Human primordial germ cells (hPGCs) form around the time of implantation and are the precursors of eggs and sperm. Many aspects of hPGC specification remain poorly understood because of the inaccessibility of the early postimplantation human embryo for study. Here, we show that micropatterned human pluripotent stem cells (hPSCs) treated with BMP4 give rise to hPGC-like cells (hPGCLC) and use these as a quantitatively reproducible and simple in vitro model to interrogate this important developmental event. We characterize micropatterned hPSCs up to 96 hr and show that hPGCLC populations are stable and continue to mature. By perturbing signaling during hPGCLC differentiation, we identify a previously unappreciated role for Nodal signaling and find that the relative timing and duration of BMP and Nodal signaling are critical parameters controlling the number of hPGCLCs. We formulate a mathematical model for a network of cross-repressive fates driven by Nodal and BMP signaling, which predicts the measured fate patterns after signaling perturbations. Finally, we show that hPSC colony size dictates the efficiency of hPGCLC specification, which led us to dramatically improve the efficiency of hPGCLC differentiation. In humans and other animals, eggs and sperm are unique cells that pass on genetic material to the next generation. They originate from a small group of cells called primordial germ cells that form early in life in the developing embryo. Several different signal molecules including ones known as BMP4, Wnt, and Nodal, instruct certain cells in the embryo to become primordial germ cells.The process by which primordial germ cells are made in humans is very different to how primordial germ cells are made in mice and other so-called model animals that are commonly used in research. This has made it more challenging to uncover the details of the process in humans. Fortunately, new methods have recently been created that mimic aspects of how human embryos develop using human stem cells in a laboratory dish, providing an opportunity to gain a deeper understanding of how human germ cells form.Jo et al. used a technique called micropatterning to control the shape and size of groups of human stem cells growing in a laboratory dish. Treating these cells with a signal known as BMP4 gave rise to cells that resembled primordial germ cells. The team then used this system as a model to study how primordial germ cells form in humans. The experiments found that reducing Wnt signals in stem cells stopped primordial germ cells from forming in response to BMP4, confirming that Wnt signals made by the cells in response to BMP4 are essential. However, this block was overcome by providing the stem cells with another signal called Nodal. This suggests that the role of Wnt signaling in primordial germ cell formation is in part indirect by switching on Nodal in stem cells.Defects in eggs and sperm may lead to infertility, therefore, the findings of Jo et al. have the potential to help researchers develop new fertility treatments that use eggs or sperm grown in a laboratory from the patients’ own stem cells. Such research would benefit from first developing a better understanding of how to make primordial germ cells. Formation of primordial germ cells (PGC) is the first step in specification of the germline, the unique lineage through which genetic material is passed on to the next generation and potentially the key to understanding totipotency. Germline defects underlie numerous human diseases, most notably infertility . UnderstHere, we used micropatterned hPSCs as a quantitatively reproducible system that allowed systematic interrogation of hPGCLC specification at single-cell resolution. Micropatterning enables spatial restriction of cell-substrate adhesion to control colony size and shape. Micropatterned human embryonic stem cells treated with BMP4 for 42–48 hr are a model system of human gastrulation, generating all three germ layers in concentric rings surrounded by another ring of extraembryonic-like cells . The innA final ring of SOX17-positive cells, roughly positioned between the extraembryonic cells and primitive-streak-like cells, was originally thought to be endoderm. However, these SOX17+ cells do not express the definitive endoderm marker FOXA2 . MoreoveWe developed improved quantitative analysis of immunofluorescence (IF) data at the single-cell level based on a 3D image analysis pipeline integrating deep-learning-based segmentation. This enabled accurate assessment of the molecular signatures, spatial distributions, and sizes of cell populations. We combined this with scRNA-seq to confirm PGCLC identity and further found evidence of amniotic ectoderm identity of the outer ring. By carrying out temporal analysis up to 96 hr, we found that PGCLC populations persist and mature during this time window.After confirming PGCLC specification, we carried out pharmacological and genetic perturbations to provide insight into the underlying signaling involved in this process. Although a requirement for Nodal in mouse PGC differentiation was demonstrated , directeFinally, we investigated how PGCLC differentiation depends on colony size and found that by optimizing colony size we can generate PGCLCs with efficiencies of ~50% using BMP4 treatment alone. When 12 hr of pre-differentiation to an incipient mesoderm-like state (iMeLC) is included, as is typical in directed PGCLC differentiation, this number increases to 70% compared to other studies reporting 20–30% efficiency .Upon treatment with BMP4, at least four distinct cell fates arise in concentric rings by 42 hr in micropatterned hPSC colonies with 500–1000 µm diameter . Cells eTo quantify the relationship between these markers from the IF data, we developed a 3D image analysis pipeline based on machine learning to handle multiple overlapping cell layers and automatically determine if cells express or co-express specific markers . Segmentation  allowed Across multiple experiments, we found ~10–20% of cells to be SOX17+ with 50–60% of those also expressing TFAP2C . About 5After identifying populations by thresholding markers, we visualized spatial patterning as the fraction of cells positive for a marker at some radius . We founWe repeated quantitative analysis of IF for PGC markers with four different hPSC lines, both male and female . We founTo further understand the identity of both the SOX17+ cells and other cells within the micropatterned hPSCs, we performed scRNA-seq and visualized our data using PHATE . This reTo systematically evaluate gene expression in PGCLCs, we performed clustering using Louvain, which yielded six clusters . We perfThe identity of the outer ring of cells has been a source of debate and is important in the context of PGCLC induction because PGCs have been found to derive from the amnion in cynomolgus monkeys, while their origin in human remains unclear . We arguThe outer cells were previously found to express markers of both TE, including CDX2, GATA3, TP63, TBX3, and KRT7, but also genes associated with amnion such as TFAP2A, with little consensus on which genes specifically mark human amnion in vivo . RecentlA fifth cluster was identified between the pluripotent cells and differentiated fate, expressing intermediate levels of both PS and pluripotency markers. This appears to be a transitionary state. In this context, we decided to name this intermediate state incipient mesoderm-like (iMeLC) as is used in directed differentiation of PGCLCs .The sixth and final cluster of cells was very clearly distinct from other cells in the scRNA-seq data; however, very few cells were captured in this cluster, suggesting that these cells are rare within micropatterns. This cluster was enriched for ANXA1, POSTN, VIM, and PODXL , suggestWe asked whether the gene expression relationships found using IF in It remains unclear whether human PGCs derive from amnion or posterior epiblast. Moreover, PGCs are thought to go through an incipient mesodermal state transiently expressing low levels of PS markers. We therefore also looked at co-expression of PGC markers with the PS markers EOMES and TBXT, and the amnion marker ISL1 .low ISL1low SOX17+ cells connecting EOMES+ ISL1 cells to EOMES-ISL+. This is consistent with a requirement for, but subsequent suppression of, EOMES in PGCLCs and places the gene expression profile of PGCLCs intermediate between amnion and PS. Similarly, we found that TFAP2C+ cells that co-express TBXT are mostly PRDM1+ and show a strong correlation between TFAP2C and PRDM1 within this cluster  versus PGCLCs (PRDM1+ NANOG+ POU5F1+) and found that these markers are upregulated over time in PGCLCs to levels significantly higher than in pluripotent cells  as has bBMP and Wnt signaling are known to be important for PGC specification, and PGCLC specification is known to sensitively depend on the duration of exogenous Wnt activation, with prolonged Wnt activation leading to PS-like fates instead . HoweverFirst, we inhibited BMP signaling after 24 hr with the BMP receptor inhibitor (BMPRi) LDN193189 . This leMany of the effects of BMP4 are known to be indirect through the BMP-Wnt-Nodal cascade . To testAs expected, Wnt inhibition for the full duration of the experiment eliminates expression of TBXT and PRDM1. Strikingly, blocking Wnt signaling after 12 hr also led to loss complete loss of PRDM1, indicating that Wnt signaling between 12 and 24 hr after addition of BMP4 is critical . We alsoDirected differentiation protocols for PGCs typically consist of a brief period of exposure to PS-inducing signals Wnt and Nodal to induce an iMeLC followed by BMP. Therefore, we asked whether the other signals that are required to specify mesoderm: FGF and Nodal, are also only required during the first 24 hr to induce PGCs.First, we inhibited FGF signaling with the FGF receptor inhibitor (FGFRi) PD173074 and MEK signaling (MEKi) with PD0325901 after 0 and 24 hr . The effNext, we inhibited Nodal signaling with the TGF-beta receptor inhibitor (TFGBRi) SB431542 . Nodal iGiven that Wnt induces Nodal, it is possible that the main role of Wnt for PGCLC specification is to induce Nodal. While it is known that Nodal alone does not induce differentiation, it is unclear what happens in combination with BMP. We therefore tested whether we could rescue the loss of PGCLCs after Wnt inhibition by adding Activin to exogenously stimulate the Nodal pathway. PGCLC induction was indeed partially rescued by intermediate doses of Activin with robust expression of SOX17 and TFAP2C . This isWe repeated the experiments with a different Wnt inhibitor, IWR-1, which stabilizes AXIN to inhibit canonical Wnt signaling, whereas IWP2 used in earlier experiments acts on PORCN to block Wnt secretion, thereby inhibiting both canonical and noncanonical Wnt signaling. A high dose of IWR-1 (50 µM) inhibited PGCLC differentiation, although some LEF1 remained . We agaiThe increased number of PGCLCs after Activin treatment with IWR-1 compared to IWP2 suggests either a different efficacy in inhibiting WNT signaling or a role for noncanonical WNT signaling, which is not inhibited by IWR-1. The inability of IWR-1 to inhibit PGCs at a lower dose  and the We then asked whether Nodal signaling might generally be the limiting factor in directing TFAP2C-positive cells to PGCLC fate and treated colonies with different doses of Activin in addition to BMP4 . We founThe dose-dependent effect of Activin treatment may be absolute if there are gene activation thresholds related to, for example, binding affinities of Smad2, or it may be relative to BMP, with BMP and Nodal signaling competing to activate and suppress TFAP2C. To test this, we treated NodalKO colonies with 25 different combinations of Activin and BMP doses. Although differentiation became less organized at higher doses of BMP, we found reproducible behavior with PGC induction maximal at intermediate levels of both BMP and Activin. Moreover, the effect of Activin was dependent on the level of BMP. For example, treatment with 10 ng/ml Activin significantly reduced PGC numbers at 10 ng/ml BMP, but increased PGC numbers at higher doses of BMP4, indicating that relative levels are important for fate determination. We also stained for TFAP2C, PRDM1, NANOG with similar results .Unlike endogenous Nodal, high enough exogenous Activin eliminates TFAP2C and induces strong FOXA2 expression by 42 hr. We asked whether this is due to increased signaling levels or whether it is due to changes in timing and duration. Endogenous Nodal is activated with a delay downstream of BMP and Wnt and does not reach high levels until after 24 hr . At thatCombined, these data suggest that PGC specification requires that Nodal signaling should be activated in cells expressing TFAP2C to induce SOX17 before they commit to AmLC fate, but that prolonged high-level Nodal signaling suppresses TFAP2C and activates FOXA2 to give rise to endoderm. Therefore, we predicted that a high dose of activin during only the first 24 hr would be able to convert all TFAP2C-positive cells to PGCs without inducing endoderm. Indeed, we were able to double the fraction of PGCs to about 20% by 24 hr of Activin exposure with Activin/Nodal signaling inhibited after removing Activin . As befoIn summary, we provide evidence that an important part of the role of Wnt in PGCLC induction is indirect by inducing Nodal, and that the balance and relative timing of the Nodal and BMP pathways play a crucial role in inducing TFAP2C and SOX17 to specify PGCs.We observed that under most conditions TFAP2C+ SOX17+ cells are confined to a ring 100 µm or less in size on the edge of the colony that accounts for at most 30% of the cells. Response to exogenous BMP and Activin is well understood as an ‘edge effect,’ excluded from the center by receptor localization and inhibitor production . TherefoAlthough strongly suggested by Our experiments suggest that cells interpret the ratio of BMP and Nodal signaling levels as well as their relative timing and duration through a network of mutually repressive fates that are acquired in a switch-like manner. Although Wnt is required in combination with Nodal to induce PS-like fates, our data suggest that for PGCLC specification only low levels of WNT are required directly and its primary role is to induce Nodal, so that many of our results can be explained without considering Wnt. Moreover, differentiation of AmLCs on the colony edge does not require Nodal, and PS-like fates do not (directly) require BMP, while PGCLCs positioned in between require combined induction of both BMP and Nodal target genes, in particular TFAP2C and SOX17.Intuitively, this would explain the unperturbed WT pattern as follows. First, higher BMP signaling on the colony edge induces TFAP2C and amnion genes faster than further inside; then, with a delay that depends on distance from the edge, Nodal signaling turns on in all cells at similar levels and induces SOX17 and PS-like genes. Cells on the outside reach a threshold to stably switch on amnion-like genes and repress other fates before SOX17 and PS-like genes are significantly induced. Cells slightly further inside reach high enough levels of both TFAP2C and SOX17 to stably switch on PGCLC genes and repress other fates, while cells even further inside never significantly activate amnion genes or TFAP2C and after a longer exposure to Nodal will reach a threshold to commit to a PS-like fate. Combined induction of PS-like genes and SOX17 may specify endoderm. The effect of perturbations of Activin/Nodal timing and duration is then naturally explained: early treatment with Activin combined with BMP can induce high enough levels of both SOX17 and TFAP2C in cells on the colony edge to make them PGCLCs before they commit to AmLC, but if the duration of Activin exposure is too long PS-like genes dominate and suppress PGCLC fate. On the other hand, Activin exposure in the second 24 hr coincides with the timing of endogenous Nodal and has little effect on fate decisions.To test if this intuitive model holds up more rigorously and also explains our other perturbations in BMP and Nodal signaling, we identified a minimal mathematical model for the GRN specifying PGCs downstream of previously determined BMP, Nodal, and Activin signaling profiles. Previous work showed that after initial uniform activation BMP signaling is restricted to a stable gradient from the colony edge and that a region of high endogenous Nodal signaling expands into the colony from the edge at constant velocity starting around 24 hr  . Like BMThe structure of the model is shown in In this study, we have shown that PGCs form in a very reproducible manner in BMP4-treated micropatterned hPSCs and are part of a stereotypic spatial organization that positions them between extraembryonic cells that may be amnion-like and cells expressing PS markers, similar to their location in vivo. Because they are close to the colony edge, we used micropatterning to create small colonies and were able to get much greater fractions of PGCLC differentiation than previously described. This potentially also explains the advantage of using ROCK inhibitor in PGCLC differentiation noted by some groups  since thIn a major advance, a microfluidics-based stem cell model of human gastrulation was recently shown to give rise to hPGCLCs and was used to study the transcriptome of hPGCs . HoweverIt was a surprise that we found no clear definitive endoderm (DE) population at 42 hr and that the majority of SOX17+ cells that were originally thought to be endoderm are PGCLCs. However, we found that endoderm is present at 48 hr and continues to increase until 72 hr. It is possible that the SOX17+ TFAP2C- cells at 42 hr will continue to differentiate to DE, which would require lineage tracing, but neither IF nor scRNA-seq shows expression of DE markers like FOXA2 or HEX at 42 hr. When endoderm arises, it localizes close to the PGCs and is still in a location where high BMP is expected. Therefore, the puzzle of endoderm localization in micropatterned hPSC colonies is not fully resolved by our study. However, Nodal and BMP have not been measured after 48 hr, so it is possible that BMP signaling is excluded from this region at later times. A different study performed scRNA-seq at 44 hr  and founOur earlier work showed that endogenous Nodal does not form static gradients but moves into the colony like a wavefront with constant velocity, arguing against the classic model of pattern formation by concentration thresholds . MoreoveAlthough to our knowledge the interactions in our model for the GRN are consistent with the literature, there are several variations possible at the level of the model, and several molecular mechanisms that could be responsible for the behavior of the same model . For example, instead of directly, BMP could activate SOX17 indirectly through TFAP2C as suggested by It will also be important to relate our results to in vivo development in more detail. One question is the origin of PGCs, which in cynomolgus monkeys were found to be the amnion around day 11 . That inWhile the focus in human PGCLC differentiation has typically been on BMP and Wnt, we showed that a major part of the role of Wnt is to induce Nodal and that the effect of Wnt inhibition on hPGCLC specification can be rescued by exogenous Activin. This further highlights the complex feedback between the paracrine signaling pathways that make it hard to directly interpret the effect of a signaling perturbation, and therefore the need for a quantitative approach. By establishing a highly efficient and reproducible differentiation platform and revealing how timing, duration, and dose of Activin/Nodal signaling affect hPGCLC specification, we have laid the foundation for future quantitative investigations of the interplay between different signaling pathways during PGCLC induction, and the downstream GRN that interprets these signals to determine fate.All experiments were performed at least twice. Quantification was performed on four or more colonies per condition in each experiment. Numbers of cells in IF analysis are shown in each scatterplot. Error bars in quantitative image analysis represent standard deviation over colonies unless otherwise stated. Error bars on qRT-PCR data are over technical triplicates from the representative biological sample set.The cell lines used were the embryonic stem cell line ESI017 (XX), and the induced pluripotent stem cell lines PGP1 (XY), WTC11 (XY), MR30 (XX). The identity of these cells as pluripotent stem cells was confirmed by staining of pluripotency markers OCT3/4, SOX2, NANOG. All cells were routinely tested for mycoplasma contamination, and negative results were recorded.β (0.6 ng/ml) and FGF2 (100 ng/ml). Whole-colony routine passaging was done using L7  on Cultrex (R&D Systems)-coated tissue culture plates. mTeSR1 contains TGFusing L7 , and sinusing L7 . All colImaging was done on an Andor Dragonfly/Leica DMI8 spinning disk confocal microscope with a ×40, NA 1.1 water objective. Nuclei were segmented in individual z-slices based on DAPI staining using two different machine learning approaches: Ilastik  and CellTo get a 3D segmentation, we next linked segmentations in different z-slices using a linking algorithm formulated as a linear assignment problem loosely based on the particle tracking approach in A gives the cost of linking nucleus i in frame n to nucleus j in frames n + 1, and is given byn,i is the set of pixels in the mask of nucleus i in frame j and d is the distance between the centroids of the two masks. That is, the cost to link two nuclei is the smaller of the sizes of the two masks divided by the size of their overlap. For efficiency, each nucleus in slice n has this cost computed only for its three nearest neighbors in slice n + 1 and vice versa, and all other costs are set to Inf . We further impose a cutoff dmax on the distance between the centroids of the two nuclei and set A = Inf if the distance exceeds the cutoff. Finally, B and C are square diagonal matrices with all off-diagonal entries set to Inf and diagonal entries set to the ‘alternative cost’ 1/IoU for not linking to any other nucleus, where IoU is an intersection over union threshold set to determine the minimum ratio of overlap to nucleus area that qualifies two nuclei to be linked. If every cost along the ith row of A exceeds 1/IoU, then nucleus i in slice n will be linked to nothing, and likewise for costs along columns. This linking operation is performed sequentially across pairs of adjacent z-slices, creating chains of linked masks in different slices that are taken to correspond to a single nucleus. We additionally impose a maximum expected nuclear diameter and use the spacing between z-slices to determine the maximum number of slices that may correspond to a single nucleus. If more than this number of masks are linked together, the chain is broken into two parts by splitting it at a local minimum in the area of the nuclear mask. Since nuclei are defined across multiple z-slices, a given nucleus has a readout of average fluorescent intensity in each channel in each slice. For each channel, we take the maximum across z-slices as the value for that nucleus as it should correspond to the readout in which the nucleus was most nearly in focus.where Ngithub.com/idse/PGCs.Using the resulting segmentation, we extracted mean intensities for each of the stained markers in each nucleus. For further analysis, the single-cell expression data obtained this way was log(1 + x) transformed similar to what is common for scRNA-seq analysis for several reasons including reduction of the effect of outliers on the analysis. We separated population by thresholds in each marker, which while not perfect performed better than more advanced clustering methods. To determine a threshold between cells expressing or not expressing a marker, we fitted a Gaussian mixture model to the expression data of each gene separately, which worked better than fitting it to the combined gene expression due to the clusters not being sufficiently Gaussian in two or three dimensions. The number of Gaussians was determined automatically using the Bayesian information criterion, and the positive cells were taken to be those belonging to the Gaussian with the highest mean. This generally produced good results but, in some cases, did require manual fine-tuning based on the scatterplot . The data were rescaled so that log(1 + x_thresh) = 1 for visualization in scatterplots. All codes are available on Coverslips were rinsed with PBS, fixed for 20 min in 4% paraformaldehyde, rinsed twice with PBS, and blocked for 30 min at room temperature with 3% donkey serum and 0.1% Triton X-100 in 1× PBS. After blocking, cells were incubated with primary antibodies at 4°C overnight, followed by three washes in PBST (PBS with 0.1% Tween 20). They were then incubated with secondary antibodies and DAPI for 30 min at room temperature and washed twice in PBST at room temperature. In some cases, repeated stainings were done following the protocol from For qPCR experiments, ESI017 cells were grown in 24-well plates or 18-well Ibidi slides. For EOMES response in Cells were collected using accutase and resuspended in ice-cold PBS. Single-cell RNA-sequencing was performed by the University of Michigan Advanced Genomics Core. Cells were barcoded using the 10X Genomics Chromium system . For quality control, cDNA was quantified by Qubit High Sensitivity DNA assay and Agilent TapeStation. Sequencing was performed on the Illumina NovaSeq 6000 with NovaSeq S4 flowcell and Control Software version 1.7.0. Reads were aligned using cellranger-4.0.0 with the GRCh38 reference. Further processing was done in Python using the scprep package, and the script used for all analyses that include all parameters is included as a supplement. After filtering for library size to exclude empty droplets and duplets, 4254 cells were left. After excluding outliers for mitochondrial gene expression and excluding genes that were expressed in fewer than 50 cells, we were left with 4095 cells and 16,151 genes. The data was then transformed using a sqrt, which has a similar effect as the commonly used log(1 + x) transformation without the arbitrary pseudocount to avoid singular behavior at zero. Rather than regress out various factors like cell cycle or pseudogenes that were not of interest or may confound analysis and lead to misinterpretation , we made This manuscript describes a powerful tissue culture model to study the early embryonic development of human primordial germ cells (PGC), the precursors of eggs and sperm. The study dissects the signaling pathways that direct the formation of PGC and provides important clues as to the origins of these cells during development. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Marianne Bronner as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Efficient differentiation of human primordial germ cells through geometric control reveals a key role for NODAL signaling" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:The most important points below relate to further characterization of the hPGCLC cells. This should be as convincing as possible, to maximize the impact of your study.1. In line 192, which is based on the data in Figure 1K, the authors stated that NANOG expression in hPGCLCs is lower than that in undifferentiated hESCs. It also looks like that POU5F1 expression in hPGCLCs is lower than that in undifferentiated hESCs. This somewhat contradicts with the observation made by preceding reports using a directed differentiation system such as those by Kojima et al., 2017, which showed POU5F1 and NANOG are substantially up-regulated during hPGCLC induction. Also, although the authors claimed that NANOG protein levels are higher in PGCLCs , many cells residing in the PRDM1-positive area appeared to show relatively low NANOG. Are the SOX17/TFAP2C-positive cells induced in the micropatterned differentiation system somewhat different from more validated hPGCLCs in other systems?2. Related to this point, Supplemental Figure 2F showed that with regard to the expression of selected highly variable genes, hPGCLCs in the micropatterned system exhibited low correlation to hPGCs in vivo, and showed even higher correlation to Non-Neural ectoderm (Amnion?), YS-mesoderm, Hypoblast, YS Endoderm, Advanced Mesoderm. Why is this?3. The authors should demonstrate that "hPGCLCs" appeared in the micropatterned system truly represent hPGCLCs in a more rigorous manner; for example, by performing a direct transcriptome comparison between "hPGCLCs" in the micropatterned system and hPGCLCs induced in a directed differentiation system. This is critical, because if the "hPGCLCs" appeared in the micropatterned system are somewhat different from hPGCs in vivo, then, this system cannot be used for analyzing the hPGC specification mechanism.4. In Figure 1D-G, which cells correspond to hPGCLCs? The relevant quadrants (top right quadrants) include cells expressing indicated proteins at diverse levels and do not appear to represent a uniform cell population.5. Line 248: The authors use MAGIC to de-noise the scRNA-seq data. On the other hand, MAGIC has been known to often over-impute expression levels of key differentially expressed genes The authors need to make a careful use of the imputation method and provide an appropriate statement regarding this point.6. Figure 2F and the paragraph starting from line 326: All the genes appeared to show continuous up-regulation during hPGCLC specification up to ~96 hour. The authors should show qPCR data of each gene in comparison to the expression levels of representative housekeeping genes in the same differentiation time point, but not to each gene level in undifferentiated ESCs, so that the readers can compare the absolute level of each gene in a more quantitative manner.7. Line 680: Methods for cell culture and differentiation should be described in a more detailed manner so that the readers can at least understand the condition without referring to other papers. For example, inclusion of TGFβ in the culture media was stated in the main text, but not in the Methods section.8. All the claims regarding the mechanism and manner of key cell-fate specifications were based on the authors' in vitro experimental system. It would be better to make discussion taking in vivo developmental context in humans or primates into account.9. The authors should use colors that match the immunostaining colors for TFAP2C, SOX17 and FOXA2 in panels B, E, G and K of Figure 4 (as they do in other figures).10. The images for TFAP2C and PRDM1 in Figure 5G are identical. It appears to be a mistake when assembling the figure as the merge image contains different blue and red channels.11. It is not clear what the x-axis label "F" in Figure 5I refers to.Reviewer #1 (Public Review):Jo et al. use a combination of micropatterned differentiation, single cell RNA sequencing and pharmacological treatments to study primordial germ cell (PGC) differentiation starting from human pluripotent stem cells. Geometrical confinement in conjunction with a pre-differentiation step allowed the authors to reach remarkable differentiation efficiencies. While Minn et al. already reported the presence of PGC-like cells in micropatterned differentiating human cultures by scRNA-Seq (as acknowledged by the authors), the careful characterization of the PGC-like population using immunostainings and scRNA-Seq is a strength of the manuscript. The attempt at mechanistically dissecting the signaling pathways required for PGC fate specification is somehow weaker. The authors do not present sufficient evidence supporting the ability to specify PGC fate in the absence of Wnt signaling and the importance of the relative signaling levels of BMP to Nodal pathways; the wording of the text should be amended to better reflect the presented evidence or the authors should perform additional experiments to support these claims. The molecular characterization of why colonies confined to small areas differentiate much better would greatly increase the biological significance of the manuscript .The authors propose a mathematical model based on BMP and Nodal signaling that qualitatively recapitulate their experimental data. While the authors should be commended for providing examples of other simple models that do not fully recapitulate their data, it would have been nice to see an attempt at challenging quantitatively the model. In particular, the authors do not take advantage of the ability to explore in a more systematic manner the BMP/Nodal phase space with their system.The authors' claim that PGCLC formation can be rescued by exogenous Activin when blocking endogenous Wnt production is surprising given the literature. The authors only show that they can restore a TFAP2C+SOX17+ population but do not actually stain for an established germ cell marker. It appears essential to perform a PRDM1 staining in these conditions  to unambiguously identify this population.The authors only provide weak evidence that the fates depend on the relative signaling levels of BMP and Nodal. Indeed, fewer cells acquire a fate the lower BMP concentration they use, including the fates marked by Sox17 expression. It would more convincing to show the assay of Figure 4F for a range of BMP concentrations at which the overall differentiation works sufficiently well.Reviewer #1 (Recommendations for the authors):The authors should use colors that match the immunostaining colors for TFAP2C, SOX17 and FOXA2 in panels B, E, G and K of Figure 4 (as they do in other figures).The images for TFAP2C and PRDM1 in Figure 5G are identical. It appears to be a mistake when assembling the figure as the merge image contains different blue and red channels.It is not clear what the x-axis label "F" in Figure 5I refers to.Reviewer #2 (Public Review):This manuscript uses a micropatterned differentiation system to explore the mechanism of human primordial germ cell-like cell (hPGCLC) specification and proposes a previously un-recognized role of NODAL signaling operating downstream of BMP signaling. The strength of the manuscript is the development of a simple in vitro system that is potentially suitable for exploring the mechanism of human primordial germ cell-like cell (hPGCLC) specification. The weakness of the manuscript is the lack of rigorous validation of the identity of the hPGCLCs appeared in the micropatterned differentiation system and the lack of the discussion relevant to the in vivo developmental mechanisms.Reviewer #2 (Recommendations for the authors):The manuscript by Jo et al. explores a signaling mechanism for human primordial germ cell-like cell (hPGCLCs) specification from human embryonic stem cells (hESCs) using a micropatterned differentiation system. This system allows relatively easy manipulations of culture conditions and is suitable for analyzing signaling and transcriptional requirements in relevant cell fate specifications. Furthermore, the authors developed a three dimensional (3D) image analysis pipeline to quantify the immunofluorescence analysis data for cell fate specification and based on such analyses under various experimental conditions, interrogated the differentiation process of hPGCLCs. Accordingly, the authors identified a previously unrecognized role of NODAL signaling that acts downstream of BMP signaling and replaces a requirement of WNT signaling for EOMES expression for hPGCLC specification. Based on these results, the authors formulated a simple mathematical model that takes only BMP and NODAL signaling into account and succeeds in recapitulating the authors' experimental outcome, and developed a simple and highly efficient (up to ~70%) hPGCLC induction system with a small diameter of cell micropatterning. Overall, this is a potentially interesting manuscript that may provide a robust system for the mechanistic understanding of the specification of hPGCLCs and other relevant cell types.Following issues should be addressed to strengthen the claims by the authors and to improve the manuscript:1. In line 192, which is based on the data in Figure 1K, the authors stated that NANOG expression in hPGCLCs is lower than that in undifferentiated hESCs. It also looks like that POU5F1 expression in hPGCLCs is lower than that in undifferentiated hESCs. This somewhat contradicts with the observation made by preceding reports using a directed differentiation system such as those by Kojima et al., 2017, which showed POU5F1 and NANOG are substantially up-regulated during hPGCLC induction. Also, although the authors claimed that NANOG protein levels are higher in PGCLCs , many cells residing in the PRDM1-positive area appeared to show relatively low NANOG. Are the SOX17/TFAP2C-positive cells induced in the micropatterned differentiation system somewhat different from more validated hPGCLCs in other systems?Related to this point, Supplemental Figure 2F showed that with regard to the expression of selected highly variable genes, hPGCLCs in the micropatterned system exhibited low correlation to hPGCs in vivo, and showed even higher correlation to Non-Neural ectoderm (Amnion?), YS-mesoderm, Hypoblast, YS Endoderm, Advanced Mesoderm. Why is this?The authors should demonstrate that "hPGCLCs" appeared in the micropatterned system truly represent hPGCLCs in a more rigorous manner; for example, by performing a direct transcriptome comparison between "hPGCLCs" in the micropatterned system and hPGCLCs induced in a directed differentiation system. This is critical, because if the "hPGCLCs" appeared in the micropatterned system are somewhat different from hPGCs in vivo, then, this system cannot be used for analyzing the hPGC specification mechanism.2. In Figure 1D-G, which cells correspond to hPGCLCs? The relevant quadrants (top right quadrants) include cells expressing indicated proteins at diverse levels and do not appear to represent a uniform cell population.3. Line 248: The authors use MAGIC to de-noise the scRNA-seq data. On the other hand, MAGIC has been known to often over-impute expression levels of key differentially expressed genes ; Hou, W., Ji, Z., Ji, H. & Hicks, S. C. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biology 21, doi:10.1186/s13059-020-02132-x (2020)). The authors need to make a careful use of the imputation method and provide an appropriate statement regarding this point.4. Figure 2F and the paragraph starting from line 326: All the genes appeared to show continuous up-regulation during hPGCLC specification up to ~96 hour. The authors should show qPCR data of each gene in comparison to the expression levels of representative housekeeping genes in the same differentiation time point, but not to each gene level in undifferentiated ESCs, so that the readers can compare the absolute level of each gene in a more quantitative manner.5. Line 350: The authors used IWP2 as a WNT inhibitor. It would be better to use at least one more different inhibitor, since the chemical inhibitors are often not pathway-specific and show various off-target effects.6. Line 680: Methods for cell culture and differentiation should be described in a more detailed manner so that the readers can at least understand the condition without referring to other papers. For example, inclusion of TGFβ in the culture media was stated in the main text, but not in the Methods section.7. The terms such as "strikingly" and "surprisingly" appear too many times in the manuscript.8. Line 206: Would it be appropriate to comment on the finding reported in the preprint as "established"?9. All the claims regarding the mechanism and manner of key cell-fate specifications were based on the authors' in vitro experimental system. It would be better to make discussion taking in vivo developmental context in humans or primates into account. Reviewer #1 (Public Review):Jo et al. use a combination of micropatterned differentiation, single cell RNA sequencing and pharmacological treatments to study primordial germ cell (PGC) differentiation starting from human pluripotent stem cells. Geometrical confinement in conjunction with a pre-differentiation step allowed the authors to reach remarkable differentiation efficiencies. While Minn et al. already reported the presence of PGC-like cells in micropatterned differentiating human cultures by scRNA-Seq (as acknowledged by the authors), the careful characterization of the PGC-like population using immunostainings and scRNA-Seq is a strength of the manuscript. The attempt at mechanistically dissecting the signaling pathways required for PGC fate specification is somehow weaker. The authors do not present sufficient evidence supporting the ability to specify PGC fate in the absence of Wnt signaling and the importance of the relative signaling levels of BMP to Nodal pathways; the wording of the text should be amended to better reflect the presented evidence or the authors should perform additional experiments to support these claims.We thank the reviewer for this comment. As described in more detail in the responses below, we have significantly strengthened the evidence for the rescue of Wnt inhibition by exogenous Activin treatment and have nuanced our interpretation. We believe that our data suggest low levels of Wnt may be required directly for PGC competence, while much higher levels are required indirectly to induce Nodal, with Nodal signaling being the limiting factor for PGC specification under the reference condition with BMP4 treatment only. We describe this in detail in the manuscript but summarize it in We have also carried out additional experiments that match model predictions demonstrating the importance of relative BMP and Nodal signaling levels and amended the text to reflect the evidence as suggested. More details are provided below.The molecular characterization of why colonies confined to small areas differentiate much better would greatly increase the biological significance of the manuscript .We believe the mechanism by which cells confined to small colonies differentiate to PGCLCs more efficiently is explained by a larger fraction of the cells being exposed to the necessary levels of BMP and Nodal signaling. In large colonies BMP signaling was shown to be restricted to a distance of 50-100 μm from the colony edge through receptor localization and secretion of inhibitors . From this one would expect that BMP signaling extends a similar distance from the edge in small colonies, so that a larger fraction of cells are receiving the BMP signal needed to differentiate to PGCLCs. Because it was not previously shown that the length scale of BMP signaling and downstream signals are preserved as colony size is reduced, we have now included an analysis of BMP signaling (pSmad1 levels) and Nodal signaling (nuclear Smad2/3 levels) as a function of colony size . This confirms our hypothesis and provides a potential mechanism.The authors propose a mathematical model based on BMP and Nodal signaling that qualitatively recapitulate their experimental data. While the authors should be commended for providing examples of other simple models that do not fully recapitulate their data, it would have been nice to see an attempt at challenging quantitatively the model. In particular, the authors do not take advantage of the ability to explore in a more systematic manner the BMP/Nodal phase space with their system.We thank the reviewer for this suggestion. Experimentally we have now tested the effect of 5x5 = 25 different combinations of BMP and Activin doses on PGCLC differentiation. We then challenged the mathematical model to predict the ‘phase diagram’ corresponding to this data with good agreement . It is important to note here that the model was fit using only data with 50ng/ml of BMP, making this a true prediction. We also point out that the phase diagram predicted in this way is different from the one shown in Figure 6d, not only because of the lower resolution, but because Figure 6f shows the steady state after uniform stimulation in space and time (i.e. the response on the very edge), whereas the predicted phase diagram shows average expression at 42h in a 100um range from the colony edge using the previously measured spatiotemporal gradients of BMP and Activin response. Finally, the data in Figure 6f shows mean expression levels as opposed to the percentage double positive cells for the same data in Figure 4q because our model does not simulate individual cells and noise, only allowing us to compare mean expression. We explain all this in the text now. As a minor change to facilitate comparison of data and model we have now plotted the concentrations of BMP and Activin in Figure 6 rather than the scaled model parameters from 0 to 1, we also further optimized the model parameters without qualitative changes.The authors' claim that PGCLC formation can be rescued by exogenous Activin when blocking endogenous Wnt production is surprising given the literature. The authors only show that they can restore a TFAP2C+SOX17+ population but do not actually stain for an established germ cell marker. It appears essential to perform a PRDM1 staining in these conditions  to unambiguously identify this population.We have significantly extended our analysis of the effect of WNT inhibition and subsequent rescue of PGCs by Activin treatment. This includes staining for TFAP2C,NANOG,PRDM1 and staining for LEF1 as a measure of WNT signaling. Figure 4 and Figure 4—figure supplement 1 now also include treatment with IWR-1, a different small molecule inhibitor of WNT signaling, as well inhibition by IWR-1 and IWP2 at different times and different doses.The authors only provide weak evidence that the fates depend on the relative signaling levels of BMP and Nodal. Indeed, fewer cells acquire a fate the lower BMP concentration they use, including the fates marked by Sox17 expression. It would more convincing to show the assay of Figure 4F for a range of BMP concentrations at which the overall differentiation works sufficiently well.As suggested, we have now included a range of BMP concentrations. The reduction in PGCs at lower BMP doses is in line with our model and does not contradict a dependence on the relative signaling levels of BMP and Nodal by which we mean that optimal dose of Activin for PGCLC specification depends on the level of BMP and vice versa. We have amended the text to state this more clearly.Reviewer #1 (Recommendations for the authors):The authors should use colors that match the immunostaining colors for TFAP2C, SOX17 and FOXA2 in panels B, E, G and K of Figure 4 (as they do in other figures).We thank the reviewer for pointing this out, the colors match now.The images for TFAP2C and PRDM1 in Figure 5G are identical. It appears to be a mistake when assembling the figure as the merge image contains different blue and red channels.We thank the reviewer for pointing out this mistake and we have corrected it.It is not clear what the x-axis label "F" in Figure 5I refers to.The revised manuscript no longer contains this panel but shows scatterplots for two conditions instead .Reviewer #2 (Public Review):This manuscript uses a micropatterned differentiation system to explore the mechanism of human primordial germ cell-like cell (hPGCLC) specification and proposes a previously un-recognized role of NODAL signaling operating downstream of BMP signaling. The strength of the manuscript is the development of a simple in vitro system that is potentially suitable for exploring the mechanism of human primordial germ cell-like cell (hPGCLC) specification. The weakness of the manuscript is the lack of rigorous validation of the identity of the hPGCLCs appeared in the micropatterned differentiation system and the lack of the discussion relevant to the in vivo developmental mechanisms.Reviewer #2 (Recommendations for the authors):The manuscript by Jo et al. explores a signaling mechanism for human primordial germ cell-like cell (hPGCLCs) specification from human embryonic stem cells (hESCs) using a micropatterned differentiation system. This system allows relatively easy manipulations of culture conditions and is suitable for analyzing signaling and transcriptional requirements in relevant cell fate specifications. Furthermore, the authors developed a three dimensional (3D) image analysis pipeline to quantify the immunofluorescence analysis data for cell fate specification and based on such analyses under various experimental conditions, interrogated the differentiation process of hPGCLCs. Accordingly, the authors identified a previously unrecognized role of NODAL signaling that acts downstream of BMP signaling and replaces a requirement of WNT signaling for EOMES expression for hPGCLC specification. Based on these results, the authors formulated a simple mathematical model that takes only BMP and NODAL signaling into account and succeeds in recapitulating the authors' experimental outcome, and developed a simple and highly efficient (up to ~70%) hPGCLC induction system with a small diameter of cell micropatterning. Overall, this is a potentially interesting manuscript that may provide a robust system for the mechanistic understanding of the specification of hPGCLCs and other relevant cell types.Following issues should be addressed to strengthen the claims by the authors and to improve the manuscript:1. In line 192, which is based on the data in Figure 1K, the authors stated that NANOG expression in hPGCLCs is lower than that in undifferentiated hESCs. It also looks like that POU5F1 expression in hPGCLCs is lower than that in undifferentiated hESCs. This somewhat contradicts with the observation made by preceding reports using a directed differentiation system such as those by Kojima et al., 2017, which showed POU5F1 and NANOG are substantially up-regulated during hPGCLC induction. Also, although the authors claimed that NANOG protein levels are higher in PGCLCs , many cells residing in the PRDM1-positive area appeared to show relatively low NANOG. Are the SOX17/TFAP2C-positive cells induced in the micropatterned differentiation system somewhat different from more validated hPGCLCs in other systems?We believe that the discrepancies are due to timing. PGCLCs in our manuscript at 42h are likely less mature than those at day 2 (48h) in Kojima et al. We examined PGCLCs in our system a day later and found that they express NANOG and POU5F1 at much higher levels than pluripotent cells, similar to those in Kojima at 48h. We now show this with immunofluorescence staining for NANOG and POU5F1 at 48h, 72h, 96h in Figure 2hg and Figure 2—figure supplement 2, and have added a discussion regarding NANOG and POU5F1 levels to reflect these observations. We also collected scRNA-seq data at 48, 72, and 96 hours and similarly found that NANOG and POU5F1 substantially increase over time in PGCLCs relative to pluripotent cells, including from 42h to 48h (we show the data and provide further discussion in the next section of this response). The fact that our PGCLCs initially express NANOG and POU5F1 at levels similar to pluripotent epiblast cells is consistent with immunofluorescence data on nascent PGCs in cynomolgus monkeys, where the earliest PGCs appear to express NANOG at levels similar to the epiblast .The authors should demonstrate that "hPGCLCs" appeared in the micropatterned system truly represent hPGCLCs in a more rigorous manner; for example, by performing a direct transcriptome comparison between "hPGCLCs" in the micropatterned system and hPGCLCs induced in a directed differentiation system. This is critical, because if the "hPGCLCs" appeared in the micropatterned system are somewhat different from hPGCs in vivo, then, this system cannot be used for analyzing the hPGC specification mechanism.In summary: We carried out scRNA-seq analysis comparing our micropatterned hPGCLCs to ‘conventional’ hPGCLCs , and to PGCs from the CS7 human gastrula . We conclude that our PGCLCs appear at least as similar to in vivo PGCs as other hPSC-derived PGCLCs. We discuss the details of our analysis below.Extending our analysis of NANOG and POU5F1, we first compared scRNA-seq data that we collected for micropatterned hPGCLCs at 42, 48,72, and 96h across the full panel of marker genes used to establish PGC identity in Figure 4e of Kobayashi et al., Nature 2017. We also added PDPN and KIT to this panel as they are also commonly used cell surface markers for PGCs . In addition to NANOG and POU5F1 we also see a clear increase in other early PGC markers over time. We share this data in We see a similar gene signature as in Kobayashi d4 hPGC+Cy with a few discrepancies. First PGCs in Kobayashi also appear to express some mesoderm and endoderm markers, which may represent imperfect FACS sorting since their data is bulk RNA-seq, or represent a difference in the induction strategy. Furthermore, we appear to lack DND1 and DPPA3 and SYCP3 in comparison to their d4 hPGC+Cy. This again may reflect a difference in experimental setup, for example we see small amounts of DDPA3 transcripts in the mesoderm but not the early PGCs and the bulk RNAseq may combine those. Therefore, we also compared our scRNA-seq data with that for hPGCLCs from Chen et al. Cell Rep 2019 and the Tyser et al. Nature 2021 CS7 human gastrula (in vivo data) and processed each sample in the same way :We see that the transcriptional profiles of the PGCLCs from Chen et al. are very similar to ours and to the profile from the CS7 human gastrula. It is difficult to say with certainty whether differences are due to technical limitations or biology. For example, we appear to be missing low levels of DND1 relative to Chen et al., but in In that regard it is also striking that the human embryo data lacks expression significant expression of PRDM1 in the PGCs. This may again simply be due to noise, especially since their PGC population is only 8 cells. It may also be a clustering error: PRDM1+TFAP2C+ cells are present in that dataset but not annotated as PGC but rather as non-neural/amniotic ectoderm. Again, similar considerations apply to other differences.Related to this point, Supplemental Figure 2F showed that with regard to the expression of selected highly variable genes, hPGCLCs in the micropatterned system exhibited low correlation to hPGCs in vivo, and showed even higher correlation to Non-Neural ectoderm (Amnion?), YS-mesoderm, Hypoblast, YS Endoderm, Advanced Mesoderm. Why is this?We thank the reviewer for pointing this out. We found that there was an error in the figure since the correlation was calculated on the wrong set of highly variable genes. It is now calculated on the intersection of the top 10% most highly variable genes from each data sets. With the new calculation PGCLCs in 42h micropatterned colonies correlate most strongly to PGCs, but the PGCLCs still show relatively low correlation with in vivo PGC and relatively high correlation with non-neural ectoderm (amniotic ectoderm) and endoderm. In summary: we believe that this is also due to a combination of (1) our PGCLCs at 42h being nascent / relatively immature and (2) because of technical limitations in how correlations between datasets are being calculated.To support that low correlation at 42h has to do with the developmental stage of our PGCLCs relative to the CS7 PGCs, we calculated correlation in an identical way for our 48, 72, 96h timepoints . We see While relative correlations in these Author response images are suggestive, absolute correlation should not be quantitatively compared with numbers elsewhere, since correlations are calculated on normalized expression (z-score) and are highly sensitive to the overall cell content of the dataset  as well as various preprocessing and integration steps. Our previous calculations of correlation in the manuscript did not integrate datasets because we have found results to strongly depend on the integration method. However, to illustrate the effect we did integrate our 42h micropattern data with the human gastrula data using the most popular method: Seurat and have included this in Figure 1—figure supplement 2f. As might be expected correlations after integration are much higher, with PGCLC-PGC correlation going from 0.24 to 0.73.To give basis of comparison of our PGCLCs to other PGCLCs, we also calculated the correlation matrices of the data from Chen et al. Cell Rep 2019 using the exact same methods as ours, without integration . These sWe see that by 96h, PGCLC-PGC correlations are similar to, but in fact somewhat lower than for our system. We conclude that our PGCLCs appear at least as similar to in vivo PGCs as other hPSC-derived PGCLCs.2. In Figure 1D-G, which cells correspond to hPGCLCs? The relevant quadrants (top right quadrants) include cells expressing indicated proteins at diverse levels and do not appear to represent a uniform cell population.Indeed, the expression levels are not uniform in each cell population. Gene expression levels are heterogeneous because of biological noise and on top of that there is measurement noise. Moreover, because of the spatiotemporal dynamics of signaling, cells may be at different stages of differentiation in different positions adding more heterogeneity. Nevertheless, coloring scatterplots for density shows clearly defined populations when population sizes are similar, as happens on the small micropatterns, and we now show this in Figure 5—figure supplement 1c . We believe our observations are generally expected and have no knowledge of any study showing single cell quantitative analysis of protein expression in this manner where the studied cell populations are found to have more uniform expression.3. Line 248: The authors use MAGIC to de-noise the scRNA-seq data. On the other hand, MAGIC has been known to often over-impute expression levels of key differentially expressed genes ; Hou, W., Ji, Z., Ji, H. & Hicks, S. C. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biology 21, doi:10.1186/s13059-020-02132-x (2020)). The authors need to make a careful use of the imputation method and provide an appropriate statement regarding this point.We thank the reviewer for this point of caution. We are aware of the risks of denoising/imputation and believe we did make careful use of it. None of our conclusions rely on MAGIC. As stated explicitly in the methods section, MAGIC was used for two purposes only:1) Visualization in Figure 1. It is easier to see the gene expression patterns on PHATE maps after some smoothing with MAGIC. We have now included the same data without MAGIC in Figure 1—figure supplement 2d and have also include raw data in Figure 1—figure supplement 2e.2) Relationships between pairs of genes in Figure 1—figure supplement 1k,n. While none of our conclusions rest on these figures, we believe that they actually demonstrate the value of imputation, because while raw single cell RNA-seq data is so noisy one cannot discern any relationship between any pair of genes, denoising yields relationships between genes that closely resemble those found from immunofluorescence data providing cross-validation for both approaches. We also discuss the differences found in the main text and state imputation artefacts are one possible explanation for these differences. Again, we have now included raw data in Figure 1—figure supplement 1lo to emphasize this.We only used magic for these two purposes and have revised the text to make this clear. For all other analysis, such as cluster comparison with heatmaps , and differential expression analysis, we used the raw data, without MAGIC.4. Figure 2F and the paragraph starting from line 326: All the genes appeared to show continuous up-regulation during hPGCLC specification up to ~96 hour. The authors should show qPCR data of each gene in comparison to the expression levels of representative housekeeping genes in the same differentiation time point, but not to each gene level in undifferentiated ESCs, so that the readers can compare the absolute level of each gene in a more quantitative manner.We have now included CT values relative to GAPDH in Figure 2—figure supplement 1e.5. Line 350: The authors used IWP2 as a WNT inhibitor. It would be better to use at least one more different inhibitor, since the chemical inhibitors are often not pathway-specific and show various off-target effects.As also described above in response to a comment by reviewer 2, we have significantly extended and nuanced our analysis of the effect of WNT inhibition and subsequent rescue of PGCs by Activin treatment. Figure 4 and Figure 4—figure supplement 1 now include treatment with IWR-1, a different small molecule inhibitor of WNT signaling, as well inhibition by IWR-1 and IWP2 at different times and different doses.6. Line 680: Methods for cell culture and differentiation should be described in a more detailed manner so that the readers can at least understand the condition without referring to other papers. For example, inclusion of TGFβ in the culture media was stated in the main text, but not in the Methods section.β and added additional description of the differentiation protocol.We have now explicitly stated in the methods section that mTeSR1 contains FGF2 and TGF7. The terms such as "strikingly" and "surprisingly" appear too many times in the manuscript.We have removed several instances of these and similar words.8. Line 206: Would it be appropriate to comment on the finding reported in the preprint as "established"?We have changed “established” to “suggested”. The work we referred to is now published in Nature Communications.9. All the claims regarding the mechanism and manner of key cell-fate specifications were based on the authors' in vitro experimental system. It would be better to make discussion taking in vivo developmental context in humans or primates into account.We agree and thank the reviewer for this comment. We have added a paragraph relating our results to in vivo findings.ReferencesChen, Di, Na Sun, Lei Hou, Rachel Kim, Jared Faith, Marianna Aslanyan, Yu Tao, et al. 2019. “Human Primordial Germ Cells Are Specified From Lineage-Primed Progenitors..” Cell Reports 29 (13): 4568–4582.e5. doi:10.1016/j.celrep.2019.11.083.Etoc, Fred, Jakob Metzger, Albert Ruzo, Christoph Kirst, Anna Yoney, M Zeeshan Ozair, Ali H Brivanlou, and Eric D Siggia. 2016. “A Balance Between Secreted Inhibitors and Edge Sensing Controls Gastruloid Self-Organization..” Developmental Cell 39 (3): 302–15. doi:10.1016/j.devcel.2016.09.016.Kobayashi, Toshihiro, Haixin Zhang, Walfred W C Tang, Naoko Irie, Sarah Withey, Doris Klisch, Anastasiya Sybirna, et al. 2017. “Principles of Early Human Development and Germ Cell Program From Conserved Model Systems..” Nature 546 (7658): 416–20. doi:10.1038/nature22812.Kojima, Yoji, Kotaro Sasaki, Shihori Yokobayashi, Yoshitake Sakai, Tomonori Nakamura, Yukihiro Yabuta, Fumio Nakaki, et al. 2017. “Evolutionarily Distinctive Transcriptional and Signaling Programs Drive Human Germ Cell Lineage Specification From Pluripotent Stem Cells..” Cell Stem Cell 21 (4): 517–532.e5. doi:10.1016/j.stem.2017.09.005.Sasaki, Kotaro, Tomonori Nakamura, Ikuhiro Okamoto, Yukihiro Yabuta, Chizuru Iwatani, Hideaki Tsuchiya, Yasunari Seita, et al. 2016. “The Germ Cell Fate of Cynomolgus Monkeys Is Specified in the Nascent Amnion..” Developmental Cell 39 (2): 169–85. doi:10.1016/j.devcel.2016.09.007.Tyser, R.C.V., Mahammadov, E., Nakanoh, S. et al. Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289 (2021). https://doi.org/10.1038/s41586-021-04158-y


Glucose-6-phosphate dehydrogenase (G6PD) deficiency is a prevalent condition worldwide and is caused by loss-of-function mutations in the G6PD gene. Individuals with deficiency are more susceptible to oxidative stress which leads to the classical, acute hemolytic anemia (favism). However, G6PD deficiency in newborn infants presents with an increased risk of hyperbilirubinemia, that may rapidly escalate to result in bilirubin induced neurologic dysfunction (BIND). Often with no overt signs of hemolysis, G6PD deficiency in the neonatal period appears to be different in the pathophysiology from favism. This review discusses and compares the mechanistic pathways involved in these two clinical presentations of this enzyme disorder. In contrast to the membrane disruption of red blood cells and Heinz bodies formation in favism, G6PD deficiency causing jaundice is perhaps attributed to the disruption of oxidant-antioxidant balance, impaired recycling of peroxiredoxin 2, thus affecting bilirubin clearance. Screening for G6PD deficiency and close monitoring of affected infants are important aspects in neonatal care to prevent kernicterus, a permanent and devastating neurological damage. WHO recommends screening for G6PD activity of all infants in countries with high prevalence of this deficiency. The traditional fluorescent spot test as a screening tool, although low in cost, misses a significant proportion of cases with moderate deficiency or the partially deficient, heterozygote females. Some newer and emerging laboratory tests and diagnostic methods will be discussed while developments in genomics and proteomics contribute to increasing studies that spatially profile genetic mutations within the protein structure that could predict their functional and structural effects. In this review, several known variants of G6PD are highlighted based on the location of the mutation and amino acid replacement. These could provide insights on why some variants may cause a higher degree of phenotypic severity compared to others. Further studies are needed to elucidate the predisposition of some variants toward certain clinical manifestations, particularly neonatal hyperbilirubinemia, and how some variants increase in severity when co-inherited with other blood- or bilirubin-related genetic disorders. The glucose-6-phosphate dehydrogenase (G6PD) gene is approximately 18 kb in length and contains 13 exons and 12 introns. This gene is mapped onto the X chromosome at the Xq28 band and codes for the G6PD enzyme. G6PD deficiency is caused by loss-of-function mutations in the G6PD gene and follows an X-linked recessive inheritance pattern. These polymorphic variants usually have decreased enzyme activity and are often reported with reduced enzyme stability as well as disruptions in protein folding , 5.The global prevalence of G6PD deficiency was estimated to be 4.9% . Due to Since its discovery in 1956 by Carson et al. the prevalence and health burden contributed by G6PD deficiency have been determined in many parts of the world, made possible by the availability of cheap and rapid screening tests. In some countries neonatal screening programs are established as part of the strategy for prevention of severe neonatal jaundice. G6PD deficiency has been identified as a major risk factor in severe neonatal hyperbilirubinemia that may lead to the debilitating complication of kernicterus. The WHO initiated a working group to address G6PD deficiency as a significant global health issue with publication of a comprehensive classification of the disease, diagnosis, treatment, and prevention . A signiPopulations with medium to high prevalence rates may have a national newborn screening program for early detection of this condition and guidelines in close monitoring of affected infants. Early detection and intervention with phototherapy are simple cost-effective strategies to identify infants at-risk, monitor and treat hyperbilirubinemia expediently to prevent kernicterus. Although the diagnosis and preventative strategy may be rather straightforward, much is unclear about the pathophysiology and heterogeneity of this inherited genetic condition that underlies the severity of neonatal hyperbilirubinemia. The cost-effectiveness of such a healthcare strategy in countries with limited resources and a high disease burden with a rapid turnover of hospital bed occupancy needs due consideration. In countries such as Malaysia, G6PD deficiency is common and is an important cause of severe neonatal jaundice , 13. ManThe G6PD enzyme is constitutively expressed in cells and plays an essential role in the pentose phosphate pathway. It catalyzes the oxidation of glucose-6-phosphate into 6-phosphogluconolactone. Importantly, G6PD regenerates the reduced form of nicotinamide adenine dinucleotide phosphate . NADPH Prdx2 is much more abundant than glutathione peroxidase and catalase. It is highly reactive and should therefore be the main consumer of hydrogen peroxide in RBCs at low to moderate oxidative stress. The antioxidant function of Prdx2 also depends on NADPH. Oxidized Prdx2 forms an interchain disulfide, which is recycled by thioredoxin and thioredoxin reductase, and this requires reducing equivalents from NADPH. The antioxidant activity of the Prdx system should be compromised when NADPH cannot be supplied by the pentose phosphate pathway. This was evidenced by RBC Prdx2 in G6PD-deficient infants, with a higher proportion shown to maintain the oxidized state and are poorly reactivated after peroxide challenge . FurtherThus, a diminished activity of the Prdx2 antioxidant system may be a contributor to G6PD-related diseases, and this could also be dependent on the G6PD genetic variant. More research is needed to study the role of Prdx2 in jaundiced G6PD-deficient infants with and without acute hemolysis. The oxygen-carrying function of the RBC results in the generation of oxygen radicals from cellular metabolism, leading to constant exposure to endogenous and exogenous sources of oxidative stress. As such, the maintenance of GSH and Prdx2 antioxidants are essential to protect the RBCs from these reactive oxygen species (ROS). It should also be noted that RBCs lack other NADPH-producing enzymes, and any loss of NADPH cannot be compensated by other pathways. Therefore, the effect of G6PD enzyme deficiency leading to NADPH insufficiency is particularly marked in the RBC. The decreased NADPH production results in insufficient reduction of oxidized glutathione disulfide (GSSG) and Prdx2, thus rendering the antioxidant system less effective . In compG6PD deficiency is a heterogeneous condition with 217 mutations reported as of 2016. The first 186 mutations were described and listed in detail by Minucci et al. , with 31Caenorhabditis elegans models, with oxidative stress damage shown to be the underlying cause (The one and only nonsense mutation recorded so far was reported in a heterozygous patient of Filipino descent and was termed G6PD Georgia (c.1284C > A), encoding for a truncated protein . As nonsng cause . The hetAccording to the World Health Organization (WHO), G6PD variants are categorized into 5 classes depending on the degree of enzyme deficiency and severity of hemolysis . This clIt has been more than three decades since this classification was introduced. A revision is warranted as more evidence emerge relating genotype with phenotype, genetic variant classes and enzyme activity, and discovery of novel mutations. A recent population-based study on the genotype-phenotype relationship unraveled complexities in the classification that need to be addressed . The G6PMost individuals with G6PD deficiency are often asymptomatic. However, this can easily change when they are exposed to oxidative stress. In the newborn period, these oxidative triggers are rarely encountered than in older children and adults ingesting fava beans, or medications that predispose them to acute hemolytic anemia. Even so, nursing mothers ingesting fava beans and the use of mothballs have been reported to trigger acute hemolytic anemia with jaundice in the neonatal period. More commonly in newborn infants, G6PD deficiency presents as rapid onset and unremitting neonatal jaundice, which if unrecognized early, remains as one of the leading causes of kernicterus or BIND , 37.Vicia faba) contain high concentrations of glucosides called vicine and convicine, which are hydrolyzed in our body to form divicine and isouramil, respectively. Divicine and isouramil are aglycones that are highly reactive and produce ROS in the blood. As NADPH is insufficient in G6PD-deficient RBCs, the increased ROS are not eliminated effectively and thus cause damage to the RBCs. The levels of aglycones produced in our body are known to be affected by multiple factors, and the occurrence of acute hemolytic anemia in G6PD-deficient patients is reported to be dose-dependent . The lifespan of the neonatal RBC ranges between 60 and 90 days, while that of preterm infants are even shorter at 35–50 days . The neoAnother distinctive feature of the neonatal RBC is the presence of the predominant fetal hemoglobin (HbF). HbF has a tendency to denature with oxidative stress and precipitate to disrupt the RBC membrane, contributing to the shortened RBC lifespan. These denatured precipitated HbFs form the Heinz bodies and, as such, most hyperbilirubinemic infants secondary to G6PD deficiency should display characteristics of Heinz bodies. However, this is not the case. A possible explanation is that there is a relatively more efficient clearance of Heinz bodies during the transition period of fetal to adult Hb. Additionally, there is an alternative pathway that reduces the susceptibility of the Hb to oxidative denaturation. A significant fraction of the heme moeity may be converted to metabolites other than bilirubin, such as mesobilifuscins, that are excreted in the bile/feces and urine . More reHeterozygote females are often considered only as carriers, leading to under-diagnosis of G6PD deficiency as the underlying cause of severe hyperbilirubinemia. A case to highlight here is a Malay baby girl who was born at term and developed jaundice at day 3 of life. Basic hematological indices were normal except for the reticulocyte count that was increased at 6.28%. The peripheral blood smear findings showed no evidence of hemolytic anemia. G6PD enzyme level taken on admission was 7.47 U/g Hb . The diagnosis of G6PD deficiency could be easily dismissed if not for the understanding that the enzyme activity was spuriously elevated due to the increased reticulocyte count. Further investigation with molecular analysis for G6PD gene mutations revealed that the patient was heterozygous for G6PD Mahidol  made accessible to resource limited parts of the world with high prevalence of G6PD deficiency may help in early diagnosis and to curb severe hyperbilirubinemia with counseling and advice on taking the necessary steps for prompt intervention. Molecular screening for gene variants may not appear practical or cost-effective traditionally because of the infrastructure and technical expertise required, but advancements are progressively making this more widely accessible in the form of gene chips.G6PD status is usually determined by measuring enzyme activity from whole red blood cells. Screening of cord blood in neonates for G6PD deficiency is a popular method especially in low-middle income countries with high prevalence. Individuals who live in malaria endemic areas are also screened for G6PD status before administration of 8-aminoquinolone compounds. The most widely used G6PD deficiency screening method is the fluorescent spot test (FST), a semi-quantitative assay. Although inexpensive and easy to carry out, this method is only able to detect cases with 20% or less of mean normal G6PD activity and misses most of the moderate G6PD activity especially in female heterozygotes .Several quantitative test kits have been introduced in recent years which are superior to the FST in terms of accuracy and sensitivity. The OSMMR2000-D G6PD assay kit  is one such tool that is available in the market. The G6PD activity is quantified relative to a control sample and is normalized for hemoglobin concentration using a spectrophotometer. Absolute values of G6PD activity can vary between different assays and hence results need to be translated to the population, relative to the male population-specific adjusted mean . The oveAlternatively, the CareStart™ G6PD Biosensor  is a handheld digital device measuring total G6PD activity through electrochemical quantification of NADPH production. This device is preferred in certain situations due to its practicality and convenience in producing a rapid, bed-side result readout. This point-of-care test (POCT) kit is able to incorporate both enzyme activity and hemoglobin concentration measurement into the result, which is automatically calculated within the device. The CareStart™ G6PD Biosensor 1 , a newer generation and more quantitative method of testing, is also reportedly more sensitive . The Hb Due to its simplicity, the CareStart™ G6PD Biosensor POCT becomes an increasingly validated test for use in screening of newborn infants for G6PD deficiency. Analysis of 216 newborn infants against the FST and G6PD enzyme activity assay showed intraclass correlation coefficient of 0.90, with 100% sensitivity and 96% specificity . In a siMolecular techniques remain the most unambiguous method in the diagnosis of G6PD deficiency. The G6PD Deficiency GenoArray Diagnostic Kit  is an all-in-one integrated polymerase chain reaction (PCR), hybridization, and gene chip processing system based on flow-through hybridization technology which can provide a rapid and accurate nucleic acid analysis of a patient sample. This test kit can detect up to 14 G6PD gene mutations in one run and the results can be directly visualized as a precipitated dot on the membrane template to provide a readout of the patient’s G6PD allele. The mutation analyses could be interpreted as heterozygous, homozygous or compound heterozygous without additional PCR runs since the wild type probes have been incorporated into the test kit for comparison. However, as the selection of G6PD mutations for the test kit may be limited to the common mutations known to occur in that locality, further sequencing is necessary if rarer or novel mutations are suspected. Such molecular techniques have been applied on a large scale and population-based studies, availing genetic counseling for families presenting with different variants , 76. App+ binding interface  and G6PD Palermo , reported by Vaca et al.  and RigaOne other aspect of correlating mutations and their corresponding disease severity is through the analysis of amino acid replacement. The basis is that different mutations affect the functional and structural parameters of a protein differently, depending on the similarity or dissimilarity between the WT and the replacing mutant amino acid. The calculation of similarity is based upon variables such as amino acid charge and size, as well as the physico-chemical distance . GranthaAs an example, G6PD Mahidol (c.487G > A) and G6PD Plymouth (c.488G > A) are variants that affect the same amino acid, G163 , 92. G6PIn general, G6PD mutations are position-specific and may cause severe deficiency if they abrogate functional domains of the protein, such as dimerization and alteration of the substrate binding interface. Besides, G6PD mutations can also affect stability of the protein, through changes of its properties such as charged interactions, hydrophobicity and steric hindrance. Resultantly, the protein could be misfolded or subjected to erratic post-translational modification, ultimately being more susceptible to proteolysis and other forms of degradation in the RBC .G6PD deficiency is the most common enzyme deficiency known to affect humans. G6PD deficiency in RBCs results in clinical manifestations stemming from the inability of G6PD-deficient RBCs to deal with ROS. In infants, the risk of neonatal jaundice and hyperbilirubinemia is increased in frequency and severity . G6PD deIt is plausible that hyperbilirubinemia and acute favism may be two separate entities mechanistically. The former, often lacking in overt hemolysis and Heinz bodies, may be more of a bilirubin accumulation and clearance problem. The role of antioxidants such as Prdx2 in RBCs are only beginning to unravel. The predisposition of different variants enhancing the susceptibility of oxidizing denatured Hb (Heinz bodies) to metabolites other than bilirubin is an area that needs to be explored to gain new insights on the heterogeneity of this disease phenotype. Acute favism, on the other hand, is a classic case of RBC membrane disruption. Stronger oxidants could be involved, overwhelming the glutathione system that may be primarily involved in preventing oxidation of protein sulfhydryl groups in the RBC membrane from lysis.+ binding site may cause more detrimental effects compared to mutations of residues in other less conserved sites. Mutations could also lead to G6PD instability and disrupts protein folding which affects the amount of functional protein in vivo. Further studies should investigate the differences and to distinguish between the G6PD variants, their combinations, and co-inheritance with other genes for bilirubin-related metabolism in favism and severe hyperbilirubinemia. It is timely that a WHO working committee is regrouped to review the G6PD classification, stratifying of risks, diagnosis, therapy, and prevention, focusing on neonatal hyperbilirubinemia.The discovery of G6PD crystal structures and subsequent analyses provided us with further insights into this enzyme deficiency, since specific mutations can now be plotted on a 3D position in the protein structure. This information enables us to elucidate the effects of individual mutations and provide us with the genotype-phenotype correlation . The perThe development of affordable and accurate quantitative enzyme POCTs will enable greater opportunities for a more extensive coverage in newborn screening for G6PD deficiency. Advances in molecular techniques have led to the determination of the G6PD gene abnormalities that are more common in various parts of the world. The molecular profile has also provided some insights into the original gene pool of some of the local ethnic groups. These databases could be useful for improved detection, diagnosis, and more cost-effective strategies in managing G6PD deficiency in affected newborn infants.Further research is emerging to characterize different variants with co-existing gene polymorphisms to relate to the bilirubin metabolism pathway and reactions to various oxidants. In this era of precision and personalized medicine there is an increasing need to develop a POCT with molecular genetic testing that produces a risk assessment based on a comprehensive profile for targeted therapy. Perhaps in addressing G6PD deficiency, the most promising in the horizon lies in drug development designed to correct the structural defects caused by the various genetic mutations that are becoming increasingly clearer .FC: conceptualization. HL, AI, RA, AO, FC, and AS: research and manuscript writing. HL and FC: review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We present the case of a man who, after receiving treatment for Covid-19 pneumonia, suffers a manic episode induced by medication.This case is chosen to present as an example of a psychiatric illness derived from Covid-19, in this case secondary to its treatment.We present a manic episode induced by covid medication.Complementary examinations are carried out in which organic pathology is ruled out, being diagnosed of a manifest episode in probable relation to the treatments used and hospital admission is decided due to the impossibility of home management. Neuroleptic and anxiolytic treatment was started at low doses with a good response, and he was discharged with complete recovery and critiqued of the episode.It is estimated that between 20-40% of patients with COVID infection have presented neuropsychiatric symptoms. Mania secondary to treatment was reported in 13 (0 · 7%) of 1744 patients with coranavirus included in a study (1). The Spanish Pharmacivigilance System of Medicines for Human Use (SEFV-H) closely monitors the suspected adverse reactions reported with drugs considered potential therapeutic strategies for SARS-CoV-2 infection. 327 cases of adverse reactions have been reported, of which 18 cases include 28 thermal cases of suspected psychiatric adverse reactions. The interest of this case lies in the fact that it illustrates a psychiatric disorder derived from Covid-19, in this case secondary to the treatment used, which causes a manifest episode with a typical evolution in this type of case with rapid improvement at low doses of antipsychotics.