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Self-assembly of mesoscopic peptide surfactant fibrils investigated by STORM super- resolution fluorescence microscopy Henry Cox, Pantelis Georgiades, Hai Xu, Thomas Andrew Waigh, and Jian Ren Lu Biomacromolecules, Just Accepted Manuscript • Publication Date (Web): 01 Jun 2017 Downloaded from http://pubs.acs.org on June 6, 2017

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Self-assembly of mesoscopic peptide surfactant fibrils investigated by STORM super- resolution fluorescence microscopy Henry Cox1, Pantelis Georgiades1,2, Hai Xu3, Thomas A Waigh*1,2, Jian R. Lu+1 1

Biological Physics, School of Physics and Astronomy, University of Manchester, Oxford Road, Manchester, M13 9PL, U.K.

2

3

Photon Science Institute, University of Manchester, Oxford Road, Manchester, M13 9PL, U.K.

Centre for Bioengineering and Biotechnology, China University of Petroleum (East China), 66

Changjiang West Road, Qingdao 266555, China

*[email protected] +

[email protected]

KEYWORDS Self-assembly, STORM, Peptides, Peptide fibers, Surfactant, Super-resolution fluorescence microscopy, Peptide gels

ACS Paragon Plus Environment

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ABSTRACT

Super-resolution fluorescence microscopy, specifically STOchastic Reconstruction Microscopy (STORM), and atomic force microscopy (AFM) were used to image the self-assembly processes of the peptide surfactant, I3K. The peptide surfactants self-assembled into giant helical fibrils with diameters between 5 and 10 nm with significant helical twisting. The resolution of the STORM images was 30 nm, calculated using the Fourier ring correlation method. STORM compares favorably with AFM for the calculation of contour lengths (~6 µm) and persistence lengths (10.1 ± 1.2 µm) due to its increased field of view (50 µm), and its ability to image bulk morphologies away from surfaces under ambient solution conditions. Two color STORM experiments were performed to investigate the dynamic process of self-assembly after mixing of two separately labeled samples and the results revealed the formation of long nanofibers via endto-end connections of short ones. No evidence was found for significant monomer exchange between the samples and the self-assembled structures were very stable and long lived.


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1. Introduction The materials and structures created by the self-assembly of synthetic peptides have garnered significant research interest1–4 due to their potential applications in drug delivery5, as antibacterial agents6 and anticancer agents7, for the creation of synthetic extra-cellular matrices8 and to accelerate wound healing9,10. This is in part due to the peptides’ inherent biocompatibility11, but also the way self-assembly can be sensitively tuned by various environmental factors including temperature12, pH13, salt14 and solvent2,15. Small amphiphilic peptides are of particular interest because of the reduced costs associated with the production of peptides that only contain a few residues. Furthermore, the reduced complexity of small peptides offers many opportunities to model and study exactly how external factors may impact selfassembly. One such peptide, I3K, is formed from four amino acid residues and is shown in Figure 1A. I3K self-assembles through a spontaneous multi-step process, involving the creation of peptide aggregates stabilized by hydrophobic interactions and anti-parallel beta sheets. These aggregates then grow to form ribbons, which finally curl under chiral forces to form nanotubes of approximately 10 nm in diameter and microns in length16. The peptide has been used as a model to study the role of salt14 and hydrogen bonding17 in self-assembly, and the self-assembled structures are sufficiently stable to act as templates for silica nanotube synthesis16,18. I3K nanotubes have also been shown to form a self-supporting hydrogel. When considering the properties of the hydrogel, the larger scale network and arrangement of nanotubes/fibrils is often just as important as the small scale details of self-assembly19. The wide range of relevant length scales makes experimentation challenging and often requires the use of many different complimentary techniques to provide a complete understanding of the system. Popular techniques for imaging the self-assembled structures include atomic force microscopy (AFM)


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and transmission electron microscopy (TEM) which can afford a resolution on the order of nanometers or below4. However, sample preparation can be invasive. A typical AFM experiment may involve fixing the sample to a surface and drying it, which has been shown to impact the structure of many biopolymers such as DNA20. TEM sample preparation is often even more invasive, involving freezing and slicing to circumvent problems with the ultra-high vacuum conditions required and the low path length of electrons in condensed matter.

Figure 1. A) The chemical structure of I3K. Three isoleucine amino acid residues form the hydrophobic isoleucine tail and a single lysine amino acid forms the hydrophilic lysine head group. The propensity for isoleucine to form beta sheets, along with the amphiphilic nature of I3K, drives it to form nanotubes in aqueous environments, as shown in previous work16. B) An example of the NHS Ester fluorescent dye molecules used to label I3K nanotubes. C) The result of the conjugation reaction between I3K and the NHS Ester fluorescent dye molecules. A stable amide bond is formed between the dye molecule and the lysine side-chain of I3K. Super-resolution fluorescence microscopy (STORM) is a relatively new technique, first reported in 2006, which enables imaging of fluorescently labelled samples with sub-diffraction limited resolution. This is achieved through the localization of photoswitchable fluorescent


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probes within a sample, which are then combined to form a high resolution image21. The technique has gathered interest due to the resolution it affords (approx. 20 nm lateral resolution), the flexibility to tag fluorescent dyes to specific chemical structures and the capability of working in aqueous sample environments22,23. Cellular biologists have been quick to adopt the technique and have been able to image microtubules24, β-amyloid fibrils25 and 3D actin networks26 successfully using STORM. Recently, the technique has begun to gather interest from the self-assembly community; primarily due to the possibility of using two color techniques to track the self-assembly kinetics of molecules in fibrils and aggregates27. The unidirectional living growth of peptide amphiphile fibrils has been observed by Beun et al28 and the molecular exchange of self-assembled protein fibrils was seen by da Silva et al29. Both of these reports involved the preparation of two sets of fluorescently labelled monomers, which were then combined with unlabeled monomers and allowed to self-assemble, leading to fluorescent fibrils which were then adsorbed to a glass coverslip and imaged. In this report, we demonstrate how the technique can be extended to the study of the self-assembly of short amphiphilic peptide fibrils imaged away from the surface in bulk conditions. We believe that STORM has excellent potential as a complementary technique to AFM and TEM for the direct imaging of selfassembled structures. We show how the technique can provide high resolution images of peptide fibril networks under standard bulk conditions and how these may be used to provide quantitative analysis of the self-assembly process. 2. Methods 1. Peptide preparation and fluorescent labelling The I3K peptide was synthesized using FMOC solid-phase synthesis and purified as described previously18. Water was sourced from a Milli-Q purifier (18 MΩ). To prepare the peptide fibril


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solutions the lyophilized I3K was dissolved directly into the water to the desired concentration (10 mM) at the ambient temperature around 20 oC. Once fully dissolved, the pH was adjusted to pH 7 using a minute amount of sodium hydroxide. This pH and peptide concentration was chosen as it is suitable for forming a self-supporting hydrogel of I3K16, which is required to obtain a motionless sample for STORM image capture. The critical micelle concentration of I3K is 0.43 mM and fibrils/hydrogels have been shown to form at 2 mM peptide concentration in the presence of certain anionic salts14. However, additional buffers or salts were not used in this case as they may interfere with the enzymatic anti-oxidant processes in the STORM imaging buffers. For the STORM experiments the peptides were labelled with fluorescent dyes. Alexa Fluor 647 NHS Ester and Cy3B NHS Ester fluorescent dyes were obtained from ThermoFischer Scientific and GE Healthcare, respectively. The NHS Ester conjugation chemistry was chosen to target the primary amine found in the lysine side chain, as shown in Figure 1. The other potential conjugation site, the N-terminus of I3K, was capped with acetic anhydride during synthesis, which means it is unavailable for dye conjugation. Due to the hydrophilic nature of lysine, the lysine side chain is likely to be accessible to the solvent and available for conjugation to the dye molecules. As the dye molecules are also hydrophilic, they do not significantly impact peptide fibril structures and act like a ‘paint’ on the outside of the fibrils. AFM imaging of the fibrils with and without conjugated dye confirms that there are no significant changes to the selfassembled structures. Representative AFM images and analysis are shown in Section 1 of the supporting information. Previous work on I3K fibrils suggests that fibril formation is completed at least three days after peptide dissolution16. Therefore, the peptide fibrils were aged for at least a week at room temperature to ensure the self-assembly process was complete. Once the peptides were ready, the


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fluorophores were dissolved in anhydrous-DMSO and then mixed with the prepared peptide fibril solutions, leaving the reaction to proceed to completion on an orbital shaker for at least two hours at room temperature (full details given in Section 2 of the supporting information). Once conjugation was finished the samples were stored in a refrigerator. For two color STORM experiments, two sets of peptide solutions were prepared, one dyed with Alexa Fluor 647 and the other with Cy3B fluorescent dye. The separately dyed samples had a peptide concentration of 10 mM and were subsequently mixed together and then diluted slightly with the desired solvent (ethanol, acetonitrile or additional water), to form the final sample at 8 mM peptide concentration. 2. Atomic force microscopy Atomic force microscopy (AFM) measurements were taken on a Bruker multimode AFM and recorded using the Nanoscope software. The substrate used for AFM measurements was initially bare silica, which was cleaned and inspected prior to use with a J.A. Woollam M-2000 Ellipsometer to ensure the surface was smooth and contained a suitable native SiO2 layer. Cleaned glass coverslips were also used for AFM as these have identical surface chemistry to those used in the STORM experiments. For silica and coverslip substrates, 3 µl of the peptide fibril solution at 10 mM peptide concentration was deposited on the surfaces. The surfaces were then gently rinsed with pure water for no more than 30 seconds before drying. 3. Super-resolution fluorescence microscopy Super-resolution fluorescence microscopy (STORM) images were taken using our custom built microscope, which has some minor changes from what was described in our previous work30. Specifically, an oscillated optical fiber is now used to deliver multiple laser lines at the back of the microscope, instead of a free space configuration as previously used with a dedicated speckle


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scrambler30. Three lasers were used to illuminate the sample; 647 nm, 561 nm and 405 nm. We primarily used the 647 nm and 561 nm lasers to activate the fluorescent dyes, whereas the 405 nm laser was used to compensate for photobleaching of dye molecules back into the switching state31. The laser beams were combined and launched into an optical fiber which carried the laser light to the microscope. The use of an optical fiber ensured a more consistent intensity distribution across the sample on all three laser lines, which is vital for two color imaging. Fluorescence from the sample was collected by a 100x Olympus TIRF lens in an epillumination geometry and recorded using a Hamamatsu ORCA Flash v2 sCMOS camera. At least 20,000 images of the sample were acquired at 100 frames per second, with thermal drift in the sample z position compensated by a Mad-City Labs C-focus system. A full description and schematic diagram of the microscope can be found in Section 3 of the supporting information. The STORM sample preparation was similar to that for the AFM measurements; 3 µl of the dyed peptide fibril solutions was deposited on a cleaned glass coverslip and then gently immersed in a small water bath at room temperature for 30 seconds. This process effectively removed loose fibrils and unconjugated dye molecules, whilst leaving a thick layer of entangled fibrils in a network attached to the surface. Excess water was then removed from the coverslip and the fibrillary sample was immediately fixed to a microscope slide using a circular imaging spacer obtained from ThermoScientific, taking care to ensure the coverslip surface did not dry. In the well created by the spacer, 5 µl of imaging buffer was deposited prior to fixing. Full details of the imaging buffers can be found in Section 4 of the supporting information. Once the images of the blinking fluorophores attached to peptide surfactants were collected, the ThunderSTORM ImageJ plugin32 was used to reconstruct the super-resolution images. Postprocessing of the localization data was also performed in ThunderSTORM to correct for lateral


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sample drift during image acquisition and to filter out localizations based upon their positional uncertainty. The final images were created by binning the localizations into virtual 10 nm pixels. 4. Fiber tracking and analysis AFM and STORM images were analyzed using the FiberApp fiber tracking software33 which operates within MatLab. The software applies an open snake algorithm to user identified fibrils and accurately identifies the backbone of the fibrils for further analysis. For the STORM data, additional MatLab code was written which used the FiberApp contour co-ordinates along with the ThunderSTORM localization data to extract localizations that corresponded to a fibril for further analysis. 3. Results and Discussion 1. Comparison of AFM and STORM data Prior to STORM imaging, we performed AFM experiments to verify the self-assembled structure of I3K. An example of the height scans obtained from AFM is shown in Figure 2; the images clearly show fibrous structures, which are typically longer than the AFM field of view (5 µm). Analysis of the AFM images reveal that two classes of fibrils exist; a large population of smaller fibrils of 11.3 ± 0.7 nm diameter and larger fibrils of 19.8 ± 0.9 nm diameter, which were much less common. The number of smaller fibrils significantly outweighs the large fibrils and this is broadly in agreement with previous work, which found fibrils of approximately 10 nm in diameter, as measured by AFM and TEM images16. The AFM images also clearly show how the fibrils twist, with a measured helical pitch of 121 ± 18 nm and 344 ± 29 nm for the small and large fibrils, respectively. Twisting is a common phenomenon among many types of fibrils formed from proteins and peptides34–36. As I3K is positively charged at pH 7, a twisted formation


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is energetically favorable for bundles of two or more fibrils due to the mutual electrostatic repulsion of adjacent charged groups along each fibril36.

Figure 2. AFM height image of self-assembled I3K peptide fibrils at 10 mM peptide concentration on a glass coverslip surface. The lateral size of the image is 5 µm and the height scale bar is displayed below the image. The fibrils are typically between 5 and 20 nm in diameter and show significant twisting. Both ends of many of the fibrils extend beyond the image field of view, which limits the maximum measurable fibril length from these images. Furthermore, as the fibrils are confined to the surface, assessment of the bulk network structure is very difficult. Single color STORM imaging was performed with I3K peptide fibrils, which were labelled with the Cy3B fluorescent dye. The resolution of STORM images scales with the number of photons emitted by the fluorophores before photobleaching, so antioxidant buffers can improve the resolution. In our experiments, the Cy3B dye worked well in conjunction with the OxEA imaging buffer31. This buffer was used as aliquots of it remained stable for imaging over one or two weeks; much longer than a Gloxy based buffer. An example of one of the images obtained


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with this fluorophore and imaging buffer combination is shown in Figure 3. The typical field of view for our STORM images is 50 µm, which is limited by the size of the illuminating beam of the laser and is an order of magnitude greater than possible with the AFM. The increased field of view with STORM means that many complete fibrils are visible, but imaging the sample in the bulk does mean that the image contains incomplete fibrils due to some of them extending beyond the focal plane. The fibril density also varies significantly between the techniques; the AFM image features a much higher density of fibrils. The initial sample preparation is very similar for the two techniques, so this is likely due to drying in AFM experiments where layers of fibrils could collapse on top of one another. This effect is not seen in the STORM image as sample wetness is maintained throughout the preparation and imaging processes. Furthermore STORM images are taken in the bulk, with the fibrils in a relaxed 3D conformation, rather than confined to a 2D surface, as in AFM. The STORM image reconstruction process involves fitting Gaussian profiles to intensity spots on the original raw images in order to localize individual dye molecules. By tuning the fitting parameters and performing post-processing techniques on the resultant localizations using ThunderSTORM, the image quality can be greatly increased. When imaging in the bulk, images feature low noise, as out of focus light does not meet the localization criteria for the fits. The final resolution of the STORM image can be calculated by considering the reciprocal space correlation functions associated with the distribution of localizations in the image. In our experiments we calculated the image resolution using the Fourier ring correlation method37. This gave a result of approximately 30 nm for the reconstruction of our images, which is an order of magnitude better than the diffraction limited resolution of the microscope. However, as I3K fibrils are approximately 10 nm in diameter it was hard to gain information on the cross-sectional


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properties of I3K from the STORM images. Nevertheless, a resolution of 30 nm does allow for the accurate measurement of larger scale fibril properties, such as the persistence and contour length. By collecting several images of the sample, both with AFM and STORM, the fibrils were tracked and the resultant contour length distributions and persistence lengths were calculated.

10 µm

Figure 3. A STORM image that shows I3K peptide fibrils at 10 mM peptide concentration labelled with Cy3B fluorescent dye. This image was taken in the bulk and a sepia lookup table is used to accentuate the contrast of the image. This makes it possible to see where fibrils are extending beyond the plane of focus.


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Prior to calculating the contour length, the fitted co-ordinates for each fibril were smoothed38. This corrected for local inaccuracies in the open snake algorithm used in FiberApp, which was caused by the inhomogeneous nature of the STORM images, an illustration of this effect is included in Section 5 of the supporting information. The measured contour length distributions are shown in Figure 4A. The mean values for the AFM and STORM distributions were 3.2 µm and 6.0 µm, respectively. The AFM data shown in Figure 4A has been collected from images with a 10 µm field of view, the largest possible with our equipment. The contour length measurements were used in conjunction with the end-to-end distance of each peptide fibril to calculate the persistence length of fibrils in our images. The advantage of this technique was that it was not significantly affected by local errors in fiber tracking and can be used on incomplete fibrils. Furthermore, it was possible to vastly increase the number of data points collected by sampling segments of each fibril using various contour lengths shorter than the total fibril contour length39. This was not necessary in our case due to the high number of fibrils in each image. Furthermore, we were close to the rod-like fiber limit, so most information on the persistence length came from the longest contour lengths that could be measured. The mean square end-to-end distance, 〈 〉, can be written as,

(

[

R 2 = 2 L p Lc − L p 1 − e

− Lc / L p

]),

(1)

where and are the persistence length and contour length, respectively. By performing a single parameter fit to this expression, we were able to calculate the persistence length for each sample40. The STORM images were a 2D projection of a 3D object, but the depth of focus of the images was so small (~300 nm) that very little 3D information was contained within them, especially with respect to the lateral dimensions of the images, which is on the order of 50 µm. Therefore, corrections to the calculated persistence lengths were not required, as only fibrils well


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aligned with the imaging plane were considered. Figure 4 (B and C) shows the fits calculated from the single color STORM images and the corresponding AFM data.

Figure 4. A) The contour length distributions for peptide fibrils measured using AFM and STORM imaging, with sample sizes of 172 and 197 fibrils respectively. The mean values are 3.2 µm and 6.0 µm for AFM and STORM, respectively, and the difference arises from the reduced field of view for the AFM measurements. B, C) The end-to-end distance, , against the contour length, , from fits of fibrils measured in AFM (B) and STORM (C) images. The fits to equation (1) give a persistence length of 18.0 ± 2.2 µm for AFM and 10.1 ± 1.2 µm for STORM.


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For a contour length ≲ 2 µm, the fibrils are rod-like ( ∼ ), and only above this threshold value does the curvature begin to significantly affect the measured end-to-end distance. The results of the fits shown in Figure 4 provide the persistence length as 10.1 ± 1.2 µm for STORM, which corresponds to an elastic modulus of 51.4 ± 19.0 GPa, given the majority of fibrils have a measured diameter from AFM of 11.3 ± 0.7 nm. The elastic modulus, , was calculated assuming a cylindrical cross-section of radius, , as follows, E=

4k B T L p , π r4

(2)

where is the thermal energy41. The measured elastic modulus of I3K is relatively large compared to other biopolymers, such as microtubules or amyloid fibrils, which have an elastic modulus of 1-5 GPa41,42. This may be due to the strong, stable β-sheet structure favored by the three isoleucine residues in the I3K monomer. As the majority of this small molecule is involved in β-sheet hydrogen bonding a stronger overall structure may result than that formed from the larger monomers found in other biopolymers i.e. a higher density oriented network of hydrogen bonds is formed. The results from the AFM data provide a persistence length of 18.0 ± 2.2 µm. As AFM measures fibers adsorbed to the surface, it is not likely that the true three dimensional persistence length was directly measured. If the fibers have equilibrated on the surface, we expect a factor of two between the measured persistence length in two and three dimensions. This is due to the reduced degrees of freedom in two dimensions and explains some of the differences in the measured persistence lengths observed40. For the type of imaging measurements we performed, better quality information could be gained when the measured contour length was equal to or greater than the persistence length. When the contour length is smaller than the persistence


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length, there is little to no measurable curvature and the end-to-end distance is approximately equal to the contour length. The large field of view possible with STORM allowed the collection of data for more fibrils with longer contour lengths and was therefore a more accurate measurement of the persistence length. This could also be useful for the analysis of other proteins and peptides which form extended structures similar to I3K. Furthermore, imaging in the bulk with a shallow depth of focus means we directly measured the 3D persistence length. This alleviates an ambiguity that may arise in surface based techniques, which can either be a projection of a three dimensional conformation or an equilibrated two dimensional conformation20,39. The persistence lengths of other biopolymers, such as DNA20, have been shown to be dependent upon solvent ionic strength, as well as the intrinsic strength of the biopolymer. This modifies the measured persistence length such that = + ,

(3)

where and are the intrinsic and electrostatic persistence lengths respectively. Theoretical calculation43 of the electrostatic persistence length predicts that it depends upon the charges on the polymer, , the distance between charges, , and the molar salt concentration, , as follows ~

!"

#$

,

(4)

where is the thermal energy. With I3K, protonation of lysine residues occurs along the fiber at pH < 9 and we estimate that the charges are separated by approximately 1 nm. This yields an electrostatic persistence length of approximately 10 nm in the STORM buffers used. Therefore, it is not expected to significantly affect the measurements in this report. However, many biopolymers are more flexible, such as DNA which has a persistence length less than 100 nm20. STORM may be a useful technique in these cases to investigate electrostatic effects in aqueous


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sample environments, due to the removal of confusing surface adsorption interactions. Furthermore, STORM is better suited to investigating the overall network properties of I3K hydrogels when compared to surface based AFM. The mesh size of the network was calculated according to the methodology from Kaufman et al;44 using binary images obtained from the FiberApp contours, rather than image thresholding. The AFM images yield a mesh size of 0.51 ± 0.01 µm, whereas for the images of the hydrated network obtained using STORM the result is 3.0 ± 0.1 µm. This disagreement is most likely due to surface adsorption of fibrils, increasing their concentration at the surface compared to the bulk in the AFM measurements. As a result we expect the STORM value for the mesh size to be much more accurate and reproducible. 2. Two color STORM imaging Multi-color STORM images can be measured with different types of fluorescent dye, however the optimization of imaging conditions for both dyes can be very challenging. In our experience the OxEA buffer used for our initial single color imaging did not perform well when combined with the Alexa Fluor 647 dye. Photo-bleaching of the dye occurred before 20,000 images could be acquired and the images were thus of low quality. To obtain higher quality images we switched to a more commonly used Gloxy buffer, which gave bright and sustained blinking for both Alexa Fluor 647 and Cy3B dyes. To obtain a two color image the two dyes were imaged consecutively, Alexa Fluor 647 first and then Cy3B, and the images from each channel were then combined with lateral drift corrections applied. Figure 5 shows an example of a two color image. Typically, the two color images were of a slightly lower quality than the single color images, which is due to the increased complexity involved with optimizing imaging conditions for both types of fluorophore.


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10 µm

Figure 5. Representative image of the two color STORM images collected of I3K peptide fibrils taken in the bulk. This image is taken 10 days after mixing the individually dyed fibrils together in the presence of 20% v/v ethanol, with a final peptide concentration of 8 mM. The red and green channels are from Alexa Fluor 647 and Cy3B dye localizations, respectively. Imaging in the bulk assisted with reducing the background in the final image. One of the immediate advantages of imaging in two colors is the ability to distinguish individual fibers which are bundled or twisted together. From AFM, or TEM images, it is often possible to spot where fibrils appear to be branching or bundling together, but there is some ambiguity involved with the interpretation of bundles, as it can be difficult to resolve each fibril within them. When the fibers are distinguishable, as is the case with two color STORM images, the bundling and twisting of fibrils can be observed directly. A couple of examples of this are shown in Figure 6. The additional information available in two color imaging may help us to better understand how fibrils interact with each other to inform our models and predictions for self-assembly45,46.


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Figure 6. Two color STORM images of I3K peptide fibril bundles and twisting; the color is removed on the left hand images to show the potential ambiguity of single color imaging. The bundling and wrapping of fibers around one another is easily distinguishable in two color STORM images. The high resolution of the STORM technique allowed us to observe separate fibrils twisting together. The scale bars are all 2 µm long. By mixing two sets of separately dyed I3K fibrils we performed an experiment to deduce if dynamic mixing of monomers occurs in the equilibrium state. Theoretical models for selfassembly are often based upon assumptions which can be hard to test experimentally, such as that monomers only join fibrils at either end46. STORM allows us to distinguish between sets of monomers and determine if mixing phenomena occur, through the use of quantitative measures, such as localization correlation measurements. I3K peptide fibrils have been shown to be extremely stable47 and therefore for our two color experiments we introduced ethanol and acetonitrile to the samples at a concentration of 20% (v/v) in an attempt to disrupt the equilibrium state of the fibrils. Once the two color images were acquired they were converted to


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a grayscale to reduce bias towards a specific color in the fiber tracking process. MatLab code was written to collect all the localizations that corresponded to each fiber to allow analysis of the color distribution within fibers. An example of a tracked fiber, which shows localizations around it in two dimensions and along the fiber contour in one dimension, is shown in Figure 7.

Figure 7. A) The localization co-ordinates plotted in the vicinity of a Cy3B dyed I3K fibril, along with the fitted contour in black. Alexa Fluor 647 and Cy3B localizations are plotted in red and green, respectively. B) The localization counts along the fibril contour plotted in A. The points where the red fibril crosses the green fibril can be clearly seen. As with the single color imaging, the contour length distributions and persistence length for fibrils in the two color images could be obtained. Figure 8 shows the mean value of the contour length distributions and the persistence lengths for these samples. The contour length distributions across the samples did not show a significant variation and are consistently in the range of 4 – 7 µm. The measured persistence length for each sample does vary more significantly. In the presence of 100% H2O the fibrillar persistence length was consistent across the experiments which was expected as the fibrils are well aged and already in an equilibrium


20

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state before the start of the experiment. By introducing ethanol and acetonitrile to these fibrils, the persistence length is seen to increase from 10 µm to ~25 µm after one day. Over the next twenty eight days the persistence length of fibrils in the presence of ethanol and acetonitrile begins to diverge, increasing to 34.3 ± 4.8 µm with ethanol and decreasing to 16.5 ± 2.6 µm with acetonitrile. The mesh size was also obtained and did not show significant variation across the study period, ranging from 3 – 8 µm with an average of 5.3 ± 1.3 µm (see Section 6 of the supporting information for more information). Previous work that looked at the impact of acetonitrile on peptide self-assembly has shown that it can induce morphological transitions from nano-ribbons to tubes with a similar peptide, KI4K. This is thought to be due to weakening of hydrophobic interactions, with the intra-chain hydrogen bonding patterns left relatively unchanged15. Zhao et al propose that the reduced hydrophobic interaction leads to wider KI4K ribbons, which readily curl into tubes. Ribbons curling into tubes is also thought to be the primary mechanism of tube formation in I3K16 and the weakening effect of acetonitrile may result in slightly thicker I3K nanotubes, which retain the stable β-sheet structure. Given that the persistence length is proportional to fibril radius to the fourth power (equation (2)), only a 20% increase in radius would lead to a doubling of the persistence length. Furthermore, the dielectric constant of the solvent reduces by 12.5%, which could cause an enhancement of the electrostatic forces on the fibrils48 (although counterion condensation could also be affected). However, electrostatic effects are not anticipated to significantly affect the persistence length, as we have shown earlier that the electrostatic persistence length is on the order of 10 nm. The presence of ethanol is also likely to disrupt the hydrophobic interactions within the self-assembled I3K structures, which may be why we saw an initial change in the persistence length. However, ethanol has also been shown to promote β-


21

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sheet formation in surfactant-like peptides, such as A6K49, and strengthen inter-β-strand hydrogen bonds in amyloidogenic insulin fibrils50. This may explain the overall effect of strengthening the I3K fibrils, which leads to a higher measured persistence length for the fibrils in the presence of 20% ethanol than those in 20% acetonitrile.

Figure 8. The average contour length, Lc, and persistence length, Lp, of fibrils from samples containing either 100% H2O or 80% H2O and 20% ethanol or acetonitrile. The contour length does not appear to change significantly across the samples and the timescales. Further data on these samples is displayed in Section 6 of the supporting information. The stochastic nature of STORM means that visual inspection of the resultant images can be misleading when trying to gain information about structures present and the distribution of dye molecules within them51. So to investigate the distribution of localizations and peptides within the fibrils we calculated both the pair auto-correlation function and cross-correlation function of


22

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the number of Alexa Fluor 647 and Cy3B localizations along each fibril. An example of the raw data input into these calculations is shown in Figure 7B. The auto-correlation and crosscorrelation functions were calculated for each fibril individually and then averaged over the fibrils in each sample, with normalization applied to ensure the autocorrelation was unity at zero displacements. In order to guide our interpretation of the correlation functions from our data, we performed Monte Carlo simulations of the distribution of localizations along the fibrils. From visual inspection of the localization counts along each fibril, such as that in Figure 7B, there appears to be clustering of localizations along each fibril. Evidence of clustering has been seen in other similar experiments from different types of peptides28,29 and to account for this phenomenon in our simulation, segments along each fibril were randomly assigned to be on or off in each color channel (red and green). This generated fibrils with segments either red, green, both red and green or neither (i.e. unlabeled). Next, linear interpolation was performed between the center of each segment, to produce a linear change in the localization counts of each channel between segments. Finally, we modulated each channel with normally distributed random noise, which resulted in fibrils similar to those shown in Figure 9A.


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Figure 9. A) An example of the simulated localizations of either the red or green channel on a single fiber. Initial points are generated randomly at either 100 or 0 localizations, interpolated and then noise is added to generate the final simulated localizations. The simulated fiber properties can be altered by varying the number of segments, segment length and magnitude of the noise. B) The auto-correlation and cross-correlation functions from the 20% acetonitrile sample one day after mixing. The auto-correlation profiles were shifted by +1.0 and +0.5 for the Alexa Fluor (AF647) and Cy3B channels respectively to aid visualization. The results of the model for each channel and the cross correlation are also shown. The model parameters were 100 segments of length 170 nm. Additional correlation profiles are displayed in Section 7 of the supporting information. The auto-correlation profiles of all samples were calculated using MatLab and then compared to the output from our model with different parameters. Calculating the covariance function, the


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resultant profile is only dependent on the shape of the localization profile and not the average brightness, which may vary from fibril to fibril. Figure 9B shows the auto-correlation and crosscorrelations profiles for the 20% acetonitrile sample after one day, along with the results from 100 simulated fibrils. The original data was well reproduced by the model with simulated fibrils containing 100 segments of length 170 nm and a noise magnitude equivalent to the resolution of the images as calculated earlier with the Fourier ring correlation method. The auto-correlation profiles of all samples was very similar (see Section 7 of the supporting information), which suggests that there was little change in the distributions of fluorophores within the sample over the course of the study. The cross-correlation profiles for most samples typically show no significant deviation from zero, suggesting that localizations from each channel are totally independent i.e. there was no correlation hole due to anti-correlation of the two channels52. The cross-correlation profiles for the 20% acetonitrile sample at ten days and the 20% ethanol sample at one and twenty nine days do show a very slight positive correlation at short length scales (Figure S9 in Section 7 of the supporting information). However, on closer inspection of the data this does not appear to be a significant result. For example, the cross-correlation of the ethanol sample at one day returns to zero when an image containing a large entwined fiber bundle is removed from the sample dataset, as shown in Section 7 of the supporting information. Furthermore, the cross-correlation is not calculated on fibrils whose localizations are over 80% a single color, e.g. > 80% red localizations and < 20% green. This can dramatically reduce sample size when most fibrils are a single color. For the acetonitrile sample, the cross-correlation sample size is just 7, compared with 53 for the auto-correlations. Therefore, these results that demonstrate positive cross-correlation are not statistically significant compared to the others.


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The results of the two color experiments demonstrate the stability of I3K in the presence of organic solvents, since there is negligible change in the measured contour length after four weeks of ageing in the different solvents. Furthermore, the distribution of dye molecules, and therefore monomers within the fibrils, also appears to be constant over the course of the study. If monomers were readily exchanging with the solvent, we would expect the majority of fibrils to incorporate both types of dye within them, however this is not seen in our samples. Theoretical models for the self-assembly of fibrils from monomers adopt assumptions about the process of self-assembly, such as that monomers join and leave fibrils from the ends only46. If the I3K fibrils readily exchanged monomers from each end, we would expect multicolored ends to single color fibrils, as in the study by Beun et al on protein fibrils28. Again, there is no evidence for this process occurring with I3K. It is therefore likely that the combined hydrophobic and hydrogen bonding interactions in the I3K fibrils form extremely stable fibrils. Previous work has used this high stability to template silica nano-tubes using I3K18 and our STORM technique demonstrates that not only is the equilibrium state stable, but that monomers are locked in to the fiber architecture and are not released to the solvent in significant numbers. The model parameters which best fit the correlation data from the STORM images suggest that localizations were clustered into blocks of size 160 – 180 nm. It is difficult to determine if this is due to the intrinsic fiber properties or if it is due to the dye molecules themselves. Da Silva et al concluded in their STORM experiments that the Cy3 dye was more prone to clustering along peptide fibrils compared to the Cy5 dye29. However, in our experiments both the Alexa Fluor 647 and Cy3B channels gave results which suggested the same degree of clustering was present on both channels. Therefore, it may be due to the intrinsic fibril properties, rather than the dye itself. Self-assembly of protein and peptide fibrils has often been shown to be a hierarchical


26

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process, which involves the association of protofibrils to form the final fibrillary structures45,53. For the short, surfactant-like peptides, A6K and A9K, Wang et al propose that the nanofibers form via short nanofiber fragments, which then fuse together54. The clustering evident in our experiments could therefore be due to the early stages of self-assembly, which have impacted upon the final self-assembled structure, and resulted in a regular pattern of dye conjugation. 4. Conclusion We demonstrated how STORM can be used to study the structural evolution of I3K peptide fibrils during their dynamic process of self-assembly. STORM can provide high quality images (resolution ~30 nm) of peptide fibril networks in bulk aqueous conditions. The large field of view possible (~50 µm) with STORM allows the quantification of the contour and persistence lengths of individual fibrils, even when they are on the order of tens of micrometers. This is advantageous for a wide range of biopolymers, and in the case of I3K, its self-assembled nanofibers have an average contour length of ~6 µm and a persistence length of 10.1 ± 1.2 µm as measured by STORM at 10 mM peptide concentration. Furthermore, by imaging a 2D slice of a 3D structure, only fibrils aligned with the imaging plane are captured and the true 3D properties of the fibrils are measured directly with STORM, removing the ambiguity associated with surface adsorption based techniques20. STORM has already been used to accurately map the geometry of 3D actin networks26 and this could be extended to peptide fibril networks to aid the design of synthetic peptides to create biomaterials suitable for therapeutic application. Multi-color STORM imaging is also possible with peptide fibrils and can be used to probe monomer exchange dynamics. We demonstrated the stability of I3K fibrils, which showed little evidence of monomer exchange and retained an average contour length of 4 - 6 µm over the four week study period, even in the presence of the organic solvents ethanol and acetonitrile. STORM


27

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offers the possibility to quantify the exchange kinetics of self-assembled structures by imaging individual fibrils directly. Further optimization and application of this technique may inform the better design of synthetic peptides in important applications, such as drug delivery and bioresponsive self-assembled materials/structures. ASSOCIATED CONTENT The supporting information document (pdf) contains seven sections: 1. AFM height scans of I3K fibrils with and without fluorescent dye conjugated to them. 2. Methodology for dye conjugation to I3K. 3. Description of the STORM microscope. 4. Recipes for the STORM imaging buffers used. 5. Additional information about the smoothing of fibrils to correct for local inaccuracies in the fiber tracking process. 6. Figures showing the persistence length fits and contour length distributions of data which is summarized in Figure 8 of the main report. 7. Auto-correlation and cross-correlation functions for all two color samples. ACKNOWLEDGEMENTS We thank Viki Allan, James Sanders and Mark Dickinson for helping with the construction of the STORM equipment and the EPSRC for funding this project under the PhD studentship to HC and also under a research grant (EP/F062966/1). REFERENCES


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TOC GRAPHIC


31

O

H N N H

O

O

H N N H

O NH2

O +

NH3

1 2 3 4 5 6 7

Lp 32 STORM imaging Page Exchange Biomacromolecules of 41 in bulk phase L dynamics c


Lysine head

Isoleucine tail

APage 33 of 41BiomacromoleculesB O

1 2 3 4 C5 6 7 8 9 10 11 12

O

H N

N H

O

O

H N

N H

O

NH2

O

O O

Dye

N O

NH3

O N H

H N O

O N H

H N

+

O O

NH2

O

HN

ACS Paragon Plus Environment O HN

O

Dye

O

BiomacromoleculesPage 34 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 2517nm

ACS Paragon Plus Environment 0 nm

Page 35 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

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10 µm

Probability

0.25 0.2

Biomacromolecules Page 36 of 41 AFM

R (µm)

R (µm)

STORM 10.15 2 3 0.1 4 A 50.05 6 0 6 2 10 12 14 16 18 20 4 8 7 0 Lc (µm) 8 9 1 10 10 11 12 13 0 10 14 15 B 16 -1 17 10 1 -1 0 10 10 10 18 L (µm) c 19 20 21 22 1 10 23 24 25 26 0 10 27 C 28 ACS Paragon Plus Environment 0 1 29 10 10 30 Lc (µm)

Page 37 of 41 Biomacromolecules

10 µm

1 2 3 4 5 6 7 8 9 10 11 12 13


Biomacromolecules Page 38 of 41

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17


y co-ordinate (µm)

A Page 39 of 41Biomacromolecules 5 4 3 2 1 1

2

150

Localisation counts

1 2 3 4 5 6 7 B 8 9 10 11 12 13 14 15

6

3

4

5

x co-ordinate (µm)

6

7

8

100

50

ACS Paragon Plus Environment 0

0

1

2

3

4

5

6

Distance along fibre (µm)

7

8

9

10

20% Acetonitrile / 80% Biomacromolecules Page 40 H of2O41 20% Ethanol / 80% H2O 100% H2O

8

Lc (µm)

1 6 2 3 4 4 5 2 6 7 0 8 9 1040 11 1230 13 14 20 15 16 1710 18 190 20

0

5

10

15

20

25

30

25

30

Lp (µm)

Time since mixing (days)

ACS Paragon Plus Environment 0

5

10

15

20

Time since mixing (days)

200

Localizations

Initial points Interpolated points Page 41 of 41 Biomacromolecules

A

Final simulated localizations

150

5

10

15

Distance along fibril (segments)

20

AF647 auto-correlation Cy3B auto-correlation Cross-correlation Model

B

Auto-correlation

1 2 100 3 4 50 5 6 0 7 8 9 2 10 11 1.5 12 13 14 1 15 16 0.5 17 18 19 0 20 21 0 22

ACS Paragon Plus Environment 0.2

0.4

0.6

Distance (µm)

0.8

1

Self-Assembly of Mesoscopic Peptide Surfactant ... - ACS Publications

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