Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell types in embryonic stem cells

  1. Dhruv Raina
  2. Azra Bahadori
  3. Angel Stanoev
  4. Michelle Protzek
  5. Aneta Koseska
  6. Christian Schröter

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Abstract

During embryonic development and tissue homeostasis, reproducible proportions of differentiated cell types are specified from populations of multipotent precursor cells. Molecular mechanisms that enable both robust cell-type proportioning despite variable initial conditions in the precursor cells, and the re-establishment of these proportions upon perturbations in a developing tissue remain to be characterized. Here, we report that the differentiation of robust proportions of epiblast-like and primitive endoderm-like cells in mouse embryonic stem cell cultures emerges at the population level through cell-cell communication via a short-range fibroblast growth factor 4 (FGF4) signal. We characterize the molecular and dynamical properties of the communication mechanism and show how it controls both robust cell-type proportioning from a wide range of experimentally controlled initial conditions, as well as the autonomous re-establishment of these proportions following the isolation of one cell type. The generation and maintenance of reproducible proportions of discrete cell types is a new function for FGF signaling that might operate in a range of developing tissues.

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  1. Version published to 10.1242/dev.199926
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    Reply to the reviewers

    Response to Reviewers "Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell types in embryonic stem cells"

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    In this manuscript Raina et al. use an in vitro model of PE specification based on the transient overexpression of GATA4 in ESCs to show that the acquisition of primitive endoderm (PE) identity is governed at the population levels by cell-cell interactions mediated by FGF signaling. The authors further argue that the specification of a defined proportion of "PE" and "Epiblast" cells in a differentiating population of ESC is an emergent property of a system where paracrine signaling shifts the balance between two alternative stable states. Overall, the work does not reach radically new conclusions: broadly similar models are outlined in several other publications, including from the authors. Yet this study makes use of elegant genetic models and is particularly well executed. In addition, it includes a very accurate characterisation of the spatial range of FGF signaling activity that is original and adds on the existing knowledge. Moreover, the authors show novel evidence suggesting that GATA factors inhibits Fgf4 transcription and the activity of the FGF signaling pathway in ESCs.

    We thank the Reviewer for commending the execution of the experiments, and for highlighting the novel insights that they bring. The Reviewer acknowledges that the specification of a defined proportion of PrE-like and Epiblast-like cells in a differentiating population of ESCs is an emergent property which is mediated by paracrine FGF4 signaling. This has not been experimentally demonstrated before. In contrast to the Reviewer’s assertion, we therefore think that our work does reach a conclusion that is radically different from previous experimental studies, a view that is also shared by Reviewer #3 below. In a revised version of the manuscript we will further emphasize the conceptual differences between published models that focus on single cell dynamics, and our experimental and theoretical demonstration of qualitatively different dynamics that emerge at the population level as a consequence of cell fate coupling.

    **Two major points deserve further clarification:**

    In this manuscript the authors claim that the proportions of cells acquiring PE fate is, at least in the experimental setup adopted, largely independent from the levels of GATA4 induction, and therefore of the initial state of the gene regulatory network regulating this cell fate transition. However, the authors should discuss how the current findings relate to their previous results, showing that the duration/levels of Gata4 induction, in a similar experimental setting, play an important role in determining the final proportion of cells cell acquiring "PE" fate. Absolute expression levels may be crucial for this distinction, but the authors seem to exclude this possibility (see figure S3).

    The different roles of GATA4-mCherry induction levels for determining the final proportion of cells acquiring a PrE-like fate reported in our previous (PMID: 26511924) and the current work is because of important differences in the experimental settings between the two studies. In PMID: 26511924, we assayed PrE-like differentiation in medium supplemented with serum and LIF, which provides exogenous signals that promote PrE-like differentiation. These conditions reveal the function of the cell-autonomous circuit, in which GATA4-mCherry levels do control the probability of PrE-like differentiation. In the current work, we likewise observe that cell type proportions depend on GATA4-mCherry induction levels when we supply exogenous FGF4 during the differentiation of wild type cells (Figures S2C and S3D, lower panel). Differentiation in the absence of exogenous factors in contrast reveals the behavior of the coupled system, in which cell type proportions are independent from GATA4-mCherry induction levels.

    Furthermore, in the present manuscript, we use new inducible cell lines in which the majority of cells can be induced above the critical GATA4-mCherry threshold required for PrE-like differentiation, in contrast to our previous study where the distribution of GATA4-mCherry induction levels was straddling this threshold.

    In a revised version of the manuscript, we will more explicitly emphasize these important differences in the experimental design between the two studies, and discuss how the specific conditions in the present study lead to new conclusions.

    Most importantly, the authors incorporate in their model the notion that GATA6 inhibits FGF signaling. It would be interesting to understand how such inhibition is mechanistically mediated. For instance GATA6 has been shown to bind in proximity of the Fgfr2 gene (Wamaitha et al., Genes and Dev., 2015). Alternatively, the authors show a direct effect on Fgf4 expression. The short time window of the reported repressive transcriptional effects (8h, Fig 2 middle), might suggest a direct regulation. The authors should test this possibility, and discuss what alternative modes of regulation could be envisaged (for instance, indirect effects mediated by Nanog). This is a key result that deserves a more detailed mechanistic characterisation.

    The regulation of FGF signaling by GATA factors has been pointed out as a central new result of our study by all three reviewers that we will be happy to further expand on in a revised manuscript. Regulation of Fgfr2 expression by GATA6 as suggested by the ChIP-seq data in Wamaitha et al., 2015 (PMID: 26109048) is one possible mechanistic explanation that we will of course discuss.

    Most importantly, we will test possible direct effects of GATA factors on Fgf4 expression that are indicated by the short timescales of the transcriptional effects shown in Fig. 2, as noted by the Reviewer. We have already mined the ChIP-seq data from Wamaitha et al., 2015 (PMID: 26109048) and found a GATA6-binding peak approximately 10 kb upstream of the Fgf4 start codon in a region that is highly enriched for GATA6 consensus binding sites. To test the functional role of this binding region, we propose to delete it by CRISPR-mediated mutagenesis in the inducible lines, and to test its ability to regulate reporter gene expression in heterologous assays.

    To address the question of alternative modes of regulation of Fgf signaling through NANOG, we have already performed in situ mRNA stainings for Fgf4 expression in cells grown for 40 h in N2B27 medium. While Nanog expression is much reduced under these conditions, Fgf4 mRNA continues to be expressed, indicating that positive regulation through NANOG is not essential for Fgf4 mRNA expression in ESCs. We will add this data to a revised manuscript, and discuss its implications for the regulation of Fgf4 transcription (see also our response to Reviewer #3 below). As a complementary approach to further test the role of indirect effects mediated through NANOG, we will dissect more closely the timing of Fgf4 downregulation reported in Fig. 2B relative to the upregulation of the inducible GATA4-mCherry protein and the downregulation of NANOG protein.

    **Minor points:**

    Fig S1: The authors should show quantifications of Nanog and GATA6 levels before the beginning of the differentiation protocol.

    We will be happy to add this data in a revised version, as part of a more extensive analysis of GATA4-mCherry and GATA6 expression at early stages of the differentiation protocol. See also our response to the next point.

    Line 106: The authors write "the initially large proportion of GATA6+; NANOG+ double positive cells". It appears that at 16h of differentiation ESCs have already partitioned between Gata6 or Nanog expressing cells. The authors should rephrase the sentence to reflect what seems to be an almost total absence of truly double positive cells. Possibly, an analysis conducted at earlier time points could clarify these dynamics.

    The Reviewer rightly points out that at 16 h of differentiation, most cells are already associated with one of two clusters in the NANOG/GATA6 expression space. The misleading classification of a large number of cells as double positive at 16 h was caused by applying a single gating strategy to the entire experiment, even though the mean expression levels of NANOG and GATA6 in the two clusters change significantly over time. We will update our gating strategy and rephrase this section to more appropriately describe cell clustering and gene expression dynamics over the time course. We will also extend Figure S1 with analysis of GATA6 and NANOG expression levels at earlier time points of the differentiation protocol, to test whether this allows detecting a truly double positive population.

    Line 124: The authors write "... concentration dependent downregulation of NANOG expression". The effects may rather depend on the time of doxycycline stimulation.

    We agree with the Reviewer that in isolation, the data shown in Fig. 1 and Fig. S2 leave open the possibility that the stronger downregulation of NANOG at higher GATA4-mCherry expression levels is caused by the extended time of doxycycline stimulation rather than GATA4-mCherry concentration. However, in our opinion, this concern is already addressed by the experiments performed in the four clonal lines with independent integrations shown in Figure S3. Here, the time of doxycycline induction is held constant, and a similar relationship between GATA4-mCherry and NANOG expression levels is observed as in the experiments where we modulate induction time in a single clonal line (compare Fig. S2A to Fig. S3B). In a revised version of the manuscript we will describe more clearly how the experiments shown in Figure S3 control for time-dependent effects of doxycycline stimulation.

    Line 192: The authors write "...and confined to cells with low GATA4-mCherry expression levels". It would be helpful to have an indication of the cell boundaries, possibly showing localisation of a membrane bound protein.

    We agree that more firmly establishing a correlation between GATA4-mCherry expression levels and Fgf4 mRNA expression in single cells would greatly benefit from co-staining with a plasma membrane marker. However, the protocol for mRNA in situ hybridization involves incubation steps with ethanol and formamide and is thus incompatible with staining for commonly used membrane markers. There is one commercially available membrane stain (CellBrite by Biotium) that promises to survive the treatments necessary for in situ hybridization and that we will try to use in our stainings. Should this not be successful, we will resort to identifying a subset of the cytoplasm corresponding to each nucleus by dilating nuclear masks that we will segment based on the DNA stain.

    It would be interesting for the authors to discuss how the spatial range of FGF activity measured in culture could affect PE specification in the embryo.

    During lineage specification in the embryo, Epi and PrE cells are initially arranged in a salt-and-pepper pattern (PMID: 16678776; PMID: 18725515; PMID: 30514631). In Fig. 4 and Fig. S9 of our manuscript, we show experimentally and theoretically how similar patterns in ESC colonies arise from the short range of FGF activity. In a revised version of the manuscript, we will discuss how the spatial range of FGF activity measured in culture provides a possible mechanistic explanation for the spatial arrangement of cell types in the embryo.

    Reviewer #1 (Significance (Required)):

    See above.

    Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    In their manuscript entitled "Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell types in embryonic stem cells" Raina et al study the effect of Fgf-signalling based local cell-cell communication for the establishment of PrE-like and Epi-like cells. The authors use an elegant, albeit artificial, system to analyse the effect of Fgf signalling on establishing 'normal' lineage proportions after transient induction of Gata4 expression. The main conclusions of the manuscript are: i) Gata6 positive cells emerge through short range Fgf4 based cell-cell cummunication. ii) Fgf4 signalling can compensate a wide range of initial levels of Gata6 expression and produce properly portioned cell identities. The authors also state that this mechanism could operate in a range of developing tissues.

    **Major points:**

    Fgf4 KOS ESCs are deficient in initiating epiblast lineage differentiation (Kunath 2007). Therefore, the effect studied by the authors might be multifactorial and the general inability of Fgf4 deficient cells to enter differentiation might contribute to the observed differentiation defects and defects of cell fate proportioning. Specifically, it could be expected that Nanog regulation is affected in Fgf4 mutants, although, to my knowledge, the specific phenotype of Fgf4 depletion has not been evaluated in Gata4 induced cell programming towards PrE. What steps have the authors taken to exclude an impact of general cell fate change defects in Fgf4 KO ESCs.

    While it is true that Fgf4 mutant cells have a general deficiency in initiating epiblast lineage differentiation, it was already shown in the original publication by Kunath et al. (PMID: 17660198) that general differentiation of Fgf4 mutant cells is restored to wild type levels by supplementing the culture medium with 5 ng/ml recombinant FGF4. This is a concentration that is well within the range of concentrations applied in our study. In initial experiments to characterize our Fgf4 mutant lines, we have measured NANOG expression to test the effectiveness of recombinant FGF4 to restore epiblast lineage differentiation. We found that FGF4 treatment of Fgf4 mutant cells in the absence of doxycycline induction leads to a downregulation of NANOG expression, to levels comparable to those seen in wild type cells grown in N2B27. These data indicate that treatment with recombinant FGF4 rescues defects of general cell fate change in Fgf4 KO ESCs. We will add these data to Figure S4 of a revised manuscript, and explicitly mention the function of recombinant FGF4 to rescue lineage differentiation potential more generally.

    Increasing the time of Gata4 expression results in increasing levels of Gata4 levels (Fig 1C). This is shown at the overall mean fluorescence level. However, it is important to also quantify how many cells do actually show some increase in Gata4 levels. Fig1D suggests that the number of Gata4 expressing cells is quite similar between 4h and 8h induction, but this needs to be quantified. An explanation for the apparent dosage independence of Gata4 could then be simple threshold effects, such that there is no additional effect of increased Gata4 levels in WT cells without any further requirement of feedback regulation after a certain threshold level of Gata4 is reached. Have the authors considered such a simple model?

    The current version of the manuscript already contains quantifications of GATA4-mCherry expression levels in single cells - see Fig. S2A for the experiments where we vary doxycycline induction time, and Fig. S3B for experiments with independent clonal lines. This analysis confirms the Reviewer’s visual impression of Fig. 1D - the number of GATA4-mCherry expressing cells is similar for different induction times and clonal lines, such that the increase in overall mean fluorescence levels is mainly due to an increase in GATA4-mCherry expression levels in single cells. This analysis therefore rules out the simple model based on threshold effects proposed by the Reviewer. In a revised version of the manuscript, we will more explicitly discuss the quantifications in Fig. S2A and Fig. S3B.

    An important point is that in the current setup distinguishing between dosage effects and effects of extended presence of Gata4 cannot be distinguished. Wouldn't titrating the amount of doxycycline used for induction be a more direct way to achieve different initial levels of Gata4 expression?

    This concern has also been raised by Reviewer #1, and is addressed in detail in our response to their comment above. Briefly, in our opinion this concern is addressed in the current manuscript by the experiments performed in the four clonal lines with independent integrations (Figure S3). Here, the duration of doxycycline induction and hence time of GATA4-mCherry exposure is held constant, such that the only difference between the conditions is GATA4-mCherry dosage. We will discuss this important function of Fig. S3 in a revised version of a manuscript.

    Unfortunately titrating doxycycline does not allow titrating transgene induction levels in a meaningful way, as sub-saturating doses of doxycycline lead to an increased heterogeneity in transgene expression with many non-expressing cells, rather than to reduced expression levels across all cells. See PMID: 17048983 for a possible explanation of this observation.

    Another point the authors should appropriately discuss and consider is that a lack of effect of different doses/durations of Gata4 expression could be due to the fact that by the time Gata6 is induced, the levels of Gata4 in cells previously treated for different periods of time are no longer detectably different. Such a regulation would equally result in indistinguishable cell fate proportioning. Can the authors exclude such a regulation? This is an important point at the heart of the authors conclusion.

    The Reviewer seems to suggest that by separating the initiation of GATA6 expression from the GATA4-mCherry pulse in time, the decision to initiate PrE-like differentiation could be independent from GATA4-mCherry concentration, thus explaining the robust cell type proportions. The data shown in Figs. S2C, S3D and Fig. 3 A - C clearly exclude such a regulation: In conditions where we supply recombinant FGF4, the proportions of the different cell types scale with GATA4-mCherry expression levels, indicating that GATA4-mCherry dose does indeed affect Gata6 expression. In a revised version of the manuscript we will discuss and consider how these observations argue against a model where the decision to initiate PrE-like differentiation occurs independently from GATA4-mCherry levels.

    The authors make some general statements on cell differentiation (e.g. l205). They also claim that the Fgf4-based mechanism of lineage proportioning could act in a range of tissues during development. However, the use of the term differentiation for the induction of PrE-identity (or Gata-factor expression to be exact, see comment below) after Gata4 overexpression is problematic. The system chosen by the authors is entirely artificial. ES cells normally do not differentiate into extraembryonic cell types. It needs to be made clear in the manuscript that they do not study a differentiation process that normally occurs in the embryo or in differentiating ESC cultures. The system the authors are using would, in my opinion, rather qualify as cell programming or transdifferentiation than as differentiation. I suggest presenting the system using clearer unambiguous language and to try to avoid any generalisations based on an artificial transgene-overexpression based system. The results have to be presented with this limitation in mind.

    To address the Reviewer’s concerns regarding terminology, we will expand on the relationship of our system to normal ESC differentiation and lineage specification in the embryo, and discuss its possible limitations. We disagree however with the Reviewer’s assertion that using a transgene-based overexpression system precludes drawing any general conclusions. Rather, the system allows mimicking Epi- and PrE-like differentiation in a uniquely accessible context, and thereby to exploit the molecularly simple regulation of this cell fate decision for studying basic principles of cell differentiation. This view is supported by Reviewer #3 in the referees cross-commenting section below, who emphasizes the value of such models and notes that they are very common in developmental biology.

    It is unclear how 'PrE-like' (as stated e.g. in the abstract) the cells really are after a short pulse of Gata4 expression. No proper characterisation has been performed but needs to be included, if the authors want to term these cells PrE-like.

    A recent study by Amadei et al. (PMID: 33378662) supports the notion that a short pulse of GATA4 expression can trigger bona fide PrE-like differentiation. In this study, the authors induced a similar doxycycline-inducible GATA4 expression system for 6 hours, and observed subsequent differentiation into several PrE derivatives, including the anterior visceral endoderm. In a revised version, we will cite this study to support our claim that the GATA6-positive cells are indeed PrE-like. Additionally, we offer to perform immunostainings with an extended panel of known PrE marker proteins to substantiate the PrE-like character of the GATA6-expressing cells.

    How is the statement in l112 that "The clear separation between the two populations suggests that the increase in the proportion of double negative cells at the expense of GATA6+; NANOG- PrE-like cells beyond 40 h is mostly fueled by the downregulation of NANOG expression in the GATA6-negative cell population, combined with a slower proliferation of the GATA6-positive population, rather than by the reversion of PrE-like into double negative cells." supported by the data?

    We realize from the comments of all three reviewers that this section was confusing and potentially misleading in the original version of the manuscript. In a revision, we will reword this paragraph to better bring out the major conclusions from the GATA6 and NANOG expression patterns shown in Fig. S1A. These data show that the majority of cells belong to one of two discrete clusters from 16 h onwards. The clear separation of the two clusters furthermore indicates that cells rarely switch their gene expression patterns. Given these observations, the changes of cell type proportions reported in Figure S1B can be explained as a consequence of slower proliferation of cells in the GATA6-positive relative to the GATA6-negative cluster. In addition, NANOG expression in the GATA6-negative cluster declines over time, such that progressively more cells are classified as double negative.

    Would the data and modelling performed by the authors be in line with a model in which the decision to express Gata6 is a stochastic choice (with a certain probability based on the levels of Gata4 induction) that is then stabilized and reinforced by Fgf signalling rather than Fgf signalling having an instructive role?

    The simulations shown for the Fgf4 mutant case in Fig. 3 D - G, right column, are based on a model in which the decision to express Gata is a stochastic choice with a probability based on the initial levels of GATA expression, and reinforced by FGF signaling. Thus, our data from the Fgf4 mutant, but not the wild type, are perfectly in line with such a model.

    We realize from the Reviewer’s comment that we have not made sufficiently clear the conceptual differences between the models for the mutant and the wild type case. We suspect that this lack of clarity stems from the fact that the two models rely on the same circuitry, except for the regulatory link between GATA and FGF. This link however makes a crucial difference: It transforms the simple single cell input-output model of the mutant case, which is common to many previous publications, into a population level model with cell-cell feedback which shows new emergent behavior. And only this population level model, but not the single cell model for the Fgf4 mutant, can recapitulate the experimental data observed in the wild type. In a revised version of the manuscript we will expand on these crucial differences when describing the model and data in Fig. 3.

    The statement in line 187 "This indicates that GATA4-mCherry expression negatively regulates FGF4 signaling during cell type specification." is not supported by the data. The authors show only a correlation and actually correctly say so in line 195.

    Prompted by the comments of both Reviewer #1 and #3, we will carry out experiments to mechanistically explore the regulation of Fgf4 expression by GATA factors (see our response to Reviewer #1 above for a detailed description). Depending on the outcome of these experiments we will reword this statement.

    In Fig 2F statistical analysis between the re-seeded conditions is required for the conclusion that "the proportion of PrE-like cells systematically increased with cell density". Replating itself appears to quite drastically impact lineage distribution. Do the authors have an explanation for this?

    The p-value in line 221 of the original manuscript refers to a test for a linear trend between the three conditions following a one-way ANOVA in GraphPad Prism. We apologize that this has not been made clear and will add this information in a revised version.

    The observation that replating drastically impacts lineage distribution is perfectly in line with the overall conclusion from this section, namely that FGF signaling is enhanced by cell-cell contacts. Replating strongly reduces the number of direct cell-cell contacts by disrupting the colony structure of the culture. Thus it is expected that the proportion of the PrE-like cells - which require exposure to FGF ligands - is reduced under these conditions compared to the condition that has not been replated. We will discuss this explanation in a revision.

    Fig 2G shows a key experiment illustrating the local effect of Fgf4 expression on first and second neighbours. The authors have investigated this effect using a Fgf-signalling reporter. Why did they not assay Gata6 expression in this assay instead of a Spry reporter? This would be the experiment to show that also Gata6 expressing cells (after transient Gata4 induction) are clustered around Fgf4 producing cells and be a strong piece of evidence to show that local Fgf4 signalling and cell-cell communication is indeed involved in cell identity proportioning. The cell lines required for this experiment (including Fgf4 mutant Gata4 inducible ESCs) appear to be available.

    We decided to measure the FGF4 signaling range with a Spry4:H2B-Venus reporter because its response time is faster than that of GATA6 expression during differentiation. Furthermore, the Spry4:H2B-Venus reporter provides a quantitative readout for FGF4 signaling, in contrast to a binary read-out that would be expected for GATA6 expression. We will be happy to discuss these considerations in a revised manuscript.

    We agree that measuring FGF4 signaling range with Fgf4 mutant Gata4-mCherry inducible cells as suggested by the Reviewer constitutes a complementary approach to further corroborate the role of local FGF4 signaling in cell differentiation. However, we would like to stress that our demonstration of local FGF4 signaling is already supported by two fully orthogonal quantitative experiments, one relying on cell replating and the other one relying on the signalling reporter. The concept of local signaling is further supported by our quantitative analysis of the spatial arrangement of cell types in Fig. 4. The additional experiment suggested by the Reviewer is therefore unlikely to substantially change the paper’s conclusions, as also pointed out by Reviewer #3 in the referees cross-commenting section. Therefore, we offer to perform this experiment for a revision, but would like to seek the editor’s opinion if this is deemed necessary to make the paper acceptable for publication.

    The authors conclude from data in Fig 3A that proper cell type proportioning depends on initial Gata4 levels in Fgf4 mutants, in contrast to WT cells where the initial levels appear more irrelevant. Is 10ng/ml too high a dose? Would using a lower concentration (such as ~2ng/ml suggested by Fig 2D to give WT-like distribution) result in a complete rescue of cell lineage proportioning in this assay? Formally a control of adding additional Fgf4 to WT cells will also ne needed to control for a potential effect of exogenous Fgf4 addition.

    In our initial characterization of the Fgf4 mutant cell lines, we have performed experiments where we examined cell type proportions upon culture in the presence of different doses of FGF4 following doxycycline induction times between 1 h and 8 h. These experiments confirm the suspicion of the Reviewer that cell type proportions similar to the wild type can be obtained with a lower dose of 2.5 ng/ml FGF4 after 8 h of induction. For shorter induction times followed by differentiation in the presence of 2.5 ng/ml FGF4 however, cell type proportions were strongly skewed towards Epiblast-like cells. These data thus further support the major conclusion from Fig. 3A quoted by the Reviewer: Proper cell type proportioning in Fgf4 mutants depends on GATA4 levels, and this behavior is independent from the FGF4 concentration applied. We offer to add this data to a revised manuscript.

    The effects of adding FGF4 to wild type cells are shown in Fig. S2C and S3D in the current version of the manuscript. This control has been performed in all experiments shown in Fig. 3A - C, but we decided to omit it for clarity. We are happy to add this information back in as requested by the Reviewer.

    Does the model in Fig 3E consider potentially varying doses of exogenous Fgf4? Can the model also predict what happens if Fgf4 is added to WT cells, as suggested above as control? In general, the value of this model is unclear. Figure 3E is near impossible to understand, no quantitative information is given.

    The model in Fig. 3E can of course be simulated with different doses of exogenous FGF4. These simulations recapitulate the experimental results described under point 10 above: Cell type proportions for the Fgf4 mutant case are skewed towards NANOG-positive cells at lower FGF4 doses, and vary with initial conditions irrespective of FGF4 dose. We offer to show the results of these simulations in a revised manuscript alongside the experimental data discussed above.

    It is also possible to incorporate into the model addition of exogenous FGF4 to the wild type. Simulations of this condition confirm the experimentally observed increase in PrE-like cells shown in Fig. S2C and S3D of the current manuscript.

    To help the reader digest Fig. 3E, we will add separating lines similar to the gates of the flow cytometry data in panel A, and indicate the proportion of cells in the respective quadrants.

    The Reviewer’s comment that the value of the model is unclear indicates to us that we have not explained in sufficient detail the conceptual differences between the behavior of the model of the wild type and the mutant case. As detailed in our response to Reviewer’s comment 6. above, we will rewrite the text to bring out more clearly the insight that the model brings.

    Fig4A: What were WT and Fgf4 mutant cells treated differently in this assay (8h vs 4h, respectively)?

    The spatial arrangement of cell types in Fgf4 mutant cells has been assayed in two conditions that give similar cell type proportions as seen in the wild type, as motivated in lines 366 - 370 of the current manuscript. We decided to show the condition with 4 h induction followed by differentiation in the presence of 10 ng/ml FGF4 in the main Figure 4 because it is most similar to the condition that gives wild-type like cell type proportions in the Fgf4 mutant shown in the immediately preceding main Figure 3, while the condition that uses 8 h induction followed by differentiation in the presence of 2.5 ng/ml FGF4 refers back to the main Figure 2. We show both primary data and the complete analysis for the latter condition in Figures S8D and S10. Fig. S10 provides a direct comparison between the two conditions and clearly demonstrates that they show similar dynamics. We do not think that exchanging the two datasets between main and supplementary Figures will add value to the manuscript.

    Does the interpretation that at 24h there is a difference in Fig 4C survive statistical scrutiny? Only few datapoints are shown and any apparent differences seem due to outliers rather than a shift in cluster radii. How often were these experiments independently repeated? This information is missing. In Fig 4B, I cannot appreciate any difference between cell lines.

    We will perform statistical testing to assess whether the spatial arrangement of cell types is significantly different between the time points, and mention the results in the text.

    To evaluate the spatial arrangement of cell types, we have performed two independent experiments in the wild type, and analyzed two conditions for the mutant case. In each experiment, we have analyzed at least eight positions per condition and control. Spatial clustering of wild type cells at 40 h is also observed in earlier Figures in the manuscript (e.g. Fig. 1D, S2B, S3C).

    The similarities between wild type and Fgf4 mutant cells shown in Fig. 4B are not surprising and fully in line with the data shown in panel C, which shows that differences between time points are much more pronounced compared to the differences between genotypes. However, we realize that the micrographs and analysis plots in Fig. 4A and B were perhaps not fully representative for the aggregate behavior shown in panel C. In a revision, we will therefore show data from more representative colonies in panels A and B.

    **Minor points:**

    a) More information on statistics should be given in the Figures and legends.

    To address this concern we will perform statistical tests for differences in proportions of the main cell types in Figures 1D and 3C. In addition, we will perform statistical testing on Fig. 4C as detailed in point 13 above.

    b) Percentages should be indicated in the quadrants of the FACS plots of Fig 3A and E.

    This is a good suggestion, we will add this information. See also our response to point 11 above.

    c) What is the underlying evidence for the statement: "The specification of Epi- and PrE-like cells in ESCs shows both molecular and functional parallels to the patterning of the ICM of the mouse preimplantation embryo."

    In the current manuscript, this statement is further substantiated in the subsequent paragraph (lines 483 - 503). We realize that this order is potentially confusing and will change it. We will further modify this section as part of our response to major point 3. above.

    d) Fig 5C is difficult to interpret without a comprehensive decoding of colour information.

    To facilitate interpretation of this panel, we will add a legend to decode the colour information of the traces (purple: VNPhigh, cyan: VNPlow)

    Reviewer #2 (Significance (Required)):

    This manuscript provides novel insights into the role of Fgf-mediated cell-cell communication to establish proper ratios of cell identities in a PrE-induction system. The authors provide some interesting data and interpretation. Overall, the significance is slightly impaired by the highly artificial nature of the studied cell fate specification event.

    This manuscript will be of interest to readers working on early embryonic cell fate decision as well as researchers working on modelling of cellular processes.

    My expertise lies in the field of cell fate decision and pluripotency.

    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    It is well established that FGF signalling plays a role in the partitioning of the Primitive Endoderm and Epiblast fates during preimplantation mammalian development. Recent work has shown that this fate decisions is associated with a mechanism that is able to maintain the proportions of the two fates stable in the face of perturbations. Here, the authors address this mechanism and show that it is dependent on FGF signalling and associated with the fate decision. In the process they suggest and test a novel mechanism based on short range FGF signalling. A series of carefully designed and executed experiments, refine and provide evidence for the model. This is an original and important piece of work that will influence the field of pattern formation.

    Overall the manuscript is well written but, at least from the perspective of this reviewer, there are places in which clarity can be improved.

    Lines 104 and ff: the description of the dynamics of the different populations fater the GATA4 pulse, can be clarified. The reference to the double negative population emerging from the PrEnd population is not clear. It is stated that the proportion of these cells increased continuously and it said to be at the expense of the decrease of the PrEnd population whose variation is referred to as 'slightly declined". How can a slight decline fuel a steady increase in the double negative?

    Also, what are these double negative? Could they be cells differentiating into embryonic lineages?

    We realize from the comments of all three Reviewers on this paragraph that it was confusing and potentially misleading in the original manuscript. In a revised version we will rewrite this section to clarify our interpretation of the data in Fig. S1. First, the clear separation of the two clusters observed in NANOG-GATA6 expression space indicates that cells rarely switch between the two clusters. Then, a likely explanation for the slow decline in the fraction of GATA6-positive cells is a slower proliferation compared to the GATA6-negative cells. Third, the increase in the proportion of double negative cells is caused by a progressive downregulation of NANOG expression in the GATA6-negative cluster. These NANOG expression dynamics are consistent with NANOG expression dynamics in epiblast cells of the embryo, and could indeed indicate differentiation towards embryonic lineages. We will mention this possibility in a revised manuscript.

    See also our response to Reviewer #1 and Reviewer #2, point 5..

    In Figure 1 and its discussion, it would be good to see a representation of the stability of the final proportions relative to the different initial conditions, a variation on 1E.

    This is a good suggestion. In a revised version, we plan to add a panel to Fig. 1 in which we plot the final proportions of the different lineages versus the GATA4-mCherry expression levels for the different induction times. This will illustrate more clearly that the final proportions of cell types are largely independent from the initial conditions.

    Paragraph lines 182 and ff: the report that GATA4 expression is able to suppress FGF4 signalling, autonomously is, at least for this reviewer, a novel and important result and one that impinges on the understanding of the process. The authors should emphasize this.

    We agree with the Reviewer that the direct regulation of Fgf4 expression through GATA factors is a new regulatory link suggested by our data that has not been described before and that is crucial for the functioning of the system. Prompted by a similar comment of Reviewer #1 above, we offer to further explore the mechanistic basis of this link through an analysis of published ChIPseq data, functional studies of a GATA binding site upstream of the Fgf4 start codon, or a more detailed temporal dissection of NANOG, GATA and Fgf4 expression dynamics following doxycycline induction (see our response to Reviewer #1 above for more details). These new experiments and analyses will allow us to emphasize this novel result, and thereby significantly strengthen our paper.

    Paragraph lines 274 and ff (section on the involvement of FGF4 in the robustness of the process) needs some explanations. The derivation of the conclusion that 'recursive communication vis FGF4 underlies a population-level phenotype ...characterized by the differentiation of robust proportions of cell types..." from the experiments requires some unwrapping. It would be helpful if the authors could reason how the conclusion follows from the experiments.

    We realize from this Reviewer’s comment and the comments of Reviewer #2 above that we have not explained well enough how the results shown in Fig. 3 A-C (lines 274 - 283) lead to our conclusion of emergent behavior, which are then further substantiated in the modelling in panels D - G. The central conclusion of this paragraph rests on the observation that cell type proportions are dependent on initial conditions in the Fgf4 mutant, but not in wild type cells. As we had supplied FGF4 externally to the Fgf4 mutant cells, the only difference between these two conditions is that FGF4 dose in wild type cells is regulated by the cell population, i.e. cells can communicate via FGF4, whereas mutant cells cannot. We will expand on this line of reasoning, and also explain in more detail the differences in the models for the mutant case and the wild type, which we believe will help to conceptualize the experimental results. See also our response to Reviewer #2, points 6. and 11..

    Their model does not seem to include the commonly agreed regulatory interaction between Nanog and FGF4, at least not directly, and it would be helpful if a reasoning could be provided for this decision.

    A discussion of the regulatory interaction between NANOG and Fgf4 has also been requested by Reviewer #1. In our response to their point above, we provide a reasoning why we have omitted it in the current manuscript. Briefly, our decision not to include a direct positive link between NANOG and Fgf4 expression rests on our observation that Fgf4 mRNA continues to be expressed 2 days after switching cells from 2i + LIF medium to N2B27, a time at which NANOG already starts to be downregulated as a consequence of differentiation along embryonic lineages. We will add this data to a revised manuscript. In addition, we propose above to dissect in more detail the temporal sequence of GATA4-mCherry, Fgf4 and NANOG expression upon doxycycline induction. This analysis will provide further information about the role of NANOG for Fgf4 mRNA expression in ESCs.

    Reviewer #3 (Significance (Required)):

    In this manuscript, Raina and colleagues use an Embryonic Stem (ES) cell based experimental system to address a central problem in developmental biology, namely the emergence of stable scaled populations of different cell fates. The experiments are elegant in design, carefully executed and the effort provides a solution to the problem: a novel mechanism based on short range FGF signalling that provides homeostatic control of relative cell populations. This is an important piece of work with sound conclusions that establishes a new paradigm in pattern formation whose implications are likely to lead to a reassessment of the role of FGF in different patterning paradigms. The experiments are quantitative and supported by a modelling effort based on a theoretical piece of work (Stanoev et al. 2021) which underpins the conclusion.

    This manuscript will appeal to a wide audience including developmental and stem cell biologists as well as modellers.

    My expertise cover the areas addressed in the manuscript.

    **Referees cross-commenting**

    It looks as if, with some nuances, we all agree on the value of the work. I do not have any issues with the comments of Reviewer 1, though I disagree that the model tested and improved here is similar to existing ones. While it is true that this work is related to a theory paper by some of the authors, the experimental test and resulting conclusions are very important. On the other hand, I am very surprised by the comments of Reviewer 2 who, after conceding the value and potential significance of the work, raises a list of queries, largely small details and opinions rather than points of substantial concerns, hinting at a need for the authors to perform extra work and analysis that will not change the conclusions of the manuscript. Some of this e.g. #9 would be a nice piece of additional evidence, but more an adornment than a necessary piece of additional evidence. The main problem of this reviewer is the lack of appreciation of what they define as 'highly artificial nature' of the study without providing any reason for why such experiments (very common in developmental biology) can lead to misleading conclusions. It seems to me that most, if not all, of their significant concerns can be dealt with in a rebuttal or by altering the text, either to discuss the issues raised, to clarify the points or qualify the conclusions.

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    Referee #3

    Evidence, reproducibility and clarity

    It is well established that FGF signalling plays a role in the partitioning of the Primitive Endoderm and Epiblast fates during preimplantation mammalian development. Recent work has shown that this fate decisions is associated with a mechanism that is able to maintain the proportions of the two fates stable in the face of perturbations. Here, the authors address this mechanism and show that it is dependent on FGF signalling and associated with the fate decision. In the process they suggest and test a novel mechanism based on short range FGF signalling. A series of carefully designed and executed experiments, refine and provide evidence for the model. This is an original and important piece of work that will influence the field of pattern formation.

    Overall the manuscript is well written but, at least from the perspective of this reviewer, there are places in which clarity can be improved.

    Lines 104 and ff: the description of the dynamics of the different populations fater the GATA4 pulse, can be clarified. The reference to the double negative population emerging from the PrEnd population is not clear. It is stated that the proportion of these cells increased continuously and it said to be at the expense of the decrease of the PrEnd population whose variation is referred to as 'slightly declined". How can a slight decline fuel a steady increase in the double negative?

    Also, what are these double negative? Could they be cells differentiating into embryonic lineages?

    In Figure 1 and its discussion, it would be good to see a representation of the stability of the final proportions relative to the different initial conditions, a variation on 1E.

    Paragraph lines 182 and ff: the report that GATA4 expression is able to suppress FGF4 signalling, autonomously is, at least for this reviewer, a novel and important result and one that impinges on the understanding of the process. The authors should emphasize this.

    Paragraph lines 274 and ff (section on the involvement of FGF4 in the robustness of the process) needs some explanations. The derivation of the conclusion that 'recursive communication vis FGF4 underlies a population-level phenotype ...characterized by the differentiation of robust proportions of cell types..." from the experiments requires some unwrapping. It would be helpful if the authors could reason how the conclusion follows from the experiments.

    Their model does not seem to include the commonly agreed regulatory interaction between Nanog and FGF4, at least not directly, and it would be helpful if a reasoning could be provided for this decision.

    Significance

    In this manuscript, Raina and colleagues use an Embryonic Stem (ES) cell based experimental system to address a central problem in developmental biology, namely the emergence of stable scaled populations of different cell fates. The experiments are elegant in design, carefully executed and the effort provides a solution to the problem: a novel mechanism based on short range FGF signalling that provides homeostatic control of relative cell populations. This is an important piece of work with sound conclusions that establishes a new paradigm in pattern formation whose implications are likely to lead to a reassessment of the role of FGF in different patterning paradigms. The experiments are quantitative and supported by a modelling effort based on a theoretical piece of work (Stanoev et al. 2021) which underpins the conclusion.

    This manuscript will appeal to a wide audience including developmental and stem cell biologists as well as modellers.

    My expertise cover the areas addressed in the manuscript.

    Referees cross-commenting

    It looks as if, with some nuances, we all agree on the value of the work. I do not have any issues with the comments of Reviewer 1, though I disagree that the model tested and improved here is similar to existing ones. While it is true that this work is related to a theory paper by some of the authors, the experimental test and resulting conclusions are very important. On the other hand, I am very surprised by the comments of Reviewer 2 who, after conceding the value and potential significance of the work, raises a list of queries, largely small details and opinions rather than points of substantial concerns, hinting at a need for the authors to perform extra work and analysis that will not change the conclusions of the manuscript. Some of this e.g. #9 would be a nice piece of additional evidence, but more an adornment than a necessary piece of additional evidence. The main problem of this reviewer is the lack of appreciation of what they define as 'highly artificial nature' of the study without providing any reason for why such experiments (very common in developmental biology) can lead to misleading conclusions. It seems to me that most, if not all, of their significant concerns can be dealt with in a rebuttal or by altering the text, either to discuss the issues raised, to clarify the points or qualify the conclusions.

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    Referee #2

    Evidence, reproducibility and clarity

    In their manuscript entitled "Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell types in embryonic stem cells" Raina et al study the effect of Fgf-signalling based local cell-cell communication for the establishment of PrE-like and Epi-like cells. The authors use an elegant, albeit artificial, system to analyse the effect of Fgf signalling on establishing 'normal' lineage proportions after transient induction of Gata4 expression. The main conclusions of the manuscript are: i) Gata6 positive cells emerge through short range Fgf4 based cell-cell cummunication. ii) Fgf4 signalling can compensate a wide range of initial levels of Gata6 expression and produce properly portioned cell identities. The authors also state that this mechanism could operate in a range of developing tissues.

    Major points:

    1. Fgf4 KOS ESCs are deficient in initiating epiblast lineage differentiation (Kunath 2007). Therefore, the effect studied by the authors might be multifactorial and the general inability of Fgf4 deficient cells to enter differentiation might contribute to the observed differentiation defects and defects of cell fate proportioning. Specifically, it could be expected that Nanog regulation is affected in Fgf4 mutants, although, to my knowledge, the specific phenotype of Fgf4 depletion has not been evaluated in Gata4 induced cell programming towards PrE. What steps have the authors taken to exclude an impact of general cell fate change defects in Fgf4 KO ESCs.
    2. Increasing the time of Gata4 expression results in increasing levels of Gata4 levels (Fig 1C). This is shown at the overall mean fluorescence level. However, it is important to also quantify how many cells do actually show some increase in Gata4 levels. Fig1D suggests that the number of Gata4 expressing cells is quite similar between 4h and 8h induction, but this needs to be quantified. An explanation for the apparent dosage independence of Gata4 could then be simple threshold effects, such that there is no additional effect of increased Gata4 levels in WT cells without any further requirement of feedback regulation after a certain threshold level of Gata4 is reached. Have the authors considered such a simple model? An important point is that in the current setup distinguishing between dosage effects and effects of extended presence of Gata4 cannot be distinguished. Wouldn't titrating the amount of doxycycline used for induction be a more direct way to achieve different initial levels of Gata4 expression? Another point the authors should appropriately discuss and consider is that a lack of effect of different doses/durations of Gata4 expression could be due to the fact that by the time Gata6 is induced, the levels of Gata4 in cells previously treated for different periods of time are no longer detectably different. Such a regulation would equally result in indistinguishable cell fate proportioning. Can the authors exclude such a regulation? This is an important point at the heart of the authors conclusion.
    3. The authors make some general statements on cell differentiation (e.g. l205). They also claim that the Fgf4-based mechanism of lineage proportioning could act in a range of tissues during development. However, the use of the term differentiation for the induction of PrE-identity (or Gata-factor expression to be exact, see comment below) after Gata4 overexpression is problematic. The system chosen by the authors is entirely artificial. ES cells normally do not differentiate into extraembryonic cell types. It needs to be made clear in the manuscript that they do not study a differentiation process that normally occurs in the embryo or in differentiating ESC cultures. The system the authors are using would, in my opinion, rather qualify as cell programming or transdifferentiation than as differentiation. I suggest presenting the system using clearer unambiguous language and to try to avoid any generalisations based on an artificial transgene-overexpression based system. The results have to be presented with this limitation in mind.
    4. It is unclear how 'PrE-like' (as stated e.g. in the abstract) the cells really are after a short pulse of Gata4 expression. No proper characterisation has been performed but needs to be included, if the authors want to term these cells PrE-like.
    5. How is the statement in l112 that "The clear separation between the two populations suggests that the increase in the proportion of double negative cells at the expense of GATA6+; NANOG- PrE-like cells beyond 40 h is mostly fueled by the downregulation of NANOG expression in the GATA6-negative cell population, combined with a slower proliferation of the GATA6-positive population, rather than by the reversion of PrE-like into double negative cells." supported by the data?
    6. Would the data and modelling performed by the authors be in line with a model in which the decision to express Gata6 is a stochastic choice (with a certain probability based on the levels of Gata4 induction) that is then stabilized and reinforced by Fgf signalling rather than Fgf signalling having an instructive role?
    7. The statement in line 187 "This indicates that GATA4-mCherry expression negatively regulates FGF4 signaling during cell type specification." is not supported by the data. The authors show only a correlation and actually correctly say so in line 195.
    8. In Fig 2F statistical analysis between the re-seeded conditions is required for the conclusion that "the proportion of PrE-like cells systematically increased with cell density". Replating itself appears to quite drastically impact lineage distribution. Do the authors have an explanation for this?
    9. Fig 2G shows a key experiment illustrating the local effect of Fgf4 expression on first and second neighbours. The authors have investigated this effect using a Fgf-signalling reporter. Why did they not assay Gata6 expression in this assay instead of a Spry reporter? This would be the experiment to show that also Gata6 expressing cells (after transient Gata4 induction) are clustered around Fgf4 producing cells and be a strong piece of evidence to show that local Fgf4 signalling and cell-cell communication is indeed involved in cell identity proportioning. The cell lines required for this experiment (including Fgf4 mutant Gata4 inducible ESCs) appear to be available.
    10. The authors conclude from data in Fig 3A that proper cell type proportioning depends on initial Gata4 levels in Fgf4 mutants, in contrast to WT cells where the initial levels appear more irrelevant. Is 10ng/ml too high a dose? Would using a lower concentration (such as ~2ng/ml suggested by Fig 2D to give WT-like distribution) result in a complete rescue of cell lineage proportioning in this assay? Formally a control of adding additional Fgf4 to WT cells will also ne needed to control for a potential effect of exogenous Fgf4 addition.
    11. Does the model in Fig 3E consider potentially varying doses of exogenous Fgf4? Can the model also predict what happens if Fgf4 is added to WT cells, as suggested above as control? In general, the value of this model is unclear. Figure 3E is near impossible to understand, no quantitative information is given.
    12. Fig4A: What were WT and Fgf4 mutant cells treated differently in this assay (8h vs 4h, respectively)?
    13. Does the interpretation that at 24h there is a difference in Fig 4C survive statistical scrutiny? Only few datapoints are shown and any apparent differences seem due to outliers rather than a shift in cluster radii. How often were these experiments independently repeated? This information is missing. In Fig 4B, I cannot appreciate any difference between cell lines.

    Minor points:

    a) More information on statistics should be given in the Figures and legends.

    b) Percentages should be indicated in the quadrants of the FACS plots of Fig 3A and E.

    c) What is the underlying evidence for the statement: "The specification of Epi- and PrE-like cells in ESCs shows both molecular and functional parallels to the patterning of the ICM of the mouse preimplantation embryo."

    d) Fig 5C is difficult to interpret without a comprehensive decoding of colour information.

    Significance

    This manuscript provides novel insights into the role of Fgf-mediated cell-cell communication to establish proper ratios of cell identities in a PrE-induction system. The authors provide some interesting data and interpretation. Overall, the significance is slightly impaired by the highly artificial nature of the studied cell fate specification event.

    This manuscript will be of interest to readers working on early embryonic cell fate decision as well as researchers working on modelling of cellular processes.

    My expertise lies in the field of cell fate decision and pluripotency.

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    Referee #1

    Evidence, reproducibility and clarity

    In this manuscript Raina et al. use an in vitro model of PE specification based on the transient overexpression of GATA4 in ESCs to show that the acquisition of primitive endoderm (PE) identity is governed at the population levels by cell-cell interactions mediated by FGF signaling. The authors further argue that the specification of a defined proportion of "PE" and "Epiblast" cells in a differentiating population of ESC is an emergent property of a system where paracrine signaling shifts the balance between two alternative stable states. Overall, the work does not reach radically new conclusions: broadly similar models are outlined in several other publications, including from the authors. Yet this study makes use of elegant genetic models and is particularly well executed. In addition, it includes a very accurate characterisation of the spatial range of FGF signaling activity that is original and adds on the existing knowledge. Moreover, the authors show novel evidence suggesting that GATA factors inhibits Fgf4 transcription and the activity of the FGF signaling pathway in ESCs.

    Two major points deserve further clarification:

    In this manuscript the authors claim that the proportions of cells acquiring PE fate is, at least in the experimental setup adopted, largely independent from the levels of GATA4 induction, and therefore of the initial state of the gene regulatory network regulating this cell fate transition. However, the authors should discuss how the current findings relate to their previous results, showing that the duration/levels of Gata4 induction, in a similar experimental setting, play an important role in determining the final proportion of cells cell acquiring "PE" fate. Absolute expression levels may be crucial for this distinction, but the authors seem to exclude this possibility (see figure S3).

    Most importantly, the authors incorporate in their model the notion that GATA6 inhibits FGF signaling. It would be interesting to understand how such inhibition is mechanistically mediated. For instance GATA6 has been shown to bind in proximity of the Fgfr2 gene (Wamaitha et al., Genes and Dev., 2015). Alternatively, the authors show a direct effect on Fgf4 expression. The short time window of the reported repressive transcriptional effects (8h, Fig 2 middle), might suggest a direct regulation. The authors should test this possibility, and discuss what alternative modes of regulation could be envisaged (for instance, indirect effects mediated by Nanog). This is a key result that deserves a more detailed mechanistic characterisation.

    Minor points:

    Fig S1: The authors should show quantifications of Nanog and GATA6 levels before the beginning of the differentiation protocol.

    Line 106: The authors write "the initially large proportion of GATA6+; NANOG+ double positive cells". It appears that at 16h of differentiation ESCs have already partitioned between Gata6 or Nanog expressing cells. The authors should rephrase the sentence to reflect what seems to be an almost total absence of truly double positive cells. Possibly, an analysis conducted at earlier time points could clarify these dynamics.

    Line 124: The authors write "... concentration dependent downregulation of NANOG expression". The effects may rather depend on the time of doxycycline stimulation.

    Line 192: The authors write "...and confined to cells with low GATA4-mCherry expression levels". It would be helpful to have an indication of the cell boundaries, possibly showing localisation of a membrane bound protein.

    It would be interesting for the authors to discuss how the spatial range of FGF activity measured in culture could affect PE specification in the embryo.

    Significance

    See above.

  6. Version published to 10.1101/2020.02.14.949701v2 on bioRxiv
  7. Version published to 10.1101/2020.02.14.949701v1 on bioRxiv