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Macromolecular crowding gives rise to microviscosity, anomalous diffusion and accelerated actin polymerization
This content has been downloaded from IOPscience. Please scroll down to see the full text. 2015 Phys. Biol. 12 034001 (http://iopscience.iop.org/1478-3975/12/3/034001) View the table of contents for this issue, or go to the journal homepage for more
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Phys. Biol. 12 (2015) 034001
doi:10.1088/1478-3975/12/3/034001
NOTE
RECEIVED
5 February 2015
Macromolecular crowding gives rise to microviscosity, anomalous diffusion and accelerated actin polymerization
REVISED
25 March 2015 ACCEPTED FOR PUBLICATION
26 March 2015 PUBLISHED
30 April 2015
Rafi Rashid1,2, Stella Min Ling Chee3,4, Michael Raghunath3,4,5 and Thorsten Wohland2,6,7 1 2 3 4 5 6 7
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore NUS Centre for BioImaging Sciences, Faculty of Science, National University of Singapore, Singapore Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore NUS Tissue Engineering Programme, National University of Singapore, Singapore Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Departments of Biological Sciences and Chemistry, Faculty of Science, National University of Singapore, Singapore Author to whom any correspondence should be addressed.
E-mail: [email protected] Keywords: macromolecular crowding, excluded volume effect, microviscosity, anomalous diffusion, reaction rate, fluorescence correlation spectroscopy Supplementary material for this article is available online
Abstract Macromolecular crowding (MMC) has been used in various in vitro experimental systems to mimic in vivo physiology. This is because the crowded cytoplasm of cells contains many different types of solutes dissolved in an aqueous medium. MMC in the extracellular microenvironment is involved in maintaining stem cells in their undifferentiated state (niche) as well as in aiding their differentiation after they have travelled to new locations outside the niche. MMC at physiologically relevant fractional volume occupancies (FVOs) significantly enhances the adipogenic differentiation of human bone marrow-derived mesenchymal stem cells during chemically induced adipogenesis. The mechanism by which MMC produces this enhancement is not entirely known. In the context of extracellular collagen deposition, we have recently reported the importance of optimizing the FVO while minimizing the bulk viscosity. Two opposing properties will determine the net rate of a biochemical reaction: the negative effect of bulk viscosity and the positive effect of the excluded volume, the latter being expressed by the FVO. In this study we have looked more closely at the effect of viscosity on reaction rates. We have used fluorimetry to measure the rate of actin polymerization and fluorescence correlation spectroscopy (FCS) to measure diffusion of various probes in solutions containing the crowder Ficoll at physiological concentrations. Similar to its effect on collagen, Ficoll enhanced the actin polymerization rate despite increasing the bulk viscosity. Our FCS measurements reveal a relatively minor component of anomalous diffusion. In addition, our measurements do suggest that microviscosity becomes relevant in a crowded environment. We ruled out bulk viscosity as a cause of the rate enhancement by performing the actin polymerization assay in glycerol. These opposite effects of Ficoll and glycerol led us to conclude that microviscosity becomes relevant at the length scale of the reacting molecules within a crowded microenvironment. The excluded volume effect (arising from crowding) increases the effective concentration of actin, which increases the reaction rate, while the microviscosity does not increase sufficiently to lower the reaction rate. This study reveals finer details about the mechanism of MMC.
Introduction The total concentration of macromolecules within intracellular compartments and extracellular fluids is known to be very high: in Escherichia coli it ranges from
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300 to 400 mg ml−1, whereas the range is 50–400 mg ml−1 in eukaryotic cells. Blood plasma contains approximately 80 mg ml−1 of solutes [1, 2]. Mitochondria contain 270–560 mg ml−1 [3] and nuclei approximately 400 mg ml−1 [4]. This high
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solute concentration is expected to have two main consequences: (1) slowing of molecular diffusion; and (2) acceleration of rates of biochemical reactions [5]. It has been empirically demonstrated that the deposition of extracellular matrix (ECM) in cell culture can be significantly enhanced by the addition of carbohydrate-based polymers such as Ficoll [6–12]. These crowders serve as culture media additives that can be easily included in any cell culture protocol. By increasing the rate at which the ECM is produced by cells and improving its quality, both the ex vivo proliferation of human bone marrow-derived mesenchymal stem cells (MSCs) and their differentiation into specific lineages have been dramatically improved [13]. Crowding has been used to significantly shorten the time taken for organotypic epidermal differentiation in skin tissue culture [14]. The use of Ficoll has helped to remove a key obstacle in the development of stem cell-based therapies, namely, obtaining stem cell numbers that are sufficiently high for the mass production of implantable and therapeutically useful stem cells. Crowding acts through the excluded volume effect (EVE), where excluded volume is defined as the volume of a solution that is excluded to the centre of mass of a probe particle due to the presence of background particles in the medium [15]. To express the power of crowders in solution, a quantity known as fractional volume occupancy (FVO), which is dependent on the size of the crowders, is used [6]. Crowding increases the effective solute concentration, which, in turn, increases the chemical potential of the solute [16]. In a crowded solution, the reactivity of a solute (e.g. a protein) is related to the number of solute molecules per unit of available volume, not total volume. Therefore, the thermodynamic activity or effective concentration of a given solute in a crowded medium will be higher than in a dilute solution by a factor that is determined by the activity coefficient of that solute. Through the EVE, crowding drives polymer folding into native states, thus improving enzyme kinetics and supramolecular assembly. It has recently been shown that crowding enhances enzymatic reactions and antibody binding on cell surfaces, and the authors suggest that this occurs because volume exclusion increases cell-surface concentrations of interacting molecules [17]. The mechanism by which crowding enhances the polymerization rate of a fibrillar protein like collagen is not entirely known. Collagen is a principal component of the ECM [18] and has been shown to play a role in stem cell proliferation and differentiation [6, 13]. We previously demonstrated that in vitro collagen aggregation and in vivo collagen deposition could be enhanced by optimizing the FVO and minimizing the bulk viscosity [19]. In this study, we consider the heterogeneity of crowded solutions and the relevant length scale [20, 21] for molecular interactions. The heterogenous microenvironment will have a corresponding microviscosity that is expected to 2
differ from the bulk viscosity [22, 23]. To further elucidate the mechanism by which Ficoll enhances stem cell behaviour through length scale-dependent effects, we performed experiments that tested the effect of Ficoll on (1) the rate of actin polymerization in vitro and (2) the diffusion of molecules in crowded solutions. Our measurements show that Ficoll enhances the polymerization rate. We used fluorescence correlation spectroscopy (FCS) to measure molecular diffusion. FCS is based on the time-correlation of temporal fluorescence fluctuations which are detected in a focal volume [24]. The power of FCS lies in its single-molecule sensitivity and its capacity to explore a broad range of dynamic events with high temporal resolution and good statistical accuracy. Through FCS, anomalous diffusion has been reported to be a consequence of crowding in some systems [5, 25–27]. Anomalous diffusion has also been observed in the cytoplasm of bacteria and yeast, and in the cytoplasm and nucleoplasm of mammalian cells [28]. Anomalous diffusion is an active area of research [29]. We observed a relatively minor contribution from anomalous diffusion in our FCS measurements. Our diffusion measurements in solution also showed a nonlinear dependence of the diffusion time, τD, on the bulk viscosity, which points to a difference between microviscosity and bulk viscosity in a crowded environment. These two viscosities differ in their length scales: the viscosity sensed by a probe on the micrometre length scale in a crowded solution is called the microviscosity [22], whereas the bulk viscosity is that which is measured by a device such as a rheometer (or a viscometer). In contrast to Ficoll which enhanced actin polymerization, glycerol diminished actin polymerization over the same bulk viscosity range. Glycerol is a pure viscogen whose solutions are characterized solely by bulk viscosity. When we measured the diffusion of the fluorophore Atto565 in glycerol solutions, we saw no difference between the microviscosity and the viscosity of the bulk solution. We conclude that Ficoll enhances actin polymerization through the EVE, while the microviscosity does not increase sufficiently to slow the reaction rate. It is this heterogeneity in the microenvironment of interacting molecules within a crowded system that causes them to experience a viscosity which is lower than that of the bulk environment. Our findings help to advance our knowledge of how Ficoll enhances stem cell behaviour via changes in the microenvironment.
Materials and methods Measuring actin polymerization by fluorospectrometry Actin polymerization experiments were performed in duplicate with an actin polymerization kit (Cytoskeleton Inc.). Assays were carried out in 96-well plates (Greiner). The final concentration of G-actin in each
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well was 2.0 μM. The fluorescence intensity of the pyrene-labelled actin samples was measured at room temperature using a Fluostar Optima fluorimeter (BMG). The excitation and emission wavelengths used were 355 and 405 nm, respectively. Data acquisition protocols were customized in the Fluostar BMG software. All raw data were acquired and polymerization curves plotted by the software and subsequently exported to Microsoft Excel for further processing. Fluorescence correlation spectroscopy (FCS) The FCS system was built around an FV300 Olympus laser scanning confocal microscope in which an additional FCS module was coupled to the microscope. The excitation beam from a 543 nm HeNe ion laser (Melles Griot, Singapore) was reflected by an excitation dichroic mirror and a scanning mirror, and then focused by a water immersion objective (60X, NA1.2, Olympus) into the fluorescent sample. The emission light after the confocal pinhole was focused by a lens (Achromats f = 60 mm, Linos), and separated from the excitation light by an emission filter (593/40, Semrock, NY). This light was collected on the active area of an avalanche photodiode (APD) in a singlephoton-counting module (SPCM-AQR-14, Pacer Components). The transistor-to-transistor logic output signal from the APD was processed online by an autocorrelator (Flex02-01D) to obtain an experimental autocorrelation curve. Measuring probe diffusion by FCS FCS measurements were performed on nanomolar concentrations of Atto565, TRITC-Ficoll 70 and TRITC-Ficoll 400 in solutions of Ficoll 70 and Ficoll 400. FCS measurements were also performed on Atto565 in glycerol solutions. FCS measurements were performed at room temperature and the acquisition time of each measurement was 20–30 s. FCS and anomalous diffusion Fitting models in FCS were derived using a model for the observation volume [30–32] and an appropriate physical representation of the underlying molecular dynamics. The anomalous diffusion model is based on its corresponding propagator [33]. FCS data analysis FCS experimental autocorrelation curves were fitted with a suitable correlation function using an iterative procedure which applied the Levenberg–Marquardt algorithm to minimize χ2. Curve-fitting was performed using a self-written procedure in IgorPro (Wavemetrics, OR, USA). Fitting models for threedimensional-1 particle-1 triplet, three-dimensional-2 particles-1 triplet, and three-dimensional-2 particles1 triplet-anomalous diffusion were used where appropriate. The last model was devised by us to suit our experimental system. 3
Microviscosity calculations The microviscosity, ημ , experienced by a molecular probe in a crowder solution is given by: ημ =
D0 η0 Dμ
,
(1)
where D0 is the diffusion coefficient of the probe in buffer, η0 is the bulk viscosity of the buffer, Dμ is the diffusion coefficient of the probe in the crowder solution.
Results In order to understand the effects of high molecular weight solutes such as Ficoll 70 and Ficoll 400 on the diffusion of molecules in solutions, the viscosities of these solutions covering a wide concentration range were first measured (figure S1(a)). The rate of actin polymerization increases with Ficoll concentration (figure 1). In the case of the Ficoll 70 solutions, the fold change in reaction rate increased over 0–300 mg ml−1. The EVE caused by Ficoll 70 was able to outweigh the bulk viscosity, even at the higher concentrations. A similar result is seen with the Ficoll 400 solutions: the polymerization rate increased over 0–250 mg ml−1. The rate appears to level off from 250 to 300 mg ml−1 (figure 1). However, the polymerization rate at 300 mg ml−1 was still two-fold higher than it was at 0 mg ml−1 of Ficoll 400. When the same polymerization reaction was carried out in glycerol solutions whose viscosities matched the viscosities of the Ficoll 70 and 400 solutions, the rate of polymerization decreased with increasing bulk viscosity (figure 1). As microviscosity is known to be a function of crowder concentration, the data in figure 1 show that polymerization rate is a function of microviscosity and not bulk viscosity. The microviscosities of the Ficoll 70 and Ficoll 400 solutions are shown in figure S1(b). The diffusion of TRITC-labelled Ficoll 70 and Ficoll 400 was measured in solutions of unlabelled Ficoll 70 and Ficoll 400 over 0 to 300 mg ml−1 by FCS. In order to obtain values for the anomaly parameter, α, we used an anomalous diffusion model for fitting [33] (see S3). The α values were plotted against Ficoll concentration and are shown in figure 2. These values do not differ much from 1, except for the α value ≈0.8 at 150 mg ml−1 of Ficoll 400, which was the lowest value we obtained from our experiment. The α value has been reported to fall between 0.5 and 0.8 for anomalous diffusion [34], which is not the case for the majority of our measurements. Moreover, there is no clear trend in the α value with respect to Ficoll concentration. We conclude that our probes do not exhibit anomalous diffusion in solutions of either Ficoll 70 or Ficoll 400 over the concentration range that we have chosen. However, what we do observe is that the
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Figure 1. The effects of 0–300 mg ml−1 of Ficoll 70 and Ficoll 400 on the in vitro rate of actin polymerization. The effect of 0–800 mg ml−1 of glycerol is also shown. Inset: plot of fold change in reaction rate against bulk viscosity.
Figure 2. The variation of the anomalous diffusion exponent α for Atto565, Ficoll 70-Tritc and Ficoll 400-Tritc with the concentration of (a) Ficoll 70 and (b) Ficoll 400.
Figure 3. (a) The diffusion time (τD) of the small probe Atto565 (inset) was measured in glycerol, Ficoll 70 and Ficoll 400 solutions. (b) The diffusion time (τD) of Ficoll 70-Tritc and Ficoll 400-Tritc was measured over a wide range of viscosities of unlabelled Ficoll 70 and Ficoll 400.
4
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standard deviation (SD) of the measurements (error bars in the figure 2 graphs) increases with probe size. The SD is lowest for Atto565 and highest for Ficoll 400-Tritc. This trend indicates a more heterogeneous environment and is dependent on probe size. When the diffusion of Atto565 was investigated in solutions of Ficoll 70 and Ficoll 400, the relationship between τD and bulk viscosity was found to be nonlinear (figure 3(a)). As a control, we measured the diffusion of our fluorophore standard, Atto565, in glycerol solutions over a viscosity ranges that corresponds with that of the Ficoll 70 and Ficoll 400 solutions since glycerol is a pure viscogen. As predicted by the Stokes–Einstein equation [35], τD of Atto565 increases linearly with increasing concentrations of glycerol (figure 3(a), inset). These results suggest that there is a difference between the viscosity of the bulk solution and the viscosity sensed by the diffusing probe, i.e., the microviscosity. Measurements performed with Ficoll 70-Tritc and Ficoll 400-Tritc as the probes diffusing in Ficoll 70 and Ficoll 400 solutions also revealed that τD varies nonlinearly with bulk viscosity (figure 3(b)).
Discussion The manner in which intracellular and extracellular solutes affect the diffusion of single molecules is not yet fully understood. An understanding of the effect of high solute concentration on molecular diffusion is needed to unravel the mechanisms underlying the transport processes that influence the assembly of subcellular structures such as organelles, signal transduction pathways, biochemical equilibria, and reaction kinetics. It is known that diffusion can be affected by macromolecular crowding (MMC). In Ficoll solutions, we would expect diffusion to slow down with increasing bulk viscosity. However, the diffusion of single molecules depends on microviscosity [36] and a comparison between microviscosity and bulk viscosity showed deviations from the Stokes–Einstein equation in dextran solutions. Other studies have also reported diffusion coefficients of proteins that suggest a difference between microviscosity and bulk viscosity [37– 39]. Microviscosity would be reflected in the altered translational mobility of a given probe [40]. We can describe two properties of Ficoll solutions: (1) the crossover concentration (or crossover polymer fraction), Φ*, at which the polymer molecules start to overlap with each other, and (2) the persistence length, Lp, which is the length, in monomer units, above which a polymer molecule becomes semi-rigid or flexible [41]. On the basis of Φ* and Lp, it appears that high-mass polymers form flexible networks in solution, thus creating a porous medium [42–44]. This porous medium contains free space that is large enough for proteins to diffuse freely, thus explaining why the microviscosity does not change as drastically 5
as the bulk viscosity. The microviscosity can be as low as the solvent viscosity or be as high as the bulk viscosity. The deviation of τD from linearity with respect to bulk viscosity reflects the microviscosity in the Ficoll solutions that we have used. As we increased the concentration of Ficoll, both the bulk viscosity and microviscosity increased. Increasing either type of viscosity (bulk or micro) would decrease the reaction rate. In our actin polymerization experiments, the actin monomers are well mixed. The well-mixedness of the reactants means that diffusion of the reactants is determined by the local diffusion coefficient (which depends on the microviscosity) and local reactant concentration. Reactants do not travel over larger length scales over which diffusion is determined by the macroscopic structure of the system and might thus be anomalous. We observed the actin polymerization rate to rise despite the increase in both viscosities. We conclude that the microviscosity did not increase sufficiently to lower the reaction rate. The increase in polymerization rate is explained by the concomitant increase in excluded volume due to the crowders. Crowding a system with macromolecules promotes association reactions that result in net reduction of excluded volume. This EVE causes the rate of a reaction to increase [45–47]. Crowding increases the effective solute concentration, which, in turn, increases the chemical potential of the solute. The reactivity of a solute is related to the number of solute molecules per unit of available volume, not total volume. Therefore, the thermodynamic activity or effective concentration of a given solute in a crowded medium will be higher than in a dilute solution by a factor that is determined by the activity coefficient of that solute. Through the EVE, crowding drives polymer folding into native states, thus improving enzyme kinetics and supramolecular assembly. Crowding is known to induce depletion interactions, as a result of which macromolecules become segregated by size due to an increase in the available free volume. The depletion force that exists in crowded environments would favour the formation of polymers from monomers. Indeed, it is known that depletion forces can promote the formation of actin bundles [48]. Depletion interactions, and hence crowding, increase the rates of chemical reactions in solutions [25, 49]. Our data agree with these predictions. Another known consequence of MMC is anomalous diffusion. We have analyzed all our diffusion measurements from the Ficoll solutions using the anomalous diffusion FCS fitting model in order to determine the degree of anomaly in these solutions. We conclude that probe diffusion in Ficoll solutions is slightly anomalous. This minor contribution of anomalous diffusion agrees with the findings of a previous study which also demonstrated the presence of microviscosity in their crowder solutions [23]. In addition, the SDs of the α values increased with increasing probe size: the deviations were smallest for the smallest
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probe, Atto565, largest for the largest probe, Ficoll 400, and intermediate for the intermediate-sized probe, Ficoll 70. These differences reflect the heterogeneity of the crowded environment. We have previously shown that in vitro collagen deposition in the ECM by human MSCs can be increased by crowding with Ficoll. We now propose a possible explanation for the enhancement in deposition. Crowders increase the effective concentration but do not sufficiently increase the viscosity experienced by the smaller reactants, i.e. microviscosity. Since the reactants are smaller than the crowders and the pores created by those crowders, they experience the microviscosity due to the solvent. The rate enhancement occurs at an ideal crowder concentration. If the crowder concentration is too high, the reaction will slow down as the pore sizes decrease and the viscosity experienced by the reactants increases. Thus, from zero to the ideal crowder concentration, reaction rates will increase. This increase will slow down as the crowder concentration continues to increase because the viscosity experienced by the reactant will increase. We show an almost linear increase of the reaction rate till ≈10 cP, beyond which the rate starts to fall. Our data help to explain the mechanism by which the rate of a reaction increases in a crowded solution. Our study describes experimental techniques for studying crowding effects and improves our understanding of the mechanism of crowding.
[11]
[12]
[13]
[14]
[15]
[16]
[17] [18] [19]
[20]
[21]
References [22] [1] Ellis R J 2001 Macromolecular crowding: obvious but underappreciated Trends Biochem. Sci. 26 597–604 [2] Wu J and Berland K M 2008 Propagators and time-dependent diffusion coefficients for anomalous diffusion Biophys. J. 95 2049–52 [3] Partikian A, Olveczky B, Swaminathan R, Li Y and Verkman A S 1998 Rapid diffusion of green fluorescent protein in the mitochondrial matrix J. Cell Biol. 140 821–9 [4] Hancock R 2004 Internal organisation of the nucleus: assembly of compartments by macromolecular crowding and the nuclear matrix model Biol. Cell. 96 595–601 [5] Dix J A and Verkman A S 2008 Crowding effects on diffusion in solutions and cells Annu. Rev. Biophys. 37 247–63 [6] Chen C, Loe F, Blocki A, Peng Y and Raghunath M 2011 Applying macromolecular crowding to enhance extracellular matrix deposition and its remodeling in vitro for tissue engineering and cell-based therapies Adv. Drug Deliv. Rev. 63 277–90 [7] Chen C Z, Peng Y X, Wang Z B, Fish P V, Kaar J L, Koepsel R R, Russell A J, Lareu R R and Raghunath M 2009 The scar-in-ajar: studying potential antifibrotic compounds from the epigenetic to extracellular level in a single well Br. J. Pharmacol. 158 1196–209 [8] Harve K S, Vigneshwar R, Rajagopalan R and Raghunath M 2008 Macromolecular crowding in vitro as means of emulating cellular interiors: when less might be more Proc. Natl Acad. Sci. USA 105 E119 author reply E120 [9] Harve K S, Lareu R, Rajagopalan R and Raghunath M 2010 Understanding how the crowded interior of cells stabilizes DNA/DNA and DNA/RNA hybrids-in silico predictions and in vitro evidence Nucleic Acids Res. 38 172–81 [10] Lareu R R, Harve K S and Raghunath M 2007 Emulating a crowded intracellular environment in vitro dramatically
6
[23]
[24]
[25] [26]
[27]
[28]
[29] [30]
[31]
[32]
improves RT-PCR performance Biochem. Biophys. Res. Commun. 363 171–7 Lareu R R, Arsianti I, Subramhanya H K, Yanxian P and Raghunath M 2007 In vitro enhancement of collagen matrix formation and crosslinking for applications in tissue engineering: a preliminary study Tissue Eng. 13 385–91 Lareu R R, Subramhanya K H, Peng Y, Benny P, Chen C, Wang Z, Rajagopalan R and Raghunath M 2007 Collagen matrix deposition is dramatically enhanced in vitro when crowded with charged macromolecules: the biological relevance of the excluded volume effect FEBS Lett. 581 2709–14 Ang X et al 2013 Macromolecular crowding amplifies adipogenesis of human bone marrow-derived mesenchymal stem cells by enhancing the pro-adipogenic microenvironment Tissue Eng. A 20 966–81 Benny P et al 2014 Making more matrix: enhancing the deposition of dermal–epidermal junction components in vitro and accelerating organotypic skin culture development, using macromolecular crowding Tissue Eng. A 21 183–92 Zimmerman S and Minton A 1993 Macromolecular crowding: biochemical, biophysical, and physiological consequences Annu. Rev. Biophys. Biomol. Struct. 22 27–65 Zhou H X, Rivas G and Minton A P 2008 Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences Annu. Rev. Biophys. 37 375–97 Chapanian R et al 2014 Enhancement of biological reactions on cell surfaces via macromolecular crowding Nat. Commun. 5 4683 Alberts B et al 2002 Molecular Biology of the Cell (New York: Garland Science) Rashid R et al 2014 Novel use for polyvinylpyrrolidone as a macromolecular crowder for enhanced extracellular matrix deposition and cell proliferation Tissue Eng. C 20 994–1002 Kozer N et al 2007 Protein–protein association is polymer solutions: from dilute to semidilute to concentrated Biophys. J. 92 2139–49 Kozer N and Schreiber G 2004 Effect of crowding on protein– protein association rates: fundamental differences between low and high mass crowding agents J. Mol. Biol. 336 763–74 Goins A, Sanabria H and Waxham M 2008 Macromolecular crowding and size effects on probe microviscosity Biophys. J. 95 5362–73 Lavalette D et al 1999 Microscopic viscosity and rotational diffusion of proteins in a macromolecular environment Biophys. J. 76 2744–51 Kahya N, Scherfeld D, Bacia K, Poolman B and Schwille P 2003 Probing lipid mobility of raft-exhibiting model membranes by fluorescence correlation spectroscopy J. Biol. Chem. 278 28109–15 Banks D S and Fradin C 2005 Anomalous diffusion of proteins due to molecular crowding Biophys. J. 89 2960–71 Weiss M, Elsner M, Kartberg F and Nilsson T 2004 Anomalous subdiffusion is a measure for cytoplasmic crowding in living cells Biophys. J. 87 3518–24 Weiss M, Hashimoto H and Nilsson T 2003 Anomalous protein diffusion in living cells as seen by fluorescence correlation spectroscopy Biophys. J. 84 4043–52 Malchus N and Weiss M 2010 Elucidating anomalous protein diffusion in living cells with fluorescence correlation spectroscopy-facts and pitfalls J. Fluoresc. 20 19–26 Saxton M 2012 Wanted: a positive control for anomalous subdiffusion Biophys. J. 103 2411–22 Schwille P, Korlach J and Webb W W 1999 Fluorescence correlation spectroscopy with single-molecule sensitivity on cell and model membranes Cytometry 36 176–82 Schwille P, Haupts U, Maiti S and Webb W W 1999 Molecular dynamics in living cells observed by fluorescence correlation spectroscopy with one- and two-photon excitation Biophys. J. 77 2251–65 Xu C and Webb W W 1997 Multiphoton excitation of molecular fluorophores and nonlinear laser microscopy Topics in Fluorescence Spectroscopy vol 5, ed J Lakowicz (New York: Plenum)
Phys. Biol. 12 (2015) 034001
R Rashid et al
[33] Wu J and Berland K 2008 Propagators and time-dependent diffusion coefficients for anomalous diffusion Biophys. J. 95 2049–52 [34] Höfling F and Franosch T 2013 Anomalous transport in the crowded world of biological cells Rep. Prog. Phys. 76 046602 [35] Berg O G and von Hippel P H 1985 Diffusion-controlled macromolecular interactions Annu. Rev. Biophys. Biophys. Chem. 14 131–60 [36] Goins A B, Sanabria H and Waxham M N 2008 Macromolecular crowding and size effects on probe microviscosity Biophys. J. 95 5362–73 [37] Lavalette D, Tetreau C, Tourbez M and Blouquit Y 1999 Microscopic viscosity and rotational diffusion of proteins in a macromolecular environment Biophys. J. 76 2744–51 [38] Chirico G, Placidi M and Cannistraro S 1999 Fractional Stokes–Einstein relationship in biological colloids: role of mixed stick-slip boundary conditions J. Phys. Chem. B 103 1746–51 [39] Busch N, Kim T and Bloomfield V A 2000 Tracer diffusion of proteins in DNA solutions: II. Green fluorescent protein in crowded DNA solutions Macromolecules 33 5932–7 [40] Goins A B, Sanabria H and Waxham M N 2008 Macromolecular crowding and size effects on probe microviscosity Biophys. J. 95 5362–73 [41] Kozer N and Schreiber G 2004 Effect of crowding on protein– protein association rates: fundamental differences between low and high mass crowding agents J. Mol. Biol. 336 763–74 [42] Devanand K and Selser J C 1990 Polyethylene oxide does not necessarily aggregate in water Nature 343 739–41 [43] Laurent T C 1995 An early look at macromolecular crowding Biophys. Chem. 57 7–14
7
[44] Vergara A, Paduano L, D'Errico G and Sartorio R 1999 Network formation in polyethyleneglycol solutions an intradiffusion study Phys. Chem. Chem. Phys. 1 4875–9 [45] Hall D and Minton A 2003 Macromolecular crowding: qualitative and semiquantitative successes, quantitative challenges Biochim. Biophys. Acta 1649 127–39 [46] Minton A 1997 Influence of excluded volume upon macromolecular structure and associations in ‘crowded’ media Curr. Opin. Biotechnol. 8 65–9 [47] Minton A 2001 Effects of excluded surface area and adsorpate clustering on surface adsorption of proteins: II. Kinetic models Biophys. J. 80 1641–8 [48] Tharmann R, Claessens M M and Bausch A R 2006 Micro- and macrorheological properties of actin networks effectively cross-linked by depletion forces Biophys. J. 90 2622–7 [49] Asakura S and Oosawa F 1958 Interaction between particles suspended in solutions of macromolecules J. Polym. Sci. 33 183–92 [50] Aragon S and Pecora R 1976 Fluorescence correlation spectroscopy as a probe of molecular dynamics J. Chem. Phys. 64 1791–803 [51] Widengren J, Rigler R and Mets U 1994 Triplet-state monitoring by fluorescence correlation spectroscopy J. Fluoresc. 4 255–8 [52] Thompson N 1991 Fluorescence correlation spectroscopy Topics in Fluorescence Spectroscopy vol 1Techniques ed J Lakowicz (New York: Plenum) [53] Yu L et al 2005 Determination of critical micelle concentrations and aggregation numbers by fluorescence correlation spectroscopy: aggregation of a lipopolysaccharide Anal. Chim. Acta. 556 216–25
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Macromolecular crowding gives rise to microviscosity, anomalous diffusion and accelerated actin polymerization
This content has been downloaded from IOPscience. Please scroll down to see the full text. 2015 Phys. Biol. 12 034001 (http://iopscience.iop.org/1478-3975/12/3/034001) View the table of contents for this issue, or go to the journal homepage for more
Download details: IP Address: 128.42.202.150 This content was downloaded on 21/05/2015 at 02:08
Please note that terms and conditions apply.
Phys. Biol. 12 (2015) 034001
doi:10.1088/1478-3975/12/3/034001
NOTE
RECEIVED
5 February 2015
Macromolecular crowding gives rise to microviscosity, anomalous diffusion and accelerated actin polymerization
REVISED
25 March 2015 ACCEPTED FOR PUBLICATION
26 March 2015 PUBLISHED
30 April 2015
Rafi Rashid1,2, Stella Min Ling Chee3,4, Michael Raghunath3,4,5 and Thorsten Wohland2,6,7 1 2 3 4 5 6 7
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore NUS Centre for BioImaging Sciences, Faculty of Science, National University of Singapore, Singapore Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore NUS Tissue Engineering Programme, National University of Singapore, Singapore Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore Departments of Biological Sciences and Chemistry, Faculty of Science, National University of Singapore, Singapore Author to whom any correspondence should be addressed.
E-mail: [email protected] Keywords: macromolecular crowding, excluded volume effect, microviscosity, anomalous diffusion, reaction rate, fluorescence correlation spectroscopy Supplementary material for this article is available online
Abstract Macromolecular crowding (MMC) has been used in various in vitro experimental systems to mimic in vivo physiology. This is because the crowded cytoplasm of cells contains many different types of solutes dissolved in an aqueous medium. MMC in the extracellular microenvironment is involved in maintaining stem cells in their undifferentiated state (niche) as well as in aiding their differentiation after they have travelled to new locations outside the niche. MMC at physiologically relevant fractional volume occupancies (FVOs) significantly enhances the adipogenic differentiation of human bone marrow-derived mesenchymal stem cells during chemically induced adipogenesis. The mechanism by which MMC produces this enhancement is not entirely known. In the context of extracellular collagen deposition, we have recently reported the importance of optimizing the FVO while minimizing the bulk viscosity. Two opposing properties will determine the net rate of a biochemical reaction: the negative effect of bulk viscosity and the positive effect of the excluded volume, the latter being expressed by the FVO. In this study we have looked more closely at the effect of viscosity on reaction rates. We have used fluorimetry to measure the rate of actin polymerization and fluorescence correlation spectroscopy (FCS) to measure diffusion of various probes in solutions containing the crowder Ficoll at physiological concentrations. Similar to its effect on collagen, Ficoll enhanced the actin polymerization rate despite increasing the bulk viscosity. Our FCS measurements reveal a relatively minor component of anomalous diffusion. In addition, our measurements do suggest that microviscosity becomes relevant in a crowded environment. We ruled out bulk viscosity as a cause of the rate enhancement by performing the actin polymerization assay in glycerol. These opposite effects of Ficoll and glycerol led us to conclude that microviscosity becomes relevant at the length scale of the reacting molecules within a crowded microenvironment. The excluded volume effect (arising from crowding) increases the effective concentration of actin, which increases the reaction rate, while the microviscosity does not increase sufficiently to lower the reaction rate. This study reveals finer details about the mechanism of MMC.
Introduction The total concentration of macromolecules within intracellular compartments and extracellular fluids is known to be very high: in Escherichia coli it ranges from
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300 to 400 mg ml−1, whereas the range is 50–400 mg ml−1 in eukaryotic cells. Blood plasma contains approximately 80 mg ml−1 of solutes [1, 2]. Mitochondria contain 270–560 mg ml−1 [3] and nuclei approximately 400 mg ml−1 [4]. This high
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solute concentration is expected to have two main consequences: (1) slowing of molecular diffusion; and (2) acceleration of rates of biochemical reactions [5]. It has been empirically demonstrated that the deposition of extracellular matrix (ECM) in cell culture can be significantly enhanced by the addition of carbohydrate-based polymers such as Ficoll [6–12]. These crowders serve as culture media additives that can be easily included in any cell culture protocol. By increasing the rate at which the ECM is produced by cells and improving its quality, both the ex vivo proliferation of human bone marrow-derived mesenchymal stem cells (MSCs) and their differentiation into specific lineages have been dramatically improved [13]. Crowding has been used to significantly shorten the time taken for organotypic epidermal differentiation in skin tissue culture [14]. The use of Ficoll has helped to remove a key obstacle in the development of stem cell-based therapies, namely, obtaining stem cell numbers that are sufficiently high for the mass production of implantable and therapeutically useful stem cells. Crowding acts through the excluded volume effect (EVE), where excluded volume is defined as the volume of a solution that is excluded to the centre of mass of a probe particle due to the presence of background particles in the medium [15]. To express the power of crowders in solution, a quantity known as fractional volume occupancy (FVO), which is dependent on the size of the crowders, is used [6]. Crowding increases the effective solute concentration, which, in turn, increases the chemical potential of the solute [16]. In a crowded solution, the reactivity of a solute (e.g. a protein) is related to the number of solute molecules per unit of available volume, not total volume. Therefore, the thermodynamic activity or effective concentration of a given solute in a crowded medium will be higher than in a dilute solution by a factor that is determined by the activity coefficient of that solute. Through the EVE, crowding drives polymer folding into native states, thus improving enzyme kinetics and supramolecular assembly. It has recently been shown that crowding enhances enzymatic reactions and antibody binding on cell surfaces, and the authors suggest that this occurs because volume exclusion increases cell-surface concentrations of interacting molecules [17]. The mechanism by which crowding enhances the polymerization rate of a fibrillar protein like collagen is not entirely known. Collagen is a principal component of the ECM [18] and has been shown to play a role in stem cell proliferation and differentiation [6, 13]. We previously demonstrated that in vitro collagen aggregation and in vivo collagen deposition could be enhanced by optimizing the FVO and minimizing the bulk viscosity [19]. In this study, we consider the heterogeneity of crowded solutions and the relevant length scale [20, 21] for molecular interactions. The heterogenous microenvironment will have a corresponding microviscosity that is expected to 2
differ from the bulk viscosity [22, 23]. To further elucidate the mechanism by which Ficoll enhances stem cell behaviour through length scale-dependent effects, we performed experiments that tested the effect of Ficoll on (1) the rate of actin polymerization in vitro and (2) the diffusion of molecules in crowded solutions. Our measurements show that Ficoll enhances the polymerization rate. We used fluorescence correlation spectroscopy (FCS) to measure molecular diffusion. FCS is based on the time-correlation of temporal fluorescence fluctuations which are detected in a focal volume [24]. The power of FCS lies in its single-molecule sensitivity and its capacity to explore a broad range of dynamic events with high temporal resolution and good statistical accuracy. Through FCS, anomalous diffusion has been reported to be a consequence of crowding in some systems [5, 25–27]. Anomalous diffusion has also been observed in the cytoplasm of bacteria and yeast, and in the cytoplasm and nucleoplasm of mammalian cells [28]. Anomalous diffusion is an active area of research [29]. We observed a relatively minor contribution from anomalous diffusion in our FCS measurements. Our diffusion measurements in solution also showed a nonlinear dependence of the diffusion time, τD, on the bulk viscosity, which points to a difference between microviscosity and bulk viscosity in a crowded environment. These two viscosities differ in their length scales: the viscosity sensed by a probe on the micrometre length scale in a crowded solution is called the microviscosity [22], whereas the bulk viscosity is that which is measured by a device such as a rheometer (or a viscometer). In contrast to Ficoll which enhanced actin polymerization, glycerol diminished actin polymerization over the same bulk viscosity range. Glycerol is a pure viscogen whose solutions are characterized solely by bulk viscosity. When we measured the diffusion of the fluorophore Atto565 in glycerol solutions, we saw no difference between the microviscosity and the viscosity of the bulk solution. We conclude that Ficoll enhances actin polymerization through the EVE, while the microviscosity does not increase sufficiently to slow the reaction rate. It is this heterogeneity in the microenvironment of interacting molecules within a crowded system that causes them to experience a viscosity which is lower than that of the bulk environment. Our findings help to advance our knowledge of how Ficoll enhances stem cell behaviour via changes in the microenvironment.
Materials and methods Measuring actin polymerization by fluorospectrometry Actin polymerization experiments were performed in duplicate with an actin polymerization kit (Cytoskeleton Inc.). Assays were carried out in 96-well plates (Greiner). The final concentration of G-actin in each
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well was 2.0 μM. The fluorescence intensity of the pyrene-labelled actin samples was measured at room temperature using a Fluostar Optima fluorimeter (BMG). The excitation and emission wavelengths used were 355 and 405 nm, respectively. Data acquisition protocols were customized in the Fluostar BMG software. All raw data were acquired and polymerization curves plotted by the software and subsequently exported to Microsoft Excel for further processing. Fluorescence correlation spectroscopy (FCS) The FCS system was built around an FV300 Olympus laser scanning confocal microscope in which an additional FCS module was coupled to the microscope. The excitation beam from a 543 nm HeNe ion laser (Melles Griot, Singapore) was reflected by an excitation dichroic mirror and a scanning mirror, and then focused by a water immersion objective (60X, NA1.2, Olympus) into the fluorescent sample. The emission light after the confocal pinhole was focused by a lens (Achromats f = 60 mm, Linos), and separated from the excitation light by an emission filter (593/40, Semrock, NY). This light was collected on the active area of an avalanche photodiode (APD) in a singlephoton-counting module (SPCM-AQR-14, Pacer Components). The transistor-to-transistor logic output signal from the APD was processed online by an autocorrelator (Flex02-01D) to obtain an experimental autocorrelation curve. Measuring probe diffusion by FCS FCS measurements were performed on nanomolar concentrations of Atto565, TRITC-Ficoll 70 and TRITC-Ficoll 400 in solutions of Ficoll 70 and Ficoll 400. FCS measurements were also performed on Atto565 in glycerol solutions. FCS measurements were performed at room temperature and the acquisition time of each measurement was 20–30 s. FCS and anomalous diffusion Fitting models in FCS were derived using a model for the observation volume [30–32] and an appropriate physical representation of the underlying molecular dynamics. The anomalous diffusion model is based on its corresponding propagator [33]. FCS data analysis FCS experimental autocorrelation curves were fitted with a suitable correlation function using an iterative procedure which applied the Levenberg–Marquardt algorithm to minimize χ2. Curve-fitting was performed using a self-written procedure in IgorPro (Wavemetrics, OR, USA). Fitting models for threedimensional-1 particle-1 triplet, three-dimensional-2 particles-1 triplet, and three-dimensional-2 particles1 triplet-anomalous diffusion were used where appropriate. The last model was devised by us to suit our experimental system. 3
Microviscosity calculations The microviscosity, ημ , experienced by a molecular probe in a crowder solution is given by: ημ =
D0 η0 Dμ
,
(1)
where D0 is the diffusion coefficient of the probe in buffer, η0 is the bulk viscosity of the buffer, Dμ is the diffusion coefficient of the probe in the crowder solution.
Results In order to understand the effects of high molecular weight solutes such as Ficoll 70 and Ficoll 400 on the diffusion of molecules in solutions, the viscosities of these solutions covering a wide concentration range were first measured (figure S1(a)). The rate of actin polymerization increases with Ficoll concentration (figure 1). In the case of the Ficoll 70 solutions, the fold change in reaction rate increased over 0–300 mg ml−1. The EVE caused by Ficoll 70 was able to outweigh the bulk viscosity, even at the higher concentrations. A similar result is seen with the Ficoll 400 solutions: the polymerization rate increased over 0–250 mg ml−1. The rate appears to level off from 250 to 300 mg ml−1 (figure 1). However, the polymerization rate at 300 mg ml−1 was still two-fold higher than it was at 0 mg ml−1 of Ficoll 400. When the same polymerization reaction was carried out in glycerol solutions whose viscosities matched the viscosities of the Ficoll 70 and 400 solutions, the rate of polymerization decreased with increasing bulk viscosity (figure 1). As microviscosity is known to be a function of crowder concentration, the data in figure 1 show that polymerization rate is a function of microviscosity and not bulk viscosity. The microviscosities of the Ficoll 70 and Ficoll 400 solutions are shown in figure S1(b). The diffusion of TRITC-labelled Ficoll 70 and Ficoll 400 was measured in solutions of unlabelled Ficoll 70 and Ficoll 400 over 0 to 300 mg ml−1 by FCS. In order to obtain values for the anomaly parameter, α, we used an anomalous diffusion model for fitting [33] (see S3). The α values were plotted against Ficoll concentration and are shown in figure 2. These values do not differ much from 1, except for the α value ≈0.8 at 150 mg ml−1 of Ficoll 400, which was the lowest value we obtained from our experiment. The α value has been reported to fall between 0.5 and 0.8 for anomalous diffusion [34], which is not the case for the majority of our measurements. Moreover, there is no clear trend in the α value with respect to Ficoll concentration. We conclude that our probes do not exhibit anomalous diffusion in solutions of either Ficoll 70 or Ficoll 400 over the concentration range that we have chosen. However, what we do observe is that the
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Figure 1. The effects of 0–300 mg ml−1 of Ficoll 70 and Ficoll 400 on the in vitro rate of actin polymerization. The effect of 0–800 mg ml−1 of glycerol is also shown. Inset: plot of fold change in reaction rate against bulk viscosity.
Figure 2. The variation of the anomalous diffusion exponent α for Atto565, Ficoll 70-Tritc and Ficoll 400-Tritc with the concentration of (a) Ficoll 70 and (b) Ficoll 400.
Figure 3. (a) The diffusion time (τD) of the small probe Atto565 (inset) was measured in glycerol, Ficoll 70 and Ficoll 400 solutions. (b) The diffusion time (τD) of Ficoll 70-Tritc and Ficoll 400-Tritc was measured over a wide range of viscosities of unlabelled Ficoll 70 and Ficoll 400.
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standard deviation (SD) of the measurements (error bars in the figure 2 graphs) increases with probe size. The SD is lowest for Atto565 and highest for Ficoll 400-Tritc. This trend indicates a more heterogeneous environment and is dependent on probe size. When the diffusion of Atto565 was investigated in solutions of Ficoll 70 and Ficoll 400, the relationship between τD and bulk viscosity was found to be nonlinear (figure 3(a)). As a control, we measured the diffusion of our fluorophore standard, Atto565, in glycerol solutions over a viscosity ranges that corresponds with that of the Ficoll 70 and Ficoll 400 solutions since glycerol is a pure viscogen. As predicted by the Stokes–Einstein equation [35], τD of Atto565 increases linearly with increasing concentrations of glycerol (figure 3(a), inset). These results suggest that there is a difference between the viscosity of the bulk solution and the viscosity sensed by the diffusing probe, i.e., the microviscosity. Measurements performed with Ficoll 70-Tritc and Ficoll 400-Tritc as the probes diffusing in Ficoll 70 and Ficoll 400 solutions also revealed that τD varies nonlinearly with bulk viscosity (figure 3(b)).
Discussion The manner in which intracellular and extracellular solutes affect the diffusion of single molecules is not yet fully understood. An understanding of the effect of high solute concentration on molecular diffusion is needed to unravel the mechanisms underlying the transport processes that influence the assembly of subcellular structures such as organelles, signal transduction pathways, biochemical equilibria, and reaction kinetics. It is known that diffusion can be affected by macromolecular crowding (MMC). In Ficoll solutions, we would expect diffusion to slow down with increasing bulk viscosity. However, the diffusion of single molecules depends on microviscosity [36] and a comparison between microviscosity and bulk viscosity showed deviations from the Stokes–Einstein equation in dextran solutions. Other studies have also reported diffusion coefficients of proteins that suggest a difference between microviscosity and bulk viscosity [37– 39]. Microviscosity would be reflected in the altered translational mobility of a given probe [40]. We can describe two properties of Ficoll solutions: (1) the crossover concentration (or crossover polymer fraction), Φ*, at which the polymer molecules start to overlap with each other, and (2) the persistence length, Lp, which is the length, in monomer units, above which a polymer molecule becomes semi-rigid or flexible [41]. On the basis of Φ* and Lp, it appears that high-mass polymers form flexible networks in solution, thus creating a porous medium [42–44]. This porous medium contains free space that is large enough for proteins to diffuse freely, thus explaining why the microviscosity does not change as drastically 5
as the bulk viscosity. The microviscosity can be as low as the solvent viscosity or be as high as the bulk viscosity. The deviation of τD from linearity with respect to bulk viscosity reflects the microviscosity in the Ficoll solutions that we have used. As we increased the concentration of Ficoll, both the bulk viscosity and microviscosity increased. Increasing either type of viscosity (bulk or micro) would decrease the reaction rate. In our actin polymerization experiments, the actin monomers are well mixed. The well-mixedness of the reactants means that diffusion of the reactants is determined by the local diffusion coefficient (which depends on the microviscosity) and local reactant concentration. Reactants do not travel over larger length scales over which diffusion is determined by the macroscopic structure of the system and might thus be anomalous. We observed the actin polymerization rate to rise despite the increase in both viscosities. We conclude that the microviscosity did not increase sufficiently to lower the reaction rate. The increase in polymerization rate is explained by the concomitant increase in excluded volume due to the crowders. Crowding a system with macromolecules promotes association reactions that result in net reduction of excluded volume. This EVE causes the rate of a reaction to increase [45–47]. Crowding increases the effective solute concentration, which, in turn, increases the chemical potential of the solute. The reactivity of a solute is related to the number of solute molecules per unit of available volume, not total volume. Therefore, the thermodynamic activity or effective concentration of a given solute in a crowded medium will be higher than in a dilute solution by a factor that is determined by the activity coefficient of that solute. Through the EVE, crowding drives polymer folding into native states, thus improving enzyme kinetics and supramolecular assembly. Crowding is known to induce depletion interactions, as a result of which macromolecules become segregated by size due to an increase in the available free volume. The depletion force that exists in crowded environments would favour the formation of polymers from monomers. Indeed, it is known that depletion forces can promote the formation of actin bundles [48]. Depletion interactions, and hence crowding, increase the rates of chemical reactions in solutions [25, 49]. Our data agree with these predictions. Another known consequence of MMC is anomalous diffusion. We have analyzed all our diffusion measurements from the Ficoll solutions using the anomalous diffusion FCS fitting model in order to determine the degree of anomaly in these solutions. We conclude that probe diffusion in Ficoll solutions is slightly anomalous. This minor contribution of anomalous diffusion agrees with the findings of a previous study which also demonstrated the presence of microviscosity in their crowder solutions [23]. In addition, the SDs of the α values increased with increasing probe size: the deviations were smallest for the smallest
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probe, Atto565, largest for the largest probe, Ficoll 400, and intermediate for the intermediate-sized probe, Ficoll 70. These differences reflect the heterogeneity of the crowded environment. We have previously shown that in vitro collagen deposition in the ECM by human MSCs can be increased by crowding with Ficoll. We now propose a possible explanation for the enhancement in deposition. Crowders increase the effective concentration but do not sufficiently increase the viscosity experienced by the smaller reactants, i.e. microviscosity. Since the reactants are smaller than the crowders and the pores created by those crowders, they experience the microviscosity due to the solvent. The rate enhancement occurs at an ideal crowder concentration. If the crowder concentration is too high, the reaction will slow down as the pore sizes decrease and the viscosity experienced by the reactants increases. Thus, from zero to the ideal crowder concentration, reaction rates will increase. This increase will slow down as the crowder concentration continues to increase because the viscosity experienced by the reactant will increase. We show an almost linear increase of the reaction rate till ≈10 cP, beyond which the rate starts to fall. Our data help to explain the mechanism by which the rate of a reaction increases in a crowded solution. Our study describes experimental techniques for studying crowding effects and improves our understanding of the mechanism of crowding.
[11]
[12]
[13]
[14]
[15]
[16]
[17] [18] [19]
[20]
[21]
References [22] [1] Ellis R J 2001 Macromolecular crowding: obvious but underappreciated Trends Biochem. Sci. 26 597–604 [2] Wu J and Berland K M 2008 Propagators and time-dependent diffusion coefficients for anomalous diffusion Biophys. J. 95 2049–52 [3] Partikian A, Olveczky B, Swaminathan R, Li Y and Verkman A S 1998 Rapid diffusion of green fluorescent protein in the mitochondrial matrix J. Cell Biol. 140 821–9 [4] Hancock R 2004 Internal organisation of the nucleus: assembly of compartments by macromolecular crowding and the nuclear matrix model Biol. Cell. 96 595–601 [5] Dix J A and Verkman A S 2008 Crowding effects on diffusion in solutions and cells Annu. Rev. Biophys. 37 247–63 [6] Chen C, Loe F, Blocki A, Peng Y and Raghunath M 2011 Applying macromolecular crowding to enhance extracellular matrix deposition and its remodeling in vitro for tissue engineering and cell-based therapies Adv. Drug Deliv. Rev. 63 277–90 [7] Chen C Z, Peng Y X, Wang Z B, Fish P V, Kaar J L, Koepsel R R, Russell A J, Lareu R R and Raghunath M 2009 The scar-in-ajar: studying potential antifibrotic compounds from the epigenetic to extracellular level in a single well Br. J. Pharmacol. 158 1196–209 [8] Harve K S, Vigneshwar R, Rajagopalan R and Raghunath M 2008 Macromolecular crowding in vitro as means of emulating cellular interiors: when less might be more Proc. Natl Acad. Sci. USA 105 E119 author reply E120 [9] Harve K S, Lareu R, Rajagopalan R and Raghunath M 2010 Understanding how the crowded interior of cells stabilizes DNA/DNA and DNA/RNA hybrids-in silico predictions and in vitro evidence Nucleic Acids Res. 38 172–81 [10] Lareu R R, Harve K S and Raghunath M 2007 Emulating a crowded intracellular environment in vitro dramatically
6
[23]
[24]
[25] [26]
[27]
[28]
[29] [30]
[31]
[32]
improves RT-PCR performance Biochem. Biophys. Res. Commun. 363 171–7 Lareu R R, Arsianti I, Subramhanya H K, Yanxian P and Raghunath M 2007 In vitro enhancement of collagen matrix formation and crosslinking for applications in tissue engineering: a preliminary study Tissue Eng. 13 385–91 Lareu R R, Subramhanya K H, Peng Y, Benny P, Chen C, Wang Z, Rajagopalan R and Raghunath M 2007 Collagen matrix deposition is dramatically enhanced in vitro when crowded with charged macromolecules: the biological relevance of the excluded volume effect FEBS Lett. 581 2709–14 Ang X et al 2013 Macromolecular crowding amplifies adipogenesis of human bone marrow-derived mesenchymal stem cells by enhancing the pro-adipogenic microenvironment Tissue Eng. A 20 966–81 Benny P et al 2014 Making more matrix: enhancing the deposition of dermal–epidermal junction components in vitro and accelerating organotypic skin culture development, using macromolecular crowding Tissue Eng. A 21 183–92 Zimmerman S and Minton A 1993 Macromolecular crowding: biochemical, biophysical, and physiological consequences Annu. Rev. Biophys. Biomol. Struct. 22 27–65 Zhou H X, Rivas G and Minton A P 2008 Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences Annu. Rev. Biophys. 37 375–97 Chapanian R et al 2014 Enhancement of biological reactions on cell surfaces via macromolecular crowding Nat. Commun. 5 4683 Alberts B et al 2002 Molecular Biology of the Cell (New York: Garland Science) Rashid R et al 2014 Novel use for polyvinylpyrrolidone as a macromolecular crowder for enhanced extracellular matrix deposition and cell proliferation Tissue Eng. C 20 994–1002 Kozer N et al 2007 Protein–protein association is polymer solutions: from dilute to semidilute to concentrated Biophys. J. 92 2139–49 Kozer N and Schreiber G 2004 Effect of crowding on protein– protein association rates: fundamental differences between low and high mass crowding agents J. Mol. Biol. 336 763–74 Goins A, Sanabria H and Waxham M 2008 Macromolecular crowding and size effects on probe microviscosity Biophys. J. 95 5362–73 Lavalette D et al 1999 Microscopic viscosity and rotational diffusion of proteins in a macromolecular environment Biophys. J. 76 2744–51 Kahya N, Scherfeld D, Bacia K, Poolman B and Schwille P 2003 Probing lipid mobility of raft-exhibiting model membranes by fluorescence correlation spectroscopy J. Biol. Chem. 278 28109–15 Banks D S and Fradin C 2005 Anomalous diffusion of proteins due to molecular crowding Biophys. J. 89 2960–71 Weiss M, Elsner M, Kartberg F and Nilsson T 2004 Anomalous subdiffusion is a measure for cytoplasmic crowding in living cells Biophys. J. 87 3518–24 Weiss M, Hashimoto H and Nilsson T 2003 Anomalous protein diffusion in living cells as seen by fluorescence correlation spectroscopy Biophys. J. 84 4043–52 Malchus N and Weiss M 2010 Elucidating anomalous protein diffusion in living cells with fluorescence correlation spectroscopy-facts and pitfalls J. Fluoresc. 20 19–26 Saxton M 2012 Wanted: a positive control for anomalous subdiffusion Biophys. J. 103 2411–22 Schwille P, Korlach J and Webb W W 1999 Fluorescence correlation spectroscopy with single-molecule sensitivity on cell and model membranes Cytometry 36 176–82 Schwille P, Haupts U, Maiti S and Webb W W 1999 Molecular dynamics in living cells observed by fluorescence correlation spectroscopy with one- and two-photon excitation Biophys. J. 77 2251–65 Xu C and Webb W W 1997 Multiphoton excitation of molecular fluorophores and nonlinear laser microscopy Topics in Fluorescence Spectroscopy vol 5, ed J Lakowicz (New York: Plenum)
Phys. Biol. 12 (2015) 034001
R Rashid et al
[33] Wu J and Berland K 2008 Propagators and time-dependent diffusion coefficients for anomalous diffusion Biophys. J. 95 2049–52 [34] Höfling F and Franosch T 2013 Anomalous transport in the crowded world of biological cells Rep. Prog. Phys. 76 046602 [35] Berg O G and von Hippel P H 1985 Diffusion-controlled macromolecular interactions Annu. Rev. Biophys. Biophys. Chem. 14 131–60 [36] Goins A B, Sanabria H and Waxham M N 2008 Macromolecular crowding and size effects on probe microviscosity Biophys. J. 95 5362–73 [37] Lavalette D, Tetreau C, Tourbez M and Blouquit Y 1999 Microscopic viscosity and rotational diffusion of proteins in a macromolecular environment Biophys. J. 76 2744–51 [38] Chirico G, Placidi M and Cannistraro S 1999 Fractional Stokes–Einstein relationship in biological colloids: role of mixed stick-slip boundary conditions J. Phys. Chem. B 103 1746–51 [39] Busch N, Kim T and Bloomfield V A 2000 Tracer diffusion of proteins in DNA solutions: II. Green fluorescent protein in crowded DNA solutions Macromolecules 33 5932–7 [40] Goins A B, Sanabria H and Waxham M N 2008 Macromolecular crowding and size effects on probe microviscosity Biophys. J. 95 5362–73 [41] Kozer N and Schreiber G 2004 Effect of crowding on protein– protein association rates: fundamental differences between low and high mass crowding agents J. Mol. Biol. 336 763–74 [42] Devanand K and Selser J C 1990 Polyethylene oxide does not necessarily aggregate in water Nature 343 739–41 [43] Laurent T C 1995 An early look at macromolecular crowding Biophys. Chem. 57 7–14
7
[44] Vergara A, Paduano L, D'Errico G and Sartorio R 1999 Network formation in polyethyleneglycol solutions an intradiffusion study Phys. Chem. Chem. Phys. 1 4875–9 [45] Hall D and Minton A 2003 Macromolecular crowding: qualitative and semiquantitative successes, quantitative challenges Biochim. Biophys. Acta 1649 127–39 [46] Minton A 1997 Influence of excluded volume upon macromolecular structure and associations in ‘crowded’ media Curr. Opin. Biotechnol. 8 65–9 [47] Minton A 2001 Effects of excluded surface area and adsorpate clustering on surface adsorption of proteins: II. Kinetic models Biophys. J. 80 1641–8 [48] Tharmann R, Claessens M M and Bausch A R 2006 Micro- and macrorheological properties of actin networks effectively cross-linked by depletion forces Biophys. J. 90 2622–7 [49] Asakura S and Oosawa F 1958 Interaction between particles suspended in solutions of macromolecules J. Polym. Sci. 33 183–92 [50] Aragon S and Pecora R 1976 Fluorescence correlation spectroscopy as a probe of molecular dynamics J. Chem. Phys. 64 1791–803 [51] Widengren J, Rigler R and Mets U 1994 Triplet-state monitoring by fluorescence correlation spectroscopy J. Fluoresc. 4 255–8 [52] Thompson N 1991 Fluorescence correlation spectroscopy Topics in Fluorescence Spectroscopy vol 1Techniques ed J Lakowicz (New York: Plenum) [53] Yu L et al 2005 Determination of critical micelle concentrations and aggregation numbers by fluorescence correlation spectroscopy: aggregation of a lipopolysaccharide Anal. Chim. Acta. 556 216–25
