Heterogeneity and Turnover of Intermediates during Amyloid-Î² (AÎ²

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Heterogeneity and intermediates turnover during amyloid-# (A#) peptide aggregation studied by Fluorescence Correlation Spectroscopy Ann Tiiman, Jüri Jarvet, Astrid Gräslund, and Vladana Vukojevic Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.5b00976 • Publication Date (Web): 16 Nov 2015 Downloaded from http://pubs.acs.org on November 27, 2015

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Heterogeneity and intermediates turnover during amyloid-β (Aβ) peptide aggregation studied by Fluorescence Correlation Spectroscopy Funding information: We gratefully acknowledge financial support from The Knut and Alice Wallenberg Foundation (KAW 2011.0218), Foundation for Strategic Research (SBE13-0115), The Swedish Research Council (VR 2012-2595 to VV, 2011-4850 to AG), The Swedish Brain Foundation, The Foundation for Baltic and East European Studies, Gnothis AB and The Estonian Ministry of Education and Research (IUT23-7 to JJ).

Ann Tiiman†, Jüri Jarvet†,‡, Astrid Gräslund† and Vladana Vukojević⊥,,*

† Department of Biochemistry and Biophysics, Arrhenius Laboratories, Stockholm University, 10691 Stockholm, Sweden ‡ The National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618 Tallinn, Estonia ⊥ Department of Clinical Neuroscience, Center for Molecular Medicine CMM L8:01, Karolinska Institutet, 17176 Stockholm, Sweden Corresponding author: Vladana Vukojević Karolinska Institutet Department of Clinical Neuroscience Center for Molecular Medicine CMM L8:01 17176 Stockholm Sweden Tel: + 46 8 517 717 22 E-mail: [email protected]

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ABBREVIATIONS: Aβ – amyloid β; ACC – autocorrelation curve; AD – Alzheimer's disease; FCS – florescence correlation spectroscopy; CD – circular dichroism; τD – diffusion time; OVE – observation volume element; ThT – Thioflavin T.


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ABSTRACT Self-assembly of amyloid β (Aβ) peptide molecules into large aggregates is a naturally occurring process driven in aqueous solution by a dynamic interplay between hydrophobic interactions among Aβ molecules, which promote aggregation, and steric and overall electrostatic hindrance, which stifles it. Aβ self-association is entropically unfavorable, as it implies order increase in the system, but under favorable kinetic conditions the process proceeds at appreciable rates, yielding Aβ aggregates of different sizes and structures. Despite the great relevance and extensive research efforts, detailed kinetic mechanisms underlying Aβ aggregation remain only partially understood. In this study, fluorescence correlation spectroscopy (FCS) and Thioflavin T (ThT) were used to monitor the time dependent growth of structured aggregates and characterize multiple components during the aggregation of Aβ peptides in a heterogeneous aqueous solution. To this aim, we collected data during a relatively large number of observation periods, 30 consecutive measurements lasting 10 s each, at what we consider to be a constant time point in the slow aggregation process. This approach enabled monitoring the formation of nanomolar concentrations of structured amyloid aggregates and demonstrated the changing distribution of amyloid aggregate sizes throughout the aggregation process. We identified aggregates of different sizes with molecular weight from 260 to more than 1×106 kDa, and revealed the hitherto unobserved kinetic turnover of intermediates during Aβ aggregation. The effect of different Aβ concentrations, Aβ:ThT ratios, differences between the 40 (Aβ40) and 42 (Aβ42) residues long variants of Aβ, and the effect of stirring were also examined.


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INTRODUCTION Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by aggregation of amyloid β (Aβ) peptides and their accumulation in amyloid plaques. Aβ peptides are 39-42 amino acids long peptides derived from the amyloid precursor protein via proteolytic processing1 (recently reviewed in references 2 and 3)2,3. The most frequently encountered forms of Aβ are Aβ40, predominantly found in cultured cells and in the cerebrospinal fluid, and Aβ42, which is a major component of amyloid deposits in the brain4,5. The Aβ aggregation mechanism is complex and still not fully understood despite of extensive investigations using a variety of analytical techniques6-17. However, the chemical kinetics of the amyloid self-aggregation processes of Aβ peptides has recently been elucidated in some detail18-20. The present study adds a new dimension to the understanding of the kinetic formation of the aggregates – that of size and heterogeneity during the time course of the aggregation revealed using florescence correlation spectroscopy (FCS). Fluorescence-based techniques play an important role in basic research of molecular mechanisms underlying Aβ aggregation and in biomedical diagnostics of AD. For example, the Thioflavin T (ThT) fluorescence assay introduced in 195921 is a standard method for amyloid detection by fluorescence microscopy. The ThT assay is based on specific properties of the ThT molecule, which contains a benzothiazole and a benzamine ring that freely rotate around a shared C-C bond. Upon binding to amyloid fibrils there is a bathochromic shift in absorbance maximum from λex,max = 340 nm for free ThT in protic solvents to λex,max = 450 nm in the presence of amyloid fibrils, accompanied by a strong increase in fluorescence quantum yield and the appearance of a fluorescence emission band with a maximum at 480 nm13,22-26. One major problem in characterizing the detailed kinetics of Aβ aggregation lies in the heterogeneity of the


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aggregation mixture, which contains aggregates of various sizes whose abundance is changing throughout the entire process. Many analytical methods involve averaging over all states present in the sample at a single time point. FCS however, is able to avoid such averaging and makes it possible to study complex mixtures in great detail without requiring any physical separation27,28. In FCS, florescence intensity fluctuations over time are typically analyzed by temporal autocorrelation analysis to derive an autocorrelation curve (ACC). If fluorescence intensity fluctuations arise due to molecular diffusion, the decay time of the ACC indicates the average time that a molecule spends in the observation volume element (OVE), i.e. it reflects the diffusion of the fluorescent molecule, and hence its size. The amplitude of the ACC (A) is inversely related to the average number of molecules (N) in the OVE, and thus is representative of their concentration28-30. FCS has been used in earlier studies to monitor Aβ aggregation, however only for peptides with covalently linked fluorescent labels7,8,31-35. In the present study we use FCS to monitor the time dependent growth of amyloid aggregates via ThT fluorescence. The effects of peptide concentration, ThT concentration and stirring on the aggregation process were also examined.

EXPERIMENTAL PROCEDURES Sample preparation. Lyophilized Aβ42 (ultra-pure, recombinant) was dissolved in hexafluoroisopropanol (HFIP) and dried in a vacuum exicator. Aβ solutions were freshly prepared by dissolving on ice the HFIP film/peptide powder in 10 mM NaOH to a concentration of 1.0 mg/ml and then diluted to the final concentration using 20 mM HEPES containing 10 µM ThT for fluorescence measurements or FCS. For CD experiments the sample was diluted to the final concentration with water and the pH was adjusted with NaH2PO4, resulting in a 0.5 mM


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phosphate buffer. The solution was transferred to a quartz spectroscopic cuvette and the sample was subjected to spectroscopic analyses. This procedure yields generally reproducible kinetic results in bulk measurements using ThT fluorescence as a probe to monitor amyloid formation36.

Data analysis. Fluorescence intensity fluctuations were analyzed using temporal autocorrelation analysis. The thus generated autocorrelation curves (ACCs) were fitted with a model for free 3D diffusion of one component27-29: = ∗

∗

∗

+1

(1)

where N is the average number of aggregates in the detection volume, τD is the diffusion time and S is the structure parameter. Aggregation parameters were determined by fitting the time dependence of the CD signal intensity at 195 nm, the ThT emission at 480 nm, and the distribution of diffusion times to equation37: = +

∗ !"#$ %

(2)

where X(t) is in the case of fluorescence spectroscopy bulk florescence emission at 480 nm at time t, in the case of CD it is the signal intensity at 195 nm at time t, and in the case of FCS the diffusion time at time t, X0 is the value of the corresponding entity in the beginning of the aggregation process, at t = 0 min, A is the amplitude, k the kinetic rate constant and thalf the time when the signal intensity has reached half of its maximum value, that is the time that is required for the reaction to reach half-completion. Detailed description of instrumentation and procedures is given in the Supporting Information.


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RESULTS Monitoring Aβ aggregation by FCS. To monitor the time course of Aβ aggregation by FCS, the peptide was dissolved in a buffer solution containing ThT, as described in the Materials and Methods. For each time point, fluorescence intensity fluctuations were recorded in a series of 30 consecutive measurements, each measurement lasting 10 s (Figures 1 and 2).

Figure 1. FCS measurements in a solution of 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES, pH 7.0, T = 20 °C, recorded immediately after mixing, t = 0 min (A-C) and after 90 min (D-F). A. and D. Ten consecutively recorded fluorescence intensity fluctuation traces recorded at t = 0 min (A) and t = 90 min (D). B. and E. Corresponding ACCs are shown in the same color as the related fluorescence intensity fluctuations time series. C. and F. Diffusion time distribution histograms generated by plotting the apparent number of ThT-reactive aggregates (N) as a function of the corresponding diffusion time (τD) obtained by fitting the ACCs with Eq 1. Ten consecutive measurements, out of 30, are shown here for clarity. Diffusion time distribution for all 30 measurements can be seen in Figure 2D top panel, t = 0 min, and bottom panel, t = 90 min.


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Figure 1A shows ten consecutive fluorescence intensity fluctuation traces recorded in a solution containing 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES immediately after mixing, t = 0 min. Corresponding ACCs are shown in Figure 1B. The Maximum Entropy Method for FCS (MEMFCS) for systematic analysis of ACCs38 showed that majority of the data at t = 0 min could be evaluated using a model for free 3D diffusion (Eq 1, Figure S1), and parameters such as the apparent number of ThT active aggregates in the OVE (N) and the diffusion time (τD) could be determined for each individual measurement (Figure 1C). FCS analysis showed that already at time point t = 0 min, the reaction mixture contains ThT fluorescence active entities of different sizes that can be distinguished by differences in diffusion time and relative abundance (Figure 1A-C). The smallest aggregate that was detected was characterized by a diffusion time of τD ~ 200 µs. Smaller aggregates are most abundant, as evident from the large N (Figure 1C). In contrast, the number of large aggregates, characterized by long diffusion times was small (Figure 1C, orange and dark cyan). Using Rh6G as a reference (τD,Rh6G = (24 ± 2) µs, Figure S2) and the Stokes-Einstein diffusion equation for spherical molecules as a first approximation, the molecular weight of the smallest aggregates that could be determined by ThT was estimated to be ~ 260 kDa, corresponding to aggregates consisting of 50-70 Aβ42 monomers. Figure 1D-F shows the FCS data recorded at reaction time point t = 90 min (Figure 1D and E) evaluated in the same way as the data at t = 0 min (Figure 1F). At this time point, largeamplitude fluorescence intensity fluctuations are observed and τD is at least an order of magnitude longer, τD > 2 ms, reflecting the existence of bright, large-sized aggregates at low concentrations. Using the same approximation for spherical molecules, the molecular weight of


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these aggregates was estimated to be about 1×106 kDa, corresponding to aggregates consisting of more than 2×105 Aβ42 monomers.

Figure 2. Time course of Aβ42 aggregation monitored by FCS in a solution of 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES, pH 7.0, T = 20 oC. A. Changes in ACC amplitudes (A; open circles) during the course of aggregation. B. Changes in diffusion times (τD; open circles) during the course of aggregation. The solid line corresponds to a fit with Eq 2 obtained using all τD values. C. Changes in molecular brightness (open circles) assessed as counts per molecule and second (CPMS). In A-C a trend is seen as apparently filled areas are made up from the open circles in areas of highly populated values. D. Changes in the distribution of diffusion times during the course of aggregation. The histograms were generated as described in the legend of Figure 1. E. Changes in the average number of molecules (Navg) in a specific diffusion time (τD) interval over time. The intervals are shown on the image. Dots represent experimental results. Lines were drawn by hand to help guide the eye. F. The same data set as in D analyzed using a coarser grouping of diffusion times.


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During the course of aggregation one could note that the shape of some ACCs becomes altered and that a better fit could be obtained by using a model with exponential decay. After verifying that this is not an artifact arising due to short measurement times, see Supporting Information for a detailed discussion on FCS data analysis (Figures S1-S8), we concluded that contribution from ThT binding/unbinding39 and/or from directed flow due to sedimentation of large aggregates40,41 may add to the observed change in ACC shape. However, irrespective of the ACCs shape and irrespective of which approach was used to determine the characteristic decay time – by determining the width at half-maximum of the ACCs or by using different fitting models (Figure S1), generally the same pattern of characteristic decay time distribution was observed (Figure S8). We therefore used the model for free 3D diffusion (Eq 1) for ACCs analysis throughout.

Diffusion time distribution changes over the course of aggregation. To monitor changes in the distribution of aggregate sizes during the course of Aβ42 aggregation, series of 30 consecutive FCS measurements lasting 10 s each, were performed with about 5 – 7 min intervals in-between (Figure 2). Figure 2 shows how the evaluated ACCs amplitude (A; Figure 2A), the diffusion time (τD; Figure 2B) and brightness of observed aggregates, expressed as counts per molecule and second (CPSM; Figure 3C) change over time. We observe that as the aggregation process proceeds, the number of small aggregates decreases, larger and larger aggregates appear and the total number of observed particles decreases (Figure 2D). The diffusion times estimated from individual ACCs show a wide distribution throughout the aggregation process, but the overall curve has the typical sigmoidal shape observed with other bulk methods used for monitoring Aβ aggregation (Figure 2B). Fitting with Eq 2 yielded an apparent rate constant for the overall process, k = (0.08


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± 0.05) min-1 and the time for the reaction to reach half-completion, thalf = (24 ± 7) min. Values of k and thalf were also estimated by fitting only the five lowest or highest diffusion times for each time point (Figure S9). The values obtained were not significantly different from one another (Table S1).

Detection of reaction intermediates and their turnover during aggregation. To further evaluate the underlying aggregation kinetics, the aggregates were grouped according to their diffusion time, and thus according to their size, into eight groups. The apparent number of aggregates in the OVE (N = 1/A) for a single measurement was calculated by fitting the ACC curve with Eq 1. The average number of aggregates (Navg) for a single group at a given time was derived by adding all N values for that group and by dividing the sum with the number of measurements (n = 30). Changes in Navg in the OVE for each group are shown in Figure 2E. A coarser classification of diffusion times, attained by grouping the aggregates in three rather than eight groups (Figure 2F), also shows that aggregates that are smallest in size (τD < 1 ms) behave as reactants – their concentration monotonically decreases during the course of aggregation (Figure 2E and F, black), whereas the largest aggregates behave as products – their concentration monotonically increases during the course of aggregation (Figure 2F, blue). Aggregates of sizes that fall in between behave as intermediates, showing non-monotonic change in their concentration during the course of aggregation (Figure 2F, magenta).

FCS comparison with bulk fluorescence measurements and CD spectroscopy. Figures 3A and B show changes in bulk fluorescence during Aβ aggregation recorded by conventional ThT assay (red line) and by FCS (open circles).


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Figure 3. Time course of Aβ42 aggregation monitored by FCS, conventional bulk fluorescence and CD spectroscopy. A. The distribution of diffusion times (open circles) of ThT-active aggregates measured by FCS. Bulk ThT fluorescence at 480 nm measured using a fluorimeter (red line). B. The distribution of total aggregate mass, calculated by multiplying the molecular weight of the aggregate (M) with the apparent number (N) of that aggregate measured by FCS (open circles) and the fit of this distribution with Eq 2 (black line). Bulk ThT fluorescence at 480 nm measured using a fluorimeter (red line). C. Secondary structure transition of Aβ from random coil to β-sheet followed by CD spectroscopy. D. CD measurement compared with conventional bulk ThT fluorescence measurement. Mean residual ellipticity at 195 nm (black circles), fit of the data to Eq 2 (black line) and bulk ThT fluorescence at 480 nm measured using a fluorimeter (red line). FCS and conventional bulk ThT fluorescence measurements were recorded in a continuously agitated solution containing 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES, pH 7.0, T = 20 oC. The CD measurements were conducted in a continuously agitated solution of 10 µM Aβ42 in 0.5 mM phosphate buffer, pH 7.0, T = 20 oC.


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It can be seen that the two curves are quite similar; both have the typical sigmoidal shape and the apparent rate constant for the overall process agree within experimental error (kFCS = (0.08 ± 0.05) min-1 and kconventional = (0.10 ± 0.04) min-1). However, changes in FCS measurements are consistently detected earlier (thalf = (24 ± 7) min) as compared to bulk ThT measurements (thalf = (52 ± 29) min). When the distribution of Aβ fibrillar mass is taken into consideration this difference becomes considerably smaller (Figure 3B). The Aβ aggregation process was also followed with CD-spectroscopy (Figure 3C and D). During the course of Aβ aggregation, CD spectral features change, reflecting a global transition from random coil-dominant structure that is observed at the beginning of the process to β-sheet dominant population at the end (Figure 3C). The isodichroic point indicates a structural transition between these two main populations. Change in the spectral intensity at 195 nm as a function of time was fitted using Eq 2. The obtained values kCD = (0.13 ± 0.06) min-1 and thalf,CD = (51 ± 14) min are in good agreement with those acquired by the conventional ThT method.

Effect of Aβ 42-ThT ratio. The effect of the Aβ42:ThT ratio on the outcome of FCS measurements was assessed by using a constant Aβ42 concentration (10 µM) and varying the ThT concentration: 20, 10 and 0.5 µM (Figure 4 A-C). Only a small difference in the number of aggregates was observed when the ThT concentration was changed from 10 µM to 20 µM (Figure 4 A and B). Aggregation could still be monitored when the ThT concentration was 5 µM (Aβ42:ThT ratio 2:1, data not shown) or even 0.5 µM (Aβ42:ThT ratio of 20:1, Figure 4C). Reduction in the Aβ42:ThT ratio decreased the number of aggregates observed, but the overall time course of aggregation was not significantly affected (Figure 4A-C, Figure S10 A-C).


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Figure 4. Effect of Aβ42:ThT ratio and the concentration of Aβ42 on the distribution of diffusion times during the course of Aβ aggregation. A.-C. Effect of ThT concentration on the distribution of diffusion times (τD) for 10 µM Aβ42 and three different ThT concentrations: A. 20 µM, B. 10 µM and C. 0.5 µM. D.-E. Effect of peptide concentration on the distribution of diffusion times for different Aβ42 concentrations: D. 20 µM, E. 5 µM and F. 1.5 µM. In all cases, ThT concentration, in 20 mM HEPES was 10 µM, pH 7.0, T = 20 oC and the solution was continuously agitated.


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Effect of Aβ 42 concentration. Effect of Aβ42 concentration on the aggregation kinetics was probed in a limited set of experiments (Figure 4 and Figure S10). In these experiments the ThT concentration was kept constant (10 µM) and Aβ42 concentration was varied: 20 µM (Figure 4D), 10 µM (Figure 4B) 5 µM (Figure 4E) and 1.5 µM (Figure 4F). The diffusion time distribution shows that when the initial concentration of Aβ42 is 20 µM, the initial solution contains larger aggregates already at the beginning of the reaction and the aggregation process is so fast that the lag phase in the beginning of the reaction could not be observed (Figure 4D). When Aβ42 concentration is 5 µM, aggregation occurs but N is smaller (Figure 4E). As expected, when the peptide concentration is lowered to 1.5 µM, the overall aggregation process is much slower, N is considerably smaller and the aggregates at t = 90 min are smaller in size (Figure 4F). When Aβ42 concentration was lower than 1.5 µM, ThT fluorescence was not observed within 90 min, even though stirring was applied (data not shown).

Figure 5. Comparing Aβ40 with Aβ42. Parameters derived from the autocorrelation curves of continuously agitated 10 µM Aβ42 (black) or Aβ40 (red). A. The distribution of amplitude of the autocorrelation curves at 10 µs. B. The distribution of diffusion times of the detected ThT-active aggregates. Aβ42 time scale is shifted 2 minutes to the right to minimize the overlap. Experiments were carried out in 20 mM HEPES, pH 7.0, T = 20 °C with continuous stirring.


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Comparison between Aβ42 and Aβ40. Overall, Aβ40 shows similar behavior to Aβ42, except that the amplitude of the ACCs varied more for Aβ40 than for Aβ42. The Aβ40 peptide also showed a wider diffusion time distribution profile throughout the aggregation process, but the overall size of aggregates at the end point, t = 90 min, was similar (Figure 5).

Effect of stirring. Stirring has pronounced effects on the time course of Aβ40 and Aβ42 aggregation (Figure 6).

Figure 6. Effect of stirring on Aβ42 and Aβ40 aggregation. A. The distribution of diffusion times in a solution of 10 µM Aβ42 under continuous stirring (black) and after stirring was stopped (red) at the time indicated by the red arrow. B. The distribution of diffusion times in a solution of 10 µM Aβ40 with continuous stirring (black), when stirring was stopped in the exponential growth phase (red) and in the stationary phase (green) at times indicated by the red and green arrows, correspondingly. Lines were drawn by hand to guide the eye. Experiments were carried out in the presence of 10 µM ThT, in 20 mM HEPES, pH 7.0, T = 20 °C. Under the conditions investigated here, aggregation leading to the formation of ThT reactive aggregates does not occur during 3 h in the absence of stirring. Hence, all studies were conducted with stirring. If stirring is turned off after the reaction has reached the exponential growth phase, Aβ42 readily forms very large aggregates (Figure 6A, red), which is not the case when


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aggregation proceeds in a continuously stirred solution (Figure 6A, black). As aggregation proceeds in the unstirred solution, the distribution of diffusion times becomes widely spread and large aggregates with τD > 10 ms were observed. Some aggregates also grew too large (τD > 50 ms) to be accurately characterized within the measurement time of 10 s. In contrast, the effect of stirring on Aβ40 aggregation was much less pronounced. When stirring was turned off in the exponential growth phase, pronounced growth of Aβ40 aggregates was not noted (Figure 6B, red). Slightly bigger aggregates were seen when stirring was turned off in the stationary phase (Figure 6B, green), but these aggregates where still considerably smaller than the aggregates of Aβ42 observed after stirring was turned off.

DISCUSSION Aβ aggregation has previously been studied by FCS using peptides with a covalently linked fluorescent label7,32,33,35,42. This approach has yielded significant new information, but it also has some limitations: the covalently labeled Aβ peptides can behave differently than unlabeled peptides43 and the concentration range that can be tested by FCS is limited. Since the ACC amplitude becomes very low at high concentrations, one can probe a limited region of the concentration space using labeled Aβ only, [Aβ] < 1 µM. However, this concentration is below the critical concentration for Aβ aggregation. Hence, a mixture of labeled and non-labeled peptides needs to be used, often in the ratio of labeled vs unlabeled 1:1000. Because of the total number of molecules present in the solution, such low amount of the labeled probe may not be sufficient to reflect the presence of all intermediates existing in the reaction medium. In contrast to covalently labeled peptides, ThT in µM concentrations does not significantly affect the kinetics of Aβ aggregation18,44. This was also verified in the current study, by our observation


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that the time course of Aβ aggregation monitored using CD spectroscopy, which is performed in the absence of ThT, was very similar to the time course of Aβ aggregation monitored using bulk ThT fluorescence measurements (Figure 3D). Furthermore, amyloid-bound ThT is sufficiently photostable to allow FCS measurements without significant photobleaching45. By using ThT as a fluorescent marker, we are not monitoring Aβ monomers, but rather amyloid aggregates rich in β-structure, that give rise to ThT fluorescence. The smallest aggregates that were seen were characterized by a diffusion time of ~ 200 µs. We estimated that this diffusion time reflects aggregates consisting of 50-70 monomers. This may represent the smallest aggregate of Aβ peptide that the ThT molecule binds to that can give rise to increased fluorescence. By recording and analyzing the FCS data as described above, each time point in the kinetic aggregation curve (on a slow time scale) is represented by a set of N and τD values describing the system at that time point (Figure 1C and F, Figure 2D and Figure 4). Looking at the distribution of decay times during aggregation, it is seen that the reaction mixture is heterogeneous throughout the aggregation process (Figures 2B, 2D, 4, 5B and 6). The distribution of decay times immediately after mixing, t = 0 min, is different for different experiments, but it is always most narrow at the beginning of the process (Figures 2B, 2D, 4, 5B and 6). It becomes wider and the center of distribution shifts towards longer decay times as the aggregation process progresses (Figures 2B, 2D, 4, 5B and 6). Transient formation of intermediates is observed during the aggregation process, with small aggregates preceding the formation of large ones (Figure 2 E and F and Figure S9). Macroscopic kinetic parameters that describe the overall process, such as thalf and k, don’t differ significantly when all or only the five shortest/longest decay times are analyzed (Figure S8, Table S1), suggesting that Aβ


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intermediates of all sizes follow the same general kinetic mechanism and that microscopic processes such as primary nucleation, elongation, secondary, nucleation and fragmentation occur during all three phases of the macroscopic aggregation process – the low-fluorescence lag phase, growth phase and the terminal high-fluorescence steady state. However, since the concentration of monomers and aggregates of different sizes and their relative ratios are different at different stages of the aggregation process, the contribution of these microscopic processes to the overall rate of aggregation are different during different phases of aggregation. It is also interesting to compare the estimated sizes of the larger aggregates observed towards the end of Aβ aggregation process, t = 90 min, with the dimensions of the approximately ellipsoid OVE of the FCS system. According to the longest decay times analyzed in this study, 5 ms ≤ τD ≤ 8 ms, the large aggregates contain (0.2-4)×106 Aβ molecules. The OVE long axis is approximately on a µm scale and the short axis is about half of a µm. A typical fibril, as seen by AFM studies of Aβ aggregation, is oblong, with a diameter of about 0.01 µm and length in the µm scale46. It is obvious that such fibrils, although still soluble, don’t behave as small diffusing particles inside the OVE. This, together with the onset of sedimentation40,41, which inevitably occurs as the size of amyloid aggregates increases, may in addition to ThT binding/unbinding also contribute to the changing shape of the ACCs that was observed towards the end of the aggregation process.

FCS comparison with bulk fluorescence and CD spectroscopy. When FCS measurements are compared to bulk ThT or CD measurements (Figure 3), the overall time course is the same with all the methods as well as the overall rate constant. However the thalf of FCS measurements is consistently shorter than that of the bulk ThT or CD measurements. This could be due to subtle


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differences in the experimental setup, mainly in stirring. However, if the fibrillar mass was calculated the difference in the thalf between the FCS and bulk ThT measurements became very small (Figure 3B). This demonstrates that absence of aggregate growth in conventional kinetic spectroscopy and the initial difference in the thalf is mainly due to the difference in sensitivity between these methods. FCS has single-molecule sensitivity and is thus able to detect aggregates in concentrations below the detection limit of conventional fluorimetry or CD spectroscopy.

Differences between Aβ40 and Aβ42. The Aβ40 and Aβ42 peptides, which differ in two amino acids at the C-terminus, have different aggregation propensities18,20,47. In our experiments, the aggregation process of Aβ40 was in general similar to that of Aβ42, except that some larger aggregates were observed early on in the Aβ40 solution and throughout the aggregation process (Figure 5B). Since no attempts were made to purify the reactants and remove contaminants such as pre-formed oligomers and micro air bubbles it is not possible to state with certainty that this is the case, but this observation is consistent with a steeper slope of the Aβ40 growth curve as compared to that of Aβ42 shown by Meisl et al. to reflect fibril-catalyzed secondary nucleation processes and stronger autocatalytic behavior of Aβ40 as compared to Aβ42.18 In addition Aβ40 and Aβ42 behaved differently when stirring was turned off (Figure 6 A and B) as discussed in the subsection below.

Effect of stirring. Stirring exerts a profound, yet complex effect on Aβ aggregation. At the beginning of the process, stirring accelerates aggregation – it increases the collision frequency by increasing the transport of peptides through the solution19,48 and by breaking up/tearing apart the existing aggregates, making new free ends for elongation and freeing new surfaces for secondary


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nucleation19,49. At the late stage of the process stirring slows down aggregation of the largest aggregates, as the mechanical shear force facilitates their fragmentation48. It is generally agreed that the kinetics of Aβ40 aggregation is different from that of Aβ42. Aβ42 is more aggregation prone than Aβ40, possibly due to the increased hydrophobicity imposed by residues Ile41 and Ala42,18,19,50 and that they behave differently when stirring is turned off47. Under quiescent conditions secondary aggregation pathways (fragmentation and surface catalyzed nucleation) play an important role in the aggregation of both peptides18,19. However, it was shown by Cohen et al. that rate constants of all microscopic processes, not only fibril-catalyzed secondary nucleation, are decreased for Aβ40 relative to Aβ42.18,19. The experiments here were carried out with stirring to promote the aggregation process, i.e. shorten the lag phase. If the solution is kept quiescent, no changes related to an aggregation process can be seen with any of the present methods within the time frame of the experiment (3 h). When stirring is turned off during the ongoing aggregation process, Aβ42 aggregates grow larger than in the presence of continuous stirring (Figure 6A), demonstrating that stirring breaks up existing aggregates by fragmentation. Aβ40 aggregates however did not grow noticeably in the timeframe of the experiment (Figure 6B) if the stirring was turned off.

CONCLUSION Thermodynamic and kinetic mechanisms underlying self-association of Aβ molecules have been in focus for many years, but detailed understanding of underlying molecular mechanisms and description of the dynamic aggregation process in terms of mass-action kinetics has been only recently achieved19,50. In spite of this great break-through, new methodological approaches


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are needed in order to quantitatively and non-destructively characterize aggregation in complex mixtures, such as biological fluids, with sufficiently high temporal resolution and sensitivity. Previous studies using FCS and fluorescently tagged Aβ peptide convincingly showed that Aβ aggregation proceeds through the formation of oligomeric intermediates, but the contribution of oligomers of different sizes, the time course of their turnover and the kinetics of their growth was not identified7,8. In the present study, we have shown that it is possible to quantitatively characterize the Aβ peptide aggregation in great detail by using FCS and ThT fluorescence to monitor the process. Even though we could only distinguish intermediates by difference in diffusion and brightness, but not by shape (circular versus rod-like), FCS revealed the large heterogeneity of the process that bulk methods cannot expose. This not only explains many of the difficulties to achieve reproducible spectroscopic results, but allows us to quantitatively characterize the presence of transiently populated intermediates and the time course of their turnover. The possibility to nondestructively monitor the turnover of intermediates during peptide aggregation is of utmost importance for basic research, as it allows quantitative characterization of the underlying kinetics. It is also of general relevance. The approach presented here allows us to distinguish oligomers which may be the targets for molecules with the capacity to disrupt Aβ aggregation. This is invaluable for the development and assessment of new drugs. Finally, our approach shows the most sensitive method developed thus far that allows the detection of amyloid aggregates rich in β-structure with an ultimate, single-molecule sensitivity, which may be of considerable value for early diagnostics.


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ACKNOWLEDGMENT We thank Prof. Rudolf Rigler for stimulating discussions and Prof. Sudipta Maiti for providing freely the MEMFCS software.


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ASSOCIATED CONTENT Supporting Information. The Supporting Information is available free of charge via the Internet at http://pubs.acs.org. Detailed description of instrumentation and data analysis procedures; additional experiments with quantum dots and Rhodamine 6G; comparison of alternative FCS data fitting procedures.


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36. Abelein, A., Graslund, A., and Danielsson, J. (2015) Zinc as chaperone-mimicking agent for retardation of amyloid beta peptide fibril formation, Proc. Natl. Acad. Sci. U. S. A. 112, 5407-5412. 37. Hellstrand, E., Boland, B., Walsh, D. M., and Linse, S. (2010) Amyloid beta-Protein Aggregation Produces Highly Reproducible Kinetic Data and Occurs by a Two-Phase Process, ACS Chem. Neurosci. 1, 13-18. 38. Sengupta, P., Garai, K., Balaji, J., Periasamy, N., and Maiti, S. (2002) Measuring Size Distribution in Highly Heterogeneous Systems with Fluorescence Correlation Spectroscopy, Biophys. J. 84, 1977-1984. 39. Elson, E. L. (2001) Fluorescence correlation spectroscopy measures molecular transport in cells, Traffic 2, 789-796. 40. Dam, J., and Schuck, P. (2004) Calculating sedimentation coefficient distributions by direct modeling of sedimentation velocity concentration profiles, Methods Enzymol. 384, 185-212. 41. van Oss, C. J. Interfacial Forces in Aqueous Media, Second EditionCRC Press: 2006; pp 7577. 42. Garai, K., Sahoo, B., Sengupta, P., and Maiti, S. (2008) Quasihomogeneous nucleation of amyloid beta yields numerical bounds for the critical radius, the surface tension, and the free energy barrier for nucleus formation, J. Chem. Phys. 128, 045102. 43. Jungbauer, L. M., Yu, C., Laxton, K. J., and LaDu, M. J. (2009) Preparation of fluorescentlylabeled amyloid-beta peptide assemblies: the effect of fluorophore conjugation on structure and function, J. Mol. Recognit. 22, 403-413. 44. Karafin, A., Palumaa, P., and Tõugu, V. (2009) Monitoring of A beta fibrillization using an improved fluorimetric method. New Trends in Alzheimer and Parkinson Related Disorders: Adpd 2009 255-259. 45. Qin, L., Vastl, J., and Gao, J. (2010) Highly sensitive amyloid detection enabled by thioflavin T dimers, Mol. BioSyst. 6, 1791-1795. 46. Luo, J., Wärmländer, S. K., Gräslund, A., and Abrahams, J. P. (2014) Alzheimer peptides aggregate into transient nanoglobules that nucleate fibrils, Biochemistry 53, 6302-6308. 47. Tiiman, A., Noormägi, A., Friedemann, M., Krishtal, J., Palumaa, P., and Tõugu, V. (2013) Effect of agitation on the peptide fibrillization: Alzheimer's amyloid-beta peptide 1-42 but not amylin and insulin fibrils can grow under quiescent conditions, J. Pept. Sci. 19, 386-391. 48. Bekard, I. B.; Dunstan, D. E. Bio-nanoimaging; Lyubchenko, V. N. U. L., Ed.; Academic Press: Boston, 2014; pp 503-513.


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49. Tõugu, V., Karafin, A., Zovo, K., Chung, R. S., Howells, C., West, A. K., and Palumaa, P. (2009) Zn(II)- and Cu(II)-induced non-fibrillar aggregates of amyloid-beta (1-42) peptide are transformed to amyloid fibrils, both spontaneously and under the influence of metal chelators, J. Neurochem. 110, 1784-1795. 50. Arosio, P., Knowles, T. P. J., and Linse, S. (2015) On the lag phase in amyloid fibril formation, Phys. Chem. Chem. Phys. 17, 7606-7618.


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For Table of Contents Use Only

Heterogeneity and intermediates turnover during amyloid-β (Aβ) peptide

aggregation

studied

by

Fluorescence

Correlation

Spectroscopy Ann Tiiman†, Jüri Jarvet†,‡, Astrid Gräslund† and Vladana Vukojević⊥,,*


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Figure 1. FCS measurements in a solution of 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES, pH 7.0, T = 20 C, recorded immediately after mixing, t = 0 min (A-C) and after 90 min (D-F). A. and D. Ten consecutively recorded fluorescence intensity fluctuation traces recorded at t = 0 min (A) and t = 90 min (D). B. and E. Corresponding ACCs are shown in the same color as the related fluorescence intensity fluctuations time series. C. and F. Diffusion time distribution histograms generated by plotting the apparent number of ThTreactive aggregates (N) as a function of the corresponding diffusion time (τD) obtained by fitting the ACCs with Eq 1. Ten consecutive measurements, out of 30, are shown here for clarity. Diffusion time distribution for all 30 measurements can be seen in Fig. 2D top panel, t = 0 min, and bottom panel, t = 90 min. 72x29mm (300 x 300 DPI)

o


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Figure 2. Time course of Aβ42 aggregation monitored by FCS in a solution of 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES, pH 7.0, T = 20 oC. A. Changes in ACC amplitudes (A) during the course of aggregation. B. Changes in diffusion times (τD) during the course of aggregation. The solid line corresponds to a fit with Eq 2 obtained using all τD values. C. Changes in molecular brightness assessed as counts per molecule and second (CPMS). D. Changes in the distribution of diffusion times during the course of aggregation. The histograms were generated as described in the legend of Figure 1. E. Changes in the average number of molecules (Navg) in a specific diffusion time interval (τD) over time. The intervals are shown on the image in ms. Dots represent experimental results. Lines were drawn by hand to help guide the eye. F. The same data set as in D analyzed using a coarser grouping of diffusion times. 85x40mm (300 x 300 DPI)


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Figure 3. Time course of Aβ42 aggregation monitored by FCS, conventional bulk fluorescence and CD spectroscopy. A. The distribution of diffusion times (open circles) of ThT-active aggregates measured by FCS. Bulk ThT fluorescence at 480 nm measured using a fluorimeter (red line). B. The distribution of total aggregate mass, calculated by multiplying the molecular weight of the aggregate (M) with the apparent number (N) of that aggregate, (open circles) of aggregates measured by FCS and the fit of this distribution with Eq 2 (black line). Bulk ThT fluorescence at 480 nm measured using a fluorimeter (red line). C. Secondary structure transition of Aβ from random coil to β-sheet followed by CD spectroscopy. D. CD measurement compared with conventional bulk ThT fluorescence measurement. Mean residual ellipticity at 195 nm (black circles), fit of the data to Eq 2 (black line) and bulk ThT fluorescence at 480 nm measured using a fluorimeter (red line). FCS and conventional bulk ThT fluorescence measurements were recorded in a continuously agitated solution containing 10 µM Aβ42 and 10 µM ThT in 20 mM HEPES, pH 7.0, T = 20 oC. The CD measurements were conducted in a continuously agitated solution of 10 µM Aβ42 in 0.5 mM phosphate buffer, pH 7.0, T = 20 oC. 71x45mm (300 x 300 DPI)


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Figure 4. Effect of Aβ42-ThT ratio and the concentration of Aβ42 on the distribution of diffusion times during the course of aggregation. A.-C. Effect of ThT concentration on the distribution of diffusion times (τD) for 10 µM Aβ42 and three different ThT concentrations: A. 20 µM, B. 10 µM and C. 0.5 µM. D.-E. Effect of peptide concentration on the distribution of diffusion times for different Aβ42 concentrations: D. 20 µM, E. 5 µM and F. 1.5 µM. In all cases, ThT concentration, in 20 mM HEPES was 10 µM, pH 7.0, T = 20 oC and the solution was continuously agitated. 150x126mm (300 x 300 DPI)


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Figure 5. Comparing Aβ40 with Aβ42. Parameters derived from the autocorrelation curves of continuously agitated 10 µM Aβ42 (black) or Aβ40 (red). A. The distribution of amplitude of the autocorrelation curves at 10 µs. B. The distribution of diffusion times of the detected ThT-active aggregates. Aβ42 time scale is shifted 2 minutes to the right to minimize the overlap. Experiments were carried out in 20 mM HEPES, pH 7.0, T = 20 oC with continuous stirring. 37x12mm (300 x 300 DPI)


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Figure 6. Effect of stirring on Aβ42 and Aβ40 aggregation. A. The distribution of diffusion times in a solution of 10 µM Aβ42 under continuous stirring (black) and after stirring was stopped (red) at the time indicated by the red arrow. B. The distribution of diffusion times in a solution of 10 µM Aβ40 with continuous stirring (black), when stirring was stopped in the exponential growth phase (red) and in the stationary phase (green) at times indicated by the red and green arrows, correspondingly. Lines were drawn by hand to guide the eye. Experiments were carried out in the presence of 10 µM ThT, in 20 mM HEPES, pH 7.0, T = 20 oC. 39x13mm (300 x 300 DPI)


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Heterogeneity and Turnover of Intermediates during Amyloid-Î² (AÎ²

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