heart field65,81). Anterior is shown on the left and posterior on the right. Also shown is the t-SNE for all 1,205 cells or 682 cells from E7.0 onwards (primitive streak, neural plate and head fold stages) indicating expression of each gene (white, low; purple, high).

Extended Data Figure 5 Expression of key genes used for sorting single cells.

a, t-SNE as in Fig. 1 showing the sorting strategy for each of the 1,205 cells. b, Expression of Flk1 (Kdr), CD41 (Itga2b), Scl (Tal1), Gata2 and T (Brachyury) superimposed onto the t-SNE. c, t-SNE showing only the 682 cells from primitive streak, neural plate and head fold stages, coloured according to cluster as in Fig. 1c, e. d, t-SNE for the 481 E6.5 cells in cluster 3, as in Fig. 2a. Each point is coloured by expression of T and Foxa2.

Extended Data Figure 6 Pseudospace analysis of cluster 4 correlates with anterior–posterior position along the primitive streak.

a, Diffusion plot of the 216 cells in cluster 4. Different colours correspond to different plates and different lanes of flow cells. b, Table showing the number of cells in each stage analysed on the different lanes of flow cells (S, primitive streak; NP, neural plate; HF, head fold). c, A direction in the diffusion space can be identified by the angle α that it forms with the first diffusion component (left panel). For each value of α the right panel shows the percentage of variance explained by the batch effect associated to plates SLX-8408 and SLX-8409 (orange line) and plates SLX-8410 and SLX-8411 (blue line). Labels α1 and α2 are the angles corresponding to directions that correlate the least with the batch effect (that is, variance explained by the batch effect is minimum). d, The use of alternative dimensionality reduction techniques results in the identification of highly correlated pseudospace coordinates. A t-SNE projection of the dissimilarity matrix was performed (perplexity set to 50), and the direction corresponding to the pseudospace coordinate was estimated by minimizing the correlation with the batch effect (left panel; Spearman correlation between the two pseudospace coordinates 0.79, P < 2.2 × 10−16). Independent component analysis was performed on the dissimilarity matrix with the ‘fastICA’ R function, and three independent components (corresponding to the two batch effects and the biological effect) were estimated. The presumptive pseudospace coordinate is the component having the smallest correlation with the batch effects (right panel; Spearman correlation coefficient is 0.97, P < 2.2 × 10−16). e, Plots showing the average expression of genes in clusters 1–3 of Fig. 3c along the pseudospace axis. Gene expression levels are normalized between 0 and 1. Dark red lines indicate the normalized mean expression levels of genes in each cluster as obtained from the fitting procedure and red shaded area indicates standard deviation. f, Expression of T as function of the pseudospace coordinate. g, Gene expression levels for example genes showing high-low-high expression pattern across the blue cluster. In f and g, putative anterior cells are to the left and posterior to the right. Each dot represents a cell and red lines indicate fits based on local polynomial functions (see Methods). h, We performed principal component analysis on the cells in cluster 4 by using markers of pre-somitic mesoderm as anterior mesoderm markers and genes expressed in haemato-vascular and allantoic mesoderm as posterior markers82,83,84,85,86,87,88,89,90,91,92,93,94,95, as well as Podxl which was shown to separate distinct Flk1+ mesodermal lineages96. The first component explained 36% of the total variance and was highly correlated with the pseudospace coordinate (left; Spearman rank correlation 0.84, P < 2.2 × 10−16). All the anterior markers were negatively correlated with the pseudospace coordinate, whereas all posterior markers had a positive correlation (right).

Extended Data Figure 7 Expression of key genes along the anterior–posterior axis of the primitive streak in E7.0–7.75 embryos.

Schematic representations of gene expression were generated from published in situ hybridization data (see citations) for key markers of clusters 4 (blue, mesoderm) and 7 (yellow, posterior mesoderm/blood progenitors). Expression of T (Brachyury)97 and Flk1 (Kdr, from in-house data) are shown to illustrate the extent of the primitive streak at E7.5. Lefty2 and Tbx6 (ref. 59) are expressed in the putative anterior portion of cluster 4 and in more anterior regions of the primitive streak in in situ analysis. Tbx3 (ref. 98) and Bmp4 (ref. 99) are expressed in the more posterior portion of cluster 4 and in the embryo are expressed in the more posterior region of the primitive streak around the amnion and into the extra-embryonic mesoderm. Tek and Fli1 (from in-house data) are expressed in cluster 7 and in the embryo are found exclusively in the extra-embryonic portion. Also shown is the t-SNE for the cells from E7.0 onwards (primitive streak, neural plate and head fold stages) indicating expression of each gene (white, low; purple, high).

Extended Data Figure 8 Pseudotime analysis of primitive erythroid development.

a, Diffusion plot of the 271 cells in clusters 7 and 8. Different colours correspond to different plates and lanes of flow cells. b, Table showing the number of cells in each stage collected on the different plates (S, primitive streak; NP, neural plate; HF, head fold). c, Analogously to Extended Data Fig. 6, the angle α identifies a direction in the diffusion space (left panel). The percentage of variance explained by the batch effect associated to plates SLX-8344 and SLX-8345 is plotted as a function of α in the right panel. d, The pseudotime coordinate is robust to the use of different dimensionality reduction techniques, as shown in the left panel with t-SNE (Spearman correlation 0.92, P < 2.2 × 10−16) and in the right panel with independent component analysis (Spearman correlation 0.97, P < 2.2 × 10−16; same procedure described in Extended Data Fig. 6d). e, Plots showing the average expression of genes in clusters 1–3 of Fig. 4c along the pseudotime axis. Gene expression levels are normalized between 0 and 1. Dark red lines are the average expression levels of genes in each cluster as obtained from the fitting procedure, after normalization. Red shaded areas indicate standard deviation. f, Principal component analysis was performed on the expression pattern of genes known from previous studies to be upregulated or downregulated along the blood developmental trajectory15,66,100,101,102,103,104. The first principal component (explaining 44% of total variance) showed a very strong correlation with the pseudotime coordinate (left; Spearman correlation coefficient 0.91, P < 2.2 × 10−16). All upregulated (downregulated) genes positively (negatively) correlate with the pseudotime coordinate (right).

Extended Data Figure 9 ChIP-seq for Gata1 in ESC-derived haematopoietic cells.

a, Flow cytometry for Gata1–mCherry and Runx1-IRES–GFP knock-in reporter genes in embryoid body cells after 5 days of haematopoietic differentiation. Cells were sorted for the expression of both Runx1-IRES–GFP and Gata1–mCherry knock-in reporter genes to provide in vitro equivalents of the developing primitive erythrocytes assayed by RNA-seq. The gate used for sorting is shown in red. b, Numbers of reads and peaks identified for Gata1 and an input sample after mapping and peak calling; 4,135 Gata1 peaks were identified. c, Distribution of Gata1 peaks between promoter, intragenic and intergenic sequences. d, University of California, Santa Cruz Genome Browser tracks for Gata1 and input sample at the Zfpm1 (Fog1) locus known to be a target of Gata1, indicating the quality of the ChIP-seq data. e, Expression of Gata1 target Zfpm1 during the pseudotimecourse for erythroid development, as in Fig. 4.

Extended Data Figure 10 Collection of embryos from Tal1 LacZ/+ crosses.

a, Genotyping PCR for embryos from Tal1 LacZ/+ crosses. Lower band is the WT allele and upper band is the mutant allele carrying a neomycin knock in. Presence of both bands indicates heterozygosity. Embryos from which sequencing data were obtained are indicated with a red star and the number given corresponds to embryo identity in the metadata available online with the sequencing data. b, t-SNE as in Fig. 5d showing Tal1 data (triangles; 377 cells) and original WT data (grey circles; 1,205 cells). Tal1 data are coloured according to the embryo stage from which they were collected: green, neural plate; red, head fold; orange, four-somite pair. c, As in Fig. 5d, showing the complete list of genes. d, Gene set control analysis105 was used to identify statistically significant overlaps between genes significantly downregulated in Tal1−/− compared with WT cells in the endothelial cluster (see Fig. 5) and Tal1 targets identified by ChIP-seq. Gene set control analysis identified an enrichment of our gene set with Tal1 ChIP-seq in ESC-derived haemangioblasts and haemogenic endothelium106, but not in ESC-derived haematopoietic progenitors106 or a haematopoietic progenitor cell line52.

Supplementary information

Supplementary Table 1

Genes correlated with T in E6.5 epiblast cells. Correlation was calculated using Spearman rank correlation. All genes significantly correlated at FDR <0.1 are listed. (XLSX 138 kb)

Supplementary Table 2

Gene ontology terms for genes dynamically expressed across pseudospace and pseudotime in Figures 2 and 4. Significant GO terms associated with the gene clusters in Figure 3c and Figure 4c (both p-value <10-4). In both cases, no significant GO terms were associated with the smallest cluster of transient expressed genes, likely due to the low numbers of genes in these clusters. (XLSX 34 kb)

Supplementary Table 3

Full list of marker genes associated with clusters in Figures 1 and Extended Data Fig. 2. (XLSX 77 kb)

Supplementary Table 4

List of genes differentially expressed along the pseudo-space trajectory shown in Figure 3. The three clusters of genes differentially expressed along the pseudo-space trajectory are listed (see Figures 3 and Extended Data Fig. 5) with their ΔAIC scores (see Methods). (XLSX 65 kb)

Supplementary Table 5

List of genes differentially expressed along the pseudo-time trajectory shown in Figure 4. The three clusters of genes differentially expressed along the pseudo-time trajectory are listed (see Figures 4 and Extended Data Fig. 8), along with their ΔAIC scores (see Methods). (XLSX 83 kb)

Supplementary Table 6

Lists of genes bound by Gata in ChIP-seq experiment, and differentially expressed along the pseudo-time trajectory shown in Figure 4 and bound by Gata1. (XLSX 98 kb)

Supplementary Table 7

List of 319 genes up-regulated in endothelium (red, cluster 6) and blood cells (brown, cluster 8) when compared to nascent mesoderm population (blue, cluster 4) in Figure 5d. (XLSX 73 kb)

Supplementary Table 8

List of genes differentially expressed between Tal1-/- endothelium and WT endothelium in Figure 5e. Genes up-regulated in Tal1-/- mice correspond to fold-changes greater than 1. (XLSX 56 kb)

Supplementary Table 9

Genes up- and down-regulated by Tal1. Gene sets used in Figure 5e, taken from Org et al., 20153 Supplementary Tables 1c and 1d. (XLSX 13 kb)

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Scialdone, A., Tanaka, Y., Jawaid, W. et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289–293 (2016). https://doi.org/10.1038/nature18633

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