1. Developmental Biology
  2. Physics of Living Systems
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Quantitative analyses reveal extracellular dynamics of Wnt ligands in Xenopus embryos

  1. Yusuke Mii  Is a corresponding author
  2. Kenichi Nakazato
  3. Chan-Gi Pack
  4. Takafumi Ikeda
  5. Yasushi Sako
  6. Atsushi Mochizuki
  7. Masanori Taira  Is a corresponding author
  8. Shinji Takada  Is a corresponding author
  1. National Institute for Basic Biology and Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Japan
  2. The Graduate University for Advanced Studies (SOKENDAI), Japan
  3. Japan Science and Technology Agency (JST), PRESTO, Japan
  4. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Japan
  5. Theoretical Biology Laboratory, RIKEN, Japan
  6. Cellular Informatics Laboratory, RIKEN, Japan
  7. ASAN Institute for Life Sciences, ASAN Medical Center, University of Ulsan College of Medicine, Republic of Korea
  8. Laboratory of Mathematical Biology, Institute for Frontier Life and Medical Sciences, Kyoto University, Japan
  9. Department of Biological Sciences, Faculty of Science and Engineering, Chuo University, Japan
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Cite this article as: eLife 2021;10:e55108 doi: 10.7554/eLife.55108

Abstract

The mechanism of intercellular transport of Wnt ligands is still a matter of debate. To better understand this issue, we examined the distribution and dynamics of Wnt8 in Xenopus embryos. While Venus-tagged Wnt8 was found on the surfaces of cells close to Wnt-producing cells, we also detected its dispersal over distances of 15 cell diameters. A combination of fluorescence correlation spectroscopy and quantitative imaging suggested that only a small proportion of Wnt8 ligands diffuses freely, whereas most Wnt8 molecules are bound to cell surfaces. Fluorescence decay after photoconversion showed that Wnt8 ligands bound on cell surfaces decrease exponentially, suggesting a dynamic exchange of bound forms of Wnt ligands. Mathematical modeling based on this exchange recapitulates a graded distribution of bound, but not free, Wnt ligands. Based on these results, we propose that Wnt distribution in tissues is controlled by a dynamic exchange of its abundant bound and rare free populations.

Introduction

The Wnt family of secreted signaling proteins has diverse roles in animal development, stem cell systems, and carcinogenesis (Clevers et al., 2014; Loh et al., 2016; Nusse and Clevers, 2017). It has been generally accepted that in the extracellular space, morphogenic Wnt ligands form a concentration gradient by dispersal (Clevers et al., 2014; Kiecker and Niehrs, 2001; Müller et al., 2013; Smith, 2009; Strigini and Cohen, 2000; Tabata and Takei, 2004; Yan and Lin, 2009; Zecca et al., 1996; Zhu and Scott, 2004). In contrast to this classical view, evidence also suggests dispersal-independent functions of Wnt ligands. For instance, a membrane-tethered form of Wingless (Wg) can recapitulate an almost normal pattern of Drosophila wings, suggesting that dispersal of Wg is dispensable for patterning (Alexandre et al., 2014). This dispersal-independent patterning can be explained by gradual attenuation of Wg expression in distally localized cells in which Wg was formerly expressed. However, it remains unclear to what extent dispersal-dependent and/or -independent mechanisms contribute to the graded distribution of Wnt proteins in tissue patterning.

Visualization of Wnt ligands is essential to understand their distributions. In the wing disc of Drosophila, Wg proteins are widely distributed from wing margin cells, where Wg is expressed (Strigini and Cohen, 2000; Zecca et al., 1996). Furthermore, long-range dispersal of Wg was evidenced by an experiment in which Wg was captured by distally expressed Frizzled2, a Wg receptor (Chaudhary et al., 2019). Similarly, endogenous Wnt ligands tagged with fluorescent proteins showed long-range distributions in C. elegans (Pani and Goldstein, 2018). In addition to these observations in invertebrates, we found that endogenous Wnt8 ligands disperse far from their source cells in Xenopus embryos (Mii et al., 2017). On the other hand, mouse Wnt3 accumulates within a few cell diameters of its source cells in the microenvironment of the intestine (Farin et al., 2016). These studies show that Wnt ligands apparently disperse in tissues and embryos, although the dispersal range varies. Importantly, in many of these studies, Wnt ligands accumulate locally on cell surfaces, showing punctate distributional patterns (Pani and Goldstein, 2018; Strigini and Cohen, 2000; Zecca et al., 1996). Furthermore, we demonstrated that Wnt8 and Frzb, a secreted Wnt inhibitor, accumulate separately and locally on cell surfaces in Xenopus embryos (Mii et al., 2017). However, these punctate accumulations on cell surfaces, largely ignored in the literature in the context of Wnt gradient formation, raise the question of whether such accumulations contribute to formation of concentration gradients in tissues and embryos.

Studies in Drosophila wing disc have shown that cell surface scaffolds, such as heparan sulfate (HS) proteoglycans (HSPGs), are required for both distribution and delivery of morphogens, including Wg, Hedgehog (Hh), and Decapentaplegic (Dpp) (Franch-Marro et al., 2005; Lin, 2004; Yan and Lin, 2009). From these studies, the ‘restricted diffusion’ model, in which morphogens are transferred extracellularly by interacting with cell surface scaffolds, has been proposed (Yan and Lin, 2009). In this model, the movement of each morphogen molecule is constrained in a ‘bucket brigade’ fashion by interactions with cell surface scaffolds. As a result of continuous interactions, morphogen molecules are slowly transferred (Han et al., 2005; Yan and Lin, 2009 #152; Kerszberg and Wolpert, 1998; Takei et al., 2004). However, it seems difficult to explain local accumulations of Wnt proteins by the restricted diffusion mechanism, because passive diffusion alone should result in smoothly decreasing gradients. On the other hand, we recently showed that HSPGs on cell surfaces are discretely distributed in a punctate manner, which varies with heparan sulfate (HS) modification, forming two different types of HS clusters, N-sulfo-rich and N-acetyl-rich forms (Mii et al., 2017). Notably, Wnt8 and Frzb, a secreted Frizzled-related protein (sFRP), accumulate separately on N-sulfo-rich and N-acetyl-rich HS clusters, respectively. Frzb expands the distribution and signaling range of Wnt8 by forming heterocomplexes (Mii and Taira, 2009), and Wnt8/Frzb complexes are colocalized with N-acetyl-rich HS clusters (Mii et al., 2017). N-sulfo-rich clusters are frequently internalized together with Wnt8, whereas N-acetyl-rich HS clusters tend to remain on the cell surface. This difference in stability on the cell surface may account for the short-range distribution of Wnt8 and the long-range distribution of Frzb (Mii and Taira, 2009; Mii et al., 2017) and suggests that the distribution of HS clusters should be considered in order to understand extracellular dynamics of Wnt ligands (Mii and Takada, 2020).

To explain the dynamics of Wnt ligands in tissues, quantitative analyses of Wnt ligands are required. Dynamics of secreted proteins have been investigated using fluorescence recovery after photobleaching (FRAP) (Sprague and McNally, 2005; Sprague et al., 2004) and fluorescence correlation spectroscopy (FCS), although optimal ranges for diffusion coefficients differ (Hess et al., 2002; Kicheva et al., 2012; Müller et al., 2013; Fradin, 2017). For example, FRAP measurements have shown that Dpp and Wg diffuse slowly in the Drosophila wing disc with diffusion coefficients ranging from 0.05 to 0.10 μm2/s, suggestive of the restricted diffusion model (Kicheva et al., 2007). In contrast, FCS measurements of FGF8 in zebrafish embryos showed fast, virtually free diffusion, with a diffusion coefficient of ~50 μm2/s (Yu et al., 2009). Furthermore, in contrast to the FRAP results, free diffusion of Dpp measured in the Drosophila wing disc using FCS yielded a diffusion coefficient of ~20 μm2/s (Zhou et al., 2012). FCS is based on fixed-point scanning within a confocal volume (typically sub-femtoliter) for several seconds, while FRAP evaluates considerably larger regions of photobleaching/photoconversion, containing tens or hundreds of cells (Rogers and Schier, 2011) and spanning long time windows (typically several hours). Under these experimental conditions for FRAP, it is proposed that diffusion of secreted proteins is affected by zigzag paths of the narrow intercellular space between polygonal epithelial cells, instead of an open, unobstructed space (hindered diffusion model) (Müller et al., 2013), and/or by endocytosis, which reduces the concentration of the diffusing species in the extracellular space. Thus, we need exercise caution when comparing data derived from FRAP and from FCS analyses.

In this study, we examined extracellular dynamics of Wnt8 and Frzb, both of which are involved in anteroposterior patterning of vertebrate embryos (Clevers and Nusse, 2012; Kiecker and Niehrs, 2001; MacDonald et al., 2009; Mii et al., 2017). First, we visualized their localization in Xenopus embryos by fusing them with fluorescent proteins and we examined their dispersion by capturing them in distant cells. We also examined their dispersal dynamics using FCS and fluorescence decay after photoconversion (FDAP) measurements in embryonic tissue. In particular, we refined FDAP-based analysis by focusing on a limited area at the cell boundary, which enabled us to quantify dynamics comparable to those measured by FCS. Based on these results and our previous findings, we propose a basic mathematical model to explain distribution and dispersion of secreted proteins.

Results

Extracellular distributions of secreted proteins depend on interactions with cell-surface molecules

As we have previously shown (Mii et al., 2017), Wnt8 and Frzb fused with monomeric Venus (mV) were visualized along cell boundaries when expressed in Xenopus embryos (Figure 1A). We note that biological activities of these proteins were not severely impaired by the fusion of mV and that the reduced activity of mV-tagged Wnt8 compared to untagged Wnt8 could possibly be due, at least in part, to differences in translation (Figure 1—figure supplement 1). In contrast, we found that only the secreted form of mV (sec-mV), which was not expected to bind specifically to the cell surface, was hardly visible along the cell boundary under the same conditions (Figure 1A, right). Since Wnt8 and Frzb colocalize with heparan sulfate clusters on cell surfaces, we speculated that binding to cell surface proteins, like heparan sulfate proteoglycans (HSPGs), affects the distribution of Wnt8 and Frzb. To examine this possibility, we added heparin-binding (HB) peptides, consisting of 16 (ARKKAAKA)2 (HB2) or 32 amino acids (ARKKAAKA)4 (HB4) (Verrecchio et al., 2000Figure 1C) to sec-mV. Addition of HB peptides significantly increased the intensity of mVenus fluorescence in the intercellular region compared to that of sec-mV. This suggests that the intercellular distribution of secreted proteins depends on interactions with docking molecules on cell surfaces.

Figure 1 with 2 supplements see all
Extracellular distributions of Wnt8, Frzb, and artificial secreted proteins.

All images presented were acquired using live-imaging with the photon counting method, which enables saturation-free imaging even with samples having a wide dynamic range. (A) Distribution of secreted proteins in the superficial layer of a living Xenopus gastrula (st. 10.5–11.5). Observed focal planes were at the subapical level, as illustrated. mRNAs for indicated mVenus (mV) fusion proteins were microinjected into a single ventral blastomere of four- or eight-cell stage embryos to observe regions adjacent to the source cells (indicated with asterisks). All images were acquired in the same condition with photon counting detection. Look-up tables (LUT) show the range of the photon counts in the images. (B) Intensity plots for mV-Wnt8 and mV-Frzb in the intercellular space. Plots along the arrows in enlarged pictures in (A) are shown. (C) Distribution of artificial secreted proteins in Xenopus embryos. The data of sec-mV is the same as in (A). sec-mV was not apparent in the intercellular space, whereas sec-mV-HB2 and sec-mV-HB4 were distributed in the intercellular space (arrowheads). SP, signal peptide; HB, heparin binding peptide. (D) Quantification of fluorescent intensities in the intercellular space. Photon counts per pixel are presented. All samples show statistically significant differences (p<2e-16, pairwise comparisons using the Wilcoxon rank sum test adjusted for multiple comparison with Holm’s method). Scale bars, 20 μm. Amounts of injected mRNAs (ng/embryo): mV-wnt8, mV-frzb, sp-mV, sp-mV-hb2, or sp-mV-hb4, 0.25.

To directly examine this idea, we constructed a reconstitution system, consisting of HA-epitope-tagged secreted mVenus (sec-mV-2HA) and a membrane-tethered anti-HA antibody (‘tethered-anti-HA Ab’) (Figure 2A, see Figure 2—figure supplement 1 for cDNA cloning and validation of anti-HA antibody). This artificial protein and tethered-anti-HA Ab were expressed in separated areas in the animal cap region of Xenopus gastrulae. As with sec-mV, sec-mV-2HA was hardly visible in the intercellular space, even close to the source cells (Figure 2B). In contrast, sec-mV-2HA was observed around tethered-anti-HA Ab-expressing cells that were traced with memRFP, even though these cells were distantly located from the source cells (Figure 2B). Thus, interaction with cell surface proteins can affect distributions of secreted proteins.

Figure 2 with 1 supplement see all
Tethered-anti-HA Ab and morphotrap.

(A) Schematic representation of tethered-anti-HA Ab. (B) Results of tethered-anti-HA Ab. The artificial ligand (sec-mV-2HA) was trapped at tethered-anti-HA Ab-expressing cells, distant from the source. The superficial layer of a Xenopus gastrula (st. 11.5) was imaged as a z-stack and its maximum intensity projection (MIP) was presented for the fluorescent images. Intercellular mVenus signal (green) of sec-mV-2HA was not apparent in the vicinity of source cells, but was detected around the tethered-anti-HA Ab-expressing cells (arrowheads) that are traced with memRFP (magenta). (C) Morphotrap at a distant region from the source. The superficial layer of a Xenopus gastrula (st. 11.5) was imaged as a z-stack and its maximum intensity projection (MIP) was presented for the fluorescent images. The intercellular mVenus signal of an artificial ligand, sec-mV (green), was not detected in the vicinity of source cells (green) (left panel), but was detected around the morphotrap-expressing cells that can be traced by mCherry fluorescence (middle panels). Also, mV-Wnt8 and mV-Frzb were trapped and accumulated on distant morphotrap-expressing cells, suggesting the existence of diffusing molecules in the distant region. Source regions are indicated with cyan lines according to memBFP (tracer for mV-tagged proteins, not shown). (D) Distribution of mVenus and morphotrap. Fluorescent intensity of mVenus and mCherry (for morphotrap) was plotted from the left to the right. Scale bars, 100 μm. Amounts of injected mRNAs (ng/embryo) sp-mV-2ha, 1.0; memRFP, 0.15; ig gamma2b-gpi, 1.1; ig kappa, 0.63 (B); sec-mV, mV-wnt8, or mV-frzb (high dose), 0.25; mV-frzb (low dose), 0.063; morphotrap, 1.0; memBFP, 0.1 (C).

This result also indicates that diffusing proteins are not readily visible using standard confocal microscopy, unless they are trapped by cell surface proteins. In fact, quantitative analysis of artificial secreted proteins revealed a slight, but significant increase of photon counts in the intercellular region by injection of mRNA for sec-mV, compared to uninjected embryos, indicating that sec-mV actually exists in the intercellular region (Figure 1D, Figure 1—figure supplement 2).

Populations of secreted Wnt8 and Frzb proteins disperse long distance

We next examined dispersal of molecules of mV-Wnt8 and mV-Frzb. Both mV-Wnt8 and mV-Frzb accumulated locally along the cell boundary at the subapical level (Figure 1A and B), consistent with previous observations (Mii et al., 2017), which indicated that populations of Wnt8 and Frzb in the intercellular space were bound to the cell surface at HS clusters. On the other hand, given that some molecules of mV-Wnt8 or mV-Frzb may drift away from the cell surface, these proteins would be almost undetectable with standard confocal microscopy, as exemplified by sec-mV (Figure 1A) and tethered-anti-HA Ab (Figure 2B). To examine such mobile proteins, we tried to capture them using ‘morphotrap’ located distantly from the source cells (Figure 2C). Morphotrap is a membrane-tethered form of anti-GFP nanobody, originally devised to block dispersal of Dpp-GFP from source cells (Harmansa et al., 2015). We supposed that morphotraps could be utilized to detect or visualize diffusible proteins, similar to tethered-anti-HA Ab. As expected, sec-mV accumulated on the surface of morphotrap-expressing cells remote from source cells (Figure 2C). Similarly, mV-Wnt8 and mV-Frzb were trapped (Figure 2C), evidencing the long-distance dispersal (over 15 cells/200 μm) of some of secreted mV-Wnt8 and mV-Frzb molecules. These proteins are not likely to be transferred by cell-movement-based mechanisms, including distant migration of source and morphotrap-expressing cells, because cells in the animal cap region form an epithelial sheet and are tightly packed. In addition to this dispersing population, mV-Wnt8 and mV-Frzb were also detectable in gradients from producing cells to morphotrap-expressing cells, unlike the case of sec-mV (Figure 2C and D). These results suggest that populations of mV-tagged Wnt8 and Frzb do not associate tightly with cell surfaces, thereby potentially dispersing far from source cells.

FCS analyses combined with quantitative imaging reveal cell-surface-bound and diffusing Wnt8 and Frzb proteins in the extracellular space

We next attempted to quantify the populations of Wnt8 or Frzb proteins associated with cell surfaces and diffusing in the extracellular space. For this purpose, we employed fluorescence correlation spectroscopy (FCS). FCS analyzes fluctuation of fluorescence by Brownian motion of fluorescent molecules in a sub-femtoliter confocal detection volume (Figure 3, A and B). By autocorrelation analysis (Figure 3C), FCS can measure diffusion coefficients (D) of mobile molecules and the number of particles in the detection volume, but inference of diffusion coefficients depends on mobile molecules (Hess et al., 2002; Fradin, 2017). FCS analyses were performed by injecting the same doses of mV-Wnt8 and sec-mV that were used in the experiments shown in Figure 1 (250 pg mRNA/embryo) to consider the relationship between photon counting from live-imaging and NoP from FCS. Furthermore, to measure the dynamics of mV-Wnt8 and mV-Frzb at a concentration equivalent to the endogenous concentration, a decreased amount of RNA was also injected (20 pg mRNA/embryo, see Figure 1—figure supplement 1A).

Figure 3 with 3 supplements see all
Fluorescence correlation spectroscopy (FCS) in the extracellular space of Xenopus embryos.

mRNAs for mV-tagged proteins or sec-mV were injected into the animal pole region of a ventral blastomere of four- or eight-cell stage Xenopus embryo. Injected embryos were observed at gastrula stages (st. 10.5–11.5). Each FCS measurement (10 s) was performed at a point in the intercellular region within three cell diameters of the source cells. (A) Schematic illustration of FCS measurement. In FCS measurements, the fluorescent signal usually fluctuates due to Brownian motion of fluorescent molecules. Such fluctuations contain dynamic properties of fluorescent molecules. Briefly, temporal frequency of the fluctuations corresponds to the diffusion coefficient (D) and amplitude of the fluctuations corresponds inversely to the number of particles in the confocal detection volume (0.3 fl with Leica system; 0.12 fl with Zeiss system). (B) Trace of fluorescent intensities in a single measurement of indicated conditions. mV-Wnt8 shows characteristic peaks, probably corresponding to multimeric forms (asterisks, Takada et al., 2018). (C) Normalized autocorrelation curves of averaged data. Numbers of embryos/measurements are as indicated in (D). Experimental data are plotted with circles with the best fitting curve. (D) Summary of FCS measurements. Mean values are presented. s.d., standard deviation. Indicated numbers of measurements were omitted for averaging (in the table, no data were omitted for C), based on Dfast values over 80 μm2/s (reflecting blinking of mVenus). (E–H) Effect of HS digestion by HepIII-GPI on mV-Wnt8 or sec-mV. Measurements were performed in the same embryos to achieve side-by-side comparison at control regions (HepIII–) or HS-digested regions (HepIII+). (E, G) Unnormalized autocorrelation curves of averaged data (number of embryos: (E) 3, (G) 4; number of measurements: (E) HepIII–, 84 HepIII+, 97; (G) HepIII–, 56 HepIII+, 87). (F) Measured parameters obtained by curve-fitting. Statistical significance (p, indicated in red, when significant) was calculated using the Wilcoxon rank sum test. Numbers of omitted measurements due to unreliable parameters (Dfast values over 80 μm2/s; inadequate Ffast values due to virtually the same Dfast and Dslow values): (F) HepIII–, 24;2 HepIII+, 10;3 (H) HepIII–, 17;1, HepIII+, 21;5. Lyn-mTagBFP2 and/or Lyn-miRFP703 were used to trace source cells, control regions, or HepIII +regions. Fluorescence of these tracers did not interfere with FCS measurements because these can be completely separated from mVenus. Amounts of injected mRNAs (pg/embryo): mV-wnt8, 250 or 20; mV-frzb, 20; sec-mV, 250; sp-hepIII-ha-gpi, 400; lyn-mTagBFP2, 100; lyn-miRFP703, 200.

To analyze the data obtained by FCS measurements, we compared the suitability of one-component and two-component diffusion models using the Akaike information criterion (AIC) (Tsutsumi et al., 2016). AIC supported fitting with the two-component model, comprising fast and slow diffusing components (Figure 3—figure supplement 1A). Consistent with predictions, the result indicated that the number of particles (NoP) of mV-Wnt8 (250 pg/embryo) was significantly higher than that of mV-Wnt8 (20 pg/embryo, endogenous-equivalent level; Figure 3D and Figure 3—figure supplement 1B). Mean values of Dfast indicate that fast-diffusing components in all groups examined can be regarded as free diffusion (Figure 3D), because theoretical, as well as reported D values of freely diffusing proteins of similar size, range from 10 to 100 μm2/s (Pack et al., 2006; Yu et al., 2009; Zhou et al., 2012). Importantly, even at the endogenous-equivalent level, mV-Wnt8 and mV-Frzb show freely diffusing populations (Dfast >10 μm2/s, Figure 3—figure supplement 1B). We also note that the diffusion coefficient of the fast component of mV-Wnt8 (20 pg/embryo, endogenous-equivalent level) was significantly lower than that of mV-Wnt8 (250 pg/embryo), suggesting stronger constraints with the endogenous-equivalent expression level. Thus, we conclude that within the small volume of FCS measurements, a population of mV-Wnt8 and mV-Frzb molecules diffuses freely under physiological conditions.

As shown in Figure 1D, photon counts of mV-Wnt8 were much higher than those of sec-mV. However, under the same conditions as in Figure 1 (250 pg mRNA/embryo), NoP of mV-Wnt8 was similar to that of sec-mV (Figure 3—figure supplement 1B) or even smaller in another set of measurements (Figure 3—figure supplement 2). Thus, molecules detected in FCS appear not to contribute to the photon counts in the confocal imaging under these conditions. We speculate that FCS measurements might be biased to choose positions where HS-bound molecules are not abundant. Otherwise, HS-bound, immobile molecules cause strong photobleaching, which results in large drift of the fluorescence intensity. In general, such a data is not suitable for analysis.

Interestingly, slow components were observed not only in mV-Wnt8, but also in sec-mV (Figure 3D). To characterize these slow components, we examined the effects of HS-chain digestion with FCS. These analyses were performed with embryos injected with 250 pg/embryo RNA for mV-Wnt8 or sec-mV, because at the endogenous-equivalent level, measured values showed a large variance, possibly reflecting heterogeneity of the extracellular space, and also signal detection was difficult for sec-mV. For this purpose, we made a membrane-tethered form of Heparinase III (HepIII-HA-GPI, also known as heparitinase I) (Hashimoto et al., 2014). HepIII-HA-GPI enables us to digest HS chains in a region of interest (Figure 3—figure supplement 3), allowing us to examine the effects of HS-digestion in the same embryos. For mV-Wnt8, NoP and the fraction of fast components, Ffast was significantly increased by HepIII, suggesting release of mV-Wnt8 from HS chains (Figure 3E and F). Thus, we suggest that HS chains contribute to the slow components of mV-Wnt8. For sec-mV, although NoP was not significantly changed by HepIII, Ffast was slightly, but significantly increased by HepIII (Figure 3G and H). Furthermore, fluorescence cross-correlation spectroscopy (FCCS) analysis indicated that sec-mV did not interact with the cell membrane (Figure 3—figure supplement 1C and D). Thus, HS-chains showed some contribution to the slow components of sec-mV even without interaction. We speculate that such a slow population of sec-mV could be explained by hindered diffusion, in which torturous diffusion results from HS chains and other ECMs, because HS chains are highly hydrophilic and well hydrated (Figure 3—figure supplement 1E).

FDAP analyses suggest exchange of cell-surface-bound and unbound states of Wnt8 and Frzb proteins

Although FCS analysis is suitable for measuring diffusing molecules, it cannot directly analyze molecules with extremely low mobility (Hess et al., 2002). To directly analyze dynamics of such molecules, we next employed fluorescence decay after photostimulation/photoconversion (FDAP) assays (Matsuda et al., 2008; Müller et al., 2012) in the intercellular space of Xenopus embryos.

Since FRAP/FDAP measurements usually examine considerably larger regions (typically containing tens or hundreds of cells) than with FCS (Rogers and Schier, 2011), direct comparisons of dynamics between FRAP/FDAP and FCS may need careful consideration. Therefore, we restricted the area of photoconversion to a diameter of 1.66 μm and reduced the measurement time (16 s), allowing us to obtain dynamic data in the intercellular region under conditions comparable to those for FCS (Figure 4A). We refer to this FDAP mode as ‘cell-boundary FDAP.’ In this analysis, we fused a photoconvertible fluorescent protein, mKikGR (mK) (Habuchi et al., 2008) to Wnt8 and Frzb (mK-Wnt8 and mK-Frzb). These fusion proteins showed distributions in embryos similar to mV-tagged proteins (Figure 4A), and retained biological activities (Figure 4—figure supplement 1). Importantly, observed distribution patterns of mK-Wnt8 and mK-Frzb were stable for up to tens of minutes (Figure 4A). Therefore, we assumed a steady state during the FDAP analysis (16 s).

Figure 4 with 3 supplements see all
Fluorescence decay after photoconversion (FDAP) assay at the cell-boundary of Xenopus embryos.

(A) Stable distribution of mKikGR-Wnt8 and mKikGR-Frzb. The superficial layer of a Xenopus gastrula (st. 10.5–11) was imaged as a z-stack and maximum intensity projection (MIP) was presented. Puncta of these proteins persisted for 30 min (arrowheads). Scale bars, 10 μm. (B) Schematic illustration of cell-boundary FDAP assay. Green lines represent mKikGR-fusion protein distributed in the intercellular region. As an example, still images before and after photoconversion (PC) are shown. Width of the blue box (area of PC and measurement) was 1.66 μm. See also Videos 13 and the text for detail. (C) Time course of red (photoconverted state) fluorescent intensity within the photoconverted region. Photoconversion was performed about 4 s after the beginning of the measurement. Means of normalized intensities were presented (for s.d., see Figure supplement 2A). Data of ‘mKikGR-Frzb fixed’ were measured with MEMFA-fixed mKikGR-Frzb expressing embryos as an immobilized control. Numbers of measurements were indicated as n, which were collected in multiple experiments (twice for mKikGR-Wnt8 and mKikGR-Frzb fixed, and four times for mKikGR-Frzb). (D) Fluorescent decay curves fitted with the dissociation model. The mean of normalized intensities for each time point was corrected for photobleaching with division by 0.9991n (n, number of scanning after PC; Figure 4—figure supplement 2B). Fitting curves are shown as black lines. Residuals were mostly within 5% (0.05) and within 10% (0.1) in all cases. (E) Coefficients and evaluation of goodness of fit with the dissociation model. koff, off-rate constant; C, rate of immobile component; SSE, sum of squared errors; R2, coefficient of determination. Amounts of injected mRNAs (ng/embryo): mkikGR-wnt8 and mkikGR-frzb, 4.0.

After photoconversion, red fluorescent intensity of mK-tagged proteins was measured in the same rectangular area as photoconversion (Figure 4B). Because puncta of Wnt8 are often internalized with HS clusters (Mii et al., 2017), we excluded data in which vesicular incorporation was observed during measurement of mK-Wnt8. As an immobilized control, mK-Frzb in formaldehyde-fixed embryos was similarly photoconverted. Its intensity within the rectangular region was fitted to the photobleaching model using repeated laser scanning, confirming that it actually was immobilized by formaldehyde fixation (Figure 4—figure supplement 2B). Compared with this fixed control, mK-Wnt8 and mK-Frzb showed faster decline of fluorescent intensities (Figure 4C and Figure 4—figure supplement 2A), confirming that a population of mK-Wnt8 and mK-Frzb moved away from the photoconverted area.

Given that the punctate distribution of mK-Wnt8 and -Frzb results from their binding to HS clusters (Mii et al., 2017), we considered whether a simple dissociation model (Equation 1) is suitable for curve-fitting of FDAP data. Indeed, bleaching-corrected FDAP curves of mK-Wnt8 and mK-Frzb were well fitted to this model (Figure 4D; residuals were mostly within 5% and all within 10%) with the indicated parameters (Figure 4E, the off-rate constant koff, and the rate of the constantly bound component C; for individual data plot, see Figure 4—figure supplement 2D). As a result, both mK-Wnt8 and mK-Frzb show large C values, indicating that the majority of these proteins can be considered immobile on the timescale of the measurement (Figure 4E). In addition, koff of mK-Wnt8 was significantly lower than that of mK-Frzb (Figure 4—figure supplement 2D), suggesting relatively rapid dissociation of mK-Frzb from the binding site. This difference appears to be consistent with FDAP spatial intensity profiles, in which photoconverted mK-Frzb, but not mK-Wnt8, accumulated in adjacent areas (Figure 4—figure supplement 2C, see also Videos 1, 2 and 3). Thus, we conclude that most mK-Wnt8 and mK-Frzb molecules are bound, but can be exchanged with unbound molecules, and also dissociation rate values of mK-Wnt8 and mK-Frzb differ significantly.

Video 1
Photoconversion of mKikGR-Wnt8 in a cell-boundary region of a Xenopus embryo.
Video 2
Photoconversion of mKikGR-Wnt8 in a cell-boundary region of a Xenopus embryo (another example).
Video 3
Photoconversion of mKikGR-Frzb in a cell-boundary region of a Xenopus embryo.

Photoconversion of mKikGR fusion proteins was performed at a cell-boundary region in the animal cap of a Xenopus gastrula (st. 10.5 st.11.5). mKikGR-Wnt8 (Videos 1 and 2) or mKikGR-Frzb (Video 3) was photoconverted at the region indicated with the blue box after 100 frames scanned (about 4 s), and another 400 frames were scanned for measurement. The width of the region for photoconversion and intensity measurement was 20 pixels (1.66 μm). The play speed is x1.

Mathematically modeling diffusion and distribution of secreted proteins

Based on our quantitative imaging (Figure 1) of tethered-anti-HA Ab and morphotrap (Figure 2) FCS (Figure 3) and FDAP (Figure 4), we conclude that most Wnt8 and Frzb molecules are bound to cell surfaces, while small numbers of freely diffusing molecules exist in the extracellular space. Furthermore, we have already shown that Wnt8 and Frzb utilize different types of HS clusters, N-sulfo-rich and N-acetyl-rich, as cell-surface scaffolds, respectively (Mii et al., 2017). Thus, we examined whether free diffusion and binding to HS clusters can explain the extracellular distribution or gradient formation of secreted proteins, using mathematical modeling.

Here, we consider two states of ligands: free and bound. The free state corresponds to the fast diffusing component in FCS, and we consider the bound component as immobile molecules. This model includes five dynamic processes: (i) ligand production, (ii) diffusion of free molecules in intercellular space, (iii) binding of ligands to HS clusters on cell surfaces, (iv) release of bound molecules from HS clusters and (v) internalization of bound molecules into cells. In one-dimensional space, the model is written as:

(1) ut=D2ux2a(x)u+bv+g(x),(0<x<L)
(2) dvdt=a(x)ubvcv,(0<x<L)

where u and v represent the concentration of free molecules and numbers of bound molecules, respectively, of a secreted protein. The symbols x and t are position and time, respectively. The symbols a(x), b, c, and g(x) represent binding, release, internalization, and production rates, respectively (Figure 5A); a(x) is equivalent to the amount of HS in HS clusters (for details, see Materials and methods). D (= 20 μm2/s) represents the diffusion coefficient of the free component in the extracellular space, which corresponds to the fast diffusing component measured by FCS (Figure 3D).

Figure 5 with 1 supplement see all
A minimal model of secreted protein dynamics in the extracellular space.

Distributions of free (u) and bound (v) components of secreted proteins were obtained by computer simulation. The vertical axis indicates the amount of u and v, and the horizontal axis indicates the distance (x); Distributions in the range of 0 ≤ x ≤ 100 (μm) are shown while the model considers a field whose spatial length L = 1000 (μm). Distributions of u (red) and bound v (blue) at time t = 100 (sec) are shown, which we confirmed as being nearly steady states. We used the forward difference method with spatial step Δx = 0.1 and temporal step Δt = 0.0001 in numerical calculations. The level of v at the position where docking sites exist (a(x) = an,max) remains relatively high even after an,max exceeded b. (A) Schema of the modeling. a(x), binding rate at position x. Note that a(x) is equivalent to the amount of HS for an HS cluster. b, release rate from the HS clusters. c, internalization rate of the HS clusters. D, diffusion coefficient of u. g(x), production rate at position x. For details, see Materials and methods. (B) Rapid internalization of the docking sites. Parameter values are: D = 20.0 (μm2/s), an,max = 10.0, b = 0.1, c = 0.1, gmax = 0.2, R = L/1000, p1 = 2, and p2 = 0.2. The decay length λ is calculated as 6.346 μm, according to the fitting curve (see Figure 5—figure supplement 1A). See also Source code 1. (C) Slow internalization of the docking sites. Parameter values are the same as in (A) except for c = 0.01. The decay length λ is calculated as 10.79 μm, according to the fitting curve (see Figure 5—figure supplement 1B). This value represents a wider range than that in (A). See also Source code 2. (D) Local accumulation similar to intercellular distribution of mV-Wnt8 and -Frzb. an,max is given randomly for each n by an absolute value of the normal distribution. See also Source code 3. (E) Distant scaffolds from the source region. an,max is given to depend on space: 10.0 for 50 ≤ x ≤ 60, otherwise, an,max is 0.0. This situation is similar to tethered-anti-HA Ab (Figure 2B). See also Source code 4. (F) Ligand accumulation in front of the HS-absent region. an,max is given depending on space: 10.0 for 0 ≤ x ≤ 10 and 0 for 10 < x ≤ 1000. Values of v in (B) (vB) are also shown with green dashed lines, for comparison. Note that ligand accumulation occurs in front of the HS-absent region (10 < x.). See also Source code 5.

Under a wide range of appropriate parameter values, distributions of u and v converged to steady states within a few minutes. Compared to the fast diffusing component (Figure 5B), the contribution of the slow component (D = 0.50 μm2/s) to the distribution range is much smaller (Figure 5—figure supplement 1A). Hense, we mainly consider the fast component observed in FCS (Figure 3D), as the diffusing population in the model. The free component, u, quickly decreases, displaying a shallow continuous distribution pattern due to diffusion. In contrast, the bound component, v, shows a discrete distribution following a(x), and the level of v is much higher than u at any position, reflecting our conclusion that the majority of Wnt or sFRPs molecules in the extracellular space are bound. Given that activation of Wnt signaling requires internalization of the ligands (Kikuchi et al., 2009; Yamamoto et al., 2006) of the bound component, corresponding to cv in Equation 4, the distribution of the bound component in this model could be equivalent to the ‘actual’ gradient of Wnt signaling, even though it is not diffusing. We demonstrated that consistently some portion of Wnt8 ligands accumulated on N-sulfo-rich HS clusters initiate canonical Wnt signaling by forming the signaling complex ‘signalosome’ (Mii et al., 2017).

We have shown that N-sulfo-rich HS, but not N-acetyl-rich clusters, are frequently endocytosed (Mii et al., 2017). In this model, different internalization rates of N-acetyl-rich and N-sulfo-rich HS clusters can be reflected by varying the internalization rate of the docking sites, c. A smaller value of c results in long-range distribution (compare Figure 5B and C), explaining why Frzb shows a long-range distribution by binding to N-acetyl-rich HS clusters (Mii et al., 2017). We can evaluate the distribution by the decay length, λ. λ represents a distribution range when the steady state gradient is written as

(5) c(x)=coexp(x/λ)

(Kicheva et al., 2012; Kicheva et al., 2007). We calculated λ by curve-fitting the peak values of v to Equation 5. The value of λ with c = 0.1 or 0.01 is 6.346 or 10.79 μm (Figure 5B,C and Figure 5—figure supplement 1B,C for normalized plots), respectively, showing that internalization rates of HS clusters can affect distribution ranges, as observed between Wnt and sFRPs (Mii and Taira, 2009). In addition, we examined the contribution of dissociation from the bound to the diffusing state, suggested by our cell-boundary FDAP (Figure 4). Without dissociation, a shorter range distribution of the bound component was obtained (λ = 4.504 μm, Figure 5—figure supplement 1D) than with dissociation (λ = 6.346 μm, same data as in Figure 5B). Furthermore, in Xenopus embryos, Wnt8 in the intercellular space exhibited local accumulations (Figure 1B). In our model, when the binding rate an,max (Equation 7 in the Materials and methods) fluctuates randomly (i.e. the amount of HS at position x), the bound ligand component also fluctuates (Figure 5D, blue), reproducing the local accumulation of Wnt8 and Frzb in Xenopus embryos. Even under these conditions, the free component shows a continuously decreasing gradient (Figure 5D, red), which probably corresponds to the FCS-measured, diffusing component of the FGF8 gradient in zebrafish embryos (Yu et al., 2009; measuring concentrations by FCS in a wide field is technically difficult in larger Xenopus embryos). Thus, our mathematical model can generalize protein distributions in the extracellular space.

Discussion

As one of the major secreted signaling molecules, mechanisms of Wnt dispersal are crucial when we consider embryonic patterning and various other systems involving Wnt signaling (Routledge and Scholpp, 2019). Among many Wnt proteins, Wg distribution in the Drosophila wing disc has long been investigated as a morphogen gradient (Strigini and Cohen, 2000; Zecca et al., 1996). Various genetic studies show that the extracellular distribution of Wg largely depends on HSPGs, such as Dally and Dally-like glypicans (Baeg et al., 2004; Franch-Marro et al., 2005; Han et al., 2005). Furthermore, FRAP-based analysis suggests that the effective diffusion coefficient of Wg is much slower (0.05 μm2/s) than free diffusion (>10 μm2/s) (Kicheva et al., 2007). However, such dynamics of secreted signaling proteins still remain a matter of debate (Rogers and Schier, 2011). On the other hand, recently we found that HS chains on the cell surface are organized in clusters with varying degrees of N-sulfo modification in Xenopus embryos and HeLa cells. Furthermore, we demonstrated that endogenous Wnt8 protein visualized by immunostaining shows a punctate distribution, specifically associated with N-sulfo-rich HS clusters (Mii et al., 2017). Similar punctate distributions have also been observed with Wg in Drosophila (Strigini and Cohen, 2000; van den Heuvel et al., 1989), but the significance of these distributions has not yet been explained. Therefore, to gain insight into the mechanism of Wnt distribution, we examined Wnt8 protein dynamics.

Based on quantitative live-imaging techniques, we propose that most Wnt8 molecules distributed among cells are mostly cell-surface-bound, while a small portion of them are diffusing. Similarly, Wnt/EGL-20 shows that puncta mostly overlap with Frizzled and a small population of mobile/diffusing molecules is also suggested in C. elegans (Pani and Goldstein, 2018). In Xenopus embryos, Frizzled may also contribute to bind Wnt ligands because some Wnt8 puncta overlapped with Frizzled8 (Mii et al., 2017). Furthermore, a small population of diffusing Dpp has been shown in Drosophila wing disc (Zhou et al., 2012). Importantly, it has been suggested that these populations disperse over long distances, similar to our observation of mV-Wnt8 trapped using morphotrap (Figure 2C,D), generalizing the existence of long-dispersing populations in various model systems.

It is plausible that cell-surface-bound Wnt8 is mostly associated with HS clusters (Mii et al., 2017). The function of HSPGs in Wnt dispersal has been examined by genetic studies of Drosophila. These studies show that HSPGs are required for accumulation and transfer of Wnt ligands. Based on these results, it has been proposed that Wnt disperses by restricted diffusion, in which HSPGs transfer Wnt ligands in a bucket brigade manner (Yan and Lin, 2009). In our FDAP assay, most photoconverted mK-Wnt8 does not diffuse laterally, even when other puncta of Wnt8 exist near the site of photoconversion (Figure 4—figure supplement 2C, left panel, Video 1). We further considered this observation with modeling (Figure 5—figure supplement 1E). Unlike the experiment, modeling shows lateral dispersal of photoconverted molecules in neighboring regions, over time. For this difference, we mainly consider two possibilities: (i) Our imaging system may not be sufficiently sensitive to detect such a small increase, or the increase of the ligands may be obliterated by photobleaching. (ii) If binding dynamics of mK-Wnt8 are slower than in our model, ligands may diffuse away before re-binding in neighboring regions. On the other hand, mK-Frzb showed some lateral dispersal similar to the model. As previously discussed, these behaviors in FRAP experiments can be classified into some cases including ‘reaction dominant’ and ‘effective diffusion’ by a balance among the on-rate, the off-rate, and the diffusion coefficient (Sprague and McNally, 2005; Sprague et al., 2004). Restricted diffusion can be understood as a kind of effective diffusion in which dynamics of ligand binding/dissociation to HSPGs are similar to those of free diffusion. Although we did not derive binding constants, at least superficially, mK-Frzb showed an effective diffusion-like behavior, whereas mK-Wnt8 showed a reaction dominant-like behavior, in which free diffusion is much faster than binding/dissociation. In order to compare our data with those previously reported (Kicheva et al., 2012), we also performed curve-fitting with an effective/apparent diffusion model (Figure 4—figure supplement 3, Equation 2). As a result, the apparent diffusion coefficient Da (μm2/s) was calculated as 0.042 and 0.059 for mKikGR-Wnt8 and mKikGR-Frzb, respectively. These values are very close to a previously reported FRAP value for Wg (0.05 μm2/s) (Kicheva et al., 2007). Thus, such small values of Da relative to free diffusion could be interpreted as the result of interaction with cell surfaces, regardless of whether the protein of interest actually shows lateral diffusion in bucket brigade fashion.

We found that sec-mV is almost invisible with standard confocal microscopy (Figure 1B). Furthermore, binding to cell surface molecules such as HSPGs and membrane-tethered antibody was sufficient for visible distribution for artificial secreted proteins (Figures 1C and 2B). These findings are similar to recent demonstrations that secreted GFP can be synthetic morphogens with specific scaffold molecules in the Drosophila wing disc (Stapornwongkul et al., 2020) and in cultured cells (Toda et al., 2020). On the other hand, secreted GFP appears visible in some tissues, such as deep cells in early zebrafish embryos (Yu et al., 2009) and developing zebrafish brain (Veerapathiran et al., 2020). In the zebrafish brain, secreted EGFP did not show slow components (Veerapathiran et al., 2020), which is different from our observation in the Xenopus animal cap region (Figure 3D). Together, considering detection of diffusing molecules (Appendix), we speculate that these differences may reflect narrowness of the extracellular space in the tissues.

Our cell-boundary FDAP suggests that cell-surface-bound and diffusing populations are probably exchangeable. Although this result can be explained by dissociation of molecules from the bound state as described above, it also seems possible that endocytosis reduces the number of photoconverted molecules (Figure 4C). However, we consider this less likely. Endocytosis of Wnt8 is possibly mediated by caveolin (Mii et al., 2017; Yamamoto et al., 2006), and we have already shown that internalized Wnt8 was detected as puncta in the cell (Mii et al., 2017). However, in FDAP analyses in this study, we excluded observations with internalization of Wnt puncta from curve-fitting analysis. In our mathematical model, when dissociation from the cell-surface does not occur (b = 0 in Equation 3 and 4, Figure 5A), the range of the gradient (decay length, λ) was shortened from 6.35 to 4.50 μm (Figure 5—figure supplement 1D). Thus, at least in cases we analyzed, dissociation from the bound state seems to contribute to the long-range distribution and rapid formation of the gradient.

A goal of this study is to link quantitative measurements of local protein dynamics to larger spatiotemporal patterns of extracellular protein dispersal in embryos. We hypothesized that local dynamics of diffusion and interaction with HS chains measured by FCS and FDAP could be extrapolated to explain mechanisms for gradient formation across many cells. We mainly consider protein dispersal within a single plain, and this is exemplified in the animal cap region since mV-Wnt8 and mV-Frzb accumulated on the proximal side (to the source) of morphotrap-expressing cells (Figure 2C). But when we consider dispersal of secreted proteins in embryos, other routes can be involved. For example, a BMP antagonist, Chordin exhibits dispersal within the Brachet cleft, which is a fibronectin-rich ECM (Plouhinec et al., 2013). In addition, several other mechanisms, such as cell lineage-based dilution (Farin et al., 2016) and cytonemes/signaling filopodia (Roy et al., 2011; Stanganello et al., 2015) may contribute to dispersal of a morphogen. We emphasize that immobilization of morphogen molecules is a prerequisite for cytoneme/filopodium-mediated transfer of signaling. Gradient formation over long ranges has not been examined experimentally in this study. However, we attempted to understand the outcome of diffusion and binding, basic properties of morphogens. Thus, we propose a mathematical model consisting of free and bound components of Wnt based on observed local dynamics (Figure 5A). This model can be widely applied to secreted proteins that bind to cell surfaces, including sFRPs and other peptide growth factors. Notably, in our mathematical model, distributions of both free and bound components converged to steady states within a few minutes, showing rather fast dynamics in the context of embryonic patterning. This characteristic could solve perceived weaknesses of diffusion-base models (Müller et al., 2013), especially dilemmas related to the speed and stability of gradient formation. From this point of view, the combination of abundant cell-surface-bound and minimal diffusing populations would be beneficial for signaling stability and speed of pattern formation, respectively. Like tethered-anti-HA Ab (Figure 2B), atypical distributions of FGF (Shimokawa et al., 2011) and Nodal (Marjoram and Wright, 2011), in which ligands accumulate in locations distant from their sources, have been reported, although a theoretical explanation of these atypical distributions has proven elusive. In our model, atypical distributions can be reproduced if specific scaffolds for ligands (ligand binding proteins) are anchored on the surfaces of cells (Figure 5E). Furthermore, our model explains the puzzling localization of ligands in tissues. In mosaic analyses of the wing discs of Drosophila mutants, Hh and Dpp ligands accumulate at the edges of clones defective in HS synthesis (Takei et al., 2004; Yan and Lin, 2009). Distributional patterns of these ligands are explained by our model, which accounts for accumulations of ligand in regions lacking HS (Figure 5F). Thus, our model provides a basic framework to understand of the extracellular behavior of secreted proteins.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
AntibodyHepSS-1 (mouse monoclonal, IgM)Kure and Yoshie, 1986
Mii et al., 2017
1:400
Figure 3—figure supplement 3C
AntibodyNAH46 (mouse monoclonal, IgM)Suzuki et al., 2008
Mii et al., 2017
1:50
Figure 3—figure supplement 3C
AntibodyF69-3G10 (mouse monoclonal, IgG2b)Seikagaku Corp.3702601:200
Figure 3—figure supplement 3AB
AntibodyAnti-HA (rabbit polyclonal)MBL#5611:200
Figure 3—figure supplement 3ABC
AntibodyAnti-Wnt8 (rabbit antiserum)Mii et al., 20171:4000
Figure 1—figure supplement 1A
AntibodyAnti-mouse IgG-AlexaFluor 488 (goat polyclonal)InvitrogenA110291:500
Figure 3—figure supplement 3AB
AntibodyAnti-rabbit IgG-AlexaFluor 555 (goat polyclonal)InvitrogenA214341:500
Figure 3—figure supplement 3AB
AntibodyAnti-rabbit IgG-AlexaFluor 568 (goat polyclonal)InvitrogenA110111:500
Figure 3—figure supplement 3C
AntibodyAnti-mouse IgM-AlexaFluor 488 (goat polyclonal)InvitrogenA210421:500
Figure 3—figure supplement 3C
AntibodyAnti-rabbit IgG-AlexaFluor 647 (donkey polyclonal)InvitrogenA212451:500
Figure 1—figure supplement 1A
AntibodyAnti-mouse IgM-AlexaFluor 488 (goat polyclonal)InvitrogenA210421:500
Figure 3—figure supplement 3C
Cell line (Mus musculus)Hybridoma anti-HA (clone 12CA5)Field et al., 1988Mouse monoclonal, IgG2b, kappa
Cell line (Mus musculus)Hybridoma anti-Myc (clone 9E10)Evan et al., 1985Mouse monoclonal, IgG1, kappa
Gene (Mus musculus)12CA5-ig-gamma-2bThis studyLC522514Gene
Gene (Mus musculus)12CA5-ig-kappaThis studyLC522515Gene
Recombinant DNA reagentmorphotrapHarmansa et al., 2015
Recombinant DNA reagentpET21b-Phep_3797 (plasmid)Hashimoto et al., 2014
Recombinant DNA reagentpCSf107-SP-HepIII-HA-GPI (plasmid)This study
Software, algorithmPyCorrFitMüller et al., 2014Version 1.1.7Windows version
Software, algorithmFijiSchindelin et al., 2012
Software, algorithmimage JNIH
Software, algorithmZen2009Zeiss
Software, algorithmMatlab
Curve Fitting Toolbox
Mathworks
Software, algorithmRThe R Foundation

All experiments using Xenopus laevis were approved by the Institutional Animal Care and Use Committee, National Institutes of Natural Sciences (Permit Number 18A038, 19A062, 20A053), or the Office for Life Science Research Ethics and Safety, University of Tokyo.

Xenopus embryo manipulation and microinjection

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Unfertilized eggs of Xenopus laevis were obtained by injection of gonadotropin (ASKA Pharmaceutical). These eggs were artificially fertilized with testis homogenates and dejellied using 4% L-cysteine (adjusted to pH 7.8 with NaOH). Embryos were incubated in 1/10x Steinberg’s solution at 14–17°C and were staged according to Nieuwkoop and Faber, 1967. Synthesized mRNAs were microinjected into early (2–16 cell) embryos. Amounts of injected mRNAs are described in figure legends.

Fluorescent image acquisition

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Image acquisition was performed using confocal microscopes (TSC SP8 system with HC PL APO ×10/NA0.40 dry objective or HC PL APO2 ×40/NA1.10 W CORR water immersion objective, Leica or LSM710 system with C-Apochromat 40x/1.2 W Corr M27 water immersion objective, Zeiss). Photon counting images were acquired with a HyD detector (Leica). Detailed conditions for imaging are available upon request. mV was constructed by introducing an A206K mutation to prevent protein aggregation (Zacharias et al., 2002). For FDAP and FCS measurements, gastrula embryos were embedded on 35 mm glass-based dishes (Iwaki) with 1.5% LMP agarose (#16520–050; Invitrogen) gel, which was made of 1/10x Steinberg’s solution. For other types of live-imaging, embryos were mounted in a silicone chamber made in-house with holes 1.8 mm in diameter. Fluorescent intensity was measured using Fiji, Image J (NIH) or Zen2009 (Zeiss).

Cell lines

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Hybridomas (derived from mouse, 12CA5, anti-HA; 9E10, anti-Myc) were used to obtain their total RNA and subsequent cloning of immunoglobulin genes. These hybridomas have been neither authenticated nor tested for mycoplasma because no assays were performed with these hybridomas themselves. Instead, we confirmed generation of functional anti-HA or anti-Myc IgG from the cloned genes by co-IP assay.

Immunostaining of Xenopus embryos

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Immunostaining of Xenopus embryos was carried out according to a previous report (Mii et al., 2017). Briefly, embryos were fixed with MEMFA (0.1 M MOPS pH 7.4, 4 mM EGTA, 2 mM MgSO4, 3.7% formaldehyde) 2 hr at room temperature. Fixed embryos were dehydrated with EtOH (EtOH treatment improves staining with anti-Wnt8 and anti-HS antibodies). After rehydration, embryos were washed with TBT (1x TBS, 0.2% BSA, 0.1% Triton X-100) and blocked with TBTS (TBT supplemented with 10% heat-treated [70°C, 40 min] fetal bovine serum). The following procedures are similar for primary and secondary antibodies. Antibody was diluted with TBTS and was centrifuged 15 min at 15,000 rpm before use. Embryos were incubated with the supernatant of antibody solution overnight at 4°C. Then embryos were washed five times with TBT.

cDNA cloning of IgG from cultured hybridomas

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Cultured hybridomas were harvested by centrifugation and total RNAs were prepared using ISOGEN (Nippon Gene), according to the manufacturer’s protocol. First strand cDNA pools were synthesized using SuperScript II reverse transcriptase (Invitrogen) and random hexamer oligo DNA. These cDNA pools were used as templates for PCR to isolate cDNAs for heavy chains and light chains of anti-HA and anti-Myc IgGs. See Supplementary file 1 for all primers used for PCR cloning. Full-length cDNAs were cloned into the pCSf107mT vector (Mii and Taira, 2009).

Cultured hybridoma cells were harvested by centrifugation and total RNAs were prepared using ISOGEN (Nippon Gene), according to the manufacturer’s protocol. First strand cDNA pools were synthesized using SuperScript II reverse transcriptase (Invitrogen) and random hexamer oligo DNA. These cDNA pools were used as templates for PCR to isolate cDNAs for heavy and light chains of anti-HA and anti-Myc IgGs.

Procedures for PCR cloning were as follows. IgG cDNAs for 3' regions of CDSs were obtained by PCR with degenerate primers (5' γ-F and 5' κ-F) and primers corresponding to constant regions of Ig genes (γ2b-const-R, γ1-const-R, 3' κ-R) (Wang et al., 2000). To obtain the complete CDSs, 5'RACE was carried out to obtain the first codons of Ig genes, using a modified protocol in which inosines are introduced into the G-stretch of the HSPst-G10 anchor (personal communications from Dr. Min K. Park). cDNAs were synthesized with gene-specific primers (HA-heavy-R1, Myc-heavy-R1, HA-light-R1, and Myc-light-R1), and tailed with poly-(C) by terminal deoxynucleotidyl transferase, and subsequently double-stranded cDNAs were synthesized with the HSPst-G10 anchor. 5' ends of cDNAs were amplified by PCR between the HSPst adaptor and gene-specific primers (HA-heavy-R2, Myc-heavy-R2, HA-light-R2 and Myc-light-R2) using the double-stranded cDNAs as templates. Full length CDSs were amplified using primers designed for both ends of the CDSs (HH-Bam-F, MH-Bam-F, HL-bam-F, and ML-Bgl-F for 5’ ends; 3' γ2b-R, 3' γ1-R and 3' κ-R for the 3’ end) and the first cDNA pools. See Supplementary file 1 for all primers used for PCR cloning. Full-length cDNAs were cloned into the pCSf107mT vector (Mii and Taira, 2009). Sequence data for anti-HA IgG genes have been deposited in Genbank/DDBJ under accession codes LC522514 and LC522515.

FDAP measurements

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For expression in the animal cap region of Xenopus embryos, four-cell-stage Xenopus laevis embryos were microinjected with mRNAs for mK-Wnt8 and mK-Frzb (4.0 ng/embryo) at a ventral blastomere. Injected embryos were incubated at 14°C until the gastrula stage (st. 10.25–11.5) for subsequent confocal analysis. FDAP measurements were performed using the LSM710 system (Zeiss) with a C-Apochromat ×40, NA1.2 water immersion objective. Time-lapse image acquisition was carried out for 20 s each at 25 frames/s, and after 4 s (100 frames) from the start, intercellular mK-fusion proteins were photoconverted at a small rectangular region (1.66 × 2.49 μm) with 405 nm laser irradiation. After photoconversion, images were acquired for 16 s (400 frames). Red fluorescent intensities within the rectangular region where photoconversion was performed, were analysed by curve-fitting to Equation 1 (Figure 4D) or Equation 2 (Figure 4—figure supplement 3A), using the Curve Fitting Toolbox of MATLAB (Mathworks).

FCS measurements

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FCS measurements were carried out using a ConfoCor2 system (objective: C-Apochromat ×40, NA1.2 water immersion) (Zeiss; Figure 3—figure supplement 2 only) according to a previous report (Pack et al., 2006) or a TSC SP8 equipped with FCS (objective: HC PL APO 63x/1.20 W motCORR CS2) (Leica). mRNAs for mV-Wnt8 and sec-mV were microinjected into four- or eight-cell stage Xenopus embryos. Injected embryos were measured at gastrula stage (st. 10.5–11.5). Rhodamine 6G (Sigma-Aldrich) was used to calibrate detection volume, with a reported value of its diffusion coefficient (280 μm2/s) (Pack et al., 2006). PyCorrFit software (Müller et al., 2014) was used for curve-fitting analyses of FCS data from the Leica system. Models considering three-dimensional free diffusion with a Gaussian laser profile, including a triplet component (‘T + 3D’, a one-component model or ‘T + 3D + 3D’, a two-component model) were used for fitting. Akaike information criterion (AIC) was used to compare fitting with the one-component and two-component models according to a previous report (Tsutsumi et al., 2016).

Plasmid construction

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pCSf107mT (Mii and Taira, 2009) was used to make most plasmid constructs for mRNA synthesis. pCSf107SPw-mT and pCSf107SPf-mT were constructed, which have the original signal peptides of Wnt8 and Frzb, respectively. The coding sequence (CDS) for mVenus (mV) or mKikGR (mK) was inserted into the BamHI site of pCSf107SPw-mT or pCSf107SPf-mT to construct pCSf107SPw-mV-mT, pCSf107SPf-mV-mT, pCSf107SPw-mK-mT, and pCSf107SPf-mK-mT. Constructs for SP-mV, SP-mV-HB, and SP-mV-2HA were made with pCSf107SPf-mT. pCS2 +HA-IgH-TM-2FT (the heavy chain for anti-HA IgG with the transmembrane domain of a membrane-bound form of IgG heavy chain) was made by inserting the full length CDS of heavy chain of anti-HA IgG without the stop codon (using the EcoRI and BglII sites) and a partial CDS fragment corresponding to the IgG transmembrane domain (using the BglII and XbaI sites) into the EcoRI/XbaI sites of pCS2 +mcs-2FT-T. To construct pCSf107-SP-HepIII-HA-GPI, HepIII CDS was inserted into pCSf107-SP-mcs-4xHA-GPI.

Luciferase reporter assays

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Luciferase reporter assays were carried out as previously described (Mii and Taira, 2009). Multiple comparisons were carried out with pairwise Wilcoxon rank sum test (two-sided) in which significance levels (p-values) were adjusted by the Holm method, using R.

Mathematical modeling

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Two ligand components were considered: free and bound. This model includes five dynamic processes: (i) production of ligands, (ii) diffusion of the free component in intercellular space, (iii) binding of ligands to dotted structures (‘docking sites’) such as HS clusters on the surface of cells, (iv) release of the bound component from ‘docking sites,’ and (v) internalization of the bound component into cells. The model in one-dimensional space is written as:

(3) ut=D2ux2a(x)u+bv+g(x),(0<x<L)
(4) dvdt=a(x)ubvcv,(0<x<L)

where u and v represent the amounts of free and bound components of morphogen molecules, respectively. The symbols a(x), b, c, and g(x) represent binding, release, internalization, and production rates, respectively. D represents the diffusion coefficient of the free component in the extracellular space. The ligand is assumed to be produced in a limited region using the following function:

(6) g(x)={gmax(0xR)0(elsewhere).

We assumed that the binding rate a(x) depends on the position x, following heterogeneous distribution of HS clusters on the cell surface. The following function was used for a(x):

(7) a(x)={an,max(p1nxP1n+p2,n=1,2,...)0(elsewhere),

where p1 and p2 are the interval and width, respectively, of docking sites. We used the no-flux (Neumann) boundary condition at x = 0 and L. We calculated the model by numerical simulation. The initial distributions of u and v were set at 0 throughout the entire space. In the one-dimensional space, distributions of free (u) and bound (v) components of secreted proteins were obtained by computer simulation, where the spatial length L = 1000 (μm). Distributions of u (red) and v (blue) are presented at time t = 100 (sec), which almost reached steady states. We used the forward difference method with the spatial step Δx = 0.1 and temporal step Δt = 0.0001 in numerical calculations. In Figure 5B, parameter values are: D = 20.0, an,max = 10.0, b = 0.1, c = 0.1, gmax = 0.2, R = L/1000, p1 = 2, and p2 = 0.2. In other panels, distributions in specific conditions are shown (see figure legends).

Appendix 1

Detecting freely diffusing molecules with confocal microscopy

When we consider detection of diffusing molecules with confocal microscopy, diffusing molecules might not be detected because of their large displacement. In our imaging conditions, pixel dwell time was 24.4 μs and observed maximum Dfast in FCS was about 80 μm2/s (Figure S3E). During pixel dwell time t, mean square displacement (MSD) of molecules of diffusion coefficient D in three-dimensional space is 6Dt (Crank, 1975). Therefore, maximum MSD is estimated as

6×80×24.4×106=0.0117μm2

Accordingly, mean displacement of the molecules at the maximum is

MSD=0.108μm=108nm

On the other hand, when the numerical aperture (NA) of an objective lens is given, the Reyligh diffraction limit in our conditions is calculated as

0.61λNA=0.61×5001.1=277nm

This value of the diffraction limit is larger than the MSD so that even the most rapidly diffusing molecules observed in FCS measurements, loss of fluorescence due to diffusion is not likely to occur. Indeed, we detected slight but significant increase of photon counts with sec-mV (Figure 1D and Figure 1—figure supplement 2) with a long pixel dwell time. We speculate that the difficulty of visualizing sec-mV is mainly due to narrowness of extracellular space in Xenopus embryos

Data availability

Sequence data for anti-HA IgG genes have been deposited in Genbank/DDBJ under accession codes LC522514 and LC522515.

The following data sets were generated
    1. Mii Y
    (2020) NCBI GenBank
    ID LC522514.1. Mus musculus mRNA for immunoglobulin gamma 2b, complete cds.
    1. Mii Y
    (2020) NCBI GenBank
    ID LC522515.1. Mus musculus mRNA for immunoglobulin kappa, complete cds.

References

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    1. Crank J
    (1975)
    The Mathematics of Diffusion
    Oxford University Press.
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    2. Yoshie O
    (1986)
    A syngeneic monoclonal antibody to murine Meth-A sarcoma (HepSS-1) recognizes heparan sulfate glycosaminoglycan (HS-GAG): cell density and transformation dependent alteration in cell surface HS-GAG defined by HepSS-1
    Journal of Immunology 137:3900–3908.
  2. Book
    1. Nieuwkoop PD
    2. Faber J
    (1967)
    Normal Table of Xenopus laevis (Daudin)
    Garland Science.

Decision letter

  1. Ariel M Pani
    Reviewing Editor; University of Virginia, United States
  2. Naama Barkai
    Senior Editor; Weizmann Institute of Science, Israel

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

How secreted signaling proteins disperse to form gradients in living animals is a fundamental question for developmental and cell biology. Mii et al., use quantitative light microscopy to analyze dispersal dynamics of a tagged Wnt and FrzB in Xenopus embryos. The authors provide evidence that the spatial distribution of binding sites and dynamic exchange of bound and freely diffusing proteins facilitate rapid formation of long-range protein gradients and that a similar mechanism could apply to other pathways as well.

Decision letter after peer review:

Thank you for submitting your article "Quantitative analyses reveal extracellular dynamics of Wnt ligands in Xenopus embryos" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Naama Barkai as the Senior Editor. The reviewers have opted to remain anonymous.

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

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is 'in revision at eLife'. Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

In this manuscript, Mii et al., use quantitative light microscopy to analyze dispersal dynamics of a tagged Wnt and FrzB in Xenopus embryos. Using protein trapping approaches, they demonstrate that Venus-tagged Wnt8 and FrzB can spread across many cell diameters in vivo and that high levels of secreted protein can be captured at a distance from their sources. Importantly, Mii et al., showed using FCS that only a small fraction of the detectable protein was freely diffusing at a given time while the majority was not. Mathematical models of Wnt8 and FrzB dispersal based on experimentally derived parameters including exchange between the free and bound populations were also able to recapitulate a gradient profile for bound extracellular proteins. The authors propose that the spatial distribution of ligand binding sites and on-off dynamics facilitate formation of long-range Wnt protein gradients, and that a similar mechanism could apply to other pathways.

How signaling proteins disperse in living animals is a fundamental question, and a variety of mechanisms are likely to operate in different contexts. This manuscript presents a series of targeted experiments that provide an important step forward for the field. However, there are key issues that need to be addressed to give confidence in the biological relevance of the results and the mathematical techniques.

Essential revisions:

Comments from the three reviewers have been condensed into a single list to help with revisions. Successfully addressing the reviewers' essential revision points 1-3 will require additional laboratory experiments, while the others likely will not. Successfully addressing the reviewers' points 11-14 would normally require new experiments, but based on current eLife guidelines due to COVID-19, I think that these could instead be adequately addressed in the manuscript text if the additional experiments are not practical at this point.

1. All three reviewers had similar reservations regarding the use of overexpression for quantitative analyses. Most importantly, this paper relies entirely on overexpressed Wnt ligand. As the proposed mechanisms controlling Wnt diffusion involve binding to extracellular factors, altering the stoichiometry of Wnt ligands and binding sites may affect the normal balance between the different pools of Wnt. This would be especially problematic if overexpression saturates endogenous mechanisms that limit Wnt diffusion within the areas used for measurements. Similar concerns also apply to the FrzB measurements. To ensure confidence in the physiological relevance of the data, the authors should perform the key experiments over a range of lower mRNA dosages down to the minimum required for FCS to test if the measured parameters (diffusion constants, fraction of molecules in each pool) are the same. If the results differ, would it be possible to extrapolate to a case of zero overexpression?

2. The authors should attempt to estimate how the levels of the tagged proteins compare to the endogenous ones. For Wnt8, this could potentially be done by comparing immunofluorescence levels for the endogenous and the tagged Wnt8.

3. Functionality of tagged proteins.

a. Please provide additional data to demonstrate that the tagged proteins used here are fully functional. This revision is considered critical due to the level of precision in the analyses. Showing only that Wnt or FrzB fusion proteins can induce a phenotype when overexpressed would not be sufficient. The authors could use TOP-FLASH assays like in Figure 4 Supplement 1 over a range of lower concentrations to confirm that the responses at low mRNA levels are the same for tagged and untagged Wnt8 and FrzB.

b. Based on Figure 4, supplement 1, the mKikGR-FrzB fusion protein is not fully functional and should be removed.

4. Some of the details of the proposed mechanism are confusing. The paper seems to measure both slow and fast diffusing components from the FCS measurements and also to propose an immobile fraction that cannot be measured in FCS. Or do the authors believe that the slow component in FCS can be equated with the immobile one? If the former, do the authors really need three different pools of ligand to explain the data? On the other hand, the model only has two components a fast diffusing one and an immobile fraction – would the results be affected by adding a slow diffusing component as measured in FCS? or alternatively treating the immobile fraction as slowly diffusing?

5. In light of (4), the authors should consider whether simpler models fit their data equally well. For example, the effective diffusion model in Figure 4 supplement 3 that has fewer parameters appears to fit the Frzb data better than the two population model in the main text. For Wnt, it isn't clear whether goodness of fit is improved by adding a second component as more parameters are also added.

6. Can the authors speculate about why the sec-mv has a slow diffusing component? This seems surprising. Again, the authors should justify using a two-population (slow and fast) rather than a simpler model.

7. There may be an error in the FDAP equation in figure 4. The data appear to be normalized so that f(0) = 1, however, plugging 0 into the equation does not give 1. More likely, the authors intend to fit to the function f(t) = (1-c)*exp(-koff*t)+c which has the correct limiting behavior at both t = 0 and t = inf. This shows however that there are only two parameters to be fit (this makes sense, they correspond to the decay rate and the immobile fraction) so it is unclear how the authors are fitting three parameters from their data. Essentially, one cannot extract kon from this data – it is lumped into the prefactor of the exponential.

8. Modeling

a. The argument in lines 402-411 is not valid, as can be seen with numerical simulations, in which a molecule with D=0.05 um^2/s does reach the end of a 200 μm long patterning field over 24 h and can be readily absorbed by a localized sink there.

b. In fact, the models (effective diffusion or binding/dissociation) are not mutually exclusive (as described in the Crank reference mentioned by the authors). It would therefore be useful for the readers if the authors could present a unified description of restricted, effective and hindered diffusion.

c. Since the proteins are detected over distances of ~15 cell diameters (~200 um), the spatial range of the simulations should be increased to the length of a Xenopus embryo to avoid reflection from the boundary. It would also be ideal if the authors could measure the gradient throughout the embryo with FCS (similar to PMID 19741606) or note their limitations.

d. Please normalize each curve to the maximum of each species to see if there really is a difference in the ranges of the free and bound components in the model.

e. The authors state that "Notably, in our mathematical model, distributions of both free and bound components converged to steady states within a few minutes, showing rather fast dynamics in the context of embryonic patterning". The cause for these unexpectedly fast dynamics might be the extremely high off-rates compared to previously measured values (e.g. compare to the values used in PMID 22445299). Depending on re-assessment of the FDAP data, the authors should repeat their simulations. It would be ideal to more directly compare the simulations to the experimental data (e.g. overlay data from Figure 2D with simulations) and to compare the fast kinetics prediction of the model to the temporal gradient evolution in real embryos.

f. Please simulate the outcome of FDAP experiments (i.e. the dissipation of concentration from one peak). Is an increase in neighboring regions detected, and how does this compare with the experimental findings?

g. Please simulate the Sec-mV findings. Maybe the simulations will clarify why this protein is not visible?

h. Please clarify why the data in Figure 5F looks different from panel B, even though it should be the same according to the figure legend.

9. FCS analyses and inference of bound/unbound fractions

a. The authors state that "[…] diffusion coefficients measured with FRAP and FCS differ by 3-4 orders of magnitude (Rogers and Schier, 2011)", but this statement is outdated and should be corrected (see e.g. PMIDs 23533171 and 28919007).

b. Figure 3B shows problematic drift that might underlie the long correlation times.

c. The authors further state that "By autocorrelation analysis, FCS can measure the diffusion coefficient (D) and the number of particles, which is equivalent to the concentration of the diffusing molecules (Figure 3E), but it cannot measure immobile molecules (Hess et al., 2002)", but this statement (and similar ones in lines 220-221 and 237-238) is imprecise and needs to be corrected. Avalanche photodiodes or the like used in FCS experiments do detect all signal emitted from fluorescent molecules, but the inference of diffusion coefficients depends on mobile molecules.

d. These imprecisions in FCS theory lead to unsuitable analyses in Figure 3G. Importantly, not only the inferred number of particles but also the count rate is higher for Sec-mV, although the same mRNA amount was used for all constructs. The difference in the outcomes between confocal imaging and FCS might instead be the result of variability between experiments. The authors should therefore measure and compare the intensities using photon counting and FCS within the same embryos if possible or note their limitations.

e. The subsequent considerations about immobile and diffusing molecules (e.g. lines 230-233) are inappropriate and should be corrected. The current logic is: 9.4 detected mV-Wnt8 particles constitute 43% of the 21.7 particles detected for sec-mV, and since 43% of the 19.5 maximally detectable mobile mean photon counts equal 8.4 mean photon counts, the rest of the mean photon counts (219.7) for mV-Wnt8 is immobile; however, this logic is inappropriate since identical amounts of mRNA are not equimolar given the protein size differences, identical mRNA amounts do not necessarily give rise to similar protein amounts due to differences in translation or stability, and day-to-day or instrument differences might influence the intensities.

f. Please use the Akaike information criterion or the like to compare the 2-component fits with more parsimonious 1-component fits of the FCS data, especially given the small differences in the highly and poorly mobile fractions.

g. Please mention from how many independent embryos the FCS measurements were derived.

10. In Figure 3 supplement 1, the comparison is not appropriate given the very different sample numbers and the small effect size. The authors should instead use bootstrapping approaches with similar sample numbers.

11. For the FDAP experiments, the instruments may just not have the sensitivity to detect small numbers of photoconverted molecules that spread locally, especially considering that a large amount of fluorescence loss is due to photobleaching and that the imaging is at a single plane. The authors should take care with interpreting these data unless they are able to repeat the experiments using new acquisition parameters to minimize bleaching. Additionally, as the authors noted that the pseudo-equilibrium binding constant may be underestimated because curve fitting does not consider photobleaching, and the simulations crucially depend on these parameters, they should execute new simulations with the parameters determined in the absence of photobleaching if possible. If repeating these experiments is not practical due to current restrictions the authors should temper their conclusions.

12. A goal of this manuscript is to link quantitative measurements of local protein dynamics to larger spatiotemporal patterns of extracellular protein dispersal in embryos. To do so, the authors assume that short-term measurements at sub-apical junctions fully capture the processes that are important for protein dispersal and gradient formation. The hypothesis is that local dynamics measured by FCS and FDAP in a particular location can be extrapolated to model mechanisms for long distance dispersal across fields of many cells, but this has not been shown experimentally. The authors could use FRAP experiments photobleaching a large area at a distance from the Wnt source followed by time-lapse imaging over a long period to confirm that the pattern and timing of fluorescence recovery across many cells is consistent with predictions from their model. Under normal circumstances, these experiments would be considered required revisions. However, in light of the current research situation it would be acceptable to instead discuss these limitations in the manuscript.

13. The manuscript seems to treat Wnt8 and FrzB dispersal as occurring within a single plane, with the dynamics at sub-apical junctions reflecting dynamics elsewhere. However, the tagged proteins could be spreading along the basal surfaces or through entirely different routes or mechanisms. These possibilities should be considered in the discussion if it is not feasible to assess them experimentally.

14. Similarly to (12), a key assumption is that the freely diffusing population of Wnt detectable by FCS is the protein population that moves between cells, but this cannot be directly concluded from the experiments. The authors should discuss the possibility of Wnt transport on cytonemes/signaling filopodia or contacts between cells at earlier developmental stages. Based on the Xenopus fate map, can the authors rule out potential mechanisms for Wnt gradient formation based on cell lineages or migration patterns? The morphotrap experiments argue against these possibilities, but the fact that tagged protein can accumulate to high levels in distant trap-expressing cells could be explained by higher stability of trapped versus untrapped protein.

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

Thank you for submitting your article "Quantitative analyses reveal extracellular dynamics of Wnt ligands in Xenopus embryos" for consideration by eLife. Your revised article has been evaluated by a Reviewing Editor, one Reviewer, and Naama Barkai as the Senior Editor.

The Reviewing Editor has drafted this to help you prepare a revised submission.

The authors have satisfactorily addressed the reviewers' combined points, and the additional experiments have substantially strengthened the conclusions. I am pleased to recommend accepting this paper for publication pending minor revisions. There are only three points to address prior to full acceptance.

Summary:

How secreted signaling proteins disperse to form gradients in living animals is a fundamental question for developmental and cell biology. Mii et al., use quantitative light microscopy to analyze dispersal dynamics of a tagged Wnt and FrzB in Xenopus embryos. The authors provide evidence that the spatial distribution of binding sites and dynamic exchange of bound and freely diffusing proteins facilitate rapid formation of long-range protein gradients and that a similar mechanism could apply to other pathways as well.

Essential Revisions:

1. The authors state "We provide the source code for our mathematical model, written in C.", but we could not find the code among the current manuscript items. Please provide this code as a Supplementary file.

2. The diffusion coefficients for 250 pg and 20 pg of mV-Wnt8 appear to differ between Figure 3D and Figure 3—figure supplement 1B, and the authors should check whether the correct data are plotted.

3. The figure legend for Figure 4 includes a panel F, but this image is missing from the figure. Please provide an updated Figure 4 including F or remove it from the legend.

4. The authors may wish to note in the text that reduced activity of mV-tagged Wnt8 compared to untagged Wnt8 could possibly be due, at least in part, to differences in translation.

https://doi.org/10.7554/eLife.55108.sa1

Author response

Essential revisions:

Comments from the three reviewers have been condensed into a single list to help with revisions. Successfully addressing the reviewers' essential revision points 1-3 will require additional laboratory experiments, while the others likely will not. Successfully addressing the reviewers' points 11-14 would normally require new experiments, but based on current eLife guidelines due to COVID-19, I think that these could instead be adequately addressed in the manuscript text if the additional experiments are not practical at this point.

Thank you very much for constructive and helpful comments to improve our manuscript. Accordingly, we performed essential experiments addressing the reviewers’ points, mainly estimation of endogenous-equivalent doses of mV-Wnt8 and -Frzb, their activity, and FCS analyses with endogenous-level expression. In addition, we performed FCS analyses with HS digestion using newly developed, membrane-tethered Heparinase III. In the current situation, it is difficult for us to perform the FDAP analyses again. However, we carefully re-assessed our FDAP data, and revised our manuscript, in accordance with the reviewers’ suggestions. We hope that our revisions have adequately addressed their concerns.

1. All three reviewers had similar reservations regarding the use of overexpression for quantitative analyses. Most importantly, this paper relies entirely on overexpressed Wnt ligand. As the proposed mechanisms controlling Wnt diffusion involve binding to extracellular factors, altering the stoichiometry of Wnt ligands and binding sites may affect the normal balance between the different pools of Wnt. This would be especially problematic if overexpression saturates endogenous mechanisms that limit Wnt diffusion within the areas used for measurements. Similar concerns also apply to the FrzB measurements. To ensure confidence in the physiological relevance of the data, the authors should perform the key experiments over a range of lower mRNA dosages down to the minimum required for FCS to test if the measured parameters (diffusion constants, fraction of molecules in each pool) are the same. If the results differ, would it be possible to extrapolate to a case of zero overexpression?

2. The authors should attempt to estimate how the levels of the tagged proteins compare to the endogenous ones. For Wnt8, this could potentially be done by comparing immunofluorescence levels for the endogenous and the tagged Wnt8.

Thank you for these suggestions. We performed immunostaining of overexpressed mV-Wnt8 in the animal cap region with rabbit antiserum against Wnt8 (Mii et al., 2017) to determine a dose equivalent to endogenous distribution of Wnt8 in the ventral marginal zone (VMZ, just over the ventral mesoderm expressing wnt8, and highest for Wnt8 distribution). The data show that injection of 20 pg/embryo of mV-Wnt8 mRNA provides Wnt8 staining in the animal cap region that is equivalent to that in the VMZ (Figure 1—figure supplement 2A). Endogenous expression levels of Xenopus embryonic mRNAs were published in Session et al., Nature 2016 (PMID 27762356). In this data, wnt8a (L + S) shows 288 transcripts per million (TPM) and frzb (L + S) shows 180 TPM. Thus, we estimated an endogenous-equivalent dose of mV-frzb as 10-20 pg/embryo.

In response to comment 1, we performed FCS measurements at the endogenous-equivalent level (by injecting 20 pg/embryo mRNA) of mV-Wnt8 and mV-Frzb (new Figure 3 and Figure 3—figure supplement 1). Previous FCS measurements were performed with a Zeiss system at RIKEN (Wako, Saitama); however, due to the pandemic, it is currently difficult to perform experiments at RIKEN. Instead, we performed new measurements with a Leica system at our institute (Okazaki, Aichi). New data with an endogenous-equivalent level of mV-Wnt8 and mV-Frzb also showed a freely diffusing component as well as a slowly diffusing component (Figure 3D and Figure 3—figure supplement 1B).

3. Functionality of tagged proteins.

a. Please provide additional data to demonstrate that the tagged proteins used here are fully functional. This revision is considered critical due to the level of precision in the analyses. Showing only that Wnt or FrzB fusion proteins can induce a phenotype when overexpressed would not be sufficient. The authors could use TOP-FLASH assays like in Figure 4 Supplement 1 over a range of lower concentrations to confirm that the responses at low mRNA levels are the same for tagged and untagged Wnt8 and FrzB.

b. Based on Figure 4, supplement 1, the mKikGR-FrzB fusion protein is not fully functional and should be removed.

In accordance with this comment, we performed TOP-FLASH assays at an endogenous-equivalent dose (20 pg/embryo) for mV-Wnt8 and -Frzb, in conditions such that the activity of the reporter is not saturated (Figure 1—figure supplement 2 B and C). The data indicate that these tagged proteins possess activities to activate (Wnt8) or to inhibit (Frzb) canonical Wnt signaling. mV-Frzb showed similar signaling activity to the wild type. But mV-Wnt8 showed 1/3 to 1/2 of the full activity. However, we want to continue using this tagged protein for this study for the following reason.

In our previous studies, we used mEGFP-Wnt3a to investigate biochemical and biophysical features of Wnt3a (Takada, Mii et al., Commun. Biol. 2018, PMID 30320232). In this study, mEGFP-Wnt3a showed roughly one-fourth the activity of non-tagged Wnt3a, as assayed with endogenous ß-catenin in L cells (Supplementary Figure 1; Because L cells do not express Cadherins, we can measure canonical Wnt activity of conditioned media by determining the amounts of ß-catenin in L cells). The activity of mEGFP-Wnt3a might look insufficient; however, we demonstrated that homozygous mEGFP-knock-in mice that express the same fusion protein from endogenous Wnt3a loci are viable and show normal development (Shinozuka et al., Development, 2019, PMID 30651295). Furthermore, mEGFP-knock-in mice show similar distributions of mEGFP-Wnt3a and a signaling range (assayed with Axin2 expression) comparable to that of wild-type mice. mV-Wnt8 used in this study has a similar tagging design, and shows similar spikes in FCS measurements (Figure 3B), suggesting formation of multimeric homo-complexes as shown in Takada et al., (2018). In addition, no haploinsufficiency has been reported for wnt and sfrp genes as far as we know. Taking these facts into consideration, we think that use of mV-Wnt8 and mK-Frzb is justified.

4. Some of the details of the proposed mechanism are confusing. The paper seems to measure both slow and fast diffusing components from the FCS measurements and also to propose an immobile fraction that cannot be measured in FCS. Or do the authors believe that the slow component in FCS can be equated with the immobile one? If the former, do the authors really need three different pools of ligand to explain the data? On the other hand, the model only has two components a fast diffusing one and an immobile fraction – would the results be affected by adding a slow diffusing component as measured in FCS? or alternatively treating the immobile fraction as slowly diffusing?

We apologize that details of FCS data and our model were confusing. As we mentioned in response to comment 9f, a 2-component model, considering fast and slow components in FCS is supported by AIC. Comparison of photon count imaging and FCS, and the FDAP experiment suggest a slower or immobile population in addition to the two components in FCS. Therefore, experimental evidence supports at least three different populations. On the other hand, in mathematical modeling, we mainly consider an immobile and a freely diffusing population for simplicity. We also examined modeling with an immobile and a slowly diffusing (D = 0.50 um2/s) population, but the range of the distribution was very short (Figure 5—figure supplement 1A). We made these relationships clear in the text (lines 551-553).

5. In light of (4), the authors should consider whether simpler models fit their data equally well. For example, the effective diffusion model in Figure 4 supplement 3 that has fewer parameters appears to fit the Frzb data better than the two population model in the main text. For Wnt, it isn't clear whether goodness of fit is improved by adding a second component as more parameters are also added.

In the revised version, we used a simpler model for fitting FDAP data (f(t) = (1-c)*exp(-koff*t)+c, suggested in comment 7). The goodness of fit of this model was similar to that of the effective diffusion model, as expected (both consider 2 parameters).

6. Can the authors speculate about why the sec-mv has a slow diffusing component? This seems surprising. Again, the authors should justify using a two-population (slow and fast) rather than a simpler model.

Thank you for these comments. We also think that this is an interesting point. As described in our answer about AIC in FCS fitting (reviewer comment 9f), newly obtained sec-mV data also show slow components, which are supported by AIC. To characterize slow components, we performed two-types of experiments. One is a cross-correlation analysis of mV-Wnt8 and a membrane-tracer (Lyn-miRFP703), and the other is enzymatic digestion of heparan sulfate (HS). In cross-correlation measurements, no cross-correlation was observed between mV-Wnt8 and lyn-miRFP703, suggesting that even the slow component is not associated with cell membranes (Figure3—figure supplement 1C, D).

For HS digestion experiments, we used a newly constructed membrane-tethered heparinase III (HepIII-HA-GPI), which enables us to compare HS-digested regions and control regions in the same embryos, to sec-mV and mV-Wnt8. In sec-mV, a slight, but statistically significant increase (about 5%) of the ratio of the fast components was observed, without a significant change in the number of particles (Figure 3 G, H). We speculate that this might reflect an increase of free space in the intercellular space, because HS is a highly charged polymer, so it is highly space-filling when hydrated. On the other hand, the number of particles, as well as the ratio of the fast components of mV-Wnt8 were significantly increased by HS-digestion (Figure 3E, F), probably reflecting the release of mV-Wnt8 from specific binding to HS chains.

Considering that there are many other types of cell surface and extracellular matrix molecules in addition to HS, we speculate that the slow components may reflect the narrow extracellular space of Xenopus ectoderm, occupied with those extracellular molecules. This could be understood as a kind of “hindered diffusion”, but the spatial scale is significantly smaller than the previously proposed idea at a multicellular level (Figure 3—figure supplement 1E, Muller et al., 2013).

7. There may be an error in the FDAP equation in figure 4. The data appear to be normalized so that f(0) = 1, however, plugging 0 into the equation does not give 1. More likely, the authors intend to fit to the function f(t) = (1-c)*exp(-koff*t)+c which has the correct limiting behavior at both t = 0 and t = inf. This shows however that there are only two parameters to be fit (this makes sense, they correspond to the decay rate and the immobile fraction) so it is unclear how the authors are fitting three parameters from their data. Essentially, one cannot extract kon from this data – it is lumped into the prefactor of the exponential.

Thank you for pointing this out. We derived the FDAP equation as follows, starting from the case of reaction dominant, equation (11) in Sprague et al., Biophys. J. (2004) (PMID 15189848).

frap(t)=1κonκon+κoffexp(κofft) (11)

This equation is to fit a complete recovery (to 1); however, experimental data for FRAP often show incomplete recovery (to Ifinal); thus, we derived

frap(t)=Ifinal{1κonκon+κoffexp(κofft)} (11).

As fdap(t) = 1 – frap(t),

fdap(t)=1Ifinal{1κonκon+κoffexp(κofft)}=1Ifinal+Ifinalκonκon+κoffexp(κofft)=(1C)κonκon+κoffexp(κofft)+C(oldequation1),

where C = 1 – Ifinal. In eq. (11), frap(0) does not give 1 as,

frap(0)=1κonκon+κoff=κoffκon+κoff=feq.

This is because, in the reaction dominant case, also known as “diffusion-uncoupled” FRAP, the concentration of freely diffusing molecules rapidly converges to feq. Thus, eq. (11) and also its derivative, eq. (1) do not give 1 at t = 0.

Nonetheless, after considering reviewer comments, we are currently unsure whether our way of adjusting for the incomplete recovery (eq. 11') and fitting such a “delicate” model to our data containing non-negligible bleach are appropriate, especially for extracting k*on. So we decided to use the suggested model, fdap(t) = (1-c)*exp(-koff*t)+c. Our data contain an effect of photobleaching, but this can be corrected with fixed mKikGR-Frzb data. Using this model and bleach correction with the chemically-fixed samples, we derived new koff values (Figure 4D, E, Figure 4—figure supplement 2D).

8. Modeling

a. The argument in lines 402-411 is not valid, as can be seen with numerical simulations, in which a molecule with D=0.05 um^2/s does reach the end of a 200 μm long patterning field over 24 h and can be readily absorbed by a localized sink there.

We agree, so we deleted the argument.

b. In fact, the models (effective diffusion or binding/dissociation) are not mutually exclusive (as described in the Crank reference mentioned by the authors). It would therefore be useful for the readers if the authors could present a unified description of restricted, effective and hindered diffusion.

Thank you for this suggestion. We added discussion about these models in lines 776-784.

c. Since the proteins are detected over distances of ~15 cell diameters (~200 um), the spatial range of the simulations should be increased to the length of a Xenopus embryo to avoid reflection from the boundary. It would also be ideal if the authors could measure the gradient throughout the embryo with FCS (similar to PMID 19741606) or note their limitations.

In accordance with this suggestion, we changed the length of the field of modeling from 100 μm to 1000 um. The revised version of Figure 5 shows results from the 1000 μm field; however, the increase of the field length did not affect the distributions of the ligands. Therefore, distributions within 100 μm from the source are presented, as in the previous versions. This is also reasonable because Wnt-based AP-patterning in early Xenopus embryos may occur in/around the dorsal lip region of the gastrula, which is far smaller than the whole embryo.

FCS measurements require mounting embryos in LMP agarose to avoid movements of embryos; thus, it is technically difficult to measure gradients in a wide field. We noted this technical difficulty in lines 707-708.

d. Please normalize each curve to the maximum of each species to see if there really is a difference in the ranges of the free and bound components in the model.

Following this suggestion, we prepared normalized curves for free and bound species to compare their ranges for Figure 5, B and C (Figure 5—figure supplement 1, B and C). The l values obtained by fitting with C(x) = Co exp(– x/l) were slightly different between the free and bound species (lu and lv, respectively); however, we can see that the free and bound species show similar ranges.

e. The authors state that "Notably, in our mathematical model, distributions of both free and bound components converged to steady states within a few minutes, showing rather fast dynamics in the context of embryonic patterning". The cause for these unexpectedly fast dynamics might be the extremely high off-rates compared to previously measured values (e.g. compare to the values used in PMID 22445299). Depending on re-assessment of the FDAP data, the authors should repeat their simulations. It would be ideal to more directly compare the simulations to the experimental data (e.g. overlay data from Figure 2D with simulations) and to compare the fast kinetics prediction of the model to the temporal gradient evolution in real embryos.

Thank you for this suggestion. In our re-assessment of FDAP data, we obtained koff values of mV-Wnt8 and mV-Frzb as 0.16 and 0.58 (s-1), respectively. These values are much higher than the values used in Zhou et al., 2012 (10-7 s-1); however, they correspond to Dpp-Receptor kinetics. In the same paper, the authors mentioned that “binding of BMPs to glypicans (and heparan sulfate in general) is reversible, with modest affinity and relatively rapid kinetics.” In our system, HS appears to be a major scaffold of Wnt8 and Frzb; thus, large koff values may be reasonable. In another report (Muller et al., Science 2012), the clearance rate constants, k1, which are equivalent to koff, are around 10-4 s-1; however, in their experiments, the area of photoconversion was considerably larger, containing tens of cells. In this condition, re-binding or exchanging reactions of photoconverted molecules should contribute to the small k1 values. Regarding our observation, simulation of FDAP experiments recapitulate the decline of photoconverted molecules in a single peak in 16 seconds (Figure 5—figure supplement 1E, see also the next answer).

f. Please simulate the outcome of FDAP experiments (i.e. the dissipation of concentration from one peak). Is an increase in neighboring regions detected, and how does this compare with the experimental findings?

Following this comment, we simulated the FDAP experiment (Figure 5—figure supplement 1E, simulating Figure-4 Figure supplement 2). Unlike the experiment with mK-Wnt8, modeling shows a small increase in neighboring regions (but this is similar to the case of mK-Frzb). For this difference, we mainly consider two possibilities; i) our imaging system may not be sufficiently sensitive to detect such a small increase, or the increase of ligand may be cancelled by photobleaching. ii) if binding dynamics of mK-Wnt8 are slower than that in our model, ligands may diffuse away before re-binding in neighboring regions.

g. Please simulate the Sec-mV findings. Maybe the simulations will clarify why this protein is not visible?

We think that modeling itself would not clarify why sec-mV is not visible (in our model, sec-mV is not degraded; thus, it will just increase continuously). Freely diffusing molecules, such as cytosolic GFP, at a similar concentration (~ 100 uM) is readily visible. We speculate that invisibility of sec-mV is mainly due to the narrowness of the extracellular space in Xenopus embryos.

h. Please clarify why the data in Figure 5F looks different from panel B, even though it should be the same according to the figure legend.

We apologize for this mistake. Actually, Figure 5B in the previous version was presented incorrectly (probably a result with different parameters). We corrected Figure 5B. The l value in previous version was calculated with the correct data. As mentioned above, figures in Figure 5 were replaced with new modeling using the wider field (1000 um), but the result is almost indistinguishable from the previous version. In addition, we changed the style of the previous Figure 5—figure supplement 1 (new Figure supplement 1C), with normalization of the plots, similar to other panels.

9. FCS analyses and inference of bound/unbound fractions

We removed “concentration” from tables of FCS data to avoid misleading data, after considering that the width of intercellular space (< 30 nm) of Xenopus embryos appears narrower than a diameter of confocal detection volume (~ 200-300 nm). Also, we checked data in old Figure 3F again, and corrected the table (new Figure 3—figure supplement 2D).

a. The authors state that "[…] diffusion coefficients measured with FRAP and FCS differ by 3-4 orders of magnitude (Rogers and Schier, 2011)", but this statement is outdated and should be corrected (see e.g. PMIDs 23533171 and 28919007).

Thank you for pointing this out. We deleted this part. Instead, we mentioned that their “optimal ranges for diffusion coefficients differ” (lines 128-129).

b. Figure 3B shows problematic drift that might underlie the long correlation times.

In general, such drift could affect correlation time. But in the case of Figure 3B, the drift is much slower than diffusion-related movements, thus it is negligible. To avoid unfavorable drift, we set the duration of a single measurement to 5 seconds (previous measurements) or 10 seconds (new measurements for this revision). To avoid misunderstanding, only traces of single measurements are presented in our new figures.

c. The authors further state that "By autocorrelation analysis, FCS can measure the diffusion coefficient (D) and the number of particles, which is equivalent to the concentration of the diffusing molecules (Figure 3E), but it cannot measure immobile molecules (Hess et al., 2002)", but this statement (and similar ones in lines 220-221 and 237-238) is imprecise and needs to be corrected. Avalanche photodiodes or the like used in FCS experiments do detect all signal emitted from fluorescent molecules, but the inference of diffusion coefficients depends on mobile molecules.

We apologize for this imprecise statement. We corrected this part (lines 244-247).

d. These imprecisions in FCS theory lead to unsuitable analyses in Figure 3G. Importantly, not only the inferred number of particles but also the count rate is higher for Sec-mV, although the same mRNA amount was used for all constructs. The difference in the outcomes between confocal imaging and FCS might instead be the result of variability between experiments. The authors should therefore measure and compare the intensities using photon counting and FCS within the same embryos if possible or note their limitations.

e. The subsequent considerations about immobile and diffusing molecules (e.g. lines 230-233) are inappropriate and should be corrected. The current logic is: 9.4 detected mV-Wnt8 particles constitute 43% of the 21.7 particles detected for sec-mV, and since 43% of the 19.5 maximally detectable mobile mean photon counts equal 8.4 mean photon counts, the rest of the mean photon counts (219.7) for mV-Wnt8 is immobile; however, this logic is inappropriate since identical amounts of mRNA are not equimolar given the protein size differences, identical mRNA amounts do not necessarily give rise to similar protein amounts due to differences in translation or stability, and day-to-day or instrument differences might influence the intensities.

Photon counting and FCS within the same embryo is technically difficult. After considering the questions raised by reviewers regarding our estimation (Figure 3G), we removed it. On the other hand, we see a large difference in photon counts of mV-Wnt8 and sec-mV, whereas NoP measured by FCS of mV-Wnt8 and sec-mV were rather similar. We speculate that FCS measurements might be biased to choose positions where HS-bound molecules are not abundant. Otherwise HS-bound, immobile molecules cause photobleaching, which results in fluorescence intensity drift.

f. Please use the Akaike information criterion or the like to compare the 2-component fits with more parsimonious 1-component fits of the FCS data, especially given the small differences in the highly and poorly mobile fractions.

Thank you for this suggestion. We examined AIC to compare 1- and 2-component models with new measurements according to a previous report (Tsutsumi et al., 2016) (Figure 3—figure supplement 1A. Evaluation of previous data with AIC is currently difficult for us, mainly because we cannot access the facility to perform the analysis). For all averaged data analyzed, the 2-component model was better than the 1-component model, judging by AIC (Figure 3—figure supplement 1A). For individual measurements (10 seconds), most measurements fit the 2-component model better than the 1-component model. Thus, we concluded that the 2-component model is suitable to fit our FCS data from Xenopus embryos.

g. Please mention from how many independent embryos the FCS measurements were derived.

We are sorry for our failure to have supplied this information. We added numbers of embryos measured for FCS in the legend or tables.

10. In Figure 3 supplement 1, the comparison is not appropriate given the very different sample numbers and the small effect size. The authors should instead use bootstrapping approaches with similar sample numbers.

Thank you for this suggestion. For old Figure 3 supplement 1 (new Figure 3—figure supplement 2), we removed the statistical test, after considering its requirement. Instead, we used bootstrapping for examining significance in Figure 3—figure supplement 1B. Using R software, we did bootstrapping as follows.

#NoP (mV-Wnt8 250 pg vs sec-mV)

> library(boot)

> N=10000

> data_join <- c(nw25, ns)

> theta <- numeric(N)

> for(i in 1:N){

data1_boot <- sample(data_join, 20, replace = TRUE)

data2_boot <- sample(data_join, 26, replace = TRUE)

theta[i] <- mean(data1_boot)-mean(data2_boot)

}

> dif <- mean(nw25) - mean(ns)

> sum(theta >= dif)/N

[1] 2567 #not significant

11. For the FDAP experiments, the instruments may just not have the sensitivity to detect small numbers of photoconverted molecules that spread locally, especially considering that a large amount of fluorescence loss is due to photobleaching and that the imaging is at a single plane. The authors should take care with interpreting these data unless they are able to repeat the experiments using new acquisition parameters to minimize bleaching. Additionally, as the authors noted that the pseudo-equilibrium binding constant may be underestimated because curve fitting does not consider photobleaching, and the simulations crucially depend on these parameters, they should execute new simulations with the parameters determined in the absence of photobleaching if possible. If repeating these experiments is not practical due to current restrictions the authors should temper their conclusions.

We sincerely considered this comment. Currently, it is very difficult for us to perform FDAP experiments because of the COVID-19 pandemic, and also because we currently do not have access to the confocal microscope suitable for FDAP. As we noted in our response to comment 7, we used a simpler model and photobleaching was corrected with chemically-fixed samples.

12. A goal of this manuscript is to link quantitative measurements of local protein dynamics to larger spatiotemporal patterns of extracellular protein dispersal in embryos. To do so, the authors assume that short-term measurements at sub-apical junctions fully capture the processes that are important for protein dispersal and gradient formation. The hypothesis is that local dynamics measured by FCS and FDAP in a particular location can be extrapolated to model mechanisms for long distance dispersal across fields of many cells, but this has not been shown experimentally. The authors could use FRAP experiments photobleaching a large area at a distance from the Wnt source followed by time-lapse imaging over a long period to confirm that the pattern and timing of fluorescence recovery across many cells is consistent with predictions from their model. Under normal circumstances, these experiments would be considered required revisions. However, in light of the current research situation it would be acceptable to instead discuss these limitations in the manuscript.

Thank you for this comment. We noted these limitations in the Discussion (lines 895-899).

13. The manuscript seems to treat Wnt8 and FrzB dispersal as occurring within a single plane, with the dynamics at sub-apical junctions reflecting dynamics elsewhere. However, the tagged proteins could be spreading along the basal surfaces or through entirely different routes or mechanisms. These possibilities should be considered in the discussion if it is not feasible to assess them experimentally.

Thank you for this comment. Judging from our data, we can see accumulation of mV-Wnt8 and mV-Frzb (low dose) in the proximal region (to the source) in the morphotrap-expressing area (Figure 2C, D), consistent with the idea of dispersal in a single plane, at least in the animal cap region. Of course, in actual embryogenesis, a diffusing molecule could diffuse three-dimensionally in embryonic tissue, we discussed these possibilities in the Discussion (lines 886-891). However, aim of our model is rather to provide a basic framework to understand our observation of protein distributions and quantitative analyses than to construct a complex model considering the three-dimensional shape of Xenopus embryos.

14. Similarly to (12), a key assumption is that the freely diffusing population of Wnt detectable by FCS is the protein population that moves between cells, but this cannot be directly concluded from the experiments. The authors should discuss the possibility of Wnt transport on cytonemes/signaling filopodia or contacts between cells at earlier developmental stages. Based on the Xenopus fate map, can the authors rule out potential mechanisms for Wnt gradient formation based on cell lineages or migration patterns? The morphotrap experiments argue against these possibilities, but the fact that tagged protein can accumulate to high levels in distant trap-expressing cells could be explained by higher stability of trapped versus untrapped protein.

Thank you for this suggestion. Because the superficial layer of Xenopus embryos, including the animal cap region, is a kind of epithelium, in which cells are tightly packed, cell-movement-based transfer of these proteins can be excluded together with distributions of source and morphotrap-expressing cells. We have never observed cytoneme/filopodia-based transfer of Wnt protein in the superficial layer, but these protrusions could be involved in mesenchymal cells, which are packed more loosely. We discussed these possibilities in the Discussion (lines 891-895). We agree with the possibility of higher stability of trapped protein. We think that both dispersal and stabilization of protein are required for accumulation on the morphotrap-expressing cells.

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

Essential Revisions:

1. The authors state "We provide the source code for our mathematical model, written in C.", but we could not find the code among the current manuscript items. Please provide this code as a Supplementary file.

We apologize for the lack of source codes. Please find the uploaded source codes.

2. The diffusion coefficients for 250 pg and 20 pg of mV-Wnt8 appear to differ between Figure 3D and Figure 3—figure supplement 1B, and the authors should check whether the correct data are plotted.

We are grateful that the reviewers pointed this out. We suppose they suggest that mean values shown in Figure 3D appear to differ from values indicated with bold horizontal lines in Figure 3—figure supplement 1B. We confirmed that mean values of diffusion coefficients of mV-Wnt8 (250 or 20 pg) shown in Figure 3D and plots of Dfast and Dslow shown in Figure 3—figure supplement 1B are based on the same data. We would like to note that the bold horizontal lines in Figure Supplement 1B indicate median values (50 percentile), which generally differ from mean values in asymmetric distributions. To avoid misunderstanding, we added “Mean values are presented.” to the legend of Figure 3D.

3. The figure legend for Figure 4 includes a panel F, but this image is missing from the figure. Please provide an updated Figure 4 including F or remove it from the legend.

We apologize for this mistake. In the revised version, the legend for panel F has been removed.

4. The authors may wish to note in the text that reduced activity of mV-tagged Wnt8 compared to untagged Wnt8 could possibly be due, at least in part, to differences in translation.

We appreciate this suggestion. We added this point to the text.

https://doi.org/10.7554/eLife.55108.sa2

Article and author information

Author details

  1. Yusuke Mii

    1. National Institute for Basic Biology and Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Japan
    2. The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Japan
    3. Japan Science and Technology Agency (JST), PRESTO, Kawaguchi, Japan
    4. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    mii@nibb.ac.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1907-5665
  2. Kenichi Nakazato

    Theoretical Biology Laboratory, RIKEN, Wako, Japan
    Contribution
    Software, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  3. Chan-Gi Pack

    1. Cellular Informatics Laboratory, RIKEN, Wako, Japan
    2. ASAN Institute for Life Sciences, ASAN Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
    Contribution
    Data curation, Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6578-3099
  4. Takafumi Ikeda

    Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  5. Yasushi Sako

    Cellular Informatics Laboratory, RIKEN, Wako, Japan
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Atsushi Mochizuki

    1. Theoretical Biology Laboratory, RIKEN, Wako, Japan
    2. Laboratory of Mathematical Biology, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan
    Contribution
    Software, Supervision, Writing - original draft
    Competing interests
    No competing interests declared
  7. Masanori Taira

    1. Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
    2. Department of Biological Sciences, Faculty of Science and Engineering, Chuo University, Tokyo, Japan
    Contribution
    Conceptualization, Investigation, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    m-taira.183@g.chuo-u.ac.jp
    Competing interests
    No competing interests declared
  8. Shinji Takada

    1. National Institute for Basic Biology and Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Japan
    2. The Graduate University for Advanced Studies (SOKENDAI), Okazaki, Japan
    Contribution
    Conceptualization, Investigation, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    stakada@nibb.ac.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4125-6056

Funding

Japan Science and Technology Agency (JPMJPR194B)

  • Yusuke Mii

Japan Society for the Promotion of Science (24870031)

  • Yusuke Mii

Japan Society for the Promotion of Science (15K14532)

  • Yusuke Mii

Japan Society for the Promotion of Science (18K14720)

  • Yusuke Mii

National Institutes of Natural Sciences (1311608)

  • Yusuke Mii

National Institutes of Natural Sciences (01311801)

  • Yusuke Mii

Japan Society for the Promotion of Science (24657147)

  • Yusuke Mii
  • Masanori Taira

Japan Society for the Promotion of Science (18H02447)

  • Masanori Taira

Japan Society for the Promotion of Science (25251026)

  • Masanori Taira

Japan Science and Technology Agency (JPMJCR13W6)

  • Atsushi Mochizuki

Japan Science and Technology Agency (JPMJCR1922)

  • Atsushi Mochizuki

Japan Society for the Promotion of Science (19H05670)

  • Atsushi Mochizuki

Japan Society for the Promotion of Science (17K19418)

  • Shinji Takada

Japan Society for the Promotion of Science (18H02454)

  • Shinji Takada

Japan Society for the Promotion of Science (19H04797)

  • Shinji Takada

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

Acknowledgements

We thank Dr. Min Kyun Park for hybridoma culture and 5’RACE for IgG cDNAs. We also thank Dr. A Miyawaki for Venus cDNA; Dr. R Moon for pCS2+Xwnt8; Dr. M Affolter for morphotrap cDNA; Dr. W Hashimoto for the HepIII plasmid (pET21b-Phep_3797); Dr. Shinya Matsuda for critical reading; Drs. Hiroshi Koyama, Yohei Kondo and Motosuke Tsutsumi for discussion and Dr. Steven D Aird for editing and proofreading. We thank the Confocal Microscope core facility at the ConveRgence mEDIcine research cenTer (CREDIT), Asan Medical Center. This work was supported in part by following programs: KAKENHI (24870031, 15K14532, 18K14720 to YM; 19H05670 to AM; 24657147, 18H02447, 25251026 to MT; 17K19418, 18H02454, 19H04797 to ST), the NINS program for cross-disciplinary study (1311608, 01311801 to YM), Joint Usage/Research Center program of Institute for Frontier Life and Medical Sciences Kyoto University, JST-PRESTO (JPMJPR194B to YM), JST-CREST (JPMJCR13W6, JPMJCR1922 to AM).

Ethics

Animal experimentation: All experiments using Xenopus laevis were approved by the Institutional Animal Care and Use Committee, National Institutes of Natural Sciences (Permit Number 18A038, 19A062, 20A053), or the Office for Life Science Research Ethics and Safety, University of Tokyo.

Senior Editor

  1. Naama Barkai, Weizmann Institute of Science, Israel

Reviewing Editor

  1. Ariel M Pani, University of Virginia, United States

Publication history

  1. Received: January 13, 2020
  2. Accepted: April 23, 2021
  3. Accepted Manuscript published: April 27, 2021 (version 1)
  4. Version of Record published: May 21, 2021 (version 2)

Copyright

© 2021, Mii 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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