Quantitative theory for the diffusive dynamics of liquid condensates
Abstract
Key processes of biological condensates are diffusion and material exchange with their environment. Experimentally, diffusive dynamics are typically probed via fluorescent labels. However, to date, a physicsbased, quantitative framework for the dynamics of labeled condensate components is lacking. Here, we derive the corresponding dynamic equations, building on the physics of phase separation, and quantitatively validate the related framework via experiments. We show that by using our framework, we can precisely determine diffusion coefficients inside liquid condensates via a spatiotemporal analysis of fluorescence recovery after photobleaching (FRAP) experiments. We showcase the accuracy and precision of our approach by considering space and timeresolved data of protein condensates and two different polyelectrolytecoacervate systems. Interestingly, our theory can also be used to determine a relationship between the diffusion coefficient in the dilute phase and the partition coefficient, without relying on fluorescence measurements in the dilute phase. This enables us to investigate the effect of salt addition on partitioning and bypasses recently described quenching artifacts in the dense phase. Our approach opens new avenues for theoretically describing molecule dynamics in condensates, measuring concentrations based on the dynamics of fluorescence intensities, and quantifying rates of biochemical reactions in liquid condensates.
Introduction
Liquid phase separation has emerged as an organizing principle in biology and is thought to underlie the formation of various membraneless cellular organelles (Banani et al., 2017). Hallmark properties of such organelles are their rapid formation and dissolution, their fusion, and their wetting to membranes (Hyman et al., 2014). Moreover, phaseseparated organelles exchange material with their environment leading to dynamic sequestration of molecules, which affects biochemical processes by spatial redistribution of reactants (Moon et al., 2019; Lyon et al., 2021; Saha et al., 2016; GuillénBoixet et al., 2020; Sanders et al., 2020; Yang et al., 2020). Probing the dynamics of condensate components is thus imperative for a quantitative understanding of how they affect the cellular biochemistry (Mir et al., 2019).
To probe the dynamics of condensates, biomolecules are typically labeled with fluorescent tags. In general, in systems with tagged molecules, various methods exist to characterize molecular properties such as binding rates and diffusion coefficients, including fluorescence correlation spectroscopy (FCS) (Ries and Schwille, 2012; Rigler and Elson, 2012), singleparticle tracking (SPT) (Tinevez et al., 2017; Saxton and Jacobson, 1997), and fluorescence recovery after photobleaching (FRAP) (Diaspro, 2010; Stasevich et al., 2010). However, interpretation of the experimental data acquired from such methods requires a rigorous derivation accounting for the underlying physicochemcial processes. This derivation has been achieved for some biological systems and processes, but is lacking for condensates formed by liquid phase separation. Processes that are wellunderstood include membranecytoplasmic exchange and transport (Sprague et al., 2004; Robin et al., 2014; Goehring et al., 2010) as well as chemical reactions (Elson, 2001) or filament turnover (McCall et al., 2019). For liquid condensates, various phenomenological fit functions have been proposed in the literature (e.g. Patel et al., 2015; Banerjee et al., 2017; Hubstenberger et al., 2013, for a broader summary see Taylor et al., 2019). However, it was recently shown that these fits lead to wildly differing estimates of the diffusion constant inside, ${D}_{\mathrm{in}}$ (Taylor et al., 2019). Taylor et al. showed that these discrepancies were attributed to unrealistic assumptions, for example infinitely large droplets or infinitely fast diffusion outside the bleach area.
Here, we first introduce a quantitative FRAP method to extract the diffusion coefficient inside, ${D}_{\mathrm{in}}$, purely based on fluorescence measurements inside droplets, without resorting to unrealistic assumptions or requiring knowledge about the partition coefficient, $P$ or diffusion outside, ${D}_{\mathrm{out}}$. Using irreversible thermodynamics, we then derive the theory that connects dynamics inside and outside of the droplet via transport across a finite interface. We use the corresponding dynamic equation to derive a relationship between $P$ and ${D}_{\mathrm{out}}$, which we use to investigate effects of salt addition on $P$. We show that this dynamic equation agrees with our experimentally observed dynamics. By numerically solving the underlying equations, we show that in theory all relevant parameters of the system, $P$, ${D}_{\mathrm{in}}$, and ${D}_{\mathrm{out}}$, can be extracted purely based on knowledge of the dynamics inside the droplet. We find that our measurements are agnostic to breaking radial symmetry, for example by introducing a coverslip or neighboring droplets. Our approach does not suffer from typical limitations of fluorescencebased concentration measurements, such as low fluorescence in the dilute phase and fluorophore quenching in the rich phase. We anticipate that this new understanding will open the door to characterizing dynamical properties such as chemical rates and rheological parameters in multicomponent, phaseseparated systems.
Results
Determining the diffusion constant inside liquid condensates
First, we discuss a quantitative method to extract diffusion coefficients of biomolecules in a condensate. After photobleaching, bleached molecules diffuse out and unbleached molecules diffuse into the condensate, until the unbleached components reach the spatially homogeneous level prior to bleaching (Figure 1a, left and middle). Inside a spherical condensate of radius $R$, the concentration of unbleached components, ${c}_{u}(r,t)$, follows a diffusion equation (for derivation, see subsequent section),
where ${c}_{u}(r={R}_{},t)$ is the timedependent concentration directly inside the interface at $r={R}_{}$. Here, $r$ denotes the radial distance to the center of the condensate. The flux ${\mathit{\bm{j}}}_{u}$ is given by Fick’s law (Equation (1b)). It vanishes at the condensate center. Moreover, we have $\nabla ={\mathit{\bm{e}}}_{r}{\partial}_{r}$, with ${\mathit{\bm{e}}}_{r}$ denoting the radial unit vector. During FRAP, the concentration at the interface, ${c}_{u}(r={R}_{},t)$, changes with time (Figure 1a, middle) and is determined by the physical properties of the condensate environment. This environment is characterized by the diffusion constant and the concentration of unbleached components outside, the distribution of neighboring condensates as well as system boundaries like the coverslip.
To initially bypass this complex dependence on the condensate environment, we propose to extract the fluorescence concentration of unbleached molecules ${c}_{u}(r={R}_{},t)$ directly inside of the spherical phase boundary between the condensate and the dilute phase from experimental data after photobleaching (Figure 1a). To achieve spherical symmetry of the recovery inside the droplet, the entire spherical droplet should be bleached. Using the experimentally determined dynamic boundary condition ${c}_{u}(r={R}_{},t)$, we can accurately determine the diffusion constant inside a condensate, ${D}_{\text{in}}$ (Figure 1f,g), provided ${R}^{2}/{D}_{\text{in}}\lesssim {\tau}_{\text{bound}}$. Here, ${\tau}_{\text{bound}}$ is the time scale of recovery at the boundary (see Figure 1a, middle), which in general features a complex dependence on bleach geometry in the dilute phase, the coverslip, and neighboring droplets. Following this idea, we fit the solutions of Equation (1a) to spatiotemporal experimental data, with ${D}_{\text{in}}$ as the only fit parameter. We find very good agreement between experimentally measured and fitted concentration profiles (Figure 1b–d and supp. Videos 1, 2 and 3). Specifically, we consider condensates composed of PGL3, a main protein component of P granules in the C. elegans embryo (Brangwynne et al., 2009; Griffin, 2015), as well as two synthetic polyelectrolyecomplex coacervate systems, Polylysine/ATP (PLYS/ATP) and Carboxymethyldextran/Polylysine (CMD/PLYS).
We first compared ${D}_{\text{in}}$ of PGL3 for different salt concentrations between $50\mathrm{mM}$ and $180\mathrm{mM}$ (see Methods). We find that ${D}_{\text{in}}$ varies by roughly one order of magnitude, between $0.009\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1}$ and $0.070\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1}$. Our trend is in agreement with reported measurements of the viscosity η, determined by active microrheology (Jawerth et al., 2018) for untagged PGL3 (Figure 1f). Using viscosity data for GFPtagged PGL3 (Jawerth et al., 2020), we use the StokesSutherlandEinstein relationship ${D}_{\text{in}}={k}_{\text{B}}T/(6\pi a\eta )$ to estimate the hydrodynamic radius of PGL3:GFP, $a=1.5\phantom{\rule{thinmathspace}{0ex}}\mathrm{n}\mathrm{m}$ (Einstein, 1905; Sutherland, 1905; von Smoluchowski, 1906). This estimate is consistent with the value reported in Liarzi and Epel, 2005. Across all salt concentrations, the average coefficient of variation per condition is found to be ${c}_{\text{v}}=0.22$. Due to the smooth cost function (Figure 1e) this is unlikely to be a stochastic artefact. It rather seems to reflect variation within the experimental assay.
For the coacervate systems, we find for the diffusion coefficients inside $D}_{\text{in}}=1.68\pm 0.09\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1$ for PLYS/ATP coacervates and $D}_{\text{in}}=5.65\pm 0.32\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1$ for CMD/PLYS coacervates; see (Figure 1g). The coefficient of variation of these measurements is low enough such that even a single measurement provides a good estimate of ${D}_{\text{in}}$. We find ${c}_{\text{v}}=0.05$ and ${c}_{\text{v}}=0.06$ for PLYS/ATP and CMD/PLYS coacervates respectively. Interestingly, ${D}_{\text{in}}$ for the coacervate droplets is about ten times smaller than the diffusion constant of the dilute polyelectrolytes, ${D}_{\text{out}}$ (ArrioDupont et al., 1996; Morga et al., 2019).
Theory for the dynamics of labeled molecules in phaseseparated systems
To understand the physical origin of the timedependent concentration of unbleached components at the condensate interface and the phenomenological Equations (1a, b, c) for the dynamics inside a condensate, we need a theory that encompasses diffusion inside, outside and across phase boundaries. Here, we derive such a theory for a system that can be described by a binary, incompressible mixture prior to photobleaching. This binary mixture is composed of condensate material and solvent. The condensate material has a concentration profile, ${c}_{\text{tot}}(\mathit{\bm{x}},t)={\varphi}_{\text{tot}}(\mathit{\bm{x}},t)/\nu $, which can be expressed in terms of a volume fraction profile ${\varphi}_{\text{tot}}(\mathit{\bm{x}},t)$ by dividing through the molecular volume of the condensate material, ν. Due to incompressibility, the solvent volume fraction in such a binary mixture is given by $(1{\varphi}_{\text{tot}})$. The system prior to photobleaching is assumed to be either at equilibrium, that is, a single droplet and ${\mathrm{\partial}}_{t}{\varphi}_{\text{tot}}(\mathit{x},t)=0$, or close to equilibrium, that is, a system composed of many droplets undergoing slow Ostwald ripening and fusion and ${\partial}_{t}{\varphi}_{\text{tot}}(\mathit{\bm{x}},t)\simeq 0$. Thus, the (quasi) stationary profile ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$ prescribes a physical constraint for FRAP dynamics.
After photobleaching, the system becomes a ternary incompressible mixture composed of bleached ($b$) and unbleached ($u$) components, as well as solvent (Figure 2a). Introducing the volume fraction of the bleached and unbleached components, ${\varphi}_{b}$ and ${\varphi}_{u}$, the physical constraint for the FRAP dynamics reads
where the profiles depend on space and time. The above constraint reflects particle number conservation of bleached and unbleached molecules and becomes a local constraint due to incompressibility. In our work, we focus on FRAP dynamics at thermodynamic equilibrium, where the total volume fraction ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$ depends on space only. Please note that that the derivation below can be generalized to nonequilibrium situations where ${\varphi}_{\text{tot}}$ exhibits a flux (Bo et al., 2021).
Immediately after photobleaching, unbleached molecules diffuse into the condensate leading to FRAP dynamics of unbleached molecules inside (Figure 2b,c). At long times, the concentration profile of unbleached molecules approaches the profile prior to photobleaching, ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$. The dynamics of both concentration profiles, bleached and unbleached molecules, ${c}_{i}={\varphi}_{i}/{\nu}_{i}$ ($i=b,u$), with ${\nu}_{i}$ denoting the molecular volumes, is described by the following conservation laws ($j=u,b$),
Here, ${\mathrm{\Gamma}}_{i}$ are the Onsager transport coefficients, often referred to as mobilities, and ${\overline{\mathrm{\Gamma}}}_{ij}$ are the Onsager cross coupling coefficients obeying ${\overline{\mathrm{\Gamma}}}_{ij}={\overline{\mathrm{\Gamma}}}_{ji}=:\overline{\mathrm{\Gamma}}$. In general, both mobility coefficients depend on the volume fractions. According to irreversible thermodynamics, the flux ${\mathit{\bm{j}}}_{i}$ is driven by gradients in exchange chemical potentials ${\mu}_{i}$ and ${\mu}_{j}$. In the following, we consider linear response for simplicity. The exchange chemical potentials, ${\mu}_{i}=\delta F/\delta {c}_{i}$, are linked to the free energy, $F=\int {d}^{3}xf$, where $f$ denotes the free energy density. Expressing concentrations in terms of the volume fractions, ${\varphi}_{i}={c}_{i}{\nu}_{i}$, we describe our incompressible ternary mixture after photobleaching by a FloryHuggins free energy density (Flory, 1942; Huggins, 1942; Krüger et al., 2018):
where we write the molecular volumes of bleached and unbleached components in n_{i} multiples of the solvent molecular volume ${\nu}_{\text{sol}}$, that is, ${\nu}_{i}={n}_{i}{\nu}_{\text{sol}}$. Moreover, ${\chi}_{ij}$ denote dimensionless FloryHuggins parameters characterizing the interactions between different components $i$ and $j$, where subscript $s$ indicates the solvent. The parameters ${\kappa}_{i}$ and ${\kappa}_{ub}$ characterize the free energy penalties for spatial heterogeneities and are linked to the surface tensions.
If photobleaching does not affect the molecular interactions or molecular volumes, the free energy density above can be simplified significantly (Appendix of Krüger et al., 2018). In this case, the interactions between unbleached and solvent, and bleached and solvent components are equal, ${\chi}_{us}={\chi}_{bs}=:\chi $, and cross interactions vanish, ${\chi}_{ub}=0$. Moreover, molecular volumes of bleached and unbleached components are equal, ${n}_{u}={n}_{b}=:n$, and the parameters characterizing free energy penalties for spatial heterogeneities obey ${\kappa}_{u}={\kappa}_{b}=:\kappa $ and ${\kappa}_{ub}=2\kappa $. Thus, the simplified free energy reads
To ensure a constant diffusion coefficient in the dilute limits of the bleached and unbleached components, we employ the scaling ansatz for a ternary mixture, ${\mathrm{\Gamma}}_{i}={\mathrm{\Gamma}}_{0}{\varphi}_{i}\left[(1{\varphi}_{\text{tot}})+\left({\overline{\mathrm{\Gamma}}}_{0}/{\mathrm{\Gamma}}_{0}\right){\varphi}_{j}\right]$ and $\overline{\mathrm{\Gamma}}={\overline{\mathrm{\Gamma}}}_{0}{\varphi}_{u}{\varphi}_{b}$. In general, both mobility functions, ${\mathrm{\Gamma}}_{0}$ and ${\overline{\mathrm{\Gamma}}}_{0}$, depend on the total volume fraction ${\varphi}_{\text{tot}}$. For the limiting case where bleached and unbleached molecules are identical particles, we can choose ${\mathrm{\Gamma}}_{0}={\overline{\mathrm{\Gamma}}}_{0}$. Applying the equilibrium FRAP condition (2) and using Equation (3a, b), we find that the concentration of unbleached components is governed by the following diffusion equation
with a ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$dependent diffusivity, $D({\varphi}_{\text{tot}})={k}_{\mathrm{B}}T{\mathrm{\Gamma}}_{0}({\varphi}_{\text{tot}})$. As we show in Bo et al., 2021 a similar approach can be used to investigate singlemolecule dynamics across phase boundaries.
Similar to Equation (1), the diffusion equation above is linear in c_{u}. However, the dynamics of unbleached components are affected by gradients in ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$ and components diffuse with different diffusion coefficients inside and outside the condensate, where in each phase $\nabla {\varphi}_{\text{tot}}=0$ (Figure 2b). The positiondependence of ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$ is given by the equilibrium condition of a homogeneous chemical potential of the binary mixture prior to photobleaching, which implies ${\partial}_{t}{\varphi}_{\text{tot}}=0$. For a radially symmetric system with $r$ denoting the radial coordinate, ${\varphi}_{\text{tot}}(r)={\varphi}_{\text{out}}^{\text{eq}}+\left({\varphi}_{\text{in}}^{\text{eq}}{\varphi}_{\text{out}}^{\text{eq}}\right)H\left((rR)/\mathrm{\ell}\right)$, where ${\varphi}_{\text{in}}^{\text{eq}}$ and ${\varphi}_{\text{out}}^{\text{eq}}$ are the equilibrium volume fractions inside and outside, respectively, $R$ is the droplet radius, and $\mathrm{\ell}$ denotes the width of the interface. Moreover, $H\left((rR)/\mathrm{\ell}\right)$ is a function that decreases from one to zero at $r=R$ on an interface width $\mathrm{\ell}$. For phase separation close to the critical point and large droplet sizes, $H(x)=\left(1+\mathrm{tanh}\left(x\right)\right)/2$ (Bray, 1994; Weber et al., 2019). We numerically solve Equation (6) using a finite element method (Anders et al., 2012) in a finite domain of size $L$ which is much larger than the droplet radius $R$ and fit the solution to experimental data.
In summary, our model has seven parameters. Four of these, namely, the equilibrium volume fractions ${\varphi}_{\text{in}}^{\text{eq}}$ and ${\varphi}_{\text{out}}^{\text{eq}}$, the interface width $\mathrm{\ell}$, and the droplet radius $R$, characterize the equilibrium profile prior to bleaching ${\varphi}_{\text{tot}}(\mathit{\bm{x}})$. The remaining parameters are the system size $L$ and the diffusion coefficients inside and outside, which are given as
For the case of a single, spherical droplet with an infinitely thin interface (Weber et al., 2019), we can derive an effective droplet model for the unbleached component from Equation (6), where the dynamics of unbleached components inside and outside are given by diffusion equations that are coupled by boundary conditions (see Appendix 1 for the derivation):
Here, ${R}_{}$ and ${R}_{+}$ denote the radial position directly inside and outside the droplet interface, respectively. Equation (8c) describes an equality of the fluxes directly inside and outside of the interface, respectively, and thereby expresses particle number conservation at the interface $r=R$. Equation (8d) describes a jump in concentration of unbleached components, which is determined by the thermodynamic partition coefficient
Moreover, the flux vanishes at the origin $r=0$, ${{\mathit{\bm{e}}}_{r}\cdot \nabla c}_{r=0}=0$, and at the system boundary $r=L$, ${{\mathit{\bm{e}}}_{r}\cdot \nabla c}_{r=L}=0$.
Above we provide a thermodynamic derivation of Equations (8) in the limit of thin interfaces. These equations were already proposed as a model for FRAP dynamics of protein condensates (Taylor et al., 2019). Interestingly, while we obtained Equations (8) for the dynamics of a phaseseparated protein component undergoing fluorescence recovery, similar equations were also used to investigate diffusion of a protein that was added to an already existing twophase system at thermodynamic equilibrium (Münchow et al., 2008).
In Equations (8) ${D}_{\text{in}}$, ${D}_{\text{out}}$, and $P$ are considered to be independent parameters. Strictly speaking, due to phase separation, the diffusion coefficients ${D}_{\text{in}}$ and ${D}_{\text{out}}$ are not independent which is evident in Equations (7). For example, in the absence of phase separation or at the critical point, ${\varphi}_{\text{in}}^{\text{eq}}={\varphi}_{\text{out}}^{\text{eq}}$ (i.e. $P=1$), the diffusion coefficients inside and outside must be equal, ${D}_{\text{in}}={D}_{\text{out}}$. For a given condensate with fixed ${\varphi}_{\text{in}}^{\text{eq}}$ and ${D}_{\text{in}}$, there is a relationship between the diffusivity outside ${D}_{\text{out}}$ and the partition coefficient $P$, which can be expressed using Equations (7) as
However, except for the limit $P\to 1$, Equation (10) does not impose further constraints for the determination of the parameters since the mobility function ${\mathrm{\Gamma}}_{0}({\varphi}_{\text{tot}})$ is unknown. For large $P$, the missing knowledge of the mobility function renders ${D}_{\text{out}}$, ${D}_{\text{in}}$ and $P$ as effectively independent parameters. This provides a theoretical justification for the assumption made by Taylor et al., 2019.
In the following, we use our theory (Equation (6)) to investigate the impact of the condensate environment on the FRAP dynamics. In particular, we consider how a passivated coverslip (no wetting of condensates) and nearby condensates affect the influx of unbleached molecules and thereby the recovery dynamics. Lastly, given the concentration at the droplet boundary, $c(r=R,t)$, we derive a relationship between ${D}_{\text{out}}$ and $P$. This can be used to investigate changes of the partition coefficient, for example when changing salt concentrations. Importantly, this method does not rely on absolute fluorescence intensities when measuring concentrations in the dense and dilute phases, which are prone to artefacts (McCall et al., 2020).
Impact of nonwetting coverslip on FRAP dynamics
Here, we investigate the influence of the coverslip surface on the FRAP dynamics of nonwetting spherical droplets. Under typical in vitro conditions, condensates sediment due to gravity, leading to sessile droplets on a coverslip. In many experimental setups, coverslips are passivated, for example pegylated, in order to suppress wetting of condensates on the coverslip surface (Alberti et al., 2018). These experimental conditions lead to almost spherical droplets since capillary effects are typically negligible for micrometersized polymerrich or proteinrich droplets (Park et al., 2013; McCall et al., 2020).
We numerically solved Equation (6) for a spherical condensate on top of a noflux boundary that mimics the coverslip surface (Figure 3a). We find that the recovery of the average volume fraction inside the condensate can slow down compared to the case without a coverslip (Figure 3b). This slowdown vanishes if droplets have a distance to the coverslip surface larger than a few droplet diameters. Moreover, we find that the slowdown is more pronounced for larger partition coefficients $P$. This trend can be explained by the size of the region in the dilute phase from where most unbleached molecules are recruited: if $P$ is small, most of the unbleached molecules come from the immediate surroundings of the condensate. Hence the influence of radial asymmetry is minimal and the recovery appears almost unchanged compared to the case without coverslip. However, for large $P$, the condensate recruits unbleached molecules from distances far away, limiting the recruitment to an effective halfspace compared with the case without coverslip. Indeed, for very large $P$ recovery rates slow down maximally by a factor of two.
Interestingly, by extracting the boundary concentration in midplane, similar to the procedure in Figure 1, and spatially fitting the solutions of Equation (1a) to the ensuing recovery profiles, we can reliably recover the input diffusion coefficient ${D}_{\mathrm{in}}$ (Figure 3c). The reason for this agreement is that by considering the intensity at the condensate interface, our method is independent of the time scale set by diffusion in the dilute phase.
Impact of neighboring condensates on FRAP dynamics
In this section, we address the impact of neighboring condensates on the recovery dynamics. We solved Equation (6) for a system composed of a bleached condensate with four unbleached neighboring condensates of the same size (Figure 3d) and find that neighboring condensates can significantly speed up the recovery dynamics (Figure 3e). This speedup is only pronounced for rather close condensates with interdroplet distance on the order of condensate size. Moreover, similar to the impact of the coverslip, the effects of the recovery dynamics are stronger for larger partition coefficients $P$. For small $P$, most unbleached molecules are recruited from the dilute phase leading to almost no effect on the recovery also when condensates are very close to each other (Figure 3e, left). For large partition coefficients $P$, however, a certain fraction of molecules are recruited from the neighboring condensates causing a significant speedup of the recovery (Figure 3e, right).
Again, despite this change in total recovery due to closeby neighboring droplets, we can reliably measure ${D}_{\mathrm{in}}$ via our spatial fitting method (Figure 3f). In particular, by extracting the boundary intensity in midplane and spatially fitting the solutions of Equation (1a), we find very good agreement with our input ${D}_{\mathrm{in}}$. This agreement shows that our method is robust for typical experimental systems that deviate from an ideal, isolated condensate.
Interfacial flux relates partition coefficient and outside diffusivity
We have shown that by using the timedependent fluorescence at the interface of a spherical droplet we can accurately fit our dynamic Equation (1) to our experimental data and thus determine the diffusion constant inside the droplet, ${D}_{\mathrm{in}}$ (Figure 1a). Our theory (see Equation 6) suggests that the fluorescence at the droplet interface is affected by the physical parameters characterizing the droplet environment such as the diffusion coefficient ${D}_{\mathrm{out}}$ and the partition coefficient $P$. In particular, the flux through the droplet interface is enlarged for increasing ${D}_{\mathrm{out}}$ or decreasing $P$ (see Equation (8c) after rescaling the concentration close to the interface). Thus, for a condensate with concentration ${\varphi}_{\text{in}}^{\text{eq}}$ and diffusion coefficient ${D}_{\text{in}}$, a given flux between both phases through the interface implies a relationship between ${D}_{\text{out}}$ and $P$.
Here, we determined the relationship between ${D}_{\text{out}}$ and $P$ by fitting numerical solutions of Equation (6) to the recovery dynamics inside the droplet (Figure 4b–d and Videos 1, 2 and 3). The diffusion coefficient inside, ${D}_{\text{in}}$, was independently determined for each experiment via our method introduced in Figure 1a. This leaves $P$ and ${D}_{\text{out}}$ as independent parameters, which is valid for large $P$ (see discussion after Equation (10)). We thus sampled $P$ (with $P\gg 1$) across three orders of magnitude and obtained the bestfitting ${D}_{\mathrm{out}}$ for each $P$ (Figure 4e,f). Notably, all the combinations of ${D}_{\mathrm{out}}$ and $P$ represent relatively good fits, and each experimental condition leads to a unique relationship ${D}_{\text{out}}(P)$. For large $P$, we find that the best ${D}_{\mathrm{out}}$ scales linearly with $P$ (dashed lines in Figure 4e,f); for a discussion on the origin of this scaling, please refer to Appendix 2.
We would like to draw attention to the values of $P$, which are higher than expected (A.W. Fritsch and J.M. IglesiasArtola, personal communication), for a realistic parameter range $50\phantom{\rule{thinmathspace}{0ex}}\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1}<{D}_{\text{out}}<100\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1}$. One reason for this discrepancy is the assumption of spherical symmetry for the fitting routine leading to absolute values of $P$ that are overestimated up to twofold (see caption of Figure 3 for a discussion). Additionally, our current experiments cannot exclude boundary effects, such as a recently hypothesised interfacial resistance (Taylor et al., 2019). Equation (6) should therefore be interpreted as a minimal model that fits the available data with high accuracy and explains the boundary dynamics, but cannot rule out additional effects.
Thus, the relationship ${D}_{\text{out}}(P)$ has to be assessed critically. Since $P$ and ${D}_{\text{out}}$, in conjunction with ${D}_{\text{in}}$, set the time scale for recovery at the boundary, any effects that are ignored would change this relationship. Since the boundary time scale is welldescribed by ${D}_{\text{out}}\propto P$, this change would likely manifest itself as a constant prefactor in each curve in Figure 4e,f. We thus only interpret the ratio between curves in Figure 4e,f, instead of their absolute values.
Considering this ratio allows us to investigate how the addition of salt affects the partition coefficient of PGL3, assuming that boundary effects and ${D}_{\text{out}}$ are independent of ${c}_{\text{salt}}$. Figure 4f indicates that for all salt concentrations we have ${D}_{\text{out}}\propto P$ for $D}_{\text{out}}>1\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1$. For proteins and coacervate components we can safely assume $D}_{\text{out}}>10\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1$ in solution. Consequently, we chose $D}_{\text{out}}=10\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1$ and calculated how the partition coefficient $P$ changes with salt concentration ${c}_{\text{salt}}$ (Figure 4g). Specifically, for salt concentrations in the range from 50 mM to 180 mM, we find that the estimated partition coefficient $P$ of PGL3 droplets decreases approximately 10fold. This trend is probably a result of enhanced screening of charged groups for increasing salt concentration.
Proposal how to determine partition coefficient and outside diffusivity
We next asked, whether we can obtain both parameters, $P$ and ${D}_{\mathrm{out}}$, at the same time, without measuring fluorescence intensities outside the droplet. Although each combination (${D}_{\mathrm{out}}$, $P$) along the lines specified in Figure 4e,f leads to a reasonable fit, we will now show that there is a distinct combination that exhibits a global minimum of the cost function for each ${D}_{\mathrm{out}}(P)$. Here, we assume no resistance at the interface as proposed in Münchow et al., 2008; Hahn et al., 2011; Hahn and Hardt, 2011. Providing experimental evidence of this global minimum can be hampered by effects due to the droplet environment, such as neighboring droplets or the coverslip surface (Figure 3a,d), inhomogeneous bleaching in the dilute phase, imaging artefacts at the phase boundary and effects at the droplet boundary not accounted for in Equation (6). In particular, in our experimental studies, interdroplet distances are sometimes on the order of the droplet size and diffusive exchange is affected by the coverslip. Thus, we decided to use our model to create in silico data and provide evidence for the existence of a distinct combination of ${D}_{\mathrm{out}}$ and $P$ for a fixed ${D}_{in}$. Fixing ${D}_{\mathrm{in}}$ mimics the approach of initially determining ${D}_{\mathrm{in}}$, as outlined in Figure 1. To determine the relationship ${D}_{\mathrm{out}}(P)$, we proceed as described for Figure 4. Figure 5a depicts the ${D}_{\mathrm{out}}(P)$ relationships corresponding to four parameter combinations in a range relevant for protein condensates and coacervate droplets (Drobot et al., 2018; Riback et al., 2020). In particular, we choose two outside diffusivities ${D}_{\mathrm{out}}$ of $0.1\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1}$ and $1\phantom{\rule{thinmathspace}{0ex}}\mu {\mathrm{m}}^{2}{\mathrm{s}}^{1}$ and two partition coefficients, $P=5$ and $P=150$. We find indeed that each cost function exhibits a unique minimum for each of the considered parameter combinations (Figure 5b,c). These findings indicate that all three parameters, ${D}_{\mathrm{in}}$, ${D}_{\mathrm{out}}$ and $P$, can in theory be determined by a single FRAP time course of the droplet intensity inside. Thus, in principle, there is no need to measure kinetic properties of the dilute phase to fully characterise the system in terms of its parameters. This possibility represents a new approach to characterize the partition coefficient $P$, which is particularly important in light of recent data showing that measurements based on fluorescence intensity can lead to drastic underestimation of $P$ (McCall et al., 2020).
Discussion
The dynamic redistribution of fluorescent molecules has been used to characterize liquid phase separation in biology via a variety of techniques, including SPT, FRAP, and FCS (ElbaumGarfinkle et al., 2015; Taylor et al., 2019; Moon et al., 2019). Here, we have derived a theory that describes the diffusive motion of labeled molecules based on the physics of phase separation. It can be applied to many stateofthe art fluorescent methods such as FCS, SPT, and FRAP and can thus help extend traditional techniques to the realms of phase separation (Ries and Schwille, 2012). Importantly, this theory enables us to avoid commonly applied approximations such as the frequently used singleexponential recovery (Brangwynne et al., 2009; Frottin et al., 2019; Kaur et al., 2019; Fisher and ElbaumGarfinkle, 2020; Kistler et al., 2018).
Our theory, essentially represented by Equation (6), governs the dynamics of labeled molecules through interfaces of condensates. As we show in our work, it can be applied to spherical condensates. In addition, our theory could also be used for nonspherical condensates and arbitrary bleach geometries, since spherical symmetry is only assumed for ease of data analysis. We were able to quantify the impact of neighboring droplets and the coverslip on the recovery dynamics. We found that neighboring droplets caused an appreciable speedup in overall recovery, while emulating a coverslip caused a weak slowdown. In order to experimentally verify our theory, we have used three in vitro droplet systems, two composed of charged synthetic polymers and one with a purified protein component. There is remarkable quantitative agreement between our theory and the diffusion dynamics observed inside such droplets. This agreement shows that proteins and charged, synthetic polymers can form droplets that follow simple diffusive dynamics. Crucially, we use the full spatiotemporal data for fitting and can thus distinguish the timescale set by intradroplet diffusion from the timescales at play in the dilute phase. We extract the intensity directly at the inside of the droplet interface and fit a spatially resolved diffusion equation to the ensuing recovery. We use the boundary intensity as a dynamic boundary condition and the experimentally measured profile as initial condition. Within the statistical fluctuations, the numerical solutions and the experimental data are not distinguishable (see Figure 1b–d). Throughout the time course, we find excellent agreement with the data and have thus found a method with minimal approximations that can precisely measure the inside diffusion coefficient ${D}_{\text{in}}$.
Building on the analysis inside the droplet, we show that there is a relationship between partition coefficient $P$ and the diffusion coefficient in the dilute phase, ${D}_{\text{out}}$. Data obtained from FRAP experiments define a line in (${D}_{\text{out}}$, $P$) space, along which a range of parameter sets can reliably account for the boundary dynamics. This relationship allowed us to characterize changes in $P$ upon salt addition, opening an alternative avenue for characterizing $P$ without relying on fluorescence intensities. This is particularly important in light of recent results obtained by quantitative phase microscopy (QPM). These results show that measuring partition coefficients based on fluorescence intensity can lead to strong underestimation of $P$ (McCall et al., 2020). While it is tempting to interpret our values for $P$ as actual partition coefficients, we would like to stress that these values were obtained from a physical model, which does not consider additional effects at the condensate interface such as a potential interfacial resistance. This effect has recently been hypothesized to solve a contradiction in time scales between FRAP and FCS experiments (Taylor et al., 2019). However, introducing an interfacial resistance significantly lowered the fit quality in Taylor et al., 2019, an issue that has yet to be resolved. In this context, it will be interesting to reexamine work for threecomponent systems (Binks and Lumsdon, 2000; Lin et al., 2003). Some evidence for an interfacial resistance across a PEG/Dextran interface has been found for some types of molecules, for example DNA, bovine serum albumin, and bovine γglobulin (Hahn and Hardt, 2011; Gebhard et al., 2021). Measuring ${D}_{\mathrm{out}}$ via FCS, similar to Taylor et al., 2019, and partition coefficients via QPM (McCall et al., 2020), will allow further characterization of the existence of interfacial effects.
Our approach can be readily extended to multicomponent systems with an arbitrary number of components, which is particularly useful in vivo. This would hardly be possible for techniques that do not use labeled components, such as QPM or other scattering methods. Of particular interest are multicomponent systems with chemical reactions away from equilibrium. Our approach can then be used to determine the diffusion coefficients and concentration levels of reactants, and thereby provide insights into reaction kinetics. Interestingly, introducing the bleached molecules via a ternary mixture also enabled us to derive the Langevin equation governing singlemolecule motion in phaseseparated media, thus providing a link to SPT (Bo et al., 2021). Approaches for single labeled molecules are relevant since high labeling fractions were shown to alter the viscosity and thus kinetics in dense protein phases (compare viscosity for PGL3 in Jawerth et al., 2018 with viscosity for tagged PGL3:GFP Jawerth et al., 2020 ). Finally, by accurately measuring ${D}_{\mathrm{out}}$, our technique can also be employed to characterize rheological properties of condensates such as the recently reported glasslike dynamics of protein droplets (Jawerth et al., 2020).
Materials and methods
Coacervate assay
General reagents
Request a detailed protocolCarboxymethyldextran sodium salt (CMDex, (C6H10O5)n.(COOH), 10–20 kDa, monomer MW = 191.3g/mol), PolyLlysine hydrobromide (PLys, (C6H12N2O)n, 4–15 kDa, monomer MW = 208.1g/mol) and adenosine 5’triphosphate disodium salt hydrate (ATP, C10H14N5Na2O13P3, MW = 551.1g/mol) were purchased from Sigma Aldrich. FITCPLys ((C6H12N2O)n.(C21H11NO5S), $\mathrm{25\hspace{0.17em}000}\mathrm{g}/\mathrm{mol}$) was purchased from Nanocs, NewYork, USA . Milli Q water was used to prepare aqueous stocks of CMDex (1000 mM, pH 8), PLys (200 mM, pH 8) and ATP (100 mM, pH 8). All solutions were stored in the freezer at 20 until use and the pH of all stocks was adjusted using a stock solution of 1M NaOH.
Coacervate preparation
Request a detailed protocolStock solutions of CMDex, PLys and ATP were first diluted to 25 mM and the PLys solution doped with 1% v/v PLysFITC. Diluted solutions of CMDex/PLys or PLys/ATP were then mixed together at a 4:1 vol ratio (16 μl), resulting in the formation of turbid coacervate solutions. Solutions were left to equilibrate for at least 5 min before imaging, up to a maximum of 15 min when larger droplets were desired.
PGL3 droplets
Request a detailed protocolPGL3 was purified and stored as previously described (Saha et al., 2016). To obtain droplets, 300 mM KCl stock protein solution was diluted to the desired concentration, achieving final salt concentrations of 50–180 mM. A small imaging volume was created by using polystyrene beads, resulting in complete droplet sedimentation after less than five minutes. Droplets were imaged immediately to avoid changes in material properties due to ageing (Jawerth et al., 2020).
Microscopy and FRAP
Confocal imaging
Request a detailed protocolDroplets were imaged at midplane by visually defining the focal position with the largest droplet area of the droplet of interest. Images were acquired on an Andor spinning disk confocal microscope equipped with an Andor IX 81 inverted stand, a FRAPPA unit, an Andor iXON 897 EMCCD camera, and a 488 nm laser, using a 60x/1.2 U Plan SApo OLYMPUS water objective. Imaging conditions were optimized for minimal bleaching at the required frame rate. Frame rates were optimised for each system: PGL3, $0.1\mathrm{s}<\mathrm{\Delta}t<5\mathrm{s}$, CMD/PLYS, $\mathrm{\Delta}t=0.03\phantom{\rule{thinmathspace}{0ex}}\mathrm{s}$, PLYS/ATP, $\mathrm{\Delta}t=0.07\phantom{\rule{thinmathspace}{0ex}}\mathrm{s}$.
Frap
Request a detailed protocolDroplets were bleached in their entirety by using the minimal FRAP ROI that encompasses the entire droplet. FRAP was performed in three focal planes, equally spaced across the droplet in zdirection, to reduce nonuniform bleaching of the droplet. FRAP rates and dwell times were chosen such that leftover fluorescence intensity above background was smaller than 1% for PGL3 and smaller than 15% for coacervate droplets to maximize bleaching within the droplet while keeping bleaching impact on the droplet environment minimal.
Data analysis
Azimuthal averaging and normalization
Request a detailed protocolTimelapse images were cropped with the droplet of interest in the center. An azimuthal average was performed around the center of the droplet to obtain a 1D profile along the radial coordinate with minimal loss of data, using the radialavg function provided by David J. Fischer on Matlab File Exchange (Fischer, 2016). Camera background was subtracted uniformly from the resulting 1D profiles. The radial intensity profile at the prebleach stage was used for normalization and to correct for optical artefacts that lead to increased fluorescence at the droplet center compared to the dropletbulk interface. Data close to the droplet interface cannot be used for fitting, since the droplet has an artificially broad boundary due to the pointspread function and likely due to curvature effects. Therefore, on average, the intensity of the ten pixels closest to the boundary were not used for analysis. The droplet boundary was defined as the inflection point of the azimuthally averaged profile in the prebleach frame.
Immediately after bleaching, a uniform recovery across the entire droplet can be seen, which cannot be spatially resolved even at frame rates $<30\phantom{\rule{thinmathspace}{0ex}}\mathrm{m}\mathrm{s}$. This recovery is fast compared to the recovery by diffusion from the outside for all systems under investigation. We thus chose to not account for this uniform recovery in our model and instead start the fitting after a time lag that depends on the system and droplet size. This offset typically consists of less than 5% of the total prebleach intensity. Additionally, bleaching is not complete, resulting in an additional offset above the camera background even immediately after bleaching (see gray lines in Figure 1).
Photobleaching due to continuous imaging was minimal in all droplet types. We thus chose to not account for imaginginduced photobleaching, in order to not introduce additional noise due to necessarily occurring fluctuations within the bleach correction.
Extracting experimental boundary conditions
Request a detailed protocol${c}_{u}(r={R}_{},t)$ was extracted from the intensity profiles as the value at the outermost pixel. In order to speed up fitting and avoid jumps in ${c}_{u}(r={R}_{},t)$, the extracted intensity values were sorted to eliminate small fluctuations.
Fitting of ${D}_{\text{in}}$ by using experimentally measured boundary conditions (Figure 1)
Request a detailed protocolThe resulting spatiotemporal profiles were fit as described in the main text, using ${D}_{\mathrm{in}}$ as a single global fit parameter and using ${c}_{u}(r={R}_{},t)$ as described above as the system’s timedependent boundary condition. Fits were performed in MATLAB (Mathworks), using pdepe to solve the PDE and fminsearch for minimizing the squared distance between data and model. Code is available at https://gitlab.pks.mpg.de/mesoscopicphysicsoflife/DropletFRAP (Hubatsch, 2021a; copy archived at swh:1:rev:7e5b59fff3c634cfce5d0f99a86c807635a090fd).
Numerical solution of Equation (6)
Request a detailed protocolEquation 6 was solved using either pdepe (MATLAB (Mathworks), Figures 4 and 5, for spherically symmetric systems) or by using the finite element method via the FENICS environment (Anders et al., 2012) for arbitrary 3D geometries (Figure 3). All fits in Figures 4 and 5 were performed using fminsearch based on a squareddifference metric. Code is available at https://gitlab.pks.mpg.de/mesoscopicphysicsoflife/frap_theory (Hubatsch, 2021b; copy archived at swh:1:rev:2c4a972a380df7f9e86ddbbf0ae921443ce0800f).
Appendix 1
Limit of narrow interfaces
Here, we derive the effective droplet model for our dynamic equation
by considering the limit of narrow interfaces. In the equation above, ${\mathit{\bm{j}}}_{\mathrm{u}}$ denotes the flux of unbleached molecules. Conservation of molecules at the interface implies
where $\mathrm{\ell}$ is the characteristic size of the interface. Moreover, $\u03f5>0$ is a small but nonzero parameter by which we define the position directly left and right of the interface. In this limit, ${\nabla {\varphi}_{\text{tot}}}_{R\pm \u03f5}=0$ directly left and right of the interface. Thus, we obtain Equation (8c) for narrow interfaces. However, the contribution $D({\varphi}_{\text{tot}}){c}_{u}\nabla {\varphi}_{\text{tot}}/{\varphi}_{\text{tot}}$ in Equation (11) that determines the dynamics through the interface implies a Dirichlet boundary condition for the concentration of unbleached molecules at the interface $r=R$. Since $\nabla {\varphi}_{\text{tot}}/{\varphi}_{\text{tot}}\propto \mathrm{\ell}$, we demand
in the limit of decreasing interface width $\mathrm{\ell}$. This condition ensures that the dynamics at the interface remain unchanged for decreasing $\mathrm{\ell}$. Parameterizing the interface for example by ${\varphi}_{\text{tot}}(r)={\varphi}_{\text{out}}^{\text{eq}}\left[1+\left(P1\right)\left(1+\mathrm{tanh}\left(r/\mathrm{\ell}\right)\right)/2\right]$, we find that Equation (14) leads to Equation (8d).
Appendix 2
Solution of effective droplet model for FRAP
In this appendix, we derive the solution for the two diffusion equations coupled at the interface for a spherically symmetric case; see Equations (8). We consider an initial condition with a constant concentration $c(r,0)={c}_{0}$ outside a droplet with radius $R$ and bleaching gives rise to $c(r,0)=0$ inside the droplet. Performing a Laplace transformation, $\widehat{c}(r,s)={\int}_{0}^{\mathrm{\infty}}\mathit{d}t{e}^{st}c(r,t)$, of Equations (8), we find
where $s$ is the rate parameter of the Laplace transform. The corresponding solutions read
where ${\xi}_{\text{in/out}}=\sqrt{s/{D}_{\text{in/out}}}$. We have selected the solutions with no radial flux at the origin and a finite concentration at infinity. The remaining unknown constants $a$ and $b$ are determined by the conditions at the interface stated in Equations (8). Inside the droplet $r<R$, the solution reads
Even though it is nontrivial to perform the inverse Laplace transformation of this expression we can obtain some analytical understanding of the behavior at short and long timescales by considering the asymptotics of large and small $s$. Expanding Equation (17) for small $s$, we obtain:
This relationship shows that for small $s$, the rescaled concentration ${\widehat{c}}_{u}(r,s)/P$ is affected by ${D}_{\text{out}}$ only via the $P{D}_{\text{in}}/{D}_{\text{out}}$ combination. Conversely, for large $s$, the leading contribution features the term
showing that for short times the system is influenced by ${D}_{\text{out}}$ via the ratio $P\sqrt{{D}_{\text{in}}/{D}_{\text{out}}}$. Note that for these short times the solution is well approximated by that of a onedimensional system. These long and short time behaviors are confirmed by the numerical solutions plotted in Appendix 2—figure 1. C, which show that the evolution of the rescaled concentrations match at short times, if $P\sqrt{{D}_{\text{in}}/{D}_{\text{out}}}$ is kept constant but start to deviate for longer times. Conversely, at longer times the dynamics are invariant if $P{D}_{\text{in}}/{D}_{\text{out}}$ is kept constant except for a shift due to a short timescale transient.
Data availability
Code for modelling and data analysis is available at https://gitlab.pks.mpg.de/mesoscopicphysicsoflife/frap_theory and https://gitlab.pks.mpg.de/mesoscopicphysicsoflife/DropletFRAP (copies archived at https://archive.softwareheritage.org/swh:1:rev:2c4a972a380df7f9e86ddbbf0ae921443ce0800f and https://archive.softwareheritage.org/swh:1:rev:7e5b59fff3c634cfce5d0f99a86c807635a090fd, respectively).
References

A user's Guide for Phase Separation Assays with Purified ProteinsJournal of Molecular Biology 430:4806–4820.https://doi.org/10.1016/j.jmb.2018.06.038

BookAutomated Solution of Differential Equations by the Finite Element MethodBerlin, Germany: Springer.https://doi.org/10.1007/9783642230998

Diffusion of fluorescently labeled macromolecules in cultured muscle cellsBiophysical Journal 70:2327–2332.https://doi.org/10.1016/S00063495(96)797989

Biomolecular condensates: organizers of cellular biochemistryNature Reviews. Molecular Cell Biology 18:285–298.https://doi.org/10.1038/nrm.2017.7

Theory of phaseordering kineticsAdvances in Physics 43:357–459.https://doi.org/10.1080/00018739400101505

BookOptical Fluorescence Microscopy: From the Spectral to the Nano DimensionBerlin, Germany: Springer.https://doi.org/10.1007/9783642151750

Compartmentalised RNA catalysis in membranefree coacervate protocellsNature Communications 9:3643.https://doi.org/10.1038/s4146701806072w

Tunable multiphase dynamics of arginine and lysine liquid condensatesNature Communications 11:18224.https://doi.org/10.1038/s4146702018224y

Thermodynamics of high polymer solutionsThe Journal of Chemical Physics 10:51–61.https://doi.org/10.1063/1.1723621

Cytoplasmic localization and asymmetric division in the early embryo of Caenorhabditis elegansWiley Interdisciplinary Reviews. Developmental Biology 4:267–282.https://doi.org/10.1002/wdev.177

Electrophoretic transport of biomolecules across liquidliquid interfacesJournal of Physics: Condensed Matter 23:184107.https://doi.org/10.1088/09538984/23/18/184107

SoftwareDropletFRAP , version swh:1:rev:7e5b59fff3c634cfce5d0f99a86c807635a090fdSoftware Heritage.

Some properties of solutions of Longchain compoundsThe Journal of Physical Chemistry 46:151–158.https://doi.org/10.1021/j150415a018

Liquidliquid phase separation in biologyAnnual Review of Cell and Developmental Biology 30:39–58.https://doi.org/10.1146/annurevcellbio100913013325

SaltDependent rheology and surface tension of protein condensates using optical trapsPhysical Review Letters 121:258101.https://doi.org/10.1103/PhysRevLett.121.258101

Protein condensates as aging maxwell fluidsScience 370:1317–1323.https://doi.org/10.1126/science.aaw4951

Discontinuous switching of position of two coexisting phasesNew Journal of Physics 20:75009.https://doi.org/10.1088/13672630/aad173

A framework for understanding the functions of biomolecular condensates across scalesNature Reviews Molecular Cell Biology 22:215–235.https://doi.org/10.1038/s4158002000303z

Multicolour singlemolecule tracking of mRNA interactions with RNP granulesNature Cell Biology 21:162–168.https://doi.org/10.1038/s4155601802634

Kinetics of Polyllysine adsorption on mica and stability of formed monolayers: theoretical and experimental studiesLangmuir : The ACS Journal of Surfaces and Colloids 35:12042–12052.https://doi.org/10.1021/acs.langmuir.9b02149

Protein diffusion across the interface in aqueous twophase systemsLangmuir : The ACS Journal of Surfaces and Colloids 24:8547–8553.https://doi.org/10.1021/la800956j

Shape of a large drop on a rough hydrophobic surfacePhysics of Fluids 25:022102.https://doi.org/10.1063/1.4789494

Fluorescence correlation spectroscopyBioEssays : News and Reviews in Molecular, Cellular and Developmental Biology 34:361–368.https://doi.org/10.1002/bies.201100111

BookFluorescence Correlation Spectroscopy: Theory and ApplicationsBerlin, Germany: Springer.https://doi.org/10.1007/9783642595424

Singleparticle tracking: applications to membrane dynamicsAnnual Review of Biophysics and Biomolecular Structure 26:373–399.https://doi.org/10.1146/annurev.biophys.26.1.373

Analysis of binding reactions by fluorescence recovery after photobleachingBiophysical Journal 86:3473–3495.https://doi.org/10.1529/biophysj.103.026765

LXXV A dynamical theory of diffusion for nonelectrolytes and the molecular mass of albuminThe London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 9:781–785.https://doi.org/10.1080/14786440509463331

Zur Kinetischen Theorie der Brownschen Molekularbewegung und der suspensionenAnnalen Der Physik 326:756–780.https://doi.org/10.1002/andp.19063261405

Physics of active emulsionsReports on Progress in Physics 82:064601.https://doi.org/10.1088/13616633/ab052b
Decision letter

Rohit V PappuReviewing Editor; Washington University in St Louis, United States

José D FaraldoGómezSenior Editor; National Heart, Lung and Blood Institute, National Institutes of Health, United States

Rohit V PappuReviewer; Washington University in St Louis, United States
Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.
Acceptance summary:
This work is timely and relevant as the field grapples with the issue of diffusive dynamics across phase boundaries. The numerical formalism in this work will be of broad interest to the condensate field.
Decision letter after peer review:
Thank you for submitting your article "Quantitative Theory for the Diffusive Dynamics of Liquid Condensates" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, including Rohit Pappu as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by José FaraldoGómez as the Senior Editor.
The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.
Both reviewers have arrived at similar conclusions. FRAP is a method that is routinely used to study internal dynamics of molecules within biomolecular condensates. Of course, FRAP was introduced in a completely different context, and as has been shown before, its adoption needs appropriate adaptation to the context of interest. How should FRAP data be analyzed in the context of studying biomolecular condensates that form via phase separation. The authors build on the work of Hardt and coworkers, and demonstrate the incorporation of a FloryHuggins free energy alongside diffusion equations to describe the dynamics of unbleached molecules, using features of mass balance. They show that numerical solutions of the derived equations – see Equation (6) – can be used to fit FRAP data for different systems. The authors also suggest that inferences from FRAP data can go beyond extraction of individual parameters. In other words, FRAP data seem to be more informationrich than originally thought. The current version, although very interesting, features opacities that should be remediable by following the recommendations made by both reviewers.
Essential revisions:
1) Two distinct flowcharts summarizing how Equations (1) and (6) are used in the fitting of FRAP data will be essential.
2) Accounting for interfacial tension and / or interfacial resistance (see Taylor et al.) requires discussion.
3) Please delete references to nonequilibrium situations since the model imposes detailed balance throughout. In fact, this point should be made clear.
4) There is considerable confusion regarding the claims regarding being able to extract D_{in}, D_{out}, and P from single sets of FRAP data and the actual demonstration of this versatility. This is accentuated by considerable confusion caused, for both reviewers, by the introduction of the cost function, which was opaque, and the sweep of parameters for D_{out} and P that clearly give satisfactory fits to the FRAP data. At this juncture, the claim of being able to extract more insights from FRAP data than one is accustomed to seeing has not been unequivocally demonstrated.
5) Several scholarly issues, specifically pertaining to the work of Hardt and colleagues, and the semantics of what constitutes a phenomenological vs. physical theory description were raised. These should be addressed and there is a strong desire to see a toning down of what were perceived as overclaims.
Reviewer #1 (Recommendations for the authors):
As it currently stands, the average practitioner of FRAP is likely to find the narrative to be rather opaque. Two flowcharts, that summarize (a) how the dynamic boundary condition and its application lead to the internal diffusion coefficients and (b) the use of equation (6) for experimentalists in their analysis of FRAP data need to be added. In doing so, it is really important to explain when and how the analysis can be used, and when and where it cannot be used.
Other recommendations:
1. phi is a conserved order parameter, because the underlying theory imposes a closed system. Therefore, the relations between phi_{tot} and phi_{u} etc. come not from incompressibility, but from mass balance in a closed system.
2. The work of Steffen Hardt, mentioned in passing, and labeled as being phenomenological needs rectification. Elaboration of their work and a clear, scholarly contrast between the current effort, the published work of Hardt that goes beyond the 2008 Langmuir paper, and the differences between the work of Taylor et al., would be helpful for the reader who is interested in understanding what is new, and what is different. In this context, please also see Lin et al., (2003) Science 299: 226, Binks and Lumsdon (2000) Langmuir 16: 8622.
3. The authors classify previous efforts as being phenomenological and the current effort as a physical theory. Respectfully, I disagree with this characterization. Inasmuch as previous efforts largely use the same equations as being used by the authors, with the key difference being the use of the FH functional for extracting the chemical potentials, the extant literature and the current manuscript are both phenomenological or both physical theories. It would be best to drop these prefaces and simply state what has been done. Specifically, the work of Hardt and coworkers does account for phase boundaries. They don't use the FH free energy functional, and this is really the only main difference. And many would rightly argue that the FH functional is phenomenological. So, please do not use terms like "first principles" for this work, and phenomenological to inadvertently dismiss other efforts.
4. Throughout the manuscript, there is a lot of selfcongratulation about the excellent fits, the striking agreements etc. This is jarring. It is impossible to know if one should be impressed with the agreements or if such agreements are readily achieved. An unvarnished statement of the facts is sufficient.
5. The description of the cost function analysis needs a lot of work. In effect, the implication is that D_{out} depends on P. Whether this is an implicit function or not is unclear. The cost function has not been defined in the main text, and why there should be a minimum is unclear. There is also a curious leap that happens: at one point, we see that D_{out} and P that describe the data can span a wide range. This would lead to inference that the FRAP data cannot pin down these parameters absent independent measurements. However, the conclusions paint a more rosy picture, suggesting that knowledge of D_{in} is sufficient, and that fitting the FRAP data will lead to reliable estimates of D_{out} and P. The section that describes this leap needs a lot of work. The flowcharts recommended in the public review will help.
6) There are important caveats that apply as well. The slow or nonrecovery of fluorescence, becomes a scenario wherein D_{in} will be very small or immeasurable. Alternatively, if the material state has changed, then the usage of the dynamic boundary condition might still yield reliable measurements of D_{in}, due to an anisotropic stress tensor, but this would be an erroneous estimate of D_{in} on the inside. Therefore, it is important to lay out a set of requirements for the applicability of the proposed approach, i.e., under what circumstances can the method be used, and under what circumstances can it not be used.
7) As a logical follow up to the preceding point, how might one falsify the theory? Fitting FRAP data is inadequate for such an effort. This will be very useful for experimentalists and computational scientists alike.
8) Finally, do the authors know that the salt concentrations across the phase boundary are equal to one another or is this implicitly assumed? This is particularly relevant for the synthetic coacervates. Sing and coworkers have proposed that there are differences in salt concentrations (even ion concentrations) across phase boundaries. This is relevant because it requires the addition of a Donnan potential to the equations that describe molecular transport. A discussion of this issue would be helpful.
Reviewer #2 (Recommendations for the authors):
Concerns on figures and writing:
1. Recurrent lack of clarity and/or consistency in how figures are presented
When dissecting the data shown in figures, we often found details in presentation that detracted from our understanding of the study. Notable, but not exhaustive, examples are listed:
a. The retrieval of physical parameters in this study frequently alternates between the use of Equation 1 to track FRAP data, and Equation 6 to model droplet dynamics. The authors do not clearly distinguish the purpose of these equations in the theoretical sections, leaving the reader to understand by context.
b. The data in Figure 3 would benefit from control data sets that entirely lack the coverslip and/or neighbouring droplets for comparison. In particular, showing the retrieval of identical Din across all simulations (including no coverslip nor neighboring droplets condition) will convey the major point of the paper very clearly.
c. In the caption of Figure 5 it is unclear why the authors have denoted the four indicated values of Dout and P as "reference systems' as opposed to example parameter sets.
We recommend the authors reassess their figures for clarity of the information that is meant to be communicated.
2. The theory sections lack needed elaboration in some areas.
a. The authors conclude starting on line 153 that P, Din, and Dout can be treated as independent of each other for sufficiently large P. Their justification is that the unknown mobility functions do not impose constraint on Dout(P) other than shown in Equation 10. While the data demonstrates that this is a valid assumption, we find this justification to be opaque and would like to see further elaboration on how independent P, Din, and Dout follows from the mobility functions being unknown.
Suggestions to the authors for bolstering the overall strength of this study below:
1. When interpreting the Results section, we often found that the nature of the experiment was unclear. This is particularly true for Figure 4 and 5. We recommend specifying the procedure followed to acquire the data more clearly and explicitly.
2. In Figure 1c, the label "Dynamic BC" made this figure confusing to interpret, especially because there is a samecolor arrow depicting time progression. The authors should consider some other way of noting that dynamic BC is applied at the max r (=R). Also, for the line going across the earliest data points, "initial condition" rather than the "fit" label may be more appropriate.
3. If available, showing corrected viscosity data from Jawerth et al., 2020 rather than Jawerth et al., 2018 in Figure 1d would bolster this figure via internal consistency with the text in line 98. We would like to be able to backcalculate consistent viscosities using your diffusion data and the StokesSutherlandEinstein relationship.
4. Since the time progression of FRAP recovery is illustrated in Figure 2c, we believe the time point label in Figure 2 to be unnecessary and possibly confusing.
5. We find that the citation of privately communicated and unseen data in line 234 does not add to the preceding statement. Optimum salt concentration is a very believable observation.
6. There appears to be a typo in the subscripts on line 138. Both read "in".
7. Regarding the concluding statement on line 306: We do not find that Jawerth et al., 2018,2020 contain discussions of altering dense phase kinetics by high labeling fraction. Rather, McCall et al., 2020 describes effects closer to this.
8. Regarding Figure 5a, labelling the "ratio" of Dout and P can be confusing. We suggest noting the specific (Dout, P) pairs to specify the points. Also, a clear distinction between "simulation generating parameters (points)" and the "Dout and P dependence (lines) obtained from simulation result and equation 6" is needed.
9. Please consider labelling the point in Figure 5b as "simulation input" or "simulation parameter" rather than "reference simulation".
10. We suggest the following change in line 230 for clarity: "Specifically, for salt concentrations in the range from 50 mM to 180 mM, we find that the estimated partition coefficient P of PGL3 droplets decreases more than 10fold."
11. The significance of the shading in Figure 4d is unclear and inconsistent with how the analogous data in Figure 4e is presented.
12. The text accompanying Figure 5 beginning on line 249 describes the range of Dout and P used as "relevant for protein condensates and coacervate droplets' without citation. We recommend backing up the validity of the range of Dout and P used here with evidence from literature.
13. In Figure 1b, individual FRAP recovery curves cut off at seemingly arbitrary points and are difficult to distinguish based on provided color coding. We also note that the curve of 100mM salt concentration does not seem to fit the same trend as the other data sets, but is not discussed by the authors.
14. Figure 1f shows the precision of Din determination for the two coacervate systems, but not the protein condensate system, and a salt concentration for comparison with Figure 1d is not provided.
https://doi.org/10.7554/eLife.68620.sa1Author response
Essential revisions:
1) Two distinct flowcharts summarizing how Equations (1) and (6) are used in the fitting of FRAP data will be essential.
We are thankful for this suggestion and have added the proposed flow charts; see Figure 1 and Figure 4 in revised manuscript.
2) Accounting for interfacial tension and / or interfacial resistance (see Taylor et al.) requires discussion.
We now discuss our results in the light of a potential interfacial resistance at the phase boundary. We have also now made explicitly clear in several key parts of the paper that we assume no interfacial resistance. For a more detailed discussion, see also replies below. Interfacial tension is related to the interfacial width which is accounted for in our model; see \ell in the paragraph below Equation (6).
3) Please delete references to nonequilibrium situations since the model imposes detailed balance throughout. In fact, this point should be made clear.
We agree that in our manuscript we focus on the case where the total volume fraction (composed of bleached and unbleached molecules) is at thermodynamic equilibrium leading to partial_{t} phi_{tot} = 0. Please note that our theory can be applied to nonequilibrium situations, i.e., in the presence of fluxes. For example, see e.g. Equation (6) in Bo et al., (reference in manuscript), where we use this coarsegrained theory to derive a single molecule description also away from thermodynamic equilibrium.
Based on the reviewer’s remark, we have revised the paragraph around Equation (2) and now stress more clearly that we focus on the cases where detailed balance holds.
4) There is considerable confusion regarding the claims regarding being able to extract D_{in}, D_{out}, and P from single sets of FRAP data and the actual demonstration of this versatility. This is accentuated by considerable confusion caused, for both reviewers, by the introduction of the cost function, which was opaque, and the sweep of parameters for D_{out} and P that clearly give satisfactory fits to the FRAP data. At this juncture, the claim of being able to extract more insights from FRAP data than one is accustomed to seeing has not been unequivocally demonstrated.
We have now made more explicit that this refers to a theoretical possibility of extracting all three parameters, by introducing a new section heading and adding descriptive text. Also, the flow charts should help, in particular regarding the cost function. We have reevaluated our conclusion regarding being able to extract P if D_{out} is known and vice versa. While we still think this is a promising avenue, we agree that the current data are better interpreted more carefully. We now conclude that we find a unique relationship between P and D_{out} for every condition, which allows us to extract relative changes in P for different salt conditions. This was not possible previously.
5) Several scholarly issues, specifically pertaining to the work of Hardt and colleagues, and the semantics of what constitutes a phenomenological vs. physical theory description were raised. These should be addressed and there is a strong desire to see a toning down of what were perceived as overclaims.
We have carefully revised and extended the discussion of the previous literature, such as the works of the Hardt group and Taylor et al. Moreover, we have removed statements such as “phenomenological” and similar formulations.
Reviewer #1 (Recommendations for the authors):
As it currently stands, the average practitioner of FRAP is likely to find the narrative to be rather opaque. Two flowcharts, that summarize (a) how the dynamic boundary condition and its application lead to the internal diffusion coefficients and (b) the use of equation (6) for experimentalists in their analysis of FRAP data need to be added. In doing so, it is really important to explain when and how the analysis can be used, and when and where it cannot be used.
We have added the flow charts according to the reviewer’s suggestion. Moreover, we say explicitly when our analysis can be used and when not (see text below Equation 1).
Other recommendations:
1. phi is a conserved order parameter, because the underlying theory imposes a closed system. Therefore, the relations between phi_{tot} and phi_{u} etc. come not from incompressibility, but from mass balance in a closed system.
Our system conserves particle number of each component after photobleaching. Since we consider an incompressible system, the volume fractions of bleached and unbleached molecules add up to the total volume fraction at each position (Equation (2) in manuscript). Please note that the latter is also true for open systems that are incompressible.
2. The work of Steffen Hardt, mentioned in passing, and labeled as being phenomenological needs rectification. Elaboration of their work and a clear, scholarly contrast between the current effort, the published work of Hardt that goes beyond the 2008 Langmuir paper, and the differences between the work of Taylor et al., would be helpful for the reader who is interested in understanding what is new, and what is different. In this context, please also see Lin et al., (2003) Science 299: 226, Binks and Lumsdon (2000) Langmuir 16: 8622.
We are grateful for the additional references and now discuss the work of Hardt and colleagues more thoroughly, covering additional references. Note, both the works by Hardt and colleagues as well as Lin/Science and Binks/Langmuir, assume a third species that adsorbs to the interface. However, we consider a binary mixture of protein and solvent and currently have no evidence that the protein component exists in several states. While we also find the possibility of an interfacial resistance compelling, we didn’t find any direct evidence of it.
We cite Taylor et al., on several occasions in the manuscript, where we felt it was appropriate. They provide a very nice overview of currently used heuristic fit functions and their (severe) limitations. They also give an effective droplet model (referenced), without derivation. However, as discussed in their work, their fitting yields unrealistic results, and even after introducing an additional fit parameter (interfacial resistance), the fits get worse, instead of better. This is curious, given that the interfacial tension model is a superclass of the model without interfacial tension and should thus fit at least as well, if not better than the original model. In our method to determine D_{in} (Figure 1 and Equation (1)), we completely circumvent any boundary effects, can measure D_{in} reliably by fitting the spatial profiles, and thus provide a significant advance. Figures 35 have no overlap with any of the references.
3. The authors classify previous efforts as being phenomenological and the current effort as a physical theory. Respectfully, I disagree with this characterization. Inasmuch as previous efforts largely use the same equations as being used by the authors, with the key difference being the use of the FH functional for extracting the chemical potentials, the extant literature and the current manuscript are both phenomenological or both physical theories. It would be best to drop these prefaces and simply state what has been done. Specifically, the work of Hardt and coworkers does account for phase boundaries. They don't use the FH free energy functional, and this is really the only main difference. And many would rightly argue that the FH functional is phenomenological. So, please do not use terms like "first principles" for this work, and phenomenological to inadvertently dismiss other efforts.
We have now dropped all occurrences of ‘phenomenological’ when referring to the work of others.
4. Throughout the manuscript, there is a lot of selfcongratulation about the excellent fits, the striking agreements etc. This is jarring. It is impossible to know if one should be impressed with the agreements or if such agreements are readily achieved. An unvarnished statement of the facts is sufficient.
We have toned down our language. The fits however, are “excellent” in our eyes, since we get almost complete agreement with the data within the statistical fluctuations due to radial averaging. From the fits in Figures 1 bd, it is clear that there are barely any systematic deviations between model and data. Almost all deviations stem from statistical fluctuations of neighbouring pixels. These get stronger towards r=0, since fewer pixels can be used for radial averaging. In this context, please also note that even integrated intensity curves with more parameters are less well described by existing models (cf. Taylor et al.,).
5. The description of the cost function analysis needs a lot of work. In effect, the implication is that D_{out} depends on P. Whether this is an implicit function or not is unclear. The cost function has not been defined in the main text, and why there should be a minimum is unclear. There is also a curious leap that happens: at one point, we see that D_{out} and P that describe the data can span a wide range. This would lead to inference that the FRAP data cannot pin down these parameters absent independent measurements. However, the conclusions paint a more rosy picture, suggesting that knowledge of D_{in} is sufficient, and that fitting the FRAP data will lead to reliable estimates of D_{out} and P. The section that describes this leap needs a lot of work. The flowcharts recommended in the public review will help.
We have now added the requested flow charts and believe they will be very helpful. The distinction between what is currently feasible based on experiments (namely only getting an approximate relationship between D_{out} and P) and what is theoretically possible with additional assumptions and perfect data has now been made clearer by introducing an extra section heading and additional descriptive text.
6) There are important caveats that apply as well. The slow or nonrecovery of fluorescence, becomes a scenario wherein D_{in} will be very small or immeasurable. Alternatively, if the material state has changed, then the usage of the dynamic boundary condition might still yield reliable measurements of D_{in}, due to an anisotropic stress tensor, but this would be an erroneous estimate of D_{in} on the inside. Therefore, it is important to lay out a set of requirements for the applicability of the proposed approach, i.e., under what circumstances can the method be used, and under what circumstances can it not be used.
7) As a logical follow up to the preceding point, how might one falsify the theory? Fitting FRAP data is inadequate for such an effort. This will be very useful for experimentalists and computational scientists alike.
We agree that our method has restricted applicability. As indicated by the reviewer, the underlying kinetic equation does no more apply when the condensed phase is for example a complex liquid with more than one timescale on the timescale of the recovery kinetics. However, we think that in this case shapes of spatial profiles should differ between such a complex liquid condensate and a simple liquid. We have added a paragraph to the Results section, pointing out the restricted applicability (below Equation (1)).
8) Finally, do the authors know that the salt concentrations across the phase boundary are equal to one another or is this implicitly assumed? This is particularly relevant for the synthetic coacervates. Sing and coworkers have proposed that there are differences in salt concentrations (even ion concentrations) across phase boundaries. This is relevant because it requires the addition of a Donnan potential to the equations that describe molecular transport. A discussion of this issue would be helpful.
The referee raises an interesting point. Indeed, theoretical studies suggest that salt concentrations can be different, though weakly, inside and outside of condensates (as shown for example in Sing 2020 JCP). Due to the large differences in molecular/particle volumes between salt ions and coacervate components / proteins, however, only weak differences of salt concentrations between phases are expected. Thus, we feel confident that the assumption of equal salt concentrations in both phases represents a reasonable approximation, at least for our systems. In addition, our results are consistent with unpublished data by Fritsch and colleagues.
Reviewer #2 (Recommendations for the authors):
Concerns on figures and writing:
1. Recurrent lack of clarity and/or consistency in how figures are presented
When dissecting the data shown in figures, we often found details in presentation that detracted from our understanding of the study. Notable, but not exhaustive, examples are listed:
a. The retrieval of physical parameters in this study frequently alternates between the use of Equation 1 to track FRAP data, and Equation 6 to model droplet dynamics. The authors do not clearly distinguish the purpose of these equations in the theoretical sections, leaving the reader to understand by context.
We think that this should now be clear from the flow charts and figure legends.
b. The data in Figure 3 would benefit from control data sets that entirely lack the coverslip and/or neighbouring droplets for comparison. In particular, showing the retrieval of identical Din across all simulations (including no coverslip nor neighboring droplets condition) will convey the major point of the paper very clearly.
Panels c and f serve this purpose. In fact, box size is 8*8*8 um, such that the case of h=4um (panel b) gives almost the same results as the case of no coverslip and no neighbors. However, even in the scenario with largest influence of coverslip and neighbours (droplet very close to coverslip, neighbours very close to droplet) the fit can barely be distinguished from the simulated data set (c and f). This is now made clear in the legend.
c. In the caption of Figure 5 it is unclear why the authors have denoted the four indicated values of Dout and P as "reference systems' as opposed to example parameter sets.
This has been addressed now, please see below.
We recommend the authors reassess their figures for clarity of the information that is meant to be communicated.
2. The theory sections lack needed elaboration in some areas.
a. The authors conclude starting on line 153 that P, Din, and Dout can be treated as independent of each other for sufficiently large P. Their justification is that the unknown mobility functions do not impose constraint on Dout(P) other than shown in Equation 10. While the data demonstrates that this is a valid assumption, we find this justification to be opaque and would like to see further elaboration on how independent P, Din, and Dout follows from the mobility functions being unknown.
We are sorry for not being clear on this. As stressed in the manuscript and as shown for example by Equation (10), P, Din and Dout are not independent. If we knew the mobility as a function of phi_{tot}, we could use e.g. Equation (10) to calculate from two of the three parameters the third one. However, mobility as a function of phi_{tot} is not known. We have now revised the paragraphs around Equation (10).
Suggestions to the authors for bolstering the overall strength of this study below:
1. When interpreting the Results section, we often found that the nature of the experiment was unclear. This is particularly true for Figure 4 and 5. We recommend specifying the procedure followed to acquire the data more clearly and explicitly.
We have added a flow chart describing the procedure in figure 4 and hope that the experimental procedure is now clearer.
2. In Figure 1c, the label "Dynamic BC" made this figure confusing to interpret, especially because there is a samecolor arrow depicting time progression. The authors should consider some other way of noting that dynamic BC is applied at the max r (=R). Also, for the line going across the earliest data points, "initial condition" rather than the "fit" label may be more appropriate.
Following the reviewer’s suggestions, Figure 1 has been redone, including the addition of a flowchart.
3. If available, showing corrected viscosity data from Jawerth et al., 2020 rather than Jawerth et al., 2018 in Figure 1d would bolster this figure via internal consistency with the text in line 98. We would like to be able to backcalculate consistent viscosities using your diffusion data and the StokesSutherlandEinstein relationship.
Unfortunately, Jawerth et al., 2020 only investigate c_{salt}=75mM, we can thus not correct the entire graph, but can only note an order of magnitude given the available data point.
4. Since the time progression of FRAP recovery is illustrated in Figure 2c, we believe the time point label in Figure 2 to be unnecessary and possibly confusing.
We have shifted this information to the legend and removed the label.
5. We find that the citation of privately communicated and unseen data in line 234 does not add to the preceding statement. Optimum salt concentration is a very believable observation.
We have deleted the superfluous statement.
6. There appears to be a typo in the subscripts on line 138. Both read "in".
Corrected.
7. Regarding the concluding statement on line 306: We do not find that Jawerth et al., 2018,2020 contain discussions of altering dense phase kinetics by high labeling fraction. Rather, McCall et al., 2020 describes effects closer to this.
Here, we are referring to the change in droplet viscosity, when labelling PGL3 with GFP vs unlabelled PGL3 (Figure S5G in Jawerth et al., 2020 vs Figure 3G in Jawerth et al., 2018). This has been clarified.
8. Regarding Figure 5a, labelling the "ratio" of Dout and P can be confusing. We suggest noting the specific (Dout, P) pairs to specify the points. Also, a clear distinction between "simulation generating parameters (points)" and the "Dout and P dependence (lines) obtained from simulation result and equation 6" is needed.
We have deleted the legend and noted the parameter values in the figure caption. A sentence was added to the caption to distinguish open circles and solid lines.
9. Please consider labelling the point in Figure 5b as "simulation input" or "simulation parameter" rather than "reference simulation".
We went for ‘Example system’, trying to stay coherent with panel a.
10. We suggest the following change in line 230 for clarity: "Specifically, for salt concentrations in the range from 50 mM to 180 mM, we find that the estimated partition coefficient P of PGL3 droplets decreases more than 10fold."
This has been incorporated.
11. The significance of the shading in Figure 4d is unclear and inconsistent with how the analogous data in Figure 4e is presented.
We have removed the shading in 4d.
12. The text accompanying Figure 5 beginning on line 249 describes the range of Dout and P used as "relevant for protein condensates and coacervate droplets' without citation. We recommend backing up the validity of the range of Dout and P used here with evidence from literature.
We have added two references for P. D_{out} can be seen for example in Figure 1.
13. In Figure 1b, individual FRAP recovery curves cut off at seemingly arbitrary points and are difficult to distinguish based on provided color coding. We also note that the curve of 100mM salt concentration does not seem to fit the same trend as the other data sets, but is not discussed by the authors.
Figure 1b was previously shown to illustrate the dynamic boundary condition and to indicate a trend in recovery speed vs. salt concentration, which mostly holds, except for the noted outlier. However, to be able to quantitatively compare droplet recovery speed based on integrated fluorescence, droplets need to be of the same size, which is not entirely fulfilled in this graph. Since it seems to also have caused confusion with the overall fitting procedure, we chose to remove this panel and instead opt for a cleaner presentation of a single curve in Figure 1a.
14. Figure 1f shows the precision of Din determination for the two coacervate systems, but not the protein condensate system, and a salt concentration for comparison with Figure 1d is not provided.
Since we did not have a large enough sample size for any individual condition for PGL3, we initially chose to not show a graph that corresponds to Figure 1f (now 1g). Here in Author response image 1 we include the requested graph for the reviewer’s convenience. We agree that it is useful to give a measure of measurement precision also for the protein system and now discuss this in the text. The average CoV for PGL3 across all measurements is approximately four times larger than for the coacervates, likely due to variations in the experimental assay (see text).
Article and author information
Author details
Funding
Deutsche Forschungsgemeinschaft (SPP 2191)
 Lars Hubatsch
 Anthony A Hyman
 Christoph A Weber
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
We are grateful to P McCall, A Fritsch, JM IglesiasArtola, G Bartolucci, T Wiegand, M Karnat, E Filippidi, T Harmon, F Jülicher and members of the Weber and Hyman groups for stimulating discussions, and P McCall and J Pfanzelter for very valuable and insightful comments on the manuscript. L Hubatsch, AA Hyman and C Weber acknowledge the SPP 2191 ‘‘Molecular Mechanisms of Functional Phase Separation’’ of the German Science Foundation for financial support.
Senior Editor
 José D FaraldoGómez, National Heart, Lung and Blood Institute, National Institutes of Health, United States
Reviewing Editor
 Rohit V Pappu, Washington University in St Louis, United States
Reviewer
 Rohit V Pappu, Washington University in St Louis, United States
Publication history
 Preprint posted: March 8, 2021 (view preprint)
 Received: March 21, 2021
 Accepted: October 11, 2021
 Accepted Manuscript published: October 12, 2021 (version 1)
 Version of Record published: November 10, 2021 (version 2)
Copyright
© 2021, Hubatsch 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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