Across the animal kingdom, gastrulation represents a key developmental event during which embryonic pluripotent cells diversify into lineage-specific precursors that will generate the adult organism. Here we report the transcriptional profiles of 116,312 single cells from mouse embryos collected at nine sequential time points ranging from 6.5 to 8.5 days post-fertilization. We construct a molecular map of cellular differentiation from pluripotency towards all major embryonic lineages, and explore the complex events involved in the convergence of visceral and primitive streak-derived endoderm. Furthermore, we use single-cell profiling to show that Tal1−/− chimeric embryos display defects in early mesoderm diversification, and we thus demonstrate how combining temporal and transcriptional information can illuminate gene function. Together, this comprehensive delineation of mammalian cell differentiation trajectories in vivo represents a baseline for understanding the effects of gene mutations during development, as well as a roadmap for the optimization of in vitro differentiation protocols for regenerative medicine.
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Raw sequencing data are available on ArrayExpress with the following accessions: Atlas: E-MTAB-6967; Smart-seq2 endothelial cells: E-MTAB-6970; Tal1−/− chimaeras: E-MTAB-7325; wild-type chimaeras: E-MTAB-7324. Processed data may be downloaded following the instructions at https://github.com/MarioniLab/EmbryoTimecourse2018. Gene Expression Omnibus (GEO) accession GSE87038 was used to support the annotation of myeloid cells (see Methods). All code is available upon request, and at https://github.com/MarioniLab/EmbryoTimecourse2018. Our atlas can be explored at https://marionilab.cruk.cam.ac.uk/MouseGastrulation2018/. All other data are available from the corresponding authors on reasonable request.
Tam, P. P. L. & Behringer, R. R. Mouse gastrulation: the formation of a mammalian body plan. Mech. Dev. 68, 3–25 (1997).
Loh, K. M. et al. Mapping the pairwise choices leading from pluripotency to human bone, heart, and other mesoderm cell types. Cell 166, 451–467 (2016).
Viotti, M., Nowotschin, S. & Hadjantonakis, A.-K. SOX17 links gut endoderm morphogenesis and germ layer segregation. Nat. Cell Biol. 16, 1146–1156 (2014).
Lescroart, F. et al. Defining the earliest step of cardiovascular lineage segregation by single-cell RNA-seq. Science 359, 1177–1181 (2018).
Ibarra-Soria, X. et al. Defining murine organogenesis at single-cell resolution reveals a role for the leukotriene pathway in regulating blood progenitor formation. Nat. Cell Biol. 20, 127–134 (2018).
Downs, K. M. & Davies, T. Staging of gastrulating mouse embryos by morphological landmarks in the dissecting microscope. Development 118, 1255–1266 (1993).
Koch, F. et al. Antagonistic activities of Sox2 and Brachyury control the fate choice of neuro-mesodermal progenitors. Dev. Cell 42, 514–526.e7 (2017).
Tzouanacou, E., Wegener, A., Wymeersch, F. J., Wilson, V. & Nicolas, J.-F. Redefining the progression of lineage segregations during mammalian embryogenesis by clonal analysis. Dev. Cell 17, 365–376 (2009).
Kwon, G. S., Viotti, M. & Hadjantonakis, A.-K. The endoderm of the mouse embryo arises by dynamic widespread intercalation of embryonic and extraembryonic lineages. Dev. Cell 15, 509–520 (2008).
Finley, K. R., Tennessen, J. & Shawlot, W. The mouse Secreted frizzled-related protein 5 gene is expressed in the anterior visceral endoderm and foregut endoderm during early post-implantation development. Gene Expr. Patterns 3, 681–684 (2003).
Makover, A., Soprano, D. R., Wyatt, M. L. & Goodman, D. S. An in situ-hybridization study of the localization of retinol-binding protein and transthyretin messenger RNAs during fetal development in the rat. Differentiation 40, 17–25 (1989).
Martinez Barbera, J. P. et al. The homeobox gene Hex is required in definitive endodermal tissues for normal forebrain, liver and thyroid formation. Development 127, 2433–2445 (2000).
Bosse, A. et al. Identification of the vertebrate Iroquois homeobox gene family with overlapping expression during early development of the nervous system. Mech. Dev. 69, 169–181 (1997).
Osipovich, A. B. et al. Insm1 promotes endocrine cell differentiation by modulating the expression of a network of genes that includes Neurog3 and Ripply3. Development 141, 2939–2949 (2014).
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
Schiebinger, G. et al. Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming. Preprint at https://www.bioRxiv.org/content/early/2017/09/27/191056 (2017).
Viotti, M., Foley, A. C. & Hadjantonakis, A. K. Gutsy moves in mice: cellular and molecular dynamics of endoderm morphogenesis. Phil. Trans. R. Soc. Lond. B 369, 20130547 (2014).
Deschamps, J. & Duboule, D. Embryonic timing, axial stem cells, chromatin dynamics, and the Hox clock. Genes Dev. 31, 1406–1416 (2017).
Palis, J. Hematopoietic stem cell-independent hematopoiesis: emergence of erythroid, megakaryocyte, and myeloid potential in the mammalian embryo. FEBS Lett. 590, 3965–3974 (2016).
McGrath, K. E. et al. Distinct sources of hematopoietic progenitors emerge before HSCs and provide functional blood cells in the mammalian embryo. Cell Reports 11, 1892–1904 (2015).
Downs, K. M., Gifford, S., Blahnik, M. & Gardner, R. L. Vascularization in the murine allantois occurs by vasculogenesis without accompanying erythropoiesis. Development 125, 4507–4520 (1998).
Patan, S. in Angiogenesis in Brain Tumors (eds Kirsch, M. & Black, P. M.) 3–32 (Springer, Boston, MA, 2004).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
Palis, J., Robertson, S., Kennedy, M., Wall, C. & Keller, G. Development of erythroid and myeloid progenitors in the yolk sac and embryo proper of the mouse. Development 126, 5073–5084 (1999).
Tober, J. et al. The megakaryocyte lineage originates from hemangioblast precursors and is an integral component both of primitive and of definitive hematopoiesis. Blood 109, 1433–1441 (2007).
Xu, M.-j. et al. Evidence for the presence of murine primitive megakaryocytopoiesis in the early yolk sac. Blood 97, 2016–2022 (2001).
Hoeffel, G. et al. C-Myb+ erythro-myeloid progenitor-derived fetal monocytes give rise to adult tissue-resident macrophages. Immunity 42, 665–678 (2015).
Gomez Perdiguero, E. et al. The origin of tissue-resident macrophages: when an erythro-myeloid progenitor is an erythro-myeloid progenitor. Immunity 43, 1023–1024 (2015).
Bennett, M. L. et al. New tools for studying microglia in the mouse and human CNS. Proc. Natl Acad. Sci. USA 113, E1738–E1746 (2016).
Ginhoux, F. et al. Fate mapping analysis reveals that adult microglia derive from primitive macrophages. Science 330, 841–845 (2010).
Shivdasani, R. A., Mayer, E. L. & Orkin, S. H. Absence of blood formation in mice lacking the T-cell leukaemia oncoprotein tal-1/SCL. Nature 373, 432–434 (1995).
Robb, L. et al. The scl gene product is required for the generation of all hematopoietic lineages in the adult mouse. EMBO J. 15, 4123–4129 (1996).
Van Handel, B. et al. Scl represses cardiomyogenesis in prospective hemogenic endothelium and endocardium. Cell 150, 590–605 (2012).
Huber, T. L., Kouskoff, V., Fehling, H. J., Palis, J. & Keller, G. Haemangioblast commitment is initiated in the primitive streak of the mouse embryo. Nature 432, 625–630 (2004).
Briggs, J. A. et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780 (2018).
Farrell, J. A. et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131 (2018).
Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).
Pijuan-Sala, B., Guibentif, C. & Göttgens, B. Single-cell transcriptional profiling: a window into embryonic cell-type specification. Nat. Rev. Mol. Cell Biol. 19, 399–412 (2018).
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).
Nichols, J. & Jones, K. Derivation of mouse embryonic stem (ES) cell lines using small-molecule inhibitors of Erk and Gsk3 signaling (2i). Cold Spring Harb. Protoc. 2017, https://doi.org/10.1101/pdb.prot094086 (2017).
Ying, Q.-L. et al. The ground state of embryonic stem cell self-renewal. Nature 453, 519–523 (2008).
Wray, J. et al. Inhibition of glycogen synthase kinase-3 alleviates Tcf3 repression of the pluripotency network and increases embryonic stem cell resistance to differentiation. Nat. Cell Biol. 13, 838–845 (2011).
Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protocols 8, 2281–2308 (2013).
Le Bin, G. C. et al. Oct4 is required for lineage priming in the developing inner cell mass of the mouse blastocyst. Development 141, 1001–1010 (2014).
Lun, A. et al. Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Preprint at https://www.bioRxiv.org/content/early/2018/04/04/234872 (2018).
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).
Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. In Third International AAAI Conference on Weblogs and Social Media (AAAI, 2009).
Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One 9, e98679 (2014).
Wolf, F. A. et al. Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Preprint at https://www.bioRxiv.org/content/early/2017/10/25/208819 (2017).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Dong, J. et al. Single-cell RNA-seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis. Genome Biol. 19, 31 (2018).
Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).
Kinder, S. J. et al. The orderly allocation of mesodermal cells to the extraembryonic structures and the anteroposterior axis during gastrulation of the mouse embryo. Development 126, 4691–4701 (1999).
We thank W. Mansfield for blastocyst injections; A. T. L. Lun and F. Hamey for discussions concerning the analysis; T. L. Hamilton for technical support in embryo collection; S. Kinston and K. Jones for technical assistance; the Flow Cytometry Core Facility at CIMR for cell sorting; the CRUK-CI genomics core for the chimaera scRNA-seq 10x libraries and for letting us use the 10x Chromium after hours; the Wellcome Sanger Institute DNA Pipelines Operations for sequencing; and K. Hadjantonakis for sharing the Ttr::cre mouse line. Research in the authors’ laboratories is supported by Wellcome, the MRC, CRUK, Bloodwise, and NIH-NIDDK; by core support grants from Wellcome to the Cambridge Institute for Medical Research and Wellcome-MRC Cambridge Stem Cell Institute; and by core funding from CRUK and the European Molecular Biology Laboratory. B.P.-S. and D.L.L.H. are funded by the Wellcome 4-Year PhD Programme in Stem Cell Biology and Medicine and the University of Cambridge; D.L.L.H. is also funded by the Cambridge Commonwealth European and International Trust. J.A.G. is funded by the Wellcome Mathematical Genomics and Medicine Programme at the University of Cambridge (109081/Z/15/A). C.G. is funded by the Swedish Research Council (2017-06278, administered by Sahlgrenska Cancer Center, University of Gothenburg). This work was funded as part of a Wellcome Strategic Award (105031/Z/14/Z) awarded to W.R., B.G., J.C.M., J.N., L. Vallier, S.S., B.D.S., S. Teichmann, and T. Voet; by a Wellcome grant (108438/Z/15) awarded to J.C.M. and S.S., and by a BBSRC grant (BBS/E/B/000C0421) awarded to W.R.
Nature thanks Peter Sims, Patrick Tam and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Representative images of embryos collected at the time points indicated. Scale bars, 0.25 mm.
a, Left, estimated number of cells present in a single mouse embryo at each time point. Points are values measured previously24; line is an ordinary least squares regression fit. Right, number of cells captured in this study compared with the estimated number of cells in the embryo from the left panel. b, Violin plots illustrating the number of detected genes (top) and unique molecular identifiers (UMIs; bottom) per cell per sample. Sample 11 failed quality control and is therefore not shown. Sample details are provided in Supplementary Table 1. c, UMAP highlighting additional cells identified when a reduced UMI threshold of 1,000 was considered. Additional cells are shown in black. Cells from the atlas are shown in the colour corresponding to their cell type (Fig. 1c). Note that all additional cells are present alongside cells from the atlas: no new cell types are found. d, UMAP plot as shown in Fig. 1c, with cells coloured by biological replicate, showing consistency between samples collected at the same time point. e, Heat map showing the mean gene expression of diagnostic markers (y axis) for each cell type (x axis). Genes are row-normalized. NMP, neuromesodermal progenitors; PGC, primordial germ cells.
a, UMAP plot as shown in Fig. 1c, coloured by the density of each cell in gene expression space; brighter coloured regions (towards yellow) are more densely sampled and darker regions (towards blue) are more sparsely sampled, relative to other regions in the atlas. Values shown are log2(density + 1). b, Box plots summarizing the density per cell type. Values shown are log2(density + 1). c, UMAP plots as shown in Fig. 1c, highlighting cells from each sampled time point and therefore illustrating the transcriptional progression along developmental time. d, Results of atlas stability testing (see Methods). y axis: ratio of the standard deviation of cell-type frequency to the mean cell-type frequency at different degrees of downsampling. Note that when the atlas is downsampled to less than half of its full size (50,000 cells), the standard deviation remains less than 10% of the mean for all cell types. x-axis is log-transformed. e, f, Abstracted graphs, which quantify the degree of similarity between the identified clusters to represent the underlying biological structure of the dataset. Nodes correspond to the annotated cell types, and edges reflect the confidence of adjacency between clusters (thicker edges indicate higher confidence). Node sizes increase as a function of the number of cells within each cluster. Nodes in e are coloured and numbered according to the legend shown in Fig. 1c. Nodes in f show the frequency of cells from each time point, excluding two samples of mixed time-point embryos.
a, Schematic representing the process of definitive endoderm intercalation following gastrulation, and subsequent gut maturation. Adapted from a previous publication9. b–g, Gene expression levels of Ttr (b), Mixl1 (c), Nkx2-5 (d), Pyy (e), Nepn (f), and Cdx2 (g), overlaid on the Fig. 2a force-directed graph. h, Diffusion map of cells selected for transport map construction; cells selected as termini for pulling mass backward through the transport maps are coloured. i, Results of pushing mass forward through the transport maps are shown on the force-directed layout. j, Violin plots showing expression levels of Trap1a and Rhox5 in all cell-types of the full atlas. k, Dorsal view of a whole-mount fluorescence image of a Ttr::cre; R26R::YFP embryo at E8.5. Green, YFP; red, phalloidin. Arrowhead denotes the increased Ttr–YFP staining in the posterior region of the gut. Scale bar, 300 µm.
a, Top, graph abstraction of the endoderm landscape after fine sub-clustering as an alternative method to resolve which cells should be part of the visceral endoderm (VE)–hindgut 1 trajectory or the definitive endoderm (DE)–hindgut 2 trajectory (supporting transport maps; see Methods). Edges along the VE–hindgut 1 trajectory are highlighted in yellow (nodes 1–4; yellow numbers). Edges along the DE–hindgut 2 trajectory are highlighted in orange (nodes 1–12; orange numbers). Bottom, graph abstraction with the subset of nodes related to the DE–hindgut 2 trajectory to resolve the origin of cluster 4 (between 5 and 6 in the top panel). Resulting DE–hindgut 2 trajectory includes clusters 1–4 and 6–9. The right-hand panels overlay information about the composition of each cluster by developmental stage. b, Force-directed graph coloured by partition-based graph abstraction (PAGA) trajectories. Note that this independent approach for trajectory identification reaches very similar results to those inferred by the transport maps in Fig. 2h. HG1, hindgut 1; HG2, hindgut 2. c, Gene-normalized dynamics of all clusters found along the VE–hindgut 1 and the DE–hindgut 2 trajectories (x axis: DPT along the trajectory; y axis: normalized expression levels). The black line is the mean fitted expression level across all genes in each cluster; the grey shading indicates the standard deviation along the trend across all genes in the cluster; the pink area highlights intercalation process; and the blue area highlights gut maturation steps. Vertical dashed lines correspond to additional stages in the process, deduced from the changes in gene expression trends. Points below the plots are the DPT coordinates of cells from each time point coloured according to time point as in Fig. 2b (from E6.5 in red to E8.5 in blue). d, Gene-normalized dynamics of VE genes along the VE–hindgut 1 trajectory, indicating VE maturation before the intercalation stage. Plot design is as in c. e, Left, Venn diagram of genes that were upregulated during the intercalation process in both VE–hindgut 1 (in clusters 3, 5, 8, and 11) and DE–hindgut 2 (in clusters 4, 6, 7, 8, and 9) trajectories. The overlapping fraction was enriched in genes that are a signature of epithelial remodelling (top 20 genes are listed). Right, gene-normalized dynamics of illustrative genes (Pcna, Epcam, Slc2a1, Vim, Crb3, and Cadm1) along the trajectories. f, Left, Venn diagram of genes that were upregulated after the intercalation process in both trajectories (VE–hindgut 1: clusters 1, 2, 5, and 10; DE–hindgut 2: clusters 1, 3, 5, and 10). The overlapping fraction was enriched in genes that encode transcription factors (TFs), including a large subset of homeodomain proteins (genes are listed). Right, gene-normalized dynamics of Hox and Cdx genes along the trajectories. g, Gene-normalized dynamics of transcription factors that were upregulated specifically in the VE–hindgut 1 trajectory during endoderm intercalation. Points below the x axis in d–g are as in c.
a, Diagram illustrating the two waves of embryonic blood development. At E6.5, gastrulation begins. Previous work using transplantation assays has shown that the proximo-posterior epiblast cells closest to the primitive streak at this stage (red) mainly give rise to primitive erythroid cells in the yolk sac, whereas the epiblast cells located in the middle of the embryo at E6.5 but closer to the primitive streak at a later stage are enriched for endothelial progenitors56. At E7.5, blood islands are apparent (zoomed box of primitive blood wave), where primitive erythroid cells are surrounded by endothelium. At around E8.25, some endothelial cells (haemogenic endothelium) undergo an endothelial-to-haematopoietic transition and become EMPs, which migrate to the fetal liver and give rise to definitive erythrocytes. Adapted from a previous study38. b, Force directed layout of Fig. 3a coloured by original clusters from Fig. 1c. c, Gene expression levels of Cdh5 and Pecam1, overlaid on the graph abstraction visualization from Fig. 3b. d, Experimental design to isolate FLK1+ cells from yolk sac, allantois, and embryo proper for Smart-seq2 scRNA-seq. e, Representative image of an embryo collected for the transcriptional analysis of endothelial cells from the yolk sac, allantois, and embryo proper. f, Sorting strategy of FLK1+ cells from the yolk sac, embryo proper, and allantois on live cells (DAPI). x axis: FLK1 intensity. y axis: DAPI intensity. g, Evidence to support myeloid annotation of the myeloid cell cluster in Fig. 3. Haemato-endothelial cells from Fig. 3a were mapped to a published dataset54 that profiled haematopoietic cells collected at E9.5, E10.5, and E11.5 from different organs. Bar charts show the fraction of atlas cells in the myeloid cell cluster mapped to the clusters defined in figure 8 of the previous study54. h, Representative images of the dissected regions collected to study the location of CSF1R+CD16/32+ cells. Scale bars, 0.25 mm. i, Flow cytometry plots indicating the frequency of CSF1R+CD16/32+ cells in each embryonic region. Two biological replicates were performed for this experiment: with pools of 12 and 13 embryos, respectively. Plots illustrate one biological replicate.
a, Left, representative chimaera embryo obtained at E8.5 (left: brightfield; right: tdTomato fluorescence; scale bar, 0.25 mm). Right, flow cytometry plot with tdTomato fluorescence distribution and sorting gates. b, Histograms showing the UMI counts for the tdTomato construct in both tdTomato− and tdTomato+ fractions in the Tal1−/− into wild-type experiment (see Methods). c, d, Control mapping results of an E8.0 biological replicate that was removed and mapped back to the atlas. c, Heat map showing the fraction of cells of each labelled cell type that mapped to each cell type in the reference atlas. Numbers above columns indicate the number of cells in each category. Of these cells, 89.4% correctly mapped to their annotated cell type. d, Histogram showing the fraction of cells from the E8.0 biological replicate that mapped to each time point in the reference: 29.2% of cells mapped to the correct time point, and 83.1% of cells mapped within one time point (that is, 6 hours) in either direction. e, Scatter plot comparing the percentage of tdTomato+ cells against tdTomato− for each cell type in both Tal1−/− into wild-type (WT; left) and wild-type into wild-type (right) experiments. Black arrowheads indicate extra-embryonic tissues; white arrowheads indicate haematopoietic tissues. f, Force-directed graph of blood-related lineages from the atlas (Fig. 3), coloured by Tal1 expression levels. Darker colouring shows higher expression. g, Flow cytometry analysis of E8.5 Tal1−/− into wild-type chimaeras, showing the complete depletion of the haematopoietic markers CD41 and CD45 (top panels), as well as of the CD71+ Ter119+ erythroid fraction (bottom panels) in Tal1−/− tdTomato+ cells (right panels). h, UMAP plots of wild-type into wild-type experiment, showing balanced contributions to all embryonic lineages. i, Flow cytometry analysis of wild-type into wild-type chimaeras, showing balanced contributions to the haematopoietic lineage from both tdTomato+ and tdTomato− cells at E9.5 (representative of 2 individual embryos).
a, Heat map illustrating the row-normalized expression of genes that were upregulated in EC3-mapped Tal1−/− cells when compared with their closest neighbours in the atlas (labelled ‘EC3’) and EC3-mapped wild-type chimaera cells (labelled ‘WT’). Genes Gm45123 and Fam212a are also known as 5430431A17Rik and Inka1, respectively. b, UMAP plots as in Fig. 1c, showing the expression of Tdo2, Plagl1, and Pcolce.
This file contains the full legends for Supplementary Tables 1-6.
Embryo collection information.
Dynamically expressed genes along the trajectory from visceral and embryonic endoderm to hindgut.
Cell numbers for chimaera haemato-endothelial cell abundance analysis.
Metadata for all atlas cells that passed QC.
Metadata for chimaera cells from Tal1-/- into wildtype that passed QC.
Metadata for chimaera cells from wildtype into wildtype that passed QC.
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Pijuan-Sala, B., Griffiths, J.A., Guibentif, C. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019). https://doi.org/10.1038/s41586-019-0933-9
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