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Extracellular interactions and ligand degradation shape the nodal morphogen gradient

  1. Yin Wang
  2. Xi Wang
  3. Thorsten Wohland  Is a corresponding author
  4. Karuna Sampath  Is a corresponding author
  1. University of Warwick, United Kingdom
  2. National University of Singapore, Singapore
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Cite this article as: eLife 2016;5:e13879 doi: 10.7554/eLife.13879

Abstract

The correct distribution and activity of secreted signaling proteins called morphogens is required for many developmental processes. Nodal morphogens play critical roles in embryonic axis formation in many organisms. Models proposed to generate the Nodal gradient include diffusivity, ligand processing, and a temporal activation window. But how the Nodal morphogen gradient forms in vivo remains unclear. Here, we have measured in vivo for the first time, the binding affinity of Nodal ligands to their major cell surface receptor, Acvr2b, and to the Nodal inhibitor, Lefty, by fluorescence cross-correlation spectroscopy. We examined the diffusion coefficient of Nodal ligands and Lefty inhibitors in live zebrafish embryos by fluorescence correlation spectroscopy. We also investigated the contribution of ligand degradation to the Nodal gradient. We show that ligand clearance via degradation shapes the Nodal gradient and correlates with its signaling range. By computational simulations of gradient formation, we demonstrate that diffusivity, extra-cellular interactions, and selective ligand destruction collectively shape the Nodal morphogen gradient.

https://doi.org/10.7554/eLife.13879.001

eLife digest

Animals develop from a single fertilized egg cell into multicellular organisms. This process requires chemical signals called “morphogens” that instruct the cells how to behave during development. The morphogens move across cells and tissues to form gradients of the signal. Cells then respond in different ways depending on how much of the signal they receive. This, in turn, depends on several factors: first, how quickly or slowly the signal moves; second, how well the morphogen binds to responding cells and other molecules in its path; and third, how much signal is lost or destroyed during the movement.

Many researchers study morphogen gradients in the transparent zebrafish, since it grows quickly and it is easy to see developmental changes. However, until now it was not fully clear how the well-known morphogen called Nodal moves in live zebrafish as they develop.

Wang, Wang et al. have now investigated how well Nodal signals bind to the surface of cells that receive the signal and to a molecule called “Lefty”, which is present in the same path and interferes with Nodal signals. Advanced techniques called fluorescence correlation and cross-correlation spectroscopy were used to measure Nodal signals at the level of single molecules in growing zebrafish. The experiments gave insights into how far Nodal signals move and remain active. The results showed that, in addition to Nodal diffusing and binding to receiving cells, one of the most important factors determining how far and quickly Nodal moves is its inactivation and destruction. Lastly, Wang, Wang et al. built computational models to test their observations from live zebrafish.

The current work was based on forcing zebrafish to produce molecules including Nodal at locations within the fish that normally do not make them. Therefore future experiments will aim to examine these molecules and their interactions when they are produced at their normal locations in the animal over time.

https://doi.org/10.7554/eLife.13879.002

Introduction

In many animals, development from a single cell to a complex multicellular organism requires the graded distribution and activity of diffusible proteins, which are known as morphogens. How morphogen gradients are formed is not fully understood. Studies in many organisms have suggested three major mechanisms to establish morphogen gradients: 1) diffusion, 2) transcytosis and 3) via cytonemes (Green, 2002; Müller et al., 2013; Rogers and Schier, 2011; Wartlick et al., 2009). For example, the gradient of fibroblast growth factors (FGFs) is established by diffusion and is regulated by extracellular heparan sulfate proteoglycans (HSPGs) (Dowd et al., 1999; Duchesne et al., 2012; Makarenkova et al., 2009; Miura et al., 2009; Nowak et al., 2011; Yu et al., 2009), whereas the gradient of Drosophila Decapentaplegic (Dpp) is established not only by diffusion, but also via transcytosis (Dierick and Bejsovec, 1998; Kruse et al., 2004) and cytonemes (Hsiung et al., 2005; Ramirez-Weber and Kornberg, 1999; Roy et al., 2011).

Nodal proteins, which belong to the TGF-β family of signaling proteins, play critical roles in vertebrate development (Arnold and Robertson, 2009; Wakefield and Hill, 2013). They serve as mesendoderm inducers in vertebrates, and are involved in many aspects of embryonic axis formation during development (Kumari et al., 2013; Sampath and Robertson, 2016). Nodal proteins are translated as precursors and function as dimers (Massagué, 1990). The Nodal precursors are cleaved by extracellular convertases, and convertase processing was found to be essential for Nodal activation in zebrafish and mouse embryonic tissues (Beck et al., 2002; Le Good et al., 2005). A recent report found that FurinA convertase activity regulates long range signaling by the zebrafish left-right patterning Nodal, Southpaw (Spaw), but not other Nodal factors (Tessadori et al., 2015). Upon activation, Nodal proteins form complexes with type II and type I Activin receptors (Acvr1b; Acvr2a/b), which are serine/threonine kinases (Reissmann et al., 2001; Yan et al., 2002; Yeo and Whitman, 2001) and activate the Nodal pathway (Jia et al., 2008; Kumar, 2000; Massagué et al., 2005; Whitman, 1998). Nodal target genes include nodal itself and lefty, which encodes a feedback inhibitor of Nodal signaling (Branford and Yost, 2002; Meno et al., 1999). In zebrafish, of the three nodal homologs, cyclops (cyc), squint (sqt) and southpaw (spaw), sqt and cyc are expressed in an overlapping pattern in the gastrula margin where presumptive mesoderm and endoderm cells are located (Erter et al., 1998; Feldman et al., 1998; Gritsman et al., 2000; Long et al., 2003; Rebagliati et al., 1998a; 1998b; Sampath et al., 1998van Boxtel et al., 2015). However, Sqt and Cyc elicit differential responses in target cells: Sqt acts at long-range whereas Cyc only affects cells immediately adjacent to the source of the signal (Chen and Schier, 2001; Jing et al., 2006; Müller et al., 2012; Tian et al., 2008).

So far, there is no evidence for a requirement for transcytosis and cytonemes in distributing the Nodal factors and the Nodal morphogen gradient has been proposed to be established by simple diffusion (Williams et al., 2004). The diffusion coefficient of a molecule is a measure of its ability to move freely across a defined region. The free diffusion coefficient of the zebrafish Nodals has been suggested to be faster than their effective diffusion coefficient (Müller et al., 2012; 2013), resulting in fast diffusion over short distances but slow diffusion over longer distances presumably by morphogen trapping at high affinity binding sites. These observations led to the hypothesis that Nodal diffusion is hindered either by cell surface interactions or by molecules in the extracellular matrix (Müller et al., 2013). How Nodal diffusion is hindered, and to what extent it shapes the Nodal gradient is unclear.

In contrast to the differential diffusion model, a recent study suggested that a temporal signal activation window created by microRNA-430 (miRNA-430) delays translation of the Nodal antagonist Lefty to determine the dimensions of Nodal signaling in the gastrula (van Boxtel et al., 2015). Repression by miRNA-430 likely plays a key role in regulation of Nodal signaling. However, miRNA-430 is not exclusive to lefty1 but also targets nodal/sqt (Choi et al., 2007). Moreover, reporter protein expression and ribosome-profiling data from zebrafish embryos indicate that Nodal/Sqt and Lefty1 are translated in a similar temporal window in the early gastrula (Choi et al., 2007Bazzini et al., 2012; Chew et al., 2013). As such, it is unclear how the proposed temporal activation window might be converted into a spatial Nodal gradient.

Some studies have suggested that in addition to diffusion, the gradient of a morphogen is related to the rate of ligand clearance or stability (Callejo et al., 2006; Chamberlain et al., 2008; Gregor et al., 2007; Kicheva et al., 2007; Wartlick et al., 2009), and a role for stability and clearance of Nodals in vivo has been proposed (Jing et al., 2006; Le Good et al., 2005; Tian and Meng, 2006). Previously, we reported an atypical lysosome-targeting region located in the pro-domain of Cyc, which targets this Nodal protein for destruction, and regulates target gene induction (Tian et al., 2008). How the lysosome-targeting region regulates Nodal clearance and how it influences the Nodal morphogen gradient was not known.

In this study, we have examined the diffusion coefficient of Nodals in live zebrafish embryos by fluorescence correlation spectroscopy (FCS). FCS is a widely used single molecule sensitive technique that can quantitatively measure diffusion and concentrations in vivo by determining how fast particles diffuse through a fixed observation volume (Shi et al., 2009c; Yu et al., 2009). We estimated the affinity of Nodals to the type II receptor Acvr2b on the cell surface and to Lefty inhibitors in the extracellular space by single wavelength fluorescence cross-correlation spectroscopy (SW-FCCS). SW-FCCS uses a single laser to excite proteins labeled with spectrally different fluorophores within the observation volume (e.g., the confocal volume) (Shi et al., 2009a). By analyzing the correlated movement of various labeled proteins (ligands/receptor/inhibitor), we determined the fraction of the proteins that were free or bound, and calculated the dissociation constants in live zebrafish embryos. We also investigated the contribution of ligand stability in forming the Nodal gradient. By analyzing diffusion and binding in vivo and from computational simulations, we show that diffusivity alone is insufficient to generate the Nodal morphogen gradient. Our findings show that in order to generate and maintain a robust Nodal morphogen gradient, ligand clearance by degradation is balanced against the binding and release of Nodal ligands with the receptor and inhibitors.

Results

Nodal ligands demonstrate similar mobility profiles

To visualize Nodal ligands in vivo, we fused the enhanced green fluorescent protein (EGFP) with Sqt, Cyc, SqtCyc2 and CycΔ2. The CycΔ2mutant, which lacks a lysosomal targeting region in the Cyc pro-domain, shows significantly increased stability and signaling range over wild type Cyc protein (Tian et al., 2008 and Figure 1). SqtCyc2 chimeric protein harbors the atypical lysosome-targeting region from Cyc, and shows reduced stability and signaling range in comparison to Sqt (Tian et al., 2008). We tested the activity of the fusion proteins by comparing nodal target gene induction by the various fusion proteins to that of their untagged counterparts, and found similar activity (Figure 1A–D and Figure 1—figure supplement 1).

Figure 1 with 1 supplement see all
Activity range and diffusion of Nodal-GFP fusion proteins.

(A) Constructs used for profiling fluorescent Nodal fusion proteins in embryos. S, signal peptide; Pro, pro-domain; Mat, mature-domain; sec-EGFP, secreted EGFP. Red arrow indicates convertase cleavage sites. (B) Injection procedure. (C) Confocal image of an injected embryo at 30% epiboly stage. White crosses mark the extracellular spots where the FCS measurements were taken. (D) Representative images of RNA in situ hybridization showing the activity range of Sqt, Cyc and mutant Nodals. Source cells are marked in brown and blue staining indicates expression of the Nodal target ntl. Scale bars, 50 μm. (E) Representative auto-correlation functions (dots) and fittings (line) of Sqt-EGFP (green) and sec-EGFP (black). (F) Table showing diffusion coefficients of the Nodal and Lefty fusion proteins as measured by FCS.

https://doi.org/10.7554/eLife.13879.003

The diffusivity of extracellular signaling molecules can determine their distribution and activity range. To examine the diffusivity of the Nodal ligands, we determined the diffusion coefficients of Sqt-, Cyc-, Sqt Cyc2- and CycΔ2-EGFP fusion proteins in vivo using FCS. EGFP-tagged Nodal fusion proteins were expressed from a localized source and FCS measurements were acquired in the extracellular space at various distances from the source cells (Figure 1 and Figure 1—source data 1). All the Nodal-GFP fusions, including Sqt, Cyc, SqtCyc2 and CycΔ2, as well as Lefty1 and Lefty2 fusions show very similar diffusion coefficients (Figure 1F). These results suggest that the free diffusivity alone is unlikely to differentiate the range and activity of the Nodal proteins.

Sqt binds to Acvr2b receptor with higher affinity compared to Cyc

Cell surface receptors of extracellular signaling molecules can bind to the diffusible ligands, and thereby reduce their distribution range. In this scenario, the mobility of ligands that bind with higher affinity to the receptors will be more effectively retarded. To quantitate the binding affinity of Nodal ligands to the receptors, we determined the apparent dissociation constant (Kd) of Sqt and Cyc in vivo (Foo et al., 2012b) with the predominant Nodal receptor Acvr2b, using FCCS. To uncouple binding from signaling events within the cytoplasm, we fused the extracellular and trans-membrane domains of Acvr2b lacking the intracellular kinase domain, to a red fluorescent protein, mCherry (Figure 2A–C). Sqt-EGFP, Cyc-EGFP or control secreted eGFP (sec-EGFP) fusion proteins were expressed from a localized source in embryos that uniformly expressed Acvr2b-mCherry, and correlation curves were obtained to infer the Kd. Surprisingly, the Kd of Sqt-Acvr2b is 65 ± 7 nM and the Kd of Cyc-Acvr2b is 124 ± 12 nM (Figure 2D–I). This result suggests Sqt binds with Acvr2b with an approximately twofold higher affinity compared to Cyc.

Sqt has higher affinity to the Acvr2b receptors than Cyc.

(A) Sqt-/Cyc-/sec-EGFP and Acvr2b-mCherry constructs. S, signal peptide; Pro, pro-domain; Mat, mature-domain; ECM, extracellular and transmembrane domain. Red arrows indicate convertase cleavage sites. Sqt signal peptides and pro-domain were used in sec-EGFP constructs. (B) Injection procedure. (C) Representative image of an injected embryo at 30% epiboly stage showing the expression patterns of the fusion proteins. Scale bar represents 50 μm. (D,E,F) Representative auto-correlation (ACF) and cross-correlation functions (CCF) and fits. (G,H,I) Individual Ln(Kd) frequency histogram and Gaussian fitting (red curve). Inset, concentration plot and linear regression (red line). X axis, concentration of bound protein (Cgr (x10-9 M)); Y axis, products of concentrations of free proteins (Cg x Cr (x10-16 M)). n = number of data points (number of embryos).

https://doi.org/10.7554/eLife.13879.006

Sqt binds to Lefty2 inhibitor with higher affinity compared to Cyc

The Nodal antagonist Lefty prevents Nodal proteins from binding to their receptors and has the potential to influence the distribution of Nodal ligands. To test if binding to the inhibitor affects Nodal ligand distribution, we determined the affinity of Sqt and Cyc to Lefty2 in vivo by co-expressing Lefty-mCherry with Sqt-EGFP or Cyc-EGFP from a localized source and measuring the Kdin the extracellular space of embryonic blastula cells at various distances from the source (Figure 3A–C). The Kd of Sqt-Lefty2 is 29 ± 1.2 nM and Cyc-Lefty2 Kd is 50 ± 3 nM (Figure 3D–I), indicating an approximately twofold higher affinity of Sqt-Lefty2 binding in comparison to Cyc-Lefty2 binding. The differential affinity of the Nodals for Lefty could fine-tune their activity range by removing freely diffusing Nodals from the signaling pool.

FCCS measurements reveal that Lefty has higher affinity to Sqt compared to Cyc.

(A) Constructs used for injection. S, signal peptide; Pro, pro-domain; Mat, mature-domain. Red arrows indicate the convertase cleavage sites. (B) Injection procedure. (C) Confocal image of an injected embryo at 30% epiboly showing the expression patterns of the fusion proteins. Scale bar represents 50 μm. (D, E, F) Representative auto- and cross-correlation functions (ACF; CCF) and fittings. (G, H, I) Individual Ln(Kd) frequency histogram and Gaussian fits (red curve). Inset, concentration plot and linear regression (red line). X axis, concentration of bound protein (Cgr(x10-9 M)); Y axis, products of concentrations of free proteins (Cg x Cr(x10-17 M)). n = number of data points (i.e., number of embryos).

https://doi.org/10.7554/eLife.13879.007

The range of Nodal factors correlates with their stability

To visualize Nodal gradients in zebrafish embryos, we expressed Sqt, Cyc, SqtCyc2 and CycΔ2-EGFP fusion proteins from a localized source (Figure 4A–D). Consistent with findings by Müller et al., Sqt-EGFP was found to reach the edges of the blastula with no more than 50–60% loss in intensity, whereas the intensity of Cyc-EGFP fusion protein falls steeply from the source (Figure 4B and Figure 4—source data 1). Interestingly, the gradient of the deletion mutant, CycΔ2-EGFP, which has a longer signaling range than Cyc-EGFP, was significantly shallower than that of Cyc, and the gradient of the SqtCyc2-EGFP chimera (which has reduced signaling range compared to Sqt) was steeper than that of Sqt-EGFP (Figure 4B and Figure 4—source data 1).

Figure 4 with 1 supplement see all
The distribution of Nodal proteins correlates with clearance.

(A) Upper, representative image and region of interest (red rectangle) for measuring distribution; lower, inset showing magnified region of interest. (B) Normalized distribution profiles and fitting. Error bars indicate standard error of mean (s.e.m). (C) Representative western blots of Nodal proteins harvested from HEK293T cell culture medium at different time points after removal of the source. The Nodal proteins were immuno-precipitated with anti-FLAG antibody and detected by western blot with the same antibody. Schematics on the left show the position of the FLAG epitope tags in each construct. (D) The profile of Nodal protein levels over time after source removal. The data points were fitted with an exponential decay model. Error bars indicate s.e.m.

https://doi.org/10.7554/eLife.13879.008

To determine the relative stability of Sqt, Cyc, CycΔ2 and SqtCyc2, we expressed FLAG-tagged versions of the proteins in HEK293T cells and examined the amount of secreted protein in the supernatant after various periods (Figure 4C,D and S2). By fitting the normalized band intensity acquired from immune-blots with an exponential decay model, we inferred the degradation rates (0.166 × 10−4 /s for Cyc-FLAG, 0.093 × 10−4 /s for CycΔ2-FLAG 0.090 × 10−4 /s for SqtCyc2-FLAG and 0.003 × 10−4 /s for Sqt-FLAG). The decay rate of these proteins shows a trend consistent with their gradient profile and their signaling range (Figure 4A–D, Figure 4—source data 1, Figure 1 and S2). These results indicate a strong correlation between Nodal ligand stability and the gradient.

Simulation of the Nodal gradient

To test the validity of our measurements, we performed simulations to model the Nodal gradient. Modeling of the Nodal morphogen gradient requires a range of different parameters, of which some have been measured in vivo and are available, and we have in this study determined binding affinities, and inferred concentrations (Table 1). We found the dissociation constants, KD, of Sqt and Cyc to their major cell surface receptor Acvr2, to be ~60 and 120 nM, respectively. In addition, we determined the diffusion coefficients of the Nodals to be ~60 μm2/s. From the amplitude of our FCS measurements, we estimated the concentration of the Nodal factors to be on the order of 102 nM. We determined that the degradation rate for Cyc is higher than that for Sqt, confirming previous work (Tian et al., 2008; Jing et al., 2006). Nonetheless, because our degradation rate values were estimated from cell culture, in our simulations we use the values of 0.0001/s and 0.0005/s for Sqt and Cyc, respectively, documented in or estimated from previous reports (Jing et al., 2006 Müller et al., 2012). In the simulations we produced particles with 0.07/s to 0.7/s in the simulation volume (corresponding to a production rate of 0.3–3 pM/s) to obtain sufficient number of particles for statistical analysis. The number of particles at equilibrium is given by the ratio of production over degradation rate, which was 700–7000 for a degradation rate of 0.0001/s and 140–1400 for 0.0005/s. Importantly, the production rate itself does not change the gradient shape and only the gradient amplitude is altered. Therefore, the gradient shape is determined by the degradation rate and diffusion.

Table 1

Simulation parameters

https://doi.org/10.7554/eLife.13879.011
ParameterSqtCycReference
Kd60 nM120 nMThis work
Degradation rate0.0001/s0.0005/sEstimated from Jing et al., 2006Müller et al., 2012
Ligand concentration~100 nM~100 nMEstimated from this work
Receptor concentration40 μM40 μMEstimated from this work
Dfree [μm2/s]6060This work
Dtortuosity [μm2/s]3030This work and Müller et al., 2013
Deff [μm2/s]0.0450.09This work

First, we determined how the fact that particles have to transverse longer paths around obstacles (e.g., cells) during diffusion, renders diffusion apparently slower, and influences the effective diffusion coefficient (Figure 5A,B and Videos 1, 2). This is referred to as tortuosity by Müller et al. (2013) and reduces diffusion maximally by a factor 2 (Müller et al., 2013). In agreement with this, we determined that for cells with 10 μm diameter and cell membrane-to cell membrane distance of 2 μm, we obtain a reduction of diffusion by a factor 1.84 (Figure 5D).

Simulations of morphogen diffusion.

(A) Free diffusion with a diffusion coefficient D = 60 μm2/s. (Dashed circles indicate positions of cells in later simulations but not taken account of in this case). (B) Diffusion in the presence of cells. (C) Diffusion in the presence of cells and binding with an average number of free particles of 0.003, i.e. 99.7% of all particles are bound on average. Simulations were done in a 3D space as described in the text and the diffusion coefficient was D = 60 μm2/s. (D) Comparison of the spread of the particles as a function of the distance from the source (the left border in panels A–C). The concentration curves were fit with a bell curve that describes the diffusion of particles from the source. For free diffusion (A) we recover a diffusion coefficient of D = 63.4 μm2/s close to the input value, and in the presence of cells (B) this reduces to an effective diffusion coefficient of Deff = 33.8 μm2/s, demonstrating the effect of tortuosity. (E) Simulations of diffusion in the presence of cells, and with different amounts of binding. The simulated diffusion coefficient was D = 60 μm2/s. The concentration curves were fit with Equation 8. The recovered effective diffusion coefficients for a fraction of free particles of 0.1, 0.003, and 0.0015 were Deff = 2.99 μm2/s, Deff = 0.09 μm2/s, and Deff = 0.042 μm2/s, respectively, demonstrating the effect of binding on the effective diffusion coefficient. (F) Gradient formation using the effective diffusion coefficients determined from graph E and degradation rates of 0.0001/s and 0.0005/s, respectively. The blue curve represents Cyc, the red curve Sqt. Although Sqt has higher binding affinity and consequently a lower free mobile fraction, its lower degradation rate ensures that Sqt has a less steep gradient. The data was fit with an exponential function yielding gradients of 19 μm for Cyc and and 30 μm for Sqt, respectively.

https://doi.org/10.7554/eLife.13879.012
Video 1
Simulation of particles moving and freely diffusing from the source.

Dashed circles indicate positions of cells in later simulations (included here for illustration only, but have no influence on the simulation).

https://doi.org/10.7554/eLife.13879.013
Video 2
Particle movement is hindered by cells (black circles) around which they move.
https://doi.org/10.7554/eLife.13879.014

Secondly, we determined how binding affects the effective diffusion coefficient (Figure 5C,E and Video 3). For quantitative analysis, we simulated particles whose diffusion coefficient was recued from 60 to 30 μm2/s due to tortuosity and assumed that a fraction of the particles is bound to binding sites that are homogeneously distributed. The effective diffusion coefficient is reduced more for higher affinities, i.e. when more particles are bound on average. For instance, when 90 or 99% of particles are bound, morphogen diffusion is reduced by a factor 10 or 100, respectively. The actual amount of bound ligand depends on the total concentration of ligand (Lt), receptor (Rt) and the KD:

(1) fbound=Kd+Lt+Rt2Lt(Kd+Lt+Rt)24Lt2RtLt

Thirdly, for the simulations we assume that Sqt and Cyc share the receptors, and a ligand concentration of 100 nM is used for both Nodal ligands. The receptor concentration is on the order of 10 μM or more (see next paragraph), and is much higher than that of the ligands. As such, the exact ligand amount does not change the outcome significantly for ligand concentration changes within a factor of ~5. The differences in the gradient length are therefore, a result of diffusion, differential binding of Sqt and Cyc, as well as different degradation rates.

Video 3
Particle movement is hindered further by binding.
https://doi.org/10.7554/eLife.13879.015

Next, we determined the effective diffusion coefficient that results in a Sqt gradient length consistent with our measured values of about 30 μm. The gradient length is described by a model previously used for Fgf8 diffusion in zebrafish embryos (Müller et al., 2013; Yu et al., 2009):

(2) λ=D/kR/Kd+1

where λ is the gradient length, D is the free diffusion coefficient, k is the clearance rate which is assumed constant, R is the concentration of the receptor, and Kd is the equilibrium dissociation constant, respectively (Table 1). Using a gradient length of about 30 μm and the other values as given in Table 1, we estimate the effective diffusion coefficient to be on the order of 0.045 μm2/s. With a Kd of 60 nM for Sqt and an Lt of 100 nM, this requires a bound fraction fbound of 99.85% and a receptor concentration of ~40 μM. The estimation of the receptor number of 40 μM is based on the value of Deff required to establish the gradient of appropriate dimensions for Sqt. At this time it is not clear whether this concentration comprises only membrane receptors or whether additional binding sites (e.g., in interstitial spaces between cells) contribute to it as shown for Fgf8 (Yu et al., 2009). Any corrections in binding affinities for the receptor or different affinities for additional binding sites would alter the required concentration. Importantly, this number is dependent on the clearance/degradation rates that we have based upon previous reports, and which could change with more precise in vivo measurements. Based on the above, we assume that 40 μM is the upper limit for the receptor concentration. At this receptor concentration, Cyc, with a Kd of 120 nM will have a bound fraction of 99.7% and an effective diffusion coefficient of 0.09 μm2/s.

Finally, we used these effective diffusion coefficients and the degradation rates of 0.0001/s and 0.0005/s for Sqt and Cyc, respectively, to simulate gradient formation (see simulation parameters in Table 1). Using these parameters, Sqt produced a gradient of ~30 μm length and Cyc gradient was ~19 μm (Figure 5F). These values are consistent with actual in vivo measurements (Figure 4B and Figure 4—source data 1), and support our hypothesis.

Discussion

In our study, we found that the diffusion coefficients of free Nodal and Lefty proteins measured by FCS are very similar (~60 µm2/s), consistent with their similar apparent molecular weight and with previous reports (Müller et al., 2013). However, their mobility is too great to account for the sharp gradients observed in developing embryos. The diffusion coefficient values determined by FCS are ~3 to 85 times higher than the effective diffusion coefficients reported using FRAP (18.9 ± 3.0 µm2/s for Lefty2, 3.2 ± 0.5 µm2/s for Sqt and 0.7 ± 0.2 µm2/s for Cyc) (Müller et al., 2013). FCS and FRAP produce different readouts because they measure diffusion in different contexts, time windows and scales. FCS determines diffusion within a small volume (<1 µm3) and on a short timescale (<1 s), whereas FRAP measures net diffusion over a large area (>1,000 µm3) over a longer time period (>>10 s). FCS has been very useful to determine local diffusion, to infer the concentration of molecules within a defined confocal volume, and to determine the affinity of molecular interactions within the defined confocal volume. FRAP has been very useful for examining large-scale movement of molecules in tissues. The diffusion coefficients determined by the two techniques are known to vary dramatically. For example, in Drosophila imaginal discs, the measured diffusion coefficient of Dpp-GFP is 10 ± 1 µm2/s from FCS measurements (Zhou et al., 2012), and 0.1 ± 0.05 µm2/s by FRAP (Kicheva et al., 2007). This difference is thought to arise from other factors (e.g., by degradation of the molecules during diffusion). Taken together, these findings strongly suggest there must be other molecules and mechanisms in the embryo that refine and shape the Nodal morphogen gradient.

The diffusional movement of morphogens can also be altered by transient binding to other molecules such as receptors or to components of the extracellular matrix (Baeg et al., 2004; Belenkaya et al., 2004; Han et al., 2004; Lander et al., 2002; Wang et al., 2008; Yu et al., 2009), so that one possible mechanism to shape the gradient is transient binding of Nodal proteins to immobilized diffusion regulators, as found for the fibroblast growth factor Fgf8 (Yu et al., 2009). To explain the long range distribution of Sqt compared to Cyc, it was also proposed that Sqt might have a lower binding affinity to its receptors, (Müller et al., 2012). However, we find that Sqt in fact binds in vivo to Acvr2b with a higher affinity than the short-range Nodal, Cyc. Sqt also binds to Lefty with higher affinity, raising the possibility that Lefty-binding might alter Sqt activity. Another potential mechanism for gradient formation is rapid clearance of molecules during diffusion, as observed for Dpp in Drosophila (Kicheva et al., 2007). Our measurements and calculations strongly support this mechanism.

Previous studies have suggested that the Nodal gradient might be influenced by its stability: Le Good et al. showed that increasing the stability of mouse Nodal protein increases its range of activity (Le Good et al., 2005); Tian et al. found a lysozyme targeting signal in Cyc that accelerates its degradation and reduces the signaling activity of chimeric SqtCyc2 protein (Tian et al., 2008); Jing et al. determined the half-life of Sqt and Cyc to be ~8 hr (~480 min) and ~2 hr (~120 min), respectively, which somewhat correlates with the difference in target induction by the two proteins (Jing et al., 2006). However, the difference in clearance rates of Sqt, Cyc, Lefty1 and Lefty2 determined by photo-conversion assays is not pronounced enough to explain their very different decay lengths (Müller et al., 2012). Interestingly, Müller et al. found that their fluorescent Cyc fusion protein was expressed at very low levels in the extracellular matrix, but exhibited an unusual punctate distribution close to the plasma membrane and in the cytosol, whereas their Sqt fusion showed a strong, uniform and mainly intracellular distribution. The punctate distribution of Cyc suggests that Cyc might undergo a much faster and/or sustained endocytosis process compared to Sqt. This supports our finding that cells selectively destroy Nodal ligands by recognizing the lysosome-targeting signal, since the ligands have to be internalized.

Simulations of the Nodal gradient show that Sqt generates a gradient of 30 μm and Cyc 19.1 μm, consistent with our measurements of 29.5 ± 5 μm for Sqt and 19.7 ± 2 μm for Cyc (from Figure 4B), as well as the estimated signaling range of these proteins (Chen and Schier 2001; Tian et al., 2008). In the simulations, 80% and 95% of steady state levels for Cyc is achieved at 0.7 and 1.25 hr, respectively, which is consistent with the timing of mesoderm induction in the gastrula. By the same predictions, Sqt reaches the 80% and 95% levels at ~4 hr and 7 hr, respectively, which is longer than expected. However, these simulations have not taken into consideration cell divisions or binding to other factors that could influence the gradient. Despite some differences in absolute values, the overall agreement between our experimental results, theory, and simulation supports our conclusion that the Nodal gradient is dependent upon diffusion, binding, and degradation of the morphogen.

An important point to note from Equation (2) is that the clearance rate k and the receptor concentration R are inversely related to each other. Thus, a higher clearance rate would predict a lower receptor concentration and vice versa. It will be interesting to determine these values with higher accuracy in live zebrafish embryos. In addition, some aspects of the system have not been taken into account in our simulations. In particular, we found that Lefty binds the Nodals with high affinity. This may not influence gradient formation as the Sqt/Lefty and Cyc/Lefty complexes likely diffuse very similarly to Sqt and Cyc given that their size difference is within a factor of two. However, Lefty will influence Sqt and Cyc signaling when in complex, even if this is not directly evident in the gradient of fluorescent molecules. We also have not considered how the Nodal co-receptor (Yan et al., 1999) influences gradient formation. It will be interesting to determine how Oep/Cripto co-receptors and Lefty shape the active signaling gradient. The extracellular matrix (ECM) has been shown to play a key role in regulating diffusion of FGFs, presumably via interactions with heparan sulphate proteoglycans (HSPGs) (Makarenkova et al., 2009Yu et al., 2009). It is not known if the ECM or HSPGs play a role in modulating the Nodal morphogen gradient although sulfated proteoglycans have been proposed to provide directional cues for left-asymmetric Nodal in Xenopus (Marjoram and Wright., 2011).

In conclusion, we find that in addition to hindered diffusion via binding to the receptors and inhibitors, the differential stability of Nodal ligands play key roles in shaping the Nodal gradient and activity range. Our experimental findings together with theoretical and computational simulations show that diffusion, extracellular interactions i.e., Nodal-receptor binding, Nodal-Lefty inhibitor binding, and selective ligand destruction collectively shape and refine the Nodal morphogen gradient.

Materials and methods

Generation of constructs

All the constructs were PCR amplified and cloned into pCS2+ vector with Kozak sequence gccacc immediate 5’ of the start codon. For Cyc and CycΔ2 fusions, EGFP or 3xFLAG (DYKDHDGDYKDHD-IDYKDDDDK) tag was inserted 4 amino acids after the cleavage site (RRGRR). For Sqt and SqtCyc2 fusions, EGFP or 3xFLAG tag was inserted 1 amino acid after the cleavage site (RRHRR). For Lefty1 and Lefty2 fusions, EGFP or 3xFLAG tag was fused to the C-terminus of the protein as previously described (Müller et al., 2012). For Acvr2b fusion, mCherry was fused to the C-terminus of Acvr2b (1–188 aa). For generating the sec-EGFP construct, the EGFP tag was fused to the C-terminus of 4 amino acids after the cleavage site (RRGRR) of Sqt. For sec-EGFP-3xFLAG construct, 3xFLAG tag was fused to the C-terminus of Sec-EGFP (Yu et al., 2009).

Zebrafish strains

Wild-type (AB) fish were maintained at 28.5°C and embryos were obtained from natural matings according to standard procedures and in accordance with institutional animal care regulations.

Capped RNA synthesis

The plasmids were linearized with NotI restriction endonuclease (NEB) and transcribed using the mMessage mMachine SP6 Kit (Ambion) to produce capped RNA. Synthetic RNA was purified with P-30 Bio-Spin columns (Bio-Rad, Hercules, CA) followed by phenol/chloroform extraction and ethanol precipitation, and RNA concentration was quantified by Nanodrop (Thermo Fisher Scientific, Waltham, MA) and estimation of agarose gel electrophoresis bands.

Microinjection and sample preparation

To test overall inductivity of the various Nodal fusions, 5 ng of the RNA was mixed with 0.25% phenol red (Sigma, Aldrich, St Louis, MO) and injected into the yolk of 1-cell stage AB wild type embryos. To test the signaling range of the Nodal fusions, single cells of de-chorionated 128-cell stage embryos were injected with 2.5 pg RNA and 0.25% 10kDa biotin-Dextran (Thermo Fisher Scientific). Un-injected embryos from the same batch were used as controls and as reference for staging the embryos. Embryos were fixed at the 50% epiboly stage in 4% paraformaldehyde (PFA) in PBS at 4°C for at least 24 hr. The embryos were subjected to in situ hybridization to detect gsc and ntl expression levels as previously described (Le Good et al., 2005; Müller et al., 2012; Tian et al., 2008).

To generate clones of cells expressing Nodal or Lefty, Sqt, Cyc, Lefty1 or Lefty2 -EGFP RNA (2.5 pg) was injected into single cells of 32–128-cell stage embryos. To determine the dissociation constant of Nodal and Acvr2b, 50 pg RNA encoding Acvr2b-mCherry was injected into 1-cell stage embryos prior to clone generation. The embryos were mounted on glass-bottom dishes (World Precision Instruments) in 0.75% low melting temperature agarose (in 30% Danieau’s solution) at the 30% epiboly stage for confocal imaging and FCS/FCCS measurements.

Protein expression and detection

HEK293T cells were transfected with plasmid DNA encoding 3xFLAG tagged proteins using FuGene HD (Promega, Madison, WI) transfection reagent. The medium with transfection reagent was removed and replaced with fresh Opti-MEM medium (Life Technologies, Carlsbad, CA) 24 hr after addition of transfection reagent. Cell culture supernatants were collected and flash frozen in liquid nitrogen 24 hr after the removal of the transfection reagent. Small aliquots of frozen supernatants were immunoprecipitated with anti-FLAG M2 antibody (Sigma) and protein G dynabeads (Life Technologies), and eluted with 3xFLAG peptide (Sigma). The samples were immunobloted with the same antibody and the signals were detected with a Syngene PXi gel imaging system. The band intensity was quantified using ImageJ. To determine the clearance rate of the proteins, the remaining supernatant was diluted to the same concentration as supernatants from non-transfected cells, mixed with sec-EGFP-3xFLAG supernatant for input control, added to dishes with non-transfected cells and collected at different time points. Proteinase inhibitor cocktail (Roche, Switzerland) was added immediately after the supernatants were collected and flash frozen in liquid nitrogen. The supernatants were enriched and detected as described above. The intensity of individual protein bands was normalized against EGFP to correct for differences in sample volume and immunoprecipitation, and normalized to time 0 for relative changes.

FCS/FCCS instrumentation

A custom-built single wavelength fluorescence cross-correlation spectroscopy (SW-FCCS) system was used for the FCS and FCCS measurements as described (Shi et al., 2009a; 2009b).

Measurement of diffusion coefficients and binding affinity

We obtained the correlation curve of the various fusion proteins by focusing the detection volume on the cell membrane at various distances from the source (Figure 2A–C). The correlation curves were analyzed and fitted with a bimolecular binding model to calculate the Kd. We co-expressed Lefty-mCherry with Sqt-EGFP or Cyc-EGFP from a localized source and measured the Kin the extracellular space of blastula cells at various distances from the source.

FCS/FCCS data processing

The experimental raw auto-correlation data was fitted with defined correlation function models.

In FCS, a one-component 3D diffusion model with triplet state was used for free diffusing molecules:

(3) G3D,1C,1trip(τ)=1N[1+(Ftrip1Ftrip)eτ/τtrip](1+ττd)1[1+(ω0z0)2ττd]1/2+G,

where N is the number of particles in the confocal volume; Ftrip is the fraction of the particles that have entered the triplet state; τtrip is the triplet state relaxation time; τd is the average time required for one particle to diffuse through the confocal volume, ω0 and z0 are the radial and axial distances where the excitation intensity reaches 1/e2 of its value from the center of the confocal volume; and G is the convergence value of the ACF for long times.

In FCCS, a one-component 2D diffusion model and a two-component 3D model were used for the membrane anchored receptors and Nodal ligands, respectively:

(4) G2D,1C,1trip(τ)=1N[1+(Ftrip1Ftrip)eτ/τtrip](1+ττd)1+G
(5) G3D,2C,1trip(τ)=1N[1+(Ftrip1Ftrip)eτ/τtrip]{iFi(1+ττdi)1[1+(ω0z0)2ττdi]1/2}+G

where τdi and Fi are the diffusion time and the amplitude of the ith component. The cross-correlation data was fitted by a one-component 2D model:

(6) G3D,1C,1trip(τ)=1N(1+ττd)1+G

Data was fit with the Levenberg-Marquardt algorithm using the described models in Igor Pro 6.0 (WaveMetrics) (Wohland et al., 2001). The procedure of calibration and quantification of diffusion coefficient and dissociation constants were as previously described (Foo et al., 2012a; Shi et al., 2009a).

Gradient analysis

EGFP fusion proteins were excited with a 488 nm laser beam and the emitted fluorescence was collected through a 10X objective lens (Olympus, UPLSAPO NA = 0.40) and a long-pass 505 emission filter with a 2.5X digital zoom. Images were acquired in planes ~15 μm below the enveloping layer of the embryos at 512 × 512 pixels with a corresponding size of 1.4 μm2/pixel. Acquired images were analyzed using the ImageJ package. A rectangular region of interest (ROI) with a fixed height of 50.4 μm (36 pixels) adjacent to the source was drawn. The width of the ROI differed depending on the size of the embryo. Windows of 7 × 50.4 μm2 (5 × 36 pixels) were binned and the average intensity of each binned window was calculated. Background auto-fluorescence was estimated from images of un-injected embryos and subtracted from all measurements. The data was normalized to the value closest to the source boundary, plotted on the intensity-distance coordinate with ImageJ. The data was pooled and fitted, or individual data sets were fitted and the gradient length was calculated as the mean of all fits. Both procedures yielded similar results. Fits were performed with an exponential decay:

(7) C(x)=Aexp[xλ]+C

where A is the amplitude of the gradient, λ is the gradient decay length and C is a possible offset.

Simulation of the Nodal gradient

Simulations were performed with Mathematica 10.0 (Wolfram, Champaign, IL). Initial simulations to determine effective diffusion coefficients in the presence of cells as obstacles (tortuosity) and morphogen binding were conducted in 3D. For this purpose, we simulated a 3D slab of 2 μm height (z-axis), 44 μm width (y-axis), and 86.7 μm length (x-axis), for 5 s (Figure 5A,B). We used a diffusion coefficient of D = 60 μm2/s, and created 1000 particles at the left border of the simulation volume. The particles were allowed to perform a random walk for 5 s with a time resolution of 5 ms per step. At the left and right borders (along the x-axis), particles were reflected. At the other four borders we used periodic boundary conditions. Based upon actual measurements from early gastrula embryos, we assumed that the space is packed with cells of ~10 μm diameter, and an intercellular space (cell membrane-to-cell membrane distance) of ~2 μm. As the height of the simulation volume was only 2 μm, we used cylinders to represent the cells within this space. Under these circumstances, ligand diffusion was reduced by a factor of 1.84. This value is consistent with the findings of Müller et al. who reported tortuosity to reduce diffusion maximally by a factor of 2 (Müller et al., 2013). Therefore, for further modeling we assumed the effective diffusion coefficient of the Nodals to be ~30 μm2/s. In the case of binding we used the values in Table 1 and Equation 1 to determine the average number of free particles at each step. All concentration profiles were normalized and fitted by the following equation to determine the effective diffusion coefficient:

(8) C(x,t)=exp[x24Defft]

Here C(x, t) is the concentration profile, x is the coordinate along which the particle diffusion is observed, t is the time at which the profile is measured (i.e., 5 s), and Deff is the effective diffusion coefficient.

Final simulations, including continuing particle production and degradation, used the values given in Table 1 and were run in 1D with an extent of 200 μm, assuming a reduced diffusion coefficient of 30 μm2/s due to tortuosity, and an average number of particles bound as determined by Equation 1.. To ensure that the gradients reached equilibrium, the simulation time was 16 hr 40 min. The normalized concentration gradients C(x) were fitted with a simple exponential function to determine the gradient length λ.

(9) C(x)=exp[xλ]

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
    Functional analysis of wingless reveals a link between intercellular ligand transport and dorsal-cell-specific signaling
    1. HA Dierick
    2. A Bejsovec
    (1998)
    Development 125:4729–4738.
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
    Nodal signaling patterns the organizer
    1. K Gritsman
    2. WS Talbot
    3. AF Schier
    (2000)
    Development 127:921–932.
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
    Cytonemes: Cellular processes that project to the principal signaling center in Drosophila imaginal discs
    1. FA Ramírez-Weber
    2. TB Kornberg
    (1999)
    Cell 97:599–607.
  43. 43
  44. 44
    cyclops encodes a nodal-related factor involved in midline signaling
    1. MR Rebagliati
    2. R Toyama
    3. P Haffter
    4. IB Dawid
    (1998b)
    Proceedings of the National Academy of Sciences of the United States of America 95:9932–9937.
    https://doi.org/10.1073/pnas.95.17.9932
  45. 45
  46. 46
  47. 47
  48. 48
    Keeping a lid on nodal: Transcriptional and translational repression of nodal signalling
    1. K Sampath
    2. EJ Robertson
    (2016)
    Open Biology, 6, 10.1098/rsob.150200.
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67

Decision letter

  1. Didier Y R Stainier
    Reviewing Editor; Max Planck Institute for Heart and Lung Research, Germany

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your work entitled "Extracellular Interactions and Ligand Degradation Shape the Nodal Morphogen Gradient" for consideration by eLife. Your article has been favorably evaluated by K VijayRaghavan (Senior editor) and three reviewers, one of whom, Didier Stainier, is a member of our Board of Reviewing Editors, and another is Hiroshi Hamada.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This paper is concerned with the formation of the Nodal gradient during zebrafish development but has general implications for the formation of morphogen gradients and embryonic patterning. These questions (formation of morphogen gradients and embryonic patterning) are fundamental to our understanding of developmental processes and thus have been under active investigation in the past decades.

This paper breaks new ground in 3 different areas: 1) in vivo measurements of the binding affinity of the Nodal ligands for their major receptor, Acvr2b; 2) measurement of the diffusion coefficient of Nodal ligands and lefty inhibitors in live zebrafish embryos; and 3) examination of the degradation rates of the Nodal ligands. The main, and novel, conclusion is that diffusivity alone is not sufficient to explain the formation of the Nodal gradient, and that the interactions with the receptors and inhibitors, as well as selective ligand destruction are major players in forming the Nodal gradient.

Essential revisions:

The reviewers have agreed on the following additional work: modeling work to test the validity of their measurements/model or additional biophysical measurements (e.g., FRAP) (the latter just in case this is something you already started doing), as outlined in reviewer 2's comments.

Please also make sure to incorporate within the manuscript a discussion of the caveats associated with over-expressing the ligands ectopically (we understand that this is how all the related studies have been carried out in zebrafish but of course, these caveats should be clearly stated and the conclusions appropriately tempered.)

All additional concerns should be addressable by modifying the text.

Reviewer #1:

This paper is concerned with the formation of the Nodal gradient during zebrafish development but has general implications for the formation of morphogen gradients and embryonic patterning. These questions (formation of morphogen gradients and embryonic patterning) are fundamental to our understanding of developmental processes and thus have been under active investigation in the past decades.

This paper breaks new ground in 3 different areas: 1) in vivo measurements of the binding affinity of the Nodal ligands for their major receptor, Acvr2b; 2) measurement of the diffusion coefficient of Nodal ligands and lefty inhibitors in live zebrafish embryos; and 3) examination of the degradation rates of the Nodal ligands. The main, and novel, conclusion is that diffusivity alone is not sufficient to explain the formation of the Nodal gradient, and that the interactions with the receptors and inhibitors, as well as selective ligand destruction are major players in forming the Nodal gradient. While some of these conclusions may seem logical/intuitive, the underlying data appear to be solid and the role of ligand degradation surprising.

Additional effort should be placed to write the paper for a broader audience (e.g., add a sentence in figure legend to explain the injection procedure; explain acronyms in figure legend (e.g., ACF, CCF); show structure of sec-EGFP in Figure 1 where it is first used; etc.).

Recent studies on Nodal signaling in zebrafish have mostly used ectopic expression in the animal pole, which is not the endogenous source of Nodal expression. One should probably temper the conclusions from these studies, especially as cellular properties such as the degradation machinery might be different in the poles compared to the margin. Similarly, the stability data generated in this paper were obtained using HEK293T cells, which may be quite different from zebrafish embryonic cells.

Reviewer #2:

Wang et al. use FCS and FCCS in zebrafish embryos to measure the diffusion of Nodal ligands and their binding affinity to transmembrane receptors and Lefty. The authors conclude that degradation and Nodal-receptor/inhibitor interactions are important for shaping the Nodal gradient and that the measured diffusion coefficient cannot explain the gradient shape.

The strength and novelty in this work is the in vivo measurements of binding affinity of Nodals to their receptors and antagonists and the new measurements of in vivo diffusivity of Nodal ligands. Nevertheless, I think there are caveats in the strategy the authors have adopted and there are several weaknesses in the data. Overall my opinion is that the study is somewhat preliminary and descriptive. To be of broader interest the biophysical measurements made in this study need to be incorporated into a coherent, self-consistent model for Nodal spread that tests the main conclusions the authors make.

In general I am skeptical about the sole use of FCS for assessing morphogen diffusion properties. FCS measurements rely on very small length and time scales. In this context it is not surprising that measurements of proteins in extracellular space produce diffusion coefficients consistent with the molecular size of the protein measured. To be useful, FCS measurements need to be coupled with measurements at long time/length scales (e.g. FRAP etc.) in order to understand the diffusivity properties of the ligand at scales relevant to tissue patterning.

Previous work from the Schier lab (Müller et al. 2012) have made measurements of some of the same parameters as Wang et al. Wang et al. mention these, but it is difficult to understand from the discussion the similarities and possible reasons for the discrepancies between the two sets of data.

A limitation of the approach the authors take is that it relies on ectopic expression of the proteins assayed. This could introduce various artefacts by taking measurements of proteins expressed from ectopic cell types or located in abnormal regions of the embryo. For example, if ligand secretion/modification machinery are missing or different in the injected cells. Similarly, the measured degradation rates might be affected by over expression, etc.

With the measurements the authors have made, they could attempt to fit and test a biophysical model of Nodal spread that takes account of extracellular movement, clearance and receptor and antagonist binding. This exercise would offer a consistency check that the various measurements are compatible with each other and don't predict physically unreasonable behaviours. It might also produce testable predictions – for example suggest what proportion of ligand would be expected to be receptor bound/free at any instant in time.

Reviewer #3:

Among nodal homologs in zebrafish, Sqt acts over a long distance while Cyc functions within in a short distance. However, the mechanism that determines the difference in their signaling range remains unclear. The authors have addressed this issue and have examined diffusivity, binding affinity to their receptor, and stability (degradation rate) of Sqt and Cyc in comparison. Their results show that the diffusivity is similar whereas degradation rate and affinity to the receptor are different. The authors suggest that the binding affinity to the receptor and selective ligand degradation determines the signaling range.

The data presented in this paper look convincing and support their conclusion. It would be much nicer if one can examine their stability in embryo (instead of a culture cell system), but this is probably a realistic way to address this. Although this reviewer is unable to fully evaluate the data obtained by fluorescence correlation spectroscopy and fluorescence cross-correlation spectroscopy for technical reason, I support this paper in principle.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Extracellular Interactions and Ligand Degradation Shape the Nodal Morphogen Gradient" for further consideration at eLife. Your revised article has been favorably evaluated by K VijayRaghavan (Senior editor), a Reviewing editor, and Reviewer #2. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

As you will see below, Reviewer #2 is concerned about the receptor concentration used for the simulations, especially in light of previous papers (although of unrelated proteins) such as:

Fujioka A, Terai K, Itoh RE, Aoki K, Nakamura T, Kuroda S, Nishida E, Matsuda M., Dynamics of the Ras/ERK MAPK cascade as monitored by fluorescent probes. J Biol Chem. 2006 Mar 31 281(13):8917-26.

Lee E, Salic A, Krüger R, Heinrich R, Kirschner MW. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. 2003 Oct;1(1):E10.

Reviewer #2 (General assessment and major comments (Required)):

The additions and changes that Wang et al. have made to their study address many of the issues raised in the initial review and substantially strengthen the study.

However, these changes do lead to several new questions:

The simulations (Figure 5) that the authors have added greatly enhance the paper. The parameterization of these rely on several assumptions, in addition to the measurements. On the whole these seem well justified. However, whether the assumption of a receptor concentration of 40microM is appropriate is unclear. It would be useful to express this as molecules per cell and to provide citations to any supporting evidence for this concentration. It would also be useful to explain the consequence for the simulations of order of magnitude differences in receptor concentration.

If I understand the simulations correctly, they predict that it would take a considerable time for the gradient to reach steady state. It would be interesting to know how long it takes for the simulations to reach ~80% and ~95% of their steady state. If the time to steady state is of the order (or longer) than mesoderm induction (1-2h) then highlighting this and discussing the consequences in the Discussion will be interesting to the field.

The authors provide a clear explanation in their rebuttal to referee 1 of the differences in use of FCS and FRAP in measuring morphogen gradients. Incorporating elements of this into the Discussion as a way to explain the caveats of the approach would strengthen the discussion.

https://doi.org/10.7554/eLife.13879.016

Author response

Essential revisions:

The reviewers have agreed on the following additional work: modeling work to test the validity of their measurements/model or additional biophysical measurements (e.g., FRAP) (the latter just in case this is something you already started doing), as outlined in reviewer 2's comments.

Please also make sure to incorporate within the manuscript a discussion of the caveats associated with over-expressing the ligands ectopically (we understand that this is how all the related studies have been carried out in zebrafish but of course, these caveats should be clearly stated and the conclusions appropriately tempered.).

All additional concerns should be addressable by modifying the text.

We are pleased that the reviewers found our manuscript on the Nodal morphogen gradient in zebrafish to have general implications for formation for morphogen gradients, to be fundamental to our understanding of developmental processes, and that it breaks new ground in 3 different areas.

In the revised Wang et al. manuscript, we have fully addressed the reviewers’ comments without adding significantly to the manuscript length:

1) We have performed mathematical modeling to test the validity of our measurements, and the models proposed to generate the Nodal morphogen gradient.

2) We show that our measurements are consistent with the computational simulations, and fit with reported values.

3) The revised manuscript has been rewritten for a broad audience, and

4) The revised conclusions take into consideration the mammalian cell and zebrafish ectopic expression assays used.

We show simulations from mathematical modeling in new Figure 5 and new Videos 1, 2 and 3, and the simulation parameters are listed in new Table 1.

We address the specific comments from the individual reviewers below.

Reviewer #1:

This paper is concerned with the formation of the Nodal gradient during zebrafish development but has general implications for the formation of morphogen gradients and embryonic patterning. These questions (formation of morphogen gradients and embryonic patterning) are fundamental to our understanding of developmental processes and thus have been under active investigation in the past decades.

This paper breaks new ground in 3 different areas: 1) in vivo measurements of the binding affinity of the Nodal ligands for their major receptor, Acvr2b; 2) measurement of the diffusion coefficient of Nodal ligands and lefty inhibitors in live zebrafish embryos; and 3) examination of the degradation rates of the Nodal ligands. The main, and novel, conclusion is that diffusivity alone is not sufficient to explain the formation of the Nodal gradient, and that the interactions with the receptors and inhibitors, as well as selective ligand destruction are major players in forming the Nodal gradient. While some of these conclusions may seem logical/intuitive, the underlying data appear to be solid and the role of ligand degradation surprising.

We are pleased that this reviewer found our manuscript on the Nodal morphogen gradient in zebrafish to have general implications for formation for morphogen gradients, to be fundamental to our understanding of developmental processes, and that it breaks new ground in 3 different areas. We have revised the manuscript taking the reviewer’s comments into consideration.

Additional effort should be placed to write the paper for a broader audience (e.g., add a sentence in figure legend to explain the injection procedure; explain acronyms in figure legend (e.g., ACF, CCF); show structure of sec-EGFP in Figure 1 where it is first used; etc.).

This has been done. We have revised Figure 1 to show the structure of sec-GFP, and explain the terms used (see revised Figure 1A, revised figure legends and revised main manuscript).The manuscript has been rewritten to address a broad audience (see revised Wang et al. manuscript). We have provided information regarding the techniques used (FCS, SW-FCCS etc.).

Recent studies on Nodal signaling in zebrafish have mostly used ectopic expression in the animal pole, which is not the endogenous source of Nodal expression. One should probably temper the conclusions from these studies, especially as cellular properties such as the degradation machinery might be different in the poles compared to the margin. Similarly, the stability data generated in this paper were obtained using HEK293T cells, which may be quite different from zebrafish embryonic cells.

This has been done. We have revised the Results and Discussion sections to take into consideration the ectopic assays used (see subsection “Simulation of the Nodal gradient” and Discussion, fourth and sixth paragraphs). We also discuss these in the response to reviewers 2 and 3 comments below.

Reviewer #2:

Wang et al. use FCS and FCCS in zebrafish embryos to measure the diffusion of Nodal ligands and their binding affinity to transmembrane receptors and Lefty. The authors conclude that degradation and Nodal-receptor/inhibitor interactions are important for shaping the Nodal gradient and that the measured diffusion coefficient cannot explain the gradient shape.

The strength and novelty in this work is the in vivo measurements of binding affinity of Nodals to their receptors and antagonists and the new measurements of in vivo diffusivity of Nodal ligands. Nevertheless, I think there are caveats in the strategy the authors have adopted and there are several weaknesses in the data. Overall my opinion is that the study is somewhat preliminary and descriptive. To be of broader interest the biophysical measurements made in this study need to be incorporated into a coherent, self-consistent model for Nodal spread that tests the main conclusions the authors make.

We have now amended the manuscript according to the reviewer’s suggestions,and performed simulations of the data (see revised Methods, New Figure 5, new Table 1, and new Videos 1, 2 and 3).

In our study, first we measured in vivo the local diffusivity of the Nodals and their inhibitors Lefty by FCS in early zebrafish embryos. Importantly, our findings resolve a conundrum in light of a recent study by van Boxtel et al., (2015) who reported that contrary to the findings of Müller et al., ligand diffusivity is not a major determinant of the Nodal signals, and that instead, miRNA-430 generates a temporal activation window which is somehow converted into a spatial Nodal activity gradient in zebrafish embryos (van Boxtel et al., 2015).

Our FCS data largely concur with the findings of Müller et al., and show that the Nodals and Lefty have similar local diffusivity in vivo in zebrafish embryos.

In addition, we have determined the binding affinity of the Nodal ligands to their major cell surface receptor Acvr2b. Surprisingly, we found that the longer range Nodal, Sqt, binds with higher affinity to the Acvr2 receptor than the short range Nodal, Cyc. This suggests that other factors must determine the effective range of Nodal signals. We then measured the binding affinity of the Nodal factors to the inhibitor Lefty. We also show a role for selective ligand degradation in shaping the Nodal gradient. Finally, we tested the validity of our measurements by computational simulations.

Interestingly, our simulations predict a Sqt gradient of 30 μm and Cyc gradient of 20 μm, which are consistent with theoretical models (Equation 2) and with measured in vivo values (New Figure 5, and Videos 1, 2, 3).Our findings largely support the ‘hindered diffusion’ model proposed by Müller et al. In addition, we found a role for ligand degradation in shaping the Nodal morphogen gradient.

Therefore, our in vivo measurements, together with mathematical modeling, and simulations, represent an advance.

In general I am skeptical about the sole use of FCS for assessing morphogen diffusion properties. FCS measurements rely on very small length and time scales. In this context it is not surprising that measurements of proteins in extracellular space produce diffusion coefficients consistent with the molecular size of the protein measured. To be useful, FCS measurements need to be coupled with measurements at long time/length scales (e.g. FRAP etc.) in order to understand the diffusivity properties of the ligand at scales relevant to tissue patterning.

Previous work from the Schier lab (Müller et al. 2012) have made measurements of some of the same parameters as Wang et al. Wang et al. mention these, but it is difficult to understand from the discussion the similarities and possible reasons for the discrepancies between the two sets of data.

Müller et al., reported diffusion of Nodal and Lefty by FRAP (2012) and by FCS (2013). Importantly, our FCS measurements are in the same order of magnitude, and our conclusions largely concur with Müller et al., 2013 in that the local diffusivity of the Nodals and Lefty is similar, although the absolute diffusion coefficient values reported by the two groups differ slightly {e.g., ~40 +/- 11 μm2/s (n=14) for Sqt by Müller, compared to our measurements of ~64 +/- 14 μm2/s (n=29)}.

The “hindered diffusion” model by Müller et al., 2013 proposes that mobility of the Nodals is slowed down by transient binding interactions of the ligands to unknown diffusion regulators in the extracellular space.

In our study, we measured the diffusion coefficient of the Nodals and Lefty by FCS. In addition we have determined the affinity of the Nodals to the cell surface receptor Acvr2 and Lefty inhibitor by FCCS. We also show by mathematical modeling that diffusion of the Nodals is indeed retarded upon binding, as predicted for hindered diffusion.

Our simulations more or less concur with the predictions of Müller (2013): we find a 1.84-fold reduction in mobility with cells as obstacles (Videos 1, 2 and new Figure 5 of revised Wang et al.) compared to a presumed maximum 2-fold decrease owing to “tortuosity” (Müller et al. 2013). We then show that mobility/diffusion is reduced further when we take binding into consideration (Video 3).

Overall, our FCS and FCCS measurements reporting binding affinities of the Nodals to the receptor and inhibitor, together with our modeling and simulations largely support the “hindered diffusion by tortuosity and transient binding”model proposed by Müller et al.

A key difference between our findings and model, and that of Schier and colleagues is that they presumed that the clearance/degradation of the molecules does not significantly affect the Nodal gradient (Müller et al., 2012), which is not what we find. We identified a lysosomal targeting region in Cyc that renders this Nodal unstable compared to Sqt (Tian et al., 2008). We had previously shown that this region is responsive to chloroquine, which inhibits lysosomes. The lysosome-targeting region of Cyc when introduced into Sqt, reduces the activity of chimeric Sqt-Cyc fusion proteins. We also find that the slope of the gradient changes with differential ligand stability, with Sqt-Cyc fusions showing a steeper gradient than Sqt (Figure 4B;λ of 30 μm for Sqt versus 23 μm for SqtCyc2). This is supported by assays for signaling range in embryos (Figure 1Dand Figure 1—figure supplement 1).

Consistent with our results, in our simulations, we find that the exact ligand amount does not change the outcome significantly for concentration changes within a factor of 5 (see revised Results and Discussion). The differences in the gradient length are therefore, a result of differential binding affinities of Sqt and Cyc, and different degradation rates. Thus, degradation of the ligands is a key regulator of the Nodal gradient.

Taken together, our findings show that hindered diffusion via extracellular binding of the Nodal ligands to the Acvr2 receptor and Lefty inhibitor, together with selective ligand degradation play important roles in shaping the Nodal morphogen gradient. We have now amended the Results, Discussion and conclusions to make this clear.

A limitation of the approach the authors take is that it relies on ectopic expression of the proteins assayed. This could introduce various artefacts by taking measurements of proteins expressed from ectopic cell types or located in abnormal regions of the embryo. For example, if ligand secretion/modification machinery are missing or different in the injected cells. Similarly, the measured degradation rates might be affected by over expression, etc.

We agree that ideally one should assay proteins produced by the endogenous loci, at endogenous levels, at native locations in the embryo. Using new CRISPR/Cas based homologous recombination (Hoshijima et al., 2016) to generate single copy reporters to tag endogenous proteins, together with new/emerging 3D imaging techniques (e.g., SPiM) might provide more robust measurements, and indeed such experiments are under way.

In the current study, for ease of analysis, as is commonly performed in zebrafish embryos, we used tagged fusions of ligand and inhibitors that were expressed ectopically in the animal pole, where any interference from other molecules could potentially be minimized. A caveat with such experiments is that the ectopic locations might not contain all the necessary components, or might introduce some artifacts.

However, our current and previous analysis largely concurs with previous work which reported that Cyc acts at short range: Genetic analysis using transplantations of mutant or wild type cells suggested that the range of the Cyc signal is short range, i.e., 1-2 cells from its source (Hatta et al., 1991; Sampath et al., 1998). Chen and Schier found that the range of signaling by Cyc is much less than that of Sqt (2001). We reproduce this in our ectopic animal pole expression assays (see Figure 1 and Figure 1—figure supplement 1). Similarly, our analysis of the gradient also show that compared to the long range Sqt, chimeric SqtCyc2 protein has a steeper gradient which is consistent with its reduced signaling range (Figure 4 and Figure 1—figure supplement 1). Our modeling of the data measured broadly agrees with the reported in vivo range for the proteins (Figure 5, revised Results and revised Discussion). We also infer that some additional binding events (e.g. to the co-receptor or other molecules) might further influence the gradient.

Thus, our measurements in the animal pole (although not ideal) have provided very useful parameters.

With the measurements the authors have made, they could attempt to fit and test a biophysical model of Nodal spread that takes account of extracellular movement, clearance and receptor and antagonist binding. This exercise would offer a consistency check that the various measurements are compatible with each other and don't predict physically unreasonable behaviours. It might also produce testable predictions – for example suggest what proportion of ligand would be expected to be receptor bound/free at any instant in time.

This has been done. We performed simulations of the data (see new Figure 5, new Table 1, Videos 1, 2, 3). We have inferred the amount of free versus bound ligand from our FCS and FCCS measurements and provide this data in new Table 1. A caveat of our model is that we have not considered how interactions of Nodals with Lefty inhibitors impacts in vivo signaling output. We also have not taken account of potential Nodal-Oep/Cripto interactions. Nonetheless, our measurements are largely supported by the theoretical predictions, mathematical simulations, and fit well with reported values.

Reviewer #3:

Among nodal homologs in zebrafish, Sqt acts over a long distance while Cyc functions within in a short distance. However, the mechanism that determines the difference in their signaling range remains unclear. The authors have addressed this issue and have examined diffusivity, binding affinity to their receptor, and stability (degradation rate) of Sqt and Cyc in comparison. Their results show that the diffusivity is similar whereas degradation rate and affinity to the receptor are different. The authors suggest that the binding affinity to the receptor and selective ligand degradation determines the signaling range.

The data presented in this paper look convincing and support their conclusion. It would be much nicer if one can examine their stability in embryo (instead of a culture cell system), but this is probably a realistic way to address this. Although this reviewer is unable to fully evaluate the data obtained by fluorescence correlation spectroscopy and fluorescence cross-correlation spectroscopy for technical reason, I support this paper in principle.

We thank the reviewer for the supportive comments. Owing to the labile nature of Cyc, for ease of assessing degradation, cell culture was performed. Nonetheless, we recognize the reviewers concerns regarding measurements of stability in cell culture rather than in embryos, and therefore, in our mathematical simulations, we now use previously reported half-life values for Cyc and Sqt (see new Table 1).

For all FCS and FCCS measurements, we used appropriate controls (e.g., sec-eGFP and eGFP-mCherry fusions), and all measurements were first tested using reagents previously shown to determine diffusion of FGF in zebrafish embryos (Yu et al., 2009).

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

As you will see below, Reviewer #2 is concerned about the receptor concentration used for the simulations, especially in light of previous papers (although of unrelated proteins) such as:

Fujioka A, Terai K, Itoh RE, Aoki K, Nakamura T, Kuroda S, Nishida E, Matsuda M., Dynamics of the Ras/ERK MAPK cascade as monitored by fluorescent probes. J Biol Chem. 2006 Mar 31 281(13):8917-26.

Lee E, Salic A, Krüger R, Heinrich R, Kirschner MW. The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway. PLoS Biol. 2003 Oct;1(1):E10.

Reviewer #2 (General assessment and major comments (Required)):

The additions and changes that Wang et al. have made to their study address many of the issues raised in the initial review and substantially strengthen the study.

However, these changes do lead to several new questions:

The simulations (Figure 5) that the authors have added greatly enhance the paper. The parameterization of these rely on several assumptions, in addition to the measurements. On the whole these seem well justified. However, whether the assumption of a receptor concentration of 40microM is appropriate is unclear. It would be useful to express this as molecules per cell and to provide citations to any supporting evidence for this concentration. It would also be useful to explain the consequence for the simulations of order of magnitude differences in receptor concentration.

We are pleased that the reviewer found the revised Wang et al. manuscript improved and that the simulations greatly enhance the paper.

We determined from our simulations that a receptor concentration of 40 μM is required to slow down diffusion sufficiently to obtain a 30 μm Sqt gradient. However, it is difficult at this time to deduce a single receptor number per cell for several reasons:

i) First, this number depends on the ratio of interstitial space volume to cell size and number. So any change in the estimated distance between cells (e.g., by cell division, movement and growth) would alter that number. However, the effective diffusion coefficient (Deff) does not depend on this; Deff depends only on the concentration and tortuosity, and we have used this in our estimates.

ii) Second, at the moment we do not know whether there are additional binding sites in the interstitial space as reported for other morphogens (e.g. extracellular HSPG binding by Fgf8; Yu et al., 2009). The binding constant and concentration of such sites, or amendments to the binding constants of the morphogens to receptors would lead to changes in that number.

iii) Third, the degradation/clearance rate is the main determinant of this number and has a direct influence on the receptor concentration: a faster clearance would lead to faster establishment of the gradient and also reduce the receptor number required to establish the λ of 30 μm and 20 μm that we observed for Sqt and Cyc. Therefore, we estimate that there could be anywhere between 100,000 to millions of receptors per cell. For now, we prefer to leave this determination to future studies when precise production and clearance rates for the various molecules become available, and when it is known if there are additional binders for the ligands.

We have now amended the Results section to state the following:

“The estimation of the receptor number of 40 μM is based on the value of Deff required to establish the gradient of appropriate dimensions for Sqt. […] Based on the above, we assume that 40 μM is the upper limit for the receptor concentration”.

If I understand the simulations correctly, they predict that it would take a considerable time for the gradient to reach steady state. It would be interesting to know how long it takes for the simulations to reach ~80% and ~95% of their steady state. If the time to steady state is of the order (or longer) than mesoderm induction (1-2h) then highlighting this and discussing the consequences in the Discussion will be interesting to the field.

The time to reach the steady state is determined by the clearance rate, k: (1-Exp(-k*t)). Therefore, for Cyc, 80% and 95% of steady state levels are achieved at 0.7 and 1.25 h, respectively, which is consistent with the timing for mesoderm induction in the gastrula. By the same predictions, Sqt reaches the 80% and 95% levels at ~4 and 7 h, respectively, which is longer than expected. We should add that we have not taken into consideration cell divisions etc. during that time or binding to other factors, and how such factors might influence the gradient.

We have now incorporated this into the revised Discussion (fifth paragraph).

The authors provide a clear explanation in their rebuttal to referee 1 of the differences in use of FCS and FRAP in measuring morphogen gradients. Incorporating elements of this into the Discussion as a way to explain the caveats of the approach would strengthen the discussion.

This has been done (Discussion section).

https://doi.org/10.7554/eLife.13879.017

Article and author information

Author details

  1. Yin Wang

    1. Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
    2. Department of Biological Sciences, National University of Singapore, Singapore, Singapore
    Contribution
    YW, Experimental design, Zebrafish and cell culture experiments, Manuscript preparation, Acquisition of data, Analysis and interpretation of data
    Contributed equally with
    Xi Wang
    Competing interests
    The authors declare that no competing interests exist.
  2. Xi Wang

    Department of Chemistry, Centre for Bioimaging Sciences, National University of Singapore, Singapore, Singapore
    Contribution
    XW, Experimental design, FCS/FCCS measurements, Input for manuscript preparation, Acquisition of data, Analysis and interpretation of data
    Contributed equally with
    Yin Wang
    Competing interests
    The authors declare that no competing interests exist.
  3. Thorsten Wohland

    1. Department of Biological Sciences, National University of Singapore, Singapore, Singapore
    2. Department of Chemistry, Centre for Bioimaging Sciences, National University of Singapore, Singapore, Singapore
    Contribution
    TW, Experimental design, Manuscript preparation, Acquisition of data, Analysis and interpretation of data
    For correspondence
    twohland@nus.edu.sg
    Competing interests
    The authors declare that no competing interests exist.
  4. Karuna Sampath

    Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
    Contribution
    KS, Experimental design, Manuscript preparation, Acquisition of data, Analysis and interpretation of data
    For correspondence
    K.Sampath@warwick.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0729-1977

Funding

University of Warwick

  • Yin Wang

National University of Singapore

  • Xi Wang

Ministry of Education - Singapore (MOE2012-T2-1-101: R-154-000-543-112)

  • Thorsten Wohland

Biotechnology and Biological Sciences Research Council

  • Karuna Sampath

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

 We thank members of the Sampath and Wohland laboratories, Rob Cross, Tim Saunders, and Hugh Woodland for discussions and suggestions; Michael Brand for sharing plasmids; Agnieszka Nagorska provided technical assistance; Patrick Müller provided critical comments on the early online version; WY and KS are supported by WMS, WX is the recipient of a NUS graduate Scholarship, KS acknowledges funding from the BBSRC and TW acknowledges funding from the Ministry of Education of Singapore (grant MOE2012-T2-1-101: R-154-000-543-112).

Ethics

Animal experimentation: This study was performed in strict accordance with institutional animal care regulations and protocols of the National University of Singapore and the University of Warwick (PPL number 70/7836 and PIL number 70/26057 to KS).

Reviewing Editor

  1. Didier Y R Stainier, Max Planck Institute for Heart and Lung Research, Germany

Publication history

  1. Received: December 17, 2015
  2. Accepted: April 20, 2016
  3. Accepted Manuscript published: April 21, 2016 (version 1)
  4. Version of Record published: May 31, 2016 (version 2)

Copyright

© 2016, Wang et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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