Here we delineate the ontogeny of the mammalian endoderm by generating 112,217 single-cell transcriptomes, which represent all endoderm populations within the mouse embryo until midgestation. We use graph-based approaches to model differentiating cells, which provides a spatio-temporal characterization of developmental trajectories and defines the transcriptional architecture that accompanies the emergence of the first (primitive or extra-embryonic) endodermal population and its sister pluripotent (embryonic) epiblast lineage. We uncover a relationship between descendants of these two lineages, in which epiblast cells differentiate into endoderm at two distinct time points—before and during gastrulation. Trajectories of endoderm cells were mapped as they acquired embryonic versus extra-embryonic fates and as they spatially converged within the nascent gut endoderm, which revealed these cells to be globally similar but retain aspects of their lineage history. We observed the regionalized identity of cells along the anterior–posterior axis of the emergent gut tube, which reflects their embryonic or extra-embryonic origin, and the coordinated patterning of these cells into organ-specific territories.
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All the generated data including bulk and scRNA-seq data are available through the Gene Expression Omnibus, under accession numbers GSE123046 (scRNA-seq) and GSE123124 (bulk RNA-seq). The data can be explored at https://endoderm-explorer.com, and any other relevant data are available from the corresponding authors upon reasonable request.
Harmony is available as a Python module at https://github.com/dpeerlab/Harmony. Palantir is available as a Python module at https://github.com/dpeerlab/Palantir. A Jupyter notebook detailing the usage of Harmony along with sample data is available at http://nbviewer.jupyter.org/github/dpeerlab/Harmony/blob/master/notebooks/Harmony_sample_notebook.ipynb.
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We thank K. Anderson, A. Joyner, A. Martinez-Arias and L. Mazutis for discussions; L. Beccari, D. Duboule, L. Sussel, M. Torres, D. Wellik and M. Wilkinson for plasmids; B. Merill for antibodies; and J. Brickman for embryonic stem cells. This work was supported by grants from the NIH (R01-DK084391 and R01-HD094868 to A.-K.H.; DP1-HD084071 and R01-CA164729 to D.P.; P30-CA008748 to C. Thompson), MSKCC Society for Special Projects and Functional Genomics Initiative (to A.-K.H. and D.P.) and NSERC (RGPIN-2018-05018 to P.A.H.). C.S.S. is supported by a NYSTEM postdoctoral training award from the Center for Stem Cell Biology MSKCC.
Nature thanks Thorsten Boroviak, Valerie Wilson and the other anonymous reviewer(s) for their contribution to the peer review of this work.
S.C.B. and D.M.C. are employees and shareholders at 10x Genomics.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Endoderm cell representation in mouse embryos, from blastocyst through midgestation, and single-cell collection pipeline.
a, Distribution of extra-embryonic endoderm cells (GFP, green) from blastocyst (E3.5) to midgestation (E8.75, 13ss) demarcated using PdgfraH2B-GFP20 (pre-implantation stages) and Afp-GFP12 (post-implantation stages) reporters. Extra-embryonic endoderm (PrE and visceral endoderm derivatives) cells contribute to the gut tube of the E8.75 embryo. b, Pie charts depicting the fraction of endoderm cells per embryo, for all stages analysed in this study. c, Schematic of protocol used for single-cell collection, with E8.75 gut tube provided as an example. Gut tubes were micro-dissected from embryos, then dissociated into single cells. Single cells of either anterior and posterior halves of gut tubes, or AFP–GFP-positive (visceral endoderm descendants) and AFP–GFP-negative (definitive endoderm descendants) collected using fluorescence-activated cell sorting, were used for single-cell 3′ mRNA library construction on the 10x Genomics Chromium platform. For bulk RNA-seq, whole gut tubes that had been dissociated into single cells and then pooled, whole intact gut tubes and whole gut tubes dissected into quarters were collected for sequencing. d, t-SNE plots of collected libraries for each time point with each dot representing a single cell. Phenograph was used to identify clusters of cells, colour-coded by cell type with annotation based on expression of known markers.
Extended Data Fig. 2 Computational pipeline and comparison of scRNA-seq data with bulk RNA-seq data.
a, Flow chart of computational data processing pipeline. b, Plots showing the Pearson’s correlation between aggregated scRNA-seq data of anterior and posterior halves of the gut tube with bulk RNA-seq of dissociated (and pooled) cells and bulk tissue, respectively. The two rows represent two replicates.
Extended Data Fig. 3 Mutually nearest neighbour augmentation to correct batch effects between time points, and Harmony unified framework for scRNA-seq data analysis.
a, Force-directed layouts for cells of the E3.5, E4.5, E5.5, E6.5, E7.5 and E8.75 (amalgamation of anterior and posterior gut tube halves) time points. Cells are coloured by time point. The graph was generated using an adjacency matrix derived from the standard kNN graph. Differences between consecutive time points represent underlying developmental changes, but are also confounded by technical batch effects—including discontinuity between E3.5 and E.4.5, and the lack of spatial alignment between E6.5 and E7.5. b, E6.5 and E7.5 cells projected along their respective first two diffusion components. These projections reveal a dominant first component with strong spatial signal within individual time points. Cells are coloured by Phenograph clusters. c, Top, the number of edges connecting cells between time points are limited in the kNN graph. Bottom, plots showing the number of mutually nearest neighbours (MNNs) between E6.5 and E7.5 time points. The MNNs are enriched along the boundary between time points, providing support for augmentation of the kNN graph with additional edges between MNNs between the consecutive time points. d, The MNN distances can be converted to affinities on a similar scale as the kNN affinities, using linear regression to determine the relationship between the two scales, kNN and (l < k) MNN distances. e, Example of the augmented MNN affinity matrix construction. Left, kNN affinities for a subset of E6.5 and E7.5 cells. Middle, MNN affinity matrix determined using linear regression (in d) to convert distances E6.5 and E7.5 cells to affinities. Right, augmented affinity matrix: sum of the kNN and MNN affinity matrices. f, Comparison of force-directed layouts. Left, standard kNN affinity matrix. Middle, Harmony augmented affinity matrix. Right, plot generated using mnnCorrect18 for global batch effect correction, leading to ‘over-correction’ and loss in signal between time points. g, Harmony framework starts with the augmented affinity matrix generated as described in Supplementary Note 2. The augmented affinity matrix is used to generate the force-directed graph for visualizing the data. The same augmented matrix is used to compute the diffusion operator for determining the diffusion components, which forms the basis for (1) Palantir trajectory detection and (2) MAGIC imputation. h, Robustness of Harmony. Plots showing the correlation between diffusion components for different values of k, the number of nearest neighbours for kNN graph construction. Visceral endoderm cells in Fig. 4 were used for testing robustness. i, Harmony applied to replicates. Plots showing the Pearson’s correlation between diffusion components without Harmony (x axis) and with Harmony applied between the two replicates of the E8.75 gut tube. Plots shown for 3,512 cells.
Results from pooling cells of two replicates at E3.5 and E4.5, followed by Harmony augmentation. a, Force-directed layout of E3.5 and E4.5 cells depicting the relationship between three blastocyst lineages. Cells coloured by time point or annotated cell types. b, Plot showing projection of E3.5 and E4.5 cells along the first two diffusion components. Distances between lineages were computed using multiscale distances. c, Table showing the connectivity between different compartments in a kNN graph of E3.5 cells. Each row represents the fraction of outgoing edges from cells of the respective compartment that connect to cells in the compartments specified in the columns. d, Force-directed layout of E3.5 and E4.5 cells after removal of trophectoderm cells, showing relationships between ICM, EPI and PrE cells. Cells are coloured by time point or Phenograph14 clusters. e, Palantir10,11 determined pseudo-time ordering, differentiation potential and branch probabilities of PrE and EPI cell lineages. f, Plots showing the second derivative of PrE and EPI differentiation potential along pseudo-time, which suggests that changes in differentiation potential—and therefore lineage commitment—in both lineages occur at E3.5. Points of highest changes along pseudo-time represent inferred lineage specification and commitment. g, Distribution of E3.5 lineage cells along pseudo-time. Each distribution represents cells from one Phenograph cluster. h, Histograms showing the distribution of differentiation potential (left), PrE fate probability (middle) and EPI fate probability (right) in the E3.5 ICM clusters. i, Gene expression patterns of parietal and visceral endoderm markers. Each cell is coloured on the basis of its MAGIC50 imputed expression level for the indicated gene. Black and orange arrowheads mark presumptive parietal and visceral endoderm lineages, respectively.
Extended Data Fig. 5 Gene expression trends in EPI, PrE, visceral and parietal endoderm lineages in the blastocyst.
a, Plots comparing gene expression trends along pseudo-time for genes that encode components of the FGF signalling pathway (Fgf4, Fgf5, Fgf8, Fgfr1, Fgfr2 and Spry4), the endoderm-marker transcription factors Gata6, Gata4, Sox7 and Sox17, and Nanog during EPI and PrE lineage specification. Solid line represents the mean expression trend and shaded region represents ± 1 s.d. b, Dynamics of transcription factor ratios as lineages emerge. Ratio between Gata6 and Nanog, and Gata6 and Fgf4 along EPI; and between Nanog and Gata6, and Fgf4 and Gata6 along PrE, compared to changes in differentiation potential (dotted line). Transcription factor ratios were computed for each cell by using the MAGIC50 imputed data for each gene. c, Plots comparing gene expression trends along pseudo-time. Gata and Sox transcription factors, Fgf receptors or Fgf ligands during PrE or EPI specification. Colours at the bottom of each panel represent time point and—where applicable—E3.5 and E4.5 Phenograph clusters. Dashed lines represent branch probabilities in commitment towards respective lineages. d, Gene expression patterns of FGF signalling pathway components, and Gata and Sox transcription factor genes. Orange, black and green arrowheads point to high expression in parietal endoderm, visceral endoderm and EPI, respectively. e, Laser-scanning confocal data depicting TCF7L1 expression at E3.5 (top panel) (n = 14) and E4.5 (bottom panel) (n = 11). SOX2 and GATA6 were used as EPI and PrE lineage markers, respectively. f, Gene expression patterns of Tcf7l1 and Nanog depicting similar expression of Tcf7l1 in EPI as Fgf4 (green arrowhead).
Extended Data Fig. 6 Force-directed layouts of single E5.5 cells reveal relationships between EPI, visceral endoderm and ExE lineages.
a, Force-directed layouts of E5.5 data generated after pooling replicates, showing the relationship between EPI, visceral endoderm and ExE lineages. Cells are coloured by cell type. Black arrowheads mark cells that transdifferentiate from EPI to visceral endoderm. b, Plot showing the projection of EPI, visceral endoderm and ExE cells along the first two diffusion components. Distances between lineages were computed using multiscale distances. c, Plots highlighting the extremes of the diffusion components, serving as the boundaries of the phenotypic space for each lineage identity. d, Plots showing the shortest path-step sizes for paths from EPI to visceral endoderm (left) and EPI to ExE (right). e, Gene expression plots of AVE (Cer1 and Dkk1), visceral endoderm (Eomes, Foxa1 and Ttr), and visceral endoderm and EPI (Nodal) markers along EPI, and PrE and visceral endoderm lineages from E3.5–E5.5. Cells coloured on the basis of marker expression of indicated gene after MAGIC50. f, Laser-scanning confocal images of E5.5 and E6.0 Sox2-creTG/+;Rosa26mT/mG and Ttr-creTG/+;Rosa26mT/mG embryos immunostained for GFP, RFP (red fluorescent protein, membrane-localized tdTomato) and GATA6 (a marker of endoderm identity). Cell nuclei stained with Hoechst, and membranes labelled with RFP. Yellow arrowheads point to cells of EPI origin that are present within the visceral endoderm epithelial layer (n = 10/20 GFP-positive cells in visceral endoderm of Sox2-creTG/+;Rosa26mT/mG embryos; n = 0/27 GFP-positive cells in the EPI of Ttr-creTG/+;Rosa26mT/mG embryos). Results validated in at least three independent experiments. Scale bars, 50 μm (low-magnification images), 20 μm (high-magnification images). g, Laser-scanning confocal images of two E5.5 wild-type 4n embryo <-> H2B-tdTomato embryonic stem cell (ESC) chimaeras. Top two rows and bottom two rows represent low- and high-magnification 2D images, respectively (n = 9/19 embryo chimaeras showed tdTomato-positive cells in the visceral endoderm). Yellow arrowheads indicate EPI cells intercalating into the visceral endoderm layer. Embryos are counterstained with Hoechst to label nuclei, and phalloidin to label F-actin. Scale bars, 20 μm (low-magnification images), 10 μm (high-magnification images).
a, Plot showing Palantir pseudo-time versus differentiation potential of visceral endoderm cells from stage E3.5 to E8.75. Drops in differential potential occur at two time points. The first occurs at E5.5, as cells acquire a distal versus proximal fate; the second occurs at E7.5, as cells acquire an anterior versus posterior fate. b, Plots of branch probabilities of commitment towards yolk sac endoderm, and anterior and posterior gut endoderm. c, Marker based (top left) and bulk RNA-seq based (top right) prediction of exVE and emVE at E7.5. Bottom, plots show the Pearson’s correlation between bulk RNA-seq replicates of exVE and emVE. d, Plots show differentially expressed genes between exVE (291 genes) and emVE (2,239 genes) derived using bulk RNA-seq data. e, Plots showing the branch probabilities of E7.5, E6.5 and E5.5 exVE and emVE cells to commit towards yolk sac endoderm (extra-embryonic) and gut tube (embryonic). Cells are labelled as exVE and emVE on the basis of expression of known markers (leftmost plot), match expected Palantir branch probabilities (four plots on the right). The branch probabilities of E5.5 cells in committing towards yolk sac endoderm and gut tube were used to infer putative exVE and emVE identities at E5.5. f, Plot showing pseudo-time versus differentiation potential of endoderm cells at E5.5, coloured by the inferred cell type. This panel is a magnified view of a. g, Heat maps of highly expressed genes specifically in exVE or emVE at E5.5 also distinguish exVE and emVE cells at E6.5 and E7.5. h, ISH of E6.25 embryos showing expression of Lhx1 (n = 3) and Lefty1 (n = 3), which are genes that are specific for emVE, and Apln (n = 3) and Msx1 (n = 3), which are genes that are specific for exVE. Scale bars, 50 μm.
a, Force-directed layout as in Fig. 5. Top, plots show the probabilities of anterior–posterior positioning for the AFP–GFP-positive and AFP–GFP-negative cells inferred using the manifold classifier trained on anterior–posterior cells. Bottom, plots show the probabilities of GFP-positive and GFP-negative status for the cells from the anterior–posterior compartment, inferred using the manifold classifier trained on GFP-positive and GFP-negative cells. b, Top, anterior and posterior cells labelled by measured data (leftmost two columns). Anterior and posterior positions of AFP–GFP-positive and AFP–GFP-negative cells inferred (rightmost two columns) using probabilities in a (top panels). Bottom, GFP-positive and GFP-negative cells labelled by measured data (leftmost two panels). GFP-positive and GFP-negative status of the anterior–posterior compartment cells inferred (rightmost two panels) using probabilities in a (bottom panels). c, Top left, plot showing the first diffusion component of the E8.75 cells. Top middle, top right, plots showing the expression of anterior marker Nkx2.1 and posterior marker Hoxb9 in E8.75 cells. Bottom, bulk RNA-seq expression of Nkx2-1 and Hoxb9 in quadrants of the gut tube along the anterior–posterior axis compares with anterior–posterior single-cell expression patterns. d, Plot showing the proportion of anterior and posterior cells in bins along the anterior–posterior pseudo-space. e, Heat map showing Pearson’s correlations between anterior–posterior pseudo-space orderings, determined using a varying number of diffusion components and highlighting the robustness of the ordering. f, Plots comparing the anterior–posterior pseudo-space ordering of GFP-positive and GFP-negative cells (replicate 2, 13,335 cells) generated de novo using only the replicate 2 cells (x axis, left) with the projected ordering from replicate 1 (8,143 cells) (y axis). Right, similar comparison with the pseudo-space ordering determined using cells of both the replicates on the x axis. g, Same as f, for replicates of anterior–posterior cells (replicate 1, 1,821 cells; replicate 2, 1,691 cells). Plots show the Pearson’s correlation. h, ROC for classification of E7.5 visceral and definitive endoderm cells (4,378 cells). i, Plots showing the expression patterns of genes that are best predictive of the definitive endoderm class in the visceral and definitive endoderm classifier (top, definitive endoderm; bottom, visceral endoderm). j, Plots showing the expression patterns of genes in the definitive endoderm that are best predictive of visceral endoderm class in the visceral and definitive endoderm classifier. k, Force-directed layouts following Harmony of E7.5 and E8.75 visceral and definitive endoderm cells, with E7.5 cells coloured in red (definitive endoderm) and blue (visceral endoderm) (left). E7.5 visceral and definitive endoderm cells coloured by the branch probability of anterior localization (middle) and posterior localization (right). Black arrowheads indicate early emergence of anterior–posterior spatial patterning at E7.5, with E7.5 definitive endoderm cells predominantly destined towards the anterior, and visceral endoderm cells predominantly destined towards the posterior. l, Three-dimensional renderings of gut tube, depicting all endoderm cells along the anterior–posterior axis. Nuclei of visceral and definitive endoderm cells are labelled in green and grey, respectively. m, Plots comparing the ranks of proportion of GFP-positive cells along anterior–posterior positioning in the AFP–GFP-embryo-derived Neurolucida reconstructed gut tube replicates (x axis), and the ranks of visceral endoderm cell proportions in bins along the anterior–posterior pseudo-space axis (y axis); the anterior–posterior axis was partitioned into 20 bins; each dot represents the fraction of visceral endoderm cells in that bin.
a, Plots showing individual Phenograph cluster densities of the E8.75 gut tube cells ordered along the anterior–posterior pseudo-space (left) and in force-directed layouts (middle). ISH of representative differentially expressed genes in each cluster on whole E8.75 embryos (n > 3 for each gene) or micro-dissected E8.75 gut tubes (n > 3 for each gene) (right). Arrowheads point to expression of representative gene for each particular cluster. Scale bars, 200 μm (except for Nkx2-1, 100 μm). no, notochord. b, Density of E8.75 cells along the anterior–posterior pseudo-space axis. c, Comparison of empirical anterior–posterior pseudo-space axis and the predicted anterior–posterior pseudo-space, using expression of transcription factors. Each dot represents the anterior–posterior pseudo-space of a cell computed by all genes, versus pseudo-space prediction by the selected transcription factors alone. d, Plot showing the ranking of different transcription factors according to their predictive power, on the basis of the regression model. e, Heat map showing the coefficients for the top transcription factors when different proportions of cells are subsampled for the regression (total cells, 24,990). f, Heat map showing the Pearson’s correlation of transcription factor coefficients in e, highlighting the robustness of transcription factor coefficients in regression.
a, Force-directed layouts showing Hox genes expressed in gut endoderm cells at E8.75. b, Whole-mount mRNA ISH on whole E8.75 embryos (n > 3 for each gene) and micro-dissected gut tubes (n > 3 for each gene) of Hox genes, depicting the distribution of Hox genes along the anterior–posterior axis. Scale bars, 100 μm (Hoxc10 and Hoxd11), 200 μm (all other panels).
Force-directed layouts of context-independent targets of key signalling pathways acting within the endoderm lineage of the embryo. Fibroblast growth factor (FGF); WNT; bone morphogenic protein (BMP); NOTCH; Hedgehog (HH); NODAL and TGFβ signalling (NODAL); JAK and STAT; retinoic acid; and HIPPO.
This file contains Supplementary Notes, Supplementary Figures 6-11, and Supplementary References.
List of differentially expressed genes for E3.5 clusters.
List of differentially expressed genes for E4.5 clusters.
List of clusters of genes with similar dynamics in EPI and VE differentiation from E3.5–E5.5.
List of genes that are exVE and emVE specific at E5.5.
List of genes that are most predictive of VE and DE classes in the E7.5 classifier.
List of differentially expressed genes in the E8.75 gut tube clusters.
mRNA in situ hybridization probes.
mRNA in situ hybridization probes cloned from E8.5 embryo RNA by RT–PCR.
Numbers of GFP+ and GFP− cells in gut tubes of 13ss embryos.
List of bulk RNA-seq samples.
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Nowotschin, S., Setty, M., Kuo, YY. et al. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature 569, 361–367 (2019). https://doi.org/10.1038/s41586-019-1127-1
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