Bicoid gradient formation mechanism and dynamics revealed by protein lifetime analysis
2018; Springer Nature; Volume: 14; Issue: 9 Linguagem: Inglês
10.15252/msb.20188355
ISSN1744-4292
AutoresL. Durrieu, Daniel Kirrmaier, Tatjana Schneidt, Ilia Kats, Sarada Raghavan, Lars Hufnagel, Timothy E. Saunders, Michael Knop,
Tópico(s)Protein Structure and Dynamics
ResumoArticle4 September 2018Open Access Source DataTransparent process Bicoid gradient formation mechanism and dynamics revealed by protein lifetime analysis Lucia Durrieu Lucia Durrieu Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Daniel Kirrmaier Daniel Kirrmaier Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Deutsches Krebsforschungszentrum (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany Search for more papers by this author Tatjana Schneidt Tatjana Schneidt European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Ilia Kats Ilia Kats orcid.org/0000-0001-5220-5671 Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Search for more papers by this author Sarada Raghavan Sarada Raghavan Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Search for more papers by this author Lars Hufnagel Corresponding Author Lars Hufnagel [email protected] orcid.org/0000-0001-7753-4762 European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Timothy E Saunders Corresponding Author Timothy E Saunders [email protected] European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Mechanobiology Institute and Department of Biological Sciences, National University of Singapore, Singapore Institute of Molecular and Cell Biology, A*Star, Biopolis, Singapore Search for more papers by this author Michael Knop Corresponding Author Michael Knop [email protected] orcid.org/0000-0003-2566-923X Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Deutsches Krebsforschungszentrum (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany Search for more papers by this author Lucia Durrieu Lucia Durrieu Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Daniel Kirrmaier Daniel Kirrmaier Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Deutsches Krebsforschungszentrum (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany Search for more papers by this author Tatjana Schneidt Tatjana Schneidt European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Ilia Kats Ilia Kats orcid.org/0000-0001-5220-5671 Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Search for more papers by this author Sarada Raghavan Sarada Raghavan Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Search for more papers by this author Lars Hufnagel Corresponding Author Lars Hufnagel [email protected] orcid.org/0000-0001-7753-4762 European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Search for more papers by this author Timothy E Saunders Corresponding Author Timothy E Saunders [email protected] European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Mechanobiology Institute and Department of Biological Sciences, National University of Singapore, Singapore Institute of Molecular and Cell Biology, A*Star, Biopolis, Singapore Search for more papers by this author Michael Knop Corresponding Author Michael Knop m.kno[email protected] orcid.org/0000-0003-2566-923X Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany Deutsches Krebsforschungszentrum (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany Search for more papers by this author Author Information Lucia Durrieu1,2,6,7, Daniel Kirrmaier1,3, Tatjana Schneidt2, Ilia Kats1, Sarada Raghavan1,8, Lars Hufnagel *,2, Timothy E Saunders *,2,4,5 and Michael Knop *,1,3 1Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, University of Heidelberg, Heidelberg, Germany 2European Molecular Biology Laboratory (EMBL), Heidelberg, Germany 3Deutsches Krebsforschungszentrum (DKFZ), DKFZ-ZMBH Alliance, Heidelberg, Germany 4Mechanobiology Institute and Department of Biological Sciences, National University of Singapore, Singapore 5Institute of Molecular and Cell Biology, A*Star, Biopolis, Singapore 6Present address: Instituto Leloir, Buenos Aires, Argentina 7Present address: Departamento de Fisiología, Biología Molecular, y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina 8Present address: p53 Laboratory, A*STAR, Singapore *Corresponding author. Tel: +49 6221 387 8648; E-mail: [email protected] *Corresponding author. Tel: +65 66011552; E-mail: [email protected] *Corresponding author. Tel: +49 6221 54 4213; E-mail: [email protected] Molecular Systems Biology (2018)14:e8355https://doi.org/10.15252/msb.20188355 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Embryogenesis relies on instructions provided by spatially organized signaling molecules known as morphogens. Understanding the principles behind morphogen distribution and how cells interpret locally this information remains a major challenge in developmental biology. Here, we introduce morphogen-age measurements as a novel approach to test models of morphogen gradient formation. Using a tandem fluorescent timer as a protein age sensor, we find a gradient of increasing age of Bicoid along the anterior–posterior axis in the early Drosophila embryo. Quantitative analysis of the protein age distribution across the embryo reveals that the synthesis–diffusion–degradation model is the most likely model underlying Bicoid gradient formation, and rules out other hypotheses for gradient formation. Moreover, we show that the timer can detect transitions in the dynamics associated with syncytial cellularization. Our results provide new insight into Bicoid gradient formation and demonstrate how morphogen-age information can complement knowledge about movement, abundance, and distribution, which should be widely applicable to other systems. Synopsis The dynamics of Bicoid gradient formation are characterized using a tandem fluorescent timer as a protein-age sensor. These analyses differentiate between proposed models of gradient formation, estimate the underlying kinetic parameters, and explore temporal changes during blastoderm development. Tandem fluorescent protein timers are used to distinguish between models of morphogen gradient formation. The spatial distribution of protein age provides insights into the underlying kinetics and indicates an effective Bicoid diffusion constant of 3–4 μm2/s. Bicoid protein degradation is likely playing a critical role in forming the Bicoid morphogen gradient and the Bicoid half-life is estimated to be around 25 min. Introduction Acquisition of different cell fates at specific spatial and temporal locations is an essential process driving development. The necessary information is provided locally by morphogens (Wolpert, 1969; Lander, 2011). Understanding morphogen gradient formation requires systematic measurement of the morphogen abundance, mobility, and distribution using temporally resolved methods. However, the technical challenges associated with this undertaking are high, leading to significant discussions on how to best assess the principles and mechanisms (Ribes & Briscoe, 2009; Rogers & Schier, 2011; Muller et al, 2013) that have resulted in a plethora of models for the formation of morphogen gradients. In the early fly embryo, the morphogen protein Bicoid (Bcd) forms a concentration gradient along the anterior–posterior (AP) axis of the embryo, triggering differential cell fate acquisition (Driever & Nüsslein-Volhard, 1988a,b) (Fig 1A). The process is initiated during oogenesis where Bcd mRNA (bcd) is localized to the anterior of the forming embryo (Frigerio et al, 1986; Berleth et al, 1988; Ribes & Briscoe, 2009; Rogers & Schier, 2011; Muller et al, 2013). The classic view of Bcd gradient formation is that the protein is synthesized in the anterior pole of the Drosophila blastoderm and forms a long range gradient through diffusion, with the gradient shape adapted by protein degradation (SDD model) (Driever & Nüsslein-Volhard, 1988a,b; Gregor et al, 2007b). Such a model agrees well with the observed Bcd gradient in embryos undergoing cellularization, where Bcd levels decay exponentially toward the posterior pole (Houchmandzadeh et al, 2002; Gregor et al, 2007b). However, several other models involving alternative mechanisms for Bcd production and distribution have been proposed, all of which are capable of producing an exponential-like concentration profile, as further outlined below (Coppey et al, 2007; Hecht et al, 2009; Spirov et al, 2009; Dilão & Muraro, 2010; Grimm et al, 2010; Kavousanakis et al, 2010). Figure 1. Protein age can distinguish alternative models of morphogen gradient formation with similar concentration profiles Cartoon of the morphogen hypothesis: a spatially varying concentration of signaling molecule can result in precise readout of positional information. Outline of models considered. Morphogen RNA (grays) and protein (green dots) distribution are shown in early (top) and cycle 14 (middle) rows for each model considered. The magnitude of protein movement is represented by length of green arrows. Degradation of protein/RNA is represented by green/gray arrows. Bottom row shows a schematic of morphogen RNA and protein concentration profiles in cycle 14. Normalized Bcd concentration profiles for the models considered in (B) at time = 2.5 h. Inset: same on log scale. See Appendix and Appendix Fig S1 for extended discussion of models and parameters. Data points correspond to mean output of stochastic Monte Carlo simulation results for the average protein age as function of position, 2.5 h after initiation (see Appendix and Materials and Methods for details). Colored lines correspond to theoretical predictions for protein age in each model (Appendix Section B). As (D) but showing solutions over larger parameter space. Solid line represents mean solution, and dashed lines represent 1 SD. Parameter range described in text. SDD: synthesis, diffusion, degradation model, NucSh: nuclear shuttling model, RNA-grad: RNA gradient model, and RNA-diff: RNA diffusion model. Download figure Download PowerPoint Efforts to distinguish experimentally the different mechanisms of gradient formation have been hindered by uncertainties associated with the measurements of the relevant parameters: local production rates of Bcd; Bcd mobility and transport; and Bcd degradation. Experimental estimates of the diffusion constant vary by an order of magnitude (Gregor et al, 2007b; Abu-Arish et al, 2010), although recent theoretical work has attempted to meld these measurements (Castle et al, 2011; Sigaut et al, 2014). Estimations of the Bcd degradation rate also significantly differ (Drocco et al, 2011; Liu & Ma, 2011; Liu et al, 2011). Finally, the extent of the region where Bcd protein is produced is unclear (Spirov et al, 2009; Little et al, 2011), with a long-ranged gradient of bcd mRNA possibly enabling local translation of Bcd away from the anterior pole of the embryo (Spirov et al, 2009). These differences in the diffusion and degradation rates are meaningful beyond the determination of the gradient formation mechanism, as they help to predict whether the system is in (or close to) equilibrium—a contested issue relevant to the mechanism of gradient interpretation (Bergmann et al, 2007, 2008; Bialek et al, 2008; Saunders & Howard, 2009; Jaeger, 2010; de Lachapelle & Bergmann, 2010a,b). Altogether, these debates regarding nearly every aspect of Bcd gradient formation argue for the need of more incisive tools to investigate this paradigmatic problem. In this work, we revisit Bcd gradient formation motivated by the observation that tagging of Bcd with different fluorophores results in changes in the apparent gradient shape (Little et al, 2011; Wieschaus, 2016). Differences in the fluorophore maturation rates could underlie this change (Little et al, 2011; Wieschaus, 2016). We employ the tandem fluorescent protein timer (tFT) reporter (Khmelinskii et al, 2012; Donà et al, 2014) fused to Bcd (we henceforth refer to this reporter as tFT-Bcd), which provides simultaneously quantitative information about the Bcd protein age and its spatio-temporal concentration distribution. We pair it with multi-view light-sheet fluorescent microscopy (Krzic et al, 2012; Tomer et al, 2012) to gain high spatial and temporal resolution images of the tFT-Bcd gradient in toto. These data are then used to discriminate between alternative models of Bcd gradient formation, estimate dynamic parameters, investigate the mechanism of Bcd degradation, and to study temporal changes through the early fly embryogenesis. Results Protein age can distinguish different models of Bcd gradient formation Broadly, two types of models have been considered for Bcd gradient formation: localized Bcd synthesis in the anterior with subsequent long-ranged transport; and pre-patterned synthesis (by an mRNA gradient) and restricted protein transport, although more complicated scenarios, such as spatially patterned degradation, are imaginable. Interestingly, protein degradation is not a mandatory ingredient in gradient formation, but it plays an important role in determining whether the system can reach steady state. Within this framework, we consider four models: the SDD model (Driever & Nüsslein-Volhard, 1988a; Gregor et al, 2007b); the nuclear shuttling model (Coppey et al, 2007); bcd mRNA gradient (Spirov et al, 2009; Grimm et al, 2010; Dalessi et al, 2012); and bcd mRNA diffusion and degradation (Dilão & Muraro, 2010; Dalessi et al, 2012) (summarized in Fig 1A and B and Appendix Fig S1A). In the SDD and nuclear shuttling models, protein is synthesized locally and then migrates by diffusion toward the posterior pole (Fig 1B, i-ii). The SDD model incorporates protein degradation, whereas the nuclear shuttling model utilizes the rapid increase in nuclei number in the blastoderm to enable the Bcd concentration to remain roughly constant in each nucleus. The RNA gradient model (Fig 1B, iii) is based on a spatially extended RNA gradient resulting in distributed protein synthesis and incorporates protein degradation and very slow protein diffusion. The RNA diffusion model starts with localized RNA and protein synthesis and then proposes spreading of the mRNA (protein synthesis) throughout the embryo (Fig 1B, iv). This model reaches a steady state when the RNA is completely degraded, even though Bcd protein does not decay (and the Bcd protein cannot diffuse) (Dilão & Muraro, 2010). Further model details are provided in the Appendix. All four models can reproduce the observed Bcd concentration profile at nuclear division cycle (n.c.) 14, as expected from previous reports (Fig 1C, Appendix Fig S1B). Distinguishing these models experimentally then requires precise measurement of dynamic parameters—which has proven challenging so far. Thus, easily measurable information is required where the models make distinct and robust predictions. What information could this be? It has been shown that tandem fusions of two different fluorescent proteins with different maturation rates can measure the average time that has passed since the production of a pool of proteins (i.e., its age) (Khmelinskii et al, 2012). Protein age is dependent on protein turnover and degradation. We reasoned that fluorescent timers could be a valuable tool for discerning the above models experimentally, which prompted us to explore their predictions regarding protein age of Bcd (Appendix Fig S1C). In models that include degradation, the average age of Bcd approaches a steady-state situation where synthesis and degradation are balanced, while in models without protein degradation, the average protein age is constantly increasing (Appendix Fig S1D). We calculated the Bcd protein age as a function of position along the AP axis (Fig 1D, Appendix Fig S1D and E, and Appendix Section B). For the SDD model and the nuclear shuttling models, protein age increases with the distance from the anterior pole, though the average protein age in the SDD model is lower due to degradation. For the RNA gradient model—where the bcd RNA gradient is the primary determinant of the Bcd gradient—the average age of Bcd is roughly uniform. In contrast, the RNA diffusion model makes an inverse prediction, with lower protein age toward the posterior pole (Appendix Fig S1E). Therefore, measurement of protein age offers a quantitative readout that can clearly distinguish different models without a need for detailed parameter estimates. To test the robustness of these predictions, we explored the parameter space underlying each model for the situation 2.5 h after egg laying (AEL), around early n.c. 14. We varied each parameter over a physiologically relevant range: diffusion coefficient (0.1–10 μm2/s), protein and RNA lifetimes (10–120 min), and the range of the RNA gradient (20–200 μm). We only considered parameter sets that resulted in a Bcd gradient with an exponential-like profile with decay length (λ) between 70 and 100 μm 2.5 h AEL (determined by fitting the simulated profiles to Ae-x/λ in the range 100–400 μm from the anterior pole). This revealed that the principal difference of the models with respect to relative Bcd protein age in the gradient is robust, and should allow faithful discrimination between the models (Fig 1E and Appendix Fig S1F). We note that distinguishing the SDD and nuclear shuttling models is the most challenging. However, combining the protein age data with the protein concentration profiles should enable discrimination between the two models. The shuttling model can only produce an exponential gradient in early n.c. 14 with a limited range of parameters. This constraint is the reason for the narrower range of predictions of the nuclear shuttling model (Fig 1E). Establishment of a protein age reporter line Having established protein age as critical information for the discrimination of the alternative models of Bcd gradient formation, we proceeded to estimate it experimentally. We tagged Bcd with tandem fluorescent timer (tFT) reporters, consisting of a fast-maturing and a slow-maturing fluorescent protein separated by a linker (Fig 2A and B). Such tFT reporters can be used to infer average protein age (Khmelinskii et al, 2012; Khmelinskii & Knop, 2014). If a protein of interest is tagged with a fast-maturing green fluorescent protein and a slow-maturing red fluorescent protein, a pool of newly synthesized protein will be mostly green, while older proteins will fluoresce in both green and red. Likewise, in steady state, rapid protein turnover results in fewer proteins being fluorescent in the red channel. Therefore, the average age of a pool of proteins tagged with a tFT reporter can be estimated from the ratio of the fluorescence intensities of the fluorophores. Figure 2. tFT-Bcd reporter reflects average protein age A. Schematic of tFT-Bcd reporter with mCherry and sfGFP fluorophores. B. Cartoon of the fluorescent protein maturation states in the tFT-Bcd reporter. mg and mr represent the sfGFP and mCherry maturation rates, respectively (note, mr is an effective rate as mCherry has a two-step maturation process). C–E. Examples of embryos expressing the tFT-Bcd reporter with (C) mCherry-sfGFP-Bcd, (D) fmCherry-sfGFP-Bcd, and (E) mCherry-sfGFP. For all, (i) images of "shells" of embryos in early n.c. 14. 3D images of the embryos were generated, and then, the interior of the embryo was erased, leaving only the embryo cortex to improve clarity (see Materials and Methods for more details). (ii) Mean AP intensity profile of each color for the embryo in (i). Shade region represents ± 1 SD. Inset shows same profiles after multiplication of red intensity by constant factor. Data binned into 10-μm bins (n = 4 embryos). (iii) Ratio of green over red signal, reflecting protein age. The thin lines represent individual embryos, while the thick solid line is the mean. The solid dashed line depicts the mean green/red ratio for a line with the tFT-Bcd reporter but lacking endogenous Bcd (BcdE1 mutant, n = 4. The scale is the same for all embryos. The scale bar is 50 μm long). Source data are available online for this figure. Source Data for Figure 2 [msb188355-sup-0003-SDataFig2.zip] Download figure Download PowerPoint In order to estimate the protein average age from tFT reporters, the timescale for the maturation of the slower maturing fluorophore should be no slower than the dynamics of the system studied (Khmelinskii et al, 2012; Khmelinskii & Knop, 2014), which, in our case, is in the order of an hour. Due to uncertainty about both Bcd degradation kinetics and the fluorophores maturation rates in fly embryos, knowing a priori which fluorophore pair to use is difficult. We constructed six tFT-Bcd lines, each containing sfGFP and a different red FP (namely mCherry, tdTomato, fmCherry, td-fmCherry, mKate2, and tagRFP) and analyzed the red and green signal intensity along the AP axis (Appendix Fig S2). fmCherry stands for "fast-maturing mCherry" and was developed using directed protein evolution seeking for fast-maturing variants of mCherry (Materials and Methods). In all constructs, the tFT reporter was fused to the N-terminus of Bcd, keeping the promoter and the 5′ and 3′ UTR of the bcd gene (Fig 2A). We validated the functionality of the tFT-Bcd fusions by rescuing the bcdE1/bcdE1 null mutant (Driever & Nüsslein-Volhard, 1988a) (Appendix Fig S3). Based on this screening, we chose the lines containing the sfGFP-mCherry (Fig 2C) and sfGFP-fmCherry (Fig 2D) tFT reporters for further investigation. These timers are informative since they display a gradient in both colors, but with different profiles. Quantitative utilization of the fluorescent timer requires additional controls. It is critical that the age of the tFT-Bcd proteins reflects the stability of Bcd rather than the lifetime of the tFT tag, or a combination of both. To verify that this is the case, we established a control line that expresses only the tFT reporter (without the Bcd protein), but still using the regulatory sequences of the bcd gene (Fig 2E). This construct shows similar (flat) profiles for both fluorophores, suggesting: (i) degradation is determined by the Bcd protein, not the linked fluorophores; and (ii) that the measured differences are determined by Bcd dynamics and not the degradation of the fluorescent reporters themselves. A further potential issue with tFT reporter measurements is incomplete degradation of the fluorescent timer (Khmelinskii et al, 2012, 2016). If some fraction of free GFP protein survives the degradation of the tFT-Bcd protein, then the ratio measurements would be affected. We assessed this possibility experimentally by making Western blots against GFP for embryos of different ages (Appendix Fig S4A and B) and theoretically by simulating the impact of such artifact on the observed Bcd gradients (Appendix Fig S4C and D). Both approaches suggest that this issue is likely unimportant here. Since we introduced our constructs into the fly genome as an additional copy of Bcd, another concern is whether the higher protein levels alter the Bcd dynamics. We repeated our tFT-Bcd measurements in embryos with a Bcd null background (bcdE1) and found no difference in the tFT reporter readout (Fig 2C iii, dashed line). To summarize, we have demonstrated that: (i) the Bcd-tFT reporter is functional; (ii) the spatial distribution of the fluorescent signals is dependent on Bcd, and not on the properties of the fluorescent molecules per se; and (iii) by tuning the maturation rates of the different fluorophores, we can alter the shape of the fluorescent profiles, supporting the conclusion that the tFT reporter is sensitive to specific time scales. Therefore, we are confident that the tFT reporter does indeed reflect Bcd protein dynamics. Imaging and quantification of the tFT-Bcd signal in live embryos Reliable quantification of fluorescence signals in the Drosophila embryo is challenging, mostly due to the relatively large size of the embryo and autofluorescence from the yolk. We utilized confocal multi-view light-sheet microscopy (MuVi-SPIM) (Baumgart & Kubitscheck, 2012; Krzic et al, 2012; de Medeiros et al, 2015), which enables highly sensitive in toto fluorescence detection in larger specimens, while bleaching and phototoxicity are strongly reduced compared to confocal detection methods (Stelzer, 2015). MuVi-SPIM imaging produces three-dimensional whole-embryo images with sufficient resolution to observe sub-nuclear details. Since working with the three-dimensional datasets for quantification of the tFT-Bcd signal is challenging, we mapped the three-dimensional data to a two-dimensional projection (Krzic et al, 2012) that represents the embryo's cortex, the region of the embryonic periphery where the nuclei—and Bcd—reside (Appendix Fig S5A and B; Materials and Methods). This step significantly reduced the image size and facilitated handling without loss of relevant information (for validation of methodology and microscope sensitivity, see Appendix Fig S5C–F, and the Appendix). MuVi-SPIM imaging enabled us to detect Bcd-eGFP fluorescence signal as early as n.c. 8 (Appendix Fig S5G), and in n.c. 14, we observed intensity variability similar to previously reported fluctuations in Bcd (Gregor et al, 2007a) (Appendix Fig S5I) and above-background Bcd signal in all nuclei, even at the posterior pole (Mir et al, 2017) (Appendix Fig S5H). This confirms the high sensitivity of detection of the tFT-Bcd signal using the MuVi-SPIM. Protein age as an independent test of models of Bcd gradient formation Quantification of the tFT-Bcd fluorescence in early n.c. 14 embryos revealed that the mCherry/sfGFP intensity ratio increased along the AP axis (Fig 2C iii). This implies that, on average, the tFT-Bcd is older toward the posterior. In contrast, the fmCherry-sfGFP-Bcd line showed a reversed ratio behavior (Fig 2D iii), consistent with fmCherry maturing faster than sfGFP. In conclusion, our quantitative analysis demonstrates an increase in the relative tFT-Bcd age toward the posterior pole. We validated that this result does not depend on the specific scaling of the measured intensities (Appendix Fig S5M and N). Already analyzing these data qualitatively, we can conclude that the tFT-Bcd age profile is not consistent with the predictions from the RNA gradient and RNA diffusion models (Figs 2C iii and 1D and E). To confirm this preliminary and qualitative interpretation of the data in a quantitative manner and to potentially also discriminate between the SDD and the nuclear shuttling models, more precise estimates of the fluorophore maturation rates are required. Given that fluorophore maturation is sensitive to the experimental conditions, we determined them in the early fly early embryo and in the context of the tFT reporter (Appendix Section D and Appendix Fig S6). We estimated a maturation time of T = 27 ± 2 min for sfGFP. For mCherry, maturation involves two subsequent reactions for which we estimated individual times of 40 ± 9 min and 9 ± 4 min. fmCherry showed remarkably fast kinetics, with the two steps giving maturation times of each 6 ± 2 min (Appendix Fig S6, Appendix). These measured maturation rates are consistent with the observed behavior of the tFT-Bcd reporters in Fig 2C and D. We fitted the models to the experimentally measured tFT-Bcd gradients from early n.c. 14 (see Materials and Methods for fitting details). The parameters for each model were fitted simultaneously along the AP axis to the Bcd sfGFP profile and the ratio of the two fluorescent signals (Fig 3A, Appendix Fig S7A–H, Materials and Methods). Consistent with the preliminary assessment, we found that the RNA diffusion model is inconsistent with the data as it predicts a slope for the timer ratio profile opposite to the experimentally determined. For the RNA gradient model (with bcd decay length of 100 μm) and the shuttling model, the optimal solutions were able to fit the measured sfGFP profile but not the observed mCherry/sfGFP ratio. Although they both predict aging protein toward the posterior, it is clear that neither model is able to produce a protein age profile similar to observations. As noted above, since the nuclear shuttling model never reaches a steady state, it has a narrow range of parameters that can fit the observed data. The SDD model permits the best general fit to the tFT-Bcd concentration and ratio profiles, being able to capture the experimental data fully (Fig 3A). We then took the fitted parameters for each model and tested each model with the data from the sfGFP-fmCherry tFT reporter. The SDD model again had th
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