The mesodermal precursor populations for different internal organ systems are specified during gastrulation by the combined activity of extracellular signaling systems such as BMP, Wnt, Nodal and FGF. The BMP, Wnt and Nodal signaling requirements for the differentiation of specific mesoderm subtypes in mammals have been mapped in detail, but how FGF shapes mesodermal cell type diversity is not precisely known. It is also not clear how FGF signaling integrates with the activity of other signaling systems involved in mesoderm differentiation. Here, we address these questions by analyzing the effects of targeted signaling manipulations in differentiating stem cell populations at single-cell resolution. We identify opposing functions of BMP and FGF, and map FGF-dependent and -independent mesodermal lineages. Stimulation with exogenous FGF boosts the expression of endogenous Fgf genes while repressing Bmp ligand genes. This positive autoregulation of FGF signaling, coupled with the repression of BMP signaling, may contribute to the specification of reproducible and coherent cohorts of cells with the same identity via a community effect, both in the embryo and in synthetic embryo-like systems.
During the development of multicellular organisms, precursor cells for specialized organs need to differentiate in reproducible proportions and in spatially coherent domains. Although the main cell-cell communication systems that drive and coordinate differentiation in cell populations have been identified, how these systems interact in signaling networks is still not well understood.
The mesoderm encompasses precursor cells for important organ systems, such as the blood, vasculature, heart, kidney, limbs and the musculoskeletal system. In the mouse, the mesodermal precursor cells for these organ systems are specified during gastrulation from the pluripotent epiblast, a cup-shaped epithelium that is surrounded by extraembryonic tissues such as the extraembryonic ectoderm (ExE) and the visceral endoderm (Fig. 1A). Precursors of different mesoderm subtypes are found in distinct regions of the pre-gastrulation epiblast (Tam and Behringer, 1997) and traverse the primitive streak, the site of gastrulation, at different times. Cells from the proximal epiblast close to the ExE, for example, gastrulate first and will preferentially give rise to extraembryonic mesoderm and blood, followed by precursors of the heart and head mesoderm. In contrast, the paraxial and axial mesoderm, which encompasses precursor cells of the musculoskeletal system, arises later in more distal parts of the embryo (Fig. 1A; Ferretti and Hadjantonakis, 2019). Cell differentiation in the epiblast is thought to be governed by a system of signaling gradients, mainly generated by the extraembryonic cells that surround the epiblast (Arnold and Robertson, 2009; Tam and Loebel, 2007). The ExE, for example, expresses BMP4 ligands that establish a proximal-to-distal phosphorylation gradient of the BMP signal transducer SMAD1/5 (Arnold and Robertson, 2009; Morgani et al., 2018a; Winnier et al., 1995; Zhang et al., 2019). BMP signaling promotes the differentiation of proximal, but not distal, mesoderm subtypes from pluripotent stem cells (Manfrin et al., 2019; Morgani et al., 2018a), consistent with a proposed role for the BMP signaling gradient in mesoderm patterning in the embryo (Tam and Loebel, 2007). The BMP signaling gradient is complemented by gradients of Wnt and Nodal signaling that promote the differentiation of more distal mesoderm and endoderm, respectively. The Wnt and Nodal gradients are established through a combination of localized ligand expression on the posterior side of the embryo, together with the secretion of signaling antagonists from the anterior visceral endoderm (AVE) at the embryo's anterior side (Arnold and Robertson, 2009; Stower and Srinivas, 2018).
In addition to these three graded signaling systems, cell differentiation and patterning in the mesoderm critically relies on fibroblast growth factor (FGF) signaling. FGF signaling is most active in the primitive streak and the nascent mesoderm (Morgani et al., 2018b), mirroring the expression of Fgf8, Fgf4, Fgf3 and Fgf17 ligand genes in this region (Crossley and Martin, 1995; Maruoka et al., 1998; Niswander and Martin, 1992). Loss of FGF signaling in Fgfr1- and Fgf8-mutant embryos impairs cell migration and leads to the accumulation of cells in the primitive streak (Ciruna and Rossant, 2001; Deng et al., 1994; Sun et al., 1999; Yamaguchi et al., 1994). Furthermore, although some proximal mesoderm forms in these mutants, the differentiation of more distal cell types, such as the paraxial mesoderm, is defective (Ciruna and Rossant, 2001; Deng et al., 1994; Sun et al., 1999; Yamaguchi et al., 1994). In human embryonic stem cells in contrast, FGF signaling has been proposed to be generally required for efficient mesoderm differentiation in cooperation with BMP signaling, since BMP4 treatment in the absence of FGF leads to the differentiation of extraembryonic cell types (Bernardo et al., 2011; Yu et al., 2011). It is thus an open question as to exactly which mesodermal cell types are FGF dependent, how FGF signaling is integrated with other signals present during mesoderm differentiation, and how this integration contributes to mesoderm patterning.
In recent years, in vitro models based on pluripotent cell populations that represent the epiblast have emerged as powerful tools to investigate the signaling control of cell differentiation and patterning during gastrulation. These systems allow the testing of the influence of individual factors in the absence of the complex environment of the embryo and its extraembryonic cell types (Morgani and Hadjantonakis, 2020). Even upon homogeneous external signaling cues, both 2D micropattern and 3D aggregate systems generate and spatially arrange diverse cell types that are normally found in the gastrulating embryo (Baillie-Benson et al., 2020; van den Brink et al., 2020; Morgani et al., 2018a). These observations suggest that, in addition to the signaling landscapes imposed by extraembryonic tissues, there exist epiblast-intrinsic mechanisms that orchestrate mesoderm differentiation and patterning.
Here, we use a simple 2D epiblast stem cell (EpiSC) differentiation protocol to map the concentration-dependent functions of BMP and FGF signaling during mesoderm differentiation. Through single-cell sequencing and integration with published transcriptome datasets from the embryo, we show that FGF dose affects differentiation speed, and that it sets the proportions of discrete cell types in heterogeneous populations. The dose-dependent expression patterns of signaling genes suggest that FGF signaling is embedded in a positive autoregulatory loop, coupled with the repression of BMP signaling. This regulatory logic could establish an FGF-based community effect during mammalian mesoderm differentiation that contributes to generating spatially coherent groups of distal mesoderm cells that are spatially segregated from BMP-dependent proximal cell types.
Opposing functions of BMP and FGF signaling during EpiSC differentiation
To obtain a homogeneous starting population for mesoderm differentiation, we cultured EpiSCs in N2B27 medium containing ActivinA, FGF2 and the Wnt signaling inhibitor XAV939, which suppresses Wnt-induced cellular heterogeneity (FAX medium; Sumi et al., 2013; Tsakiridis et al., 2014). Under these culture conditions, cells were stained homogeneously positive for NANOG, but were negative for the pan-mesodermal marker T/BRA (Fig. 1B). We triggered general mesoderm differentiation following previously published protocols by exchanging ActivinA and XAV939 for 1 µM of the Wnt agonist Chir99021 (Chi) (Chal et al., 2015; Loh et al., 2016; Sudheer et al., 2016), and varied the concentration of BMP4 in this setting (Vallier et al., 2009). After 3 days of differentiation, both T/BRA and TBX6, a marker for distal mesodermal cell types, were expressed in the absence and presence of 10 ng/ml BMP4. In contrast, expression of HAND1, a marker for more proximal mesoderm, was observed only in BMP4-treated cultures (Fig. 1B). To determine more precisely how BMP4 concentration affects cell differentiation, we used quantitative PCR (qPCR) of a panel of marker genes that show regionalized expression in the gastrulating embryo (Peng, 2016). Expression of Hand1 and Gata6, which are expressed in the posterior proximal part of the embryo, was highest at 16 and 32 ng/ml BMP4 (Fig. 1C, top). The expression of Tbx6 and Msgn1, which are expressed more distally in the embryo, peaked at ∼4 ng/ml BMP4, while the most distal markers Foxa2 and Shh peaked at even lower BMP4 concentrations (Fig. 1C). Expression of the pan-mesodermal marker T/Bra paralleled the expression of Tbx6 and peaked at 4 ng/ml BMP4. To corroborate these findings at the protein level, we performed an In-Cell Western assay in which we further increased the concentration range of BMP4 (Fig. 1D,E). Even though the high cell densities required in an In-Cell Western could potentially impair the activity of BMP4 (Etoc et al., 2016), this experiment broadly confirmed the qPCR results. HAND1 expression increased for BMP4 concentrations higher than 8 ng/ml and peaked at 64 ng/ml BMP, whereas GATA6 expression increased across the whole BMP4 concentration range up to 16 ng/ml and then declined (Fig. 1D,E). Taken together, these results show that BMP promotes the expression of more proximal mesoderm markers, Hand1 and Gata6, while inhibiting markers of more distal fates, in a concentration-dependent manner. This is consistent with the proposed role of graded BMP signaling for cell type specification in the embryo.
Next, we set out to determine the concentration-dependent effects of FGF signaling in the mesoderm differentiation protocol. We tested titration series of FGF2 and FGF4 in the presence of 1 µM Chi and 8 ng/ml BMP, because previous experiments indicated that this BMP concentration gave a mixture of cell types. We first focused on T/Bra expression, using the T/Bra:mCherry signal in the dual Sox1/Brachyury reporter (SBR) cell line as a read-out (Deluz et al., 2016). For both FGF2 and FGF4, reporter expression levels increased up to a concentration of 12 ng/ml, the standard FGF2 concentration in EpiSC maintenance medium (Brons et al., 2007). While for FGF4, reporter expression further increased to a maximum of 77.9±4.6% T/Bra:mCherry-positive cells (mean±s.e.m., n=3 independent experiments) at a concentration of 96 ng/ml FGF4, for FGF2, the proportion of reporter-positive cells decreased for concentrations above 12 ng/ml (Fig. 2A,B). This biphasic effect of FGF2 is consistent with previous reports showing that high concentrations of FGF2 lead to reduced FGF/ERK signal transduction (Gharibi et al., 2020 preprint; Kanodia et al., 2014). However, because FGF2 is commonly used in published mesoderm differentiation protocols, and because FGF2 and FGF4 showed similar behavior at concentrations up to 12 ng/ml, we focused on FGF2 for subsequent experiments. To determine how different FGF2 concentrations in this low range affect the expression of a range of mesodermal markers, we performed quantitative immunofluorescence. Consistent with results from the reporter cell line, the proportion of T/BRA-positive cells increased with FGF2 concentration, whereas the downregulation of NANOG expression occurred independently of FGF2 (Fig. 2C). The proportion of HAND1-positive cells was highest in the absence of FGF2, and decreased with increasing FGF2 dose (Fig. 2D), in contrast to TBX6 expression, which was detected only at FGF2 concentrations above 1.5 ng/ml (Fig. 2E). CDX2, a protein that is expressed both in the proximal extraembryonic mesoderm as well as in the paraxial mesoderm in the embryo (Beck et al., 1995; Morgani et al., 2018a), was expressed in the absence of FGF2, and its expression slightly increased at higher concentrations (Fig. 2D). The observation that FGF promotes the expression of the more distal mesoderm marker TBX6, and inhibits the expression of the more proximal marker HAND1 in a concentration-dependent manner, suggests that FGF signaling opposes the function of BMP in the mesoderm differentiation protocol used here.
FGF dose sets the proportions of cells with different transcriptional states
In vitro differentiation protocols often yield heterogeneous mixtures of cell types (Jang et al., 2017), and the immunostaining analysis above indicated this was also the case in the mesoderm differentiation protocol applied here. To characterize this heterogeneity, and to determine how it changed depending on FGF concentration, we performed single-cell RNA sequencing (scRNAseq) of cells treated with different FGF2 doses from 0 to 12.0 ng/ml (Fig. 3A). In addition, we analyzed cells treated with the selective FGFR inhibitor AZD4547 (FGFRi) during differentiation, to assess possible effects of autocrine and paracrine FGF signaling. We observed massive cell death at micromolar FGFRi concentrations that were previously used in the embryo (Morgani et al., 2018b), and significant cell survival was only obtained at FGFRi concentrations ≤31.3 nM (Fig. S1A,B). This prompted us to use a concentration of 30 nM AZD4547 in the scRNAseq experiment. Even though this FGFRi concentration only led to a mild decrease in ERK phosphorylation, a major target of FGF signaling (Fig. S1C), it strongly changed the transcriptional state of the cells as we show below. After quality filtering, we retained ∼2000 single-cell transcriptomes per condition (Table S1).
A correlation matrix of pseudo-bulk transcriptomes showed that the samples with a similar FGF signaling strength had the most correlated transcriptomes (Fig. 3B). When comparing neighboring concentrations in the series, the biggest pseudo-bulk transcriptomic difference was observed between 0 ng/ml and 0.75 ng/ml FGF2, such that FGF-treated samples clustered away from the non-treated and the FGFRi samples. Analysis of the marker genes T/Bra, Tbx6, Hand1 and Cdx2 confirmed that their concentration-dependent expression that we had detected at the protein level was recapitulated at the transcriptional level in the scRNAseq dataset (Fig. 3C). We also analyzed the expression of Cdh1 (E-cadherin) and Cdh2 (N-cadherin) because cells switch from Cdh1 to Cdh2 expression as they undergo an epithelial-to-mesenchymal (EMT) transition during gastrulation. Cdh1- and Cdh2-expressing cells were found in all conditions, but the fraction of the former decreased, whereas the fraction of the latter increased, with exogenous FGF2. Thus, FGF tunes the proportion of cells undergoing EMT in a dose-dependent manner.
To characterize in more detail how the composition of cell types in the population changed with FGF signaling levels, we visualized the entire dataset as a dimensionality-reduced uniform manifold approximation and projection (UMAP) plot (McInnes et al., 2018 preprint) and identified transcriptionally similar cells by Leiden clustering (Fig. 3D-F; Table S2). FGFRi-treated cells showed little overlap with cells from the other conditions in the UMAP plot, and were mostly found in clusters 5 and 6. This clear separation of the FGFRi condition from all other samples, including the one without addition of exogenous FGF2, indicates that autocrine and paracrine FGF signaling affects gene transcription and cell differentiation. Cells from the other four conditions each occupied different parts of the UMAP plot, and preferentially populated one or two of the 8 clusters (Fig. 3D-F; Fig. S2). However, for each condition, a fraction of cells mapped outside the condition's main clusters. Since each of the clusters is characterized by the expression of a specific set of marker genes (Fig. 3G; Fig. S3), these observations suggest that, rather than inducing discrete cell states, FGF2 dose shapes the proportions of cells adopting specific states during mesoderm differentiation.
High exogenous FGF doses promote less advanced cell types
In order to identify the developmental stages and identities of cells differentiated in vitro, and to determine how these changed with exogenous FGF dose, we performed asymmetric dataset integration with the ingest function in Scanpy (Wolf et al., 2018). We used a fully annotated scRNAseq dataset that profiled cells from whole mouse embryos between embryonic day (E)6.5 and E8.5 collected at 6 h intervals (Pijuan-Sala et al., 2019), and transferred annotations of developmental stage and cell identity. Projection onto a UMAP representation of the embryo reference data suggested that the heterogeneous cell types differentiated in vitro corresponded to a wide range of cell types and stages present in the embryo (Fig. 4A). Furthermore, cells treated with different doses of FGF2 mapped to different parts of the UMAP plot, indicating that FGF levels affect the representation of cell types and stages (Fig. 4B).
We first analyzed annotations of developmental stages transferred from the embryo reference dataset (Pijuan-Sala et al., 2019; Fig. 4C-E′). Depicting stage labels in the UMAP plot suggested that the in vitro differentiated cells represent a range of developmental stages (Fig. 4D). However, when we plotted the proportion of cells with a specific stage label at different FGF doses as a heatmap, we found that, for all FGF stimulation regimes, the most prominent stage label corresponded to the E8.0 embryo, followed in most of the conditions by the immediately neighboring stages E7.75 and E8.25 (Fig. 4E). Given that we started from a cell population that is similar to the preimplantation epiblast at ∼E5.0 and then differentiated cells for 3 days, this observation indicates that differentiation in vitro occurs at a similar pace as in the embryo. However, we also noticed that the FGFRi condition stood out with a large proportion of cells labeled as corresponding to E8.5, while the condition with highest exogenous FGF contained the largest fraction of cells with the stage labels E7.5, E7.25 and E7.0. This shift of stage labels along the FGF axis suggests that high FGF signaling levels can delay differentiation. Consistently, when we analyzed how stage labels were distributed in the clusters determined in Fig. 3E and F, we noticed that clusters 6 and 5 had an overrepresentation of cells with more advanced stage labels, whereas clusters 3 and 4 were enriched for cells that were classified as less advanced (Fig. 4E′).
To corroborate these findings, we repeated the integration with a second, independent embryo dataset that covered a similar developmental range at lower time resolution (Grosswendt et al., 2020; Fig. S4). Again, the in vitro differentiated cells occupied different areas in the UMAP space when projected on the reference data, in an FGF-dependent manner (Fig. S4A-C). Analysis of the stage labels transferred from this alternative embryo dataset revealed that the large majority of in vitro differentiated cells were classified as corresponding to E7.5 and E8.0 cells, further supporting a similar differentiation pace in vitro and in the embryo. Finally, we found a shift in the proportion of stage labels from more advanced stages in the FGFRi condition to earlier stages in conditions that were treated with high FGF doses (Fig. S4E,F). Taken together, these analyses indicate that heterogeneous cell types emerge during in vitro differentiation with a pace that matches differentiation pace in the embryo, and that exogenous FGF can promote the maintenance of less advanced cell types.
Dataset integration identifies FGF-dependent and FGF-independent cell types
Next, to better understand which cell types differentiated in vitro at different FGF signaling strengths, we analyzed the cell type information obtained by dataset integration. A UMAP representation of the cell type labels transferred from the embryo reference dataset by Pijuan-Sala et al. (2019) suggested that a broad range of cell types were obtained in vitro (Fig. 4F,G). We produced heatmaps to visualize the proportion of cells that were assigned a specific cell type label depending on FGF signaling strength (Fig. 4H), or as a function of the cluster assignments from Fig. 3E and F (Fig. 4H′). We only plotted cell types that contained at least 4% of cells in at least one signaling condition or cluster. To facilitate interpretation of the results, we ordered cell types in the heatmaps, starting with epiblast-related cell types at the top, followed by mesoderm-, gut- and ectoderm-related cell types. This analysis showed that the composition of cell types changed strongly along the FGF concentration series, indicating that the FGF doses used in our experiment reflect physiologically relevant concentrations. Focusing on specific cell type labels, we found that epiblast-related cell types were absent in the FGFRi condition, and appeared at low-to-intermediate FGF concentrations (Fig. 4H). This suggests that the maintenance of these pluripotent and developmentally less advanced cell types is promoted by exogenous FGF signals. Inspection of the mesodermal subtypes in the heatmap revealed two broad groups. The first contained cells labeled as ‘nascent’, ‘mixed’, ‘somitic’ and ‘intermediate mesoderm’. These cell types were absent in the FGFRi condition, and their proportion increased with FGF2 dose. The second group contained cells labeled as ‘paraxial’ and ‘pharyngeal mesoderm’, ‘hematoendothelial progenitors’ and ‘mesenchyme’, which were found in the FGFRi condition and, to a smaller degree, in the condition without exogenous FGF, but were nearly absent at FGF2 concentrations ≥0.75 ng/ml (Fig. 4H). This suggests that there exist FGF-dependent and FGF-independent mesodermal subtypes. Finally, we observed that ectodermal cell types, in particular cells labeled as ‘surface ectoderm’, were most prominently obtained in the FGFRi condition as well as in conditions with low exogenous FGF signaling. This observation suggests that a threshold level of FGF signaling is required for converting the previously reported function of BMP4 to induce surface ectoderm differentiation (Li et al., 2013; Malaguti et al., 2013) towards the induction of other lineages. When we plotted the relationship between cell type labels and clusters identified in Fig. 3E, we found that some labels were strongly enriched in specific clusters, such as ‘intermediate mesoderm’ in cluster 1, or ‘surface ectoderm’ in clusters 2 and 5 (Fig. 4H′). In most cases, however, clusters contained several related cell type labels, indicating that cluster boundaries in the in vitro dataset do not align with cell type boundaries in the reference dataset. A possible explanation for this lack of alignment is that cells both in our in vitro differentiation protocol as well as in the embryo have not yet reached defined, distinct identities at the time of analysis, but rather represent a continuum of transition states.
To substantiate the associations of specific cell types with FGF signaling levels, we analyzed cell type labels that were transferred from the independent embryo dataset by Grosswendt et al. (2020) (Fig. S4G-I′). Here, few cells labeled as epiblast were detected, in contrast to integration with the dataset from Pijuan-Sala et al. (2019). The proportions of cells with labels corresponding to the primitive streak, different types of mesoderm and ectoderm, were broadly consistent between integration with the two datasets. Most notably, we again found two groups of mesodermal labels: one group containing the labels ‘secondary heart field/splanchnic lateral plate’, ‘amnion mesoderm early’, ‘pharyngeal arch mesoderm’ and ‘hematopoietic/endothelial progenitor’ was populated in the FGFRi and the 0 ng/ml condition, whereas the second group containing the labels ‘primitive streak late’, ‘presomitic mesoderm’, ‘NMPs early’ and ‘posterior lateral plate mesoderm’ was absent in the FGFRi conditions and increased with FGF2 dose. We notice that the FGF-independent mesoderm cell types identified in vitro are the ones that differentiate first in proximal parts, whereas the mesodermal cell types that we identify as FGF-dependent emerge later and in more distal parts of the gastrula. Thus, this systematic mapping of FGF-independent and -dependent cell types suggests a potential role for FGF signaling in the spatio-temporal patterning of the mesoderm.
Finally, we analyzed Hox gene expression in different FGF signaling conditions and clusters as a molecular read-out for developmental progression along the body axis (Fig. S5). All Hox genes were lowly expressed in the FGFRi condition, and their overall expression increased with FGF signaling levels (Fig. S5A). Across all conditions, Hox9 paralogs were the posteriormost genes that showed strong expression (Fig. S5A). In the embryo, Hox9 paralogs are expressed at E8.0, whereas more posterior Hox genes are only activated thereafter (Deschamps and van Nes, 2005; Noordermeer et al., 2014). The Hox gene expression pattern therefore supports the developmental stage of the in vitro differentiated cells inferred from label transfer. FGF dose had only minor effects on Hox gene expression patterns. Along the FGF concentration series, expression of Hoxd9 peaked at the highest FGF concentration of 12 ng/ml, whereas expression of the more anterior Hoxa7 and Hoxd4 reached their highest levels already at FGF concentrations of 0.75 and 3 ng/ml, respectively (Fig. S5A). Consistently, Hoxd9 expression dominated in cluster 1, which was associated with highest FGF signaling levels, whereas Hoxa7 expression dominated in clusters 0 and 2, which were preferentially populated at lower FGF doses (Fig. 3E; Fig. S5B). These differences support the notion that strong FGF signaling biases cells to more posterior identities.
Differentiating cell populations generate divergent endogenous signaling patterns
Having determined how FGF signaling levels affect the proportions of cell types, we next sought to use our data to explore why heterogeneous cell types differentiated within each condition. To test whether this heterogeneity was caused by differential FGF signaling between cell types, we analyzed the expression of the FGF target genes Spry4, Dusp4, Dusp6 and Etv4 (Kalkan et al., 2019; Morgani et al., 2018b; Fig. 5A-C). Expression of these genes was variable across all cells (Fig. 5A) and, as expected, increased with exogenous FGF2 dose (Fig. 5B). FGF target gene expression also differed strongly between clusters (Fig. 5C). This variability of target gene expression between clusters also held true when analyzed separately for each of the FGF stimulation regimes (Fig. 5D). Thus, even upon homogeneous exogenous FGF stimulation, cells show heterogeneous signaling responses. This was not due to differential receptor expression, as the main FGF receptor Fgfr1 was expressed more evenly than the FGF target genes, both across treatment regimes and clusters, and other Fgfr genes were not strongly expressed (Fig. 5B-D). However, when we extended our analysis to the expression of endogenous FGFs, we found that expression of Fgf3, Fgf4, Fgf8 and Fgf17 clearly increased with exogenous FGF dose (Fig. 5B). Furthermore, their expression was mainly associated with clusters 0, 1 and 4, when analyzing the complete dataset (Fig. 5C) as well as within each stimulation regime (Fig. 5D). This indicates that exogenous FGF2 stimulation triggers endogenous FGF signaling. It also raises the possibility that a positive feedback based on short-range endogenous FGF signaling amplifies initial heterogeneities locally and eventually leads to the observed cell type heterogeneity.
Next, we asked how exogenous FGF affected the expression of Bmp, Wnt and Nodal signaling genes. Similar to the pattern of Fgfr1 expression, Bmpr1a was expressed to a similar degree in all experimental conditions and clusters (Fig. 5A-C). The expression of several Bmp ligand genes such as Bmp1, Bmp2 and Bmp4, in contrast, decreased with increasing exogenous FGF, and was strongest in clusters 6, 3 and 2 (Fig. 5A-C). Bmpr2 expression also decreased with FGF signaling levels, but its expression was not as cluster specific as that of the Bmp ligand genes (Fig. 5B,C). The expression of core Bmp signaling genes is thus anticorrelated with that of Fgf ligand genes. This mutually exclusive expression of Bmp and Fgf ligand genes by individual cells could act to locally separate their opposing activities in promoting more proximal and more distal fates, respectively.
Finally, we extended our analysis to components of the Wnt and Activin/Nodal signaling systems and found a similar pattern of broad, FGF-independent receptor expression and FGF-regulated, cluster-specific ligand expression (Fig. S6). The Wnt receptor gene Lrp6, for example, was homogeneously expressed in all clusters and conditions, in contrast to several Wnt ligand genes: Wnt3, Wnt4 and Wnt6 expression decreased with exogenous FGF dose, whereas Wnt3a, Wnt5a and Wnt5b expression increased with FGF2 dose. Expression of Nodal signaling genes was overall low, and expressing cells were predominantly found in clusters 3 and 4. These clusters contain many less differentiated cells (Fig. 4E′,H′), consistent with Nodal's role in maintaining pluripotency (Guzman-Ayala et al., 2004; Mesnard et al., 2006). Taken together, our analysis of the expression of signaling genes suggests that populations of differentiating cells can autonomously generate distinct signaling environments.
Pulsed FGF exposure is sufficient to maintain FGF signaling and T/Bra expression
Finally, we set out to probe the existence of an intercellular positive FGF feedback loop in cell communities, and its role in cell differentiation. We treated cells with FGF2 for one or two consecutive days at different times during the full 3-day differentiation protocol (Fig. 6A), and first used the expression of the T/Bra:mCherry reporter in the SBR line (Deluz et al., 2016) as a read-out of FGF-dependent differentiation. Only few reporter-positive cells were detected when cells were exposed to exogenous FGF2 during day 1 or day 3, but FGF treatment during day 2 strongly increased the proportion of reporter-positive cells, to levels similar to those obtained for 2-day and continuous treatment regimes (Fig. 6B,C). This indicates that cells become competent to respond to FGF treatment with upregulation of T/Bra only on day 2. This behavior extended to CDX2 and HAND1 expression, which was broadly similar following pulsed FGF treatment on day 2 and continuous FGF stimulation (Fig. 6D,E). Although we cannot rule out that the timing of FGF exposure could result in more subtle changes to differentiation trajectories that are not reflected in the expression of a small set of markers, these results suggest that continuous FGF stimulation and pulsed treatment on day 2 lead to the differentiation of cells with a similar identity.
The strong similarity of T/Bra reporter expression in cultures treated for day 2 only compared to cultures treated on both day 2 and day 3 could mean that FGF signaling is only required for a short period of time, or that FGF signaling maintains itself in the population via a positive feedback triggered by pulsed FGF stimulation at day 2. We tested this possibility by pulsed FGF treatment of a Spry4:H2B-Venus reporter cell line that is a faithful transcriptional read-out for FGF signaling (Morgani et al., 2018b). Expression levels of this reporter were similar in EpiSCs growing in the FGF2-containing FAX medium, and in cells grown for 3 days in FGF-containing differentiation medium (Fig. 6D,E). Reporter expression was strongly reduced in differentiating cultures that were not treated with FGF, and those expression levels could not be further reduced by FGFRi treatment (Fig. 6E). Upon pulsed FGF treatment on day 2 (0-F-0 condition), reporter fluorescence reached 69.4±2.9% (mean±s.e.m., n=3) and 54.2±7.7% of the value in EpiSCs for FGF2 and FGF4, respectively. However, when the FGF pulse on day 2 was followed by FGFRi treatment on day 3 (0-F-A condition), reporter fluorescence was more than halved compared to the 0-F-0 condition, reaching only 31.8±2.3% and 22.8±1.9% of EpiSC levels for FGF2 and FGF4, respectively (Fig. 6F,G). This suggests that differentiating cultures maintain FGF signaling following pulsed exposure to FGF. Even though we cannot rule out that residual recombinant FGF bound to the extracellular matrix contributes to the ongoing signaling, this observation is consistent with the activity of a positive intercellular FGF feedback loop in stem cell cultures.
Here, we apply single-cell analysis methods to dissect how FGF and BMP signaling interact to regulate cell type diversity during mesoderm differentiation. We delineate opposing functions of these two signaling systems in mesoderm differentiation from EpiSCs, comprehensively identify FGF-dependent and -independent mesodermal cell types, and demonstrate that exogenous FGF dose governs the proportions of cell types. Based on the expression of signaling genes in our single-cell dataset, we propose an intercellular positive feedback loop centered on FGF signaling, which opposes BMP ligand expression, forming a signaling network that may contribute to mesoderm differentiation and patterning.
By integrating our data with high-resolution single-cell transcriptomic maps of embryo development (Grosswendt et al., 2020; Pijuan-Sala et al., 2019), we could characterize the heterogeneous outcome of in vitro differentiation in a more unbiased way and with finer resolution than standard marker-based approaches. Consistent with a previous study that applied a similar approach to scRNAseq data from human embryonic stem cells differentiated on micropatterns (Minn et al., 2020), we find that a wide range of embryonic cell types differentiate in vitro. Working with murine cells, the high time resolution of the reference datasets allows us to exactly pinpoint the developmental stage of the in vitro differentiated cells. This analysis indicates that, at intermediate FGF doses, differentiation pace in vitro is the same as in the embryo, in contrast to results from a previous study by Morgani et al., which suggested that cell differentiation in vitro is slower than in the embryo (Morgani et al., 2018a). Different starting cell populations may be the cause for these discrepancies. Furthermore, our analysis revealed that FGF signaling levels can impact differentiation speed in vitro, with high FGF levels favoring the maintenance of less differentiated cells and low FGF levels leading to more advanced cell types.
Studying mesoderm differentiation with stem cells offers opportunities for long-term signaling manipulation that are not available in the embryo. We exploit these opportunities to identify a cohort of proximal mesodermal subtypes, such as amnion, mesenchyme, haemato-endothelial progenitors and pharyngeal mesoderm, that can differentiate in the absence of FGF signaling, in contrast to mesodermal subtypes, such as lateral plate, intermediate and somitic mesoderm, that differentiate more distally. A proximo-distal transition from FGF-dependent to FGF-independent mesoderm has initially been suggested from the phenotypes of Fgfr1- and Fgf8-mutant embryos (Ciruna et al., 1997; Deng et al., 1994; Sun et al., 1999; Yamaguchi et al., 1994), but the complexity of the embryonic environment, as well as the interplay of cellular migration and differentiation defects, precluded the exact mapping of this transition.
A key result of the present work is that BMP and FGF signaling have opposing functions in the allocation of mesodermal cell fates, mirroring the function of these signals in protocols for the differentiation of neuromesodermal precursor cells (Edri et al., 2019a,b). Consistent with BMP-FGF antagonism, phenotypes arising from loss of BMP signaling can be rescued by inhibition of FGF signaling (Miura, 2006). How then is the antagonism between these two signaling systems mediated molecularly? FGF signaling can inhibit BMP signal transduction via phosphorylation of the linker region in SMAD proteins (Eivers et al., 2008; Pera et al., 2003), and BMP-induced expression of ID proteins can modulate the activity of bHLH factors downstream of FGF (Row et al., 2018). While these previously identified mechanism operate within single cells, our data suggest an additional – not necessarily exclusive – mechanism that operates at the level of local cell communities via the mutual repression of ligand expression. Similar regulatory interactions between BMP and FGF signaling and ligand expression have been found in the mouse tail and the zebrafish gastrula (Anderson et al., 2016; Fürthauer et al., 2004), indicating that this may be a general mechanism.
If BMP signaling promotes the specification of earlier-differentiating proximal cell types, and FGF signaling that of later-differentiating distal cell types, then how can mesoderm patterning switch between these two signaling modes? In the embryo, BMP signaling from the ExE triggers Wnt3 expression, which in turn activates T/Bra expression in the epiblast (Ben-Haim et al., 2006), and T/BRA finally targets the Fgf8 promoter (Evans et al., 2012). Thus, FGF signaling in the embryo appears to be activated through a relay mechanism that could create a window of opportunity in which first those cell types differentiate that are BMP dependent and FGF independent, until FGF signaling has built up sufficiently to specify more distal cell types. In our 2D mesoderm differentiation protocol, the proportion of FGF-dependent cell types is relatively low in the absence of exogenous FGF, in contrast to the situation in 3D aggregates in which FGF-dependent paraxial and axial cell types differentiate efficiently upon pulsed stimulation of Wnt signaling without addition of exogenous FGFs (van den Brink et al., 2014, 2020). A possible explanation for this difference is that 3D environments may be more conducive to endogenous cell-cell signaling than 2D systems, although we cannot rule out that the continued presence of BMP4 in our protocol inhibited the differentiation of FGF-dependent distal cell types.
Both single-cell expression data and timed stimulation experiments suggest that FGF signaling during mesoderm differentiation operates in an intercellular positive feedback loop via the upregulation of ligand genes. This feedback loop could be mediated indirectly via upregulation of Wnt signaling by FGFs (Ciruna and Rossant, 2001; Kelly et al., 2004), followed by the activation of T/Bra expression downstream of Wnt signaling (Arnold et al., 2000; Yamaguchi et al., 1999) and finally activation of Fgf8 expression by T/BRA (Evans et al., 2012). Fgf ligand genes and FGF target genes tend to be expressed in the same restricted groups of cells despite widespread Fgfr1 expression, suggesting that this positive feedback loop operates locally. Spatially restricted, positively autoregulatory cell-cell communication is the basis for the community effect (Bolouri and Davidson, 2010; Saka et al., 2011). The community effect describes a situation in which a critical number of cells in close contact produce and respond to a paracrine signal, ultimately leading this group of cells to differentiate along the same lineage (Gurdon, 1988; Gurdon et al., 1993a,b). The differentiation of muscle progenitors in Xenopus, a cell lineage related to the somitic mesoderm that differentiates at high FGF2 concentrations in our work, has been proposed to rely on a community effect based on eFGF (Gurdon et al., 1993a,b; Standley et al., 2001). We therefore speculate that an analogous FGF-based community effect may orchestrate mesoderm differentiation in mammals.
Populations of pluripotent stem cells have a remarkable potential to self-organize into reproducible patterns of differentiated cell types, both in 2D and 3D (Fu et al., 2021; Liu and Warmflash, 2021; Warmflash et al., 2014). It is clear that these processes must be coordinated by cell-cell interactions through mechanical and chemical signals. Most studies to explain self-organized patterning in stem cell models for development have focused on the intercellular organization of the Wnt, BMP and Nodal signaling systems (Chhabra et al., 2019; Etoc et al., 2016; Tewary et al., 2017). Our findings on how FGF signaling is regulated within populations, and how it interfaces with other signaling systems, call for including these connections in future models for cell differentiation and patterning during mammalian gastrulation.
MATERIALS AND METHODS
EpiSCs were routinely grown in FAX medium on fibronectin-coated tissue culture plastic. Coating was performed with 20 µg/ml human fibronectin (Merck) in PBS for at least 30 min. FAX medium is N2B27 supplemented with 12 ng/ml FGF2 (Cell Guidance Systems), 25 ng/ml ActivinA (Cell Guidance Systems) and 20 µM XAV939 (Cell Guidance Systems). N2B27 was prepared as a 1:1 mixture of Dulbecco's modified Eagle medium/F12 (PAN Biotech) and Neuropan basal medium (PAN Biotech) with 0.0025% bovine serum albumin (BSA; Gibco), 1× N2 and 1× B27 supplements (Thermo Fisher Scientific), 1× GlutaMAX (Gibco), and 50 μM 2-mercaptoethanol. Cells were split with Accutase (Merck) every 2 days or upon reaching confluency, and replated at a density of 8000-10,000 cells/cm2. For mesoderm differentiation, EpiSCs were seeded at a density of 1300-2000 cells/cm2. The day after seeding, medium was changed to N2B27 supplemented with growth factors and small-molecule inhibitors: CHIR99201 (Merck), murine BMP4 (PeproTech), FGF2 (Cell Guidance System), FGF4 (PeproTech) or AZD4547 (Selleckchem). During differentiation, medium was changed daily.
The wild-type EpiSC line used in this study is from a 129 background and was obtained from Dr Jennifer Nichols (Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK). This line has been derived from the epiblast of an E6.5 embryo by culturing on fibronectin-coated dishes in standard EpiSC medium, consisting of 12 ng/ml FGF2 and 25 ng/ml ActivinA in N2B27. The SBR Sox1-T/Bra reporter cell line and the Spry4 H2B-Venus reporter cell line have previously been described (Deluz et al., 2016; Morgani et al., 2018b). Both lines were transitioned from their original culture media to an EpiSC state by culturing for at least eight passages in FAX medium. All cell lines were regularly tested for mycoplasma contamination.
Cells for immunostainings were cultured on eight-chamber µ-slides (ibidi) and processed as previously described (Schröter et al., 2015). Primary antibodies used were anti-NANOG 1:200 (Thermo Fisher Scientific, eBioMLC-51), anti-T/BRA 1:200 (R&D Systems, AF2085), anti-HAND1 1:200 (R&D Systems, AF3168), anti-TBX6 1:200 (R&D Systems, AF4744) and anti-CDX2 1:250 (BioGenex, MU392A-5UC). Secondary antibodies coupled with appropriate AlexaFluor dyes were from Thermo Fisher Scientific and used at 1:500 dilution.
Imaging and image analysis
Live cells were imaged on an Axiovert 40 brightfield microscope equipped with a Leica MC 170 HD camera. Fluorescence imaging was performed on a Leica Sp8 confocal microscope with a 63× oil immersion objective. Bigger fields of view were imaged using the multi-tile settings of the Leica software, with 10% overlap between single tiles, followed by stitching with the BigStitcher plug-in in Fiji (Hörl et al., 2019; Schindelin et al., 2012). Nuclei were segmented with the Fiji plug-in StarDist (Schmidt et al., 2018 preprint), using a custom-trained model. Training data for the model were obtained by first generating masks of Hoechst-stained nuclei with the pre-trained StarDist model ‘Versatile (fluorescent nuclei)’, followed by manual correction. The custom model was then trained on the StarDist 2D ZeroCostDL4Mic platform (v 1.12) (von Chamier et al., 2021) for 40 epochs on seven paired image patches, starting from the pretrained model ‘Versatile (fluorescent nuclei)’. This custom-trained model was used on the Hoechst channel to determine regions of interest (ROIs) corresponding to individual nuclei. To check for correct segmentation, the ROIs were projected onto the nuclear channel, and any segmentation mistakes were manually corrected. The ROIs were then used to retrieve fluorescence intensities for each of the marker channels. On average, eight independent fields of view were analyzed per experiment and condition to capture the spatial variability of the expression of the chosen differentiation markers. The data were further analyzed and plotted with custom scripts written in Python. Thresholds between marker-expressing and non-expressing cells were determined by fitting a two-component Gaussian mixture model to the distribution of fluorescence intensities. Cells were considered marker positive if they belonged to the component with the higher expression levels with a greater than 70% probability, otherwise they were considered marker negative.
Reverse transcription (RT)-qPCR
Cell samples for immunoblotting were washed twice with ice-cold PBS supplemented with 1 mM orthovanadate, and then incubated with lysis buffer. Lysis buffer was prepared fresh by supplementing a commercially available cell lysis buffer (Cell Signaling Technology) with complete EDTA-free protease inhibitor cocktail (Roche), phosphatase inhibitor cocktail 2 and 3 (Sigma-Aldrich), and benzonase (Thermo Fisher Scientific). Cell lysis was aided by scraping, and lysates were collected and snap-frozen in liquid nitrogen. Samples were then analyzed for quality control and quantification with a micro-BCA assay (Thermo Fisher Scientific). Lysates were denatured using a standard Laemelli buffer and boiled for 5 min at 95°C. Samples were then incubated into ice for 5 min, and between 10 µg and 20 μg protein was loaded on a Bis-Tris SDS gel. Gels were run with 1× MOPS buffer (Thermo Fisher Scientific) with fresh sodium bisulphite. After the run, the gels were transferred onto methanol-activated PVDF membranes (Millipore) at 40 V for 90 min with a NuPage transfer system (Thermo Fisher Scientific) and incubated with primary and secondary antibodies for 1 h each. The membranes were imaged with the Odyssey Infrared Imaging System (LI-COR Biosciences). Primary antibodies used were anti-Tubulin 1:5000 (Sigma-Aldrich, T6074), anti-pERK1/2 1:1000 (Cell Signaling Technology, 4370S) and anti-total ERK1/2 1:1000 (Abcam, ab36991). Secondary antibodies used were IRDye-coupled donkey anti-mouse 800 at 1:500 and donkey anti-rabbit 680, both at 1:500 dilution (LI-COR Biosciences).
To obtain the high cell densities required in In-Cell Western experiments, 7000 cells/cm2 were seeded in fibronectin-coated 96-well black plates with transparent polystyrene bottom (Corning). Fixation, blocking and incubation with primary antibodies were performed as described for the immunostaining experiments above. Primary antibodies used were anti-HAND1 1:500 (R&D Systems, AF3168), anti-TBX6 1:500 (R&D Systems, AF4744), anti-GATA6 1:1000 (R&D Systems, AF1700) and anti-Tubulin 1:5000 (Sigma-Aldrich, T6074). Secondary antibodies IRDye800-conjugated donkey anti-goat and IRDye680-conjugated donkey anti-mouse (LI-COR Biosciences) were used at 1:500 dilution and incubated in the dark for 1 h. Fluorescence signals were acquired with an Odyssey Infrared Imaging System (LI-COR Biosciences), and ROIs corresponding to individual wells were selected in the images with the Fiji plugin MicroArray Profile. Signal intensities for proteins of interest were normalized against the anti-Tubulin signal.
Cells for flow cytometry were cultured in six-well plates, detached with Accutase, centrifuged, and either resuspended in PBS supplemented with 1% BSA for immediate analysis or fixed in 4% paraformaldehyde at room temperature for 15 min, followed by centrifuging and resuspending in PBS with 1% BSA. Before analysis, cells were passed through a 40 µm cell strainer to obtain single cells and remove cell clumps. Reporter expression in the SBR line was measured in live cells on a BD FACSAria Fusion flow cytometer (BD Biosciences). Spry4:H2B-Venus reporter cells were fixed before measurement and analyzed on a BD LSRII flow cytometer (BD Biosciences). At least 20,000 cells were analyzed per condition. Data were further analyzed in FlowJo (BD Biosciences).
Single-cell transcriptomes were generated with a 10x Genomics Chromium Next GEM Single Cell 3′ Reagent Kit v3.1 according to the manufacturer's instructions. Briefly, cells were differentiated for 3 days, detached with Accutase and counted. The cell suspension was diluted to a concentration of 900 cells/μl to recover ∼2000 cells per sample. Cell suspensions were loaded on a Chromium Controller (10x Genomics) to partition cells with gel beads in emulsion. Reverse transcription, cDNA recovery and amplification, and sequencing library construction were performed according to the manufacturer's instructions (10x Genomics ChromiumNextGEMSingleCell_v3.1_Rev_D). We chose 12 PCR cycles for cDNA amplification and 10 PCR cycles for index PCR. Concentration and fragment size of sequencing libraries were determined with a BioAnalyzer High Sensitivity DNA Assay (Agilent). Libraries were sequenced by paired-end Illumina sequencing on a NovaSeq6000 instrument with a read length of 150 bp. We first performed sequencing at shallow depth with a target of 20×106 reads per sample, to estimate the number of cells captured in each sample and to confirm the generation of high-quality single-cell transcriptomes. In a subsequent deeper sequencing run, we adjusted sequencing depth for each sample depending on captured cell number, to a depth between 125×106 and 180×106 reads per sample. Analysis was carried out on data from the deep sequencing run only.
Demultiplexing, alignment to the mouse genome mm10 (GENCODE vM23/Ensembl 98, from 10x Genomics) and read quantification were performed with CellRanger (10× Genomics, v4.0.0). The libraries were saved as annotated dataset objects ( ) with extension ‘.h5ad’. All further analyses were carried out with the Python package 1.7.0rc1, (Wolf et al., 2018; https://scanpy.readthedocs.io/en/stable/tutorials.html). Briefly, we first performed quality control and preprocessing on each of the five anndata files retrieved from CellRanger separately, filtering out barcodes that had less than 2500 genes per cell, or more than 10% of unique molecular identifiers (UMIs) mapping to mitochondrial genes. Following concatenation, the resulting dataset was normalized to a value of 50,000 reads per cell and log transformed. We selected for highly variable genes, and regressed out effects of total counts per cell and the percentage of mitochondrial genes expressed. Finally, the data for each gene were scaled to unit variance, and single count values exceeding standard deviation 10 were clipped. Dimensionality reduction was performed by first running a principal component analysis, followed by neighborhood analysis and visualization in two dimensions using the UMAP technique (McInnes et al., 2018 preprint). Leiden clustering (Traag et al., 2019) was performed with a resolution of 0.33. Hierarchical clustering and calculation of Pearson correlation scores of aggregated, pseudo-bulk transcriptomes from each condition were performed with the function ‘scanpy.tl.dendrogram’ in Scanpy, using highly variable genes. Scanpy was also used to produce density plots, and to visualize the expression of specific genes as violin plots or in the UMAP plot. To produce heatmaps, tabular data from Scanpy were plotted in Prism 7 (GraphPad).
Our own single-cell transcriptome data were integrated with two independent published single-cell transcriptome datasets covering the development of the mouse embryo from E6.5 to E8.5. Data from Pijuan-Sala et al. (2019) were retrieved in R with the Bioconductor experimental data package ‘MouseGastrulationData’ available at https://bioconductor.org/packages/release/data/experiment/html/MouseGastrulationData.html. Data from Grosswendt et al. (2020) were obtained from the authors as an R object. Both datasets were translated into a Python anndata object, and processed as described above for our own scRNAseq data. Dataset integration was performed with the ingest function of Scanpy, using exclusively genes that were common between the reference embryo dataset and the in vitro dataset. We used the integration to transfer labels describing developmental stage and cell type identity from the reference dataset.
We thank Jenny Nichols and David Suter for sharing cell lines, Philippe Bastiaens and Ivan Bedzhov for helpful suggestions throughout the project, and Melania Barile and Max Fernkorn for help with scRNAseq analysis. We are grateful to Helene Kretzmer and Stefanie Grosswendt for sharing processed scRNAseq data. We thank members of the Schröter laboratory, David Turner and Melania Barile for comments on the manuscript, and the International Max Planck Research School in Chemical and Molecular Biology for support.
Conceptualization: M.G., C.S.; Methodology: M.G.; Validation: M.G., M.P.; Formal analysis: M.G.; Investigation: M.G., M.P.; Resources: C.S.; Writing - original draft: M.G., C.S.; Writing - review & editing: M.G., M.P., C.S.; Visualization: M.G.; Supervision: C.S.; Project administration: C.S.
This work was funded by the Max Planck Society. Open Access funding provided by the Max Planck Society. Deposited in PMC for immediate release.
The authors declare no competing or financial interests.